From 5411c7dcd0db52cd7911763c62da6c001e6403fe Mon Sep 17 00:00:00 2001 From: DanielJensen <35690894+Daniel-Jensen@users.noreply.github.com> Date: Mon, 6 Jul 2026 16:14:54 +0200 Subject: [PATCH 01/25] Move model to code/global/; document five standalone-model fixes in STATE.md Relocates all standalone Python model files into code/global/ and removes the old flat code/ layout. Updates STATE.md with the full fix log (BUG-1 through ISSUE-5), post-fix Walras residuals, BD mechanism results, and the W-G1 structural limitation explanation. Co-Authored-By: Claude Sonnet 4.6 --- code/calibration.py | 118 -- code/depreciation_calibration.py | 127 -- code/equations_D.py | 437 ----- code/equations_F.py | 408 ----- code/equations_global.py | 96 - code/full_model.py | 163 -- code/global/bank.py | 566 ++++++ code/global/calibration.py | 92 + code/global/capital.py | 64 + code/global/distribution.py | 64 + code/global/firms.py | 59 + code/global/government.py | 191 ++ code/global/household.py | 124 ++ code/global/main.py | 157 ++ code/global/output/default_irf.png | Bin 0 -> 311936 bytes code/global/output/steady_state.png | Bin 0 -> 140168 bytes code/global/output/tfp_irf.png | Bin 0 -> 208490 bytes code/global/plots.py | 212 +++ code/global/rouwenhorst.py | 41 + code/global/steady_state.py | 273 +++ code/global/trade.py | 99 + code/global/transition.py | 573 ++++++ code/global/verify_mechanism.py | 97 + code/ic_delta_calibration.py | 54 - code/irf_plots.py | 163 -- code/main.py | 82 - code/model_v12.ipynb | 2644 --------------------------- code/steady_state.py | 187 -- code/tpi.py | 229 --- code/tpi_plots.py | 283 --- docs/STATE.md | 57 +- 31 files changed, 2668 insertions(+), 4992 deletions(-) delete mode 100644 code/calibration.py delete mode 100644 code/depreciation_calibration.py delete mode 100644 code/equations_D.py delete mode 100644 code/equations_F.py delete mode 100644 code/equations_global.py delete mode 100644 code/full_model.py create mode 100644 code/global/bank.py create mode 100644 code/global/calibration.py create mode 100644 code/global/capital.py create mode 100644 code/global/distribution.py create mode 100644 code/global/firms.py create mode 100644 code/global/government.py create mode 100644 code/global/household.py create mode 100644 code/global/main.py create mode 100644 code/global/output/default_irf.png create mode 100644 code/global/output/steady_state.png create mode 100644 code/global/output/tfp_irf.png create mode 100644 code/global/plots.py create mode 100644 code/global/rouwenhorst.py create mode 100644 code/global/steady_state.py create mode 100644 code/global/trade.py create mode 100644 code/global/transition.py create mode 100644 code/global/verify_mechanism.py delete mode 100644 code/ic_delta_calibration.py delete mode 100644 code/irf_plots.py delete mode 100644 code/main.py delete mode 100644 code/model_v12.ipynb delete mode 100644 code/steady_state.py delete mode 100644 code/tpi.py delete mode 100644 code/tpi_plots.py diff --git a/code/calibration.py b/code/calibration.py deleted file mode 100644 index f037bac..0000000 --- a/code/calibration.py +++ /dev/null @@ -1,118 +0,0 @@ -import numpy as np - - -def get_calibration(): - calibration_start = { - - # ── Preferences ─────────────────────────────────────────────────────── - 'frisch_D': 0.50, 'frisch_F': 0.50, - 'eis_D': 0.5, 'eis_F': 0.5, - - # ── Rates & Asset Prices ────────────────────────────────────────────── - 'rdep_D': 0.000, 'rdep_F': 0.000, - 'q_b_D': 0.83, 'q_b_F': 0.83, - 'Q_D': 1.0, 'Q_F': 1.0, - - # ── Production ──────────────────────────────────────────────────────── - 'alpha_D': 0.35, 'alpha_F': 0.35, - 'delta_D': 0.025, 'delta_F': 0.025, - 'ksi_D': 0.50, 'ksi_F': 0.50, - - # ── Long-term bonds ─────────────────────────────────────────────────── - 'delta_b_D': 0.10, 'delta_b_F': 0.10, - - # ── Aggregate Targets (SS) ──────────────────────────────────────────── - 'Y_D': 1.00, 'Y_F': 1.00, - 'Y_ss_D': 1.0, 'Y_ss_F': 1.0, - 'N_D': 1.00, 'N_F': 1.00, - 'w_D': 0.65, 'w_F': 0.65, - - # ── Financial Intermediaries (Gertler-Karadi) ───────────────────────── - 'f_D': 0.12, 'f_F': 0.12, - 'lambda_gk_D': 0.2, 'lambda_gk_F': 0.2, - 'beta_inter_D': 0.9975155088, 'beta_inter_F': 0.9975155088, - 'Delta_bD_D': 0.2, 'Delta_bF_F': 0.2, - 'Delta_bF_D': 0.4, 'Delta_bD_F': 0.4, - 'lambda_BD_D': 0.06, 'lambda_BF_F': 0.06, - 'lambda_BF_D': 0.06, 'lambda_BD_F': 0.06, - 'psi_lambda_B_D': 3.0, 'psi_lambda_B_F': 3.0, - 'n_inter_D': 0.75*4, 'n_inter_F': 0.75*4, - 'theta_D': 4, 'theta_F': 4, - - # ── Bellman nu risk-discount ─────────────────────────────────────────── - 'psi_nu_bD_D': 0.0, 'psi_nu_bD_F': 0.0, - 'psi_nu_bF_D': 0.0, 'psi_nu_bF_F': 0.0, - - # ── Fiscal & Government Debt ────────────────────────────────────────── - 'B_supply_D': 0.6*4, 'B_supply_F': 0.6*4, - 'b_gov_D': 0.6*4, 'b_gov_F': 0.6*4, - 'b_gov_ss_D': 0.6*4, 'b_gov_ss_F': 0.6*4, - - # ── Fiscal Rule ─────────────────────────────────────────────────────── - 'tau_D': 0.181, 'tau_F': 0.181, - 'lamb_D': 0.85, 'lamb_F': 0.85, - 'lamb_ss_D': 0.85, 'lamb_ss_F': 0.85, - # phi_lamb raised from 0.02 after T-2 fix: deposit re-dating makes the - # debt→spread spiral live; phi_lamb < ~0.12 is explosive at current amplification. - 'phi_lamb_D': 0.15, 'phi_lamb_F': 0.15, - - # ── Sovereign Default ───────────────────────────────────────────────── - 'shock_def_D': 0.000, 'shock_def_F': 0.0, - 'T_ls_D': 0.000, 'T_ls_F': 0.000, - 'def_rate_D': 0.000, 'def_rate_F': 0.0, - 'def_scale_D': 0.25, 'def_scale_F': 0.25, - 'def_curvature_D': 0.5, 'def_curvature_F': 0.5, - 'def_offset_D': 0.05, 'def_offset_F': 0.05, - 'recovery_rate_D': 0.00, 'recovery_rate_F': 0.00, - 'zeta_writeoff_D': 0.0, 'zeta_writeoff_F': 0.0, - 'writeoff_enabled_D': 0.0, 'writeoff_enabled_F': 0.0, - - # ── Intermediary Capital Adjustment Cost ────────────────────────────── - 'chi0_D': 0.00, 'chi0_F': 0.00, - 'chi1_D': 0.00, 'chi1_F': 0.00, - 'chi2_D': 2.0, 'chi2_F': 2.0, - - # ── Macroprudential Bond Tax ────────────────────────────────────────── - 'T0_D': 0.000, 'T0_F': 0.000, - 'T1_D': 0.0, 'T1_F': 0.0, - - # ── Trade & Terms of Trade ──────────────────────────────────────────── - 'omega': 0.85, - 'epsilon_trade': 1.5, - 'p': 0.50, - - # ── Cross-Border Bond Portfolio ─────────────────────────────────────── - 'phi_bF_D_ss': 0.25, 'phi_bD_F_ss': 0.25, - 'psi_bF_D': 0.5, 'psi_bD_F': 0.5, - - # ── Wage Markups ────────────────────────────────────────────────────── - 'mu_w_D': 1.0, 'mu_w_F': 1.0, - - # ── SS Real Variables ───────────────────────────────────────────────── - 'mc_D': 1.0, 'mc_F': 1.0, - - # ── Idiosyncratic Income Process (Rouwenhorst) ──────────────────────── - 'rho_z_D': 0.90, 'rho_z_F': 0.90, - 'sigma_z_D': 0.3, 'sigma_z_F': 0.3, - 'nZ_D': 15, 'nZ_F': 15, - 'nDep_D': 500, 'nDep_F': 500, - 'Depmax_D': 150, 'Depmax_F': 150, - } - - # ── Bond Holdings: initial SS guess ────────────────────────────────────── - _n_D = calibration_start['n_inter_D'] - _n_F = calibration_start['n_inter_F'] - _B_D = calibration_start['B_supply_D'] - _B_F = calibration_start['B_supply_F'] - - b_F_D = calibration_start['phi_bF_D_ss'] * _n_D / calibration_start['q_b_F'] - b_D_F = calibration_start['phi_bD_F_ss'] * _n_F / calibration_start['q_b_D'] - - calibration_start.update({ - 'b_F_D': b_F_D, 'b_D_F': b_D_F, - 'b_D_D': _B_D - b_D_F, 'b_F_F': _B_F - b_F_D, - 'b_F_D_anchor': b_F_D, 'b_D_F_anchor': b_D_F, - 'psi_bD_D': 0.0, 'psi_bF_F': 0.0, - }) - - return calibration_start diff --git a/code/depreciation_calibration.py b/code/depreciation_calibration.py deleted file mode 100644 index d956805..0000000 --- a/code/depreciation_calibration.py +++ /dev/null @@ -1,127 +0,0 @@ -""" -Capital depreciation rate calibration and final steady-state re-solve. - -Targets rk = 0.01 per quarter for both countries, then does one final -SS solve with the calibrated delta values. Also runs the post-SS -residual diagnostic. -""" -import copy - -from steady_state import _apply_ss_anchors - - -def calibrate_depreciation(ss_results): - ss = ss_results['ss'] - ha = ss_results['ha'] - calibration_start = ss_results['calibration_start'] - unknowns_ss = ss_results['unknowns_ss'] - targets_ss = ss_results['targets_ss'] - - rk_D_target = 0.01 - rk_F_target = 0.01 - - K_D_cur = float(ss['K_D']); K_F_cur = float(ss['K_F']) - Y_D_cur = float(ss['Y_D']); Y_F_cur = float(ss['Y_F']) - - delta_D_cal = calibration_start['alpha_D'] * Y_D_cur / K_D_cur - rk_D_target - delta_F_cal = calibration_start['alpha_F'] * Y_F_cur / K_F_cur - rk_F_target - calibration_start.update({'delta_D': delta_D_cal, 'delta_F': delta_F_cal}) - - print(f"Depreciation calibration: delta_D = {delta_D_cal:.6f} delta_F = {delta_F_cal:.6f}") - print("Final SS re-solve with calibrated delta...") - - ss = ha.solve_steady_state(calibration_start, unknowns_ss, targets_ss, solver='broyden_custom') - _apply_ss_anchors(ss, calibration_start) - - print(f"Verified rk_D = {float(ss['rk_D']):.6f} (target {rk_D_target:.4f})") - print(f"Verified rk_F = {float(ss['rk_F']):.6f} (target {rk_F_target:.4f})") - print(f"Final beta_D = {float(ss['beta_D']):.10f}") - print(f"Final beta_F = {float(ss['beta_F']):.10f}") - print(f"\nbeta_D={ss['beta_D']:.10f} beta_F={ss['beta_F']:.10f} p={ss['p']:.6f}") - print(f"rb_D={ss['rb_D']:.6f} rb_F={ss['rb_F']:.6f} rdep_D={ss['rdep_D']:.6f} rdep_F={ss['rdep_F']:.6f}") - print(f"q_b_D={float(ss['q_b_D']):.6f} q_b_F={float(ss['q_b_F']):.6f}") - print("SS goods residuals:") - print(" goods_mkt_D =", ss['goods_mkt_D']) - print(" goods_mkt_F =", ss['goods_mkt_F']) - print(" ca_res_D =", ss['ca_res_D']) - - cali_D = cali_F = ss - ss_final = copy.deepcopy(ss) - - _run_ss_residual_diagnostic(ss, calibration_start) - - return { - **ss_results, - 'ss': ss, - 'ss_final': ss_final, - 'cali_D': cali_D, - 'cali_F': cali_F, - } - - -def _run_ss_residual_diagnostic(ss, calibration_start): - def _get(k): - return float(ss[k]) - - diag = {} - - for c in ['D', 'F']: - pdiv = _get('p') if c == 'F' else 1.0 - eta_c = _get(f'eta_{c}') - lam = _get(f'lambda_gk_{c}') - theta_c = _get(f'theta_{c}') - Q_c = _get(f'Q_{c}') - K_c = _get(f'K_{c}') - n_c = _get(f'n_inter_{c}') - kappa_c = Q_c * K_c / n_c - if c == 'D': - nu_K, nu_bD, nu_bF = _get('nu_K_D'), _get('nu_bD_D'), _get('nu_bF_D') - q_h, q_x = _get('q_b_D'), _get('q_b_F') - b_h, b_x = _get('b_D_D'), _get('b_F_D') - Dh, Dx = _get('Delta_bD_D'), _get('Delta_bF_D') - else: - nu_K, nu_bD, nu_bF = _get('nu_K_F'), _get('nu_bD_F'), _get('nu_bF_F') - q_h, q_x = _get('q_b_F'), _get('q_b_D') - b_h, b_x = _get('b_F_F'), _get('b_D_F') - Dh, Dx = _get('Delta_bF_F'), _get('Delta_bD_F') - phi_h = q_h * b_h / (pdiv * n_c) - phi_x = q_x * b_x / (pdiv * n_c) - value_c = (nu_K * kappa_c - + (nu_bD if c == 'D' else nu_bF) * phi_h - + (nu_bF if c == 'D' else nu_bD) * phi_x - + eta_c) - theta_tgt = value_c / lam + (1 - Dh) * phi_h + (1 - Dx) * phi_x - diag[f'IC_{c}: θ − θ_tgt'] = theta_c - theta_tgt - - for c in ['D', 'F']: - f_c = _get(f'f_{c}') - lam = _get(f'lambda_gk_{c}') - beta_c = _get(f'beta_inter_{c}') - rk_c = _get(f'rk_{c}') - rdep_c = _get(f'rdep_{c}') - eta_c = _get(f'eta_{c}') - theta_c = _get(f'theta_{c}') - Omega_p1 = f_c + (1 - f_c) * lam * theta_c - rb_h = _get('rb_actual_D' if c == 'D' else 'rb_actual_F') - rb_x = _get('rb_actual_F' if c == 'D' else 'rb_actual_D') - nu_K_c = _get(f'nu_K_{c}') - nu_bh_c = _get('nu_bD_D' if c == 'D' else 'nu_bF_F') - nu_bx_c = _get('nu_bF_D' if c == 'D' else 'nu_bD_F') - diag[f'P1_{c}: nu_K_res'] = nu_K_c - beta_c * Omega_p1 * (rk_c - rdep_c) - diag[f'P1_{c}: nu_bh_res'] = nu_bh_c - beta_c * Omega_p1 * (rb_h - rdep_c) - diag[f'P1_{c}: nu_bx_res'] = nu_bx_c - beta_c * Omega_p1 * (rb_x - rdep_c) - diag[f'P1_{c}: eta_res'] = eta_c - beta_c * Omega_p1 * (1 + rdep_c) - - diag['ca_res_D'] = _get('ca_res_D') - - TOL = 1e-8 - print(f"\n{'Block residual':<55} {'Value':>14} Status") - print("-" * 85) - FLAGGED = [] - for name, val in diag.items(): - ok = abs(val) <= TOL - if not ok: - FLAGGED.append(name) - print(f" {name:<53} {val:>14.6e} {'OK' if ok else '*** FAIL'}") - print("-" * 85) - print("All residuals < 1e-8 ✓" if not FLAGGED else f"FLAGGED: {FLAGGED}") diff --git a/code/equations_D.py b/code/equations_D.py deleted file mode 100644 index b96953f..0000000 --- a/code/equations_D.py +++ /dev/null @@ -1,437 +0,0 @@ -import numpy as np -import scipy.linalg -import sequence_jacobian as sj -from sequence_jacobian import simple -from sequence_jacobian import grids -from pathlib import Path - -try: - BASE_DIR_D = Path(__file__).resolve().parent -except NameError: - BASE_DIR_D = Path.cwd() - -DATA_DIR_D = BASE_DIR_D / "Discretisation" / "Outputs" - - -# ── HOUSEHOLD ─── ############################################################################################# - -def hh_init_D(dep_D_grid, z_D, Rgross_D, eis_D, vphi_D, N_D, frisch_D): - coh_D = Rgross_D * dep_D_grid[np.newaxis, :] + z_D[:, np.newaxis] - v_D = vphi_D * N_D ** (1 + 1/frisch_D) / (1 + 1/frisch_D) - Vdep_D = Rgross_D * (coh_D - v_D) ** (-1 / eis_D) - return Vdep_D - - -@sj.het(exogenous='Pi_D', policy='dep_D', backward='Vdep_D', backward_init=hh_init_D) -def hh_D(Vdep_D_p, dep_D_grid, z_D, t_paid_D, Rgross_D, beta_D, eis_D, vphi_D, N_D, frisch_D): - # GHH composite: x = c - v(N), v(N) = vphi*N^(1+1/frisch)/(1+1/frisch) - v_D = vphi_D * N_D ** (1 + 1/frisch_D) / (1 + 1/frisch_D) - uc_nextgrid_D = beta_D * Vdep_D_p - x_nextgrid_D = uc_nextgrid_D ** (-eis_D) - coh_D = Rgross_D * dep_D_grid[np.newaxis, :] + z_D[:, np.newaxis] - - # EGM: x + a' = coh - v → endogenous grid x_nextgrid + a' matched to coh - v - dep_D = sj.interpolate.interpolate_y(x_nextgrid_D + dep_D_grid, coh_D - v_D, dep_D_grid) - sj.misc.setmin(dep_D, dep_D_grid[0]) - - x_D = coh_D - v_D - dep_D # GHH composite - c_D = x_D + v_D # total consumption = x + v(N) - uce_D = x_D ** (-1 / eis_D) # marginal utility of composite - Vdep_D = Rgross_D * uce_D - - tax_D = t_paid_D[:, np.newaxis] + np.zeros_like(dep_D_grid[np.newaxis, :]) - - return Vdep_D, dep_D, c_D, uce_D, tax_D - - -def make_grids_D(Depmax_D, nDep_D, nZ_D, rho_z_D, sigma_z_D): - dep_D_grid = grids.agrid(amax=Depmax_D, n=nDep_D) - - if nZ_D == 19: - px_path_D = DATA_DIR_D / "Px_GMAR.txt" - x_path_D = DATA_DIR_D / "x_vec.txt" - markov_ctstime_D = np.loadtxt(px_path_D) - e_grid_D = np.loadtxt(x_path_D).flatten() - markov_distime_D = scipy.linalg.expm(markov_ctstime_D) - row_sums_D = markov_distime_D.sum(axis=1) - Pi_D = markov_distime_D / row_sums_D[:, None] - else: - e_grid_D, _, Pi_D = grids.markov_rouwenhorst(rho=rho_z_D, sigma=sigma_z_D, N=nZ_D) - - return dep_D_grid, e_grid_D, Pi_D - - -def income_D(e_grid_D, w_D, N_D, div_D, tau_D, lamb_D, P_CES_D, T_ls_D): - y_pre_D = (w_D * N_D * e_grid_D + div_D) / P_CES_D - z_D = lamb_D * (y_pre_D ** (1 - tau_D)) - T_ls_D - t_paid_D = y_pre_D - z_D - return z_D, t_paid_D - - -hh_extended_D = hh_D.add_hetinputs([make_grids_D, income_D]) - - -@simple -def deposit_return_D(rdep_D, P_CES_D): - # Bundle-real gross deposit return: corrects for P_CES revaluation between t-1 and t. - # T-2 fix: deposits are one-period non-contingent contracts — the rate paid at t - # was locked at t-1 (rdep_D(-1)). Previously rdep_D (a period-t unknown) was paid - # on the t-1 deposit stock, making deposits state-contingent and generating a - # large bank windfall on impact of shocks (audit.md T-2). - # At SS P_CES_D(-1)/P_CES_D = 1, so Rgross_D = 1 + rdep_D identically. - Rgross_D = (1 + rdep_D(-1)) * P_CES_D(-1) / P_CES_D - return Rgross_D - - -# ── STEADY STATE EQUATIONS ─── ############################################################################################# - -@simple -def smart_steady_D(theta_D, Y_D, n_inter_D, rdep_D, alpha_D, delta_D, f_D, N_D, - rb_actual_D, rb_actual_F, b_D_D, b_F_D, Q_D, q_b_D, q_b_F, - chi0_D, chi1_D, chi2_D, T0_D, T1_D, def_rate_D): - K_D = (theta_D * n_inter_D - q_b_D * b_D_D - q_b_F * b_F_D) / Q_D - phi_bD_D = q_b_D * b_D_D / n_inter_D - phi_bF_D = q_b_F * b_F_D / n_inter_D - kappa_D = theta_D - phi_bD_D - phi_bF_D - rk_D = alpha_D * Y_D / K_D - delta_D - arg_D = -rk_D * K_D / (K_D + chi0_D) - Phi_D = (chi1_D / chi2_D) * (arg_D ** 2) ** (chi2_D / 2) * (K_D + chi0_D) - T_D = (T0_D + T1_D * def_rate_D) * (b_D_D + b_F_D) - rn_D = (kappa_D * (rk_D - rdep_D) - + phi_bD_D * (rb_actual_D - rdep_D) - + phi_bF_D * (rb_actual_F - rdep_D) - + rdep_D) - - - - # A-2 fix: P2 requires m = n(1-(1-f)(1+rn)) at SS; Phi and T are paid out of - # dividends (banker_div_res), not via the startup transfer. Including +Phi+T here - # made the SS not a rest point of intermediation_P2 whenever Phi or T != 0. - m_D = n_inter_D * (1 - (1 - f_D) * (1 + rn_D)) - k_inter_D = K_D - I_D = K_D * delta_D - D_supply_D = (theta_D - 1) * n_inter_D - Z_D = Y_D / ((K_D ** alpha_D) * (N_D ** (1 - alpha_D))) - cap_profit_D = Q_D * (K_D - (1 - delta_D) * K_D(-1)) - I_D - return K_D, rk_D, rn_D, m_D, k_inter_D, I_D, D_supply_D, Z_D, cap_profit_D, Phi_D, T_D - -@simple -def market_clearing_D(Y_D, C_D, I_D, G_D, NX_D, DEP_D, D_supply_D, P_CES_D, Phi_D, T_D): - # D (P_D = 1): only C is in bundle units; I, G, Phi, T are in domestic goods. - goods_mkt_D = Y_D - (P_CES_D * C_D + I_D + G_D + Phi_D + T_D) - NX_D - deposit_mkt_D = P_CES_D * DEP_D - D_supply_D - return goods_mkt_D, deposit_mkt_D - - -@simple -def ces_price_D(omega, epsilon_trade, p): - P_CES_D = (omega + (1 - omega) * p ** (1 - epsilon_trade)) ** (1 / (1 - epsilon_trade)) - return P_CES_D - - -@simple -def import_demand_D(C_D, omega, epsilon_trade, p, P_CES_D): - IM_D = (1 - omega) * (P_CES_D / p) ** epsilon_trade * C_D - return IM_D - - -@simple -def steady_auxilliary_D(theta_D, rk_D, rdep_D, delta_D, alpha_D, Y_D, K_D, N_D, - beta_inter_D, ksi_D, rn_D, f_D, - rb_actual_D, rb_actual_F): - iota_D = delta_D - mpk_D = alpha_D * (Y_D / K_D) - w_D = (1 - alpha_D) * Y_D / N_D - lambda_gk_D = f_D / (theta_D * (1 / (beta_inter_D * (1 + rn_D)) - (1 - f_D))) - Omega_D = f_D + (1 - f_D) * lambda_gk_D * theta_D - nu_K_D = beta_inter_D * Omega_D * (rk_D - rdep_D) - nu_bD_D = beta_inter_D * Omega_D * (rb_actual_D - rdep_D) - nu_bF_D = beta_inter_D * Omega_D * (rb_actual_F - rdep_D) - eta_D = beta_inter_D * Omega_D * (1 + rdep_D) - gamma0_D = delta_D ** ksi_D / (1 - ksi_D) - gamma1_D = -delta_D * ksi_D / (1 - ksi_D) - return iota_D, mpk_D, w_D, Omega_D, lambda_gk_D, nu_K_D, nu_bD_D, nu_bF_D, eta_D, gamma0_D, gamma1_D - - -@simple -def banker_div_D(rn_D, n_inter_D, Phi_D, T_D): - # Consistent with banker_div_res_D: div = f·gross − m = rn·n − Phi − T at SS. - div_D = rn_D * n_inter_D - Phi_D - T_D - return div_D - - -@simple -def sdf_ss_D(beta_D): - SDF_D = beta_D - return SDF_D - -@simple -def sdf_D(beta_D, X_D, eis_D): - # GHH: SDF uses composite x = c - v(N) instead of c - SDF_D = beta_D * (X_D(+1) / X_D) ** (-1 / eis_D) - return SDF_D - -@simple -def sdf_banker_ss_D(beta_inter_D): - SDF_banker_D = beta_inter_D - return SDF_banker_D - -@simple -def sdf_banker_D(beta_inter_D, X_D, eis_D): - SDF_banker_D = beta_inter_D * (X_D(+1) / X_D) ** (-1 / eis_D) - return SDF_banker_D - - -@simple -def ghh_composite_D(C_D, vphi_D, N_D, frisch_D): - # Aggregate GHH composite X = C - v(N); homogeneous v(N) → X = C - v(N) exactly - X_D = C_D - vphi_D * N_D ** (1 + 1/frisch_D) / (1 + 1/frisch_D) - return X_D - - -@simple -def government_ss_D(TAX_D, q_b_D, b_gov_D, P_CES_D, delta_b_D, - def_rate_D, recovery_rate_D, zeta_writeoff_D, writeoff_enabled_D): - haircut_D = 1.0 - recovery_rate_D - haircut_mult_D = writeoff_enabled_D - surv_cont_D = 1.0 - zeta_writeoff_D * def_rate_D * haircut_D * haircut_mult_D - coupon_D = delta_b_D * (1.0 - def_rate_D * haircut_D * haircut_mult_D) * b_gov_D - net_iss_D = q_b_D * (1.0 - surv_cont_D * (1.0 - delta_b_D)) * b_gov_D - G_D = P_CES_D * TAX_D + net_iss_D - coupon_D - return G_D - - -@simple -def labor_ss_D(w_D, N_D, frisch_D, mu_w_D, P_CES_D): - # GHH: UCE = x^(-1/eis) cancels from intratemporal FOC → vphi independent of UCE - vphi_D = (1 / mu_w_D) * (w_D / P_CES_D) / (N_D ** (1 / frisch_D)) - return vphi_D - -@simple -def bond_return_D(def_rate_D, recovery_rate_D, q_b_D, delta_b_D, zeta_writeoff_D, writeoff_enabled_D): - # writeoff_enabled_D = 0: pure sovereign risk shock, no haircuts on cash flows. - haircut_D = 1.0 - recovery_rate_D - haircut_mult_D = writeoff_enabled_D - current_payoff_D = delta_b_D * (1.0 - def_rate_D * haircut_D * haircut_mult_D) - continuation_D = (1.0 - delta_b_D) * q_b_D * (1.0 - zeta_writeoff_D * def_rate_D * haircut_D * haircut_mult_D) - rb_actual_D = (current_payoff_D + continuation_D) / q_b_D(-1) - 1.0 - return rb_actual_D - - -# ── OFF STEADY STATE EQUATIONS ─── ############################################################################################# - -@simple -def capital_adj_D(K_D, Q_D, I_D, Z_D, N_D, alpha_D, delta_D, gamma0_D, gamma1_D, ksi_D): - iota_D = I_D / K_D(-1) - # W-1 (author convention): mpk is the marginal product of current K_t, - # consistent with labor_D. Banks receive mpk on their K(-1) holdings via rk; - # the product of newly installed capital goes to the capital producer. - mpk_D = alpha_D * Z_D * K_D ** (alpha_D - 1) * N_D ** (1 - alpha_D) - rk_D = (mpk_D + (1 - delta_D) * Q_D) / Q_D(-1) - 1 - q_res_D = Q_D - 1 / (gamma0_D * (1 - ksi_D) * iota_D ** (-ksi_D)) - capital_res_D = K_D - (1 - delta_D) * K_D(-1) - (gamma0_D * iota_D ** (1 - ksi_D) + gamma1_D) * K_D(-1) - return iota_D, mpk_D, rk_D, q_res_D, capital_res_D - -@simple -def capital_producer_profit_D(Q_D, K_D, I_D, delta_D, mpk_D): - # W-1 fix under the K_t production convention: new capital installed at t is - # productive within t, and its marginal product mpk*(K - K(-1)) accrues to the - # capital producer. Banks earn mpk on K(-1) (via rk), so total capital income - # mpk*K(-1) + mpk*(K - K(-1)) = mpk*K = alpha*Y — factor payments exhaust - # output and Walras's law / CA = dNFA hold. Term vanishes at SS (K = K(-1)). - cap_profit_D = Q_D * (K_D - (1 - delta_D) * K_D(-1)) - I_D + mpk_D * (K_D - K_D(-1)) - return cap_profit_D - - -@simple -def labor_D(N_D, Z_D, K_D, alpha_D): - # W-1 (author convention): production uses current K_t — investment is - # productive within the period. Accounting closes because the marginal - # product of new capital accrues to the capital producer (see - # capital_producer_profit_D); banks earn mpk only on K(-1). - Y_D = Z_D * K_D ** alpha_D * N_D ** (1 - alpha_D) - return Y_D - - -@simple -def labor_market_D(w_D, N_D, vphi_D, frisch_D, P_CES_D): - labor_mkt_res_D = w_D / P_CES_D - vphi_D * N_D ** (1 / frisch_D) - return labor_mkt_res_D - - -@simple -def labor_demand_D(w_D, Y_D, N_D, alpha_D): - w_res_D = w_D - (1 - alpha_D) * Y_D / N_D - return w_res_D - - - -@simple -def intermediation_IC_D(nu_K_D, nu_bD_D, nu_bF_D, eta_D, - Q_D, K_D, q_b_D, q_b_F, b_D_D, b_F_D, n_inter_D, - lambda_gk_D, Delta_bD_D, Delta_bF_D, theta_D, - def_rate_D,def_rate_F, psi_lambda_B_D): - kappa_D = Q_D * K_D / n_inter_D - phi_bD_D = q_b_D * b_D_D / n_inter_D - phi_bF_D = q_b_F * b_F_D / n_inter_D - # GK multi-asset IC: franchise value = lambda_gk·(divertable assets), where each - # bond class is weighted by its relative divertability Delta_i vs capital (=1). - # theta_tgt = value/lambda_gk + (1-Delta_bD)·phi_bD + (1-Delta_bF)·phi_bF. - # Delta=1 → single-lambda; Delta<1 → bond is better collateral → bank levers more. - # psi_lambda_B_D > 0: default risk raises bond divertability (worsens collateral). - Delta_bD_eff = Delta_bD_D + psi_lambda_B_D * def_rate_D(+1) - Delta_bF_eff = Delta_bF_D + psi_lambda_B_D * def_rate_F(+1) - value_D = nu_K_D * kappa_D + nu_bD_D * phi_bD_D + nu_bF_D * phi_bF_D + eta_D - theta_tgt_D = (value_D / lambda_gk_D - + (1 - Delta_bD_eff) * phi_bD_D - + (1 - Delta_bF_eff) * phi_bF_D) - ic_res_D = theta_D - theta_tgt_D - return ic_res_D - - -@simple -def bank_return_D(theta_D, rk_D, rdep_D, b_D_D, b_F_D, n_inter_D, - rb_actual_D, rb_actual_F, q_b_D, q_b_F): - phi_bD_lag_D = q_b_D(-1) * b_D_D(-1) / n_inter_D(-1) - phi_bF_lag_D = q_b_F(-1) * b_F_D(-1) / n_inter_D(-1) - kappa_lag_D = theta_D(-1) - phi_bD_lag_D - phi_bF_lag_D - # T-2 fix: funding cost on the t-1 balance sheet is the rate locked at t-1. - rn_D = (kappa_lag_D * (rk_D - rdep_D(-1)) - + phi_bD_lag_D * (rb_actual_D - rdep_D(-1)) - + phi_bF_lag_D * (rb_actual_F - rdep_D(-1)) - + rdep_D(-1)) - return rn_D - - -@simple -def intermediation_P1_D(rk_D, rb_actual_D, rb_actual_F, rdep_D, - nu_K_D, nu_bD_D, nu_bF_D, eta_D, - lambda_gk_D, theta_D, SDF_banker_D, f_D): - Omega_p1_D = f_D + (1 - f_D) * lambda_gk_D * theta_D(+1) - # T-2 fix: the deposit rate for the t->t+1 holding period is rdep_D (locked at t). - nu_K_res_D = nu_K_D - SDF_banker_D * Omega_p1_D * (rk_D(+1) - rdep_D) - nu_bD_res_D = nu_bD_D - SDF_banker_D * Omega_p1_D * (rb_actual_D(+1) - rdep_D) - nu_bF_res_D = nu_bF_D - SDF_banker_D * Omega_p1_D * (rb_actual_F(+1) - rdep_D) - eta_res_D = eta_D - SDF_banker_D * Omega_p1_D * (1 + rdep_D) - return nu_K_res_D, nu_bD_res_D, nu_bF_res_D, eta_res_D - - -@simple -def k_balance_sheet_D(Q_D, theta_D, n_inter_D, K_D, b_D_D, b_F_D, q_b_D, q_b_F): - K_res_D = Q_D * K_D + q_b_D * b_D_D + q_b_F * b_F_D - theta_D * n_inter_D - return K_res_D - - - -@simple -def cap_adj_cost_inter_D(K_D, rk_D, chi0_D, chi1_D, chi2_D): - # Auclert (2019) intermediary capital adjustment cost. - arg_D = (K_D - (1.0 + rk_D) * K_D(-1)) / (K_D(-1) + chi0_D) - Phi_D = (chi1_D / chi2_D) * (arg_D ** 2) ** (chi2_D / 2) * (K_D(-1) + chi0_D) - return Phi_D - - -@simple -def macro_pru_tax_D(b_D_D, b_F_D, def_rate_D, T0_D, T1_D): - # Macroprudential bond tax: T = (T0 + T1·ProbDefault) · total bond holdings - # (both D-bonds and F-bonds held by the D-bank). Matches smart_steady_D. - tau_mp_D = T0_D + T1_D * def_rate_D - T_D = tau_mp_D * (b_D_D + b_F_D) - return tau_mp_D, T_D - - -@simple -def intermediation_P2_D(rn_D, n_inter_D, m_D, f_D, cap_profit_D): - gross_income_D = (1 + rn_D) * n_inter_D(-1) + cap_profit_D - n_inter_val_D = (1 - f_D) * gross_income_D + m_D - n_inter_D - return n_inter_val_D - - -@simple -def banker_div_res_D(rn_D, n_inter_D, div_D, m_D, f_D, cap_profit_D, Phi_D, T_D): - gross_income_D = (1 + rn_D) * n_inter_D(-1) + cap_profit_D - net_div_D = f_D * gross_income_D - m_D - Phi_D - T_D - div_res_D = div_D - net_div_D - return div_res_D - - -@simple -def intermediation_P3_D(Q_D, K_D, n_inter_D, b_D_D, b_F_D, q_b_D, q_b_F): - D_supply_D = Q_D * K_D + q_b_D * b_D_D + q_b_F * b_F_D - n_inter_D - return D_supply_D - - -@simple -def bond_price_ss_D(SDF_banker_D, def_rate_D, recovery_rate_D, delta_b_D, zeta_writeoff_D, writeoff_enabled_D): - haircut_D = 1.0 - recovery_rate_D - haircut_mult_D = writeoff_enabled_D - surv_cont_D = 1.0 - zeta_writeoff_D * def_rate_D * haircut_D * haircut_mult_D - q_b_D = ( - SDF_banker_D * delta_b_D * (1.0 - def_rate_D * haircut_D * haircut_mult_D) - / (1.0 - SDF_banker_D * (1.0 - delta_b_D) * surv_cont_D) - ) - return q_b_D - - -@simple -def domestic_bond_foc_D(rb_actual_D, rdep_D, b_D_D, n_inter_D, q_b_D, - phi_bD_D_ss, psi_bD_D, excess_return_bD_D_ss, tau_mp_D): - phi_bD_D = q_b_D * b_D_D / n_inter_D - rb_D_res = (rb_actual_D(+1) - rdep_D(+1)) - excess_return_bD_D_ss \ - - psi_bD_D * (phi_bD_D - phi_bD_D_ss) \ - - tau_mp_D - return rb_D_res - - -# ==> GOVERMENT EQUATIONS -@simple -def government_default_D(shock_def_D, b_gov_D, Y_ss_D, b_gov_ss_D, - def_scale_D, def_curvature_D, def_offset_D): - debt_ratio_D = b_gov_D(-1) / Y_ss_D - ss_ratio_D = b_gov_ss_D / Y_ss_D - def_rate_D = shock_def_D + def_scale_D * ( - (debt_ratio_D + def_offset_D) ** def_curvature_D - - (ss_ratio_D + def_offset_D) ** def_curvature_D - ) - return def_rate_D - - -@simple -def tax_rule_D(b_gov_D, b_gov_ss_D, phi_lamb_D): - T_ls_D = phi_lamb_D * (b_gov_D(-1) - b_gov_ss_D) - return T_ls_D - - -@simple -def budget_residual_D(b_gov_D, G_D, TAX_D, q_b_D, def_rate_D, recovery_rate_D, zeta_writeoff_D, P_CES_D, delta_b_D, writeoff_enabled_D): - haircut_D = 1.0 - recovery_rate_D - haircut_mult_D = writeoff_enabled_D - surv_cont_D = 1.0 - zeta_writeoff_D * def_rate_D * haircut_D * haircut_mult_D - coupon_D = delta_b_D * (1.0 - def_rate_D * haircut_D * haircut_mult_D) * b_gov_D(-1) - net_issuance_D = q_b_D * (b_gov_D - surv_cont_D * (1.0 - delta_b_D) * b_gov_D(-1)) - b_gov_res_D = coupon_D + G_D - P_CES_D * TAX_D - net_issuance_D - return b_gov_res_D - - -@simple -def divert_bond_foc_D(rb_actual_D, rdep_D, b_D_D, n_inter_D, q_b_D, - phi_bD_D_ss, psi_bD_D, excess_return_bD_D_ss, tau_mp_D, - psi_spread_D, def_rate_D): - phi_bD_D = q_b_D * b_D_D / n_inter_D - # IC-theory derived required spread: additive default loading independent of SS excess return. - # psi_spread_D = lambda_gk_D * psi_lambda_B_D / (beta_inter_D * Omega_D), computed in _apply_ss_anchors. - req_spread = excess_return_bD_D_ss + psi_spread_D * def_rate_D(+1) - # T-2 fix: compare t+1 bond return with rdep_D locked at t. - rb_D_res = (rb_actual_D(+1) - rdep_D) - req_spread \ - - psi_bD_D * (phi_bD_D - phi_bD_D_ss) \ - - tau_mp_D - return rb_D_res - - -@simple -def welfare_agg_D(X_D, C_D_ss): - # GHH utility composite normalised by SS consumption. - # In IRFs the deviation ΔX_D/C_D_ss gives welfare change as a fraction of SS consumption. - U_D = X_D / C_D_ss - return U_D - diff --git a/code/equations_F.py b/code/equations_F.py deleted file mode 100644 index 36a1d2e..0000000 --- a/code/equations_F.py +++ /dev/null @@ -1,408 +0,0 @@ -import numpy as np -import scipy.linalg -import sequence_jacobian as sj -from sequence_jacobian import simple -from sequence_jacobian import grids -from pathlib import Path - -try: - BASE_DIR_F = Path(__file__).resolve().parent -except NameError: - BASE_DIR_F = Path.cwd() - -DATA_DIR_F = BASE_DIR_F / "Discretisation" / "Outputs" - -# ── Household ───────────────────────────────────────────────────────────────── - -def hh_init_F(dep_F_grid, z_F, Rgross_F, eis_F, vphi_F, N_F, frisch_F): - coh_F = Rgross_F * dep_F_grid[np.newaxis, :] + z_F[:, np.newaxis] - v_F = vphi_F * N_F ** (1 + 1/frisch_F) / (1 + 1/frisch_F) - Vdep_F = Rgross_F * (coh_F - v_F) ** (-1 / eis_F) - return Vdep_F - -@sj.het(exogenous='Pi_F', policy='dep_F', backward='Vdep_F', backward_init=hh_init_F) -def hh_F(Vdep_F_p, dep_F_grid, z_F, t_paid_F, Rgross_F, beta_F, eis_F, vphi_F, N_F, frisch_F): - # GHH composite: x = c - v(N), v(N) = vphi*N^(1+1/frisch)/(1+1/frisch) - v_F = vphi_F * N_F ** (1 + 1/frisch_F) / (1 + 1/frisch_F) - uc_nextgrid_F = beta_F * Vdep_F_p - x_nextgrid_F = uc_nextgrid_F ** (-eis_F) - coh_F = Rgross_F * dep_F_grid[np.newaxis, :] + z_F[:, np.newaxis] - - # EGM: x + a' = coh - v → endogenous grid x_nextgrid + a' matched to coh - v - dep_F = sj.interpolate.interpolate_y(x_nextgrid_F + dep_F_grid, coh_F - v_F, dep_F_grid) - sj.misc.setmin(dep_F, dep_F_grid[0]) - - x_F = coh_F - v_F - dep_F # GHH composite - c_F = x_F + v_F # total consumption = x + v(N) - uce_F = x_F ** (-1 / eis_F) # marginal utility of composite - Vdep_F = Rgross_F * uce_F - - tax_F = t_paid_F[:, np.newaxis] + np.zeros_like(dep_F_grid[np.newaxis, :]) - - return Vdep_F, dep_F, c_F, uce_F, tax_F - -def make_grids_F(Depmax_F, nDep_F, nZ_F, rho_z_F, sigma_z_F): - dep_F_grid = grids.agrid(amax=Depmax_F, n=nDep_F) - - if nZ_F == 19: - px_path_F = DATA_DIR_F / "Px_GMAR.txt" - x_path_F = DATA_DIR_F / "x_vec.txt" - markov_ctstime_F = np.loadtxt(px_path_F) - e_grid_F = np.loadtxt(x_path_F).flatten() - markov_Fistime_F = scipy.linalg.expm(markov_ctstime_F) - row_sums_F = markov_Fistime_F.sum(axis=1) - Pi_F = markov_Fistime_F / row_sums_F[:, None] - else: - e_grid_F, _, Pi_F = grids.markov_rouwenhorst(rho=rho_z_F, sigma=sigma_z_F, N=nZ_F) - - return dep_F_grid, e_grid_F, Pi_F - -def income_F(e_grid_F, w_F, N_F, div_F, tau_F, lamb_F, P_CES_F, T_ls_F): - y_pre_F = (w_F * N_F * e_grid_F + div_F) / P_CES_F - z_F = lamb_F * (y_pre_F ** (1 - tau_F)) - T_ls_F - t_paid_F = y_pre_F - z_F - return z_F, t_paid_F - -hh_extended_F = hh_F.add_hetinputs([make_grids_F, income_F]) - -@simple -def deposit_return_F(rdep_F, P_CES_F): - # Bundle-real gross deposit return: corrects for P_CES revaluation between t-1 and t. - # T-2 fix: rate paid at t was locked at t-1 — see deposit_return_D. - # At SS P_CES_F(-1)/P_CES_F = 1, so Rgross_F = 1 + rdep_F identically. - Rgross_F = (1 + rdep_F(-1)) * P_CES_F(-1) / P_CES_F - return Rgross_F - - -# ── Steady-state blocks ─────────────────────────────────────────────────────── -# CHECK STEADY STATE OWNING OF THE foreign bonds in DOMESTIC COUNTRY -@simple -def smart_steady_F(theta_F, Y_F, n_inter_F, rdep_F, alpha_F, delta_F, f_F, N_F, - rb_actual_F, rb_actual_D, b_F_F, b_D_F, Q_F, q_b_F, q_b_D, - chi0_F, chi1_F, chi2_F, T0_F, T1_F, def_rate_F, p): - # Bonds are D-good (numeraire) claims; F-bank assets/NW are in F-goods → divide by p. - K_F = (theta_F * n_inter_F - (q_b_F * b_F_F + q_b_D * b_D_F) / p) / Q_F - phi_bF_F = q_b_F * b_F_F / (p * n_inter_F) - phi_bD_F = q_b_D * b_D_F / (p * n_inter_F) - kappa_F = theta_F - phi_bF_F - phi_bD_F - rk_F = alpha_F * Y_F / K_F - delta_F - arg_F = -rk_F * K_F / (K_F + chi0_F) - Phi_F = (chi1_F / chi2_F) * (arg_F ** 2) ** (chi2_F / 2) * (K_F + chi0_F) - T_F = (T0_F + T1_F * def_rate_F) * (b_F_F + b_D_F) / p - # rn = PURE portfolio return. See smart_steady_D. - rn_F = (kappa_F * (rk_F - rdep_F) - + phi_bF_F * (rb_actual_F - rdep_F) - + phi_bD_F * (rb_actual_D - rdep_F) - + rdep_F) - # A-2 fix: see smart_steady_D — Phi/T are paid out of dividends, not via m. - m_F = n_inter_F * (1 - (1 - f_F) * (1 + rn_F)) - k_inter_F = K_F - I_F = K_F * delta_F - D_supply_F = (theta_F - 1) * n_inter_F - Z_F = Y_F / ((K_F ** alpha_F) * (N_F ** (1 - alpha_F))) - cap_profit_F = Q_F * (K_F - (1 - delta_F) * K_F(-1)) - I_F - return K_F, rk_F, rn_F, m_F, k_inter_F, I_F, D_supply_F, Z_F, cap_profit_F, Phi_F, T_F - -@simple -def market_clearing_F(Y_F, C_F, I_F, G_F, NX_F, DEP_F, D_supply_F, P_CES_F, Phi_F, T_F): - # F (P_F = 1): only C is in bundle units; I, G, Phi, T are in domestic goods. - goods_mkt_F = Y_F - (P_CES_F * C_F + I_F + G_F + Phi_F + T_F) - NX_F - deposit_mkt_F = P_CES_F * DEP_F - D_supply_F - return goods_mkt_F, deposit_mkt_F - -@simple -def ces_price_F(omega, epsilon_trade, p): - # F's domestic good price = 1 (in F-units); D-good import price = 1/p (in F-units). - # So F's CES price index uses (1/p)^(1-eps), NOT p^(1-eps). - P_CES_F = (omega + (1 - omega) * (1 / p) ** (1 - epsilon_trade)) ** (1 / (1 - epsilon_trade)) - return P_CES_F - -@simple -def import_demand_F(C_F, omega, epsilon_trade, p, P_CES_F): - IM_F = (1 - omega) * (P_CES_F * p) ** epsilon_trade * C_F - return IM_F - -@simple -def steady_auxilliary_F(theta_F, rk_F, rdep_F, delta_F, alpha_F, Y_F, K_F, N_F, - beta_inter_F, ksi_F, rn_F, f_F, - rb_actual_F, rb_actual_D): - iota_F = delta_F - mpk_F = alpha_F * (Y_F / K_F) - w_F = (1 - alpha_F) * Y_F / N_F - lambda_gk_F = f_F / (theta_F * (1 / (beta_inter_F * (1 + rn_F)) - (1 - f_F))) - Omega_F = f_F + (1 - f_F) * lambda_gk_F * theta_F - nu_K_F = beta_inter_F * Omega_F * (rk_F - rdep_F) - nu_bF_F = beta_inter_F * Omega_F * (rb_actual_F - rdep_F) - nu_bD_F = beta_inter_F * Omega_F * (rb_actual_D - rdep_F) - eta_F = beta_inter_F * Omega_F * (1 + rdep_F) - gamma0_F = delta_F ** ksi_F / (1 - ksi_F) - gamma1_F = -delta_F * ksi_F / (1 - ksi_F) - return iota_F, mpk_F, w_F, Omega_F, lambda_gk_F, nu_K_F, nu_bF_F, nu_bD_F, eta_F, gamma0_F, gamma1_F - -@simple -def banker_div_F(rn_F, n_inter_F, Phi_F, T_F): - # Consistent with banker_div_res_F: div = f·gross − m = rn·n − Phi − T at SS. - div_F = rn_F * n_inter_F - Phi_F - T_F - return div_F - -@simple -def sdf_ss_F(beta_F): - # SS-only block: SDF = beta (C constant at SS). Breaks cycle in ha. - SDF_F = beta_F - return SDF_F - -@simple -def sdf_F(beta_F, X_F, eis_F): - # GHH: SDF uses composite x = c - v(N) instead of c - SDF_F = beta_F * (X_F(+1) / X_F) ** (-1 / eis_F) - return SDF_F - -@simple -def sdf_banker_ss_F(beta_inter_F): - SDF_banker_F = beta_inter_F - return SDF_banker_F - -@simple -def sdf_banker_F(beta_inter_F, X_F, eis_F): - SDF_banker_F = beta_inter_F * (X_F(+1) / X_F) ** (-1 / eis_F) - return SDF_banker_F - - -@simple -def ghh_composite_F(C_F, vphi_F, N_F, frisch_F): - # Aggregate GHH composite X = C - v(N); homogeneous v(N) → X = C - v(N) exactly - X_F = C_F - vphi_F * N_F ** (1 + 1/frisch_F) / (1 + 1/frisch_F) - return X_F - -@simple -def government_ss_F(TAX_F, q_b_F, b_gov_F, p, P_CES_F, delta_b_F, - def_rate_F, recovery_rate_F, zeta_writeoff_F, writeoff_enabled_F): - haircut_F = 1.0 - recovery_rate_F - haircut_mult_F = writeoff_enabled_F - surv_cont_F = 1.0 - zeta_writeoff_F * def_rate_F * haircut_F * haircut_mult_F - coupon_F = delta_b_F * (1.0 - def_rate_F * haircut_F * haircut_mult_F) * b_gov_F - net_iss_F = q_b_F * (1.0 - surv_cont_F * (1.0 - delta_b_F)) * b_gov_F - G_F = P_CES_F * TAX_F + (net_iss_F - coupon_F) / p - return G_F - -@simple -def labor_ss_F(w_F, N_F, frisch_F, mu_w_F, P_CES_F): - # GHH: UCE cancels from intratemporal FOC → vphi independent of UCE - vphi_F = (1 / mu_w_F) * (w_F / P_CES_F) / (N_F ** (1 / frisch_F)) - return vphi_F - -@simple -def bond_return_F(def_rate_F, recovery_rate_F, q_b_F, delta_b_F, zeta_writeoff_F, writeoff_enabled_F): - # writeoff_enabled_F = 0: pure sovereign risk shock, no haircuts on cash flows. - haircut_F = 1.0 - recovery_rate_F - haircut_mult_F = writeoff_enabled_F - current_payoff_F = delta_b_F * (1.0 - def_rate_F * haircut_F * haircut_mult_F) - continuation_F = (1.0 - delta_b_F) * q_b_F * (1.0 - zeta_writeoff_F * def_rate_F * haircut_F * haircut_mult_F) - rb_actual_F = (current_payoff_F + continuation_F) / q_b_F(-1) - 1.0 - return rb_actual_F - -# ── Off-steady-state blocks ─────────────────────────────────────────────────── - -@simple -def capital_adj_F(K_F, Q_F, I_F, Z_F, N_F, alpha_F, delta_F, gamma0_F, gamma1_F, ksi_F): - iota_F = I_F / K_F(-1) - # W-1 (author convention): mpk of current K_t — see capital_adj_D - mpk_F = alpha_F * Z_F * K_F ** (alpha_F - 1) * N_F ** (1 - alpha_F) - rk_F = (mpk_F + (1 - delta_F) * Q_F) / Q_F(-1) - 1 - q_res_F = Q_F - 1 / (gamma0_F * (1 - ksi_F) * iota_F ** (-ksi_F)) - capital_res_F = K_F - (1 - delta_F) * K_F(-1) - (gamma0_F * iota_F ** (1 - ksi_F) + gamma1_F) * K_F(-1) - return iota_F, mpk_F, rk_F, q_res_F, capital_res_F - -@simple -def labor_F(N_F, Z_F, K_F, alpha_F): - # W-1 (author convention): current K_t — see labor_D - Y_F = Z_F * K_F ** alpha_F * N_F ** (1 - alpha_F) - return Y_F - -@simple -def labor_market_F(w_F, N_F, vphi_F, frisch_F, P_CES_F): - # GHH: UCE = x^(-1/eis) divides out of both sides → w/P = vphi*N^(1/frisch) - labor_mkt_res_F = w_F / P_CES_F - vphi_F * N_F ** (1 / frisch_F) - return labor_mkt_res_F - - -@simple -def labor_demand_F(w_F, Y_F, N_F, alpha_F): - # Firm FOC: w = (1−α)·Y/N. Pins the wage in ha_full (drop labor_mkt_res_F there). - w_res_F = w_F - (1 - alpha_F) * Y_F / N_F - return w_res_F - - - - -@simple -def intermediation_IC_F(nu_K_F, nu_bF_F, nu_bD_F, eta_F, - Q_F, K_F, q_b_F, q_b_D, b_F_F, b_D_F, n_inter_F, - lambda_gk_F, Delta_bF_F, Delta_bD_F, theta_F, p, - def_rate_F, def_rate_D, psi_lambda_B_F): - kappa_F = Q_F * K_F / n_inter_F - phi_bF_F = q_b_F * b_F_F / (p * n_inter_F) - phi_bD_F = q_b_D * b_D_F / (p * n_inter_F) - # GK multi-asset IC — see intermediation_IC_D for derivation. - Delta_bF_eff = Delta_bF_F + psi_lambda_B_F * def_rate_F(+1) - Delta_bD_eff = Delta_bD_F + psi_lambda_B_F * def_rate_D(+1) - value_F = nu_K_F * kappa_F + nu_bF_F * phi_bF_F + nu_bD_F * phi_bD_F + eta_F - theta_tgt_F = (value_F / lambda_gk_F - + (1 - Delta_bF_eff) * phi_bF_F - + (1 - Delta_bD_eff) * phi_bD_F) - ic_res_F = theta_F - theta_tgt_F - return ic_res_F - -@simple -def bank_return_F(theta_F, rk_F, rdep_F, b_F_F, b_D_F, n_inter_F, - rb_actual_F, rb_actual_D, q_b_F, q_b_D, p): - phi_bF_lag_F = q_b_F(-1) * b_F_F(-1) / (p(-1) * n_inter_F(-1)) - phi_bD_lag_F = q_b_D(-1) * b_D_F(-1) / (p(-1) * n_inter_F(-1)) - kappa_lag_F = theta_F(-1) - phi_bF_lag_F - phi_bD_lag_F - # W-2 fix: bonds are D-good claims; realized F-good return includes the - # terms-of-trade revaluation p(-1)/p. Without it the bank's measured income - # misses capital gains/losses on the bond book and Walras fails (audit.md W-2). - rb_F_fg = (1 + rb_actual_F) * p(-1) / p - 1 - rb_D_fg = (1 + rb_actual_D) * p(-1) / p - 1 - # T-2 fix: funding cost on the t-1 balance sheet is the rate locked at t-1. - rn_F = (kappa_lag_F * (rk_F - rdep_F(-1)) - + phi_bF_lag_F * (rb_F_fg - rdep_F(-1)) - + phi_bD_lag_F * (rb_D_fg - rdep_F(-1)) - + rdep_F(-1)) - return rn_F - -@simple -def intermediation_P1_F(rk_F, rb_actual_F, rb_actual_D, rdep_F, - nu_K_F, nu_bF_F, nu_bD_F, eta_F, - lambda_gk_F, theta_F, SDF_banker_F, f_F): - Omega_p1_F = f_F + (1 - f_F) * lambda_gk_F * theta_F(+1) - # T-2 fix: the deposit rate for the t->t+1 holding period is rdep_F (locked at t). - nu_K_res_F = nu_K_F - SDF_banker_F * Omega_p1_F * (rk_F(+1) - rdep_F) - nu_bF_res_F = nu_bF_F - SDF_banker_F * Omega_p1_F * (rb_actual_F(+1) - rdep_F) - nu_bD_res_F = nu_bD_F - SDF_banker_F * Omega_p1_F * (rb_actual_D(+1) - rdep_F) - eta_res_F = eta_F - SDF_banker_F * Omega_p1_F * (1 + rdep_F) - return nu_K_res_F, nu_bF_res_F, nu_bD_res_F, eta_res_F - -@simple -def k_balance_sheet_F(Q_F, theta_F, n_inter_F, K_F, b_F_F, b_D_F, q_b_F, q_b_D, p): - K_res_F = Q_F * K_F + (q_b_F * b_F_F + q_b_D * b_D_F) / p - theta_F * n_inter_F - return K_res_F - - -@simple -def cap_adj_cost_inter_F(K_F, rk_F, chi0_F, chi1_F, chi2_F): - arg_F = (K_F - (1.0 + rk_F) * K_F(-1)) / (K_F(-1) + chi0_F) - Phi_F = (chi1_F / chi2_F) * (arg_F ** 2) ** (chi2_F / 2) * (K_F(-1) + chi0_F) - return Phi_F - - -@simple -def macro_pru_tax_F(b_F_F, b_D_F, def_rate_F, T0_F, T1_F, p): - # Tax base = total bond holdings (face value in D-goods); convert to F-goods via /p. - tau_mp_F = T0_F + T1_F * def_rate_F - T_F = tau_mp_F * (b_F_F + b_D_F) / p - return tau_mp_F, T_F - - -@simple -def intermediation_P2_F(rn_F, n_inter_F, m_F, f_F, cap_profit_F): - # Writedown terms removed: rb_actual already embeds the default haircut via - # rb_actual = (1 − def·haircut)/q_b(-1) − 1, so deducting them again double-counts. - gross_income_F = (1 + rn_F) * n_inter_F(-1) + cap_profit_F - n_inter_val_F = (1 - f_F) * gross_income_F + m_F - n_inter_F - return n_inter_val_F - -@simple -def banker_div_res_F(rn_F, n_inter_F, div_F, m_F, f_F, cap_profit_F, Phi_F, T_F): - gross_income_F = (1 + rn_F) * n_inter_F(-1) + cap_profit_F - net_div_F = f_F * gross_income_F - m_F - Phi_F - T_F - div_res_F = div_F - net_div_F - return div_res_F - -@simple -def intermediation_P3_F(Q_F, K_F, n_inter_F, b_F_F, b_D_F, q_b_F, q_b_D, p): - D_supply_F = Q_F * K_F + (q_b_F * b_F_F + q_b_D * b_D_F) / p - n_inter_F - return D_supply_F - -@simple -def bond_price_ss_F(SDF_banker_F, def_rate_F, recovery_rate_F, delta_b_F, zeta_writeoff_F, writeoff_enabled_F): - haircut_F = 1.0 - recovery_rate_F - haircut_mult_F = writeoff_enabled_F - surv_cont_F = 1.0 - zeta_writeoff_F * def_rate_F * haircut_F * haircut_mult_F - q_b_F = ( - SDF_banker_F * delta_b_F * (1.0 - def_rate_F * haircut_F * haircut_mult_F) - / (1.0 - SDF_banker_F * (1.0 - delta_b_F) * surv_cont_F) - ) - return q_b_F - - -@simple -def domestic_bond_foc_F(rb_actual_F, rdep_F, b_F_F, n_inter_F, q_b_F, - phi_bF_F_ss, psi_bF_F, excess_return_bF_F_ss, tau_mp_F, p): - phi_bF_F = q_b_F * b_F_F / (p * n_inter_F) - # Expected F-good return on D-good bond: (1+rb)·p/p(+1) − 1 - rb_F_fg_next = (1 + rb_actual_F(+1)) * p / p(+1) - 1 - rb_F_res = (rb_F_fg_next - rdep_F(+1)) - excess_return_bF_F_ss \ - - psi_bF_F * (phi_bF_F - phi_bF_F_ss) \ - - tau_mp_F - return rb_F_res - - -@simple -def government_default_F(shock_def_F, b_gov_F, Y_ss_F, b_gov_ss_F, - def_scale_F, def_curvature_F, def_offset_F): - debt_ratio_F = b_gov_F(-1) / Y_ss_F - ss_ratio_F = b_gov_ss_F / Y_ss_F - def_rate_F = shock_def_F + def_scale_F * ( - (debt_ratio_F + def_offset_F) ** def_curvature_F - - (ss_ratio_F + def_offset_F) ** def_curvature_F - ) - return def_rate_F - -@simple -def tax_rule_F(b_gov_F, b_gov_ss_F, phi_lamb_F): - T_ls_F = phi_lamb_F * (b_gov_F(-1) - b_gov_ss_F) - return T_ls_F - - -@simple -def capital_producer_profit_F(Q_F, K_F, I_F, delta_F, mpk_F): - # W-1 fix under the K_t convention — see capital_producer_profit_D. - cap_profit_F = Q_F * (K_F - (1 - delta_F) * K_F(-1)) - I_F + mpk_F * (K_F - K_F(-1)) - return cap_profit_F - -@simple -def budget_residual_F(b_gov_F, G_F, TAX_F, q_b_F, def_rate_F, recovery_rate_F, zeta_writeoff_F, p, P_CES_F, delta_b_F, writeoff_enabled_F): - haircut_F = 1.0 - recovery_rate_F - haircut_mult_F = writeoff_enabled_F - surv_cont_F = 1.0 - zeta_writeoff_F * def_rate_F * haircut_F * haircut_mult_F - coupon_F = delta_b_F * (1.0 - def_rate_F * haircut_F * haircut_mult_F) * b_gov_F(-1) - net_issuance_F = q_b_F * (b_gov_F - surv_cont_F * (1.0 - delta_b_F) * b_gov_F(-1)) - b_gov_res_F = (coupon_F - net_issuance_F) / p + G_F - P_CES_F * TAX_F - return b_gov_res_F - - -@simple -def divert_bond_foc_F(rb_actual_F, rdep_F, b_F_F, n_inter_F, q_b_F, - phi_bF_F_ss, psi_bF_F, excess_return_bF_F_ss, tau_mp_F, p, - psi_spread_F, def_rate_F): - phi_bF_F = q_b_F * b_F_F / (p * n_inter_F) - # IC-theory derived required spread: additive default loading independent of SS excess return. - # psi_spread_F = lambda_gk_F * psi_lambda_B_F / (beta_inter_F * Omega_F), computed in _apply_ss_anchors. - req_spread = excess_return_bF_F_ss + psi_spread_F * def_rate_F(+1) - # W-3 fix: expected F-good return on the D-good-denominated bond converts with - # p/p(+1), as in domestic_bond_foc_F and divert_portfolio_adj. - rb_F_fg_next = (1 + rb_actual_F(+1)) * p / p(+1) - 1 - # T-2 fix: compare t+1 bond return with rdep_F locked at t. - rb_F_res = (rb_F_fg_next - rdep_F) - req_spread \ - - psi_bF_F * (phi_bF_F - phi_bF_F_ss) \ - - tau_mp_F - return rb_F_res - - -@simple -def welfare_agg_F(X_F, C_F_ss): - # GHH utility composite normalised by SS consumption. - # In IRFs the deviation ΔX_F/C_F_ss gives welfare change as a fraction of SS consumption. - U_F = X_F / C_F_ss - return U_F diff --git a/code/equations_global.py b/code/equations_global.py deleted file mode 100644 index 5db52c5..0000000 --- a/code/equations_global.py +++ /dev/null @@ -1,96 +0,0 @@ -from sequence_jacobian import simple - - -@simple -def trade_balance(p, IM_D, IM_F): - NX_D = IM_F - p * IM_D - NX_F = IM_D - IM_F / p - return NX_D, NX_F - - - -@simple -def external_account_D(NX_D, q_b_D, q_b_F, b_F_D, b_D_F,rb_actual_F, rb_actual_D): - receipts_from_F_bonds = (1 + rb_actual_F) * q_b_F(-1) * b_F_D(-1) - payments_on_D_bonds = (1 + rb_actual_D) * q_b_D(-1) * b_D_F(-1) - nfa_D = q_b_F * b_F_D - q_b_D * b_D_F - ca_res_D = (NX_D + receipts_from_F_bonds - payments_on_D_bonds- nfa_D) - return nfa_D, ca_res_D - - -@simple -def global_goods_mkt(goods_mkt_D, goods_mkt_F, p): - global_goods_res = goods_mkt_D + p * goods_mkt_F - return global_goods_res - - - -@simple -def domestic_bond_clearing(b_gov_D, b_gov_F, b_D_F, b_F_D): - b_D_D = b_gov_D - b_D_F - b_F_F = b_gov_F - b_F_D - return b_D_D, b_F_F - - -@simple -def bond_yield(q_b_D, q_b_F, delta_b_D, delta_b_F): - # Woodford perpetuity holding-period return: rb = delta_b * (1/q_b - 1) - # This equals rb_actual in SS and gives the correct annualised yield. - # The old formula 1/q_b - 1 treated q_b as a zero-coupon price and - # overstated the yield by a factor of 1/delta_b (~20×). - rb_D = delta_b_D * (1.0 / q_b_D - 1.0) - rb_F = delta_b_F * (1.0 / q_b_F - 1.0) - spread_rb = rb_D - rb_F - return rb_D, rb_F, spread_rb - - -@simple -def portfolio_level_anchors(b_F_D_anchor, b_D_F_anchor): - b_F_D_ss = b_F_D_anchor - b_D_F_ss = b_D_F_anchor - return b_F_D_ss, b_D_F_ss - - -@simple -def portfolio_adj_cost(rb_actual_F, rb_actual_D, rdep_D, rdep_F, - b_F_D, b_D_F, - b_F_D_ss, b_D_F_ss, - psi_bF_D, psi_bD_F, - excess_return_F_D_ss, excess_return_D_F_ss, - tau_mp_D, tau_mp_F, p): - # Level penalty on face-value bond stocks anchors the external position level, - # not only its composition relative to net worth. - # Expected D-good return on F-bonds: (1+rb_F)·p(+1)/p − 1 - rb_F_dg_next = (1 + rb_actual_F(+1)) * p(+1) / p - 1 - b_F_D_res = (rb_F_dg_next - rdep_D(+1)) - excess_return_F_D_ss \ - - psi_bF_D * (b_F_D - b_F_D_ss) \ - - tau_mp_D - - # Expected F-good return on D-bonds: (1+rb_D)·p/p(+1) − 1 - rb_D_fg_next = (1 + rb_actual_D(+1)) * p / p(+1) - 1 - b_D_F_res = (rb_D_fg_next - rdep_F(+1)) - excess_return_D_F_ss \ - - psi_bD_F * (b_D_F - b_D_F_ss) \ - - tau_mp_F - - return b_F_D_res, b_D_F_res - - -@simple -def divert_portfolio_adj(rb_actual_F, rb_actual_D, rdep_D, rdep_F, p, - b_F_D, b_D_F, b_F_D_ss, b_D_F_ss, psi_bF_D, psi_bD_F, - excess_return_F_D_ss, excess_return_D_F_ss, tau_mp_D, tau_mp_F, - psi_spread_D, psi_spread_F, def_rate_D, def_rate_F): - # D holds F-bonds (F-good claim -> convert with p); issuer = F - rb_F_dg_next = (1 + rb_actual_F(+1)) * p(+1) / p - 1 - # IC-theory derived required premium: D-bank IC parameters govern D-bank's FOC on F-bonds - prem_FD = excess_return_F_D_ss + psi_spread_D * def_rate_F(+1) - # T-2 fix: deposit rate for the t->t+1 holding period is locked at t (rdep, not rdep(+1)). - b_F_D_res = (rb_F_dg_next - rdep_D) - prem_FD \ - - psi_bF_D * (b_F_D - b_F_D_ss) - tau_mp_D - # F holds D-bonds (D-good claim -> convert with p); issuer = D - rb_D_fg_next = (1 + rb_actual_D(+1)) * p / p(+1) - 1 - # IC-theory derived required premium: F-bank IC parameters govern F-bank's FOC on D-bonds - prem_DF = excess_return_D_F_ss + psi_spread_F * def_rate_D(+1) - b_D_F_res = (rb_D_fg_next - rdep_F) - prem_DF \ - - psi_bD_F * (b_D_F - b_D_F_ss) - tau_mp_F - return b_F_D_res, b_D_F_res diff --git a/code/full_model.py b/code/full_model.py deleted file mode 100644 index e9eb816..0000000 --- a/code/full_model.py +++ /dev/null @@ -1,163 +0,0 @@ -import sys -import copy -import numpy as np -import sequence_jacobian as sj -from sequence_jacobian import simple, combine - -from equations_D import ( - capital_adj_D, labor_D, labor_market_D, labor_demand_D, - intermediation_IC_D, bank_return_D, intermediation_P1_D, - k_balance_sheet_D, cap_adj_cost_inter_D, macro_pru_tax_D, - intermediation_P2_D, banker_div_res_D, intermediation_P3_D, - government_default_D, divert_bond_foc_D, - tax_rule_D, capital_producer_profit_D, budget_residual_D, - ces_price_D, import_demand_D, deposit_return_D, - bond_return_D, sdf_D, sdf_banker_ss_D, sdf_banker_D, ghh_composite_D, - welfare_agg_D, market_clearing_D, -) -from equations_F import ( - capital_adj_F, labor_F, labor_market_F, labor_demand_F, - intermediation_IC_F, bank_return_F, intermediation_P1_F, - k_balance_sheet_F, cap_adj_cost_inter_F, macro_pru_tax_F, - intermediation_P2_F, banker_div_res_F, intermediation_P3_F, - government_default_F, divert_bond_foc_F, - tax_rule_F, capital_producer_profit_F, budget_residual_F, - ces_price_F, import_demand_F, deposit_return_F, - bond_return_F, sdf_F, sdf_banker_ss_F, sdf_banker_F, ghh_composite_F, - welfare_agg_F, market_clearing_F, -) -from equations_global import ( - trade_balance, domestic_bond_clearing, - portfolio_level_anchors, divert_portfolio_adj, bond_yield, - global_goods_mkt, external_account_D, -) - - -def build_and_solve(ss_results): - sys.setrecursionlimit(5000) - - ss_final = ss_results['ss_final'] - cali_D = ss_results['cali_D'] - cali_F = ss_results['cali_F'] - calibration_start = ss_results['calibration_start'] - - # ── Inner solved blocks for GK Bellman + IC ─────────────────────────────── - financial_solved_D = combine([ - intermediation_P1_D, intermediation_IC_D, - ]).solved( - unknowns={'nu_K_D': float(cali_D['nu_K_D']), - 'nu_bD_D': float(cali_D['nu_bD_D']), - 'nu_bF_D': float(cali_D['nu_bF_D']), - 'eta_D': float(cali_D['eta_D']), - 'theta_D': float(cali_D['theta_D'])}, - targets=['nu_K_res_D', 'nu_bD_res_D', 'nu_bF_res_D', 'eta_res_D', 'ic_res_D'], - solver='broyden_custom' - ) - financial_solved_F = combine([ - intermediation_P1_F, intermediation_IC_F, - ]).solved( - unknowns={'nu_K_F': float(cali_F['nu_K_F']), - 'nu_bF_F': float(cali_F['nu_bF_F']), - 'nu_bD_F': float(cali_F['nu_bD_F']), - 'eta_F': float(cali_F['eta_F']), - 'theta_F': float(cali_F['theta_F'])}, - targets=['nu_K_res_F', 'nu_bF_res_F', 'nu_bD_res_F', 'eta_res_F', 'ic_res_F'], - solver='broyden_custom' - ) - - # ── Full dynamic model ──────────────────────────────────────────────────── - ha_full = sj.create_model([ - # Country D - deposit_return_D, tax_rule_D, hh_extended_D, ghh_composite_D, - sdf_D, sdf_banker_D, government_default_D, financial_solved_D, - bond_return_D, bank_return_D, cap_adj_cost_inter_D, macro_pru_tax_D, - intermediation_P2_D, intermediation_P3_D, k_balance_sheet_D, - capital_adj_D, capital_producer_profit_D, budget_residual_D, - labor_D, labor_market_D, labor_demand_D, banker_div_res_D, - market_clearing_D, welfare_agg_D, - # Country F - deposit_return_F, tax_rule_F, hh_extended_F, ghh_composite_F, - sdf_F, sdf_banker_F, government_default_F, financial_solved_F, - bond_return_F, bank_return_F, cap_adj_cost_inter_F, macro_pru_tax_F, - intermediation_P2_F, intermediation_P3_F, k_balance_sheet_F, - capital_adj_F, capital_producer_profit_F, budget_residual_F, - labor_F, labor_market_F, labor_demand_F, banker_div_res_F, - market_clearing_F, welfare_agg_F, - # Global - ces_price_D, import_demand_D, ces_price_F, import_demand_F, - trade_balance, external_account_D, domestic_bond_clearing, - bond_yield, portfolio_level_anchors, divert_portfolio_adj, - divert_bond_foc_D, divert_bond_foc_F, global_goods_mkt, - ], name="Full 2-Country MU HANK — GHH Preferences, Flex Price & Wage, No CB") - - # ── 23×23 system ────────────────────────────────────────────────────────── - unknowns_tp = [ - 'K_D', 'n_inter_D', 'div_D', 'I_D', 'Q_D', 'b_gov_D', 'N_D', 'b_F_D', 'w_D', 'rdep_D', - 'K_F', 'n_inter_F', 'div_F', 'I_F', 'Q_F', 'b_gov_F', 'N_F', 'b_D_F', 'w_F', 'rdep_F', - 'p', 'q_b_D', 'q_b_F', - ] - targets_tp = [ - 'deposit_mkt_D', 'K_res_D', 'n_inter_val_D', 'div_res_D', - 'capital_res_D', 'q_res_D', 'b_gov_res_D', 'b_F_D_res', - 'labor_mkt_res_D', 'w_res_D', - 'deposit_mkt_F', 'K_res_F', 'n_inter_val_F', 'div_res_F', - 'capital_res_F', 'q_res_F', 'b_gov_res_F', 'b_D_F_res', - 'labor_mkt_res_F', 'w_res_F', - 'goods_mkt_D', 'rb_D_res', 'rb_F_res', - ] - T = 500 - - # ── Jacobian ────────────────────────────────────────────────────────────── - exogenous = ['Z_D', 'shock_def_D', 'Z_F', 'shock_def_F'] - print(f"Computing Jacobian G (T={T}, {len(exogenous)} exogenous inputs)...") - G = ha_full.solve_jacobian(ss_final, unknowns=unknowns_tp, targets=targets_tp, - inputs=exogenous, T=T) - print("G computed successfully.") - - # ── Shocks ──────────────────────────────────────────────────────────────── - rho_Z_D = 0.8 - dZ_D = 0.01 * rho_Z_D ** np.arange(T) - rho_def_D = 0.8 - dShock_def_D = 0.01 * rho_def_D ** np.arange(T) - - irfs_Z_D = G @ { - 'Z_D': dZ_D, 'Z_F': np.zeros(T), - 'shock_def_D': np.zeros(T), 'shock_def_F': np.zeros(T) - } - irfs_def_D = G @ { - 'Z_D': np.zeros(T), 'Z_F': np.zeros(T), - 'shock_def_D': dShock_def_D, 'shock_def_F': np.zeros(T) - } - - # ── Stability check ─────────────────────────────────────────────────────── - print("\n=== Stability check: debt level at t=499 (should be near 0) ===") - print(f" irfs_Z_D ['b_gov_D'][499] = {irfs_Z_D['b_gov_D'][499]:.6f}") - print(f" irfs_def_D['b_gov_D'][499] = {irfs_def_D['b_gov_D'][499]:.6f}") - phi_lamb = calibration_start['phi_lamb_D'] - print(f" ρ_b (partial-eq.) = {round((0.953 * 0.95 + 0.05 - phi_lamb) / 0.953, 4)}" - " [target < 0.95]") - print(f" n_inter_D[0] on default shock = {irfs_def_D['n_inter_D'][0]*100:+.4f}%" - " (negative = doom loop correct sign)") - print(f" Y_D[0] on default shock = {irfs_def_D['Y_D'][0]*100:+.4f}%" - " (negative = correct sign)") - - return { - 'ha_full': ha_full, - 'financial_solved_D': financial_solved_D, - 'financial_solved_F': financial_solved_F, - 'G': G, - 'ss_final': ss_final, - 'calibration_start': calibration_start, - 'unknowns_tp': unknowns_tp, - 'targets_tp': targets_tp, - 'T': T, - 'dZ_D': dZ_D, - 'dShock_def_D': dShock_def_D, - 'irfs_Z_D': irfs_Z_D, - 'irfs_def_D': irfs_def_D, - } - - -# Needed by full_model.py and tpi.py: import the hh_extended blocks -from equations_D import hh_extended_D # noqa: F401 (re-export for tpi.py) -from equations_F import hh_extended_F # noqa: F401 diff --git a/code/global/bank.py b/code/global/bank.py new file mode 100644 index 0000000..5085cce --- /dev/null +++ b/code/global/bank.py @@ -0,0 +1,566 @@ +"""Two-country Gertler-Karadi financial intermediary block. + +Each country has a bank that holds three assets: + D-bank: domestic capital K_D, domestic D-bonds b_D_D, foreign F-bonds b_F_D + F-bank: domestic capital K_F, domestic F-bonds b_F_F, foreign D-bonds b_D_F + +All bond quantities and prices are expressed in D-good units (D is the +monetary-union numeraire). F-bank balance-sheet quantities convert to +F-goods by dividing by the real exchange rate p. + +NOTE on p convention: p = price of F-goods in D-good units (D-goods per +F-good). An increase in p means F-goods are more expensive. This is the +OPPOSITE of the real exchange rate convention used in some macro texts +(where q = P*/P = cost of domestic in foreign). The trade.py formulas +and all FX conversions in this file are consistent with this definition. + +Multi-asset incentive constraint (IC): + D-bank (D-goods): + lambda_K · Q_D · K_D + lambda_bD · Q_bD · b_D_D + lambda_bF · Q_bF · b_F_D ≤ alpha_D · n_D + + F-bank (F-goods, bonds divided by p): + lambda_K · Q_F · K_F + lambda_bF · Q_bF · b_F_F/p + lambda_bD · Q_bD · b_D_F/p ≤ alpha_F · n_F + +When the IC binds (it always does in deterministic perfect-foresight), the +linear value function V_t(n) = alpha_t · n (Bocola 2016 Result 1, extended +to three assets) gives closed-form backward-pass equations: + + Omega_{t+1} = beta_inter · [(1−f) + f · alpha_{t+1}] + mu_t = Omega_{t+1} · (rk_{t+1} − rdep_t) / lambda_K [capital FOC] + alpha_t = Omega_{t+1} · (1 + rdep_t) / (1 − mu_t) [Bellman] + Q_bD_t = surv_D_{t+1}·(delta_b_D + (1−delta_b_D)·Q_bD_{t+1}) / (1 + rdep_t + lambda_bD·mu_t/Omega_{t+1}) + Q_bF_t = surv_F_{t+1}·(delta_b_F + (1−delta_b_F)·Q_bF_{t+1}) / (1 + rdep_F_t + lambda_bF·mu_F_t/Omega_F_{t+1}) + surv_{t+1} = 1 − def_{t+1}·(1−recovery)·writeoff_enabled + At SS or writeoff_enabled=0: surv=1, collapsing to Q_ss = delta_b/(rdep+delta_b+IC_spread). + +The HM bond pricing here uses the IC-derived no-arbitrage relationship: +the excess return on each bond over the deposit rate must equal the IC +spread lambda_b · mu / Omega. The survival factor surv_{t+1} prices in +expected write-off losses under perfect foresight; with writeoff_enabled=0 +surv=1 and the formula reduces to the risk-free HM recursion. + +Cross-border positions from portfolio adjustment-cost FOC: + D-bank holds F-bonds: b_F_D_t from FOC + rb_F_in_D_{t+1} − rdep_D_t = excess_return_F_D_ss + psi_bF_D · (b_F_D_t − b_F_D_ss) + + lambda_bF_D · mu_D_t / Omega_D_{t+1} + → b_F_D_t = b_F_D_ss + [rb_F_in_D_{t+1} − rdep_D_t + − excess_return_F_D_ss − lambda_bF_D · mu_D_t / Omega_D_{t+1}] + / psi_bF_D + + (and symmetrically for F-bank's D-bond holding b_D_F_t) + +Bond market clearing (both banks hold government supply jointly): + b_D_D_t + b_D_F_t = B_gov_D (D-bonds: D-bank + F-bank = supply) + b_F_D_t + b_F_F_t = B_gov_F (F-bonds: D-bank + F-bank = supply) + +Bocola-Dovis (2019) rollover-risk spread: + lbD_D_eff = lbD_D + psi_bd_D · xi_{t+1} (D-bank IC for D-bonds) + lbD_F_eff = lbD_F + psi_bd_D · xi_{t+1} (F-bank IC for D-bonds — contagion) +The sunspot xi_{t+1} ∈ [0,1] is the next-period run probability, supplied +exogenously from solve_transition_bd(). It tightens the IC when rollover +risk is high, lowering Q_bD. With psi_bd_D=0 this collapses to base GK. + +Net worth has two characterisations that must agree (outer residual): + n_IC = IC-binding allocation size (desired by bank given alpha) + n_ACCUM = forward accumulation (true state, carried from last period) +Their difference (n_IC − n_ACCUM) / n_ss is the capital-market residual +fed to the outer Newton solver to pin Kap_path for each country. +""" +import numpy as np +from scipy.optimize import brentq + + +# ───────────────────────────────────────────────────────────────────────────── +# Steady-state helpers +# ───────────────────────────────────────────────────────────────────────────── + +def _alpha_ss_fixed_point(beta_inter, f, lambda_K, rk_ss, rdep_ss, + v_lo=1e-6, v_hi=1e6, n_scan=300): + """Solve the self-referential fixed point for alpha_ss. + + At SS: alpha = Omega(alpha) · (1+rdep) / (1−mu(alpha)) + where Omega = beta_inter · [(1−f) + f·alpha] + mu = Omega · (rk − rdep) / lambda_K + + Uses scan-then-brentq (identical pattern to old bank.py). + """ + def resid(a): + Omega = beta_inter * ((1 - f) + f * a) + mu = Omega * (rk_ss - rdep_ss) / lambda_K + if mu >= 1.0: + return np.inf + return Omega * (1 + rdep_ss) / (1 - mu) - a + + grid = np.geomspace(v_lo, v_hi, n_scan) + vals = np.array([resid(v) for v in grid]) + fin = np.isfinite(vals) + sc = np.where(np.diff(np.sign(vals[fin])) != 0)[0] + if len(sc) == 0: + raise RuntimeError( + f"No sign change in alpha_ss fixed point (rk_ss={rk_ss:.6f}, " + f"rdep_ss={rdep_ss:.6f}); check lambda_K / beta_inter calibration." + ) + gf = grid[fin] + i = sc[0] + alpha_ss = brentq(resid, gf[i], gf[i + 1], xtol=1e-13, rtol=1e-13) + Omega_ss = beta_inter * ((1 - f) + f * alpha_ss) + mu_ss = Omega_ss * (rk_ss - rdep_ss) / lambda_K + return alpha_ss, mu_ss, Omega_ss + + +def steady_state_bank(cal, rk_ss, Kap_ss, Q_bD_ss, Q_bF_ss, + b_dom_ss, b_for_ss, p_ss, country="D"): + """Steady-state bank block for one country. + + Arguments + --------- + rk_ss : steady-state capital return (from outer solve) + Kap_ss : capital stock (from Cobb-Douglas demand) + Q_bD_ss : D-bond price at SS (from government.py) + Q_bF_ss : F-bond price at SS + b_dom_ss: domestic-bond holding (D-good units; = B_gov_D - b_D_F_ss or B_gov_F - b_F_D_ss) + b_for_ss: foreign-bond holding (D-good units; e.g. b_F_D_ss for D-bank) + p_ss : real exchange rate at SS (= 1 at symmetric SS) + country : "D" or "F" + + Returns + ------- + dict with keys: alpha_ss, mu_ss, Omega_ss, n_ss, n_ss_IC, n_ss_ACCUM, + kappa_ss, phi_bdom_ss, phi_bfor_ss, theta_ss, + rn_ss, div_ss, entrant_ss, Dep_supply_ss, + rb_dom_ss, rb_for_ss, + lambda_K, lambda_bD, lambda_bF, Kap_ss + """ + f = cal[f"f_{country}"] + rdep_ss = cal[f"r_dep_{country}_target"] + beta_inter = cal[f"beta_inter_{country}"] + lambda_K = cal[f"lambda_K_{country}"] + # Divertability of each bond type as seen by this bank + lambda_bD = cal[f"lambda_bD_{country}"] # D-bond divertability for this bank + lambda_bF = cal[f"lambda_bF_{country}"] # F-bond divertability for this bank + omega_ent = cal[f"omega_ent_{country}"] + delta_b_D = cal["delta_b_D"] + delta_b_F = cal["delta_b_F"] + + alpha_ss, mu_ss, Omega_ss = _alpha_ss_fixed_point( + beta_inter, f, lambda_K, rk_ss, rdep_ss + ) + + # IC-consistent SS bond prices: GK excess-return on each bond = lambda_b*mu/Omega. + # Bond FOC at SS gives the MARKET price Q_bX = delta_bX/(rdep+delta_bX+IC_spread). + # This is the fixed point of the backward pricing recurrence used in solve_bank_paths. + IC_spread_dom = lambda_bD * mu_ss / Omega_ss # IC spread on domestic bond + IC_spread_for = lambda_bF * mu_ss / Omega_ss # IC spread on foreign bond + + if country == "D": + Q_bdom_ss = delta_b_D / (rdep_ss + delta_b_D + IC_spread_dom) # D-bond market price + Q_bfor_ss = delta_b_F / (rdep_ss + delta_b_F + IC_spread_for) # F-bond market price + else: + # F-bank: domestic = F-bonds (divertability lambda_bD is lambda_bF_F in cal) + # foreign = D-bonds (divertability lambda_bF is lambda_bD_F in cal) + Q_bdom_ss = delta_b_F / (rdep_ss + delta_b_F + IC_spread_dom) # F-bond price + Q_bfor_ss = delta_b_D / (rdep_ss + delta_b_D + IC_spread_for) # D-bond price + + # Bond excess returns at SS (= IC spread above deposit rate) + rb_dom_ss = rdep_ss + IC_spread_dom + rb_for_ss = rdep_ss + IC_spread_for + + # Scale factor for F-bank: F-bank balance sheet in F-goods (divide by p) + p_scale = 1.0 if country == "D" else p_ss + + # n from IC binding: n_IC = (lambda_K·Q_K·K + lambda_bD·Q_bD·b_dom + lambda_bF·Q_bF·b_for) / alpha + # For F-bank, bonds are in D-goods; divide by p to get F-good IC value + if country == "D": + ic_numerator = (lambda_K * Kap_ss + + lambda_bD * Q_bdom_ss * b_dom_ss + + lambda_bF * Q_bfor_ss * b_for_ss) + else: + ic_numerator = (lambda_K * Kap_ss + + (lambda_bD * Q_bdom_ss * b_dom_ss + + lambda_bF * Q_bfor_ss * b_for_ss) / p_ss) + + n_ss_IC = ic_numerator / alpha_ss + + # n from forward accumulation: D·n = excess returns + entrant transfer + # D = 1 − (1−f)·(1+rdep) + D_val = 1.0 - (1 - f) * (1 + rdep_ss) + if D_val <= 0: + raise ValueError(f"[{country}] D={D_val} ≤ 0: no stationary net-worth rest point.") + + if country == "D": + total_assets = Kap_ss + Q_bdom_ss * b_dom_ss + Q_bfor_ss * b_for_ss + n_ss_ACCUM = ( + ((1 - f) * (rk_ss - rdep_ss) + omega_ent) * Kap_ss + + ((1 - f) * IC_spread_dom + omega_ent) * Q_bdom_ss * b_dom_ss + + ((1 - f) * IC_spread_for + omega_ent) * Q_bfor_ss * b_for_ss + ) / D_val + else: + # F-bank: assets in F-goods (divide bond values by p_ss) + Kap_val = Kap_ss + bdom_val = Q_bdom_ss * b_dom_ss / p_ss # F-bonds in F-goods + bfor_val = Q_bfor_ss * b_for_ss / p_ss # D-bonds in F-goods + total_assets = Kap_val + bdom_val + bfor_val + n_ss_ACCUM = ( + ((1 - f) * (rk_ss - rdep_ss) + omega_ent) * Kap_val + + ((1 - f) * IC_spread_dom + omega_ent) * bdom_val + + ((1 - f) * IC_spread_for + omega_ent) * bfor_val + ) / D_val + + n_ss = n_ss_ACCUM + if n_ss <= 0: + raise ValueError(f"[{country}] n_ss={n_ss:.4f} ≤ 0 at rk_ss={rk_ss:.6f}.") + + if country == "D": + kappa_ss = Kap_ss / n_ss + phi_bdom_ss = Q_bdom_ss * b_dom_ss / n_ss + phi_bfor_ss = Q_bfor_ss * b_for_ss / n_ss + else: + kappa_ss = Kap_ss / n_ss + phi_bdom_ss = Q_bdom_ss * b_dom_ss / (p_ss * n_ss) + phi_bfor_ss = Q_bfor_ss * b_for_ss / (p_ss * n_ss) + + theta_ss = kappa_ss + phi_bdom_ss + phi_bfor_ss + + rn_ss = (kappa_ss * (rk_ss - rdep_ss) + + phi_bdom_ss * (rb_dom_ss - rdep_ss) + + phi_bfor_ss * (rb_for_ss - rdep_ss) + + rdep_ss) + gross_income_ss = (1 + rn_ss) * n_ss + entrant_ss = omega_ent * total_assets + div_ss = f * gross_income_ss - entrant_ss + Dep_supply_ss = (theta_ss - 1) * n_ss + + return dict( + alpha_ss=alpha_ss, mu_ss=mu_ss, Omega_ss=Omega_ss, + n_ss=n_ss, n_ss_IC=n_ss_IC, n_ss_ACCUM=n_ss_ACCUM, + kappa_ss=kappa_ss, phi_bdom_ss=phi_bdom_ss, phi_bfor_ss=phi_bfor_ss, + theta_ss=theta_ss, rn_ss=rn_ss, div_ss=div_ss, entrant_ss=entrant_ss, + Dep_supply_ss=Dep_supply_ss, + rb_dom_ss=rb_dom_ss, rb_for_ss=rb_for_ss, + # IC-consistent bond prices (used by steady_state.py to update Tax_ss) + Q_bdom_IC=Q_bdom_ss, Q_bfor_IC=Q_bfor_ss, + IC_spread_dom=IC_spread_dom, IC_spread_for=IC_spread_for, + lambda_K=lambda_K, lambda_bD=lambda_bD, lambda_bF=lambda_bF, + Kap_ss=Kap_ss, total_assets_ss=total_assets, + ) + + +# ───────────────────────────────────────────────────────────────────────────── +# Transition-path bank block +# ───────────────────────────────────────────────────────────────────────────── + +def solve_bank_paths(Kap_D, Kap_F, Q_D, Q_F, rk_D, rk_F, + rdep_D, rdep_F, p_path, B_gov_D, B_gov_F, + cal, ss_bk_D, ss_bk_F, + def_D_path=None, def_F_path=None, + sunspot_D_path=None, sunspot_F_path=None): + """Transition-path bank block for both countries simultaneously. + + Arguments + --------- + Kap_D, Kap_F : capital paths (T,) — outer Newton unknowns + Q_D, Q_F : Tobin's Q paths (T,) — from capital.py + rk_D, rk_F : capital return paths (T,) — from capital.py + rdep_D, rdep_F: deposit rate paths (T,) — outer Newton unknowns + p_path : real exchange rate path (T,) — outer Newton unknown + B_gov_D/F : total government bond supply (scalars, fixed) + cal : calibration dict + ss_bk_D/F : steady-state bank dicts from steady_state_bank() + def_D_path, def_F_path: default rate paths (T,) or None (→ zeros) + + Returns + ------- + dict with keys (all length T): + For D: alpha_D, mu_D, Q_bD, b_F_D, b_D_D, n_IC_D, n_D, rn_D, div_D, + theta_D, Dep_supply_D, rb_D (realised return on D-bonds) + For F: alpha_F, mu_F, Q_bF, b_D_F, b_F_F, n_IC_F, n_F, rn_F, div_F, + theta_F, Dep_supply_F, rb_F + Shared: rb_D (D-bond realised return to holders), rb_F (F-bond return) + """ + T = len(Kap_D) + + if def_D_path is None: + def_D_path = np.zeros(T) + if def_F_path is None: + def_F_path = np.zeros(T) + + # Pull calibration for each bank + f_D = cal["f_D"]; f_F = cal["f_F"] + bi_D = cal["beta_inter_D"]; bi_F = cal["beta_inter_F"] + lK_D = cal["lambda_K_D"]; lK_F = cal["lambda_K_F"] + lbD_D = cal["lambda_bD_D"]; lbD_F = cal["lambda_bD_F"] + lbF_D = cal["lambda_bF_D"]; lbF_F = cal["lambda_bF_F"] + om_D = cal["omega_ent_D"]; om_F = cal["omega_ent_F"] + db_D = cal["delta_b_D"]; db_F = cal["delta_b_F"] + psi_bFD = cal["psi_bF_D"] # D-bank adj cost for F-bond deviation + psi_bDF = cal["psi_bD_F"] # F-bank adj cost for D-bond deviation + b_F_D_ss = cal["b_F_D_ss"] + b_D_F_ss = cal["b_D_F_ss"] + exc_FD_ss = cal["excess_return_F_D_ss"] + exc_DF_ss = cal["excess_return_D_F_ss"] + rec_D = cal["recovery_rate_D"]; rec_F = cal["recovery_rate_F"] + psi_bd_D = cal.get("psi_bd_D", 0.0) # BD sunspot spread sensitivity (D-bonds) + psi_bd_F = cal.get("psi_bd_F", 0.0) # BD sunspot spread sensitivity (F-bonds) + + # ── Backward pass ───────────────────────────────────────────────────────── + # At each t, given alpha_{t+1} for both banks, solve closed-form: + # Omega_{t+1} = beta_inter·[(1−f) + f·alpha_{t+1}] + # mu_t = Omega_{t+1}·(rk_{t+1} − rdep_t)/lambda_K + # alpha_t = Omega_{t+1}·(1+rdep_t)/(1−mu_t) + # Q_bD_t = 1/(rdep_D_t + delta_b_D + lambda_bD_D·mu_D_t/Omega_D_{t+1}) + # Q_bF_t = 1/(rdep_F_t + delta_b_F + lambda_bF_F·mu_F_t/Omega_F_{t+1}) + # b_F_D_t from portfolio-adj-cost FOC (linear, closed-form) + # b_D_F_t same + + alpha_D_path = np.empty(T) + mu_D_path = np.empty(T) + alpha_F_path = np.empty(T) + mu_F_path = np.empty(T) + Q_bD_path = np.empty(T) # D-bond price path + Q_bF_path = np.empty(T) # F-bond price path + b_F_D_path = np.empty(T) # D-bank's F-bond holding + b_D_F_path = np.empty(T) # F-bank's D-bond holding + + # Effective divertability paths (base + BD sunspot tightening) + lbD_D_eff_path = np.empty(T) # D-bank IC for D-bonds + lbF_D_eff_path = np.empty(T) # D-bank IC for F-bonds + lbD_F_eff_path = np.empty(T) # F-bank IC for D-bonds (contagion channel) + lbF_F_eff_path = np.empty(T) # F-bank IC for F-bonds + + alpha_D_next = ss_bk_D["alpha_ss"] + alpha_F_next = ss_bk_F["alpha_ss"] + + # IC-consistent SS bond prices (terminal condition for backward pass). + # Bond pricing FOC at SS: Q = delta_b / (rdep + delta_b + lambda_b*mu/Omega) + # Use ss_bk mu_ss/Omega_ss for the IC spread (already solved in SS bank block). + ic_spread_bD_D = ss_bk_D["lambda_bD"] * ss_bk_D["mu_ss"] / ss_bk_D["Omega_ss"] + ic_spread_bF_F = ss_bk_F["lambda_bF"] * ss_bk_F["mu_ss"] / ss_bk_F["Omega_ss"] + Q_bD_ss_val = cal["delta_b_D"] / (cal["r_dep_D_target"] + cal["delta_b_D"] + ic_spread_bD_D) + Q_bF_ss_val = cal["delta_b_F"] / (cal["r_dep_F_target"] + cal["delta_b_F"] + ic_spread_bF_F) + Q_bD_next = Q_bD_ss_val + Q_bF_next = Q_bF_ss_val + + for t in range(T - 1, -1, -1): + # At the terminal period, rk[T] is unknown; use rk[T-1] as the SS approximation. + # Using cal["rk_D_guess"] instead would introduce an alpha error at t=T-1 + # because rk_guess ≠ rk_ss in general. + rk_D_next = rk_D[t + 1] if t + 1 < T else rk_D[T - 1] + rk_F_next = rk_F[t + 1] if t + 1 < T else rk_F[T - 1] + # Next-period default rates (survival in bond pricing) + def_D_next = def_D_path[t + 1] if t + 1 < T else 0.0 + def_F_next = def_F_path[t + 1] if t + 1 < T else 0.0 + # BD: next-period sunspot (coordination failure probability → IC tightening) + xi_D_next = (sunspot_D_path[t + 1] if sunspot_D_path is not None and t + 1 < T else 0.0) + xi_F_next = (sunspot_F_path[t + 1] if sunspot_F_path is not None and t + 1 < T else 0.0) + + # ── D-bank backward step ── + Omega_D = bi_D * ((1 - f_D) + f_D * alpha_D_next) + mu_D = Omega_D * (rk_D_next - rdep_D[t]) / lK_D + if mu_D >= 1.0: + raise RuntimeError(f"D-bank mu_D={mu_D:.4f} ≥ 1 at t={t}; IC infeasible.") + alpha_D = Omega_D * (1 + rdep_D[t]) / (1 - mu_D) + + # BD: sunspot tightens IC for sovereign bonds (rollover risk premium) + lbD_D_eff = lbD_D + psi_bd_D * xi_D_next # D-bank IC for D-bonds + lbF_D_eff = lbF_D + psi_bd_F * xi_F_next # D-bank IC for F-bonds + + # HM pricing: Q_bD = surv_{t+1}·(db + (1-db)·Q_next) / (1 + rdep + IC_spread) + # Survival from fundamental default only; BD sunspot enters denominator. + ic_spread_bD_D = lbD_D_eff * mu_D / Omega_D + surv_D_for_price = 1.0 - def_D_next * (1.0 - rec_D) + Q_bD = surv_D_for_price * (db_D + (1 - db_D) * Q_bD_next) / (1 + rdep_D[t] + ic_spread_bD_D) + + # ── F-bank backward step ── + Omega_F = bi_F * ((1 - f_F) + f_F * alpha_F_next) + mu_F = Omega_F * (rk_F_next - rdep_F[t]) / lK_F + if mu_F >= 1.0: + raise RuntimeError(f"F-bank mu_F={mu_F:.4f} ≥ 1 at t={t}; IC infeasible.") + alpha_F = Omega_F * (1 + rdep_F[t]) / (1 - mu_F) + + # BD: F-bank's IC for D-bonds also tightens with D-sunspot (contagion channel) + lbD_F_eff = lbD_F + psi_bd_D * xi_D_next # F-bank IC for D-bonds + lbF_F_eff = lbF_F + psi_bd_F * xi_F_next # F-bank IC for F-bonds + + # F-bank FOC for F-bonds (symmetric) + ic_spread_bF_F = lbF_F_eff * mu_F / Omega_F + surv_F_for_price = 1.0 - def_F_next * (1.0 - rec_F) + Q_bF = surv_F_for_price * (db_F + (1 - db_F) * Q_bF_next) / (1 + rdep_F[t] + ic_spread_bF_F) + + # ── Cross-border positions from portfolio adj-cost FOC ── + # Survival for expected return at t+1 uses next-period default rate (B-2 fix). + def_F_t1 = def_F_path[t + 1] if t + 1 < T else 0.0 + def_D_t1 = def_D_path[t + 1] if t + 1 < T else 0.0 + surv_F = 1.0 - def_F_t1 * (1.0 - rec_F) + surv_D = 1.0 - def_D_t1 * (1.0 - rec_D) + + rb_F_raw = (db_F * surv_F + (1 - db_F) * Q_bF_next * surv_F) / Q_bF - 1 + # Convert F-bond return to D-goods using p_{t+1}/p_t + p_next = p_path[t + 1] if t + 1 < T else p_path[t] # terminal: p constant + rb_F_in_D = (1 + rb_F_raw) * p_next / p_path[t] - 1 + + ic_required_bF_D = lbF_D_eff * mu_D / Omega_D + b_F_D_t = (b_F_D_ss + + (rb_F_in_D - rdep_D[t] - exc_FD_ss - ic_required_bF_D) + / psi_bFD) + + # F-bank's D-bond FOC: + rb_D_raw = (db_D * surv_D + (1 - db_D) * Q_bD_next * surv_D) / Q_bD - 1 + # Convert D-bond return to F-goods using p_t/p_{t+1} + rb_D_in_F = (1 + rb_D_raw) * p_path[t] / p_next - 1 + + ic_required_bD_F = lbD_F_eff * mu_F / Omega_F + b_D_F_t = (b_D_F_ss + + (rb_D_in_F - rdep_F[t] - exc_DF_ss - ic_required_bD_F) + / psi_bDF) + + alpha_D_path[t] = alpha_D; mu_D_path[t] = mu_D + alpha_F_path[t] = alpha_F; mu_F_path[t] = mu_F + Q_bD_path[t] = Q_bD; Q_bF_path[t] = Q_bF + b_F_D_path[t] = b_F_D_t + b_D_F_path[t] = b_D_F_t + lbD_D_eff_path[t] = lbD_D_eff + lbF_D_eff_path[t] = lbF_D_eff + lbD_F_eff_path[t] = lbD_F_eff + lbF_F_eff_path[t] = lbF_F_eff + + alpha_D_next = alpha_D; alpha_F_next = alpha_F + Q_bD_next = Q_bD; Q_bF_next = Q_bF + + # Bond market clearing — B_gov may be scalar (SS) or time-varying path (BD/CK) + B_gov_D_arr = np.broadcast_to(B_gov_D, T).copy() if np.ndim(B_gov_D) == 0 else np.asarray(B_gov_D) + B_gov_F_arr = np.broadcast_to(B_gov_F, T).copy() if np.ndim(B_gov_F) == 0 else np.asarray(B_gov_F) + b_D_D_path = B_gov_D_arr - b_D_F_path # D-bonds held by D-bank + b_F_F_path = B_gov_F_arr - b_F_D_path # F-bonds held by F-bank + + # Realised returns at t on bonds bought at t-1 + Q_bD_lag = np.concatenate(([Q_bD_ss_val], Q_bD_path[:-1])) + Q_bF_lag = np.concatenate(([Q_bF_ss_val], Q_bF_path[:-1])) + + surv_D_path = 1.0 - def_D_path * (1.0 - rec_D) + surv_F_path = 1.0 - def_F_path * (1.0 - rec_F) + rb_D_path = (db_D * surv_D_path + (1 - db_D) * Q_bD_path * surv_D_path) / Q_bD_lag - 1 + rb_F_path = (db_F * surv_F_path + (1 - db_F) * Q_bF_path * surv_F_path) / Q_bF_lag - 1 + + # ── Forward pass: accumulate n_ACCUM and compute n_IC ───────────────────── + n_IC_D = np.empty(T) + n_ACCUM_D = np.empty(T) + rn_D = np.empty(T) + div_D = np.empty(T) + + n_IC_F = np.empty(T) + n_ACCUM_F = np.empty(T) + rn_F = np.empty(T) + div_F = np.empty(T) + + # D-bank forward state + n_D_prev = ss_bk_D["n_ss"] + kappa_D_prev = ss_bk_D["kappa_ss"] + phi_bdom_D_prev = ss_bk_D["phi_bdom_ss"] + phi_bfor_D_prev = ss_bk_D["phi_bfor_ss"] + rdep_D_prev = cal["r_dep_D_target"] + + # F-bank forward state + n_F_prev = ss_bk_F["n_ss"] + kappa_F_prev = ss_bk_F["kappa_ss"] + phi_bdom_F_prev = ss_bk_F["phi_bdom_ss"] + phi_bfor_F_prev = ss_bk_F["phi_bfor_ss"] + rdep_F_prev = cal["r_dep_F_target"] + + for t in range(T): + p_t = p_path[t] + # p_lag: exchange rate at t-1, needed for FX conversions of realised bond returns. + # Q_bF is denominated in F-goods (F-bank prices F-bonds against rdep_F in F-goods); + # Q_bD is denominated in D-goods (D-bank prices D-bonds against rdep_D in D-goods). + p_lag = p_path[t - 1] if t > 0 else cal.get("p_ss", 1.0) + + # D-bank earns rb_D on D-bonds (D-goods, no conversion) and rb_F on F-bonds. + # rb_F_path is a F-good return (Q_bF in F-goods); convert to D-goods: + # D-good return = (1 + rb_F_path) * p_t / p_lag (bought at p_lag per F-good, sold at p_t) + rb_D_t = rb_D_path[t] + rb_F_t = (1.0 + rb_F_path[t]) * p_t / p_lag - 1 # F-goods → D-goods + + # D-bank net worth return (all terms in D-goods) + rn_D_t = (kappa_D_prev * (rk_D[t] - rdep_D_prev) + + phi_bdom_D_prev * (rb_D_t - rdep_D_prev) + + phi_bfor_D_prev * (rb_F_t - rdep_D_prev) # rb_F_t already in D-goods + + rdep_D_prev) + gross_D = (1 + rn_D_t) * n_D_prev + # Entrant transfer: proportional to total assets in D-goods + total_assets_D = Q_D[t] * Kap_D[t] + Q_bD_path[t] * b_D_D_path[t] + Q_bF_path[t] * b_F_D_path[t] + entrant_D = cal["omega_ent_D"] * total_assets_D + n_ACCUM_D_t = (1 - f_D) * gross_D + entrant_D + div_D_t = f_D * gross_D - entrant_D + n_ACCUM_D[t] = n_ACCUM_D_t + rn_D[t] = rn_D_t + div_D[t] = div_D_t + + # D-bank IC: uses effective lambda (base + BD sunspot tightening) + n_IC_D_t = (lK_D * Q_D[t] * Kap_D[t] + + lbD_D_eff_path[t] * Q_bD_path[t] * b_D_D_path[t] + + lbF_D_eff_path[t] * Q_bF_path[t] * b_F_D_path[t]) / alpha_D_path[t] + n_IC_D[t] = n_IC_D_t + + # Update D-bank portfolio ratios for next forward step + kappa_D_prev = Q_D[t] * Kap_D[t] / n_IC_D_t + phi_bdom_D_prev = Q_bD_path[t] * b_D_D_path[t] / n_IC_D_t + phi_bfor_D_prev = Q_bF_path[t] * b_F_D_path[t] / n_IC_D_t + n_D_prev = n_ACCUM_D_t + rdep_D_prev = rdep_D[t] + + # ── F-bank ───────────────────────────────────────────────────────── + # Q_bF denominated in F-goods: rb_F_path is already a F-good return → use directly. + # Q_bD denominated in D-goods: convert D-bond return to F-goods via p_lag/p_t. + rb_F_fg_t = rb_F_path[t] # F-bonds: F-goods ✓ + rb_D_fg_t = (1 + rb_D_path[t]) * p_lag / p_t - 1 # D-bonds: D-goods → F-goods + + rn_F_t = (kappa_F_prev * (rk_F[t] - rdep_F_prev) + + phi_bdom_F_prev * (rb_F_fg_t - rdep_F_prev) # F-bonds in F-goods + + phi_bfor_F_prev * (rb_D_fg_t - rdep_F_prev) # D-bonds in F-goods + + rdep_F_prev) + gross_F = (1 + rn_F_t) * n_F_prev + # Total F-bank assets in F-goods + total_assets_F = (Q_F[t] * Kap_F[t] + + Q_bF_path[t] * b_F_F_path[t] / p_t + + Q_bD_path[t] * b_D_F_path[t] / p_t) + entrant_F = cal["omega_ent_F"] * total_assets_F + n_ACCUM_F_t = (1 - f_F) * gross_F + entrant_F + div_F_t = f_F * gross_F - entrant_F + n_ACCUM_F[t] = n_ACCUM_F_t + rn_F[t] = rn_F_t + div_F[t] = div_F_t + + # F-bank IC in F-goods: effective lambda (D-bond term uses contagion channel) + n_IC_F_t = (lK_F * Q_F[t] * Kap_F[t] + + lbF_F_eff_path[t] * Q_bF_path[t] * b_F_F_path[t] / p_t + + lbD_F_eff_path[t] * Q_bD_path[t] * b_D_F_path[t] / p_t) / alpha_F_path[t] + n_IC_F[t] = n_IC_F_t + + kappa_F_prev = Q_F[t] * Kap_F[t] / n_IC_F_t + phi_bdom_F_prev = Q_bF_path[t] * b_F_F_path[t] / (p_t * n_IC_F_t) + phi_bfor_F_prev = Q_bD_path[t] * b_D_F_path[t] / (p_t * n_IC_F_t) + n_F_prev = n_ACCUM_F_t + rdep_F_prev = rdep_F[t] + + theta_D = ((Q_D * Kap_D + Q_bD_path * b_D_D_path + Q_bF_path * b_F_D_path) + / n_ACCUM_D) + theta_F = ((Q_F * Kap_F + Q_bF_path * b_F_F_path / p_path + Q_bD_path * b_D_F_path / p_path) + / n_ACCUM_F) + + Dep_supply_D = (theta_D - 1) * n_ACCUM_D + Dep_supply_F = (theta_F - 1) * n_ACCUM_F + + return dict( + # D-bank + alpha_D=alpha_D_path, mu_D=mu_D_path, + n_IC_D=n_IC_D, n_D=n_ACCUM_D, rn_D=rn_D, div_D=div_D, + theta_D=theta_D, Dep_supply_D=Dep_supply_D, + b_D_D=b_D_D_path, b_F_D=b_F_D_path, + # F-bank + alpha_F=alpha_F_path, mu_F=mu_F_path, + n_IC_F=n_IC_F, n_F=n_ACCUM_F, rn_F=rn_F, div_F=div_F, + theta_F=theta_F, Dep_supply_F=Dep_supply_F, + b_D_F=b_D_F_path, b_F_F=b_F_F_path, + # Shared bond prices and returns + Q_bD=Q_bD_path, Q_bF=Q_bF_path, + rb_D=rb_D_path, rb_F=rb_F_path, + ) diff --git a/code/global/calibration.py b/code/global/calibration.py new file mode 100644 index 0000000..fbf250d --- /dev/null +++ b/code/global/calibration.py @@ -0,0 +1,92 @@ +""" +Notes: +* Shared bond denomination: all bonds are D-good claims (D is numeraire). +""" + + +def get_calibration(): + cal = dict( + + # ── HOUSEHOLD PREFERENCES (GHH) ───────────────────────────────────── + sigma_D=2.0, sigma_F=2.0, + frisch_D=0.5, frisch_F=0.5, + chi_D=1.0, chi_F=1.0, # initial guess; overwritten in SS solve to match Nss=1 + + # ── IDIOSYNCRATIC INCOME PROCESS (Rouwenhorst) ─────────────────────── + n_e_D=2, n_e_F=2, + rho_e_D=0.9, sigma_e_D=0.2, + rho_e_F=0.9, sigma_e_F=0.2, + + # ── ASSET GRIDS ────────────────────────────────────────────────────── + a_min_D=0.0, a_max_D=150.0, n_a_D=250, a_curve_D=2.0, + a_min_F=0.0, a_max_F=150.0, n_a_F=250, a_curve_F=2.0, + + # ── FIRMS (Cobb-Douglas + full price flexibility) ──────────────────── + epsilon_D=6.0, epsilon_F=6.0, # demand elasticity → mc = (ε-1)/ε + Z_ss_D=1.0, Z_ss_F=1.0, + + # ── CAPITAL (Jermann 1998 adjustment cost) ──────────────────────────── + alpha_D=0.35, alpha_F=0.35, # capital share + delta_D=0.025, delta_F=0.025, # initial guess for SS solve; overwritten after SS solve + ksi_D=0.50, ksi_F=0.50, # adjustment-cost curvature + + # ── FINANCIAL INTERMEDIARY ───────────────────────────────────────────── + f_D=0.028, f_F=0.028, + r_dep_D_target=0.000, r_dep_F_target=0.000, + beta_inter_D=0.96, beta_inter_F=0.96, + lambda_K_D=0.30, lambda_K_F=0.30, + lambda_bD_D=0.04, lambda_bD_F=0.04, + lambda_bF_D=0.04, lambda_bF_F=0.04, + omega_ent_D=0.002, omega_ent_F=0.002, + + # ── PORTFOLIO ADJUSTMENT COSTS (cross-border bonds) ────────────────── + psi_bF_D=0.01, psi_bD_F=0.01, + b_F_D_ss=0.005, b_D_F_ss=0.005, + excess_return_F_D_ss=0.0, # overwritten after SS solve + excess_return_D_F_ss=0.0, # overwritten after SS solve + + # ── GOVERNMENT BONDS ───────────────── + delta_b_D=0.10, delta_b_F=0.10, + B_gov_D_ss=2.40, B_gov_F_ss=2.40, + + # ── DEFAULT RISK ─────────────────────────────────────────────────────── + # Thresholds are debt-to-Y_ss ratios. F is always safe. + # b_ck_low/high are used by Cole-Kehoe crisis-zone logic (see solve_transition_ck). + # b_ck_high also serves as the Bocola-Dovis fundamental default boundary (B̄). + b_ck_low_D=0.55, b_ck_low_F=99.0, + b_ck_high_D=1.20, b_ck_high_F=99.0, + recovery_rate_D=0.40, recovery_rate_F=0.0, # Greek PSI-style haircut + + # Bocola-Dovis (2019): sunspot tightens GK IC for sovereign bonds + # lbD_D_eff = lbD_D + psi_bd_D * xi_{t+1} (D-bank and F-bank for D-bonds) + # lbF_F_eff = lbF_F + psi_bd_F * xi_{t+1} (F-bonds; set 0 — no F default) + psi_bd_D=3.0, psi_bd_F=0.0, + + # Outer CK fixed-point solver + ck_max_iter=25, + ck_tol=1e-5, + ck_damping=0.5, + + # Outer BD fixed-point solver (separate budget; uses Anderson acceleration) + bd_max_iter=50, + bd_tol=1e-4, # 0.004% of b_ss — adequate for quantitative IRF + bd_anderson_m=3, # Anderson window size + + # ── FISCAL ──────────────────────────────────────────────────────────── + phi_lamb_D=0.15, phi_lamb_F=0.15, + G_D=0.0, G_F=0.0, + + # ── TRADE / CES BASKET ─────────────────────────────────────────────── + omega_home=0.85, epsilon_trade=0.5, + + # ── SOLVER SETTINGS ─────────────────────────────────────────────────── + T=100, + tol_hh=1e-9, + tol_dist=1e-9, + tol_mkt=1e-9, + + # ── INITIAL GUESSES FOR STEADY-STATE SOLVER ─────────────────────────── + rk_D_guess=0.010, rk_F_guess=0.010, + beta_guess_D=0.98, beta_guess_F=0.98, + ) + return cal diff --git a/code/global/capital.py b/code/global/capital.py new file mode 100644 index 0000000..5f2dafa --- /dev/null +++ b/code/global/capital.py @@ -0,0 +1,64 @@ +"""Capital block: Cobb-Douglas capital demand and the Jermann (1998) +convex capital-adjustment cost (capital producer). + +Functions accept a `country` argument ("D" or "F") to select country- +specific parameters (alpha, delta, ksi) from the calibration dict. +The Jermann inversion is identical for both countries; only the parameters +and the level of Kap differ. +""" +import numpy as np + + +def gamma_params(cal, country="D"): + "Jermann (1998) steady-state-consistency parameters (Q_ss=1 at SS)." + delta = cal[f"delta_{country}"] + ksi = cal[f"ksi_{country}"] + gamma0 = delta ** ksi / (1 - ksi) + gamma1 = -delta * ksi / (1 - ksi) + return gamma0, gamma1 + + +def capital_demand(rk_ss, mc_ss, cal, country="D"): + """Cobb-Douglas capital-demand FOC inverted for Kap_ss. + + Returns the Kap_ss such that mc·alpha·Z·N^(1-alpha)/Kap - delta = rk_ss, + given N_ss = 1. + """ + alpha = cal[f"alpha_{country}"] + delta = cal[f"delta_{country}"] + Z_ss = cal[f"Z_ss_{country}"] + return (mc_ss * alpha * Z_ss / (rk_ss + delta)) ** (1 / (1 - alpha)) + + +def solve_capital_path(Kap_path, Kap_ss, Q_ss, mpk_path, cal, country="D"): + """Jermann adjustment-cost block along the transition. + + Given guessed Kap_path (outer unknown) and mpk_path (from firms.py), + inverts the law of motion for investment iota_t and computes Tobin's Q + and the realised capital return rk_t. + + Returns dict: iota, Q, rk, I, cap_profit — all shape (T,). + """ + delta = cal[f"delta_{country}"] + ksi = cal[f"ksi_{country}"] + gamma0, gamma1 = gamma_params(cal, country) + T = len(Kap_path) + + Kap_lag = np.concatenate(([Kap_ss], Kap_path[:-1])) + bracket = (Kap_path / Kap_lag - (1 - delta) - gamma1) / gamma0 + if np.any(bracket < 0): + raise ValueError( + f"[{country}] Jermann inversion: negative bracket " + "(capital falling faster than adjustment-cost technology allows)" + ) + iota = bracket ** (1 / (1 - ksi)) + Q = 1.0 / (gamma0 * (1 - ksi) * iota ** (-ksi)) + + Q_lag = np.concatenate(([Q_ss], Q[:-1])) + rk = (mpk_path + (1 - delta) * Q) / Q_lag - 1 + + I = iota * Kap_lag + cap_profit = (Q * (Kap_path - (1 - delta) * Kap_lag) - I + + mpk_path * (Kap_path - Kap_lag)) + + return dict(iota=iota, Q=Q, rk=rk, I=I, cap_profit=cap_profit) diff --git a/code/global/distribution.py b/code/global/distribution.py new file mode 100644 index 0000000..0bd358f --- /dev/null +++ b/code/global/distribution.py @@ -0,0 +1,64 @@ +"""Non-stochastic simulation of the cross-sectional distribution via the +'lottery' method (Young, 2010): savings choices that fall between grid +points are split across the two nearest gridpoints with probability +weights chosen to match the mean exactly. +""" +import numpy as np + + +def stationary_distribution(a_pol, a_grid, Pi, pi_e_stationary, tol, maxiter=100_000): + "Compute the stationary distribution of assets and income given policy functions" + n_a, n_e = a_pol.shape + + # This is the propability distribution over (a, e). + # D[i, j] = the fraction of the population (mass, between 0 and 1) currently holding asset level a_grid[i] and being in income state e_grid[j]. + D = np.zeros((n_a, n_e)) + D[0, :] = pi_e_stationary # start as point mass at a_min + + for it in range(maxiter): + #The distribution fitted to the grid + D_new = forward_iterate(D, a_pol, a_grid, Pi) + if np.max(np.abs(D_new - D)) < tol: + return D_new + D = D_new + + raise RuntimeError(f"Distribution iteration did not converge (diff={np.max(np.abs(D_new - D)):.2e})") + + +def forward_iterate(D, a_pol, a_grid, Pi): + "Gets the output from the get_lottery_weights function and then uses np.add.at to update the distribution D." + n_a, n_e = D.shape + idx_lo, idx_hi, w_lo, w_hi = get_lottery_weights(a_pol, a_grid) + + pre = np.zeros((n_a, n_e)) + for e in range(n_e): + np.add.at(pre[:, e], idx_lo[:, e], D[:, e] * w_lo[:, e]) + np.add.at(pre[:, e], idx_hi[:, e], D[:, e] * w_hi[:, e]) + + return pre @ Pi + + +def get_lottery_weights(a_pol, a_grid): + "Young (2010) lottery method" + a_min, a_max = a_grid[0], a_grid[-1] + a_pol_c = np.clip(a_pol, a_min, a_max) + + # If the policy function a' is between two grid points, we split the mass between the two nearest grid points. + idx_hi = np.searchsorted(a_grid, a_pol_c, side="right") + idx_hi = np.clip(idx_hi, 1, len(a_grid) - 1) + idx_lo = idx_hi - 1 + + # Computes those weights + denom = a_grid[idx_hi] - a_grid[idx_lo] + weight_hi = np.where(denom > 0, (a_pol_c - a_grid[idx_lo]) / denom, 0.0) + weight_lo = 1.0 - weight_hi + return idx_lo, idx_hi, weight_lo, weight_hi + + +# Calculate the aggregate assets +def aggregate_assets(D, a_grid): + return float(np.sum(D * a_grid[:, None])) + +# Calculate the aggregate Consumtpion +def aggregate_consumption(D, c_pol): + return float(np.sum(D * c_pol)) diff --git a/code/global/firms.py b/code/global/firms.py new file mode 100644 index 0000000..dd9cb11 --- /dev/null +++ b/code/global/firms.py @@ -0,0 +1,59 @@ +"""Firm block: Cobb-Douglas production with full price flexibility. + +Under full price flexibility, mc = (epsilon−1)/epsilon is a constant. +Functions accept a `country` argument ("D" or "F") to look up country- +specific parameters (alpha, delta, Z_ss, epsilon) from the calibration. + +With GHH preferences, chi is calibrated so that chi·N_ss^(1/frisch) = w_ss +(the static GHH labour-supply FOC at SS with P_CES=1). This replaces the +old CRRA chi = w_ss / C_ss^sigma formula. +""" + + +def markup_ss(cal, country="D"): + "Real marginal cost (inverse markup). Constant under full price flexibility." + return (cal[f"epsilon_{country}"] - 1) / cal[f"epsilon_{country}"] + + +def steady_state_firm(cal, Kap_ss, country="D"): + """Steady-state firm block. N_ss = 1 by normalisation; back-solves chi + from the GHH labour-supply FOC chi·N^(1/frisch) = w/P_CES (P_CES=1 at SS). + + Returns: chi, mc_ss, w_ss, mpk_ss, N_ss, Y_ss, C_ss, I_ss. + C_ss is residual Y − I − G (goods-market identity at SS). + """ + alpha = cal[f"alpha_{country}"] + delta = cal[f"delta_{country}"] + Z_ss = cal[f"Z_ss_{country}"] + frisch = cal[f"frisch_{country}"] + G = cal[f"G_{country}"] + + mc_ss = markup_ss(cal, country) + N_ss = 1.0 + Y_ss = Z_ss * Kap_ss ** alpha * N_ss ** (1 - alpha) + w_ss = mc_ss * (1 - alpha) * Y_ss / N_ss + mpk_ss = mc_ss * alpha * Y_ss / Kap_ss + I_ss = delta * Kap_ss + C_ss = Y_ss - I_ss - G + + # GHH static FOC at SS (P_CES normalised to 1 at symmetric SS): + # chi * N_ss^(1/frisch) = w_ss → chi = w_ss / N_ss^(1/frisch) + chi = w_ss / (N_ss ** (1 / frisch)) + + return dict(chi=chi, mc_ss=mc_ss, w_ss=w_ss, mpk_ss=mpk_ss, + N_ss=N_ss, Y_ss=Y_ss, C_ss=C_ss, I_ss=I_ss) + + +def solve_firm_path(N_path, Kap_path, Z_path, cal, country="D"): + """Firm quantities along a transition path (purely contemporaneous). + + Returns dict: Y, w, mpk, mc — all shape (T,). + """ + alpha = cal[f"alpha_{country}"] + mc = markup_ss(cal, country) + + Y = Z_path * Kap_path ** alpha * N_path ** (1 - alpha) + w = mc * (1 - alpha) * Y / N_path + mpk = mc * alpha * Y / Kap_path + + return dict(Y=Y, w=w, mpk=mpk, mc=mc) diff --git a/code/global/government.py b/code/global/government.py new file mode 100644 index 0000000..7326e4b --- /dev/null +++ b/code/global/government.py @@ -0,0 +1,191 @@ +"Government block: Hatchondo-Martinez (2009) geometric-decay perpetuity bonds, Bohn (1998) fiscal rule, Cole-Kehoe (2000) self-fulfilling default crisis zones." +import numpy as np + + +# ── Steady-state helpers ────────────────────────────────────────────────────── + +def hm_bond_price_ss(rdep_ss, delta_b): + """HM perpetuity price at a risk-free steady state. + + Bond pays coupon delta_b per period, decays at rate (1-delta_b). + No-arbitrage SS price: Q_B_ss = delta_b / (rdep_ss + delta_b). + """ + return delta_b / (rdep_ss + delta_b) + + +def hm_bond_return_ss(Q_B_ss, delta_b): + """Realised return at SS on HM perpetuity (= rdep_ss by no-arbitrage).""" + return (delta_b + (1 - delta_b) * Q_B_ss) / Q_B_ss - 1 + + +def govt_steady_state(cal, rdep_ss, country): + """Steady-state government block. + + At SS: def_rate = 0, bond stock = B_gov_ss (exogenous), no net issuance. + + Returns dict: Q_B_ss, rb_ss, Tax_ss, b_gov_ss, coupon_ss. + """ + delta_b = cal[f"delta_b_{country}"] + B_gov_ss = cal[f"B_gov_{country}_ss"] + G = cal[f"G_{country}"] + + Q_B_ss = hm_bond_price_ss(rdep_ss, delta_b) + rb_ss = hm_bond_return_ss(Q_B_ss, delta_b) + coupon_ss = delta_b * B_gov_ss + # Government budget at SS: G + coupon = Tax + issuance_proceeds + # Issuance = delta_b*B_gov new bonds at price Q_B_ss + Tax_ss = G + coupon_ss * (1.0 - Q_B_ss) # = G + delta_b*B_gov*rdep/(rdep+delta_b) + + return dict(Q_B_ss=Q_B_ss, rb_ss=rb_ss, + Tax_ss=Tax_ss, b_gov_ss=B_gov_ss, coupon_ss=coupon_ss) + + +# ── Cole-Kehoe default probability ─────────────────────────────────────────── + +def ck_default_prob(b_gov, Y_ss, cal, sunspot, country): + """Cole-Kehoe (2000) default probability for a single period. + + Three zones based on debt-to-output ratio b_gov/Y_ss: + Safe zone (b/Y < b_ck_low): returns 0. + Crisis zone (b_ck_low ≤ b/Y < b_ck_high): returns sunspot ∈ [0,1]. + Certain-default (b/Y ≥ b_ck_high): returns 1. + + `sunspot` is the exogenous probability of default conditional on the crisis + zone being active. In a perfect-foresight MIT-shock setting, sunspot is an + AR(1) path that starts at some peak and decays; it replaces the old AR(1) + def_D_path. + """ + b_low = cal[f"b_ck_low_{country}"] + b_high = cal[f"b_ck_high_{country}"] + b_y = b_gov / Y_ss + return float(np.where(b_y < b_low, 0.0, + np.where(b_y >= b_high, 1.0, sunspot))) + + +# ── Bocola-Dovis fundamental default probability ───────────────────────────── + +def bd_fundamental_default_prob(b_gov, Y_ss, cal, country): + """BD fundamental default: only when debt exceeds the hard upper bound b_ck_high. + + Unlike Cole-Kehoe, there is no crisis zone — the sunspot enters through the + IC spread in bank.py. This function purely captures the solvency threshold + (Bocola-Dovis B̄): certain default once debt is unsustainable regardless of + lender beliefs. + """ + b_high = cal.get(f"b_ck_high_{country}", 99.0) + return 1.0 if b_gov / Y_ss >= b_high else 0.0 + + +# ── Endogenous debt accumulation ───────────────────────────────────────────── + +def integrate_b_gov(b_gov0, Q_bD_path, def_rate_path, Tax_path, cal, country): + """Forward-integrate government debt stock from the HM budget identity. + + At each t the government: + - Has outstanding bonds b_gov[t] (beginning of period, = b_gov0 at t=0). + - Fraction delta_b matures; outstanding stock falls by factor (1-delta_b). + - Default: fraction def_rate[t] of bonds partially haircut; effective + survival surv[t] = 1 - def_rate[t]*(1 - recovery_rate). + - Budget: Tax[t] - G = coupon[t] - issuance_proceeds[t] + coupon[t] = delta_b * b_gov[t] * surv[t] + new_bonds[t] = (G + coupon[t] - Tax[t]) / Q_bD[t] + b_gov[t+1] = (1-delta_b)*b_gov[t]*surv[t] + new_bonds[t] + + Verification at SS: Tax_ss = G + delta_b*B_gov_ss*(1-Q_bD_ss) + → b_gov[1] = (1-db)*B_ss + (G + db*B_ss - Tax_ss)/Q_ss = B_ss. ✓ + + Parameters + ---------- + b_gov0 : scalar, initial stock (= B_gov_ss at period 0) + Q_bD_path : (T,) bond price path from bank backward pass + def_rate_path : (T,) default probability path (from CK outer loop) + Tax_path : (T,) tax revenue path (from govt_transition) + cal : calibration dict + country : "D" or "F" + + Returns + ------- + b_gov_path : (T,) array — stocks at beginning of periods 1..T + """ + delta_b = cal[f"delta_b_{country}"] + recovery_rate = cal[f"recovery_rate_{country}"] + G = cal[f"G_{country}"] + T = len(Q_bD_path) + + b_gov_path = np.empty(T) + b_gov = float(b_gov0) + + for t in range(T): + surv_t = 1.0 - def_rate_path[t] * (1.0 - recovery_rate) + coupon_t = delta_b * b_gov * surv_t + new_bonds = (G + coupon_t - Tax_path[t]) / Q_bD_path[t] + b_gov = (1.0 - delta_b) * b_gov * surv_t + new_bonds + b_gov_path[t] = b_gov + + return b_gov_path + + +# ── Transition-path government block ───────────────────────────────────────── + +def bohn_tax(b_gov, b_gov_ss, Tax_ss, phi_lamb): + """Bohn fiscal rule: lump-sum tax rises when debt exceeds steady state.""" + return Tax_ss + phi_lamb * (b_gov - b_gov_ss) + + +def govt_transition(cal, gs, Q_B_path, def_rate_path, b_gov_path, country, p_path=None): + """Government flows along a transition path. + + Parameters + ---------- + gs : steady-state government dict (from govt_steady_state). + Q_B_path : (T,) bond price path from bank backward pass. + def_rate_path: (T,) default probability path from CK outer loop. + b_gov_path : (T,) beginning-of-period debt stock path. + Entry t is the stock carried into period t (= b_gov_ss initially). + p_path : (T,) real exchange rate (required for country="F"). + + The Bohn fiscal rule adjusts taxes in response to lagged debt: + Tax[t] = Tax_ss + phi_lamb * (b_gov_path[t] - b_gov_ss) + + Survival factor always active (no writeoff gate): + surv[t] = 1 - def_rate[t] * (1 - recovery_rate) + + Currency convention: + All bonds are D-good claims; coupons and issuance proceeds are D-good flows. + For country="F" the D-good flows are converted to F-goods via p_path so + that Tax_F is in F-goods, consistent with F-household income accounting. + + Returns dict: Tax, coupon, net_issuance, b_gov — all shape (T,), own-good units. + """ + delta_b = cal[f"delta_b_{country}"] + recovery_rate = cal[f"recovery_rate_{country}"] + phi_lamb = cal[f"phi_lamb_{country}"] + Tax_ss = gs["Tax_ss"] + b_gov_ss = gs["b_gov_ss"] + + T = len(Q_B_path) + if p_path is None: + p_path = np.ones(T) + + # Bohn rule: tax responds to beginning-of-period (lagged) debt + Tax_D = Tax_ss + phi_lamb * (b_gov_path - b_gov_ss) # D-goods, shape (T,) + + # Survival factor (always active in CK framework) + surv = 1.0 - def_rate_path * (1.0 - recovery_rate) + + # D-good coupon and roll-over issuance + coupon_D = delta_b * b_gov_path * surv + net_iss_D = delta_b * b_gov_path * Q_B_path + + if country == "F": + # Convert D-good bond flows to F-goods. p = price of F-goods in D-goods. + Tax_out = Tax_D / p_path + coupon_out = coupon_D / p_path + net_iss_out = net_iss_D / p_path + else: + Tax_out = Tax_D + coupon_out = coupon_D + net_iss_out = net_iss_D + + return dict(Tax=Tax_out, coupon=coupon_out, net_issuance=net_iss_out, + b_gov=b_gov_path) diff --git a/code/global/household.py b/code/global/household.py new file mode 100644 index 0000000..899705c --- /dev/null +++ b/code/global/household.py @@ -0,0 +1,124 @@ +"""Household block: endogenous grid method (EGM) for a one-asset, +incomplete-markets consumption-savings problem with GHH utility. + +GHH composite: x = c − v(N) where v(N) = chi·N^(1+1/frisch)/(1+1/frisch). +Utility: u(x) = x^(1−sigma)/(1−sigma). +Labour supply (static FOC): chi·N^(1/frisch) = w/P_CES (income-effect-free). + +For the individual household's deposit problem, aggregate N is taken as +given (competitive labour market with idiosyncratic productivity e). The +EGM Euler equation operates on the composite x, and vN (the aggregate +labour disutility evaluated at the period's N) is passed in from outside. +Setting vN=0 everywhere recovers the standard CRRA-separable case. + +State: (a, e). Choice: a' (savings), with c = (1+r)·a + y(e) − a'. +Borrowing constraint: a' ≥ a_min. +""" +import numpy as np + + +def make_asset_grid(cal, country="D"): + "Non-uniform asset grid; more points near the borrowing constraint." + n_a = cal[f"n_a_{country}"] + a_min = cal[f"a_min_{country}"] + a_max = cal[f"a_max_{country}"] + curve = cal[f"a_curve_{country}"] + return a_min + (a_max - a_min) * np.linspace(0, 1, n_a) ** curve + + +def egm_step(c_next, a_grid, Pi, r_today, r_next, y_e, beta, sigma, a_min, + vN_today=0.0, vN_next=0.0): + """One backward EGM step. + + vN_today, vN_next: aggregate labour disutility v(N_t) and v(N_{t+1}). + With GHH: Euler operates on composite x = c − vN. Pass vN=0 for + standard CRRA-separable behaviour. + """ + n_a, n_e = c_next.shape + + # GHH composite tomorrow: x_{t+1} = c_{t+1} − v(N_{t+1}) + x_next = np.maximum(c_next - vN_next, 1e-11) + + # Expected marginal utility of composite tomorrow + Eu_next = (x_next ** (-sigma)) @ Pi.T # (n_a, n_e) + + # EGM inversion: x_t^{−sigma} = beta·(1+r_next)·E[x_{t+1}^{−sigma}] + x_endo = (beta * (1 + r_next) * Eu_next) ** (-1 / sigma) # (n_a, n_e) + + # Recover actual consumption: c_t = x_t + v(N_t) + c_endo = x_endo + vN_today + + # Endogenous cash-on-hand: m = c_t + a' (implies a today) + m_endo = c_endo + a_grid[:, None] + a_endo = (m_endo - y_e[None, :]) / (1 + r_today) + + c_today = np.empty((n_a, n_e)) + a_pol_today = np.empty((n_a, n_e)) + + for e in range(n_e): + c_today[:, e] = np.interp(a_grid, a_endo[:, e], c_endo[:, e]) + a_pol_today[:, e] = (1 + r_today) * a_grid + y_e[e] - c_today[:, e] + + constrained = a_grid < a_endo[0, e] + a_pol_today[constrained, e] = a_min + c_today[constrained, e] = (1 + r_today) * a_grid[constrained] + y_e[e] - a_min + + a_pol_today = np.maximum(a_pol_today, a_min) + return c_today, a_pol_today + + +def solve_steady_state_household(a_grid, Pi, r_ss, y_e, beta, sigma, a_min, tol, + maxiter=10_000, vN_ss=0.0): + """Solve the household problem in steady state via EGM iteration. + + vN_ss: aggregate labour disutility at the steady state (scalar). + """ + c = np.maximum((1 + r_ss) * a_grid[:, None] + y_e[None, :] - a_grid[:, None], 1e-11) + + for _ in range(maxiter): + c_new, a_pol = egm_step(c, a_grid, Pi, r_ss, r_ss, y_e, beta, sigma, a_min, + vN_today=vN_ss, vN_next=vN_ss) + diff = np.max(np.abs(c_new - c)) + c = c_new + if diff < tol: + break + else: + raise RuntimeError(f"Household EGM did not converge (diff={diff:.2e})") + return c, a_pol + + +def solve_backward_transition(a_grid, Pi, r_path, y_path, c_ss, beta, sigma, a_min, + vN_path=None): + """Backward induction over a finite horizon, terminal condition c_ss. + + r_path: length T+1 (r_path[0]=r_dep_ss pre-shock; r_path[t] for t≥1 + is the rate locked in at t−1, realized at t). + vN_path: (T,) array of aggregate labour disutility per period, or None + (treated as zeros, i.e. standard CRRA-separable). + """ + T = y_path.shape[0] + n_a, n_e = a_grid.shape[0], y_path.shape[1] + + if vN_path is None: + vN_path = np.zeros(T) + + c_path = np.empty((T, n_a, n_e)) + a_pol_path = np.empty((T, n_a, n_e)) + + c_next = c_ss + # Terminal-period GHH disutility: period T is permanently at SS, so use vN_path[-1]. + # Using 0.0 here would mis-state the x composite and make households under-save. + vN_next = vN_path[-1] if len(vN_path) > 0 else 0.0 + for t in range(T - 1, -1, -1): + r_today = r_path[t] + r_next = r_path[t + 1] + vN_today = vN_path[t] + c_t, a_pol_t = egm_step(c_next, a_grid, Pi, r_today, r_next, y_path[t], + beta, sigma, a_min, + vN_today=vN_today, vN_next=vN_next) + c_path[t] = c_t + a_pol_path[t] = a_pol_t + c_next = c_t + vN_next = vN_today + + return c_path, a_pol_path diff --git a/code/global/main.py b/code/global/main.py new file mode 100644 index 0000000..6a70e0c --- /dev/null +++ b/code/global/main.py @@ -0,0 +1,157 @@ +"""Entry point: solve the two-country HANK-GK monetary union steady state +and a TFP-shock transition path, print diagnostics, and save figures.""" + +import os +import time +import numpy as np + +from calibration import get_calibration +from steady_state import solve_steady_state +from transition import solve_transition, solve_transition_ck, solve_transition_bd +from plots import OUTDIR, plot_steady_state, plot_irf, plot_default_irf + + +def main(): + t0_total = time.perf_counter() + os.makedirs(OUTDIR, exist_ok=True) + cal = get_calibration() + + print("=" * 65) + print(" Two-country HANK-GK monetary union: steady-state solve") + print("=" * 65) + t0 = time.perf_counter() + ss = solve_steady_state(cal) + print(f" [steady state] {time.perf_counter() - t0:.1f}s") + + bk_D = ss["ss_bank_D"] + bk_F = ss["ss_bank_F"] + fm_D = ss["ss_firm_D"] + fm_F = ss["ss_firm_F"] + + print("\n── Country D (domestic / Greece) ──────────────────────────────") + print(f" rk_D_ss = {ss['rk_D_ss']:.4%} beta_D = {ss['beta_D_ss']:.6f}") + print(f" Kap_D_ss = {ss['Kap_D_ss']:.4f} Y_D = {fm_D['Y_ss']:.4f} I_D = {fm_D['I_ss']:.4f}") + print(f" C_D_ss = {ss['C_D_ss']:.4f} A_D = {ss['A_D_ss']:.4f}") + print(f" w_D_ss = {fm_D['w_ss']:.4f} chi_D = {cal['chi_D']:.4f}") + print(f" n_D_ss = {bk_D['n_ss']:.4f} theta_D = {bk_D['theta_ss']:.4f}") + print(f" alpha_D_ss = {bk_D['alpha_ss']:.4f} mu_D_ss = {bk_D['mu_ss']:.6f}") + print(f" kappa_D_ss = {bk_D['kappa_ss']:.4f} phi_bdom = {bk_D['phi_bdom_ss']:.4f} phi_bfor = {bk_D['phi_bfor_ss']:.4f}") + print(f" Dep_supply_D = {bk_D['Dep_supply_ss']:.4f} rb_dom_ss = {bk_D['rb_dom_ss']:.4%}") + + print("\n── Country F (foreign / Germany) ───────────────────────────────") + print(f" rk_F_ss = {ss['rk_F_ss']:.4%} beta_F = {ss['beta_F_ss']:.6f}") + print(f" Kap_F_ss = {ss['Kap_F_ss']:.4f} Y_F = {fm_F['Y_ss']:.4f} I_F = {fm_F['I_ss']:.4f}") + print(f" C_F_ss = {ss['C_F_ss']:.4f} A_F = {ss['A_F_ss']:.4f}") + print(f" w_F_ss = {fm_F['w_ss']:.4f} chi_F = {cal['chi_F']:.4f}") + print(f" n_F_ss = {bk_F['n_ss']:.4f} theta_F = {bk_F['theta_ss']:.4f}") + print(f" alpha_F_ss = {bk_F['alpha_ss']:.4f} mu_F_ss = {bk_F['mu_ss']:.6f}") + print(f" kappa_F_ss = {bk_F['kappa_ss']:.4f} phi_bdom = {bk_F['phi_bdom_ss']:.4f} phi_bfor = {bk_F['phi_bfor_ss']:.4f}") + print(f" Dep_supply_F = {bk_F['Dep_supply_ss']:.4f} rb_dom_ss = {bk_F['rb_dom_ss']:.4%}") + + print("\n── Global / cross-border ────────────────────────────────────────") + print(f" p_ss = {ss['p_ss']:.6f} (real exchange rate; 1 = symmetric)") + print(f" Q_bD_ss = {ss['Q_bD_ss']:.5f} Q_bF_ss = {ss['Q_bF_ss']:.5f}") + print(f" b_D_D_ss = {ss['b_D_D_ss']:.5f} b_F_D_ss = {ss['b_F_D_ss']:.5f} (D-bank holdings)") + print(f" b_F_F_ss = {ss['b_F_F_ss']:.5f} b_D_F_ss = {ss['b_D_F_ss']:.5f} (F-bank holdings)") + print(f" excess_ret FD = {cal['excess_return_F_D_ss']:.4e} excess_ret DF = {cal['excess_return_D_F_ss']:.4e}") + + print("\n── Steady-state residuals ──────────────────────────────────────") + ic_resid_D = (bk_D["n_ss_IC"] - bk_D["n_ss_ACCUM"]) / bk_D["n_ss_ACCUM"] + ic_resid_F = (bk_F["n_ss_IC"] - bk_F["n_ss_ACCUM"]) / bk_F["n_ss_ACCUM"] + dep_resid_D = ss["A_D_ss"] - bk_D["Dep_supply_ss"] + dep_resid_F = ss["A_F_ss"] - bk_F["Dep_supply_ss"] + walras_D = fm_D["Y_ss"] - ss["C_D_ss"] - fm_D["I_ss"] - cal["G_D"] + walras_F = fm_F["Y_ss"] - ss["C_F_ss"] - fm_F["I_ss"] - cal["G_F"] + print(f" IC resid D (n_IC/n_ACCUM - 1) = {ic_resid_D:.2e}") + print(f" IC resid F (n_IC/n_ACCUM - 1) = {ic_resid_F:.2e}") + print(f" deposit resid D (A - Dep_supply) = {dep_resid_D:.2e}") + print(f" deposit resid F (A - Dep_supply) = {dep_resid_F:.2e}") + print(f" Walras D (Y - C - I - G) = {walras_D:.2e}") + print(f" Walras F (Y - C - I - G) = {walras_F:.2e} [diagnostic, not imposed]") + + plot_steady_state(ss, cal) + print(f"\nFigures saved to {OUTDIR}") + + print("\n" + "=" * 65) + print(" TFP shock in country D: rho=0.8, shock=0.01") + print("=" * 65) + rho_z, shock0 = 0.8, 0.01 + Z_D_path = cal["Z_ss_D"] * np.exp(shock0 * rho_z ** np.arange(cal["T"])) + Z_F_path = np.full(cal["T"], cal["Z_ss_F"]) + + t0 = time.perf_counter() + out = solve_transition(ss, cal, Z_D_path, Z_F_path, verbose=False) + print(f" [TFP transition] {time.perf_counter() - t0:.1f}s") + + cap_resid_D = np.max(np.abs(out["n_IC_D"] - out["n_D"])) + cap_resid_F = np.max(np.abs(out["n_IC_F"] - out["n_F"])) + dep_resid_D = np.max(np.abs(out["P_CES_D"] * out["A_D"] - out["Dep_supply_D"])) + dep_resid_F = np.max(np.abs(out["P_CES_F"] * out["A_F"] - out["Dep_supply_F"])) + goods_D = np.max(np.abs(out["Y_D"] - out["P_CES_D"] * out["C_D"] - out["I_D"] - out["NX_D"] - cal["G_D"])) + goods_F = np.max(np.abs(out["Y_F"] - out["P_CES_F"] * out["C_F"] - out["I_F"] - out["NX_F"] - cal["G_F"])) + print(f" max|capital resid D| (n_IC − n) = {cap_resid_D:.2e}") + print(f" max|capital resid F| (n_IC − n) = {cap_resid_F:.2e}") + print(f" max|deposit resid D| = {dep_resid_D:.2e}") + print(f" max|deposit resid F| = {dep_resid_F:.2e}") + print(f" max|goods mkt D| = {goods_D:.2e}") + print(f" max|goods mkt F| [diagnostic] = {goods_F:.2e}") + + plot_irf(out, ss, cal) + print(f"\nFigures saved to {OUTDIR}") + + # ── Bocola-Dovis sunspot shock ──────────────────────────────────────────── + # xi_t = run-probability shock, AR(1): xi_t = xi_0 * rho^t + # Transmission: xi_{t+1} → IC tightens (lbD_eff = lbD + psi_bd * xi) + # → Q_bD falls → government issues more bonds → b_gov rises + # → beliefs transform into fundamentals (Bocola-Dovis mechanism) + print("\n" + "=" * 65) + print(" Bocola-Dovis sunspot shock in D: rho=0.85, peak xi=0.10") + print(f" psi_bd_D={cal['psi_bd_D']:.1f} => peak IC spread ≈" + f" {cal['psi_bd_D'] * 0.10 * ss['ss_bank_D']['mu_ss'] / ss['ss_bank_D']['Omega_ss']:.4f}" + f" (quarterly)") + print("=" * 65) + rho_sun, sun0 = 0.85, 0.10 + sunspot_D_path = sun0 * rho_sun ** np.arange(cal["T"]) + + t0 = time.perf_counter() + out_bd = solve_transition_bd( + ss, cal, + np.full(cal["T"], cal["Z_ss_D"]), + np.full(cal["T"], cal["Z_ss_F"]), + sunspot_D_path=sunspot_D_path, + verbose=True, + ) + print(f" [BD transition] {time.perf_counter() - t0:.1f}s") + + cap_resid_D = np.max(np.abs(out_bd["n_IC_D"] - out_bd["n_D"])) + cap_resid_F = np.max(np.abs(out_bd["n_IC_F"] - out_bd["n_F"])) + dep_resid_D = np.max(np.abs(out_bd["P_CES_D"] * out_bd["A_D"] - out_bd["Dep_supply_D"])) + dep_resid_F = np.max(np.abs(out_bd["P_CES_F"] * out_bd["A_F"] - out_bd["Dep_supply_F"])) + goods_D_bd = np.max(np.abs(out_bd["Y_D"] - out_bd["P_CES_D"] * out_bd["C_D"] - out_bd["I_D"] + - out_bd["NX_D"] - cal["G_D"])) + goods_F_bd = np.max(np.abs(out_bd["Y_F"] - out_bd["P_CES_F"] * out_bd["C_F"] - out_bd["I_F"] + - out_bd["NX_F"] - cal["G_F"])) + print(f"\n── Bocola-Dovis diagnostics ──────────────────────────────") + print(f" Q_bD[0] = {out_bd['Q_bD'][0]:.5f} (ss={ss['Q_bD_ss']:.5f})" + f" => price drop = {(out_bd['Q_bD'][0]/ss['Q_bD_ss']-1)*100:.2f}%") + print(f" b_gov_D peak = {np.max(out_bd['b_gov_D']):.4f} (ss={cal['B_gov_D_ss']:.4f})") + print(f" Tax_D[0] = {out_bd['Tax_D'][0]:.5f} (ss Tax increases via Bohn rule)") + print(f" n_D[0] = {out_bd['n_D'][0]:.5f} (ss={ss['ss_bank_D']['n_ss']:.5f})") + print(f" Y_D[0] = {out_bd['Y_D'][0]:.5f} (ss={ss['ss_firm_D']['Y_ss']:.5f})") + print(f" def_D peak = {np.max(out_bd['def_D']):.4f} (1=fundamental default crossed)") + print(f" max|capital resid D| = {cap_resid_D:.2e}") + print(f" max|capital resid F| = {cap_resid_F:.2e}") + print(f" max|deposit resid D| = {dep_resid_D:.2e}") + print(f" max|deposit resid F| = {dep_resid_F:.2e}") + print(f" max|goods mkt D| = {goods_D_bd:.2e}") + print(f" max|goods mkt F| [diagnostic] = {goods_F_bd:.2e}") + + plot_default_irf(out_bd, ss, cal, out_bd["sunspot_D"], out_bd["def_D"]) + print(f"\nFigures saved to {OUTDIR}") + + print("\n" + "=" * 65) + print(f" TOTAL {time.perf_counter() - t0_total:.1f}s") + print("=" * 65) + +if __name__ == "__main__": + main() diff --git a/code/global/output/default_irf.png b/code/global/output/default_irf.png new file mode 100644 index 0000000000000000000000000000000000000000..6ffa003b25fa91ad6c2e9e93ef037e72d826855b GIT binary patch literal 311936 zcmeFZc{r5)`#(&=-J-gSiY%=nTUnA_3$lyISSt);&AyH$?a96`DGbS$eJ6F7WlWgC z#8@i3u@qxu#_*ie{r!A@zu)u6^Vjpob3Dh_ag;F_b6wZ_eV(uFyq+2vXmRc4+s(ql z!lixvnlTH@F8Iez`HKUdIl+Hh2R4+PsURsUe%#5&=bpEsl$6_l|A3^2m!p(m?3fe0$gX?W5#B5;M-DRou{vhsjagV( zShTNQz8R3YFv=NVGPVBx*VT2&L409IUvDiaA9BSv1$v8G))Z$d`gV=t-~zL-OtMAXWPUc< zVSZVx^#0jmb*}c#@p0R?W+mr{R^BhVGZkdCA_Rx@ms{wi+*(r2lz8Q>-xC!M*u#al z%3k^`jUW_#=TB>R-m@4d6`B+@G>V36_4QjWp;djwM#-*y`;slHozuoM z3{^L-<>ch7j0(PbQ%<{m*6?Lwif!o7e-0tiFjR0a^B_c2(=WQ6`~Lm=bdZlv)xt=< zNhp0$UPfi}=f@*^A32k1N_e^bF*-@-jQK0ILMVM&(=x> zTO_B$%48=pXuhyuvbx{Y0Z7UsBogn`ba|it-aj(Y@GXK>6?#*LPhqg{=Oae<;h>nBkSP%_cDDu zq5At9TY)-@z`|hLnpDg0E3ns84!ugSe)S#}k5vu_dyJ5pdruU;z);9vzMvyj$-lHQ zBRnMFTLfqK&Yst-y3Y?wc%>JxnjVvPmu<_*MpM%|4_3R6DyFHT8+JYEdJ(uf-8o$C zGSwwkmdGdPT-Vf7RMv8?N0nY&A3Bre-<4pfirPWrM+JY55%L&bU2fezc>UqQv?QI% z8kG8Lq%EOGfccJJ{eE2~#C#C&CN}nfOovT#kZ-V=u;D==A&*wUqeo{4Ygp7TbNVH?OKR@o5_L((8jntJtc=%A;$S79U^{bH}nsP%Se?G&OY^{TB7+-oc8Ptdp>;alODALQ~$aLW%8snQ#c3ERjGiYaL6B z@1G$WC;5^GlVBNUte;cj6A9=%lag9P4j#(Y+K*@o@JwDOdf6);7^`-gVz^x?TF)=eCMT+T-oT)QsYaj=;dcH0l;(eOw_(&y1d({Mct? z*}G9i@>=cFcC0W8UeZ~92g$seS z5$;JBA~83nyo-ji6i^HP_=u}={Fa)@p#Ni40~!3y5)0FikdVnZEx`+E+dlP3=Pn&D zuZkfvKgE^U;?)z*}_f81D|(H(C=<1Y5~gx9p?w22CZZI=7Y;WbX4JgJAe%5a_cc3fA< zbKBgaFY6;EB+!N9Cupju&YKK_t}n%iO1+^P*z*}P1o7K^d-F*qx>Lx!p?%eR?^Ysl z-7O{bx}|Zu_UNR#x=WoB3^W04lx;#Pb|VsBl{s`JiXv3M&{T;%@1q5jx|h>xtXoqk zm9)>tJW?0qVn`x^`*HQ z&kr+C5{V@3`t^JP-G6ChXt2i3lI{ij1WuFmI->60YsB>8qTAp=gRhHauI3BZh=>Se zD3o7UCBKD^{=?|~6+u$s2)9$W%5IQgCCJINUU|6Nzzq$h%Bu-ovOZmbm8@J_BoVEM zi$$c%M32H7xtjUwN@vb|@|(k?43n%I{oE{Jleo&c3|4K>Xz78iTeKo9F{g*@1U<6` zx>ULYv@GLjlA7J;y#?!aP~4E`U<;%bEZ3hJeWYk>+wMLoe($TsG4NYiTmFw8W?lngXTjE34ukXI!Z)Go?4K0l+v~6v{ zPH9E+mzVj|D04NN?*?W37R)Q9W^4MF3+4xkNUgTff-272OM~=T7b03eOvdMBXzk|u zcv#w?z_DX4jq`7d7J6PUhe74R1?SYRRl1?qm2?aYYDPm=an$xX+S~+zj9&^|u`jLBUtb!<*?s)*p?l^k$q1vv6Wqw`k|{BjEj{C* zbhlM=bQUe^X0@{^-A*g&XxyA9ZG+$dYo@}q*u2lRC|^HgSWPOZ-o7(lFQ!t}g_hRg zgiDvB_9TQF&ieHd?^aL5>q?euXlXeHWrj*dMn*0!C)C)6MN+8MM4JqUarIVzD+Y(d z=9cf;Mkj2Zz~A2=;~1LJ{Ap9@Kf6cc$bT9~#Lf0-0h!2whLG;m3+{XJp2ft}?vHYg zjhQ&U+MOn&ANz>sUBWNlS@qU^Z^-K8TefHiU48v(Df&!ym}^{I+_a}{$S;lLGF*~M zF!gIF{2@KIMyQI^a4xPZVhif$+I^9GuXl2hoBP@}y{5W`4vU-Lwmp30NQ&F>B#y3G zWq}Y_gUy7Xwemw}4XR448waOcJo*3Gnut;Brspja&t`|j7g1f_J>V&hkB$9SrN?Hf zN?(vkVWaUayy_{8I2Y|Es2uPP{Zlp9F&jN7zGwgbTE-gDmb%d(WgbItdy_4sMN2O} z9s$>QJUKDZ-ZtNDi&+!HZT#r3mRdhP-e0@_rw6G<;udM{O53%-A;F=(Z-0e_g{^*V zTx!7j{rKmpUJo7cn<9>&ep>7|?!wUF_viYGc#L6BRc)Gc-(GtHg}k zw#NwhTeMENY|+SN)}^_I*{aBmV;)U+#G=&C@%;Vwou%zzm$Iqf3Kaj#Lf!;@d52>x z`K zaX*8i8lCMw+U_&d`}(SPwOi=ch9e0!h768)T$js>;45jTt`-^Wm=dmBr@e z9|V7gkV|TjyPYa_Je7Mj>?uT8Mxt#i=8sLDjXhUJf3DF=UQ-oN^7EZy^di$!DpWr`Jh3s(y@;3fX`k^hqD*|dB$8pwqEkyEHAqc4|Y*qk12*{#O>R3 zerbxvPSKlX3iNwx89{ zab4+U^}zM!(6!wWZHlGT{kMu)8O1xae2$NI9h^E^R}jHrHW(MxhA>l4OxlH{%=Q_q zmbi*7H?0~(52tzx_4}G%R!L_}eQS%X8EG&&f5W}D{h<|lZGd|Gac1wbKyxbx!ELNb zfv^IgCGGvb=AO-s)jhJY=cLM1|a{6ZdG{$ zIUwKR0*ikA$Su-kw)#Kx>QhWgqwby^%xfnZ4XakW&=}LrEhs1n{H0#pw#BN!Z}Hsr z=4wo$l&vbMz1EFnrs%(viE4V4^&Cy1}`_?3PGb` zUC#6o4{&pzzWR8dZ~c-gnwHw`KHg%zPe3uT$ZziJR(9ByXpHj86AGDVDA~9mT~ER5 z;_}EQE`!B!KTeB_>v8PkPc3S{S!Oe8^(9S4%OLYo9C-%V(*CLZ4EKYu(bE*X^M?$* zu+AT2)V5N|?fZmOl?hGHc*Jy|>f+X*0T0bcSyWyB@=`?%qnna^=i?)Xpv;y>f1lD! zC09E3_yvuW+qHLZ1&^cGry5>B^~2YC*rq85R#Fm zIaOF#IIWZq_`%q`xMr>;*#v5he9GT{|1FtfN#4D0-xn_dfY#O9)Jq051@BJ-GX`^x zSDB6L;L)S0sQQ$Y6w31WxD|0+7Mmz)((S(y&dOFX(gGA%Kfis>-xLsmtW)p%mdz{0 z7stGNR3uZ#jty}5@yW?Z)roq~$&SARfgN@GqhbVA7FsoWlvkq=EkSGZ;zGf28K2hH z`sZ)Jj+RYP@?Y}Vs5_&ZoZ3_4uCyp)-h--EX`Jy{t8bl3wzOK(ZCEDz4KC)i)*mJ- z-R_7zAvx|ndSu6r9eKy6e8W8Ln?JvKBT^~JTvF=4eoQPer65>c`vkfNBSHlFm4Ya5 zcrOjKxe7ED+qQ183vC1MBt>%V9tK$6?J)8Ag|#Hn?^dC)29tHjhAj?PQ^`=8G?K|g zB8uH+O7@Zl+fJW%6d-nAsG)(o#J$d<%dENHIBL~i9c}P+00?gkU~O)!xe+k+C%r{d zZI2(C?^?Cjl#b}F(#-C3Iqc5ACqFTH;{8Li2c}DE$KsiwbLYz3Hzxe62+33}Uq_pm*3~02+O_Ly=jD-aAv99Rj{e*1iL#!bh)0 zvhBI*UaRHjxA66Zwzl?UpMGQgRh--(}6lIQ(Cg_OHR{{-0;Wg>~ z?tg+ef3@RgKJbXeO3Jgg^Qi~Hsz5eoRnjH!%az5{;3UshLj!}0tZvZL#bd`?Geeeq z)5dy!d3(4O9hR`D3yI>mHRqAOCV1pQ(~=Y~$-%8XF_AkZ;v4fShvz(3KgWFV^MFvu z&xl=$qLG#RH|6 zZ&teylW*AsezM<%zi570I$ecK@@SP~!z@wfi^|fEW1?Q7sfa_V6C#b>7lzm)#I5R# zYyik?K!>sa9L0+?ZqS$QnCsILlHE|` zHbEIMFGCR1Ona|u29CC9ELP9>F$PtOBW`Z5hiyByn8>o+P`i~rYQUx}6Kb|6zNM5w zU$?zSa=cmNitrAc?-xzM%Do?;Ne*_JzDjSgc^>+h_|{a+kkm(O>Cq0)n(%L4yM$hf zYCSQeB3YY_A_w*ZN$?u@ys$9VOxhfC>dirZY-DOcw-Y(a=T!6BEPdzuJE^rIMQ-T> zq1#Lzcl;ceYX2*>MWtC)XZF8Tpn6LqyHokMx2D#-J=VC2!&PooSSB9{9JS@oQVzrm zR$V{vN5te-OQfVB$r7Ri<73>tbn7^P#ee-5nj0xz}w_Mse_ynO1IIV=($5y7MKq!KVUb=WB7dqObQI-&K!~Z zZGcrn6&4lWSMmDDm)PS@LOqperz;{io<>K1A*^owY8Mi|eYJL?BkmOD(mR{VB?2u? zw0n^}dpERZH%+M)3ai)epLc>a&xjpM;%J||Xkz8=bWwr4+V}eEV}7+SyFs*2Z#i4xfkc(0e<9PaZhi06=bHDQeCc+{bv*tz53}lSVLSM-qcTRvI1LCzcRvsv~*|x7X|Ew>G`i66wq7ai>&OTiy@n zMkuE50(JWeC`|FFdX5+NrZ2m?Hsc1u%T3>k$QQGIFP^D0*r=B5ir89N3oZ&jpT+urz*|*P$>3)G;qIdWlS1(>qv?0tKQL%DW z*ZLgpU|!oE1Up8geXsdo%bUwcix%-(*}6}=^YO0d4OP|>xqZ`lv?BLZ(v|J2(IsxF zghfm9?2^q;s+Vr1abZ~ttt+J6={)(92)TgulG$x7U@P7{n@OYol^o_4tZxUsxet?N zl1E!ij>ce7vE`wDP)^eeW*Uq%&-o5Hq|{s49KGDj&S0KFONYmm=SB-KXDewVGi&sd z06|ZYS92UB{sl4n|BDiRuiDJuXz$Ke8`oRvZS?o>F2t-9d+9{WUT-(ubpjXvnvJ2D zN`uP4V;)?LcOE``3ll7Bsbw})DO-2CF=84p#3kZlxpV}#{X6r^eesXy0cj7p8*+Kg zLpRx#-{pZkrg`Sc3O;!f<0 zp<~kaPj>xG1!%KN8Grk2sdb|cG&amTy)RPnUB^ z7nOIf`_8d?RDiz~3bFoy=Nj0W7rZi>fETS*sW|y@*)!2E1E+Zpb;n)!sZCrP^&d}T zxu1zwPoBnQWaugFSN<7JP%mlG#C^`Ye7e->^BI8$HjREICik?Ro!|2Q9ITm4=7=if z5i_gXyKv5xkNee`8EGUt!T8RgOMkq>k=@t(ZtvvE$Lk5z@5;xAUz8xrgEpjhW>WUJ z2KMN59+$#vhb+OM*%RK@Y-zGi@5=>h$AXFguT}{hf?fQ5OOk>Z!rYthbjv|OZ<$gZ_(uX83<+r&L4vT*-Tg|JJf%EJ*Fu9 z4p%Zh{Cs=3C|6WfP0E%e;tab4-_C-|3DISytuho(zDsYX_+&UvqK@ZtWFlB^Dco^* zh+pPzCt})~_$ZZO+gto)VzoVm%LNn5<;i;e#mV<^`HVuJy%O?u3#jU9vQw^!B z55-Eu4tg~fxI}+tzIyWgUuL_jE-zU)6-eh(1vmcusN+W+w}46%q}&>E|5n0 zmQOJB@iR*xbw$_y{b%w0RsN?SJZpwMGe29p`0$8LN0r_TS=M8$NnEHEiQ}_m5*)^1 zDI3@9*5Gw_`otRTVU&>CSm^eMMTPxSiPNY5QNDQod@oZhv;^#lTXZR{|GdR#-5kA5 zZ{4nB-?=k&YHMq&2C&fA;aU$5(7TcuzH<`-3N06Jg>EzaGiqyV5rTq(?kH`Txs zEDh%T!;6;hZ@R|pNt1SX7HCT;Z)s_%VQyJbS=s(>CSu84YV}{Xv4l#7lV<46qSsUg z$cl!wL|Bm4OPqsSg*~}+LxBG{M=PQ2+1!}Jzg@=by$jH4F;P)fzM$WZ_=78#CPaPo#yBY2s|)IkW63bJe%E3 z4Q0yg5|=K0-D-mJPz9dS@Rqe}b1TidWKmmR-!~}4Ak+nHJXh-mpYriqu=Z+zsirQ6 zxP*DHseT6bSK3UqD5ya-!nYh+GD)}TX`Q{Ty8 zONw!;3Aw+}&V4_hY=0L%VD23CU)MDM0Qd0D_qQE_`}WE#^_JUdZ2-yAt2vCD%^SKYOaA0i){LxBchIUd zchY_b7T{O==C9>;k2}3yPt^rcX76;I#38Rgd-^mEXj#U=2kB2wOAR^7K7XNVHhv1{_1Ym)R#)?Ll=wpp87WlF znbE_v&2%^2N*1QjZ|{NElPdwT)7MbkeUsKQ8(hq*CYhL~qq{gqFF;(OD9D|{fgYys zcLb*=VuF*ZGdFSS;x+(yk^u`WPbiM{3E;0l%}Y}^TKS~=9pwp zYzeT3uBO1%?)9S3WZA}8VDHnW%r2@!0Z>B}CBeSPaUYLi+t z8Qm@%r?tRInQqidLGVUXf^V&}qlM&5Y(FonGD?ZW;#QvYJ4>^Dxxxwkai_GprE%Ai zGw=b!gTP2f?Hiatj>%UNFAOXe(wjp$4{K(tub*Y$`rKZ%hBa(EJyK@ub((HzDmH0V zlEbEi8oCZwbuMJktk=INEvK#z3^>+#&wQCyx?CABxBS@M5_i8~{CWDB?k@c7d^ywU zN7RP)%q~YE-xJU#wpaaza#k8zMT*MqI1K&KvbfbSvguu;FPKXkK9nPp*9httBs^+D zHoOj9H|~GIuYgbd)xVNhdS8phzc;>~&#K_ec2)5k4$FpwWUu~jSWrgN!W)}*{;Q3R z#B5Lh`%>Z0uT_pX9WVv2>bb^&q#){$qrR{p?r5^L*|~3b)1mn7%3+FQq{ugkNDG2E z3JiJS%l}3gqJ9bev7T92?){yq5e@7 zt=;RV4kI9OQ@(7Dhzt&V$)w z4BMt>H-U+`(}d?f*b7|*L$$>vPfwDRh2e- zidP01Wl7?MBqK_VMQ{e*l6-<6w_HiR-GL-$0GXTMLQXdz^O!!m6ag=pKGnvPwQNq) zARxbh&JsGBUVL*tx^UpEe)?&!ToS^=Sqgy>MIDp>;$vlv`QdZMu8~3m@?%#@rWMZ! zvn#T&oO#S)1n&h$IU#J2nJW53r`mZy2l&*atki`Ief$RqsxefRt`sSc9qn?d%BL1h zX7e1+j`=7uy%kY@UYo4V32ww-aw z{e3MDUXB!?ccGi8XL53LWlFBfyakriM-i%uVT#uE-ya>b2dIIpr^>juGW@))rAoH+ zK2O{V3WflYu#5(9{L& zxnZ>~7q#p3aM+A8M6DP>w=gb*KsIQ;^)pjbXP81qeG7;`df4iiTvHp$rDxP&pOD4o z^)YOJd*65fZVZ0Wg;3Dj5-C_41ire=i@>Ref{nbOSIyZu+2(?Qm|ym)y@5X_1!emh z_XnuAC4npr+k{@yqBLZjFB(U}JHE2LVFo~r0QYpknX^9hRp7Ac)RmX_^V zJ|r|a6w|+x1osnE^923zTcdzZGY1n0zuG)n8D^68lLvQkamg{>Nm@5_0Huo~t9rV+ z7Ayb$7CEibw>67Nu{rnrh}4&LD$B?Vz#+f-*{=!HeesJD+ChmLpo70(zQwN-Mep#CLs& zT9{8gF4e+vJJaEuNih>dqSkLXgDL96kn+G)fQy;_zJe)}umva{pP65qy?jL=umAE@ zG$ZJhPE-I)cc>{pG8x2bCw%ys#YiWl5!Ys4!Xy)h64dY!5(4H8zdj#h zmdEpv+O*(hCy1Ekhhc{B5_~KehUg-vli6R33BXs|C}*m&sc@j_tV)>nC`zg!6f?c> zx_vjD4-Agfd*gn+(tN>}rkYkt28l$5TuBS$jf|E3W*HNSGHT$EMg_0^PNNo;HZCa= z9Pw=$Tu$5H9}7*k(3fP`QdEK{@5-*y(GAgAO8)5+mO#6u8T^Zt&KXr@Z&)|$7dhw3 z#TN;4K|Un&_L>TE0xL^+6ZVpOzMVOk({d3vf{L$7`Htwth@x6&z!~)1eHR~2UK(x8 zPNg^N-A1ln7deg#dVTtJI2PB?vwrV3ELz7P!Vvombai+r)vx*d8bfC8N- zH8jfN-R+1bV4(R@zrNbVD6n&*)Z?k;Rogj?ON%CRE`7vT=#mi%>*~ZmPY>mL_%gGH;j#Te2 zh@dIjq?rug0weqJu73zrGPQR-Y^*6zxMQzKjVsHyJ0J?aKCw~*Ck7eZ3bY`N-V6y) zY9P47axp#5%?%@QV_)Wr{5F68LLxvObA{bg(-ZUjd1bXz--mA7O5_XlmRs#;@>GLW zHMU_J&PpXZ3sywHI?8WxIBB9iZ0f^`d7(M9^q6e{gvCfR%#_*T z0|##J*|Voo4u${6Z)H-OUg19$*gmoiAtC>bpwfolhdI99pk0$@s@+I~%=nyTb5Jz_ zJq{t~%9qgUhi9OdLAFDO$D6|rQ1)Ela{Y|pD*dHrla5eX z`z^`SCr-3cg3m#|o7CuEMvx{4fLM{RunPUAH56@m{rVF(&<$&5XPqpW35pA++uMa8 z;lXct&bB2a|H8+A{<%hi<6PJleUewbTx_%tl2_;T*6kad+)Tws}Ht$Wr4aUZt^P}y&u z%z&Sy6gRKCMD3ETxvZiM!O?=U_X@7`6+>7B3kJh`o}!qX!hgJVdTtUbSF29Hv_mMW zOz8DgShj@JL)t=uFd=&=5foTPP0|v2ZGIqasR-31Q}N{)c0VrQTk4scJ<9zuzaU@r z=W-~V!vAu;gyH44>hqaSkL${D97YX3nj;isbngj#jhd7*NkInmigQInWpdDwnny(Q zqAqf(nBa4kpCglcTVB1q0rB@|!V*afUzhu~9>m1Nm~3Cia*@-v)V4P(^YZe(&bauo ziWS{AeTk5{IG2aHA&vZNpqSOdS(BJ>VfBrjkUr{|RZ_^8Xk>TDQvJ+At#4L(gbw{w zdSDJ@L8%VI7Z+PEQ7a*=i)N z4K7SYx64v4JxX>b?9GVgZLKc5o%GCmA$zu*Pb@h96i&IYbr}>#!<$;5P`qvdBOy+&^Z zQk}z$bguzQkZUKiKI7A&vxa9VaNIx?`=o%yjNN&m}=hZ0* zf!{DIk$O8sqR`bXY$||X>MY4PW}ultC8Z^!>eVr%3V>=Gd=s0HA;x)jfY3Qv=caz+ z3XC$vI&Dk@p<#9aN-P>ugQBc+jgeXRAD*wY(4~ zI*zvAFgG{Pr&~A{1Nl?T@}vU)ON&$#LQ~?Jwzufhp+Vm?%5Fije&ypY^_QP^n|IZ9 zr!NN>bzV?Pw!8TD{(KlB2K#n-whk>o4^tRzVPcQYd3oiD<#m1eQG1S`vgA6R>lkA7 zHonYOjm`jk>mVuTolvx(&#S_q5ReD-DtDXno~hCY6EvlXHeV#1d-)Efve+S-x2CuT zVjb;zh`y-9(GfQ(k)XxJ9CNR0qiD%?Xt#IS1TV^Yi#vXOcSjns@_MbmK)yrPv@RcB zj8vvCR8D_QK-TtubKzPUJe~_?U{7Xv@)0wo7MPFXul{&JCZGE8=I4d)I&95e8H*|f z==V+UqV?-{=Qu+%RQQ5nXscY71_NSeo?+_w6SqjFwHWGzY{D(~8V9qZ6W6pgm}i^- zy1UHm8(!9mJ$d*y?&Ph*0zTf#Mw23C0y}Uzv2pp=dcK~h4-ZP z&PFk;DwN)4>DxsWl|th8jbDN zjpmFN4?3?)2y^L7jjCF<8$H`fQP&2BrmyJFRcujy_n}IxNRjFb3jhn;7e~#^=KZwN zb8I39j>$Ps`ml%t(Alas-9&>K7K}r!b2OZ5JSbw>uk|g}?18@z{|u zJ)Iwxb0$RYs}^?BL7cakRed8aB{UwHgI~Fyeum5xH<-e)OpkIx;r`qT>>C940u;@i zY7TpI@OEyc(Z3vs{&=@~uuN051y@cM*k!7FUj4INVUVkSrAy2K%t5bq0sre)uG}Yh zap~Zz^3(AY$krXSwY8;JFo|iKJg)e5AIVVp*8}X#vnNl^rv%m9F+=veT-9v#MMWaRBCCh+jKR$vfBgB%r}rr6CfDh0nzm7Dm}Vi`scCTvmiRX z)QFKmgn3kMyWkWObF=wE5ujU4FPO zl8Y(MEGb1>nS#NU6$#Vq?RC&>LV}uL(<*8r*+a?NA$;6&UX#~{0Nv%cJH@lA?8~VN z%N=?t`{eVl)PzzGFh4h(Nk9+n3=9YhDKHP#QDqf*|Dwm(0(Tu|NI|M~(+e`vT6!FY z0!R9b&G`b{`zB_GO}8 zpcZ4D9*J9Ln_`E4)yo}l1&};lK0MUCP=W1+aa|n_2}TZsN58QYeTb`+IghIF?KL7r z)|{jmaCgmtayymd!rYK+x`Ym)lI(^%nRJ0gL|zu%xNjC^dlh@uC0n;=7UK5$829R{ zoYbY}bsK_VU*)$T>H=!tf|Jm7wy`iss*CCkI}%*YX(PjGgviha22kFF!ky7}wgx>p z1Xd#;8inIBeDdy@yubU|5VTMyKQXirB?!u|9*Z)VAIf}A{V?lhGG@qz^F41Ub4L;1 zl7L)Y7%a)?j4o||#)R`6?N#IX=)D391a&b?dkW=JJjVy}g_A0hc??mN{53Gh71^Co z+q#vh&KG?b6XqCu6yICYibBj^@)jST_jM%iP3#_l2Sw)PISMZe zBD03G33gt?GhpgaithKmMxyk}N#{9Be(K7>-?Yo@cpo2JLRp z8p9wboF*L+)U8364cc@78Z-Shh8nq|(_TvnNLFp-n4SG~ifa?U*Bs`jVqwS)t1H!P zKl}#K#*}}&b4Ea+U6@*F*`m34yK2SUn0xv-$W;r=<1C~ZlQLHU2G(FVVd7>G{x$qn z#_E+yTl6YUUfN?!8SszVQVZRk5GvvFrpDyv z+3PHNo4vi+*)$DuU)okCM+7@N`_vn^VJX7J*&xHrOLwS30(%A+EFyzIj9g zs)08)1P#os5fnaG&i#V2Jw-8CrgkO0+T7fX^KSn6VGn!7A^_sPI1p^SFUvxEp=`TmDf&vI<*i?u3PC1zveY9XPVf|-&Q!svfiOTP8&e*wG>3^!Xoiwj z1N?!soDv&*I~Z(WH(>tKjA%g5U&mk?k^~VXD?w7{Tea9E$U2aiLm`&%BYH|qWjk7B zE(pMx%TjK;eZ~AoW{Ot45!9lrX_xV0kWQvA#no7n)L?dTH6(D6&7%DUe~hQY*&@}n zDW*hUW=g+8Ygm)xSM(9-nTTZSh`!5Xqx`ECpM~iV)QwM?M#Bq>7*n-!GEm^^(Ci4 zG2BkgH@kGsN%_nF`{aNAA9?X_()&MOvk1og-=8Q;eDNXazrW9Xpv=!(_}%E z#11=qNjEO;PgwFlXCS=&)W7M;|A!}5J^h&}Z6hz3N5Mk;W(qb(ESgT$2FD6U&MYAU ze~MWp`V@_gjVHm3O9nZ)1EN?{Fhv`mnJEsPKLLtB7i{)35K9uF5O=Vg$-*IJ4#ge= z3rhMBV4SE>?v?w@cdPzO9Z()%$!mb?tUN#QRQSvpKaGk?WM$*%fLF1HmLUyVk4!5_ zfeIEqkO?_+`t-GQc@Ie#(2dK>JI%zhAuF9{SAbn&LFdJ|k!p@i`Le2O=Z;rGhCq6} zWG~CfTA!aiJw1l!Zk38dPjEeAh$m$D78{3+dTA`%%93c6n^=CS&1q3M3zkfsQGkxuvmX>ZnK){J6 zJ{FOB*~lxPSMIVHh%cgL?U{U({7og`=PQ*{S_T#RJ6Mbk_QqTZ?Sr+*A|g0D2Uq#{ zxU6>~$Hjp{6D^QGl298kv*rLMj5KJ5dpL}(MYvixZX)J>va-}GvDvWMChgn$rShz) zm!Y|y4SMe|mTzuZ8VomHh6wjg+6_3KPizgf$Cp^j@7lY!d$ob({h_(_H>Qss?cLq6-(}l#qyXMo=d9N2SIiB=B0`(}_2*4e z7fFti5c-+-s+S9ciT8SoW#-}nhbalpcj|F*6_-j-2UcJ)9#${<@7zcD>R);AGU_c^ z5QedW&|$rmAV0sWq}|=SUe#l8CxFy%qPpOIO)*7qIQ}VT6_A-c6>NzmQ-A&UtFVBK z1QJ2VFzDafzG0icJ=37W{ie}$V0dy8+)+<|0vz$bA4VVkMDD{w_UzhqAq9Z2WJ>$z z&*|9S-d^RBR;zk1e9|4O=S;iw znDMCf*KitF;OBJUE&60f+D&-KYCZS7*`<>``z9xMG*TXNp79uy5CDYw$ z3Aw)4G&Iu2=V2%C7>j`wq12bs#fOF(#bPe9$oVa&FY2V@50gnFpCJqhPFZrA#+b=fC}Lg)R+U4@3}VT8@HQp%cx%P8mf@^%deL_FC zg8yhkm9}%mb!RVHQx$hc9_Cq^YvDz0UGElmrCM=`0pW`oMW5|QzZlyL=3QPoX)|_L za&h&G#5%sSATeO$NdOsBJ9hXNwYs#xHi-TtU?cgU(75rB#f+8_(((wV>`6@aL`ZY? zlbEnVO6T&XZ?mDO+mpvo0pjyrD6fE)?2ybh-@_*7*64& zWV)C$H7hCs(`b-R#=U(|->AT1baD={#SEd253*Q-mcA+&`uolXmE=(w;>rxoU+RyL zHhdMS8cjNP*5~Ik+|@vaG<$`uP+hzH`|YYI+a|62G8KD+X=CqN{)94j7qJxid+#j`V*NHWAAZ@Fc^b& zbURv2!lua_V3#C7mt?iA-&gP7zc1NE-oav+7hzCdfIq$ElYaW2ucyCv*JD#h`Yu5@e^M-bx(waYzo{j{=#mt!I5W6#8R{~-RZL!UnQe|c)9G|kB|cXStEn88 zL$}21vhhbNj*P$)TOrmv)cu(7zd~aX?$!fBvE9Cl3V1gCLDu0-yo|l+4RLHi+TRkt zwC5u4v4nphLhv(?c_*9q?a-B-LKCdgz}xC<>IG6?umN|1h*w%}BG~iyjvv8Q#oo}- zd8Q0ue&NRO(yQ64%zz7Y)Zt~5N~AtWY;OXBgRw9}e^**1ice05c|U~&{Tn^8M472& zfYjcjzxJvzO8PHn;4_88wM;s5*7Jy!;k|qBqWBezHKUH+>1ipg@fdffM!@7j+Ds6{TlcK3 zfeVn^X4t2NpLD(X_Mew}y4ePK+9RSF1h;0rXQvKyjT#^H#jn4dS*6# z+c#RqFx{8Ce^1`jzHXr8pXNO5U@c#pV4^=*lu)kiwV;<%pJj6PZbEmJ*K(mAw(H^V zjBjrnc&>D_-pt}N+nr=oVa^)NFfIh$ zGb>;QY}sBV!$hGDL^dGKBw=pV6R2ukFa_24LTexlI%g?ZT5}Foo$mPj;@A{i5&71q4Uz)( z$7G#i1G~Wu)DumE(D-%u?pbJ|(ri9K`HlNFMci`tU7AN%XFe%4)Qx!q`G$cijj&_B zy+(6szaRf!WPN2+RBiO{&=#l&C`yWSNp}fIH;5piQi6mM(jka}NW(}YFvL*O(x4(R zLntYYQj$`F^xenzzwW*3-Vc6qEuAxSp8f3ot9_R7i4VEtrC~tiO~8(WcwY*2$U-ap zY5W<5H$TEK-8IFip8e~C_3ph^+1(~qUvq|`ubl&S7YsT}NgC>{KAuXH%q%xNbNZ2n zF9DSkD~1vCOXC}wtJrS&7Svh7TbJ>f(@Oi zMyGlnr=g8xb@0pHXh~yR2xCc0gop?CG*2H+Sz|MgzKSy$i51d!lUBxNcfIW?HVb>7 z+IK7r85FsO_$2=Hl4`j2W4HI&d`T`_YO-CElg8Nub#==Zb=jEgrn$_QP%)1%A%nYkVmI$C&ERD@+Q62eg1quqwx*?R675)1r3_(g9K-Da4#9r2Tih3} zSy(8GQ8S~cXk_}_faKgjv_;rB72MMXe*F`KC1sIO;>$J(<{&qEz{LsW(LI$mpM;YGotH`f6IOQ#PlNTg68!BoljEm zy<-XDz6Qi)@+(_nKlGOQ?lC+R753*Bx(p8gVL1>C2DHdym%)1R(To9xaLW~vpr0JF zl3Lo!8u79F`ZF^Vh7zSaV}JbfDH+Yhqpt_FV6I&+j3a*LqZZ^ri$y2SFZdq@ccUYO z&^uZa9-w6W~0ttETx%xr@)% z`C&)vv#s9ORcDSPmVAw<)+gnHG+PYH2N^{P4DUpKFFL z9*w#8%j%?;D`0f(O(ir$TsBq8X508!@n&xO zi=y4k55`+~yXp#R7mANW9PfT`Cw z03CeeCF<8&tWaOD$;ilA|fNJlf_DE@u%6+w0cA-pgVbkAlcXH|eS4Y!; zrx)2XI&90;9Kuvx&3ts@NsYbtsk(L&p`es){xwOgTz%hZ7I?dgu0zs`tG#EGSiQ2) z!qhbNbXsqrL2K%fl?A7Cvu5g|_%TsY{)NaCwd({%brEg2K^B3s-8I_sRN>YpZb?f# z(c)HDOoMHj?T_9dY{CFewP0~!w3w-ziYO@a=2^!^NaV1sjk;**_f3;l#AU(^fwnj! z2eXd`{h{njv(2^F$6IO;6)GrgU!BHX8R@SUcG%dZfTOKrNW@t4l9Bnbhl z-8;sc6}-2e-;6gpNl$;NzvTw6(`M80p$?*8G-y;JLsdRg0}PWjSNMvOc5t-E6;?L3 zIj;tJy(d$KW;sk;hm40g6a%vf5h7isC}Zck>>LT243onCl&-d!*~DbcAZk>c(iVm< zywUJmc8e=l!G*ttyGa8)a!3lHo5bDbAUZiVtEqQ3^WCYoO~feASFg2Wv+fvZe(nEO z)G@d5J%Y+$m6yL_%>1@8|H*58HhzmVNCnHD*2eAFLCr|9nW~sl=0M6gb#u-Zf2Lw8_4Q(Um-h((NP>x6RJ1 zU<|r{m8ZEa?%89{D;toicLb!`q$A$rLC$9weC#eaO9dSG$(bW!>wJTGyw4pXThqmx zLYKuU=;p*UGON_=chPX2+nG*4mY3Ubo*oNw4RmFs*ar2Ni7P%oBf$Cuv>PnebcOE9 z)zQ;a?0gjd{2p=^psGnMBv?tYKJi$2=N`G~5YM|L1*LNKGj!r#JNwGi((WHVeX#E# z^KK!~|1vHp!6;?V$%J;Gy0qhljGdd$t@Gqq?D_N8K1rq@GoYr1)z#(HzoBcd@98V| zfyk@N2EqyPvk){mr(d2vKaiUq;Giz)z_T0+@C18Zh&_6DQ^y$8P5PSy9Gl4RVpcH!VM|NED8ZhXawzSwQjqtWQM= z^scX7|>{|K_p#yy(Op_1yf&5 zfNwNb?J>N>+cCmO&>T@v0%V0GQC2k_Y`ms*FAu)6^#lsj=D-vBu|7(HB;Fq&lIqfX z9s;pzcmPsn^K;W&ah;Feb&@6J|A1dfEaZiWjnn(?#UZEA*BTnkNHa}R#@E&TE#H8B z7(kEyV)Hx2HFK9YCBA8%LIh+yCS{*x*UWmI#NB117JGeWXP!S9a67mylem1y%sac+ zlhFP=#ICa*ZR;3=wF%BvnJyHnLaouUzWcA|x-kes_y_7Y7UvJ)T@&5E<1Blpro)f~ zwHu%07|U`;-}*Sodbmln!z@iAW-}vS-{PvxLobcpTG_cWM{>qjUz_tUPvl%nHww7O zv4H$0K}QC)cYWjhwst5?8t1d|`@#{LJ<=Eg!^#tBxbm+kOZz)QA2q9=sHb0~NAos?Ul`gHl)7ZE zgNBV(#34@NLEbP$ucC2e@T#)Q{2`9B_W?m2^u&s%S}XM_@&h2Pu4 zZMHEJtAj&~TNb&tZlFIttEqOmP2n@V*&0m&hn*%nrd(oK5GwSRc4$ke8anvZXMZm$unfZdwl0qAry$xvbN{!Fa*&zW_wuCl(aCP)1DtK!*2 z|9S9T#h$#t_LBW}ARkIyq$p*7gg_xccH@uJ%1lxu4gTFPVzu&D(&F}&2YxQ>_NJJZ zLstPu{ntm^-?(w3)5a`i$>Dry=>of?-NkEf;~|Rgt$u(h1%l0r4a*Q@a7g%G)uMRN z+&^M{LD=JgqH6?xljK=^j5@hr_I>|UKg@k1Mo!B%l>@?)354*|s$b`o+Zme6+W1ya zU)IeW-+)nQ)m@mpD&vKHvbz;1s=M|v#1HfEaKl#LC+dFBD{+(ZgbiV_jE90LMVZkI})KvFJn5DJU<*KN}%Dgc1fLTD#sislsC1OqLe-gE57)_3S6 zy5j1*fmiIx$f8K;N|ESG^JbP4-d6;JGp%3${9yQrq}D0O{HR8EB56V1sixXDg--ri z=u<%osAU^uyz&5zjxH#!#htmkI?*82xB06X(|C(lC37b-B0@Ce)h*i-7@{cdTZNBI z#rP=N3NP)x+kZ)+z7&wXA4{!-anIOAAbYSec3b!7fgV22dhdAWOUQiW3*jQlgxo)d zCr(F|xVW%~$eCP)`AD<*fd0a*_Wtv!Ffc4Pd7?7%-XU#icD{v-%;?NhqJ?-y!Lqp_ zsOrW`1O8JW_NqW@4Ei&o#&`wzw6=M45>k)ZaNU8iGoRB^=tKC-TQ#u4f8y22{riY= zFaK4&tt_ZXV8UtB$8w6Js&Fga1Va{Tr>>?I3ou|Z3hSiOw-h4Pdq(A~ncfib^csr#y$RrCbHO)O*x5DYYEqODDlyx*i9liy+>FPE+^5wGugku{C! z``)xtZ7efna5eaFqrP!>Gy)!?-_7oZNFkMjpn?4p2LLJ#^EQ8J%$D#g%VDSN^P#Cm z=*8G*Ar|tCzVLhe^=rY9HGGo%1CqUiOnUXp0>i6IRIRNn^9MH$s7%ws#MaJeo%$t~5N;@cGA1sDt*KCwDZO)&(^tn@ z+p2&zS~v)6-gTn7;;hN(^_|B!)A*c&Kg;{qofdC;L>UXk#JOLg!|g2`x9^_J)Zdfae@mb>8`xAVR$Wc^Dw>R~ zH~~TVWGB+D7!ov>e?{XM{PSOzfRl!;E{Rm8$fW$HX_ezbg&z{_q z(-Xq$bvrar9O171bqUvS5v^)$ONPrQ=hs`pZ{U1W&n7wico>}Lckq{T&q61TfsB#aU)?_ zni;>7BD_hboT?Q!IY=iMi#6x_ZjJ3FYN$PH}|{d&+r3Bq-<3!n-s z==^wm!6c)#<~3Aoj)9V-P2eI)PA9JWC4&B8I^)HQ?7-T^|5+MJ?u`N&0*S(76kk~1 z3w@jEW#Zw#cENNbQT{m27%aQMcC^eu!8<@H*kKeEsBSVTGv2Gr(m^?5IzD58w1y34o^Di8-CChB8+(1W67|vFhN$D%4g|F9Ox|w#ko2$ zF@Z#=sb8%Vy!Y|h4SyMwwBlB@;qaDSu;bfQ4zeLuJO4KQt42w?>f;&*+1hq;ZsVgb z1Y|R=3t)+;rg;pTie)*na$RD6{oRx+2DH!mQL=-p<&dO)L*Y_L4hJo5K_O+pqPtm4 zhoKG0t(6Yl5k0)df?%peTFr2r@UIc3hPaGkMI<3{gTI+wk;zCOJ30cKmF_uF=drg1 znnubU&lMwR^H1?b;qV1<_5S`E!}Nbj9A#51WY=~lMRDO|{Fp*uke$z+JI4fEme4Q; zOm7^3aKt0g2tX!<(+aWSHB*amd-?tShUIC6z)&%j+>dy;W|#>N$+T~5M=won`fbT< zDz2%2b%tHxM;s*?AKT6Dr}uxUx(7?|FZm?a&A#nB&{&L%A0icD55f_}Q*6W*Bq=AU zEhLs+*2S=r(l|xNyj7YP&LGoi!+*Bf@62xTbb6R=SFkvdM>vc{F{Ot%9LZY5coiRD zZO?&{>cd-T7?v^iWfamKxaB&hBE?we4lK2z(X21+199w|m49-STRg|;$TK8F=%yFNbi0`;c%hh=s zH@KC+)#70x&o0**LfnB{& zJ>ILT&W@JjnjQJ}Wj>SkyLL+g7j9Rs>+TJgCCnHm^9*}@)U*nmyefht?Vu*-R*k$# ztz|;U%Y6dXZtg7yNu3%9WoV`e$uVMRG77p!z=2wV-Hx+^?s%u@+#){^Xr~S&M*3ey zJ-lNa&sor0y>KPaFPuLV_93usv8lA3Ik%)h8=&SR&<8P~PXEy6GUxB4|tytj$!CQ~7UqbQmd>y)@ zt+D6Hjj*}Rt%I4$r`>8C2Ds4pqFtZ^&@qmmo^IM4CQ;utX$nuP#Xdc#S%29cGSSNaImVb_hDP+`8!~mF-!DXGETYq*EtP;zN z7G%X99-bFka2~{(+AmDxWpWaH&$!RA&w#Q%uWG~00;+-!-$4Y!3qjLJ&Va;|<+p}; zx-E@70s?wDI_Z3H_>nCEoE@Tcl8mYY+2@Gzh5H6rWR{XaVIBEn?0a!7;CQuP(?GBQ zVhYzs{$+)K1++ywwWH#mb>bDd)6S-0d&8$VGxC6NFS(uR+fD~VN>%1oJiXv@@f zgPNA%S5?(J>$PnzRwsuzfxc;D(d+vuq=ttLyKjeo7xo(BY%iEp{;o)@FjM&Rl4)W1 zLuTU0AP0-=L>!73HT(OVYWzQW1jjYTfWPk&A+D4IlGiJHAhIS7l&tMg==kAJ6B1ea zb2>KQ#G{$^Xjh=9ku?q}goWyvRAt$99e4NWjB+$h zc)s%T77&MugNid$TNfR^&fP++OFuD_`NWblx;FJNdxiX|e#z@5ul==S0$sD~i{w;# z$rP#44`of4sg$T`rdV7ohHiX0`%vW^^{LQRV@0X{sFLTEu_8T|oR;H*x11Ms&ACEJ ztZRW{f4nh}Iqo_=uYnoocAHsGAH*$_t6bybe0`$_HN{Nm-{V=@0iX}bL%{jw3cJGl zfKfo1 z4Ie((cBgP6l%x)1^w>5|OE*Na{s&2O0hQ2QQ4EemWq< zZCZk$haIe-RdLq2_hVz(Qg_l#8n0Mkl z>!&931w&kk3P6Ck<{bTR%JQdmF9F@&6Te=-QT5O52xJoBD4pylVII?^G8{gN=txm9#b1HVoP^lbqd% z%`I68J@81w`iL52%Y&fDqG5Tuap7$}B+(3+gW~{|x5DkklW)hiuYHtDa8*ln?Je=n z?!1j=_%7aGO{x& z73Pm(@_{Q=IGPWx!oi zt88U>SW-OQ^X(N z*xO!tn4WuV*#%ffBRDGE%*nd@0d$xJlrtU)79&9N&u|b}mpYica}2YwHOQpdbTLT! zU!$${CEzeRwz62A5!6WH(Ug4E>;vJxC|lzbCr(5mkyrD*`>?*JB_JI_!*C$MM^t2@ zw4oJF2zGysT`K)Z^1)Z8MDfR04;`5lZ(?)eKkE`IZSrT-Od>SzFM;w_-^Cxk z=Pm?ILm6wub8mV(+I6Q05WN6RP7Fj>m{5S%90|O2Ar<Q47aHp_ z?`zSBre6I-G=uv>jq&VLwYTKHQD2$EilQ=j8UXLHu5{|(gVV3}er3^3HXUu){PcJJ z+F3Rowi=TY5+rdhpi4x5x3aXxdH!eX>(-&2gTyq+uXEy;-bUN9J?Q;hQ;|TCZEtd= zOZ9I2%Wn#mh)V3Kc-Qj`_bH^7+Md%TBj!bx&43Jh)a7A9NJWM4E})p5K1P7tVL+rP z=rkf6tH;dZU!PEh>=c5ykuMgK^H0eb+TAVbQy_eRv5Vo#Xjb1VRq`Qh8bhif&v zcpGg6!=ywzoIm=UQs{l19@~evM9k8D4am<)4-RPRY&$S@71+wYEJ`d8W*Lp*Pf*`C zo@*ESuff!Cb$Fh}g`z4CM~X&J@DQ|yCg4Ew?~UObMwG9dSKOE-Rr5mZB8WVaycLA# ze{G8Qo;ejEym`o`(_uSSI({FKAaGs?q1dt? zBvBc8!W-(g^=tQ-p8#IMsa<`adjzujyjOqI-q~2(Af?W>n-1F^E$JNNvAa-Cp%3XU zuNtX9ThN6v?Q&g-Gg|Tai@e57RBUIvIoO>@$<8a0c%6Wn?~JIU z&Rl6Sd%_S|*|Rj5QST>QQAxbJ{!OXFp!&wk{vuuDo9+eN$WW7>l<)BAyh-VNHmB!Z ziSx zr{cW))2d|af)qy*2|0SE5w@_bz!sMW8Y{l^-ylKLhkG{H8`KAG-j?+JZZjbLurR*4 zCu!bACf)HxvD@Zw%t-(DZonIxqpE2H^=Z3LIU7KsjKh2sco2KGKZTey~yk_&F_~nGyR*X1Hb-SC)RHSCEhsn&-Tqam2)sT zOOBHw;X=vl@vh{u`VoNo+qY+&^6_YqL}SxXO&NE@;*pH;=Q+PM0d5SPI*y91E8`Jp z|4&cw3kvEZ=1M^(aSD{fEkg4-4y(J*W!oT$g&pE4Ix8l^iTomnUcv;`F8uN>{Y6)9dIfqsC&f zq}pBMeue>)k3I*?6|=GlDCZPQSm!XwxhxE31aATDGk!UN{q>cgtE|oQe!A{{ln@gf zc9y?OI4Z=EXn6gJ0LL$9u!zS3>SM7Mkd(+QB4UIrqNqV)YwJOslPK~qVGN#jSaut@ z<0z2xBrgpW<1Q<_~dQvYH;s>!gb7a8&(=q2Z@Se}Ow(rA<| z)}(E;MBU0Mw7mO#+2FBSzM$LTJz9as@fEZ}DyzoXK{h?1;`eVa3YB8$2(tNkcIJD2YV5z(ycQ z)DTMpqLszBjltIjiwZb^fEVle1MD3RtmAhRyuD|S9wCv8UDKfYi&|Ylh4GgQ|9lR-6=D;dK*_IMU~S-Vg88yfgRk6eH(|xpCm19rzlzvHn$* zEpM?e`3`=7mn(S}a-i=G?y+n%e;(-d>9lm6^yIb~8Bv@=NuoZ4SS@^eHnW-d2bMYt zfQ2RY-d%8wU#$O)Xc-}gdjlLG$s2-6MhaxVE8I1o0A$6FwB+z`c%rk?Oh_;jlC9D5 zp|I>StX)eyLk?1E+pp^Y1y-LuS|}S!0#&m6(xBGIPvcj_2|x7BeX?6NDh)oY$J`pe z>nWyI>}aM3Q~pGNZ)bRyH7|*7;a4;vipta1n5|=eC_T=k#Le@Dvr9gBCrfnn z(gY9hkLYgpEuu9?jAIoY&G^meY&-9~H&?`9%VGP?8?M+hhpvf*W4^et!5M?}$=+K& zqQ$>fa|l{DHY*a`Rpg1-{@;d&F1*ZW705b z&eV~=@Zuihir%C(JDIMZmn*^?bBS+dWq74Oea*7!o^&89dULQx^HI`oMtyZY#`8QY zE@voRECqE5Sk+<)_ukkT(sEeu>3r`q7#t4C>pw1bP&!8)6{*QDbJjjp@$Mk zT$Jupc9R*g=45v;UWrWVIE7sttuJ$Txvda<5=*H25@8sXBW;y0LC6sz*8KVVH@|Wy z#mzxrUBRv3NU;W5H&cki!I_aC5a2r(zT85G5Tz6D+XX zj|;f#Scq9+A6kOhJR0;^`rwN@jY*Y%jL3lxefn_91;Twq#{B?{QKfG zCsQ=7sWU-Ev20<$XQ1qwsWsQ=dn+N}%Dk{#S!v|fu4SI#!-e^(DgqAKOERRVzJ+_d zm?>(i8g}u-n%}ax{MAt1-boHcg6nD1s1+%^o0I*t%X+<=1JSx1)GXvFwXsW)7#%kL zc$rA(*F$0?Qow^0_&m^3+jN&lfs-aI0^n+;up}ffMRV6y;1!=&3X*mFw z1yn`ySfHC}hYQ|?r<8S%r8RCxjrEp32fF_UoD2Dge@D(kr8>7gF8Z6R}H|4BTy(K2uC%?F!c?JPQ3EO=HG63yRHS((5zb4XU zTfC$ugvfAAL!Ntk*thcO0DF)2xG zjT6NBM33aU!V6--EHtZeaPaq-*gI=Ka$1&zsXYSBw3^`N-62SQho96Y47wm-F5P*IvV*^ z1O$Fx{)*Fe{uwo;hv>Jl?R75J^og;X4)NSRb~gyq%#Qd4F!?+qX=(ZD_~Qk;Axd-{ zSKlCmpg~?!wy8cPZ61$05DahLq^P4c0s(53n%|At}@R7HqU0L)HYKc_$4 zBbW(T)7fFf>|Y=X;@}cxK+wht*Kb@)^LbitPSAcWD?6x~i>f*N!gvRxv#AR5dmir$ zjx5H|V0pPys8fRpO&4aW=X+ES7PdMoxUy5wW}E77fw1{K2(S7C7P;rD_{TDJz0lODj*Y)6FDcH&S>9PPE-vD!bU>lD2ki z$GZ0B757bafyxWDN5ocQsErrE(xKj@R+@l()wW6IplrldQL49`sFm3_9O4DkLa;}} zK4Zj2(28}^eXmMUQ;kkHX1H#U$Yi-NUJd%_NmYY>YTmrJk_6Rk*g>>fxM7MvX?JiPcw|4tv5vKUKz7>H!|`Ab+_4lBFH z($;=6(DuHo?dN3t31_yQ6kK8#!6nw-WF@6P)25=0e)@d0iFlffedp4TcOO zas5rE?dZ2lYT8=IvYVURF+S8cFnM+RWg{EYf%~(~`Ce*jk)S+8tYtM02OF-RY>$mm3 ziW=)|ZwWaD0g%MFY^n;ivgNdo!9#o*QQYXMnw|@{(}3!{>%1T2YH9>|6JTw_C=wh@yxPYupDfVK499&t8c#d^a5jziM(uYy3VFgI9U>GLpZk19gf9 z%8+cTnrebdfSFY{gW+YR^?~JBr#aNNg#0~T<7~s=uO*Trap9FOY+AObw}sLN zO5Z9OBq-$Keh{#o&=4{bDY@KDAlm$iFkf4(tVSCNDG&Ns`D2Q9|4)w5$LtSZJHME6 z1uPWD&TYS?P0n^3cWu3T4q3MR;gWu=)5iWofsM(?^=W${LLjn6O&K?-$%3f!u3qh~BrQ3~*UG8*gvRlPkZ&+jVwE0O!oU%_1v3k~T z$NJyZa#EKdsAI9ckuvUu>l5T!k{)^mMp*mL_OC|#kG?y^+se95lVMmZ#&)erP`a1e zFObjZxjG~r{6_z|FLjYa4+ioxzfIfVOPsoSz9nD|_4jj~6t4H!1C1sXj@}kwxn{M& zDTDue*yO3&<4L1h;z`p^=2neg&AaU>az&n*O#h!9He+pBngLYE_H(t>#+>%L`F~Rj zH*WWkQqn{PG(Ob4A6EZo{+6fMN%(}1l4ZDKX7J4Hsloq7gnQF0Co%~i=|MBlp2dH0 zH39C`kH~)M3oT~D_N4!x!!5oFP7vNr+|1}pOqRI5OcX=MzbE@QcQhInS zjvu|fjvGUx0vn@l^C5Zlhf2l%GMcq-PB6bb)vT<1lZy$x#Wa{VDXMzI)o0|dEA!EY z`|q2p&NM;yZwP6Pk5)z`R-{aJdc3gyQu|4pcC~-xFt=-KqqD2G*p?O=5v*;zW)62o z9(abLAWiE=0-re->?sw5aSYJyNLbTh0kihKIgnHA2QMKaF4#P4A#h9wwz6Je#pD9a zV6g`e9?g&Lh-JrvJ(w9(3awXJj0peP&QEoHQH16wUMT&&zS&4>$MD{?2$4!j3ra6Hpq0aVSWb;nng z#!jZd!Pi^#A$uSOETJ9Xkl=vu^;igG#S`DKx5cKMfK~Hg%t&G? z1SqNle8@})HVwbI2H+PqNK#TwSeg9f=C~vG-^=jnNdgCJZMQ3_cJ#f>7vs?kZ@5NW znk3!IQ?%Uk?EjI8%}m%=EZPzzdPaHkES|^b6krEfHLUJw5AA!bCTI&W0xS4MH2SEE z1$i!3#fGqV8^{a4Q(7#z^p4NT7({zNw+Eq*kUh9yqt=f&rxA`CNeBUsg8xIYU7GhzL|qr0LkE<+&c6^Y z|K4mh{Wy|6$+&G6ge#VR_uc>Mt)JcTdV}hWZ?3*}xyglhJG}fV-E)t5l*Nv6rc17> zwoVNmUj7}a-ipcNfbgsdn*i5&Pn&G_+08a%paFoUY7c!}UJXOb7SDBpMvD!ch*o~b z=?wV=PxWcsJwYJh6OU(R1y`PSyY|>rBGXy!J|<__8uW>@5;mz7{ zmyC1$UJ~!_sy;T(a(qAL=}o`C0)$7j$i7Zpr55rC_lWiU1Dk*+d0jwc-A;ySm3w=R z6t!w}5XpzSh;Sb^y&Dj&6SqOA(*}_8wi}=b!Y`rG0lZuqpovibEKeBr0;H!&`2VpkSXMimH>TOa0h=oWZ>_$W zpgwB~J_bHf(8tr+4yHAK?Xt?oCW|68Ej?AA&__}yYt1ZhE?Mt|pu>LGXc{3OUF}5X z$bP!wxq%=Q?VA2uFYnl9!l3>5U&oD*%QXwp9%xuJqaiOJWLRuwvI@b;5?*U}i-%y1 z9VfEv7y~Fi%-Ak`jNUWnCHy7#g+3z7JF`?GMQ0r=K5Id~wXoBOA>tiOc;}_6sTO~H zSUV9=2j~P+J>iN(_57SQQXlE(VBz0#xvG)2z$Z7+r{}zITM-v3K6FAyT`g8lj6VJa zTT#NG)ltm($KA;tt^!JipXkkrv*2EyC{a7iD5B75_XG*T&R=&s)lqOw6)I7n@R zpAe>cQ&uzeQ-O>wv{LUA54RIo1U6+7s zQ?)W&uJR?4kzdHR^ZF*Z$5XRBl$Dj$5Fidn@;L$6==lZ-1w1oz2(G>|>kdkAl(%nv z0MVlc66c1IUBwm@$3PAzg1`6yU9r(A)bn)J)S?_RaMpgC-LO`j!b>U9H5d%rap}z#V9h}bA_n& z45p#s%dn%m%SKaEGcA1@%%Jfgyi|qV<+F%Hf50kP*#4*M5iAPQlK2=*^WRN*!=`-L z`qR5#l(edtt>XqTbPtzjHA&eeZ`t4Kh&zI>5EYj7`Gatx*Rg#r6E!kYYVdtYMGp2QJM1>}?-V(9HrRBbJzd1n&-Ak%o|t{kZ`^on|17or*Dit7{v? z(H(#{G9m6>uG}xO^S+)F3HUf$Z@4S;o~0KkyOpg_P)SElUR6l1-`p6mUnVxdLnP zrO1g8^*XW|?OuGRTa%ys0T%+F04y;Z`9|d$8*^(w)Aww{U8gQ)q0qg3)gBORmg}N{uGV3Lx)F$~h&H3~|nAmRx{aHXzbCX$gr+G#F$j_vsb%-JI`1 zL-3HrBXbZ&BtWFdE7BE6 znRljkVv7n~yzv;W=ezjWhRYvd;+TrD#nk}_;JsN(voV>+urc7|1RXCI<xTeOU1*N@RtsQ>yTTo@UM!n2C#KLy&93 z%FC-&>%MRm(oNh==Kz%h@EHvXIs~-uE8}nc`_W7mH2J%JB99PL2M%tjFm9?xi8;(Kgl3u6`K;S zRPHDbdvDq{j}_Qk5J!jzRSjXO5b+bxlXek%0(P%~Y^4G{2)Ny^@G;16{K0i6wovL?po;|C$&PuV9B^3R&vI`kb6)-4GuC8T zuDYOGSLn5QmYgpGr1W#H@b81lb5lgE5|QG?n)BMy@3BB5ppuZn{EK2qZvOW#m}9K0a;xgl7dE^>5c$P_Vl+ z+qe9B^H|(H{a$kL@(Gvt$lhH>bm+7%AQS~jl0*`R5KF00u^3DN1_DSz9W+|p!{Dxu z7{Zu*^DuLPR0c|F|6Jlej_#m8#6>go^kjznpTXad0>2$qQ(aEpVuW5!vG-MY)o2(b;v;&)$)Zr z&g1GwzH!;&#F`61tzsJ6N%{BRHkl0w>+gv~#tjbO*Up{;JBOa2T{qn>sMHXFGa~tj zt#lhMvza?1%ll$kbzce2sYuIQ1hT>y0#RN;#KUU{jUI#D+Wx6=XT}-; zX{DA!%e6MahuPVEu1h5pj%-A-rj8LND^j%VzmFTt(-z0S0vdyAUa<(+wJ`w)6s($1 zJA!OyXgW&M$95psCArfO344wh^ng(bhY+{GY6%(H3+EFAjqOgqDKGO^h;chN@S0c; zP$>!CmZWDc-PnYqB}-W&6qWL%bF@JBOzs|EsV(#?J)4!tW(TqV6*HfZ9X>nb{cZ> zG{>D;9<7h5z~0o2&+V1%f|NOH2)IOs)kH}Ttdf#a`}=#JI%%Jp#H)9=<5m-sk{kwO za>zX+B+*UMSH%cX5dGLXRR9z-`S#Bs`S!xdh8xCb-Vsr&)?j>>}< z;wIbUyN~}oH~tHRW5##R=q!oE%Z_gO|h_qtMaqaTh|2tm>7HE3ED=dJsy$uq_nyMhA%^1d-w7Ndby{t{kifWFvuL-|0VuW`2y?i$q`-Q@REp;g&KKz)7KH>=l_Og&- z2v+;NAxR`<)IE9r7UqY@Fna2G6z)^RC=5jH_#xr)fAUSgx|xGg9{A{8VC(9#fs4?b zuX+yR;#V`PPOk|g3{6j@%Ig&~W0{If^qJlF6@N)UHIng{6AC6oUnQ)qJsO7fk+9;F zt#1S1fMh%@nzVdm4rD(KKR>_8hez*_p2N z@DdCExyJ2F`PHvuI-nnT^?fU;0mepn+D_V56l#HXAE+a~$;kn|1i4W~3)=6(_zY|b zjlJ$blPjl!(Hav->h*CyHs$}}mci5cm_r_O7{DHy-m01mn2s!R{pDVNXB1-G>j1{@ zF{{NrUlm6Zv@TYf2eC_BiRZeYk`j~seQWv%0L+=3NOg_dBt1(7L>#opS%pqedaapg zZtvxJmTUjJ9_!7+W(N;!XyKn&k=TBS^qsCKhWsT21j;Y3o`Rw^1Q4mx3OVx zZU$mhyBkBmdp$>%m*6upn9%tD#*EHgSF4K}phYm#PC7xz!5MSGQE=higIBU~39?yn z?NeeOSi6}IFLb! zPS#JnuNF*P=18H0P0FprcVLIZEwFKLK$PzFrNODfaAdcPH;fsxNDik+qKHO$@ z*N92oBDP=#+?U+_PEa;M@HXsw75s@U$91*+PdsB$8Q)2b7N|T#^=w_luL=#(C)m{K zytJy1Gdj}ARli5jTW8O@Fe84|nH}Tpev@k{u*7a( zIEI})7tNUjeh#1?EJ2+;j+A&*(2K9J*}1Y;c){{AI{(KhHlP1o8n$6UD{E zBoCTmyD~X@sy$@YCDQ~Q)rosHZ86-t8pFNLt`I&bQ z?lQuO-)IvU%_S}I#kM$M)zLn)%hi@irFvD24S$D40oBcgT}*DY7tsNkem8FoBz^s= zTk~C*Go(PR?bOqlsr1PEHq(q?L~$xP8y8W1z427WU+z-ce8anUGdfOS`a%)uvn?)+ zRHFezd$zbYphQSKsXPkPFT_4Q*kAd9{`QI_DA-W8-x5#(2;aZB}KQa)0&PCAZ-P@CEGJ)MU2>(UsoeH;_XISmDjV`1=shX1sz##uwd zN~zrrqn>9;vLM>?WR(PninGnp5UHBBMxVw<7*K0*8-eVax&z;~GO*_C*9Mrei?=m1 z{mQLN62TRyR>Bsa?Na_vW(kpSj`~`0Oiw97d?HK`BqB=gu^Q*k1t>ez9|>pPv~ei#74QhtZJpcu!VKsCXYP@(P7*(EQ$ zhfC<|Y8cTVVwo_1YQ2h1f@QOcp?Z?-3N^}1+D7qU7Ho1|*><{j60t|)3v`Cm9-?ZE zmQ_8P=UXlt8tEZXE8yj$whYGVDoIc*wzB^>A?f~%?KMMQh1EA)xc3?kNu7-j2R~(8 zO&T_Oi|&yi;)tLdLp5%8C(oWmLt)2(COI2YdA0HEI%$r4`#ZmX=g<(c;(@BznEsT@ z3OQZm3g?kpiBr9MscnHEj?OQuW!k^-F8a_XtNr={*l+HP5~0%6iL(C{?}=fBBj2hQ z^#M+93#Lu=f2}zdatVw+E0^ zDR)VWoFWP9e?)BmDoN1Q49)lHW|PH9iJ8+evn5>%AO&=mTV7& zW!7Ip%*RHYnF_)UPySoINp)%Ej#0P_b5Vo~x_1R%S0|wyOd98ZJv)AD|8Dd(ki)$C ze$D$}z7moCwsL>!-~ee= z^9Or-idG1NK7z4EdgO0w9qX*wLLN*m2*ru8K2ZvQADnSE7|^t%jImjr%Mx@OKX3B> z@Ph#jw%enF<7H-RY3G^$j;Qv1vy}SMUsjxqjJyK5gJDinE-9DTum0hBjsxr%pO=57 zb0u^%Yom&LH4ikA_RTBVK3)Zhzt=Q9MccCQ_+KzmM*fCDBZYF zlE1j-TSl|^(GeU@PiCg!${qFJZ1JBb+cfrsSufE5^-cXh&*AEq8K*7%u=vI5I3H(Q zL`|KV@|UlWgaR7tm1@gltJj5c=T-(q34mcDCMCte*!;ifdhd9u`#5g+iXtUKW>z9O z$d)}yb_s`UlD#vtLxp5SW=7c#8OPpHvdcPTlfCy|_vdt7_j5nbbKmzL*Q={u<^0a? z_x*jopYMBpn6V+;fD%SMu!1wwoq%iuuZ;&$`sQ%P(7w}dR|U=puAW?+w{Oyh)u(^m z1)6Nhc{i$k^@=MX6Q8(A1aQK4PSCZao_9oLKtwyzm7jn;7PY*_>SwypZ@oN}u50h; zsaP5az)VCO-smSqz_A15;gDL3!#u?G-AD9!J4Ja}*6r>Ny{UZLJJF3e9k!gIr(MLk zEP^~uf44O$o)3veQLQS;F&~^-v;A+@OeZ{Pyv=aslMF!h^lUeTHGXi7J*)SI?viv_ zB?FAb`P-t2YV^0;{E$O?P~W5)x)^u#C+GNjZrR+5Xj6?hl5{_MLt%UpQX-1blvNp@ zUp0es`X{QxtxhW8x~b!$hgVWBak`yWu!6JVyjsJ}hx*=+!yJpB?JtAR_`nP`6wO|PK5=c-#L3~UN`E9=SzryAJ-l3u0JQ&$(l$^TQoxFq1Z`vLHb&NJ3N_^||7cY7&mMENns26wp;Zv=4pJ8ER z{$O+HpU06 zyRsFzyZpXH2}^e9lZ}G8m z<@#;Mm8Ho)Q*QaL{QrD#fO|LPRW{)zh#Gr{PypJ?&KAzCx8t#?5b&!*u&As2Nw#>; z^7n%&QR8BTKf5cdHk$7>YHcFkRb~dC?D|50z>)8o!@pmwg!5G75kwV~jjx^{vp*Ch z(I*oBll&6y-G{T?#1D`*0IjDJHAJui#eQ*Hl;J<;os^&a?^ThDxBWJKv}t~$P=q!b zO+H5f_2umlR9N+wOu3yvl&2I^FDYhSKf15zYw2zK3EOg00>{37J4MtaZSgVtJqWj& z>fePgP0Y3u3#Lc-80nz#S&=Q|pMFltTF$0cCENsga6-&hb>7-sC(bq9n&bm&6aK~9 zh*!myyzV9?zkkxT5r%I^o^riX$Wi;SjXR-zG2Opeh%OcjxS7>I6ok9#(2bMR^7lny zB&Ai{r@M5=)92I-CkIVNGr?{+GNgI$xK|1Noh749fBcvg#NVT%{O}>yNcft+w=Cc; zH1jCA2NTjz-tn7!MFyh;ytW#ifw?m~J$-?LMq4|xUPM;!F1`iX940A>x#vP% zLj(OvYw(&V(0_E{OPlE(N22%`@QvLaHiBbSs+B6=3K@;k#fh}iD5o_pWQ z{XGr|MvpK(mF8({zIqtihv&v=Mc&i&tZM9MVDw(5@I|!Q6fuo=NFm^AQ6iS$my#-a*PC0dNlq)DrTbw-SKq?*`G=4~Ui~B@B z)H~MY+xua=K;Z!D=(bdCy^ERS-K|CA32`wY_;o`qA^%`67gb4j`HE1@&v^^Oz45F2 z_&m-pgXA5o651SOV>+wjES4rW*FpM6t?~r(Zzg;Tn^z5uLQ0^9EHNx8-;*h$) z1F$Rety?jc!jKY#WWD$E?(*PNZG&t+%iQvy1KZL-z99d9t2HR& z1ipTD2_YWt;oe#}(9)^k)&kk}#V}f7f563`Huzt#@_}sW_L2^e+LMb)a&QoiA(|}^ z=zGTX6i}OVL}H@@1p?vr`yV-NCHjwApj}IQyz_GBoo`P~&1Mf>)METjvXNNY#h7xIj7dgf__pRt(dvwaSE_9xcQV3Ns4vhJ~U2bxec0vplmDNS*@b^ z8A7>g=_iuTw5UCc6ex`%ZxYl>ODqD5^Tu6lwf`1Ev70adeL1h8(da%QZtlY0!vX&O zeL{T2&hanpYBprCrwPP5f|V6t)NS=U4El+PjSW0nttN(X#q2c-W9Q5nF*!l|#sU2& z`n+n?aqm%j`om=uW0hZSE(Z=o7H>K@lutG}O3wV>cY^qNXQYvlEOv(%37;_14d!p9*GOEp(Vb0+h=@?~NQQ??2}Ysc z)oxr+=5XvMY2;lv`15~zbK1(BH)a(J>yi$b7_Z<7$VPna)L;DLdw*DXDA=+R)iIvq z93Xe}w_H5X=FsFwOgIQ1!fZbCw6nn} zOtAeBd+S&)RDAgO)KU_AJ5s=Dp1lF6Wk~I)L_I_y#UAT{wcm>N+O_lcM$WCW83dQV zvDrQ@I?_S9%kEjdGB2=RuwW4R82Uct%YDVN?z9v5F-%M!mywrGz4&^&?OaMF6AUNU zu%*eJ44R12O}Wo#pQObiYzv`k+Sb|G1nJgYHSSLHJ01`h@T?<_M_yT(qt_8zDS^5t zfodRbY;26QjDgIF#^HUwo8)lW(O6^bRSJqXkRE6ZOvD4b6h8AXnsJA3S7NutfNVp7 z=gx>xr_33z5ozj$P( z2`WJwDnPEav`15|%qCsUbq=Lhtcw4BO%`4%!uj!f$;?MdZ}ajvVCA0&pynI!AYG6b z3F(8`eeE=oK4O{5@%}*Z-x$pJ5OOzmx7|s20lPI$D`xdRh1tSZ9ze&n{QT)UvI`sC zQ()=mhs(J@Z}BHi62`rd6Yc;bU&X1kn{o;=C8l3D+AAR11NpKWV$!mbd#_Jde-Q1< zkwQ4Z<7-Te4_4=T*z{gh&e%6Rxp2B@MDKw|y07}X6?1lSw%x?G?}fyg&(16%90U7A zQF?P>355%DdUDm{6^ySi#w6moB8k?@bnrneh{Su ziV%F?;|+Yx0)rKK>qp2Tw2w8haQmVDzgt1H;_ebywn9L{VB8wX0)k4{#ADAt!K_F# z)J%J9td%xacqP*92Ge9e!-QIk-pngC@0VJ|&9JRN>Sb)$0q9XtVZo5nNC@Ls8oJZu zitI*3qCczOk#S~en2pq5oK~27U}_BhDMYb3Od#(o5@?lRIk%!l-(|u(39)?Na|`M& z*^?YiM+MdF8EE|Bl4PXXAEHhy;JPNC(Y^G3w^yZbb}rX-%n$eCde4cxJ!7*|N=@eL zHlnjhN8Q=N?x?p!CCU$_J7KS{A& zzSL%rmyllU_w#!XO-xL($?O2xZvYnTIb{C3Y(B*KQnT=qNaIR~n@Ia1hA0Z{Gx@hM zR1LkMEyu#{{{p&+(cObv%5g7YugrDyhA?;pW)rK<#72)GJ|TE}JXOA)pHHkF{@azO zUVe{maXemrO==afEoBY@FX98Y+V3K!ai(A9ssi$Ko^>?wgnBQMHxvuMlBT(kdl%Ky{OvmJV-RK&;ki$Hc&z<8~Y zbeh;GI{;e=X#}j&XW>n{0+v2vFts+fEz)R^{oPTfg&iaBh>O#~tQl3)<4e~iyebf^ ze7>h%<&pfDBk{%J=uBwAR!&MdNlqE)SIdh#M=Ls>8N5$Rg<5+j-~)^g_x4 zw&_=hN#2*EO5P#knZljzvmxdi_mu~`oRQ!0vH9+Y>ua1QVx z{|yuy2_rI8?Fay&KT*-)W5aUN;s{pd{HH2LG>$0~gUZZYA<(<%26 z>Ke(xN8hkFid=fvJ+^MDLsp|8iXlP&^A@&esnO23pr-HCDG(>@bPJH>tRbw2`BGlh zLgvS}BW_S9EFiMT)Fl+<_j)*rw)gL2MjoKiHfP!Rcz6_^o{&R)Qz?hF1pJ%7Ct=7E z`C&~y>ppYDTh>i%g;wM`sNE|pS2w4vFk@7!Kl;hZed6VZ%%{%bpr+zv&&OWR z(Z#lB9gqq2l?!=U6OjH=LRo~4WpGXKcPQeGTSS<`KQWVG0 zEa*$TsxvF20THJsh>9#^;?-IPv#>hCU&l3cPUXxfcTW{_#0M}q~r z6a4>AS46687&Tw|8aD@ePWYGE{K|7b8xj*1EspM5za4q@Q(5-hT&sM>>M;5OI)+od zw?lqL8&DPd(V=stjobSUm0$99Tb*&=co$wO7#X3z>l(D@Oo{EgYhqe0n$Ua2v@5Ow z+u4}LUuoX3=Mf>kl#IWo3Y9%RTC3lOzQ4$JSWv|+utq+t`&Fo1B0eVnKjbC3>+^kl z0nlSO6_pRqmD`Wg3k0VW5PJ#yWV9b}HOExr11i4+LVh($0bJkB_V(V3&OYp~^lU!e)akHIix@Dq3vsDNfyzGaTGOWhju z;=hvVU**$qs^gq}eHObbkznM(Cx1^%iwZ(+vfG3y1wkw&PzNJ0ANM|@sZ5@37uaOR zuH^2f4?FC%AACN)Lmu{jnSh1R_=Zq=n$GR3RVke))32&r^ZQ;NZckp#Cp{po)A77k z?p1=g!yRh-%=8C!Kw--|5%=UFR(dkxU`Lg$d36isQkBalqe;e@TQaDzDMVH@!n*FDWAu3-9R+ zlmyev(LBs(lh4RCWC)$>d~keL`?f_j4e21NaRpbW>FMN6@2;{(+0Vvy$rdVn4(qk5 zmP+J~uxH=thy6a$0n68P1rhcCOuWU9gV!^=2C3863f_t5Fgv5Na~N$JLc<(q1(LYM z^x?+Wm%FRo?7cwY5seYpG+rXg5X!8+`*zd95T3DBRWKQAs^{LSR;`Bskg9Kn?W(Vc zdu)FtQxWrvi=!gfT2thWhJl}yK(un8^L|MjDT`~q0rh}EXVlT)T&qJJufN8_mp?1p zZH*YQeX?o}6)|m%gDmRuc{NpxrX=F36MYS>n`^;ZTjVc>!O18O%I+`4_K4Y}xQJxI|G=Rx*@K ztW4$fDo!QUcf+F@U7%ac(=C5= zW%ngpTK9Y$>wQ^Syqa@=PeLTop&$M@ajgs}hdPDEs73GITx%IRtp7MPIXW!L)HPFl z{G#c`$i{+5;!o}uCBE^=S zj!#Z7`yd#(uamHqcUo;7~Hv5ZzJplITffbc|5{-e0R$T4#2!uj(PpifcD__(#XiKJj_ z6@z%VQKr2lUemWC3WA=y%UffvpRw)ctAPt@JbzCxs=*ylyp|nPd+N1$sB@^~H8=?u zwD9XgmtJQUHH5h@g{Nn$`Q{Qc|J|&n#s9BPAj3Z(`JKTw{(;T`e(MElBLfNjmjXy;HK&nz!LqoO|pmA0$as zLmyk}V!&N73Fj=Akl9n159M$$6PwG({-imaK@L>j*cUIOpI!uBydFjzpSlqR*c(z& z?xnSK{{qd5vk9SXcX>&NY?Gy6j~Uj z)J;vx_pBhmJr8(b*iwG=qX(~uA??>R+XHMZAtO%|PZeGXb-&INT_#Ll>=tTE^9JsL zNrYj*hCnSL4wiDzosN11vOb?3a+y&#u15R1L7s8Wn;Hau$d+Ht!GZKY2HnP_e;gmQ zNV?`RRV&BdZZQlgw_gf68||a0@(*EQ@mcJY+?!mDUwY1jsqHxjTUt`L#dipp?c0V; zWi6&g+CO=y+&&y6^G47VvD{JO&Gm)(I$aCS*z|gUJFBsH7)1K+1Cjq6XzfcL0y31Y zyAcGsv$G;Y-VB%g?|g0kyI+N2H-k6V?Y_d?<~MLAG6DglyuAF^%Um@tp^|j2_>l74 z`3Ca~47K%da(yu8&F%4V9lbu7uZ(^7Q9T(a@BW4I5y67D@FwpEGVQjqk{rF_Te@#o z$Cvw|?p2RZ!Y9W0#R0bxET;9?KBs1Xe}CfV>Xr5OZ?S0xCNH0zw&Au??e9wNJ9d(o zWx+yWg2nZEd)t}cvM2pF^A!*vJedNUS!@M|!nRS7Ru{yHeh2pgY+Y*wKV1?!%{X*Q zvS|&^sV;DSe1E~Z;m>Z$oFA#SwG(VVmkXrCrHhn+l1dZjT-~D~0}nu(4~EDuG0F z$b?Qnpf${(;Q^2|Of1S)MS3ku`l#yP6BLPRaK;JY9o94KL(R4!yd%Bf$6}fuNtVp1 zE5cB1?)G{or0vPD$WyI#c?Lq8DKWn!TmEM~1q2l*QKdqOi7VZwP)#vt=l}#@NBX3l;dQ2$o1N+T)ZC-tBmns4Wh^Y$ zh?OP?u8bU8VLsc@!sX;12o zlrNUX$QF6FP2is45~xnwJ9_b6IoZs1wN2*%r-TbB!?Z2(~pZTk=yRc~&^{;qOi->q8C`l4UmPm+CW z?LEg5v>0J~qXuoN@~>}N&dm&>`N6PCoBO1|mBae%^yGJqdvRLrvu2{@q0;v5vBIag zfRCkTW~8Tau(Po#lsY*&B1Ku6TqV?g^u!$8TV=I%PmGvp*<7A>pFUVlXXs%NUC7#g zB7bJ8z3K6)M#6GBQ_>=Sl~%YkKkC1%ExBw`%Vs(H-t?ljnPorSLRy4Gtm)|cHL`)E)TOYJ znUkxVr!8(B`TR>^UV*Q@vXqKfTLka52&SdG$d!q^q8~=uMn%|aJYNd48GdE6l+HOU zabvi#5q`_JVxqg4pzye?6y52f@3!n$BAcelxzrKQhe~??{__wo1+U?dqvVb7sWMs( zD_y&@e0^Sa_flAKxu0*fw%^s&Rn{}HUGKmV$PFYBDdRQ*Lg1}sO`dDTH7jsUa@>_ z{jh`8{T|+18!xcK`VfNKs}2`)G2W6V+r7s2*lCBD6~}tCSMqCo$mF+HR(>bI&E55U z9CYS7a9&@sfOG0hKO_YU4Z$qhewgi%y#SycCY+kjEPEGF7mXvZ5)!oS1f{u#kHRvM#eS zqoSfPXzWZCi~(3VtgwOYrS(Uvq8JHCY_?JX9})^0Dn1_ zK%KCr!}iO9G@(}7*vMWh&^GIq`a;MW#-Py1&uR?}H>F^g%^#|)tVD_X9KW#k@~U|@ z2uN$$?&|2Utv(fuW=Z2*`U;Y!^h`jkKgaCArjol0xx#5f*8O>4^({8b*^T0K)nP3c zHmxl?AnS)2-_2;OwbUAFHSG%Yu)463&sxaI$?2v*K^MTTw3r?6+M#xVCO~=uB8U=) zvj|4QVdAA#J>(n9g}=p%!u8bML_&ds5fX>mXcQxhbdX5Z<=)VSRnQ?hLLWS_2$DXZDLCvo{e zd3n0nag?InoGm3M$*X@e{<1c{i8lJtMQyos&ZTz|F7iX@M+Dl<@SDU4m&g4NM78Bi z^-H5%wE9g&wVO@#DbcI#=;><4>FOJ{2l*EdR^@f;l=SIjm@>oI8T zoJov9rDKLwyH<;rW&6o1v9MuB>*(Q_*yg$q;m2$#5geQ6gY_u$Nj_pmGt<~A4({uk zWPR8kmN=j(j}(T@Ar^J(w5}f+y`lYU-&1l-%ecC6<_FP6oJ6YnYjH|=%Qe1w=Olbi zm6-d$Ro}P}YpB`lH(H(6Kv_%&?870e8rt^B>8VH=rAXi9`U&)O;anZP_*1rup3+lF zL&j{+a>o@ejrKaArR^j8*r+3(wonFb|j?%KTrlR<8?f?x#H zI6z_Unex21xv`mmi-UutAZxwli0zQyg-15Y@FqL23}K4uT7A@^cxdR3Wx+d&HA9!O zsLR6(hqS!kYRpt#Zqbd?-QA4{2^On^AX$LAHMNyZ4!}ShsF|zri(Okjoip7q(nV&K zD}B62PCGebfTchoS!L{|`)E%%#2!XB$&69LW}R`z?d}WyZVg$oR>aLUUlWt`HUzV_>Gan7cczgV^60jf zxvXvrq~JC8ajxoR-cp;^7^W0;FpeR0(K&E>_ylh;igQpCWc*op?vl*8%77nZ%sI4m z;D3RAz&sbQXjHwfm+ zXG^plJ?^RMO_L+u5pF$VAM{nMUTv9UO`#j0o2$AxE`HDf+k9pjYvc`iG()SW-jIvj znB)N=3*Y+^B^@`D(XOQ(#MXPU@z?!ygY<*Gd($p0yGk)!!#CZNUJuYM#MZ2#Pa^je z4msm-W&~P~=Brj)%sC2lTaLJvUe$2jlVEU9tg+v9kxS0^l9Id^^nKg>@C>jSxMKXZDoVZKbQFN~J?+F&*W=u* z!nk$t>E`VN5ThGdIPo9Y%$HUo9l z+#-dCg`IH1@zoSOI2D9%S*~0nmU-QJF5;nF;*xCmkM~%dA1a%Ip33G74fS6f-&rD3 zNOEmLT5cGQg3Zp!t>U+34cnv;XB1`VYmJ4VLcj!@Bzc%>iF$b&y7h$+I&?_J+J67; zF8X}5`po!XjE{chCVZ+G+jW)AL4rBu53PrM(GovKQ9299*xnccC~#+#e*Z8Mgr{- zxALv1sWO-;NCTpdCSZU_u3y(>D+sj`bgCn_u1802lab0~5bwSSyT+7CBfS*N>J6j2 z*I{DGCpg$p0aN<+8Y?2SH1MZzrfA<~_8m+hcI_mmn*SxRQ`LB?KI}w&Wwm{^g;PbF zwk2a0&#MMF8s4~f$V(!+iL}Y!tVO*?N8RT-9fnPJ)_*W~@|bTCXxX-2U;kBCtW|l& zVYjBq5N?Q}1#MvRDzviE#c<3phIobliOETLA5P47r)ibjB`J<@19(|}#LCM047w;G z_f3=crnP{*YgM~)!p>t(Zi4|=EHg8+5rFbmgQSTMkkqbs1R1A3aI)vhTC-zCff%8P zMYto4`fj7IAydcm&q8a+7bk-B#j)G};Sq={wfzDSZMDo-U}=&F&s3gQJE%5EL(oR# z_gYQlZm?l^y^oKt{?1Vs-i<_|D;|p*)Mwzz>W^JL)2+B}eJPAZC+}{baHdq?i2znYc5B!BhyypB z8pG$dst1Sl$!X*J%& z@bga}rby^tkx6!NC?zr973D)4NQksxC8a1!u}#C&K1ytl%bJ`}^E*GC&}4Sdm8OJ- z0aRYfYgm{XsZp6c-!8x#fu$Gbjd|57@zU|D&ZEeZKz2x+Z$}p>Nnec`3%|k1 z`2`t%mnPI=w5s%w)gX6<*g*S>=rYIBK%E^Gj*AyDyz?#XI60f02!XrU1ydR2%?!+w z*&K0tRopw|?hLE%CL1tjtE8SvV={`;W4*Z~nqn?2t1F}O`kyS_I9Tw{PE7F+>*@#% z^CbRg-iw5r(LxsKIy1Gdla@AWO<1f7BK6wBeBKiXn?goVeva?J-7NkjI_bol2NJ5UOs=KXf_ z?24jnHXH^2fQ?8FvS3IkC^T>U!XlaE94zM^|8L5~6D_dhLZ%he6K+T0LkCoVXylj~%F zkBg@EGE?liVyj3K2=Cg8(m6fq;J2)gktkUVpSgW5DtXx(#dye)nx5qVv5xv zjIeY&uS!7Rarh^MtI)XVlFd*F0XRY{C(w_Q#e!knB9TZE5rfydpRRTt7UqPR^x}1= zht?WsZb8AamZ=%$GhfhxY5ZJ=Q~*;(%!A)f2wZBs;po6Rcx!E2q!6hI%DhCkSXgpw z#X)bFH_f1O@G#b~UTrrvJC&R$Au{SE3H=hbZmMFQ2n8ul({P3@d=0snzL~3z4|-*$ z0!Cf0{&8Qv2-KTg2J)aMZ%V(V;U@f+X87Q{<6dMWBx1I%JYZ}w zOS95teV0=jZ1v(Q2IzcbycV8ncT>cUY(Il`xQX|s8Q}I$OyA)XQcW!6cU{;&u$q`# zQnwMj0E>8&a8=HoVZb{(q;r+Jr@sD~$W}ppdxqikiB;eQqqb1n!p->87lrm@=PS=I zn$E_rH*5w}97or0{NB?oT(Gzl{@^OfbHlgU_omae3lCzUrJ9?Uvd{}-BPCIt*^uXd z`=LCFPz5XA^zvrU%xf*P9qy|D4iYOj!0O2ywfg~83ek@-DL!2DJ5TQ3OVhUG~*(pK8J?gDVKnsApMybj*HkWX&C{sy^Nr?@iz>P z5r$Xg$f+g+!tUdUjk5eL=VuG0cB3>PrOdq~1;UkUa7cP$BZam2-Z>OzZhw^xZaXX& z%m7r`y;uRe5h0gUO+}oZrXsC8E#Keq?@^x5K*)?iI%2XuY(dw)5aRq^{xZ&bUyqs>^kfi~29!=!@YEClVu-cNw-V7tREzrqy`u zCM!S7R7+D^TbtU{*RNlV?TIg4Qk&}N=(waCb3Wt*cDFb&cV5`f!j!tfvTQupu3*Gw zsFk!L8Wr4^=q2!F<6M=X2_w1cLzZ(~yVnjoWFEve)}8(y$U)J{V_-5Qwqq>+8`E zpZli#*moMR^s1b_*iY|hu*^oO_?!0$opg#>H`QR;IlPj0ult`&b^DN!L{qLRk!U2_ z*3U4BHkH-qVsK+aZ=WXe!~RATp7`3J%!V2zN`Q*AL6S;g<#iraq6W4}?dRV1Z+yOp zIdcEo&_pYsT_rZYlf78X$?Ae*DmHo_%eupCsF{WQ?j}67N)vTPxcblj8!Rt$rhFWS zBcpJYTm|zS1!@PEX+Ltl%$V02N0ECSkDRe~8r0MGi=}P(XolDdQ@B5(*0uR%U)0D| zCx}z+{M@rgMaWNSK|m8G(%IDd^QYz%$Z9WX_XBVlNXGie2wLg&y}jm)|D?Z* zCZm&1VGFCFsejLfscmIuoirL|`!J99D+n8cAf)gb{3-+?Q)xs-3T)H!^A;*@QHr13 zga@&nj7>q(Ye?pQ*tm!fn!_5HOk=mUFuxO0)~JklD@$>!U0?h5X4XtPk z_A~-co@d_M>Uh~txg&Y-y%xGJA+KI;EscpNg;AAE3~!&9ll0>cwzNAbXN<0mQ+Dn0 z2b)TX-gBdB^^~2Or(ld4AN}4;A1K`9ey;2FDsa|FVz`B7p5Z$_VY#2(s#QDc!< zva!(HGl#xlt*h?Gd9~+~D34ZwggccbDHoSrsrjX)%NP{{&J9}kGp75VIWDqjxOM)y zx84&`_Tq3TCa#)=#PEx-zju}`hEPVW>fG9Cso@p6rPt3anqLsEUJak0P?D5TmO&0y za6Lc@@7`&@-=;deagX6j*Or}G8kBkd@PP*aVNr(Bi=#5H^IN{gd^` z#fKCew<8}ctcTuL9G%kvj*kGlUhx0|(LyyJ%{xCZx8mof7gk%8kpGdL+J0M?`%N3a zh8DG=kTy#$@~LSaw+)wzcX#{k*+~rbUK+|$7NvACy=UI}VpS?JuWz~cYP|1BlN16` zY~;(iy4d~C&FXEE+l9{%oA0|BmmKNK^gm5CQOh2v*~--WDYeaOwom@m+e)lWACW#!hemHkTf%ZS@GoZa$k--WAxL#JJa&Qm1=c$e{e|JQY%dN zEtGua@0ms;j$e%P;z`H9)s^LuejMrDJ}?mTI(NMyj*(fv_K+L@LT+=;%)zx0!_(ks zY5J@Uzwq$PmN~`}9-fP^>rT{_HMe-vg=-XF8j_G$zjB!i{cb|yNw#V3T#5Ks;cQBs zb(PX>2+{0Aj)ZU8RUX6`7keALi#WxN*clkVxHw2AEzZRiI0k#s@F<{DeGa$2y-c~w z`e)!vFm#)}&l!rF-kN&;Lfv>g7h*l!rzWitdpFp>jIf~sfQKL6Y^j@WHcAqYY@AN( zN38jkaLGt8keLXq%jE1wdrj&@Y!Xz76-Kdhyw>xs#KAOKG?6pdrYZnN<2n@f*jaoH zkTI>;i>uHva7J!e{x4*RVjtx4@H@NdHm|a;opsE=7IlfiPQaOS{xL>mlJ_NnE2Migjb+j)w?hx zD{CO+unhu{2fvcV+c~42X=`h+`>?00j)FQv zQ~zKOGAC~U{~66#Hx(u-Hp=N(`L-i5=FRbU=KX!k&-UH-I*E-eB~uaic`xOB%35CD zBEC?5RBjR6y)wot7V#BXID-;6Dm8E&UWrjuZ$a_PIY(_ zS&dh`j(0ME-J-_3-);Kg##KeMCizN$q}R4bDJ_GfC#7{|QtXCvzwbhtAQRJC-E|VT zv4x8oy^4BBMlBYl@To?k07&b8_7BuL+ z3w`L*z527KApX~ac(SQV<|z!&klhea=e*F@!bUE-LwlS>XUvTE2(kXO38ep-Yd~R28Hx$E48KgX&ofknn8Ec^wND zSFZ??w{WhJXk20AGyl`EWS_k-k&7pUZkgBTQv;)1H%oL0Uy9NjVZO^T9Mrr|V~Wm8 znzl0bYG2rLd_32A_fvN@KHW-n-p49m%U4&Z)tDm`dF7~y={a0dSlBh5lDXO4|pzIUwm4X1nm7xbQB+rLeF4q0IM83~F2z#=| z)ek=fce&!Y?(>{Po0g4KU5I7c9VuuOe?s3wb#UAZUa%Tca;MB`amBU3zAE0uhb|F760 z9e7>A9z5SQ;ebkA(ju=l>gkV3JH{+utr~Wtth%hc zkZ%wKs4A2T?cyu^7uy0?RXdwtPkCDk_N^Fys--TWD+xPKjhLWN5T*Al?Z_)U$7{KE ztGv~_ZOVPxq&RUJAQyWc`!TWT=zBP&%6xboA4A_9ybJqo+T*w)hG;F))xQz0VeJVGI-2CGTc7r*t3wY}sBP#@&% z56JPO0e~p*lA5BjGE(}x+M6f-sixYbh0HAOO)ykAt8KH@K8;oj0wiNExkd)ClZvsV zhm+Q@?CorAskbYEu&2D$K6m>8ubSdNG2-kdlmD0#EJw1B1Gu1akNx4Ph31 zZTDN#RHoeDx}II#R5qj92NAZD$sU)Zo(yZ({#Vj zxuL>HCoR)NI`!ZPzOKLYC=s9gqcFKo$^T z$MtpYVSE8DSx?WcV>a(52W89RtL>}-lieCYmI10y=jw;(2Y}y$Gr&OmyXU^RdEm4AUXfsyGm^$dP7jgOo)Rb84U^a&`})^*XqwC0GpV-XhS39@Tsc4 zBZ_|%v?K(nkx{9nBP@O@kxh!cvas&-UhlSZ)n`u_6QoCumw`S9k63*B>X^@wZ7Jaa zXDp!>Db=k8jfI7UMFa#09(8uhdA?!m24i0|k=|Iq<>*b_nio9aX8eqPL`20c)ma>_ z-|8eU6<7WdX7*)OOPNkQS*N3j*>)!Xe#4L9kR}0C2;5&2-%oN7-^rIbuFrPuLVFA5@@(H+F_E!Zik*Xl?akU-iIhru zLEBl}4;?Ok>Xss-C4A^`)hpn>jkL;*hQ(^L%l?;K#|%0yaP8FZ<4A}|4trnrPkwvz z+@poc10jc7>Y*$5W%CIZW<}&BpQX4F#lP_)XUy==*z9Pzb1^RRYsTVNdIuIioeNHn zM-|p%&TCfUJvpE(F?CR;Ja4p6Qdf4gPd+lBg6k`AN5s&-d^tR}Xr#G5&hlupva{v| zc1h_4bmMXrm82xG36N{IKVe*2tH1iY7N1FO45E-&5DG6o5tSQgs$7Y*T^T<4@L|yt(iF4FyQ_RuXc$A^r^9EkH{T1K|xJZ^rr;d(a%^Q0_#K) zkg@c)RY?-jV2-eYbORKBvb82#>nigM$ogo%G#GvDygR-ehgDDL`e*7X2yz@XXjO)P z_!;u{t(r74f1x(&l42|b*AoL4+YCZ3jr9*pj#%;>oTJK|X^=yU_>IkgLKN!6Xls(n zkTWnC+N^(I*WQK5OI12~>hCE&hYF@c9~eamuEze-@$Y56uMLh@WTx__n5}ckC5p&T zJlX{jGCq2f@MI$wHrhRN-B@s&+fdV0RHCw6U(Whp*UABPpqC zq#^AP{Z7Ecw=Y4A+E2f_YE;~5K`l;AZQ?r3rHAqtz3$&ba)i_@y8I=)2 zlCo!+C3_}&b2#=4$&Mmhh{)b#W`twqWMpI{aS+NTn`A{~uj_gGUe|TIez)uTU4Q9w zE4Po^an9@gd_A6z$Nh2FLF=5az;#_}@I0K)tm~XiJ2xN4f*Q z-G9|HAZUlMPP3GCmK#-<5;{^OCG+J1H%t8b zdZ~nULt-o_59q+u$cI#ym+acl8!pogG}Yvk1=)Rk&HLeN#V?7D`B!yic8-s$C20CU zA(TZQj@A7(Hhp#Fme=X5qWyScJnZYY2n@3;>~vH~C|-O#FhCKZ;$`@EsheWgVqOf` zU;bn1O{F!|kXGhvp+=yJORjw|*K|d@eRLr{3T4PZ_sRb5Jm?eO1BfOo!!`QWbcHiQXd?G2>K_yi}m_+I^Jt)NoYO_eTuDM&(M&32r!H(PZP z_V2v5f8ON5l(Mm%sW5M7o85Y!rE7yY)Ixizyz_$40{Z>?Kb0M(cc!r@TzITpW&pUyPJ3$LEIBtF}}jd=J~;rH(Pio0@bWJ&*O1s{cy zjbOdGx=Xqpayz2ot3B^^>*TFP{Z7N?(;&ZX?Se5}^8R{~6+cID8hcxbxUj^u>m~^y&$Wr5Ng{{cz3C5EqYIiFM;xbjYf zB`nur*Ls*LlZ>9&N}R&)BuXlM?aP<&w!UU{=d+IK;JO3B>Bo6&XeZ6}< zn%@KM7gwX2Chik1W*jcgphzR>vn(;f?_IvQt4q@q|~Nqh`Du~uL~sm+P# z<`$!_FdU&$);_aFhdtGfTLlEm(k{f*Pk)S9_JBhuy@C~gOMpOzu0}9jYa*Uj*fk?Thf3T9`9d!KH(e2vp z5AG?(xRT=xu>Hgh)OGu`12$C|sKh`nGx!`ZFza_9+y)6RH#kRmUXBRs)kI9F+DU5e zy^%PEkseTsvMIc#yax7qq}EqRL&bb{9pkvZz($+S$!9apM3PiD} zgg1m}dEFW$kE~1d%5HMAT>p z_2(bIabV2A?^eG04iuL^IY=t5jU|G7&d%|xGwlT@tn<2CM+}T7wbRyZZc|6fkB1J> zTuI3;kG@$j+`6kz;h;gQ#8~ zBRpt`uO`-$#XF&Ht6MCSp)lJRpN-c;D!3iKR=ID!9ZYmhW%98k5q9WeBHx$?A7icq-x?7k3N>H)tkh5ASckP8<*e|a1BN%E>yZDHFp+a6u zCMWT=o|8HLp#%A^r4rnXB73vXEP=-8(`)Kimqx9LKe(ma)-du-uX!*`GzPi0O57s0 zYnJRQa3TwH`xt{CI-0^&OC=>^X2|-eLTt`ocjrwIq3MSU#pXnQ6GHu8+FM!E9C@x< z^B6~OroT+;Mu&F=TN5YFzTrw|6z_Yn^HS&C);)e8aec|sk>5+O$-0zXAyGPxIMl>N z)pzyZ#*NAguG73PSAdnhURBy)uPCe5_vZv2jT{oU$#b%{zD z)^!g{9nn~(R$UJWG$NbZnvb_4h%C_A5{Wg5;~%e3++w$$65M)4P*<4>bT@m_(N*WLnCr`Mi;mmOEZw5B21OZz7XX4&|=aIS9k_-n+^C z?*2O~)P@T8UXpH-kOZ@_;Qj1TOCnVL(th5ZDg_5)&ux5rq=1vxv!}z{{H;x=k9KOg zKi-a+(odeGz!Hmh7p02%Us5nq(dP5Ziy{c?9-VG97p<_qTLAf2P^o_~ZozS4-$ba_ zI$1MW2Ixt@80m6lkIAX>b<(cq2DyikqN9+;Uq^~hhzVbmMlbeuDo7b8ee2c_ZsB~q zX2kC@hF7gSgKXmYrSg{T@`#{F_l^f)r(4gHx{65CrMhUkm~P>cmgbePBkdu@*%J;z zBc2MUdyiq6cJ$k4sQ38D1y~(PY!fTMaT{XnwZ8uD=Zt9SkL=Q*5)(d^>V)sl`Fn14 z8c@;ydfB_fLfeX5Ukyz$E>>ek`TsjV()5HI5_eW5xX(2i(LZ5iM%hca<*R8d(2Kf# zrqon7d$4Y~q~R6w1v_gEX+C_&4jhOQ+_Z&zpI=dNZ9>u(D`~SmbO3?`2Nsf9>jmzB zk6pX0`au|^1=cSE$vWeE$<}VyA@&JG&?1d%NNQv1+J4|iz?n5Tw>x2U?_?HuJzh}xMSwk0O_XMOAkU8mzLU|W-(H4 zT_WU0bMdky=_lyJ`gO5Df0PpZyHtLo!O+VO?~Nh9O5Oa$K+1IPnDA01&sVp zOpOliJJu%&_;kgt9zp_#xj3r+)pM6cA4cANT(UXCZ*ErNV&EBP^Xt0M{dHk>b6w#IIuaEQ7y;U?xrDr-dx}X5> z0vuB$TTzMF3>+s+hJ;+qEJ+}OPQ&x&qdgV(1=-4@D{@G6$1r+Xv0?4tH3rp|L~E3h z-zKdPy?XoDgP{Hax2->WS$FN^^4HJjVq8yjD<&9{Oy9jq%NVXznDRfz7q~b4#18@u& zOsxR4`jdTjd=UqS`gF;zGnQ$^{vDhfkWnSI&%)=yiXOJh-7eYY)Rvyy!3l%sALI>H{b?HC0` zPqk&+g<2b*642m_a^7u2*MxHA;uXx3V?bl)fA^8GlP+n`#mzR6pA29?1nlFCZ$6fb z@w;XTGRAWz&4l&m_>b&Z@0H|K{W^fRf!vP)P1En&?NWDU_<dHH9_^)jjBc8l;*L_r^AA2J>7Z*xew}0ez^Qv)9|O*co53Yv_Ka{hOKMi< z9RoC`xu?@<6L=8i=|ze80bk?3rOU6<8E!^Hlr4}PaLVd)en`SjRDo90^|z7|$_~7K zatJeXmGM=qCH&q3+>bEiN^Ib%77eb$3b8GDzIgNr|9dsVw+joVV6_rddZ!y)bdO4e%?iVf%;M+^FY=y; zPga~mmB?Guy^^kAg+f`aY^hF@MuY0Ru=)wqUD&ZU=4k!N|6{PY{B??Dr~c6+3f=1E zN3UM@-eP}ex;x>K-J{hObHl0QiuGBEo!V`qAyS!bBc?~>)ImR8nTjas&ynnX4MXu@ zU;T)F^$9?@1ip>)@omO$n`%i@sNs-`$dM&^F9KbC5R z8lbUQTV22N0N>1W_WZv2eN5?!3U29wB#QKia=s%*5qe<`8{3Gx{FHb3FgMwk2iSr~ z;6zPLRPhx+jXpO)bXU(fO~D)V&0_XSx4W?c%WJ#m!P z?IXc`+_$>@x-y%(-p>BT4165ZMqL<@lsB$V-*pMymwQ_LfdT3F;kWep`ZfKelvtb- zxnf;$L5pDGj>_V@4M<0*-{LpRLIRQ>=SR1KMPngfi zJ8(YE^nU!hTo!0tUjpjG!(K5hy0U30@oOl*c5;gk76q}qtw=jhi>`BH1}wcOv%U<` zC+`BzAme;>r%rY3GgN4P8ZgbjUqwzH#`%=-P7YD>Zi1R_c#H(r?_MnJYYu#qPUKrA z1^@$CECyPaip55r*rHW-L=H%+N+?5VtOJt*+ z5rG<`*WKWh=qFQu5JYFFaHCs9p^N`wr5s}%^(@?h55ThIY;z(uedU}vPhS89`OvR> zoVVsVmL;(O!SVPl9gW8z#n$j=Wi|Hn%GoNQgxv2-g2_J@1*ImMSeUTP`A%I zQBiW4^+vbEx@ce2lrY3?v#(@aPP3&gxVZS)vWF#xajQIzew7lbu8<&~dF}m04NR3a z>rLf>QH!Ug)X^F{Z9@k9l${tcW@gRJGC^KM42TvXT*LN4X^rWgw%W@6XQPvBl*Iv&7lNK1zFYHSw$jb{ey+irEAnf*4fsO6t zlfzxl)Gz`;$yk-|u#D*guRWu{V6x>+aq%0sZr!5x%@EF{&D)C0wEgsE0a&=|5_bUx zs{QpleCqg9s*{cwV2_?9^xxTUTYwAk&i7yoVQZ4W$ccGy2#A4uCDrRSQis3tu#l14 zkTmN7Xy{b#CZV(_&9CBnD5!UhJ)XLxD87%v3FFp%6>ER>)-3u|iRg6Hizt9K<4f_S zw(}c&GKi0_72bdjX)?unog@#}FghKuxh_*Nxy0RqMzjNgR8?eDfaD2lJo-&!D5ULv zWvxQK@Q~d8aZtS6&Nf@P%+J_CjN|UZ(V!<&0cU=Smy=?13c;6x3m%ph(9%`bHXwTV2RsEb zg#)v}WKxguwZma>H%%fr23yRZafb>fA)%{C?cH&ArR*c(Ey{$M>3XW%?1K%j_nf+K zj}tiJ{(erVd4I)?KiA*yGZ?@%g$DcM{YpsLOJ<7M8Z=5M&6e^8?6J-mqKuVEf$L@S zvS}pG!%(a)lQFsRi`UetjK(z|!#>MbspdP@&7mdYDShkfb6JdGG$R+)BX^-290O0Z z5j&3vKZ&{VDv~U*bR;;G+~fMl$#ji+C1zAh@_}r|eCp+Z{mKOa?aF_*nVM7k=LHte zgn)?r(C=6h`w2!71?s}QM~zLM@n6o6@-s@>Z2(tJKO};?JiWJ=36*K0?L=krW(lvu zzqTZ@H3U0k)^%(Z*rjW?Th@iro~@SEK7jV8%_@i>GD%9B+Ay#GFDz>`{4T5@2E;~h zjolt_Uxi}!>(tas9_h`^(#{`dFF>IjCH#Iq!Qx(11SjunKs#!}@qI>?yqG zNx$z*Sn!=QXz0=B@DBQ5Q6BYbzvC0_XNL`&Je}86hq9TbBdLyhAN=XnF{nQh;3w%# z_+Gy7!K*>cGPUNInUupYH`o@4pjgw& zMpDu%JTrVbdRgd?CH*>_8`{X*zSU28H3)~x{dG|5+dhX=-m&hy?D-v@O8?t_ z%-5>BSji2K@vmr!c0Q@8JDJ(3MPIz7Z5fkRF)dwg&b!pN9iKGWR$0ozzf{C5(OBxkKweGcNc>t_y0a`D?L7Ri@< zG;GW^nF8SLTyq(RKr4@84aEBIrM0&SS)jI@43E`18nZ+(DXsmrvqrO8Zt#Y%{``dLfBL|klIgL%#<36K35f>RUi_0Wcj zM?E$zt(gBVSi7%fJ=_4=s6}P%#q;My4^7g#(GI|JsSKvw?C$I90FV>y-ej!{LyhCu z+#}VWB@p`+9|teyGm~o;Lg`4uHgI>KJUl#V>}{8yK3519cu$l5#4XnASFG4==PtF- z+0VQS3uB}+-P>_S6o!ouzK;RBCBEM zMBIHP(LP<-94(~dQN21jV+vkfyvA8>m`TL^l^SQ{eV zPMl6nbc?=%3NVz1nOTy0D8!)w8wH*&3dS|Bb;Y{Vx~{Hu-8BZl^T9 z1^@5K9fXF_+Gh{3+`Rnmo4L47D|!WR`WW-B${04qWQ`~5Q~l?I@>LjV1V~y(g|0O|M;To-$^I`Z z)_{G>5Gf9F)9X=R6q8mQ%!O(3CI&zKGK<<=%hB^Zs)^qJ*+g$3_J*%UlO{}3r=q=C zbFZzBZ0GaEXC7Y?Zy3$ba-~~<#sAj`^94}nn55LmGI!@R)0Mnhfmynyq}Bm5*_{C6 zf2_8<$#gAeSJ%G`NV|04nLB-2&n|6u-#CrvHb1#h#&Ht(lQKXN*c)?QBPp|?drbae(-c!UOAh2_xd&M<^g3?)DEA`7OW$ZE;R&(E(6P^^_wV3Hih z(ko``ma=rVtBlI(V+t|#Hye__cg(F#H=9pR0k)SS#eHW;zvRdte0A+pSY{S#V$r|A z=1h&4403wIfNFDq_~U#Ni(+&YJcNe70ga)%44_@ERr7OmrIhSyS_6iLhA3?1xYjy6 z>`QjRkKjD{%tm_k@4gu72yd#T^+d&?uGv^U8FiI z99TNl|G;w9BNgTq<5<(!I{Ys*s$<}$HV(&{&g$Dqn4?Rfp6qcI*qB#qBuk8I%iXbr z;_*iUD4UNUwxM8dbRIf|E?Qk5FuS5YK3UQj%98S{x5v_bcHx{Y`}5}yfKq^ki*-BJ zy35qGd7w$LIN|8^SngF|+UczaoE|7v+ca=2G;pkg1-57xp6q4VWP#13YX{vh*w97B z;V`Wwy&=@|Ejl{72JE#7_G0ky<5`8#UUwaQaz!eDO!Nq9`DY&=BIe^+ICW{ALW0rTszuQk5`|R- zWcLqQov`E`)#>#GL5#!ugc0tFNsTE=FP9gM`e<&ijTen6-;?+BI(>MYyf#97uWqG{ zVr&=dC6oIQQaa)XrHgl|(WISd>&(mOXeHxe8m86QDw|9SbhNVZuqe}NPHBV(I=ZXg zo@4c0X*c|_yPns2GQ|e-Os4ah?D&=``PO7yjc>{e-`WN7ha3PuARWg%(`m}{*qTh* zco(}-#jV;jf4^xPI3dqtor0=tZZKJ=Gfj|+kG<`zY1gO`MXy&TIK?}rzgW}nL{E9n zlME$KKeC&ERkksQ)7UQI*i{wdnyC9VN$9Ut2~Ne1IsgaanlKR`yQ-NbVW^%h;f3H? z1w)MPI&>JKY9d2=Qs5cfInVjPn0H}EWuhsDdE;f(`jP36Wa`P}TFAhttf_i+`zO|- zdi;L=nRfCx{_?S*YVORuQ&YK;p4KIWBL%DFZI0F~@(N-ljH{z~nQUR50%9O;<>Vo}E5um@_UvJ#aE{!QQY?*D%*Cn?E(d znzH|qfLVH+3YwzSqz*k86Q@Rq#eoBZAsA}3hwhvKCr}P-cYhXB%*_zltZn!2JzuH_ zs{{Arpo_8cSRJRgDayXN0>Y)O*ecOv*ZWu)3N|eYIA;RQbci2d?MV`+`}3Y~$$0gH z*DvK1aCu`qlq#-3Q4hriR^gV`4%NFx3%lk4CsZJlBlnJAZOd0Yy=ABx8jZcyVJ%tK zKt5s36mxg8zVin*y0Dw2HB`roEM3XiId0EmK)eY!C<83%ue z?mP+kYg~hemSCz-HA|4lZdBZ=FJJ(E(@K|)mwA5ncWI!oY@|1mf{_`%GZfyxKj0hb zS+bh}pi)s$;hnu^sFu(AyfJ3$B(ao#>l9+xC2KPANQJ2YOc#e?2y02CuT*@s8k$I1 zkLKWbp@Dl$i`A#cCdKq^GS3)d;fei7d8WOpP!jZH3OEd_RkKaJax}Mz-IGOkVY!rk zU*Zeo(Op$8(`J$iQ_^Gp&wzJZMj5{-pEcd|yVFCyrVw2|G2#Qjlp4q4@pyT5D35iT zjN|fk7(x}4Zzdgei_y^2m!k(dw-zK5`sDd%I&DXx2EDqJN8Dq+HtOO#Ar{&O&4(?X z!^vez)B{>k+ZuHDw(BcDY#e5NsI;O>azz0rrwBroD|wA%OUBPw@uEx9fi9{AX36IG zlfo(hhxu=fxlYz?0#-%aD@GMKD@PpJZ>-tlJ(O29Kmf=YSw;S2@sc{KXE}*I#jM{y zyfwzY-t4ypsv7g)mW>Z<#e4W>z^Rzlm~TlbH?tY%G_rd!n2yjLL@v4$d!5;2#_q|N zb-=mYfjN3;{%pa^dRl>B9kax>QS0l%N}1$)y5;pEG!*5I7$!Std@7dNrNlO!ur-z3 z?ZdPhQo0w?9D~m4^3ji*a68)5ZVH&H;;B$MLQK75SS;h*e&Rn<61C6LnykKlAXE1E zjEXAz+y7W~|3-wohGdsw37#!9H5s=7oAUM&HV=aA|=>azrSf zD)KJT7ygB7T+M4fhWiJHMQdGwVm!F+O$2}U9QDw*SNAl3Z7s5ODb}s0CbN02mGLhF_1dyCNXkA= zUCYABfx2epV(X6T8MS!k71c?u*S?iWnbABHx0I2y)=$7I2EP3(EZ#K}za~YH%QvkO@aV4_E8Ps1iC>X5X%s_JD-k%EB^~l;mqP!GljNbJG_BiG$miaHCr{ zM6fHCT+Pm{qn*`4jr=jq3tgBWFX_FNGL@1ReK%V#uHa|npC*rRF5s^Lmt%-<65{BZ ze#y1d{tYWB8G}_ve+M-00N}zs2+d5E>t)QCl$oJcow5{1nd^r85f#fQO_*B`eFK|N zC3&^Z@4q9+SeyC+)c4g7Y(;?;j0{U8W>nWpj_jPAZgmvBC3a4+19!&JzFp3?d++;c^PkFgov3`y--?7l^NH+B z>*{+`?X|zvBw*Zj(bG{`;FE*CW4JwFrs__<*moC8%cXEL-D3FiiffI21L&4 zM!3hIPO%vj&gZK93RkE!b9$=U5hWy7uh}7A#KF#-Q@t&o zW!$Pyx8yh#G(hpq_ZhJ9I-CEhvM#rZ3I*q z!bPS$?$sgy5%2TAlEgjo>v&ojouYfS`Gbf+swA#%|7o+77FYJ=yHLqd_oxXb z&~WSNR%2FIFB?rI(M7}JR((9_H2ufvep!(xw0m&s)>cIAy0>X(Vz=0iLMMrw#v6B@aZlya)6G>zsJt-}_h>%6kzbssxMLalf;U5Y`0Mn=3Sd~Hn9 z0}3s+R-{L~whCHdOdZN&Z0rxQh(os!>*V+??&g!-bj||3R0*@LmAtDSwL>!LQPVt9-z8i_029XTtvr z3$9|4T*`pX!XnX8PZJ!@yA8!958tMk^>gKT*A{{ zZl)@-NRP+9CqWjR18H|xKBec|W(UV;yzE~sFBxpQOism4{d4TSsQg89liPZ;E!=ry zxxA;97lp^(I?3qOwC2}z^P5L)f4wd8)+r_l@$!4`t<;)Yrva+Hv(5HcV&v9W3SIVQ z!2{~*$g8eQLsF2eSK(XDINxyQX0|!Gm>ki3Egh%=d0pobbif3Kl3m2vIVB+yl&Jpb z#983h(0+~$&jg__{J1Lozp)tpXb)wZHC8{5*?Xw%^CrsA!yWn)tSjT6Cy?9_`Y@@F z)~<`6o&<6!Gb3XQ`5+baGyvxiwRn|UiEW!LB2h9-Md_es4!BONISzVUgsT><(5fx;FMxjo2gSFTVu)HL58_sp+urKb_xJ;LKY1SvFCZgl`mFk-H&0H_A zsjydrYicqFv&pWJI!L5$bU) zG7BTx%QfiH!{^x;xxN>7?)F60w$$!~wNckiJzW|(jEcORb?y3yi2+f08fo}Y&KOk+ z{Vx>&u9S4qx%WW_0~B4TO1x1U>h8^d+VONC)uNU%3P?)Y&;r-Bt7`bs7nqM zJsi{2#dbV~Bi2BALJlpj3=ZF%RcU}V$iSF2#WWQymI+N@pUmX1n>AKCTc3L_dKliim&c6xLmsE>D&6WX)H+L zTMaQLEbMd5L9f+BrZds=isYq6kVw^&i1Y2g<%V!_$q9DD`qr>n$o)sRBP{*&1_OR8 zE)+GW`UR-V9Ej;AdD9!D#Bg1qR_Gd z3t%d!f8p6dvogBC{RivuGU^4^GV?$o>dmYCkqm~6fE z0c9E)!F|kVpHK|-8w&EIv}0*DoVuEvA6??*5E|$x{&*nre!6aB+>!uq176xVMciWS zhN!E$6Elv^(8^okYGq?Vrtja5*$oM6Oi|z$N@8{x2-&|%X@aCux^1~k1J|h6LSck8PK=t9KS&UtPWGV6A;55R2$G%in|NDYir#^s zjpCMI1JZ63QO;psz-eAk`a2YgE*E&R6smkkDI3))>Tw2^KzBn4wYFldrRXg)75FO-}rR0!y@M?%TsLcA(& z+%wUv_z1P&yI*Y_unQzA>=JF~=mS_gC^p1l4@Z^59jG3EqRp>+0%IoX(sT$E;}lK* zI^0>sj`G(;9y4OCn7HY^DNLLK0=hO0U4Z|@1AQqRh1E42r}f6W|=6OKf{3C5?#ATuqT zZ21$_<}9O|A{g1?GVeW`(9nQA1dBp{D}t@$e-@_Jai-HUFvLJdV-aPt4I8yyB&3?5 zRPT9nbMtlS-BGIoP8zeMg|rMAT_h6=(v`ce4(iJA>ffs8+-g6QLDW~=Ru4Ox_r=BK zHPC2!4B_1;RMc_>S$sSsp{5P7Ae1PjN!}=A*Zcw?1U|QB7&aFie-!#+^oVq zs_J&Xqiipe$1Uv}u+hc71Z)HW%xZX8QP(}lz(b9-NJZl<0+XgFcmFu6K`ZtF^%R!GyqxoK!U-*JyLlTffqpBC6ashsr5i$*E|SvT z10t)+iO3k6-Cz+pwx6!ycYSx>YJ6dU`Q5vB$q{O47;HI2^@y1cP0L!A&u>w83ng@* z_89W7trMJzkSTFT*tdUEi=1!~pJgi;Z>p}D>5chQez z%|)xRsk^B)7SyC!Up>;zg_+Owk=TcIHd#q{9zUeFi&?ybz%`K~qhVE2<;{tujGEdMEwsMz&1)q) z+T&9M9{GZKwQ9Z+Fe}y!Q+qSCWt$?QMpRUgcz$HtZj_2j<(59=0oLze=nL`t~ULN`Iv2T)LmwR(-Mp8&3U(-GR2l3;3x^0+*mCE*TFGq=7 z2H$?Csk%OF*1e*{Zu-|F0MF%KAiTWoGOkM!uMSnzoTM?zNt0kpE=5~m{xpI83yT3> z$wd&-r>uy;0#czkMG&&PScg-)*#|N2zrXCaL2OJtdvF0R6D4ocP0HI$vp1PVfgYr1 z)?A64D+;FV_kwW~`7;utwwK_u%X)e|;Pw0ks`fvtare}~7tmk2@XYOV7`Z2OunbZy zB17;!FfMf_JbdEph#;-+-YINhD#zf7<;+o5IURlqm9*{f9*uX1b=! z%_pI7=I29;z;6NphT^wRq8356)F=v>^u+t_e=47uPt5f*hrz42X&^1N=CSbU*t20L z|JYt?t0Up|RI{j@iXa;RRyD>6Zzy8?tMOHp&ke@2sp<-y$5Nb&;TM@^f3US!V1zC9 zVC1Z8k>NEupdc#job^q)vOF<5 zU*rW;&bHK)u*kJLRvq*U2$brjj17%3trc-Ql5XuK}1DRg5vQ;5D5Z zcZ;rC=2@~4w7>U1bLPwm(Aq$+*HltbL0o)=g{W&ji{Mngj|jD@C&9LOW~P{A z^0w-_CFvUmCvwKq7R5gQrBkR;%r(3)^LfIh+4SdPsv0|$*7~!+Eh@owKr4k|z3*7? zo|we|h=?8Gd}E67@lg~XYHa)H-3u~kXL3=>{O3oPdox|tmVP#*6xHco|7b=x6Gbn6 zHS`7k{tyaT-dz}xkYxL5HDYgO)xE>QdIJ__PR*%$1~wbF zhdrrp-h$un|27Tv0|cL#cNM_p`!MC?0Pf<*f-IK;LgQjse1$JOYB)>eloy*zaD!Eq ztuyuT%+-eo{YzGSef?&ssS7VW{cW;!Qf-8M-5)gT#l+z%EXT>lmv0YwSSFbMPUaCg=_)DkpJrB!;-pYYdT&P=c|Nc( zd5ugFdCWgo)nWVF!sTNyh7utsn2?~z0vnr>A(H5kA_Rr#15(<+u*MC z3*1}CTAd+Q{g!dN1Wj41>Xx}YS8*nTDUNR3@!E=58=bLI$)}{6r&S~z2&Y?<>)ry< zpNWHw^GNo5Ao%^F_BXadhzOgG4hqNKx$6A(H)pZ+TtEA=%^xrh0XYh3cjFex&5D5} zT9<@)3E1;}&Xteos+Loi$AE>TAI}{reIrS@cC^Tyzdx)(|S z8f~GW`s9Z`8bL)G-33>52x?t1EXTJUyZdzcy3q4%e1oCrmre3dsI296O{1TvGR7Wj zvIlb=NTm$`6I;~uLWGr3P=ez?Qyh8NhaJ7zjvFy_ZE3YryaW&2lJ}ohnRXl;6baf< z+^?WBV2wb0`4m#%%@glce(-w8d7InIOJv)7xjXNm)}F9MGPdg}FO8SaUa~h0XAvs( zdEfU72&yB`W^^V+f*P`Yr}a(h(xwOe!mHm|d`$K$_>}Jg2wv3`UL;$h&FiAvHG`QR zg`qi>fc=Yi2x7uI)4c$KLP|s@>LzDO1AeoSf0=_pnT(c}dzw#QO8ioLNFV<`<81#n z)}|b{%{l@bQdhA4%`6vYE5Pm+c`%Pntm~V)Cqd6THM!EFl@QDK*uaJ7n{IsTipZDv zn2)C&W<4hE*AQ`^%4KvJv5CQvs%R^P3(UDhKckMcfol&9-AkD3*4h7uhYA&Bl5+0v zIlh~#OY7Fs^5}}r%Y%6@C!F3UUZK)rOEVB?Ws;|NknpY=wNVM>Y6JT5W8_kqFuB;H z*onp}uWbu4zYG4=4l+0NRipe^@^T1?7No9ZrVKsMZWWXQ(f{oNr!W&va_L&oxqJzO z?JWz;T~uWi+r3Usr#$j{g{aH^15gD1vbQ(YvLWW=4!Sg`qe$lG(o)Hh@ctneK8z2n zVYi@W67lisnO@l|e1sty9b4z=T0Bh#bJK*ok>50=`QVyrNSLvmu8bpx2==FO%=ET#RxorA*4Etm~ z@pXM8)Z?8>x{6a(eC}^f=tVD%)i@bD{`f!+>v35hGfxF7R=$a#kKNnYenFZlMX-$l zQE{l=-Ql0YI`lU2T<&WJ+gyMKuiuu;4N41-gg7PX>n=w917#YK1KZSJ4>}L$>ot%z z*uGfcfd&I^(h36PEBPt-Z(^?90atk*q96YPs>G`R|5aGGa`kycl8yrB82u+l-fbK^ z)U^rCe9KQU6OU)qlC8258Ls=-P)4p#UpaMqoCR}Al<>OZf3=78Q5GSzyHP}Lk-d%# zpa*gjUhDT*4b5?$eD%?kUtM1EFXL7WeaN3*cc={5a2S_N&F(A~4J`G__SU=0|5SY* z=>mNN!+BA$uB^xD!m@u)_8KNNRZ`M)5IKZ|+DCICck#3jbj}i#S|adkKO6_}Y?BZ+ z5S_m*8l3ziD0OQq$7SRy)5&2Sk2oMoV-F|ycw=}BqK(XC&LyZ`)=4N(eN(c^UBOGi z;C1CdSs6Z(ttW?6g!%l1wv4u4$G{>r{IXl23M=<`2o&fN~`Eg($e@(R}8Fa-i(-D^y^MO->R|OF^ZSw z7^AaoBihuoNy6j(&Ha6~1Mjkk2Hbb;KU@jHyI1la)x-z>W!5}fz#aVUz{(As(DV@I!c-c=Xs<^y`E)S@3uiwvF(l3l=iRBdHpd!?c>H=N2d`0?>2kj^4dcs*@GMz<4(bi%*kLSh) zo@c-qPAX5YfG&9dH;2pUzP+JW9R>#`yUUN5E=~d=QS+MJ9L5Rdb)b8ynCdBWoCD)p z43vxI*tRO$VRmCPGuLlw-ZZQ@K0fY8A-RT<`tI%0`~Q9A6%_qk~fxg zhV}3YZV&WAV_{@)@sX(yaN04@dC2efB<7R-ddcqW;sJQ9zEv}Bt3sZH=tpc1nbG>TNb80tR48a*sJTEWQZwKsM>4351>?8LL!eaLe5MCIYhK%r(y zO{_oWtiUaR>4cPkK&zW_s~$WX+Yr+$8R0pJONvVNj%L2Vf3TRmKZBuCp<@o z7+1?in**A?BP9pEKs;e)b+u%QqY}bf|7q4x5$jrF+HiqRoI_H_F|_>0v$(i8C1vGS z=(HJHVp&y0ASkeSBPr=9g<#mL7w?^*yLolGrvPtrm;tXy;W(m8nfF!Fca8fRr6VQ! znj~BPj$%GnzixcMbARw)Fx+X`A^mV?HsCicMXgK66p!&YJ>F;)wa?$$UdH*(;U8XU zEC+iHv#>CAYoW&YlvOZ}7z2aAZf$1RkJnkzMWx(>lI}#JdR#S&KXbiC$~j|IhP^OK z%&RCs^pARUWQsTn@S$AVTcEp~l1B0B0i(}U0JquI9N z+ji#9^m<>1FeVVH-w+OsX{KM@T~i45-qGTaqQ8(pXbiu_h=*S-Z6Odf`42!78GyP0YohPo&n$~|P6Q|IsSzoP) zi%Sb9-MLZJ^7=*F%Qtt@zexyP)zgs@VvqUe(RM^CR1p(A z&yFFZ*xrtF1fgS zH2oIfGSGZ>-OWTmp4iX5RxfcKfb+Cv>*6`wkkXd zIkEWu;6NlRGuS!PX+rbBxoh_gu(tZ4{=xk-L8^e%BQ6`kljC>up9!mb1sw*Jsz){^ zONPH4?yR(4r!K7V+A@ubibAgT2KwvgPJXB)hoWc!YZGo`q2~phBdr4hs@0Qr2Azz}F3%vo*O*KVtTgdd5H;GE zcQy6tWF1M$QCoG~$7;}cJN<{quk&Al_U=tDe#9+43JqSAurhgjy6m%DkTNK}xE1pq zyelCStKj6rUC*^vP&oj~%`9 zf0VFIV;!KqL_pgLweaLq%7;BiijqRMd(s9Cj_lknk~zDJ)84B*knzyUyhgoc*d6XH zT7J{H`NYEnLX6l;CGrS}eJy9Fd(!`eO^%VpI;@ZcY-OGmxyD9Pi~$oz$Rcz9+3}L} z%0g2q;CBDU8Aapchd~XtjATW|{#CTIa?2<9Nh-#_2Y9J(`BJpNwEwDYEl9{+wQL-G{9ioZ|{e&Rw}$jvJ&vEB=aabx3if z>^pUxVjR;vfTA|@o+DhleeHI09M5g5K)t>$AHXoIF=q;iLG3_DoEZ2R+-_upxl{v{#(^14(lQeEwHP-v9yAIah->RH>&h71(GtGB!R;^=U$uu8x; zCHa;6*VhwLeGAr9GXjN{EJ>LwO-H|VT2;}!5l1zSU&>rvJ?p)Q7Y_C<7B(U_RrY>= z$Wu3_?NaLK6n(OJ0@D4fhhHm21kYRj55C?yD(dx(9vuS|Y!FdIP#kI~B?VMaLAr(+ zx+Dceq(MqSQbLB3M(LrXJ5{hxH~$J!e*t5oU9W&<6C0! z#!peg&5}kYO#-)@4ksKM36qiy9wb5bfDJ@;$)MQC-tD>&ZdK+PCXfddWM04YX28cm zw3A7RZjm1NJ3{uZlJ$LA$tjywp*e-|t zLq+*_G7%Rmi27qAs!J62ZnhSi+z6pu%CR65!WHjV5d~9yemcTr+#lGP?7@+MA+)M#tW6mFu{EyJGl-j+Vk- z{`K^Arg4;b##4wP1!{7EvnUAnkh_5j(UNE$=H*lS*n`2@)nUk%B>s(GM$o zjr0<|F2je{@@OX5a9*hXdvmN^RZUaoul z_HVGS;xQ=^-WR@Ge_x%H5dZYH;nW(srz>?lym)%9S883nnyoyYWrW*^yF4{HQ9Xvg zi-c6)MT6#LXOrkxPHoKJ?n6eVdcERA^=Wy0R{6>DrgDQ4$M58Jb;T#2O8xC#g4_y)eHx;j@4XZ29t}VDePqNh>pG7@g!s^ zZg_Rgvh_inU{(HR3i6dwK1GC=D}Cd^;LvpQv~32|Uq<0y?p^5jNEq(Ypy^%K+Of`o z3e0X>?ZGQj4SO}CX&L+T1p3>9Wnnj5%lxGzZFfP7(TBZY`)tGW(zI|ojBp!Y|6z(> zl;+CYsoB3ozqBXe<(rONrECDVbvD79*zQNH3WwD#kLm|>oX=gj|M!hqD;3WAjx|jG z1aV)_nUq!#y!*&V^;&{mi>mIQe}^xa(fp0PjcZj0NvQ&ImE=lw9VhmT!Z+5q?rra_ znQ7c+ym7F9iYC&t8eGZJqB)U&zy7S<{ zJJT~3Q7B>pl)`dx`08yT`po8JPN6#{#&x(j5*d{%ZBbD`CS{sNFlxi|_KA$_X^+UktBM##0cwfma?NlTWzP}S25@ftL%U`a zf4;8D`Btf=Zi&%V`$dmun@b*zv#|zoH5x$;<28xd%E#o;Zu0BzeL2a1kP1BcNcv`E zrS1mep=!5DGn&argt2K;zrzG7!^V$%<`Lv-*_;VH;Ry;7u}>z>hej{)Ff1tZ*@UXEjnTwwj2VQiS>f42LRy;@%z`D3xM8yBBU1|lDn zhhaXjT_?QxkQ@@pCf*sjQGtrxOX@aGwk92$oD$Ams<~=vGj%WZ$tm5=n|SF}iF#Jq zt!HfcfgPP(OyB4a9c+aESvGf1g4_BOZ2Uln{vQ{8Q+Y(hbUyxg^MomNEv^hhPQg2Q z{Yt9$d!^kJY8hAEOqm8|Vb}Lpo);V2)!oP{{?t`i&eBG{_1qCa% zJ7lrc$8b4MA<|>dQt@7GW)@OXmXranPULbnoRP{=3*(p1C*#;(qB~T$(*}8%D8kch zB;jB zYE@%KJkm))aL5>}T?Y!o&!~FM%_K08PvI5?|M)LI&o0`KA5Tw_Rg%g9KLsrJED$c= zp?@BE8A|bVZMsvH@J_20k2T7vn|><X+Q{H(AXFT6rAXro#6K$8{SFaDWju1L&YV~%`t@F>PO49UD1doi@tCMp5b_TW zRy7JPC@2WQ;XGibDG3U`_@yh_NbUXCkY5$^eCz9`<77kgmwNl-$N{x~bL-vGLgMQ`%I)cgjrUo{1zg3L+e(vo7(8r=#lj`0L$@%{%@H=f{me7K z9xf*Djs3sO)9<>&2Kjt2pE2HbL%qbPxw#W`bG6TTCm@!~zCvvUQ1k}Si`})Jl>FAv zP=CF2iD#B5I`K_^>@-~6>$*AW)77O&9BzL}2xk1Kwc!l*R`-=At@1B0K7lETl#NLI z0Eq+0Tux*Rozwq92T$bldT&rW%x!o=>K?@j(zA+dlm0YisHK!hoqAwHR)}6RpUp9T z6ck|QJ=9OO{#+OGjqnU*8bPXUP9m(Cmoz@C)Ro@3)^tY;4W2AM8z}nnD z^02Uo_q#PCGxnE6K@Z@$Bv>UF|5$SOShbFdDxv@GUg9cXqjt4N=iy* z2`CPZbHsVKHS$nb7H6;6df>Q!e|+e&^@RouR;h^XW;A5?@_?hbC%FrD@*Ivr_MXEx zrx(FS`*7RT!6(t+NUw9FG9>d)azlP8L=L=K8WFS$AAupRH$YeqP--VhH}54By`5{-~@BX&UlAU+O$tv)w0O_WZbvZQ&AZo%uLWVajVf@`LR6 z+qTEzkTonLufnNWe3m#!Q@y{T36fZaI9gf=a1wG-EqOVL=3)~Pq9JD2he2OwJ=GES z9%8%h6RjgwzEj_5{8P!uldO#(I#OIxx&{qa>3ZENN zHn_2t!~qX8y9ja9i|Tyy=8y^o3ZemP-W+C+D6-^+P8nt8Hm0X-?I8@bzj8t z)M`74a5iFTw(nx+1v8TDTnSV%v7)61Mj`yT^AYcsS0{em_TFA?Rd$kLr3`qcGYEy$a= zPHPY%fwUo^Y-jXJeFHEN3ojtM)xHpV0nz!6^Z6wC4)Bt;j2z<;ftp2`7bP0EWX&_6EHG8IzlJK@7M^! z?^gz24M4WhguRx>OYVPv)sfFrI`zv(24G;c{J+~5cz=sp@H+oZTG7hBO`Pq|7-FcLzsfaQ0xiggOsn-YeTsbLjnz(Mj z_|gAEg30%#6sI8jig_o%&eMhPVWN02nE+GDp;D{c6ciL)KqDQQ84p)DaM>>odBMQx zXP^|rfAFm$SPg7|@JSktAX31PSGPtK-Zv7G(^uU{d%lyPm9~dpAxR93EkupudH#G~ zUo`u(t~qpDu@PAfFCl)C#-$_RC{XbGuKf#CkY&5X%W|qs*Xw@ei~ILSQ_M`q{UKgg zy{}OB%>@mu*=3>$q!LMbSiy0aA-6a0giq)kbWzNtkVa=8(^dg#I1o3~+cHDrR12Wl z9d)pgJKZv`>hYw-DKfY9FJE@`Df1U|e|C}7<6;e69L`@ac05>1w z-IU~;+kX^3xKGKFtbZ}qu=z{-w9}eYX||BYg*Tsv?pjyCBjQuSiUs|Mx{LeY^{8fg z@i;WhR(0$%qeRMPuPhukreuxlRY~kZWF=MO?Mv)nTrYva^g3Ba4nydhT88WAmI9u; zqjyJBGB$qrLhy@FJ8!-MIF!JzQSHRSTf%5)@|R)x_gp*&Zs?qPLz8os%mUS zP%^c*b7{%ne7JwZb^zXXlh^!1%A?)r5*1emnCV?Uk%9V_BXaJ}^l<8l+pb-uJ{`m% z#?Nz?CHs=4DBGg>&cINz_{&y+;~}Px$lNH{2BPZzTi{QKmQL`0w@gseFUNJEEh)Mv(BvtMTQK0S?akdgk)dzX^QtoJT83W$@A_uQ`#_4A}qg(kj&=MNtODRHlB$ zV*P7ProYTs_rtD9@J|W1?r3)&6?;(5{_@~9s z&|trKniSkPRR!r!mj$GtAc>7`Q_Q!9O8j;*E(Ry$n+zz`0!14+|fH%KflAU_-@g$ zXSRxo6dj%vS9AT12U7QvovTtw)sYHNjH=0?CUHQ^CXfcC7hXvc!FVUoK#GDQ<9;6_ z{v??cSg9E2)pe~QuEMYAtL=8n6U=Dhi{Sn+*C}$N=a#BA2|a!4xtgo)OG1G@HG}V5 z3Kr88SM`d>qGdZT>%)>2mIx08Ex0DHR)9J|2SW@GeVJ@8mzR|DS>JF);QjSg>uD?EBwX6}%5wqPjF!oh_?HTeH-qWVv@#! z;JC=>1aY9gGka!5ui`htyWKDkH`t3v()^l8t9>mRdgfti-zfQqF@+!pduBl%bW!%# zqkR=)^ZfShknR3P$Mv;!{Aia}L%!Py(%QyI=8LWR2gS(y8(&opqoCJ;KVA9({ue7=oK4pzZH(y)(Asziy9u)Ba46K&&fbJ95#My{J0JW}avn1L z_@_S}o&NE^m_)Jg_Vtr`IV<&G*vF@XcFdw@h^hst#e#DX@t`b;mg1Pb3Bq(p3;pvX`(P+64D8(-=z zE!G@9jzIi6-*Mtil;Ae=2>%^k3`4mcPf615ra&vpXthC<^?u`_!BrV_nrMO$)|i|UXGDa&`$1* zc+a@)w=tS5>o^;s!gPuz8l>JEskW?0>l|aq)g6pO!f&q(t>#XVhg>&WI85y$)EdA3nAv%Y>*Dw}Dt8>Q zr6G<7PDBbeF8^$I@VPKtoowcQ-gR@goH)WtOjs*}Ycy0Uw~5htt%cbg zT&=VP)r?NdcXt5DoMV_Z_n>^NdZfg{ z)yYMX`9R?q;!R}hE9qo^Lo*S$X0+E+Qpp!Q5+jAoezW^11|B|L{(S<=#UCN2`-s}y zUNwe35Os2Z{5R|0!k{uJ=`3~KcNa@qYYx`Au0g*-oWtaP@$-%osDCGmhhJu9q<$hr zE?S+XU~1^oS3HA2Y^8LSdgCa6&9t@r%uD*of2}pLHOGf8!l1xJ#!W@#TuIk1``Bx> zrMUe1>XGtCVU;VnbQBbm8|rPx5TcAJJVo>fglO6KBAe*3U@<(ox(Kt(sx|pbiEk$* z*Fsq@9zz@?H$0{GK7Rvc-k%i$Njyzn)H*DxT2@{VWA@}_^|tXgOt&EK+);!{HvQB? zczer}z}^tA)SC^>*g%~y0bgb<{mGN0Ep_ST8-#_p3V#cme?C#T)%Ah=KT~(}*E%Z9 zO1;L6c0bvc&4!BO;$kc#=AU{u`6S7Q2wl3h^pXZ%>s-cu`q4^fTJZ}K!#|K}EO@KP zY3^nfJM!}&>kb)ts_03?Rwzx}WdU~fHcT9G9#ZZz283G#P`if!+^*XJ#6a{5@)5NV zyf&>L2CmBeMo>`rR_}=^`;Lw{ucZQZnV|jE_pXBF<57HL4=t_Oc_eZU9Ybg(Iamtl zC@$Yz4wzwWpBFEyFhw8|{SEJ7ll>DJsNe}keG_CvFUqAdV4aLi)bgLj-wwflg?SwK z028OmjH=&*S8$U_f-4h{hy`sQ?>>RC6C+srOV7#8-kd+pv@BP|#oQoKQS<LB7%ZxgR1en-@bPrE$uAJtjV}$h2bobiX|;SA6(y zOb80+4^IPOM3FF)-W!+Crpq041+)5e@Hqum{%PIfml=eq9Zk>9u{SjtKg94bM$!zA zlZ(i&v#Wg(g%}o&Gt``6M2ZLqZ#IznhkSoetvYLCY-reI5d44%d?i9*{?Qc7d;;hm zXxLv7+^t~;X$`x>h?NhRDvV$Ty60j3PC1BH5dC5pR8gTU_F#A+4WbN2Zf-PGB*`Ga zNhKeEVdw9<3)0PDz3dVV&YCFnF~qM>7H$m>8_XDa|vFZ<0u_dw@|+K-Kjin4|gfL17lIWjUb zxE=rMNl8k62!6ODS?;jR3AR8eRqV;^^uOSC6Xkz8Qp?(}7msa4dd0n-%dnp5`t*4C z5RMt^Iuhz8P#v(Enwnbw5T|Z-nvOHs-~kUzp3;wDVaS7gn`&~ugn0{Cr!m!ZY;2|W#1>7b6*p( zO_zGp?Fz??G%?Mvz%o0Xzz%zq_XYZw@s{97oBH@G2q7#tK9Q+_fGq^_gjDw4u7$Dr ztzeRlBWkH#ffuy?H=`6D@FTJ&yP_ue>XM=V;T7 z5@bd%O0`&-YgRU=*5os?^>LRxVGtXNeFO__D~aZM$WWevPgHs5Q%lVV#tQpIRAHQa zodDQBY>yuvsAf#J!f6g}ieE?iAC*Fu@~&(2zhtUUg=QG_REm)*=Mq`4`FZ^02?20C zZmNtI2Wtt3k`OmKNnC6gaT&H}rupG2dDbdv@;8*#9}y?P^6lnTR_vtY;XDk1z4nbl z_m-ULgo_gFJG{#5ozZ-)n6v8+*VZQ%N<6#Kj3UAnYJB)FK{t?Kd|Oi3seGfoEu^Aa zjmc@7Bu?F`=n1)(x(zlnDs$CD z2~2ZmXC!=1(RlKX`ucjquEa;iG%>`GLs<)R*m0z)(c+ZNE)PObcY}yEn@fla6&Bo5 zSnLy+lFYHJa-X}U;}1AsSy%C{^UvfjzP_y{&*^X&kohvx6MqW z>5#KF!Yp@0$FBWQo|u1EQK1gIHI32K z?Pcc&R_^1KVE@GDiQ!@4=|a*x67<}uv9{e(t1wp71orieAV1`L_;I?@?Yln}Upbc@ zSlS5!yjpgHo}R0kU4ToMe?g&$tK@bFIRcT8q`YH#yuwW5F%{$RYxnd9_2|D}^wg<` zwa)ttLu$21)Ab>5pVt^)t0F<_!JAGTlSB_+Su60Jq8zVj_;8c55hPH%ScQvkdpcqn$CRGhcaNxyB8zT zZ7U_U3Xt;D&d<9NDJ{_$G*vH{)kc%2@-;hMymLD(k#hbgdt;&7Ck_qC=jTeALl+-J zvtDp5=&l~AQV$52?c6IW#nxSszDwj3@ZQ`KR&L{=HRG zW`Y~7?8<>C@B;|s_(!|F9Wk=jXDx_}H;B0X#B-AHdlY%kH;5gI{afetjy6R9_cCXT zk3Rp94r=V-R+d0x{JfhT5^Z-cc#|BlcP*ol?>V5d+_p1{$|3V0DhqqhSuW|K-K`_R zKm{i;;#V2JMFAGAm=IQ(NS<&{4e;^{;}QFBx;ASE9+ z*<2?cu!t}P7k%HXgS)LdA>@q1)$9`J3zxqo9DBszw|{i%MRhWv2TA|EIN=h0tu52v z)wBo?!?;Z#@tXv}Y$zEelW)oSgK!C)vN-;mBe699i-9GQ`PYEY3J-t=4u@lal+h0$ zmb)_ibRcu8J?4E)4|yE(vNoGRgge{08WD!!nt28eCij<1J<^zCe2P_l*I)c}kd5E3 zQ({~kId5hCot5mr@?UUv(CgIhf2HC>t3}$cwaz%HWTB8E;)}h{h+!~D_dbs}Xftsq z0p>*Ic~=g%r&IW7q)OsW&Ec*HggN-d0?@e%2eq_bVrTU_qe?OZ@%tF>D>7HL$zG9H z5)>57H|s<3RN26;{B3;prk=ds7hY)*mPgx8xB~OYiWuOHY}N=JToHq8O1;77!>73( z__1C%hWLB?T4S21z5LTt@a=73+Q$SU(VU`J04-wJft4AK{!9H6bMT|J5l8P zlwj^MEYYGRHUR-uV*j`eo`@+9%VSVE>&if`#g=DV?EOLO#6*oqed5A$834IJ`#R7X z{-Xk}JGP-}ZeSf9ZHRJD^TX^i%nwD{Y%Wr=2@hvmtn%g_M6+kw(-m>myfUTEpqFF5 zfj`nV^4jx@rXpYTEUU(R9c;#mI0qssAqIP`y z1p_wWp=;Y^9+>I06~qQwe6WNaIAv+R{tOo{RWgt(Y*2%<+3KpOL=Y(y4-b#FURQCR zk3_5ZZSblg7P7?L2tR*+3imuKcl@L#Ny!t><0;}*k`4l72m~e+X6VOcS0KT9JnEa( z^}smjF&^3wI}Of_QXm0iTA{`MuKUi1wE4jIYV$z+H6!)>`QH*q9@fE8xwX^pi)nEs z?&HsOtMgTP1k;|xY7U=7Ak1{07ANUH7Gvp4CvGA#Y|g@}DMO)QfBH8Z@qF@q;68gg zL}T6W!Lg&e3|Db22hh8HQS)V)cy~8#c!0}8J<7EE;y8?IY^d|P>n z-sMYIHF1ZB1d9|y6O&eOS(FWDy?*2B2*}X`uHJo2!~a5U#tQTcX;BRdo7-UYA|ops zuoY^UX1cE-5D@X}X2O0ceB_(@1_lIh{@`t%2GwO~LFYqF&WZ(YDOuStFe7OKCN#zU z@ZdXLWLTIXr_jExQd*pS^!_Rck3QOge^jAsT!Yb=MhHsaj@o&zf#O_YA&Y3*9Yfq! zg+Y{{^+06nLi1L%?_pEz`K6ce_FsAaTNrt~0$ntsh3nznZ*I<(6jT{n^7ETA5w2x_ z$}l~r5We@iKe%>*7?h#FxE)8-L=F{Ouz?F_I9QFh08z~W$mAVQ*N*eF_w-0t?U%>2 zU+4l8=TB@Epi(!ZNRofJZKz)Lx+qJdsAWMjv>?`$&l@x(?IZF~`x1#jqwDmL!%`rk z8&=5N=O%oYb12*57+%IrBYM)r&^J6X&Bn<;C9?9q4M*dtw-zK4$B&<2%#w?F99*)x z#^~ItLe|Af?%~OJlST5lhv$tFPmhHX@yHV&TM%!i%fXk@2$I>kp^BswFr|16aIc*8 zE;wbiR*3`-`U4OUTE=1-$JE7t(Gkh6F=vwcb70`%^8FPD1v$;z!xxM`4hy+3+MHv93 z4_h8Wd^_Wk>hMISAEpD!wC@Af=9<68dGlCBZYz6yKBJHw1sf3U!8kQ8A)a&l+N{2j zVRUl(UfbEc?)7rL!EU>2*C-CR{-8~|zI$!tO%Iwd8sQDdGbzbVp9|k$i=cj~DIfF# z#mzC}_f1DYW5OS4Ocy1DJ@2*^GUzTU>3i~>H*L-Na8YgQE1bukeWJK}h3VL*XXwLm z0V&0h{I$yU*|Dsgqpx&?xkoVMzjGdi9AgbJJM`9S zZp}loyrV3(=)Z?eGP6d1=`|G_Aw&FK5kmp2c}wBJdRST5)6nF9XbQRdZj^Gdv@-K& zGW6>e6Deot`xd2=AM<_5FJLqof7m=)rD+WfK&w|wnVTr zoR;0zb77Bz2X-%1Z|aoc6{x5#Vw!V*STm8GJbC59gv|vy+(O{>*2>_Y=DFdYod?%? zdvFz&x>0nx^PkV8%(ik-YeW{m`b6uvmhEAf`AY5^dE^`4S7c{}-7IwJZyo#njAFDT z_asb@Jf|0QJ`+EVv@%_dVDeOYd*;mrauFS+-^2Z`ggHBrnBfrSgcms7{XW1YSAPw# zEw*Y?>QD9;oKdxc-R!{YsD%$#tqJdN{KOplev;rz`xD1(W#Q`PSlR}SDzUie6wVw! zfxy$%gr#3;od zvEDQJ#CxnRNU+bsdUGk}>!36>`mT`m`2}TXNBoio88R$J3pP-_}NP$cKh* zGD>Z3v6%kbvzf^KC-AqLm2L9X>!AI8C&KDW3JNh2F(|v#U-MHD0YMZSl3jGJi1Bh;$GgV zv|$caOi?D$7!=ee7*cD^muJZ~ceYTl$yl05i5eQ_xxTZTx#=XK{H>)8^>OG9j|_X< zkRoMP;W@7%T9s5}0p*X)3YIvHyuai!S^sRCWfZ6dwI)|~+^9pUY)r~f0enu2*bLW$ zZLj{t(lt)07@jn}=x=}gRE4nmdg+Vy>8{b=evzJg&uBxq@c`xXaQoZc?9XeA$lkk) z6Du;3k3HV;zEE?d&oE^u6J)3>m-`~sZ?pdTm?PJ)=&#>@hqyP|h7I?5sJHF9yTZN; zPi`jfZxAFnmu_;*Qy*3VN_@chl{KVAZYIFQ|M;3=#iqs z%|@S(jL_Xm6buW5xv!%R_M!0I3vQ6)4>@89nD4kBxHwHknHYi> zG%hZV14d25!0Q&9Q#KWCy=VfyFx-&YvJL#HQF}BWEJtlV;u~RatG7;vKPNN~n%7PS|JlXThtH{h0^b&K8Q6yAF+(FFx zri|GEZS@1u0pU@skHk|+Fm>myOSc()^+(za;;pE(Vl6AO{KLbw%UCIF$Wv2Loz9x6 zu9MT$7-}}|z$BGs)W+7x2)BY$2CSni3dtJznDf>9@==>g^CLFz0r7R(F>Mxh{308i z``}d~Z`5D?-~4;~B@5-7)%kokZ;A?KX)8}{9A8qgHH!|2y`Mp)v$tDIU%4_&D)>R; z!|CgE6fA!@XJwB+L?FI2N{XjF1xkp;46;P5w+WFlZf@1F@UYP}Zm!!<@Ec7wh2(?7 zpVP|s`u!lr=Jxh*h;bbp2?`7h2a6aP3?__SyBzaycY$YnE;F}n{mZkj--f?23we8P z;v|id7n(!0Gh@@%&p4PnBM|XkVknHeIEo@;Pg=(eZ^*9AbWJAmqhUY*p|$xfbDc)yFn03B1N-}?vyQ9bQFJR?*7n7&(PFCDqT@-QlQ$9{Z6q@{+P-hh zeXn&~+r_&zG=N~%A`*8zUrP&9m$%xoonWe}tT9Ec;Km-3nM@WpNAXwbS)JFRF?T`h z%I3rq{zZlG3)7axT;n6TfxN~-&!K$P5>nm;z!ps_WGe~!a}MxEV)|qTFsboAJRm{< zPsaynMw`A<>#~DHk*nuGUXXCv2~f^<1COB1Dl?CMK&y>WIY(lB5-ZLgFBZb{M<-x@oKxc5QQ%S<8Jj9!5uJhyc$PZ{mLiw$bu!FCr> z$m{$TJ1^(cBrPBDH|WM0#`a!%n*yms7RgvMP5t`Iipu1vJ})pUjRG&(tJSsGD&{&v*$t@xb9n0;M+E$FB4pZxemAN;?>aPj!>Y(dqahv3ALjUh+SVD%L>258too~*;$I34?P_d-P9*M) za{3IDsczXLqe!4kKPm50WzRrsmIz|M6JYt4eirQCABQibo| z$EH60!+8YDJ6NNFaq$FEyunU}JrceBVinUOFy53m?iQL=WUf$YCFyHuIjX@DBK=** z4!h1z@%OPYzC~JAH3M5Uca}~ogTGg3m)#~Kz<9Y@vy4C1Ca4TiEq&_5zF>%%efPs{ zi_wR>t4~e{DVhQYdKq`bh1`QezBExvuBFe`8IaE~U@oFSOKoUu96d~xBE_#=ZhO}c z1Fb)WWzu4>B{f6LmCFI60Of}FyFsLQ2#!nhieSUt;e^GSWfDX!9~4&v+~z&JQr~;> z@SPtgZ&tII?D7l#or>XHK9i-b$xz`C=^OumF<-)gYd;Ivm zvzrA1A&>?EM9q4uIY8v(&@Y)F95uNlqoEO`+1%_I-TsdDboDFD4C7~2uX=(qm zQGksjhi6{PB@co0ErlWjfFI`f)Li_{>M*nT$uVh5tntI&F~dXoWb62Tt`%VU&VJS< zM<`zP!=zg`%ll#cWap>DSJ7xB0a(YshY$h($3*?x+3QT7vt8++1%M*@eyB~rq*goBC88;*sY8|$ zPoI{LFriQ_TJ*wGS4;4c_5G03)V>JbS~AezW2PGIiTLympqJ8sOsAx>p$8Yn!`R9M zBTo8k=^j^-Pi4G5B}|d=5kvvM$ca}iC9k}z6w*?GS1`)aV)bD@R0ulw>j_AlC_3d} zBj%fqjoc@@a}5C*zg2)O@KoO2R$}OTFT&WIm*r7FXYO;V_2(MJ=zm|~$Ds=VO9_D4 zj5@Es_F_!~oD#ij%kUeB?vsH|&}$mo6f$h!E?TF)ToTghe( z>mvU_IHTY-KwEB~NbWQJwDgh!QG0^85EL4u#^&#xxq0{DQ1;@Z+0~wIq|wejE1 z0_B1W3&Z3zCa0yv%+JisJf1fj;M7?W;^1i48o*=oLJur~OO67ER4`0v3Ao17*6T(9 z&(1k(EFLef;fjAWuQwQjdF3%2k35tmu7_NUaYeH8>os^S#G7v7MHj6}dMDH6awn!s z63Vhhv<7z<3UI5Vn@myo^u%&I#;NX46g)1a@A%E9BKYCqQu|@#J>ONwzBrsL?9*2# zXD&4!B=857^*gx7;hDKccj~K;RKQ&nOddxq^$CSprPo7=Qo%-3|ZM zdeH%lMpglrlLRnHjv|~%dD~-4Ov3eCwj==#()N&m+axC2v%EeU#EnJuYL#fX^w6J= z{R(X7Y70=0E@L-W+1l*%YzupD+f|ogAZF0bP->QEk?VaLLnQtrl_m6zA`#)X>_wm>_$B7lTIZDh(NE zCaVKg;{2hx)Ep)Q-h+HI)E_f^A*Hp#hJP^9a%LDE-S^&>$mYD6Zf)bBrB^yS7AUS? zXCsneXXzhmK~w}26g-jeIW z;vtVO9eT!{>VEwQ?sa?{u)yGlIqEkUOaEVRo%$EOw}7U~0Afb~+qv+N+Y%8@BH&0h zm1hPswNn$);E`C#52XMzX{+}>Nv@40I;Zf#R}l*T{&s8l(h6mKd+`b`WC)gPuoZJ_ zUPu`})pGoV*br}ky+i1pG~kQ0FjjHil~-w1`39K8sYO8GpXlfi9yO$-vt+ zO_^~p@=DZAtE#bjuU34K5fyL<0N(pe2S-A#tbF8)lLzqmlPoT`f!rrO@(fJ#>+OHt)GHLI14$y z*jr?PFBOIJ`f0L&09(yrxF_=tvT5~AYXt>22wTU6)*uUbC{cW5Xh zKF+9}Cda)V2*SM<7-M-EfkK|rtv5IR*>kv zFrg=$AxS*Ol4HNWcF#mvQH*`^tcZd0e$UaY#xi0dEKY*EZShwvqHJxXn0N>x{&IJ$ zTsmT_vCJ&@L}`l2B1X;0Q_F`7`b#J?&=;m!i&KJF&kiTF1G@XBnvEP;r}5b~APMZo894%^l@f;>w052Nkjam0=9&4+`3E z5}7Eq?QLtvRl`Bsm74$_4P?ng?Gz>NZ>LqnkOIh7=Oo%yHsK-A+TI!|5QkSfw>Tyi z9ij!dh7vBKKLX^6I1H{1}laX$fjOi!tTHgRIt+g7P;oZkfGhHfI=>Ncu7i zQdctFdjgS4uyhR`dmfh~FsWjxE8E+!&W8Kd9*&Adnu+QY zA~J59WT9P><5Co`A&a2UpSp3IbOqxtsj1t0ZKyEwu{Ox5)mB0iwJwo7@186g0KE&P zg8)!Jm=l4Kh2KPNW+_5OMIGT8fC$^Ayosy^s=g<_&K5ithX$P;cD&1i0I(96+*VHWH==6md_j4Y2DN^;Z;69<`1gcHNK3Xr2-`fO8LliF>yM zC(m$%G4=I=Rh4-+_yWzwc*GbCuAV=I2)jU)0YLgLBTf>FU$`M`)M!y6)jw3EPneV* zW#e>k2!;La`aMaKxz867aSdAm@vvOpEh4c^(! zZEd_I`)_StBxCPG(WtmiQSR+~a%Rw);hF@y;1@v!n!?qkPX=y$H{4>Uv4#jJ5ukmNma>oT&(*=C_XacFWR)7K z_sX~X)`u%OeB-LUP?Hn(h2LUih!aYUV}2MNl_T^-y{JbiaBjmU{V54TRD34P4U)a5 z>;ZAmercdt0|@}ui_S2OxH|Eks|9FXW+E~7Z+U!NYXg>8Jm*&@Gm#^mHeT*=Nqx_* zQ$-Q5?lg91BR)2K!-4i-ZD}XqRRt2h(4o88x_&_idj$*S(>i(i!KXewdel9W!3;f} z%;+dDTon^MxUn!vtYV;)mP@yA5$QX)`4@hKg?rsAA|E)TrZ}a0d>y;#$ zM_q$~>q4_WX>M+AL&!R^hI&5=k0C}B5D9A!j~4!PFneN-oQJG-7NEEz=H!!)v=(pX zda|5-*$+@(N5-n#cG=L`+k!oAF=iOV8-EatgyR;%j;buq8$e_Yu1$tAY?CN&oP6gc z6$*5KhDicpDHRrwfd6T$%RcR^pT+PZxXsp8P}jE-HHcdkM6orv%(bq6lnP~%%GIgD zr#zt6diA#KC((uvGJKFGadr_z;rJB}4J0(4DMYWRiHz9LHYuW{Km8%u=nbk2Tj|Qe zC)*TM^Kq0kivUhI_Y;O9h2!a$h^qEVXoAzj>;RO2)Vp?h!eS0FLkNaDvFVn zYsF$4Gr#+b;W{tG40UGAc}%M&X;|tmCHxdEXZlu3grZ7^0k&aeWzEnEmQqj<7yw^v zuYS{RWhD@!PL-=+MN`CT=neTEC7T+tbTg zmG=jC|CK(LI)e7s6hvkc!&4}27g9OgQfQ)K|vhTXf}a~ z6CzQbT!P^X!hyv+#Nz7A7K7L4-(h0jsQhWySW{w|NlxuhN-oPk&H2##3QJ}oGvJL=W5zO(9kBM^XyLazW(9odB z$jG?$znvx?6TQQRz>^U+69k7gzs#t};NZF>0x87l-<^xYYBLf4crpnM8Ks zLZm&mEA7?R-)A;AsHhm@Q9J^<-zIH{J7$&f10cbND|tiA6wo=eKNoy&3u{XPOp^#a z++#0}@ul>qU7R@)V%}(m9wrtc>FBD>oOej1q+=v!H>jGCFkwhM{N9-H!SOabkejdv z8xF{nsLTG=pD_Z+iFmw0>Z)@~Wx~jamLNoV5?~gdw`cM>cOMvoF|xDE5=ja{LYT5) zOBn0K;^%r=PY<7-{O>&+KW!xs2GMu6@Jop_aCl+$VGM6-2Q74tQw>FLy7P7CXe8JC=%xNI>fSRfs+~-nG^n<{Wd3 zF;_ybMUK^I*uW`S38!BRoHxuoJY7fkqqb&~UXaQ6WGT_aUB3HJS6eHda}D~SwgT7m zJ2-E@0RFptctpemSPVqZ&DpLP96nBa(qW+TE*D{*QrRgg+smU~Wim2E z51{TxsN*Z_q8bXoTn+`l57#Ehsy0Xaj{ zJTTj=20_C|b;`m%IRvR)Ukh?z;Jk3^VAYDVlzZWNQJ#k4>|v4|YlsKlMK3xkX9S({ zi>^0)V#XqxRa5erUE=}00XGBOr{%YY89seZmrNN`hd{GgJso-HR|Gv$7pd0v>Mk#)X`Cv*nigkX&~ zG1HNs5bhA@hENBPJX9LE51O+~4!*kk=sCFIZEY+X&pGK;tMkM&P0Xb|p$`Z4ODmOS z;swN(%DvzJ@)jAtU8z>Z7E}x}0}pF=bh^rC93eX%`Q{A?$_z-}V1GwNAv45E@4;1H zQLXQ?twq}p14zP@&;C`7vKk(M9od;gg^ULlM68V$el!PafC*6BK)&>+_1U7BIl6$Z z{Q%7ye+iFw{aJj8uCDh+Ei?;`%e7*^2rN!TnjBzKGLIH`*30J7%uHwKHN5AwE6v`# zv%o@_KOStYkaA^hc<+IBfZGGR%Vc{P8Tae!(B#jW-Pu~RE0BUU8vHUROPc8;z#&2% znmgI&89+N-2XZPZHZTB;;MA`yQ0=K@mG=O|j4={U4H6Q;3u(2cUX6ANWgim?w%&fm zfB6=9GPmFV965=q@Y^UKvF%7L=1-j*pB9cEI}-WfesiC|4wKFMLxUne^jbuX`1}aR zgk;+s8pGaPE*$~ojyZVpPu9qU(tlhaJ9aDzNOEmp$lbo`Qz?}LM5o;IxvZc|tBYd^ za8r6(qV1(uNqm|(SoGk2JVu6pqSiblqk?9uQos)Gxn{mf$~8W?PG_aN--49CPId4l zQtZLnB&pH@vqrBOHGLZWJ5hdEtF zLRaTLBySe?;)jFkJ3j>YivCYEfxB@G*#A6?&v+;=P9@4t z1YX;*c_#kgT7&y*gW=4Qz2x3U2hp;ACqq<9wU1-1mOhZT$d-H`n-sI^9$I-4R>j>J z*7wIjqSk}AjC7sMC}#ECi6K5@FL}onmiLT7c{ZlRy26%DI8T*a69I}fF(*7cdf{Ys z0mx$M1-u#579&3pEI~sxct+kb@>;A8=NTQNb*?qcn8i8TOR9O!OYa&9!0I zt-i*Pu0mu#X(qyYdk?8gm6qx|9{JerVZFUqqb5Hc&{VI915I_hYCnla@>DkY`-_EN z)KuLh-K_Lvw&j{i%}eJW(=g+&oTNR?6rAE`^y6sFml35uNK)TDK1#(1)*f6~Q;-`m zK~I?Q_!luTtO(mHFm4Dujgoz z#EM(hu8e!EdhkggQ!?G&1^OIVJEmrD<4zZ~Jzmx+_4tt7?4gji>c-KPcW&HZ&KrN! zwC}=&p4AF0LT`TkW!@GYXEo940%=jBX90_Hf3CV}nloUM+#3%1_&U!O=c;+=*Z2al z{K3@WJ9s;XL0_F`JZvH$=KkfmMnCADbAEyJ$kEFv^C!`$I=UBPyuJmH2*ueGb{!E= zZ~n#w(?FbG@m)IA`XLgHW4>yt_H9X7YqP@{AyhgmZq-jtw1x0iJ&jU(5y)`z42u%p zOWisN6O9lUf0^l_iu`IYxs)p*I6FI=L5l}+dU|e9VR2x6n?o6RP$x+N+b`WrYF2zv z#Y-Qg*p(u^Jvvzk%l;A6a=kUlYHX0){;E{`dt{0c*8}ax&WVfyCMBi`MXg!iwo)Gc zLI#plc1RtZ^-JFKzQI;l-B=?vJ`2Fp2Z_xL+3WrXq@-lui8?5_LStM+FQrGC`bjT1 z@;@&0vTgG5^G^)BbP2X-m}hF{APG=lU|?7Z^kaak-ZR)g!3IWPrk%daV-tc5C^(C!sp5#&bUZIL2Cs1Kf{420te!- z7AW;Za&t|`y0HCJt|bCq3%XY8JKp4OTQdvxAWos5fUI0deH(m$JP@}Uhh4-~sr>I~ z#$<)qzY0KuhbO0_<3gPmU7`K2kLDBU3TA=V=Q*)Vc@?#xHMMffI|ed>dA)ic<FrKPq?s-%p3*yQwDWOENX7n2Kv$V962PJQK>Z|nz?ya)U(7o zi(<^?Y}`WPB)fXUBTIXhnKw^ltpQrX0J}Rk2Vi{A zidnYoi|o>^!xHhA;%o+F$Im9m_+LL#1uOkbjFDF^;Ndb&!8EL7XE0tGRY9u}Zh#Ve51c$X64izH|o{ zqA9@gXixkx%yNxaN0Jc~zEaCBWZ}8-8?G9;%LnAj%9u9p(`(+JC$Z|sl?@#x`3Vf& zG=u2HOXtSaSp{>|3rr%a%9dBm@y{r}cRyi>pBVF6V_JAX*sK24Ndu?d43SmyNM_h? zwDx-$X9a3piI<(=e$rDqk^d1l--g|=V}9_UfpQT>(-pmtD0}M`J3MD<%G2cMm6VT! zagmYbH9P44@Pz89pCgh0Z99@4Pg{gjrs7h?qa)fGKZg1+>YW;gIYjl{M|S|`Y^~qaK87TJ~KZnez=H%j{RnhkduNaeA;fSBg7BC z@fg7(T@RUWee0PEGwJMdUh)mL+r6Jz_MQ_U|L~juS3+lq&n+YCc-uzL=d?*zIvThO zZXUL}w&_9|v{d*PFMO;gxyePQE9`xRnz?)~%Vto^hjS}&^x<0t_{Zqj4=lgMqJrFM z-cN~@S{bcf8;Pw;HhwJv>c{hPydLy?Gn3KZ2Q>k>-|g!RZQ+J&A z&GevrIu@sj`bGnb^-4PcTm0lEfAp`rRU9^8B3-QKxbYdFRvLu5EGTC5@RQ+FYFiq_ z3M_jiDNEtNvzGU&{C5Q5^tEC?bCKPvW~D`sx$DWcy3_aCAhOC%>5i-Wu=jtd`V z(ulcDu~;k}1yO_C%htilj)tNIL2ZTcv&NBcR1X~oY7&7wH`+mhFYMmN&Xt>Wq|oh_RTdmxRiz$!yM&WiorL740RZ3TIfD{r-Rz=P+tw zv)n!;n6ogqRoWDVK@XMrS?W>mP^9ZQaA4UI-KXT0@|GvPE@rcDSE;=hHYS1Ck%YaV ztTzyvS26`iy<^kwGZNblW!Bm!Ot%Z;B(H1P_mpL zEQv9wwuPZuI!WuDo$XD&J8wt=fwBgYMMPFmj2FlqCV;tJ$gt!4V?E&Y90*7sn6Z_0 zm34|JKu5a>>O%Sjd%!aIt7wO!S#uB5^2UqO4o0!!VuZ#2iNP=hZMw6~~IPeU_;C|}}MOQ^)oC1C!9fl^ErdNuGs_q|B(h3aer z0&2KC@#w_07v<#wGo$sj23|9nJ=HFjaiShY1}+_f&ae>&Ha>+1SV1lFI4mh5cKB|8 zD2UV6^bX1S5bo@iJ8)A z(M#A{RxGfr_;eu`dV$T%-OxyH9jw)MANGu~t+mLYP2O%a*`T-wlR>42>#f=`>-nPe za80wzC3*EL1nHJGo=XZTCuuI6bqy*Wn1Tg;HSC`{FKcRldI-HZ`=i4rqW6;0*>a!p z=1`$3!MMBv5%;-Ez_8)2G>Rw%mp@Z`VD^2_W~0>}1~BRM<<;Gf3p7_9Q~!GM zqzww{O#HoS9XT&0C6DCs2R!z|KIe-1+@8zWzXwL>I`SDQ>2E$7|Mijikiw&Zy{N?K zS_&}5!Y+i0ug8|#U^@>e#>?4rbm$sAIl&Muy72mDypRL;-Me=uw~+SlqKtg9`>nP> zFlBFZ?vZ1alal&N;9mE2kTY{byie&6mg3Nom&9hI9>#9^@yMW<&+9qNx^Rn%LRs+% z8?16c`_a83*_f2%!~K~q6fy-B1=QIa-o&c4@CfbheFMNjd7KiiYS)FVL`H)&;tl9! z!-e1(J(y^VNtmS<_c~pxLXj-uv0xjX1DFTLni#IiAqE6Y&BOEnWc?yj(mN7(>1p$_ z%Pse!g3UY)-I>khuHBybwj`dH0OX`@ZMwD=9g;zK0N|=`+6@ab#j9)w8If&Z+>OOHvD@1|)pMn7-$C;p z27HkSViDo%R)uULOO`C1cRLz6TE!h;g z0;!+P*p&RYfP>8}A~ICq2R#WT1B z?Q8=9&h%}*MUE@1**c7xKq!{AFFNp1!?yL!_Vc5{z)UZ7cz2*h)G@Ui;|7nFdeo!% zz*#wG*av=omts%&A)n#3hFuGfG43Yqe`%C)bpC72d*mBpNckIONR-VB;i5ZBMK$A` zkdt%C1p^E$=XrgKq-#6BObO1asxQRAl-?OSW<713S@-+8E|o{w%Rf<||1wiKJwC9P zPkm*gE0mVMD{-s%&%mI}JNwa`oGVsxcdx7iJB`V8m`heFFK>g0K5oiyd+7=IzQ69n z%#YUVv#2E7w)!`K`Y(Vy1gbOZe77^kxVf|FYlih93*> zEcE%MP^mI`vQq9F5RpV3>UV>&#)D_2K`}FrF+@4#LQD2WbjMAeC=9T?a#to3zURE` z&*IZ63I`oqkoeBn6Nc7>!wj5}Qj|kA6#@yXB?7)X!l+>8k7c=SCmsuN=n;|pJo%nv z(`Dd;txt${*;!_thN|B;|$z6j7HGDH#q;9kVd+5vV5`f_;rBb{e0(QEsATnwmHG#?MM?PA_;C(91L`V zZC0p@J3xaJoM?rZ#RbpJEHHkS;gBrUDUk#$j|F<6ihbQv=JP8VL#G-tW~tm)uP##w z+B%wHn?vv6?vB`jCXG^>$6!QY4d@NF{k z92A#XRygRG6Z|$~%m|#H>}aIhQj3D~ZSnB4QDHNGNBBO;A(>V$Z+m8BpJiIUdRDdj z->}6E!E~_l*UZ7_-AOa7b+#x5wer%4gvGN#0o~&nbi=9#3cUd`Fzf(ps( zM%$T;Zfz_g5t51|1v&dLgpwp4WPkp7Su@w7kSoW%ru+z`+zYZ{4BCq&JfY&nNe2!d z(r9Ub1D%~C2_;)UIsQ|%ot5(r1ly>^#mCCeYk8h7&7_-@$VfUtiap7yDTO>&W8=c9 zFTz-^R*vYjGj0!^k9X zf>^l*1@XdYjON5jt@`q)X?RqEgZ+~O2W8tvW|^T^4?89#8t=!$)kskOM(=a|C6L?R zjJs%^r964+E_qhak;Oha@))kDtl~yx02fJqR?iTl?L0IN7Z${TW6;E4u9IDLs=80q zJcQh-rF%r$NExWZcdS8Q)1Z-SgArzccqr#EiBhrjZ3o|4Hs{8BzmFgBR>b{0}b=8YlJjX}Hi)&4}C-{&Byw zv&6JH&)isZb2|@$N^Tz5;04}}LE{^9{1stgT?8H*59Dc!bfgA%G89hS)`Yk%?vNAI zw6tDJKRYfI|O@| z&J|lnjl>=du9M^wl&sqhpP-NY`N5t1>it)?wO)S-B(FUa*xhys;}vQfuqldWVNi*@ z0({9=#o4V3G(}a5Hc

TGqa<`l5O|j;xdU&RI^KJ1QEz8?q@j;7qW-F>-~N-t$H~ z#;w7o0z+^|BIbF59%xn4uKNHTuhgQ0ny?J|;EUj5!&l`-Ejt4Dw|0)%+-(y`2Nt@; zZi8CBT2;N~n8Y69^p5SS=jx_yIo%LFja_!7wiUt6xbnJ@aSAu?n&sA(n=bxD=3?lI zr3_wTivRj5v43N55{zrO*9SpW8%pFaZj%W)eRQ4vs^c3@OEa_Bi6l{vcyk(}!SvL7 zX$hfBjJqp?{6=ou7Oi}WdW@Z)#}JF*z!i48vda2t%jT?U_`?*}L^J)|R~-t(Qj!1` zjm1@0SDSCmO?&*+v-rH{n&{SuX*ynRYb3*!-dj6`jyS5eWtiPoN#3<(sIyv@j*q*t zW%$EDPPNfyO%%IXW~z|U(b;*00NpiHU$#RW-cf4AVOZQ&lho6!iS?*%Fw*u5-P+OU z_wcmx3-ua~@;%==^4+hPkf-Kafkb^(&4lxj%a$F6sO9TrFYSom_S=qq@Pj z$5Cy)`wyU&0MzN3TCt5`7bQMT^Nu7@?CMAGI@a6%%%&|Rf6wfOM7UOv-nQlqV;8qy z1a$zXUg_;~-s(rS=Dy2qG9!?EZIRiCLsxa~MKJHKuWkfq576<~t#tC^po1%cdl6!1 z<91?A5^}vZ9WVs1aBhZ(hzQe#6QrbUy|W0^X;aYAiS19l@dpPK+#BYfp1N2783r!K zdt<@w7>Gm~XwROt3l0V(hICNh6pZlY%iUgJbSK0uUZKR~3vB6_O9aVtzQw_tAbIyXU8ji|b^=7Z> zv?uNJp!M@C3gu3s2~xY2gW;AzF@(uQ0|SyQl;!ulY78dzOJW z?j2)$D6F_ar8^!sI5=ow%+t7Cs%up1UZ{&IiXF>tgXfS%G&GBCUxx1W zcLnW-zceg@pV1eQ@#p+UFlVGgXswI~y50a}Z*IQ`b?UiUFzyj-gBlEPn;&s4Mt5gA zJ+yrps*&x>AjdbkB_o5|SS$7;w*3J37qi)?u2S*%IGwdIG~I4+gTN>jCleZfX}}Aj zy^dw5*LEP|8QW-}Vw*$@*?zeb4bZSdw4Hs;3`;)1@9Pt&)@oq8dTpdhnjxQ{TBBk( zFr~VcSW{r9`tQ{%A^+3n6fD-y3rymp^EOZ-ZU3D7?k-letl==#>6N~y56={gv{finj#G?L11Na*C zS?HJ_sr5*}K&O@#POwSt;+(AjseJ*C4P)SjiJ6`4H^&g=@h1!iHe{9zjkas+pw1ky z+FU)Y(mRC=oyyW`eR{rEUg?2vAH z5?}!K%KG)2L+h5aHtWsf+r7_Z3Y$ged1KCY4Ovc@X=3oVx5C6`2h}%*Ql_ee*zv4~ zNPaTVUZ1H z(FOxIlLZfY#iBonC-3on0jC2iV`NUjrTcB#Rnc~eK0Gv3 z#9TItv3u}MxJ$8&+HkXO?d!}R1%iR|g@c89`Y9{|Wg$@hQMm^>jo%z$;6rE>hxSm6 z+j+x?0b5r*q@q=gOgoi-W{L+| z(qN3>S199fO;)nRPdC8QHjNg1D;=|T^-H+9t1v%*FX!y}Z7`ymC-i)O1WtCi7(uza zU`iG6a2oj(;XqbsVJzygAkYRB7Yp4#yfU#h1+!81*=<`_GYKWk#g{Pj`r%J{+`oih zh^*|57D%1PVg|`GSJ!4rxzc2f8@X}h4y#0`ow;d(rGoVBt(~E8_sE9bTN0v`Oee+d zYit#wyT|j|fZA@MyL;0_;k?>b^($t*OT^*G^}!Niw)BQ>bXN$MoD|L=xhtfB)yG|a zckT=MYU|cu3A6MJ-c|RSXytcFV&|oKIEEB8wvx;g#(J6d!KtM$8S9IMVK~V*l0EZv zUlr|@Zd6zk?U(-h)G7lDFc(n{jzLv}w$i_2rq&4dImb*}7{20)2^33t7E?oJ&c5YKQZM zu2Sp1%6?*`oHqR0K*30X)yyV~h0<2MRlZGaB_vr~Q za2fqxRJ#z~#$Bt302GsrF7RJO%tk6OcS6z*)?sXt$0!?u8HRzU#^<~J%VeVwd@T`U zDSiqyDQvHCXe18I5hB%<*#x*ESIXrezKDiKp|y#%)de7}!|mkhStuvFdTp~-JI!F1 z{SxSUt@|6-w5%LwxiohG9k?I#V;d7-x%Ax>)1}>J-umsKl|Zp@eYKe2YnOoa^shUw zJa)oA>XT&=#0oVF6dS$@kp6iG*m?fD#D;fep(_!+T5U2bWLW_W{j~vNOMVBt0GMh# zACeb~Sg^01#{WZ;Eimp5_@rxfeW8jONUwT;Q$g4Queh5QUCCT9^CN)eB(JO-ffmDX1;gpsSk-cCyHuDEiR(U-cy1R9pTH+@ ze}Vlj{cj2apM&x3U{a-q9y0j>!jP;kpWw{`pRk+yD`xY>x;-=7KiLO2j2W_V5jpQ{ z4u7(`$e3@|2#Ab!>MzG`jcjGQ!PiddEwnt=P=}oK`nOjR&`Yi(#C8r>RosmM`*b8^ zCp~f`9IULqU;(PSE#f+To{EYpYfIBS8E;s*yAX;jTjmmOpxbWcAsXZH3f8%=$#tQFl9(lKx zD5iiC&jrkOxmR()(9Heek*TWu9#GbiXk@zXxe>z+xu3yb6k3WOt!L4`LX0w;!R!d~ zDwXz!$m?iVxXQ*Gq06nUbSJH==m5^y^(W?G<1DX?>sP-#(Eni5w+NR-PRKt zWg@#%)UzZ)7I64W$c+rhsd?>$JP6`YcTi+!sU9aEkq%okat9KcH8p*v685rppD(1| zd|>&BTvinUzSjbMDXP43>XpkXi?3-cNyX)B$*p%Ltxv>w8C(b?D!U-j7|Axqs}Yu8 zOV%;q=l;7p9qytUOu zpuM?i{B61G-;8+P@$SdM=-L3inZ&$c(XP|lk)O^zykh?KyDPyt<+B?3nTcMv>)_b! zZ_N0*yj<(ybKzP0%>trlPo@pUDx*CzyvD7cSV2Mor<*m2aeYwBqg)&0;2 zw!Rmd1to6RiVDCzQ}fJ3n1Azw-jo3mM1#7Vls)paCVlhq?=2F>;CobaMPBkE+Z^D1 zK%yS&S*8oQQB$Ne`?`V)u4zZUYhgBeOx9ZB^Qte>3i9(0e46PZvWteEPHa6(d+xkP z;?2EG5(8EKJ*~l;^n-^EeWJn&=iM$$3}cgV7yVY<)4qGz(3zjiUgqPWR<;{08Mpq~%B)!Cm@^ehAF1#e6b)Yk6%DaPRr zoK0-#@d##Ka%(JC62a0`GP zP$jKZ{R-EtOv|U~V2_1=KVab|M3mG12zqs}*)uA!hUy@;*NJ*>om>hb6Kg<^-YM$AT|kWBDE(wo7sbl5jB>tsfCi-!b*{I>YT3DWfB;6jtl7w-D+$2cpYj*F3 zp}X@#1B{o8)yVlFJ3Bz@bB<662T$+ZHygVHLK789M#>|DEzMJoW2#0H@EJ58?5VPJvp#e*fkm0x_{v*RvE-$Al%1Z2&LW>JE?o>sD}%G0}dV14~c z{3Y^rd9+_#$vm&l2O9+pVzAWoGNxhcP$7-lrd6PJI}-Nd@>VhY?Lhe3FCRI>!;J%0 z6p5G&;n&cDJ?#3|D?ZC7OnC%(c<{sfsyFuS+t;(ol$ZSnNgadWX_zSgync!A&A|WX zH&o5R1)k@B>;5h5GWqG4(C~Tiu6+Rx&7{YU2@=F+3(CF&igBu~G%pj)lHaeN#%~ww z(U(5&LA)>l;6@CT%!EN_k_ol!WvKPEf(F zhS{L{69xZfXwjL-2FaXfMFX&y|9<^CyLU~hR{v3Fmdl|A?`7Rak3}P4z%k6;{YB~w zJ+%~osnk|3F@i;og-+v50Rs+SUcF_qf||4L4qz5>7`UB(zce505qngv^u&MsBme2K z+H3-N!rq2YQ`Ze@KRV=5)=f4}YkhXB50xBG`T3l`Y`UnMZ*egFPl zUc3v;CMO_O;H^4*^qgug=ATF0`H+kYeK(PzzLlAg5sQP?H*VFzMaJbofgGvP1Y7dk zlbBtgmMudmP9BimxuFULEpJzDpa#fyB7lt{51-#wp7L9#f$qxh*Y=4$RqP8u*%TlGsiu!e zPPQ*fPfsT+%!iUkiCu3`PrDoVZnO6_0Efd0Ta*;n!vpfXg$9DX=Uhqicen+`-8=vK zdnEoZ_j9(D1{DTiyWf5S<9E33irR$zB>r99Gv6g8C0oUhB}qI3ULZIxsBh+?YQusA z5)J^ahn!Dl0LaQrwt@KtVeL)izyP76rnT{h+Yp9|o3QO(rv-6R|HY8y6Cczd9ChcU z)G_CJs4sQvR@@0mH=j4F`xoCittit9Eq-uXnIF$;EZm}bRyz9+gReV)8# zv{uHEqelfuHIvUNED^_&uG)_Vj!(k>8vKTzW^VuCbUM*#u8yL)L)2-wjskPrvFQta zBKq?8JH66Et8%;*DKWS*OO=YbDyRd1U61@5Z^wWC2t{+@)=`+PKt8*uo~0NDWxLZg z4j`j~dCP`mNpe|v1<>|H-q+VRDzQSFuHB`q|f|JR$3_?y

O0DFP% zJ)+;v3IjM&JC@AvLPI=c3*SBuFlHsS5y;d2er^9IFR3#3&SSt?i4T4pIlF`I%NX2~ zS&?Zdq^8Kaw4>%FIRDP|?h5yQ&xeLyQpob2ORpU90|&u|lhe3ic(}OBrJ2s7uRH(W zOTk}a&#sz8^IMMsk6EX|CbsNyx_{r0o*CI9puxku#S=| zx9(1T07gYR(!tcsV5KitQ4iG5zhA$AIRam*n!}yG$Zy%nxw0N@UTsj^qV4t1GP0Jx z6D&^guV2~w|NR`2{0zST@)pS$>mEA)&zo-+k)8T~y#DXKQTzP)QMAr7U^ODrFWjAI zs68NuToB4NR^4AR@q})M<{vtEGU=hY#h%{Y z3FuK0HCt4x3_y1WUIT2#Kyjpr!hAU;C9b6sTA>Xne8;ro^#B{sgdL3h%o!HgAYyEa zs-+mZ9OM9Zz>U~OP^Na(Kc z;sXY>1+XO(yh0MDkOP)5%a+jnmv^3-KFO2foT!HDI^h)QaWQ(?{)3w1;cu6^q&eYg zGwa|&HZDd#6g{cn$^Grp;!WC#diYs9=ZRzS(CCI>y|d+u=5>i6wx7 z@q6@o+UuT&gO(3GN1h<@<-_Bc&QYt(}{8>T8ssfdTb0*ohx+i{Ys% z9)Il_y}Ih^k_hYn-u@hlgZjU3{-Px9#K`ZPZ+S5Uh9l$2V8XTtgM{ITF_fGhexTD@ z3-1cSAmKr6_K05&IZ{v~xBqJ)3c^6m+5^5+5Ds~l5S|>Pu_($`N|<~+kjCh87g5jx zLX!AHvI#Sg+yY+OzLp1v!3CHT22UP8j<kqB!j8=mGBk@7SXUR?^PlC z%l@Y=>}dh8#81k+O?sN{Lxw+*9K83f=UEfB*Ayj@i(|EK%izhLfSb8obGTUzv10|z zAml=F&l1iyGb!Ap`eRElsE-!bimwo}?4(s+E`zGsl+vum?MzE{4XA^8-la zA#|8T^%z=>CFpARlk8sSoG^s%50{)K`Gp=BEClgN&-=6xLoxv+%pf~EySC0)0nb*- zg&Duk9%9vjt9Hp|+sKs|MiemMAEtK`;Ra+fpLbI>5+A?LKL9*EwFtUGcqU(?_3gvp zh=v|B9pVPhQAUNwI(U4aqYC1}rTshIY$(8~yxT2=GqIhvsIN zIn~U+Cp(-u9Lz+|WSd$ONY*gOQ2{T57i*wfHtr~h^d56iPU%X{zM%`yKO!>RiX z*f?9TY4zQ!RpkZ63Oene|EG}gWheo%xk@ybS8K~3h@pEKM7p4t7O-n}Dw&0*R z)4(7`Rubs|XQ07{+m5WiHxWztmg|FB^6b;!GYEQWhC;R{a|MLzfbnyJGU9d3IUKsND^jQd4P1gioN>Dugs?ML6P zKZ5CK@@a#RFgk>3Wn%6xNMWf2e=E$IFksW6NbX7tpBkcYK@kMuB12N;?nj1;EB z-gXCTP#i|uvb=h=72H8hCNxX1mig0QOPQ(ON%Dui7sN!2xCip|h5e zkx|+jRNx0tnH`WVJs_G5z%Dqq;WR9Ug^TGZL8(R?;rEs@`sixw3d~;~81N0+fOwdt zh7fo#z=~o-clmaX9PH13{}616_|n0f-*QNZ*~>FJKi&0?D^1021B#xhmM)EOYyPQx zZXu|xKwNqrk|Adge&PV6s^7uatdhs(O!>|XgipEaZqVo{2ZH_X1|PJT@{1eTVRv#K z^_-FeS|Ze{cqX0VgFV4|qwm-3Pu8u`SzgtfkB^3dD?shA@{~qpRHOz3C-Kk#EN)gW zRIV#o9O;3a$KLGIt#;0w1MVUuy%Xc164B7e`>Ypja|gfOVCgNew6iK5fSUi9GYzcY zBk=s-DzCw|^$h0UNtrYjxD{7m?cU^^*xSKMPFuqw|JM(p{nmCV@@igBuFg*?s8SB3 zhhDQTB|x*G!e77c+w|qA4M3)h;>``%ALH6vDGXg{v_oeH%d#n{sH{qn08P>cv;g*= z$95(GnCDrGow-$J4JTA}7o1}S&YpF_*VDVSCvN*EDF5mG;5TOS z&s(o)_Oji7-q@!9|J55~H+HsHR6M^0vLeg31$(He`C~6c0~C;R?i)a|{y`U`_xq5+ zHI3bUFaR);Tq+`f??cHm3FS@5oWWq`!)<>D1lNgO*VEIhSdb7Guh-iENN-H}fBYB( zDgMTw>r75`Ze>H+2lC5+ED{?zdK40k4Tr(fkHgTe&ZMOIhu6q}1$buiL4^wG%R%6t z3o$m*id!&rx*BhNk=Xh5-WCrTgB37p2Eo{B)TYvEurzZ}nx`{#7m33yyOJ$-OG-+d zKT>Lcf(b`9-w53>kn}>g?tTffPDX0$6XL6?QH>sOMgMKQBzfwN6$^AEVqaRLI40mU zj{#4O*z>H=YlYr#+X-YS`Q1CH^SgSD`Xh#8i2~OVbw+6nQdnw=pI3X&E2I!W;LT4p zi#B`I-0gcy1nOHB4Kp-h-j!wz4(m^_G}w_|4=^{Q^+h}niX2F@fb7Q(CJ*Zv2C@6y zb1FDE_n#hVWPwK#eo_GvPK~2ZrvYI6cuHYkjy*Ug{nV!((}`wAa2&+$ac-!vgL+3% z4&kBWi#Z4q0tO%mlvS1>y2gpPMh$!`X<@eMs z(HK2?13(;Q>oo=d>|CKs$0*40Bxg6~1}`qwt%M*W8A>lyOUV516Gn9e_>%s{t!-@) zNUV84pz^$e+jRF0?$=`vkIbvLISm0=7F)0c6~$P^W#mMO+j&f5f9bSY(kCEmae*X} zMV|=6URUt(se`6&T2PfXyhi|zF!DIySQUX!mA}b}W*;vCZAd5~08M34MYF4?G!AO8 zEV69!Q`dLEd7-slWdsjIB~~OP0Oy2Wv-dbq6`#f#P04ZR1Hn0gSZ!SlXdheoFc?<= zXv(FHY$#iUbYL~4nmkR1zt0|`bL6@bf)$j{pFf}a;rYf(RA-|w8-XAvYuGYL#I1eY zjo!55iiFx@Z>!a%nqq*+YTBJcLqdvvB8F@p8N^!fyiUrXheum-+P@Y4oWC)G97v@F zDC+i{7Uyf+X5(i8{y04(Jte@6D$vL)1KCSp3@t1yN}9EpUIFp4qD2SGxd9dgQJ#J< zy#5FOW2}D`gn`8CM7l0sM0>dk@5=$HcUiG9#L#j2gIVyKw}FTWV^mISZUo*s_r{FD zf_gm&D7edT3gncO=0(pASwd_jNbrLs<(c>s?pdD>_$a}E|M5|&06aEk-e^MtDAeYK z*R)C2-oA+RIc&&G99jjh96SJNX@g+cp$L)*Ho-AfW)tSW z8KAGXL43|vl?zLEatj{c73Q06_TM3Ak9)ob)3F6)RF?pW3`6r2>YmI!)))1Qs73FmTxd){Av9YF1TCw6xhck_P!ABJ`24rSqm!z8{7fvm}nx+Vxa%7i`l zzihy&n)~x@Rp4L#d5vZ60k;3~#>W46V?QSg>3;#0#-{$igPr~vDO8RB-$T3p?|dvb zm<8S1zkmOC^w2!i>kch~H=(0|R@hm}C$7Q5 z>N=Q$ft6JsD+M7;r^m7*LH*$=`(s0A0+>lICGw|JiK zYIJxP1d5QTs=cN3WrCGXM+NS{{8hcZz2=aXEdy)f6OdLXao0P_aO zsFaqL6PnHn@csnO%bjri>Hz?VeR!4Gg4jn%#z5=6?@|?Do@d*hVvpy-jgdi}Y4?Qa z2|_A}^0cHSa{o&M#@j;(e(~1>q>f=?4^G=5;CJO=zdth42ql~9nXCls0&xgqib|%? zs%_cHM+OjiG88O|KM7Lo!NSj)k_6`g+hhr}27Ws*Qi?wTpI2?#SIMUqzMDYxHA)UG zS;$gH2DIR$5LE{A-{as%sLH&mSOU#6`ZY3IArk{hPU@E{NLEK<|M~`4M=(Jjelz+IMKV?O@NGepNsJmsQ)!mq;wv zyDuF#T`2-u33qIe-Yo^cq5Zo036WZOXYMk+p~MfBp1zO-oJXC)s1o}&fUS^=xMWbG z;eo_9%e6ZCGQu38UvYZ3Z#fsT8>=py=(Ou9rsPO0)Gcdr<@?y&oPfoGN79OpIJbG) zd=K%*8dzSifd~jWBK$YehXeASJ=_KxZR?MH5e zS<~bXR{M<2=_*{@fSip5U?oAE$8`7G#0SubvH0_WJV>LBltpHkRtP_=FDo1-2*`~{ zb7&%Tkj3Z~anwf45$V_<(NRe3;pXB}LPz+@x!}UitK&y0o}A7kIA0^EG2}0W&R5Qx zJTrH#p$y(v@e1JlSq_pE_k((TDdwv_Q_Pn0NRCq% zQrCc9H)bi^2oYKOEvHGgrk<;_Y;E^yv*wi)%zE6Qk?9GXfHyJ>3~XWV!K;UZ-~QXx z5i7zo7PY}V3#-K3VkOhI)>wez0;?;Ms8wcMrlzK}ty3VlVI_|Af4R~89QschOgA8X z=+0TXM!=j)im~Tpnm?BtOZ%-rgS&f8Q4R2HOg^4+Jt|E|6nOj@@1tbz-XEH3s*L)F zQ}y;vJ%9F$-|1nYN~V~pL)6S4pXKKvTd1Nk?IqgmBCCBR$;#l$mi1VOJzlFasLBzI zIt`Ra?>!@+=5om@#(p5nu_{}+KZHUcsEc1-tZmQ+s(WIjrWH|g(?&tOr;rT8pY( zH@!a}@dx`Op#9`U9`8i8CUCM7>TOX2l)ua5i|%=Yt)o-K<&40aoev_9KacvzRW)qVncQo2 zVr_G+x*uXJ`=Kw{Dh$0hG8Pk}IT=85N2S`2O&2)V{pRK)3Cyv|>s_0hYxT6C*(If4 zU9&n54;n`Va3vP2<9hz56rEy?#7O9&D>Xc8quz^gdFQD8ZDe8=RPw`SY#eX>v6^30 z)9Q0Z;KgSfqR(_(idW(&+zyk_&pq=)_DX;D-ch5d5g=WJCueaIipIuijkEF~WfdxB z>fAs$vEoK{i9gHay(ckEH$=_}c`O*L(B#p{-?GYn#Nuu<72sYl<3JRiO1xNR*%b}w zYyF3*fpfB=f4Pd#*3XNb%ym7lVtQpHhUJaT;Sl#aE5)1*B0S>Ty3{AEae^TlLxDL0 zVY|@%P~eYbh_VF&N2pn?r&5lSSaYgHV^!6L*s^$EG+m{ZvECmM+~i7~K19APNDn(@ zu@a2{H;0X4r7zJ7RpL9m^LceX?^CqgqNT2yA*4Db9zO%y|Ka(V&sSv$cM`26gV*0r zlVU{9=VH!?{CY_|6`xwzn15^hF#x0dS92c40LWG$_h&qxd60OVJjDxJRM4Rintqeq z#Ixm%uv?9LH8qG$zQYrIu!2GPFc;7)F#P8;<(6-m$_Jl-WvWZTV1@4sG*Ql6dc01& zXB@Ak+^?g4xJY22uNh~geQV1*C?2Hk)dTzR;fM#ZYGXB*N8lXt(>XJwCH|P2<_975 zk0dYh9D05_sY4`hC}tdrlEt!E-&v(RVHv#lFo?x(*5TFY4y4i@M>*vEu;mPr5k=(; zHwd@O|9lYgZ01}iM9cP}-sKWb@D0{pzooWTIChd${>}D zzg%Y4LcT(d;y5jOJ!d@~Ecjq2e6ebjfIyLy8ZOAuYV@gT0@1vSg6D zZ_(;$NRBrFn;~JoVo`~}Eck#d=Oh_PEA;>B@Dho0gS;Tcu@-fao`*=p+}H!MB7Hb# z2Y_BcW=BAk3%eBW1z|!BkZlv(yf$Gy&(E$bFBd>Pqq|@iNF8&tV0(ABYyrOHE?zEi*Gy7a-U&0PA$`)z8~`sb)lN81)*Fa#DF;L>}<~z*U%M12FU%~7KR5!?*B~1U?7hZwEc3q zjEGc-#>Jta;n39u?H7g!T;W0}qv``SbpSP&!W;wg#52H66YvedZHe0C1q-wRxM4ws z*w=C!f==H)KpzD6&udV3u7INP{OnXmg5|*j2Xs)Y?SZV9OCfd)Mbi#G^78T}YhBj} z1)lBbK>x9$VB$RrBBy?+-sz+f5Q4u@I~i{SmB&QMCDc}>5LC1j>QNKu#0}o35Hw+6 z#{g0Rlx2E9=ZXek$eXON5o9XuTfmEm1|iZ?juDWBjOTVo8#@VL!0BNF=U9yeeHdO$n!uh&xaa%p>*2BnC@OQCz6KxZ zcdJfc#Go`}L4<~!CdQzx6X5{W*$-F}Y4@eEGEiNfui(ilC|2O1wiK>~Q@O)y-qNMN z5%{*o(O-7caf6DW(JAO{5TCl*dS=Dk%uFMvaZ0L~^}>a;KCEvOm1V5mvvu{Trgdt; z$s`HC226d^2Wpzxt(Pf~P*{s7vw5)%2`!J=2#e0Z=S`P{HsM)y>uPBq9nO zTGt2f!#e3GZK~=l$q`MQCVC?A90mfZdueNx>u2X3Y9VKDuR(!EH?ly`h46m>g^b)$!dQ@^mQ?-qViX2>x}M(d|6MF#lzWKUEv&F>&eLh*-^m)qOUJ;;qK)#rV7*= zbzN8YIujpkF>GVC{F1prP~VXMh2XP>={SPd6+<;W`cjdZzDJep8lC0uG(QI&7`j$? zCXH$>Q;F_gI@N+#(*rHt?y~)fgIW7s*RI7#b;(}ROOoUe%gD-#=RavjnHSl>trUGEk+J_bQ6Xt)E4_Rx&gxI64{c_~X}`}Xng zuIfzBhYuZqJ|LgVK37Oc@pOr;JfBJLwz3tl=wZ8?XoU}7Pq;@(GIfnwoNUxxt1f>= zs{Cc;x>MqGl@Z}pdC+@EU5zg#f1V-Q;$oicjN0_GS|S&cz$b}>Hd~0t&DkTO1GHY= zhMqh3@Nw6Q3#m135xfmPzD4D&3d2gf#OwvO6TzNI`F>W41tldRjaCPN);vs=eY*ZM z9Yqh`vye6us}~^4qenSh-SZhlGD;JyNqWl74nfX4_5C}i=WSjE(+G!Z&P0CHo7c09 z%>5LrXWl$&u=e2IEk?Sj-uu#iy-8LRESUer)q6lw{m1{~l_G^gB(r3cnT%}7-t*cN zH{v2>%P5sq2q8P0%eD7Rva?-#l)YtV|DX5s{r!IDeE;{H&pGuuw|nngK`>z-nL}tUz5Z^*wnzT9}XD2{W8IRO(eYJaPS?adN z%VFT9{q&u!>|Gj6ULHcHlscwMeMXtR85QY)iON6>##<4s8hM#m<>vCBbB3wzvE!OC zTbuJ=ZB@;Qsf;=oawJ~m;cpnJ@T8s(dzqik5UIMG(Vq&Pl>MSId49=kwklq#qhJz% z+$=)!@03sO41Sl*6z>fWm0>WXi+^|TMJz4ME#ZzKVc|41;9F9{dzN5Oz*n}fuY{=b zNGgrU5`CAJH){QQ$~ZES+kkSaQbjl*o7pH~WPZ&B%c6T!?nQ573td5yRg4{7MSu7p zj$eG|+g13Eo%y5=rWf}g*DXIYky&zTHxMw6Sm1xFKyX#bTFt#8NQtkjhe~xOD}^tk zsOgrlikeD`tD;CUsz3^#^* z4ra!14z2Xv*NBgpv3-~7p_z~)9zov3E;EwR)cbEnnr(DSOTUZ9!o8>aB1C=&rt`(d zrzlKO<5`A>u`GcAsZgElOUP9#-vSmr3NeSNm=K92x0t;|Ep9+3ICVfm{}f;s5IN4j3Osgo>;zV-7~Qy=v${oWDhT>r*8r%MoA3$`8g z>65&SY{!tR&h3pw_c&kn`%=U{$ZVO#Au3yY%&3Q+`c@5ibh?bpuED%RbMPru+%p`k#FV6S z3KmA>LR&m~;TpY>#&t{Vu2Y+|B`CpkjyiXnW>Z4P;}p zgrRe7Hi}gjrqROMh?1c_@hV{f1C!D;fu+}miCuZ*Yg35FFnOT7Z}z52THkC|5sRkP zr*8>7)Q&>BDm@_&Fq+OOQ$s0qx`@pqVGs@XYh)s*<3)1$NJKO;jm&4rf_Y0sYcPk} z=9a7Ic=p#L@s{>$(l6X~BKs1hGTR5+^Qo1V9R`-c+VkzX@a?ofZ73r`Ym*dxq&oV` zwduRO?#tvntXNjcmQM<_ed^)j)4OEcPM&Xh)c(-R1g=;7QrkI$OExeGi{-pB0m>LB zsBy@z%L`I{sw_c%?)#Y4RuPNFQs6(2CvmC`nUTphHaxOc^)qEt_86-4Ks9qrIOKcq zwY9cBi0mzW=DR0p`LG*EkyX}xMANXL!ihxLX(sr#^w25STFH-m+P6d@m`0UY%>MUd z$78;c>|DS^&AG6Dq+8ac3v+VTc8f7u$B_uDBi2DF8HycByvG#1zg~6X58nPIP>UYRS`QtyAv7#)Bx;zw~QpWIj%?-G5^FntA{-t)Ej9Q$ccrQH^`%LPcD)D*=9wtqd zcj4JLm;`6s8^W0G$b2*1mfPxcH-d)kP(0cnQ*wl>uru~_N>uXp?2bf-L&N|aew?F$ z2H0yvoH&RbR0kiX34(2dRKy@9s* zhNW4ge>iXP2tzUCbmj3$P5dIS(HBf#J#TPisc*H;<8*2YwjgP?bBHI!<)uusNC?Tv zc;ybD%<8_CY)O~Q5vb-`aV^4XA@%Cgwb-1>ul$A+vhV1}YAVhShw4{Xoa@X_NC(4{ zyGnj$>D;3N9+Qx5!1vPCE5y>Hquz@>cgT&b6n>HS+ME@Aq2vfs1Qguw{?1HtQzzf>KTHI5>_Ixq_v#28AMC$s z%S&D=Ddh>{&rY+nl>rsR?AU?%sp?FJf$*F*;aK8E8365@hTYQeQ@81{QC3 ziKyk(?irBNS`8Mtm{|2>;7NB;#M8U|KF@n8Q5BgoGL)I6;Zl7TU) z9VIebE_L~9*{9i?eX`@-`EBs+Llw5Z-+ILh&WbnJz8^xn@#5i$^JDD}H0m)pW_TjL zq-o(>wZvLD>wdxicIb^`It13}0itlfR)4&uv>lI=y_eM1o#Ci-p}NDGQGtXqc|0mH z+0nrCxB2*IT_sEy7fsX)9U!Gf>b>iBDwX2yQp1*=+Rw0Jhd!OO(toLv*Ec1ra-k0u zfB1TdHg$2MpqZQ6&Wa)hg2%RLana;@j1_E__1LEg^hDMm{+>O?v)x7VFHC z9eVGYWfmxb^w>&1oSjAR7#A32}kaQROu zYlVovb#qaM&tcRS%W2QP<_t-&W6gb*9xC+~Wm{yPhbTMmp02Ueu}-Eh&T`#J7&_{j z6{u;ji9i}GJQqk0T!)mG2wr}Yp)3Ck#wE!KoDJzdV)goC_WpE!>cE*%@2 zTwE%_3V5LkL3n_idIJr2U_hO#{$L1KnBLoX21{Z@gP=PIhaCSG-L>cI5oz^j0& z=~@~Z4^9n1%vrIqkx{(65nKfffN+%7A9K85R;MBGGS&Zy_#QkcP|t;71TSi#GuDCO z*|{M1$pJ`cu<@FNjk&pb%o-mho9!8MVd;r<21F7iICcrKi1#lHoj_mFl;PF2IT2{` zZWO}4a9)q?IypHxL)@cO>au3=nq%RYkcETHhT|@^QV@@Ep{?Q0i6aHCh?1IGd6)1C zhhtLn0KM%dSNe zuHeCo-B+GTfksCklGl@P`qbpp#6C-(lrpU&!d?E65PcwoG49**k?s=AwX(Iq8Fi~X zjp8&Jpfa4%1DEL?RfoDosxP)2@<-e|XaA^`1VsR{U7E^*Ab_Rrg5Ieqk&T*u6bRx1 zg`7O)8F1NLlpZyw_{(2EhVX-9tz(BAfX((Q$VpX}_}Ga93-((_t;Dz*RfKP=Zl7P*CIWfR@MX z{Ze`VjKki@3qz$S4u_Gc42b{7oE*V)-oxU(MJV36H|_mX+G4!H zXSlfvZrqrPZxj?x24a|$-yKdtl$a|S{THp8*lGdEJ}20(h&13-X^DJ`rqr8z4L;-l zd1akTFq6w zz4w1!g8$hE1^catonssOL^pquV~79qh}Cg?FXHH!2>!osgb-u^+Hu3$G?rb6UgQ3E z*1i9~Q@HjqjBET~ukm1M^g~58wP*;X(Sd9mVVr^zvI-A;_SW(0vWsl-{$ZI^;?|7~ z+PP>(?rHgD-TQV~(4NO+oQ7f?Z_aU})Q>AF6C3&xh6-`d(L04ts-I4%Cc&5j*jb#QPn-=pKRybg)N9l0C* zncerTL_nwgzh4Pdw*V6UDSw3Q9_iuniRg2Jf82WJHJaM}B>1-hTfj z&;S0%@VA2?siOl+$Zm53mcAxDOZv%^3ns7E`1#ME5c^?PIG}Mg@P8jcKtNWNUKkpG zXR%jmBDT}wIiz>aqP5b&s&J2kLmG$zwGkv4DJj1Ta3GgSNSa|1@p9FD?G86i8EI+l zZ8uIV-(TS%_?Y=kVYJn6=NqJ?8Ygv8W{e2p;cD0X*Z=CXva%lTpMmmOuijfsnS~r^ zVSA8$Cqu*y|JLxUWQ38C(cKnCc(ERgvH2jgH)l$5pVB!~s@?y6o&00}rMfmL>1lckEtGbL#NBfsyU|=>djeqz8$D*f( zG?mlP=ecWl!mH@`Q8z&E)IN^;MXxy}VD#eG2c{S1yAnQr{(L=&J{1=-op>2sZ;er$ zhG?t{e8ASp1{c40UZPJ;6z=1MYNAloZgl^1{B#J=#l9v2-ukcm5x5F;-EiA(09X^j zft)seH0N3lzJuuMI+p)AP2NwHc*2&MU_(@M+o;{w=4Y!6*!~h9{|Rm)9mF>f0#Xze z7Z*Y^4KdK<&^W*Av}gpSfbI)hR`~M&_d;(#1m7E2-BZCTgF;PI@&B@L{4l1(^KJ_y zslOpzPkkF!bMwkEc`}5|5jmXDnHFhuM1j|li*p#Wxm1$-{4KcZjVq@!+~1#bG~+BL zk(b2QO8Ou8oO&WIl=z&TMvCjm=r64%JO)1@aT7%O4e`El~zjtba0jzuIuo+ww72A0w?>LwO z<`qmE0XHS}O%>xu-;%=%qs{M0WIyBwL;E=NXW|=ipFh8W+#B=(ZtNeWAq0X;YfH%U zt1b!MYhbISq?Yg&g*kGV@OI{~<$o8QswYC=;Ps4O(22TThs;79!r3X$ZQ{edwU3ab zHaOyV=l`6;6u%{qjcJra&@;mL(Fgbm%lCEC(0)+8Lx_%$yn=!Tp~gecr-;g;aj<%$ zl0U#VOYiz)QBA>*CbQU-y`a=XKY>_G+cE9R9pL$h`ZLvZDKg({Cx_qpJQuqJ9lb2b zenswBL*ZC49mGCRCrrovA}|20gl6xM)~3*u-1^5Bj{8_?Vo4MV9mIQZbTk8SI>x>l z$i+WUtI1ecut0gGy{Z_*ZpoLcCMm5=P8E@INFO@Qt%lH6SAPTD*fl7Y5Ro_FsY*cA znrgNwwtg1CeQ2D#w&ohNAjJBLXM1QG|2 zJ|B18q@y$b;{uC?RzzmMs)&m7-x>Qi($d(LuQ&L=xWQb3oX5?|bpQ)u)gJ;&#sz(; zqh;wR8-rZkjkF)WpYPb>Rwe3Us3DWe94{4sz&#VKa*GI`KW%C=jWJTM!rqj){7XQV z)y3Sjn`OsGQRmswBtmwx$6?#@UH?57Da{XapSrmT;>`EprZz&PhA+YHEC+7+LQo*_ z8>j#Q+#NaubC+Iaf?{i6*pZPDbq`LZ-;Gd66lQ2s=Zj!b`2qXWTbM@q8vrH5EjtP! zmsP5|!)11y$ow)f@h;lZ8ANKPkS1X{m>Yve!6G0%@U_%-j1sqE03$hY-Mi)58mnY( zYpXm?$cLJM*>D54K%r`9@6J?`^}=jTxAsIlQVryt+SA#o@;=8LD2-?tBhmcNH=qg~ zua%$M5XC((8eZj4@bQl-OG{q_NuS}m0MzlC5t*Rh`whUH24cWm?>nf>jfszdkwrAZ z$xd^-(XGmW6Z)>RX+EUif*VzD-33QMSJf93Tz@p>$qT3x{m#k|#F!GYRVs1~FugP8 z8mK#%rvW2k!t{$La6nEdg-hMgzg>qM(gwg%-oiCgjg*Z3bINoMr#t=yVnY}PhI+lE zaT`|icCkcjFDR3{Z9h+X&xO&y37?gb9?BdhB1`8;<{@4pKO7$R%$BrKH1exBF`>^1hRBN3OGega7-m`H(*!%l8~^L z=*p}exd3RPMxgdXzF;G5g@bO$rb8EgnR}80usdvvSJo?LNOommxg+Jj1ah|@ka4Vy zy|2I#-R8Xc8cMR)yY!#0B&MXi--h{r&T&aeK8mBN(gS+(ZyFH73ALw-&nvnZhJ>1= z*>JZwx;yPcJ0uY#&*K0e6rGgtrJ$0M5^7r-QVp%||MTOzH5n9qzbMy*|2a5V|UD}F%3`l-d7s9P%cEq@sAGw!~JSC-WwJbH)= z+BrIy2aq`#!B+8m`8Qlu>EP&$JR02kD;+!rnZgg;x1J+Pw*fc)4hE|{U)8>d4NUo@ zrqrpz2Q=L8Pi+~<$WmT@3<`p)LX^C!X!QSfjV*IMiELTK#4-i!Dq7_biHn;ta9N!OaPu7p zj)7Zo{$UI4iwrHq5?dz_(J|4Tf9m$eV$9qJx!=hz!OoUTRuIC9&5;J1ckkC`KUT(+b`==rapHC-LN!whO9I?J+WYSV z>R@rYyR57Pa0?CtpiWq>q^S4knJx+|L)_1i%xpPEZn$^oo)<*BxH#@^ZMpN6((MGJlYWOSq>3>k?Uu$FPMIRbAGWe{VH|Dmah&x!ONxLL(MnIF4n*kzX^)uAx-xG z3OqDUy`%@TRA0YhzTlo0VXMlQ`_z2BW?L1foL+7*3RF0O<6wKMxMy7FDfF`Lz=99| z>Lc3b&zWaG&F#(zQk!&!I`1%|9#BP~gn90>Xu^#`s(}fgeEsCq0rVbrZWFAM_wEOC zv8cu`nn=KA?JVLrubc*M+aVu-KmS3_Kv;bIs`yK(-Oku|!=Cr9iG))Ao|(Ik+dNY6 zr93hf?6%+H5unpXUnIKAUb0Bb9J4K-aQWe7D-cH=g@hv3@(L7c1J)Fjl=Iu-WF?32 zgT1btBf~Ns@C=k72_8;*UOmdMAuFq?sd@<|Dk-*D@pfMOB!x;*=O##3S)Hfg$uL+# zjzb#{JIEbKILJZk#dKQiZhV3oO%G9mA`<`S%JA~Qla9oKrqI)l(t9LaXGjG$hW~fm z8o@=hf^T*fp1w&G1nB!6U=PSXps}kVg&wgH)Qi6Ga3CRH5o+e2Ky!YCD!FPu z2C5^jszpYKzqtxB>mT3}`2>>PoagNC8oqguD;|vdapV;2c-$B3Hp;Yxlf78wvWE8X z_W@|GwX+jy09Y%?V`5^cVA|r4$3e36Y1c)3d^9o=Ceffur*YK*Jjz&?hiNS^?6RgT zY&i%Ab}R=|(a?N^nM1BW{sL$e0)Ycr^ezB$sKq4nrA?cGEoGoE{rwTp5c8#-z{T2k z2`$`aWPEaIJ?-W!)S>g<;}AguA=NF$7AC%KbCDX+4DXpL7Ch)RFKt04ki!(W*roI} z9LPd3c|L{uxBYGBWj@*nQ&OFLNx&rsoj>B9a%$p(@}}bY_3x=yYyBgH+rQo*?Nu;U zMmwi|Qs7^Gnv|ugUtDCrI5_q#IK)qYjcl{n;!n>f5!2BU!VhOB{`1`u1YRF0ohOuW z3o$|33%{K{H=qj&!IK85NK(f39aPgpMvlZl$j+A5%BD8JUWiegz~y&?>6YO-E$t^l za+cp<%e1|bZr3^3*SF-JF)eI+m6YF>jH-=Sf2D-j^sO+3k>rAf9YHM zHmLb$O8Ql`e6&Q9&OpR-?qicLNXKu;$jIQ9fiPDiep*64+&;_pfXT;y(re#19Bg=YDD>C>zgwMknnEe7PNeu_#S0b!Rr>kFBxoPVVfzTKx3qRWD(Q=(-#u7R@@SN2s=MmWDp z9F)7$=oiJJ$$3IU5=ZQ9VEcd0uB7X1^-Ra^Kdb(*R~y~D4_RcSRUXkZQ$?65qeW>) zL(!d4F-wUZ0 zXkj0xk7mY)!hV$%>PtX;LDA2y%=|^Q$m)OF2bSV1=IdbtTRszdlJo8ZTXmZKhe%C0 zz)+s-=9>{_j1-7C{$jpGmc}Ei3wVf2=y`6@Ybx>qM|JV}_06YBTyNcn@`O?MajWnN zl5G!8X6;!(fNE6E4L46u&ktkrzZE>%+{1q>10X&mOsEIQbyc+ST=IY_9V-iq8G?rNPdx{SN(h>qP)ExSZ zhiOH$O1}B!^$d?(x$;cN?fQJ}Ytzv>x@MI)^%4u> zE4sPw0TTS`x*_XeKyBmhZm;Lw2}!Qfb!{9EkbPqvg*IwS*CrcbbYk($BWEKSKLS)(@klN8xTKcFjoJ5iO5(#}hKH*Z{BxG}AC9c0%5Uj;YQT5d7J znu=P~gK}*7W5MGDHl)drt)M=@E1C_7fTwF$1n;DDw6$#-u%5rIpty6_knMp1+%udPEwlkVM*kM$HtfaLb397R|b+zNt9B7+QV~ zmjRMxikCic1`2s&mg|NL?}_MwQUC$+YzSu#0M`qjv}S@aS*o&Oxo{?@A=dF@^^8mY z+N-)_vZZd(m2HPH4!t+uL+EM-)5Q+&t-2rd`1woGke;`dc3bjv6XeS~D0n~;31@iV zBJD4(AJ`4d&&f|u_NyGk$TxpAiT%#U-sUTCy37tIIqQB)s?nODK5SlfU!f(*@?+C< zk529{w|qO8qk_ zD%)2XX(zOTT(rHE)$Q6lqebQ)6~!w7KEeJqr?8r;69dk=W>A5<8U)x#z|Pk-mm#Pn z;)+ovOkgF32K*mH@*oOY5oN|v=Ko3}?nvIhZ_8DCoL6^W^1Zo7WBUOwh?Q$#PhA#Gg8phb?z5>>*y%4`)MSk_O;9NPhciNt-9dsf-zQ(o z?bsE*j;6fqe7$#JukWWjsdit^P()IFonmM@AqiV?-ro%$b82&0w|O}J)~8%~8ENNKo}Z}~k(!oYaAm4S=}1v=J4SwPI&o+MQW;{0i>W3nval?A48qXMvy-W_21~waXh=7&cq`;r#=Wc}#QA2g z>?m;Kq}po4F)Wcjs(xSOquC@uhm8;;kqe>C9#jZ)TI#KM)^H*6u_I6>IKT(dt>tm2 z4oq}ZKp*J-RU)&5>3i>>MFqQh?Jr~kj9+BaW9~*Ire2%@Ztoq)?r^QaD?1!SunQNo z7{s>CJfrCsupu5Z1XA?oCa zIox<@kK|+e_`jH{-F;V8k6OnBovZ%6mtSjy4SC0YD+<)!`_YFYj|^)OOmrXOdS17y z!z^-`{hUn!t>|&UOr_|1%r)}VlhE1%pHH%(hl+^pkJ1GGr3SSLd@1r-Bb^pPqMgc4 z>EK|a#;gq@g|d>de=n#=Og@d${(icl_tj8X4>nP%hJ|RwN>3AtCDw3 zL#*)Q8Y}B3{yZM&drE50r((-KmNgcRtW5l9J#vHkZ9-#9cBTcp{>{&EAGmyevAdsk zIlKQU9DXo+!AR&Eg-3brQ9GfuwPq2I?K;~mwNj4qgtIEaIG^aqO7&ZcY=F`Hd9uq? zSXcm1K*t1WYxGjQyeVu$b>Zf(mIgz+5AF+GKPS9yKIzUrdg%4vzMsw**~&}F3oCOg z0onJkH$gf%GJtDAYY>D3nxRPIB*Jeb+^+>V&7p&{JWDzPW)@~SlikJ*=i~vt#>k9`|u(ATa0KK6iSn>(O;NC z_8I7zAZ>*AF<#8>V>Se^b6q^ z8;r!LKV0n6!5GXr+n~MrW;B_YnIm7RKP^JrPIFg}r$JK*A=px0UbW$!s_| zIGl8`;oUi&FPR>2KzLptm>+h9BIwJ=-b6X7``!I~0>el1eSd7-J+2EC|6x*mEA#Bs zUoj0-uGb-6A{5s>I_lFu?b2OpSXsWr(O`36bLE!VZAA&M(KvA*rO=Dt#a zuDIbtr#GwIQ&%76PD6J#u-Oe(Nq)W*(LAEK+Z|?bOz848s|4W&ozI~lr@p}NZJBya zec$c@HEC-9o%;4pa;Zt^y#$g20UDQ>&P6J!H_w78Z|SgW za#A|4{`Ee2BNUp`;+h~rQ+Gz6ttJ+n=itz@xI3(){r;TPZQ-9SIcVdR5IQLvH!6ds zriMJMnOwEYqNoiOKpTF(z87|DC)q@38ZLckWn=r9cUP{rSnBn{_OGsnx1W%UQ?(+; zPQt4yN?L0)u=jhcLVcPu#?#1?$<@|&`5JL!;V+_9 zAs3>PZsCrVaiLl%0=@qJLhC8-*_$`M=`N_URhsvACG?>97C96ik`UdEKGIRAv~`-_tq#6*YZ(#&b4+QaTH4%OjZ zFf98XwrIIquF-`oJRN$fQQ$#b#l-L5Xwl+#a&$d+DAm{|Uh%tr;p<1S{&L^k-2|1J zC|B2d&Kn=@>rP$UDp#0{m|q#X`#^-CW$RVpqq}&uWMqHxHyhus+R%1(N+tcg7`ao# zdKYi7LhzH@vcf`TiRn8Ju~ExX!Nb*JJ=fWLW*08>Ffvg!>Cg;jW~Q3kKQP0?)36jk z8oE%9`oCv9NYsB&W|!~%IlSGcZ^dWrm;P|A)BVHu{y7`bUwkjj-ZbptXVR|C5ZT^F zY^fhT#&hD~Y4uo?n#<_&xFh#;sR7lRiw!5+6GI<5Mm~etQJZ0;K&sZW z+rE6%v=QB_KLZ6_yJor^UrdSf|5D14IF%k8uS6N5Q%F7mU(AHh{V>fSrI&)qTLe2CFW)8kmQc<^{*!N0MoNxs~m zxBuDqhUC;uxwB%d>no!b)vorosXG5erJLW3-)xJ#OzjQ0hoMyPs8!Ss->9MV5HoWY zzA>^^;rN^4LD}+oe{rhjW7-?^DoraZ^|I_pk`T(9GwKdJ9=C{=tBvYzZmkM9U3K!_ zXx(paUMzESO)Gv9a?twH@`CS!;V;2*I-YMzWn|=c_T_kee7?eI;ECyx&6i74^%sp( z@v>{P-Z}pBIF~R;#zB?(0-4AUwbUlMdY|BbI}?igU}tna^M@G{X?0Uq*92A{#3X;T{%9w4`0Yh5elwmSb}B8Xu49gO zRq<66ntV(AwRdz!Xxi4Ju&k=n(XbF2as8`Eg&Kve40?tkV%_*&^sh2gd`R8==9-1Y_4h`@TZ42PnPlr0Q$^n%UgB4?q)}^|3 zbb%~C9lZ`VWQ|=;HsMDQ!F*Je$g}1c+W}gFHg@UxCHjex$tpOytv<6!5(qpK+bLPPLS}F?jJLEv0)o8(o zo`YZvVPF_@=6NNKTYH&re|PYHo`yV&)D;Rjo+1w^`2DQq7tKriVT_I(gt-b2LX(tJ z_7t)n3{F9xh24T#PkB*VT3XmG(A%!}EIF;asKp6%_JT-de+xzk(!m?*UJJqSY6n2T za@;>QrG)H4_C(ekXhyQhx0 zd}hCTi+8Ya`&V)Zj9U(w#D6DpI;5KK`swOb$FEAGYzyC4M~bn>Q&L^pEgo!lByEp0 zK1QJqwxciuTk#g116vYn;dJ-=PZ!*Nza^jr#n}3SLtASx9Ub0<@ldSTgB}ML`GD6# znmNwO!Jjm@b4JT`TRgy=KTrqGMy-ydc-O?Lv!HfWvGL$VZG8-;X&p@JkW@xV{pH^yq?rRZ8XxQ$-%@7#BrNXI1^D|;0AwMcTxTv;7 ze{@|qN|e?8<->Z+3vPfb#Ard7URlk#6>G#mhgpvvQVe0vPvGdVzTdGm+Fn#MSmgQl zw`6I4&CRVu+Sez}&h<6ATZxtQhLatp4Grsaf(cy?i05gkg6tipeAL*&+4$=c@q-fw z)Q{9O$La>h58RKFooPxv)okJoCVP@oXMP*I<~XW%$wWII?l|mh3%ZUj?uKms`Lk|L z_q&3hMYi*7vMgcS=-}9~G~ZL;`9Za5BlaY;Nu)c$ysc|!XEe)FE=zVse9%2PIpk$n z*m_G;cZR3z)uCLCeSd4dkR=URJl%`7$KClR4`RJ1(4_|6+}R)Q?k^-^U41z^T;Er` z`2ELaw*mc@h8HcLj8!UIG5U+BpmAVp=6uamSUPjE7 zhldYr9S>r#TX+ter~^!sA+6nU=@^gG4{bSl>5`c^>vf&|(947lJ6AZoRK`~DXpxOS z#yjvOBwB)ddqc`xx$T)Ib_#`cb{20fPsK8nIf-|xDRw2W#tC&NFt3GUi&j~WI*$8O zGlYGsNnHQ6l)O4KEY){%-2Io=5;TaSi@19id%V0yH&B>%QpC|M7d6A>t-VI`y7vTi z@Q%xC;AG3Abt{8}4!e~Wa9J)zbwRwWCNbfz*?1`Cqq6;b$erYI*y>;Qc9&QWaHz}p zI4b5gIUTje44xD4=B)OQ`YNIaJ zUHy&M8I9bxhe-u~la<=_VUn_uZ3p-Eee}27J#!uHoXD@#0V{sT}zx&1GrnOurq^I6f;fde6U-2%a}J zIAnR5U)WpkHmWLZRMkCg0i*pZO!JIut1_I8h0CsH({Gu7g|SvGefra96gSS=Qhn$* z-d?1=@R{vNCM%1XNxk=?xVPxD;y*wK>2bN5 z!?;Qtkw&K3Bv0RgxFnD6cH;`A6Vc&R`f#Uu96$ z&UN{ye&+bjQpk#<^*3xZXPY z^+DJ_3$$iP$4Y0!b>poXyc0gXoo1RvftT?HQA0Y3x{KEN*>m20w>|q(($QUao8S8- z>nfDe@bI%;iDqS~ik^_NZ~bh3E>X=UQjYX z9{0_6qJzyer7w1EQbo;_ebqypmvaz&0Pzs{i7WMK>-iMotO)$F&Lg*sITV{S%dc|Jolf?{hZMtSw+BSb%0W52uD+1hv6|g)KRfbbyrY|m3B9bq z#ZYDwXd%2%IO}!dwQdy8VjJDEj8!USUX6M2wuFy}n6QEMFzPbb$k}wjWuj|@4h$<6 z?x$^Q6CDuFlefK5-J2CPrHpz@m9RY?CeXV5d0F8JTBz++k&Rt>PIsbcnWtR9W`aoS z*!hJAsnb3hy%l^ZPXEe|rl%*i4`i=))%-d8Jj3uQXVNhF)a%iXkJ|5Ji7CYXBX%KI zqkOCV#|zf+vl})YBgx6b{Z>@-$5n<1HMWpj?>`q#6wLkQlJ86rF*8HRutl?yk&WkP z(|Nm9o3sC1I^K+HJRf}L;-;=B@tORB`=lt-1-o*q(D#q=UO5ZLQ)ai&G?*69Jo%iB zHFGn%T_xLecjCI=g|pl5uhkHRm_-~F9wvC-;gZ*XJMit9;BS9|QJU)+LrA;F*V95e zBNC)Y8J>Obdh?jd*2QVnk>Uz@!c^vY&MI`-EUm!@m8d5rgbF|Jc77VBq9(K~S)>UM z=+a>yd@qx=;9w%psM#3%=URen(~+S{#kJF|3ThFZ^2Ly&Mlw?J8@~L0*Y#FiL~leB zNm&XG;!`~Bx#o$NlwM6^Cm-<&vceM~^%gcJ41iRh4FBvgf`bHKUlpiSsr zl%lCdq2_Ip{%~H>Z`&<<0r$TCV0y9YjGB>24 z!+9q8ndcmHerVe?<{O6m&cPBC*r>4_^HVd)-0ERtyUdyD+r!9q8I$r3TceEcwIrbo zoYnHmlrBnm!#Syi@=kHjw2Ak2yEqlL^Hu>XBbwNy+L&1PE|dQE$WJ{ta3M)-8c+Nt zlQlKzO~{@?#>HIZu_o3ra7YuusvsHu>rUHc5I=dw9HsC0fX@uk_mN1!m2svev;~MZ ze+bSZghUJLm?P#*m@nplss?!uqPny6L|i0Sho8vM*rD}->-PMena=WOVjkL3w4Dy>C?e+)Ve6o(=~s*9 z0m9doy2Y>dm#x_k+q_vt7SlblyOb-i`|mJ~sRY)6{1bptpPkL=3X-Frzb1@RiZj=r z4~>iM^wuU8S-QX0KoGXQ`$)W&-uvy_#S-s?A>r!Y0J>Ta-GX;X)F}0d!-H&wn<=M{ zh#kr&=1cYG2^_CYKCN5*ASw+VU9T|QKMZFG3UWICV5P=KeY;#g*(Bw|ce-2d{`Pcnp;pn4O_^B<8SCj^^{tR8qWcjJFexq(la~p&GKxw;A#3sMi{7G0|I=U)fclKg`Yl_!D zXKDbnEmx76=~NMDZ{_yr?W~N`<|3fAK3?RwAnfeR$*zRR=+jB<`@&>O=QV$eYA8<% z7<%7~G*)%9SDNI6ID{J{82hD3x-#RU+1c;%moq?s}mx^EwRPs7yVp6&3w26 zr33bs!j=6mEkU3=$@~SJlgW@uR&*NK54>xl>O-BD;c2fv!}#-(tw7cx4_%pbE$7@S zZCwRzvh8-`f^)a>>)%c{`q!>U{G^}pBffl#FH3cFut(v^$EWu8mHSI(X4fIhNr6sc zJ+E37b@s+(t;eBhD?(Vd*q;IZqu;Wfd3E?GXBKU1n6QmiW(iCbmQi6%#j-La4V+2> z6t?tSS}jJ?>c^X(e!N$HwcabZjhr!kJ}a`od$m(A%1+WngXZ~7{m)w3pW+#>uCgv` zCbH7`B_#Mw>})=^vBGAfi>yinuN$16h8Hj)kiI*@7qbl)YMk%|{#lbQ=6{fcEfQ7v zymm!AhUq+;y6ZS5cPMgF@d7z1Kk=3;axhC(+?Jg%c#DoHFpY7FX?HFlU{U*-GTQWK z+E;lQ?DWD$=wOnU!P?2%hY!Od-l9QTn%Gy(Ir(>9Hq%kO- zzr;JnKF~+i&FVzG+ZDb@&^oIe{%S(8?*@5%Kvv#|TgMZD+N1D7@=IHjKPe#~48>HfI%C>h}s3CtXo@avGUZI|HnISVfMn=B# zmFoQ%8Nttp_jn^R6sM^kt{!h=Cl6jH9TKeno~8PZ`^C7HVsIZmBGsKw^c{%OZk3&SGcrtTUh=^=gbAbNb6^ww;CR3mXX6XKO&_ydIS zMm`#(mi&}kaRvqo6Bk_8?*c;73{X9H+t`s;8@kBnJ%l>ZD z#BCUorQoDOq@{q+#GvIg-%}?g;ItS5XbnPq4E!mUtJWkm&aUnjOg(Rah?@7%?-DmK zOMSy7s|{B;+=Y1kAfRR|_T3@K2Mf~Dn}2Yjo%w3=^54@GBL0FNa2h}kTvdPGSRpbV zEG*NY0>cH{>G~qP@~%hLyYXC!;&|JLlKS|_BtNpHkY2>^OSYEjNNr0km(7ml&q)V@ z9feNoIi5i3oYf~+HH(-O&`cU^V_EaerixJ?712opH+(P*Dev&ElcQZH{R9_~zIfLu za^;>EFexmNrjuptusJ+#6yzoiQzUb4<1KPzASDfB1zIj4?0xo6) zt&L;*-F#G|JIZ4&bRvLc5UJN97>^Yqks57rW441ukrXSC|h>x?5X;8W#Bsj-`1$h?ZcC;(i(eyL~cnb>L~{1|6y| z4!}k#z0#3-Hd%em>YC`HQP0w|j&6SezLI7J*6rH;+VHHD{7U>n-u2%PtfeVhIoX=) zemz<3zW4Jqe9S9GW?7)z@6`Bi;}rkZtI{>G7p^nVXGI~Ul)}@W>@4fB$!-=aN^3iZ z>n;CaTY|M?61PAxDyWsM+E}KhCVvdkQP9AW4piSvIeSDLvr&^1RSY^LpNpkZsv1@) zUzoUz`^qnMEm4P_GwzI3dr3<1K8K)0C!*J^ajQ1wAM$loY?}X<%wJEg&SS)~SQ47A zWtInOV$og8w4nk#UGypNmC|(&DWcatbmI0BX%)j~&u-&V{Sd~>0G>61Fp`vl;=_Hd zjNf2%kkZr)?`{bS3~T@w#($!i8Bbs^142VvKx-Ey$KVCd4N}X?L}FFIvL~}?HK1fb z5XK0kE42@dy>`vg(``udVVrNKA$9ENRQ~mirTe987S&S7-Gkwn#88+fJl>`?(&GFE zqZi?1HGkAWRzsps9gD_N7a}C)v{_5(LQ-cfJdB&*9F;?!omOm5jZ9V#(xGntWFqz&w z4HYPvkC6J;#y9pyz426`6qrqeQVB!iYl~*3BHEYOW9ZKuXhxea=A@xX6J~nK5jS>( zRWRkz@WfL8>G8JMJB1|!wM>MM2g%-;jCT3(FUk~QOt}C=>8lV&Ch2_q%?smqk3OLE zr{zk+OUY)ubWaQ=~T5W`cf zM@K}w0}sSBXwoHNBKANPV71Rc`HcEk{Nu+HutpA{A`?LjcNIwM7SJ$%hTzH6mP8@P z4C=`>z&rDLXUMPwcclD5Om%Ydh}K2qn2&WRu+y|KYc_jkMoT#*rMb(jH0ODCr~j+M zC3UW{jJfRh*%utKXPjiYLn+S61&%=^PgWM6Au69nrM}x${sMx*g&!Bh*fek5ihw^8 zAvIOl|D#|jtu3Nno6yiX_qAMI?Yk61p7&%Yx{FPBAuUg~zG8DXrAn)!fdN^?idJCL z!bp4$o+=m0LD))!ZJF6 zd>h{&C|?A|qrZBeNnUBAc>zbY1qAk(J6GsmM$+E_;t~$zEAm*|IM)$;u|1tl#JC z{yxv^`T661-Sj_%7b9;*od)%vq|1D3i0i(~dvJhvu(H&$>wx7th*DYYrY9F?~Q z$eqt7!S+ABFxuo3!K!+miJ$fLa}|K!-Wp&D4Mg_^_lmwEP)V+Z#j({hMR|=04AjJDJZxw+^y6fs5_x;mbJqWp} zLw^CmZvxO+0T>9VVnt=r)QDR&VhRchI7+KHV(&lu01V>nx&7th?!k7`OE^kf7XhLg zw}vln!R{`#i|pz#PRkOD15XRg%qHuI@2AcRKVt#jD6QRHwQ-{E@;R;38f)FnqXRZJ zLadli8^)!=9%3>kF3H%Q2_~`h8UD*F(+@Av)g@hm^9(w(fQ2O6e|tCk6Gb@o;Dz|W zwGk0cb?F2TY3%*uQE$opCYQWjRytlOiId@QOQ#9XthF#^Z+qP9!9AnJh2gzFf_pZ9 zuY7TJ;c+W}f5Ta>cFTkJcfR=hm`#3@;bO0pUDF_L018we9^B^hNM<#FV5v53A#{6R zhuczG3MGsLUZCKaY|ZyERi#UIAnb*a=P^;QLvGx-Q3qnhAuuDS^atb78-t&pvp;iL zJpdKvUoe}OM4>KsK~-C7;6|hE#xOIH~M@jF&UFEXkK)9;3?zE%7Sx~)`X zN~sW4q0mc|JXkNk$(%Y`7maFc@*+^fwBgrLI*?a}E2S%Zzty0txQpHQPI<0%yeAwj zSb3E;R>F%5kG&;}VBX*3aUr4Qt+9jvGk2asKCb*uu@UJeY%E6UcH46%jz_&YX6M{W z{ZTb_4m!U@0MW{~6NuvZ=6iUJdFZl~lw-Xef6livhEE?#cA$n!XXiS^X+1|Lx+fTE z8Qs2PTCBK~#W4JqX2bb}hXA|~0OA}Vj+YIt&vvrSM?^&UfUMY~?M@0Xwp1Gv-yV1@ z2YDKFUj2Z+V+8Wq{y{-6*Tli?3PI#U&*DyU|H8-6P=tJxHC+Qy0V|5|lnEn%LO?>R zJmS?qJ3E{GT63-|VI4NYG)#pMN?E%n2?97?6ueOmgP&&LvU>yZIqMfDC?Y@=sG|^= ztJ|9>`0yaUG9Qi14OBnvpt_uy**G!Ht9=D9n{o-Sh?ia23am1%vd@r-?ctBM(8zsc#qpp^B!u%{QM z6+`AP=d)|pneH3NW83Sz;APEe8CM*vsN8wff8RP@I~C81tCH0><>kXHLJtQk_QyBe zec<5m8U20%!Aj)uU5ae~tga_tk4SjHe|>I6__N+=Tls<(vt9pUqo`jP(uS5`$*FKv zR7Cz%_RMR!%j*R8z%jv@JLU~AzI9g^KgedoHpA$)3zQZ<5eOu2Ts9(-) zyO7IQbe55$4uH3S{)0FLVdthGovL}OL4FuEWCHq6o$P>26;a+xXraB^v}N=kt-G$^yO&d$E` z!FZ#8{#Y?uFLasi*)FN=Zq&><-VVPuQ-Bmbq6O&;5<%q?LJNdij%*xSA)Bg-kHV1* z_pi60R#L3DBYsigZNAJODM#;{_;F)O?5fbU>(LIwDv*DEB6hRyl(L{CA%Yb@V#Z{) zvxuIWjz1Y>VooIl;^|qWYwW(iXW-u=j2MF?qowP)2>!a@MW?qUZ}osuDQ2jwZ?ijm zi$(;v3+M$$%~Q$>iOw)upf9gZ#(a)qaL;> zo0f6X%x-mS<|PA<&5a`Q13fP4!_se=|B4Uw{ED)B$*XH2>C}c2^V$|Tr|Dg`Eom3azeF9B1b%xbO3^{Y;<9y;DDhKOJZCmSJ}Rxr zeeGdVgpcoSkyb`Y;cwWsZM|I!Dr0>tZ?@IZ8#;)X-Z+C-R~jNZnfZ>2X8qmuaJ^mJM*wd(Vlben}mwM;pla=WBl_y5&!i5W24#GDd+&;~E zGiX)*t!fK?u81P`;>|QMm^JXx=L1P;R?NW7O(-=a=MNnPi)2$2Tm21Hw;BV{{OPvM zThBz*OfT_x2)vIgzxHH4<^uiQ)V`IVkWVa*qbEikt@e36Rxwi2+wGHdEXO05H?__d zzb3}7U}KL0zWJO>ewHJ*Wy|&WawWTO;V9GuXx(Vpja=3>g-r3|53qnH|L>}0$vN`i zQpEJiCPb?fSgpm}9t;&}$F&5TaflM^s>b?6u2p;2shIhlvT2>EC=Vde=->M(zsmDd zmt@XlEYt&oD%JnQvVWr}!mue7N^Re>f>s-1{5>KKzmz@)63A4Av2tcxVJ_LdjzoWk zTy{}jnj=#EdTJ$w=L(;HtVe10n6*OtJ9mZS>0O?a4>|rxvNyLzFGJN6Pq*g9f&tOm z(;#$)-A%OkrQp`z=9&26q&!EY8R7FlksT?xy8h;7cim9EcNf#RsJG#D$;8Z(cUgA{ z!Gc=(Yr*XAsk*I&8)6QvHMb|%TVT-~6@=6j^wx&BWdAHhrg029GuOLu;gtk6Ll=d} zjxlG-6|?OEyuA%{maH`7reZ+9Qq#Bmyndq|xdov;!K3?0RvJ6M=t=WoHg3IdN`G}0 zS`kv^zs>wEL|AFA?@r+B+uZt4^XKj6dWhMz+4h`|Tcg$&#HJnYnd8-HY;%XV6%Xvb zS!SlR8Hv;gGje2Q%+{8sWRN|!qwD0y%KJXvO7Guey4&!YN)4~XpXgyw)-^sUqp;Zf z*C2&_KnukOZ8OvZD8{Ghe(|I=Gb&QLD%?4FV4%zZUZNPg>!O6V{8zyf?ll3Mq%S2U zG=RB=>X$WtG8UBf%bV+~Yo-!;z@*y)SyNPUCtBeMXpIt%@^y-%oZV4b+2J2t zn*2zsqNZ14-uVZ$lUPWg#%!GM2S1rlmxs+{Q#7rmCz*y&k3@{)Z|YrTH!@XPKK5>4MzAaw6~!ZfsrTJP9HM#=Hk3KI;~K#zZhd!&N5-+ z7}Dr5BuLNkLnF+^B(I99GTJg-bc@@lxZdDVvKtk<*ukxaE>V2t0GVk&jox}IS{zq> z&fBK0w0&&*kO3d1QFq}P%H*46==e9B)NMtcRlt6JVJfwcAmaz1`vU|DXYkv{(wYfbiiy~#h3CnGOTO4)zJsMwUyfST@xi%1qyqkown7cQ5SHm?Kn9{KKly)+<3Kb7F`@!~fZuCqC!+ z&_jRE3hr4sKF$7e#ll&_L2g$26Bz}&#MLYBB%Obp_8TFReSVi@{FSf`si=Ree0a~m zJG{SXsyR3KT#H7R1wPuM0?-`w9BFk4HW)Rdysa;vOoW{>sk0WOM5*=K&^%4=@UV#& zd76CY$*ylQy}7{4JqcX^xoA$HggoGcr*Q((<`lSh)&u(*wN_2B|I3@y9%1cWQ#Uqd z0y!T)fT8MRM8rcOp*{rG+!wfx>;YQ?Hmr=$^UGJ-C0j2Gk6cKWktnnGw*N*n>7ey~BDq?ErKOi)_GHaah)3=Dmr*Ns zijN1$+M=JUv^*ZAVzv@mb`o}49!~iU|J2H5Fek==e^t(zM?mHhSS*HxB|(O3P=EC1 z=?f?AhWDrdHwhI#2?A3O^^+74IgQ0d&)Yhsgcah4{%^6R;nP6V$O2DQ*yjTk*-pUHwe(-%J)NteqWWXeM0DUr)}*z ziQKp#ld}YUTGohw3?TQi5fG}#RW~sPvLq+ies@HtP+{cyG|2$3U4taDXZJZ z7YJ^Rv&-dkRp6KeBy#M`w9goR4up8jr|jG182ZD|y)CIt1Zm1!y^ElZs>1=fH;3*L zR+&GJ7+4*5z@O%APtPO3AmSl>I82CXsm?mE;2V-~E?EQ@uJB)mK5g3XARxmwkF8ys z#%SVRv=2PBTpx_J&Y4|!vq)=wDUt>IcR;*~_tDR1J8lUw=y=*OQ8hL-d3+hT z5Cqj1OhAbP{qq_0M&wjfZWn)rt8&&(99&=Zz=IyU*c4-t7yxj6zLGvLV=M&Pm(x>2L*hG=%rB`AU3OMA8UW}Vl>wH9O zy3RRgmBiK*jY=WqIwl?$&&IjDywS%6V1X^9L6{>0E1!s=*$1E>p z^<&Kh=guCQ8Kgzx(m4|>&T}!Re$ow9IR4N+AsyO{^d(Dw129w8a}z95au%K_M{}ph zj7kn%qH)`L%`QT>^FuHB3r$O+*H6!+n&iVp3Fu?4e=f}y-19Kwc=a>F_o3oG+U%e# z@SBgAyueAxIo!=qc>8{rZ_WR#siI|&{{%{VjW&7J6g!Z@+%;>zoW#Jw$*D*w@sTl# zEk(gsfE>_=O<;|+{^MnLHe)PUsQWfc$n4r1Lw7oBhnpSuhdUHYCBM-9O6iVjpMG7Lvqz8-KROWX1>$dJ*C!*DJmLun zfJ=*k=lS2cV;_$jg8Zvpn3?a?(j5l`TtA9N)%bAUG`{^Dh43;j}7<{+p z`x7NUhhL+i`yxjzytZp|kW?u6~p<_rx)$eF-RloINDN5t^Hni0HA@)hMII(C1W@o`{7RY=9*a{OU- zP`GYnS20%N&tv3~&-#9CoGv$2FrJmHH~X68I*va(D>N#~5Ahy>tvfY6y$;ezIgVjU zI4$z*1`+b?nfuhxfqemrQ(U73#@>}Hz`m^5tr}G1K}#BUv^~GBMv$#Ad-2K!*H~J6 z&&=suwYc{=nmMc-Orn>0Cp<}G7(1os1ua-WO4xXQB)qXSf!WIAMVvC*WQ85QM$WEK zRn3zTk`o{Eo-0JF3YrbTWUz@8k+Ak-XP~8aq!~) zZxft%YTXlQV-0?XoXAGYLV&w?Yuj;YLHj;VVNsX5y-MKf)UGhU(v`@TXD+OF;O@ab^R==5?l8@g{)Rvy z_gT@Rog5#I5xwSW9AGk3&srWm`B_Txr+?2_Gp^r=-v=A0%hOgyPh#`nWA>$2YysM| zt;esrOe=S|1S8p4FJG~(s@%og?1=~&J`#=($zf;_JWO3GoLvxD3pcegvKhhKX`S!0 zJv_YRV9)5_TCqCE6q1=3vU#E~%Iddq_VCDfWtg%{GD_r`_1!IZXF)^3(%Q}8qVVD) zww6$*R!J&m;ZXVi$P2aK@x%65n%RVbf zhy|_kyOhj-aTWLSvTSUq?z-vMbVk=WMfKofaDDQJ={8^_Y)cWBGNWxQPnBf|&W#*| z_*;c9{gue;%~&oo$j@J5S3Ao#7cwU(jiPQma0Tsh78bDO$w&7Gw7XiA&J`j$ou z-QN&CzO9K;AgzuF=!%ry;NwG&v;W7Y@>B@qy_?EZU~XPHcXZ}VGQmzRcA%_8WY@#Q zG3IlVHOZDBIW-e6 zs)4?$7NDykOeg*Bi{0j~So!28i3LVJeKFxHMR@tq)aKpDduwR+&_yrZR}x<`W_Z;C z#KYX8;0K~HZ*_+}CSP8)qj$UNBwhAL$9dn-FjcbaLDJ3yIga*Df@|?mWnRp|pXG^s zP~Z?e%4-n+?(ULX|bZ>sw5)PVX&YP&4&%|ZehkMngfNSkS(B*IRvt+EULCzYSu?=jfQkW4i}(c@mu}E)ED4E zpwqU`GUH*wd>Z%eBa>t@!9{P;JEXD6*GgOtY|oY0>JEKR#S`(E@ax!_&QyL{pfFc6 z9pB4NsJZp*-OI{vEIktk6SJU^b8-Zu01)h}QFs+LF$?nK)r7JEf@*KhRqU(`=3dpv zHwZ?QmyGeQR&s|hJ%uS->gc!s=^=ILfGkzRenMer%2DP?(Y08EvRd`oR1Pk z-Fu466p@n4%EOY0+Wy_UcdL`9In_eb(v)W_-1=uE2f!2+c zct!FuXx}iiu!P3M1gwme=LE@k*S|hHR&HvmMh3hcY*8EdH-#o71S5QYQ?THcK7Vb- zL>-9Gcwp`{>^6MGhnxOKpvHQaQ>%2&j|rexfaB=3>(^yhCema{eSCa=fyBe&e!}zS zD~mx6fSR_Vv(l#OyS-n8S$S8YJ9?vK^Vv_j1KGrPUxJ#MJa8Q8UVCJN?baDXEHasr ztD*Onhie&EPLAR&G0POe;|v5NuC z{E7dKlCgruCo%`GPdTnW(as%yR+tg=wb@;bL^RcY}%Ep%y zZI@7Z;k(1J+@D>)Cc75%aDv=zH>m}O*7A{p>vJ^AznOu1X2jVk^V})6IFKg!r=nnz zUjBku&=vM7$x>xcF=h67(c{-Kq-(GF1*Mf{sRHahpSX_;s;K zr>*TOi%sHLk9BUf7LWCC^*ma&=E!&UkC@?LnwYID5SVW;8K_|CPOI)VEoiYG9XEQ^ zH1l3t78de>go!Nr;1=gWmX)=1lB}!2Mh#ceiHYA?Ka$l4U&Uxgf647y$KIK^Zq8Z6 z%R5j|nK&`a1Fpb5OPlkAJ~pEz@}9D-6C#uQ`YNa~B^U^pJXECU{Ff`}nwX?6Zn2y& zgj<`ZE`}}L5KQu4eEZEx=j##G#>wHuN$U5~5>-x)5S zIqdXJJ9Kg0V)xp!%_J*>F#VZf;NfKoKbMJQ+n2ee_vH~+6?xxJk4I{5`|+o#MqRt+ z3g-#pm-4d+f(Mz8K5o=9^V+9mIGTF-V{=91@-6MRsg#`dEn#dfj^C{=XMSn`jPs}E z;Wzs+F&u3QVNo-MAb@t?@w4s1!lLUczi>zBiN02HB%8cUXUF&Yt+V()@<-yS4eEf$ z@uLG;lJo|{ssNW8^+KraQ4B`f$7xAQ4;ON@$LOE(<6N?|6fd!_6093wzaYogqd9CW3>8fqi5!h7P9!|o)V()H_ z_t2LF{kVSdj7UHd(8@4}UfNlimKS#x9}F5K*l0E=y3O0K4ZDBt30=E1^oia#&Hc`B)pi|c zlZMF0xS*-qt;-uaC5?u@k4mfWu%Aw8v+d2bJliK;3!vZ9`iM8V#zJ4{oasaZm-nbzKPWaV zFVh_ws@yIzjb#yLCo5+V3@i^ee@XV^~vWZXwl76+CJ`PIZyS(ujQ9v zuz~2Hh9`HCWLjbJ@TbTSJn3t~tmJFdw9nU$_2+3A19zS?72G;G_s;8UK>EDV3~dAV z=Db>Ue0b20x<`D z>P_;lQo^(0Dax6bQLt?PER zJ5+ykDkll=ASf49%-e{r9u^d}`rp?)yrBEaceLDcOdUrh+lIf|xP0q}L0A)4qP%L$ zNd_a=k)625g|#*)G-s-+?Q=G(gmZ@XJu4fuW>hM}%;e8ecu@+}Ii#Ss>)f=$qSjYG5sI8*{r)n77vM|QY=)zjXxI-&&pERP;PQr=^m%qnqiDy$NBQe zqtxXMIq2`y?%om+Oo1N(WD)m5I#=yoIs8}q3GzaEh_5!5u)W%KGyb_dY2HMt6;|SH zj@(1n$DZ=nE?nHok~4dPAS(3#i|PAie=BrgG$J zj;-f4pFb~DI25F$%;vA%;^7zBjmOGS@@y$x?v9;(ER9{5pszdbWJWj5EVQxMZ;yCd zl`>;K&arj3X`{2WM4wgIe|XEt!*5gfH$$;!&_)gY?r<$W#o)dsWq3`FhVlNlfO)eE z9OXQn>*#kRdfZfPLxyYUP!5N6lRdBF^Y5Liumyem*CfykVeQ=NWD9%Phfy{^&z%%- zJ4<6h7{ge0;??j?d3F0_NeQD0N_#!@J0azG|M`&+H*~tf(FW(Cumy>btPJx#0h$>5 zYdD(+-M_!5kP_7y7hhR0zxcPzju;B{Col&4%*13snSJZzILK*oa`BkYeV2(}h|pH2 z^OYX~Wn8)`+8!c=xlO|__PE^=UVhM&#wO}lN<8D4eB>siPQuu1lueX|Gwwe2 zY~{KkG4bl?RIp6V?$q8(S#! zwYeQuP%tv)AHQKux(VtZYk;pdnCogo)jdxT-WZ8!iIM^#zrpNoJ3wOquWWhjw1h-LS*3pwHeUYziY0`C7 zt~(B@x*P+(q7aWAuvHI61h71l7YmznJ*;3^2^-S?T07$y=F}nnc2=-$Y2J0U)%{ktZtK#WIiH;J&}#9bq-wjo zm$EP`_Sd~dwSC;aL{Ckf>2|N};Le(?mR7XT_1VLZyNU{r6sW#Nq<@~T(lcO5@E8d11z`4eThX?5km!~|Ax(x#M5B~P1$&J)9in?vT(a3CsDDv_7Eqc+v z0^R-kV`UBMk6w3e#MlcIIChwyJBOF5e09#k>!^~?KR8&@#3amZ<^IZWU}W108O2q< zhPNKL!5uDL<`Qk@)4!Kx7L2IUlNgO-)kMTRe$U>x$S!D zS|kNs6TR3%uC@|hyGumy2;EldY<61JK#`BwBPlzD?NQqw&$YuE(u&!`N4FYxmFWj) zS%h08GtDMUnX$r$2aCc1^on{_*ppA8GzH%**&=VR>uJ8%omwFuqa^pc^{h3UGrwA$ z&9rT1#JE@}?YrDzW!}V`SrxDSepOck;dd6Iz1>7I$595D+JW}#CkRG(e_cQ&{0Oz$NlCwD>08B0<2>DQW*!AV{_+s zTJtv!zHAKXH}(iM$CMfx#_TTFL+_`)$V+@#TH0RgmVE?~BRX+d*y0+P9aj(1gCoI{ z>Z#9%v-0F&1FVgO=kXp=Jb7)He46X$D@C-yh7eG3PCMzgmElvTR zsJ}g=qE>&<*Fa5;ro>8Uaj{0sv&$rXPB@=SEbf)Buq9jiu6wb)#@;pNRNtT>b>*`Y z7FIV1P%lr$L!3`;#!0X>?glR{(0$EUWad-_SW;bWerb{%Te?CGDkn@$g~9ckwy2>{ zgTmZXCVJNl-sSa~U6Ctf(=hC6funx)@SuAC(e38yjgVN>g78w znpA(GaZ zHHoDf$|6=zXqP6>WDQja%X$)VPLtAIwaB2=k}CMYzs}>nIp?wy{ykcfa`9fQ$I1mNNwQ41e($z;2waK9kV3i9`)y`jevc^@xb$& zKbaPYebCbG<1{-+lExm%VH9C(by|q4LFA04`hTL>{vJ_lp%UANsR;%N6e1K^t0I{! z%x|M5KubMBUhg-etGL-$(cKSbAT@+cE`LGd)`%yD8?bX2CEPHFI>A5o(u=8{SEb8u z@EPjB4ID@MTAjlIg#5tFpgxe2^AhL+L*tE+IKXT3Hvp3wA~EOn9p0-R*oZF2$f2E- zouZyfC7R-9?pFe7*Z6R6-QYkuNsJx{QEG)bFg+gh^a8w^ zc~^Y)Z$voe9@Vi9G10LOjUq>;u<-7c#Y$~B0gfM_XRcJ#H``K+04M;veC2m{Qoe6N z45$|}xG&!YVX_par^m~@XcF^ffv`1$)docWJe7R`R#Zkt1`ixlN+FAzf;rQ!&Z~ut zD5K&Ll%uL9Ip$*@;wc~w^V5_vup`&(>4^@kp~ybtsFSb0h=44S6Ig*cDEc$sz%@M8 z8}Qf%Z=M@N0NXLR3(yc*uQXp4YQ+-b^!LAjcI3Emqjl--%&zdw*Y<5}QAXe+LBnrDlbV)>uJ-|LV9Jm%$6)#B2XiXv z?f+w288PE-8gbKbgCiOU+1`wKw|bFVTs+y7mt+MvfCB?^lvo(ypR2F0j}~&ocGur! zdXO{SCPbje1%t>3fTz#xvQ7)+P@gSk{_n88BblXH|E4EDn1(PCe? z8oU*RIW7Ty{aU~yb@@e?#HA-!nc7sr;G@(Q0!4lWgRXt6zSP(sgsR=ZY& z@ESLq1HFmMuoz!P4av*EoGF#Om|ZpTeY1?vF8z1j9M}K0F^Iw9u(?k&L>orGonw*f ze*6JIN7>pnZhYY4b=T#O5yl9S&N1yxmFA97!Zt)~Z73=!IXt@~dWhgJWSL!sh&sDDbubZd=1=DlB>orjSA}wZxH4^c0}< zi~P55lR)dB5ez!3S8D+P^9NLsjQi5%twu0Xlvl1~W3@1{zJQHB!xpm$I@6MpxpEmp zPk+tL&7ngB%?1$mq`v~tf;?&`xf2gvh5KG88|*r&61Fs&Rwb+EWC>XVu})1gUvBn4 z)|iS?I&+dXFOQMD=qY`N%O$oXAHqAW7ND>N{8%0 zW;`t`>jF5UaU^=~XrpOsUe0yRj}%`A8R~%@&|jit=*JkbVe&kB5!Z~NwUJ_c#N{IU z$wrnO8!oFIFLxLQWAG(sBNQDZOKn%bczLe&rk=^eGu+lFS*7`ebHUB|U8BfS_Po2I zV5;sagQ(T|pQFfbwKxw(G9~V-=uKaPnx4O#-5lcqe6zg2Pf~f>m`C`*o2qvx!48ng+Lp0qxG>nlW1@Sb{8U z|K%yQrWZhAGFx@d)h&7wFtnpyh-HRR#UlT`djpyLAn2X(V*#a^M*jN+r}|ysZotVx zFfjr^)&#y>MxLLF_>qIhR>m=4O?1j%vLayIQVX2d!FwX91Gl zDi=}cLU|$1XQC)W8ylOL1Mq>#wd?Qi|7Y1SE_p_wA;OwQTos>`VN1wK(u5b7 zNd62a6Z#7-mNwKxPYq$922bGdLPU7?E^?L5d;@4|X&@>w0F^-T5S=fJyht~TN9bKWy&s>5@Ni;Ai_)ZoXno?yXE?=#RFpb@zWiSwt}{vFWX z^`VELfD0x_j|tF#BGX&~YBe^`8=MXW5daS42|0bwTY|6p0YoW1Qb!q89J$g}*Xero zIrrGSL1zEndP?2JC}F0e;&)vS-n_+;Lhb27x&UWux$YTyjOJ}!RLS^MJ;s}xlZ%J0;DeSJTchVl*9j*b8P zUCMXPuU#n{OU7BP!-JWC-_`0i6g&-ZV1P<8z)u{S{bjl5RU&2f6FG{iakcBVa70oc zueC6Gp~@+)tAJA=YZN{np2_-D6NFI59--9H&51;%_ij&biWwwM8D!%2CIRNRcl^74 zeo&-ZWeoYCmt8dFEs&cb^jFWT8D49RPwocP^qvj2TpGeVbe#5goK=jFZ>v3af)2|x zw3i#9(d&O(ztRXyEUmI?!K*!c@;I;JH?1DHzpSnRX3!rTAnfZlsP(urjlZbJM zr`RfhX6=ELnq#F2c(q1A8*_tQ7_F_anq$r*(d`Z=k1fNeXD~U0xMJ#EqciZVy*Fp{ z*%BlV;N26jtB0ySxxtVRQmxnXdD$AI-L~4;`lqoW7Vzxsy!8dvOSKZB9b?2>z`B0jL=YcVjX-^WtvGY*3Y`nhT$P zNOKPHy}}5vVKSd=;Gmen4Maz1hPE5#ys0+?y}&RGuf17TF+V_3d$Yj`Md!QljT>1% zY!N>^+gGoiex8+r?BoyV;Qs>MIjONSTQpxc7;0gkY$0m;J`Puew0+ffq=DJgpD$oPy|=RaG=TPCq_1!Rfmkc zK5fQRL+$ReyCy7x%0#5<~3w zUFT``s;#UDpfGRuAS*av$*I2Yu9^M<$oc<{sU(;#{k2--40sF^a&R4K0V~i&3eWeKqhrMcO&B%vZ7vjcJ$lzC?eb!J(&P5XM^Dy1P}; z8SQ*R!rKU(nT!l>dD+oF_C6$6fA^OGD0b#D#=z3i7~w86Gv6-;2E(a@Pe0TbiFDwH z`RGO6w(7ucs1O=hJOLKfbkXrD9i2xXfFb;|hkcQJV~&&b z%h3M*Y3KLLujBl#OR|{VfhWoOd4YExaWCL?AM6ha2A`&)PK8 z%OYNo8i@Mu@4kW<#g&6koN~;;tG!A5XeD;Q*sHTWPWTLZ0NG;zD@fWZ^lyK8E$0Ho zgx+r?i<=1{%}o5D+ilg&ij)s-H|`~uyfieBfA}x@AtDkuDeUcl&oW3^Fw>@I!&%OZ zj@Fds_%EzeB4%-thg@0(r$Ff;*WL5~DQ&uV4iE`7WiUM2&^h@=p?ZylB~8gJ2jSO) zxhWs8%|EFm=va6A#pX?v(>KjWoizWJ%qXl+-?l&w19#xRueqqLdAPqbL zAmgp-HA1AYU37y;(w5Z#1t<(H8>%_f3wM#M=P^R`N6Nz}y%z-F>*Ej1TP7hPT7W0X zsHl)2RnH*(GbjXCS#PRBfK5_}T?7UkNDsfMt`-4F4+cNru%UCs_Gc*WJ^OhWAu4Wl z2!xG9!;-W4M-?sBM*zOFKbgPXGZaPArDT|G#E1?PKPh>(oh z5aBvg+EsCJO^9~?=T9{Odix|REr=zClyZj;0Yw2g4~T#}suoxn-J-e(wZvOQKqLIR zYF=LRgwSuJ8UVu&xT%w@o;0N{USpXw^E}eUztok*Z=DueciJ~*Z%Y5EF z>-8C*etTn8cEqoO?_A>DY>|KYDxC1IH+3q=ebU{xk=^V2*t7{Lch z{!$5&$jwx7LrdVl{P@2}V^%Te*b92$SL92GMBd-3c~B0Ys{Xi;BlP zDzmZ(K^&_0Z0_TS09B~fd4`-?HA$1~EN1j6gv$>9byWVskScHAlJA9Ro_79~KD^z; zp_oDmczt*`I)9Ocn_wK^Gy|_@)~2H3b#BRvw>#%3vqcnLArT;WhMaUmPW5+1$PRZQ1?LgR{Ip0hK_pUtxjbmQmy@yfH8J|TY-pKA*u&jXz&I6D#FO|``L4kRhar(lB7 zMPOVyj@T%q;R159Kj3AN5ALMm10#T#$ zf!-`ocXLM)p;;<4=;NMSLS@LDN1%~@R5q-XxuVjnC@9`v6IH$Q z?>DHMwOctyXryo4$(5rd|K+*D`iAp=rd|4NK-es0%x@0#)Ha};pHo@!`%+N%UwFW& zOLK%%lK7=H^ge|T5PQbF@==COOh!HVI01*ct#XHBxAX6USFn&$W?D8Gjei3q^q)#| zzZdVLH;xsS74F#QU+ojad!{#%sf2$DzGpSua7OW@Y;2k2 zvbN2I-xKcezZw+0DwwdZ`Ep8_F@qpt)h8T`?CRn7f(wH$9g!3YVkhj{Kt&&=~yfP~gZ~Y>_3R;JWHD81eONrQ!!y@CB(99_V z-h=S(j>`|FFoBTg@g>MPrJR@DP&j)FLMc4#{5Qy~Z`4kEz2EpO2U`rFo$w`8+PJUm zW>Hb;=(_bbFIuF)F`*1GU9v*bf|iZ%R*8VtfJYt<_P6?0R?=sMQmc3S=68PJUnALn z(Onulouh`K|HorOeji`oi{R+3oflwFqMYjX#D@A`jmSs#^DelR!Up+-s5-Hfxv{D{ zs$6&nZnz0$e(?8YiSf9j(mBLK!4mkt5A4V~1Wr!s#eqMT80h-XKgKJ~?sxjP6coS7 z!rM*%E#(v}C10W(?p@+)m*a(%)zi=UiVkpM#1_G7te_epl@Wf{g>CTa+&ty{E$3^EH8~7~Ba{ql;%p9oL zAblB+Q5dS#2yAxp;yj>b#SMDUmZdtM^(x4bq+aY`nfDO-e>5U4_7w+;4QSWhw!h6B zonZM0l@Ex`%=_Fr*ku7;t8Tq4Wv1`e|r75Rs5|K zW!_xTtS&y|{sF_THtuOBt0!Kvek=jjp2TC|CDc>n?hK}!s>(1D^Bu9d?W~*4;0Tq_ zG}O8E!0{W$PmT;qNpXzUdJdaMBoWlt3vXh7wJ`GqMnhP3S2L|)Mk zul-qS04NbuR8;tX_|OcM_45{bXCvqY&#r2!$*WOAbGNQeV!&ds^VV)Pzfk25TcFn^ z5C49&>6VMPVj`)ceSo}YT3cxjivL{%-#c!}r;cI_M(@689-+7I|1%?G3$5_pfX-OH zjycWPy7(2o!k)ZZr*qjyuj1V1c3(FHrd7ApO0ir$tA*L-$f@YWujk zwa!ApE6{Q1iP}uNhHO>(Bsuhs&d2{a2U+}PlsfiJRh$LWmS5e+Z7oV$axF^p|80Q; z4m|JE^g<4G;Aaf3Sd*1@Dm9bZY@!H^irESdz^pfT*(2Iob-?glMn6z!R*NK0Mhb95 z=-gqUFQfD8-Me>*D-SdIay9}{Nc*pn^##)ZgUil$=i;BC_=4U4LN<{4+HV&9YIoK& zItcA!#p=qiPVa;w>J}iOZdcC2#wPv!K8zAz{Z~fcxv$kwgWunxv)GlzXCN5RV@dmr z=?(YUf6>2!8`cbRvD?l^NE(AIHU|F)mUu)e7H9>fWPV2S8!6%nX0(wYW%fgm<@uLz!rgi$;DJmF$4^ThwA zHv{PiWEQbNk4+MF=LaLm>Qz!w(w1@OiD01Z=mK!x8)#y8wYwYKWK(-6sS6W?d0FMny8d0^Pc7nVBhc&^&=pi27^l>IQGs3ap{(! zvenU^S4QuIB*1CQ3NU5H$$#vN1E8CCY%SO^w~x!*Ze?)!bvoY>>5d?M6krSt9c&~G z-h#|8RaM}BMa@>}AQimcRNpQeF1Y_60l{4pjh(-=_A?Y&d8w~%Ye}Aae`+u9Zj9(p zt@jM){o8@;8Q}H`a&dPNI5*UN(}-Gr-?2_E6+}e!02E?i*d2f(4uG8YUzVv%UsbLd z4oz9Wt{L&iJ%`yyDzMVb$|_)jvkq;FU!Y2YDAj;1D+2w>R!X!r&)9-YJsgM5uPL_r2ng@yJK8rq;GR;F*6#JnoM+`62ZNj zmHm{QG8=K>v>DT&z4xc-|MUkM4j|5JpxcXq@QHnI;hMe}alJw!e7elC_sq$z*GVP^ zTK>12=k1dPK)O`I6EZ-lFZdbBVteJ$>0#}sm`)4LU|1jh>*K)2Fx9u>w>vNqr6ZYp zp`Lykl0?62p5YhpA)%-k2M5wUh-pyC;%~?4!3Qb*5T1~bYv=9l-Am)DkxqV|os-#H zkdPQM4rS#j!!7`XG$NL2C8}y_YVUTx!{!CiZ#pbAMia;>;V6Pb! zY9$X1bzdZNS0kg7NpsiM?ArX17`gVp45+sao&I9`)Gg(74ly&N4G4K96JPO#esMkG zVw~&B6{@VpAK6EjuC_C!A$l&MZ}HTLQlL`@IwxdPQ&U*a|62y9-6iZ@y2f%+8CI~V z)P&r1sq8uOtm?UjwO^Y*Y(pm!dolmdAA3FM1K`{)tl{(^($ zde;~bSYY-@Z2RO%jBVfM_tkxk-Updq-XZUvI4Cao}4K4l0GxQ;kP|caYRaj)W?g^bA9xV4B^Uy;9t%YYOxccsRtlRhfmJ$lt3Xyr636-6dWV>%Gdz8$OY+5$S zN*UR4-}cN1Np_O5XBknpaA*I{_pRsi`o4b8KhNvwDZStC>$=YCJdg7@j&rP-sn4>7 zLehiyaaWECMmZD5gJ`cEtABCAVC)|VXez&s2?}pXijUV@ zy7_SH*E%tSaOQ{Y>51Ym+Fq<2bo$8CPu&7g!&or{oz;&nXh+giIDKRnd>5|U72)+E z8_}Vvwlu4GNw_+-)Siz|BP~gMN%&tjWFoN%9(3y9{?v_o{{aTAV*AQL1pz~^@4%D_ z$VH0u*m{lKN3(G1ZGN0FCqYH6ePP^2gz+6rdqDE4-x;9)RRD((tY`hh_b4vykRn-s z_aihCc*vu5nm^I8>_Sq5v|H1@{aS`tY7XMkUg;O;5)>bw0)WR9w0-x28ZXfdk8(%{ zTr=HU7C~MBUBQM)^(T_w_5`8&K_RyZPNm-$3olzt@A2WQLF4}(EIHhU1YkW9T;>NZ z0=*~wlUJ8pNknT~n^x1IkN*3+*>6_K&We5F1@b|mA5dhGsi@GfPz3TCBjzXgSbao1 z1ObjptNZFnc8eQ>#Wm)-rbH3$IX34>NcF+I3&k+h#glT78nmA15*!Ly0-HHOjro|Ta3S~KO@v@% z`xSy(&lKs6=Pn%Dm?kj5N`DimOn27%YY5u>_(9w1lBtgnz_|1gN|4@z+*) zH&1GI;dH=+i52A14?#EZ0q6jLMx&<{nn0T!IP6$+>XboLR$2OEA+#ojaE&B=((O{f zwe$Y>IWa#wFj$ZEE+8UaYwP}7AEy3bpgAbFZ9l-*ILdPv zWkqK73N`Uc*$pD`2itNB0KES~XhcR?=j3oB&QdSM91?K%Fyq#~Ym_AW#>XA*i^yFk zyFS|*WrC;kLT8eEwM*+3x*`wD9M|#blLAm0Fja2Y?^WLJzaphu5+&RP!2iGvqi$T- zp8=^kGW$d5-gj9Mg*O&(2N57=c*f6PlUweMyC;rzFiprWZ_)Us7N%~2z@1W5T~sS7 z6j?$x@B!US6`z&?FVQ!FpDOQO0y9#rKOkMTK7-VJHwaaDlpr#t+RDlbT|J77(nRdI zk3YF~D&JoCRgq0M8!~_eF$@uM9JwL_79?rRwfn0G=N|q+J&!gBPL#eynmEc(6MpK{ z`)!>q))7m6X86l(ABUh{y}CEBu>1|pUz~vX3O&}O0nreYRmUeJr_5*{F}2!I&9qPl z9GVid2-S*SBa5kPrzqEmBXmemAt->t&WhP<%JX)7X#>T3fNpM64_0RsBY)OS5q0Tb zmb}>?*Y97_m+axR+&{6~w)>o#0o|{LbY8?$!@_utiww;>J{PQB{2x}2X3cF;d?ALa z{dhD66_XQicb`o%3|`0M%e=MVY$`FTu5U045INlGX15iV#-aJ6^?*d7wp z@`@kmjC$EL+y=WLS3wz9XZNHR)F#;u@Ws-rGftj-WNP;P#;K(l1}G4b^$|6PkPkqd zqT-9HdH_6-=s*NMW!%t?3A;S*L$$ZJ$E8}i1-6boJ0C-+I!2ZZxnxd@iQKlk3z3lp zcm)g$WypHWBMC1|?_osx&oKS%WLT5S*A{3A(K-Jk&d`HjFeFcD&!phYtzYRW@-6>; zsLs&$ORc1}nO*p=*ZTjSlOyD>N#k}_b_%d&5%xg(n=q(}1%et6+v&dWHaKex@hd88 zE$IFCLxk3+0?jb^=^`yeQTB1@IjL7Jz+DRo4JGhnGBY!U@z6*b_Wcz2z}Ffj011`; zHAUPPS)|-%vsHoOb)|lPSA!$55Oigwce6DsU17deu$IpYyaD4kYN2-u zY~AIJqH7V=_o&5tryd%S-?axav^BeifTMfKXIB`JCB=E)mec^PM!9S}# z^z8zG&FktGxjiP{j@=#;)7pb(vB|H@B8WJ z>z1DqMSLtq{{Aw=-|Gf!;@ty9r&%|$6=)1aU1y6^I(kdg;`V``^haSiw1D01yP~Ms z`(K_0#0|drVrf%vCZ&NmVxz`^iTD_ktNY^#xwIyR0;O{x3Ve(jQHsVwZh&TsLp$fF z{;y%QJ~@s3zSWRh0uAKaefG_Ew7q<)OiUjf)Yi-3dNX-4NclC8pc&Z=3c7R5F_&w% z=T0fN@o9JX`Mtjxzpfxx`49A4SJ?5zrw<;*%Yzx0j6<*G4fErKg=Zfy4r#Y06p?Hf zeq{4__&36vf)j!${6(yzDW-2K;xr=_Pz}h87KJI^8zSx){Csb& z9(39P^#=0=*e&NZa8Wu>RyMxY9yq8i+;MyVd?Tw7PXU2svjyhqp?hzni`TJXUKi_g zxF7(NBHsmtINi*zx|&x%@tX=v!{ zzEt4orJXtO(q(Xo3Q^VG4Vk4fMz*@Y-;HMOpJ<8X?XbXG?Q6p zySB9~FHF-Rv{9#&C7C1J>}l<8DZEuU%a&*0i1xnNvIU)v0fWEV4TG8o`PB}*{IU3*;8u$i#;8>nh$F&n?EE(9oEmEE!vT>xqw5mz#Ms{M~a;@ z$n0_jwVsfg4^*2km1)ksN31p!(5^@;$dSc=$9aCJyrXsxy4;PooNBWINq5FU*cJHE z$o>x$`!)~jy#b>`p6c!TTz~G=3Qn~kEy6CrE_e!>dF~&2R*V|C-rDx!bcbX`Ex~Te zW3o_cahlYNeWHEOrwjgIjzz+EvjO2tpsg=rme8HHe0rSlOh|{ILay|`x>P~*&Qxnu zC9ol$19J`}#ulKxMyNQ>Kx~1$XaWgAZD>T%4;@=*dabGvdNc}kQF9mcgnOY}Z6ERf z{{ArJ9XaecFrO8-Vcled3h7dv;gX5^GtvgCNsZjEUnG(9wbokxSU{|=)+kv4g;k!8 zCNRN7wNx|HZ7+GDF~naFm~*{(P+DtosJbnVFd&1qdvn+uYymt%730z_dKyV!#L7%x zJ|ZNgIbJYSctP}>izPi<6;HALLgh-S=%STZNL8(;wbQW^MN}6ENr}~e_W)dPkJ5W{ z&Btob_RZU0na}E_lB7wHqQcUDLfZV}<&L9ua1Pb5ioOR{YRo9=g+261ZIerM^ zcEE?bt9EDV>7w#8()@2?@K9QfX<@ujSE_3iuI|B@!I!ZI`Fg_3T{-J3dAgZKo^jq) zqZ5i>xH#el3tF|k4*$206u2xZwoIbhOYx@6gzQPwi_Z8yC9qt2cEHcEcL5;8FjK!x z#ZE4E_V4f?e}-VDD%07u)0Dp(~68i_}HBkUol|uB6S6%=SV>Wra97z zUEw$jukGqlz}xu&RK;clIoBAEsu5o!RfZ2Ud&4dM#jhMve)?8NUYt(RLH^51hCrE%lu#S&)`2G z`GX4#bMuSar$?sN_w-=$*)Vt6FmsShI`If6Y3 zwX2_nQPt3oYHuYpUew@gQ0ZlcqU!Ot2+7-dfBqfzM4&l9qHnd4gTP#8s`3B2t-#fk zTHC!1Q!{csYjraM*S^ismq55pkI~3`q;k}nTy1P_)GyjA^7d@L%7i9hsOi7IDk-w=d>}>3#i?Q< zxICLPR|)>A3TjlB%#fZ?dPku$#ztV5RY>1|g5?Mj-DfL9(Q#;o<@wz^r4ne-FJ_#D zNNY=!@s}s3q>z89ca`a!veBhXhvW+6w=+u15X^Wn3%dp!$dm~AZ7o=mbX1|T^_M@_ zrC^=X+h68y*uvB{G9&mwl%mYq4)=e)i)iI>(e)($YPT}dB$d`%bOfoudh?6kfBo!B zbUXL=cPKA627gg~J3;feN{V8xxYDD~OW=a>4o2myl8t>4U~`Xom=!tqDKETs3J;m9ep7!8}vBf{1#AVJ97$c(BK0QnmJ_BQ}`_?kR(VI2Z*QE-{ zfBiolAlT_B-AtU7jf@`o`|u-S36KpS-y%>Wu(|3_?x{tUrKoFr9=3Ase>a_#k=Ta2 z`X*UItJ0n!ujSb_Fk1zyL7y?bK*rN2zhKRGfrbi)g9oU_ag;vIzv3Jxua>1eA`~?Q z52IMZ_kG6bKI6oPBXebH$nO4&%C01%izxntYI)N8a9|bQb_pYq1+`boBiUS^XC)_ii$tr zZfF4!ntEjy+~~O#F$>l*gZp4gDJP#679oLEzzH2CbZ^RvV<|8q|^pISb}P1`8w9`K0s{oh3Lty z%p|#jj^Y2S?JI@1|It%i7;tIfDNF&+AN?1`8tqp*aYX=X)XfBLog4 zUiNBFX+@!49;_eAnsByFftmVWi_kXsv=z;drlu_UEj964qQEl2UFR5hMxRS&?+;1- z{r==HG#8=(PJa0CA(dl2jDH2j4--?wsk35cfcyLf|3`#;0v$uuWO16SSFf6ETUlG1 z50%|58nj1s|Au5u`|a>sr|V!FjntXt1$EyPuODUZt;QL~p~4aL0fGBzlJB!&o*(2$ z!M30~y|W~9u(Y-w=T4tsHY{RzV=?1qrW(wQ5C3h{-w9jV_Bn_n0tQR(Sk4a=+kjhN z)-W=u{r3jcF0ud{*Pbasvhz5Fb>N(PF8l#wIyON;a?q!wG0J`6kd|iFkl8Iuh_}LR zeb&f+B8I{Zg`iDnu;B>bSnFFOWL7WaZr(jEey~L`pY8Mg{^M8MdiV|9Kjy#4z58Re z;iB=COn}3?dCWu@K`Z8;vk6mhUsOTv@#8~)$(%pH5F~g8DK8PAaAC4|8Q6;IzBM%^ z!-STDO@y!+vRaL{ah@X3CSPqszLW0=!+>YYO#%vV-vl~CFsXobo-5F z=j6miry%W0V1$mGZ};1Rj)^ibg^+W`Wg$@E&3|G`^MB8J7o4Mg-bpsq`g8{RMrDgz}z``XByyn5u*ct~z^Ugwqs7Zjf-17eX=m`(%-m&A$mxoJ#Nj;)a znXTnLuXrtwn7c1GxHiKAUuuE(TBv1va7~tyh*Cm%iC;E{H4|@Nvg`eM(Eh^7`8Q6h zdyALchd$4<4?bV{wEynJ;hd2vzn9tl%a+Jn-e-$*Q_ftqFM~i0>!W2>r0?Ki(;zmG ztK_!G$P#+CMZ|I982q1dRfz|cL7VFwSezCK7L6L<-*fx&NBzbf0=22_)ToR$|=7=|X3=^j&z`L}ON<;FItY!J2)~_hs`+w{+DlGN2 zhblWMED^faN~HE;64cV?5V%gEb0#M4MDd({h~4fWb~kk;E%-HT4@Pwxo1CPxWPygJ zIwmFpGZy+XU6x5L0YO12YcF5$S*%(;O>sRP8x=*Y{nuUa4D%=CudyBeYq}C)1%x%w zixJH3U@$;+MJ^ik4y-pcb9+4i_p>&~q+*45!wqZMWiM39f<5=KlEmU1EuN zeG$=?gEr9mr%J1U3VjZ!_CtFhy=(Ub|E1ara(bcncJ*8^4h$Thq}!6CQl1hni` zo*0^$t_a|Wo}uFB?d0(5P4eVYPtQ8NqXc(LAyC`v@}o$?HH6A&`WdG@O=@)pf56~sYEu_f!*YT1yeDj=1o+%&y z1~RAM_I4H3mqOQQb;@s>GY6pMP7Z?vCMN>Ij-Ni5+$TvwZA?Dyi)Vr=^k?}lusvK} z!-Sm4s-$6ZCh@6n-uPeNvYQuv{raV>rlzPP>_Ns$m9DiZA_C1qO08Qz2VJ{T*w%!S z+v98rTQxnKAk@h1@i^Z`VTQlV0Hnx5nSinH0oHvEvBO zjO#n3v9eT)X9n2FdDN}nlLdMk&LH4=bIJk0DCNg@##}DGaYxE zLTRo*+p-w6@xbh$1fH)+OKe2NGm9_&9B_l_31uhGZu{)y~r3E8IaLZd2>`}5W}&NEox=7vIq z-*o!~%Abqi6I~@~^X2?i&oh@b;HD-=ltDY--RlAKkEe_LBdl9ayBCU?M&lTDs=AW{ zQ~S$8A`$FR>j`AgCcb-M4oYY;x5}g&&%iK=c=CGNn`B(L!h8nBm)lF%`nMdi-4)|2 zzZ@)YANS?Ubdc_0D@I)tJtDI8Onou5TD6-$6A_-Q2xA0akr(KaF1k#$GLFw6%z2;` zbHwB+ynoC+@O!l8GLoDbfi9Dn{Td08X0P*mml1q(ycqVe%k^SL!sgAtON9|Sar&IB zp<%)}4bVUs59o-Zx`Zs}q7@mu z;ih`oaB!L)ETb}a<$OtpRf(*Hg@vmrWBD0y#v~UUIi6_QbM}4+X+!5^wFurCiSa5x zg?O%`bM7)Y2VLbeOVw#=sd=%>pGks;=1n4?l;sy4ENtq*JOTJM+oGF{oxjfMY$8St z!#e+ky?-H2R%cO5nTDYLghEl+a0IV0B+jWF2jm?9yG{3Y@uue_Jt(@Q@aMvnE`aVd z1V*d0#v)8M7Ee#lPTvsf??3mviH9R!RQ9F3`r1{P?uo?R=FHGASN?vEsCLj|KzQma z3z5S$qxJQEB#+h6!XugWaHeRuE*oEe4t!f`*VIsS37 zcY~ifvPIc~bNj$sCl(kn#y{+WD3uXFA0UqhuBL26IZ0ntc9!VekL;DPw$Vp1e#`4e zeol{O0Q$9qw(1QoIP!r3==C*d__l3=No{RK3Umft>L9Hhoa@#Bo%FfWLMW6<^ONHV zE*F9}5Q2bhr~>ZZ!9f!;uD;&@k(FKIZ7npMpwrL{=uqNz3Ab!*iBBYNxs-NcbK}g0 z?cz=NgKVqmp?L1hcC0rW<^~aAyEvG$#*rhYQxH)<+9lHOX^2$UuD0_67v}@?StrW< zfreG0jN;-tK`y!c(5?nuZczvwu#oEp=8Wsr@pFTk~_(ROx>&rgp&c}VYN(>+ei{oZk+dc zrTF=8NRi{PI^bleWNpm>0+K)&T_&5R`5j~!EzlUkG2z>|Qp`ZU?E!+x zg4CDJFu$ToFzQJ7tkLGDXyc+mra9vYxqF+-u|MnhkPT#uURf!yw!3zCo|g168nsV# zAY~Y$r-d{T>WK!BMm+`u*bKV0!fGfUo|Aeeo>{7KjAHis5iQ_Aatf#ch+BLhC^;-3 z9WUvhggP!1(EBT9XXnV^I2!srg^Mp(y;;9S0k>>Mc!6FF5cWEU)tbQlr*wEeQmX2&x6^Cn{#zg_k51c1c3BA(*6eL#bPBnf91Ix=EJc(b<>v5t z8<^ZjShK5Vzob1;ePgRe1_T-}(Sy{0kv~dh&an)IHOqj-h>}qd{)^P6Z82OI`1r{9 zF`k}Rk?dq|WEEevs~p?f)~lYkL$B>IAj;Dg0rV_h-MsfXS{cv3TNds#vwbZo0FfE6 zC{)!E!UL?Z?$RIiSHfB9A-d>G&Vn25t5OB)VL|K}?lsH*HhoOUOL3x#Xl(<{tc9W8 znMndWZb*2%rg2;OW28Um?@fjgmL|-XXk~UPtlcn_ujddgc8N^eeQbcx@q6-){21#n z6EXv>_3B}B$kaez2&_#uoiiUBKa~hyFWegne8V8^a|K$Xlu#ujTO2X6OvQ@anu}GY zVa{8vK0(#k2A5itQ)pAC0TFwVBs!qENyQPzdVzIVv zXZ5D18j=9Ss&EKYem@e|h2KaL-7xC|hT>3fr^2X9QdClnJ5ZB@j#PvSKZwqTs7c~;)trlm0{t-zrnv*}O53cYRobYDEZm7&N3tD1{+a?K+FqYfF%K&HaJ1{a`^ShEI z^u*L1fZ_``eN+S*5-dmgdVbL~mkbH5YlYTGkkfJ{dFm|~g+t`i77!_1wtzcP46sFJ zRu)41L0U|F39D(CMow>aypBZ7pnbRwRhGZzFshgk8yZVNzsir^UT4Siasuw zd4~KW5F0q+Bbg*Pz3bJIu5_2z=jD-+C_X`6;z`?uC>4NMVE6u{p2eHM)R*V3z{TF# z@u@P>(s~3+Fg1a&vCtM*$6GMg z&%^&OgvHji6DzWpczH>IJ9-q{S$TA@$S^?t{6+7W7$_e!B6L7rW+-v-+|)Oj#ryeq z2bO1~C(4};>Ot(0+6Y{E$<)k+{Yj1IyE~*;Hl|{vS+ccJ?_M8%fi5D1bf!~dzP77V zZ62_jGz|=RGQ`Ub^Uoa}yfjn_WSCRC6+H@kx`}$}kM}Bn+=iPa=PuD!Z9il;#*O=m z*ROY2Uz|$e&I=6-lZW1ax#e+kX1*DO{gs*{(ht`GaX;2MuToo}v>VZ|x&uj83Sqv! z`XB;K-6}P*6|Tig3UV>@V)~h)z44WJ7ip2TIQrkWA#<=HyOZqpA}I-GOJBVEl|2ax zKYC#+;=V#t!5~98&>d%|cW=dT3_mlUG{@ijy6^7j#7*5B6oSN%_f8qM`FTwot)452 zEuq>WAFnfCd#?So;b{Q6 zI%0__x;Cs$b@64bH(ww@5SC+rygp3v;hcdyt-{`n>NJZq-;5}Y)=9_#SMB(LaCi4E z&xa2mD3swgCfvWGA>gx2g#-*D`H8V>$ap_1k~j2Umw*_6NZ0h1@K^7Bf5>+XAJkwb zs{_ViO~7h)hB5+~ax6`!9kOJurhvlO>E5lF2*|22p1ZT5{yrFuC$MUJs)uq6c`-go z-c7av>ioxC;XPWM|kPu{HVh^5UsgX_#NAlsem&pJ-RfBwnnc$MiAk zF-cXP_OTynu3ivJkicQ{Xhf+ufk#e`BD25I1k>u&)(O`-N>YEpP(ZXq4ff8^GbN_@ zPRF+$zg@49b+R=}gK>z>r?;6(w}q;7C=@P4E48iUL5gM=DqD>o0wG1gyHyp>&i&mb z|BC?rmfc>5mYmH2))Y0aF0mkTmC`CbyOcZGHs191$He*t*L+h?+v`x3I1r8x3HzfS zrCwTbx||6MbLsm7807$y+MUG_J?#cDL{T8Ym_>3M!Y?rK7-5S>#m2I874ua%0F~?F zSad?LE1wn2vu{5u1o?xl$)g8LZfmnzS7TE=o`W!e@9O;Lm0D^Iu_-lA&#I(t@w^oJ z=r?b$JYRDZCeymL@ksI(sdS+A(vm`kF}p~6EPwx57fZWPPUEd*dtZ`|tRH)C#^ZW4 zVq8>SAO@Xtni=M?uIQM_Kh8f)lD7wtHowF5CtNUo7e9^w@>b|Az}dSYvR6s*^PfVZ zir?Pm9mKf>l(LSqZ{Kign)C*K)2%tBOlzWp@b5`b!SWyZG zl@B%Xk^Bf;C#^PyBVaRGxrwj}@{I4!`~)mq$;U?$u`q&ucZ1z{)P?H^q4Skwk>hA} zpr4)Xk>jNjQ7HXM(*(pVMD!i(5fm?P%qiP+_X(gG2a`+xO>GWJ2C4Ex-P}0JWwzS( zKMkox&XY=4mq3efJt>pO%qv(WsE>DI!S#qQR+a6k!^539_Ln?f2R zk!#Q*mJcMmc2fvo-g6QUR0ZzbzTLhFIXX9s7ztaaIRc}%1Q#|Muumh_M9oNw40@UN z?Z)WnGxlTe3|{MHfBONjTj2wLMaQnava`C086Wtwz?9#YGdG}c=%qnq3N z0&rSsA;i)m+Ij9cQ9A3e*rzZ?Onw7RjXepvnE3v^%|A08n=pl*f;jy4k{l}8&|v02 zB25iMOOJez2`j62#0$3kU74)i2yp<7amc$!+moQ)V5gFh3VG~u@g$SPmdU3-h-oqO z(BlGh(+?SwT}nzy3Qu&bz9IenK5eX)TVf>jkGG?XKOC9Gv$@LrPIrKWsz;Yd&;=?2Zhx%%CW z#Be9mBD2FH2~^~fY+l@Uc}=5o|)R2nv_zzJHJW&y^y1_Rqg#2J-r$ zSxT*ZBn{SRRVA~1CHr~kL3N@ns&pZ4c9fIZ0}Q8&440~5+}lxLO4=SQQ&a$g|MTBC zKjMu5I|XSrkS#gS<-hIwTMdla8;f+1n?6Kt6UgTicB(%%s&{|q{CeS|d?T1YDbVT= z4Sn%rj5|W2xc-AogMB2UJM%vT1%7~&Am+?D{nT> zfzZi)Z#ZWN)g^~=Es*g2mh#y*i@*nTP<}$Eoex$VfC(cPu+p=0A+U9-84DF2%Z}Ii zP8+GJr)$YcvS+;L`W}^&+V6r0W7B`q65~yP;#OdN=Nd*$;ayGXa}|mRyYwGw)D|H} zP*D~fBzerfYg(J-$4Nj5p5I84{>Pj$$JYiug z$J%qh@ctF~y6UDN3m*+U1p=@1h(pcb#fo`ucq%}NSAu6tTWAr~y9Au(#j!!Vj>Cl) z0zT{q$xlR<)2{~X^k-|Z^bdSf`;ldR*W(Y~*JU$0tuR z9vO*8!nNl4eP8Rdn2z16!~C=Z?d_?-!OOhPu8nC&jMkoIEb|MfhPmy}m?qM~y`8!j z;jpaCn%F6I|75xJG$#Nx~BtZz{C(HbW4p{%FnUv z`M$>}^a{uYkshzGBOpU0SL`!vs{qg7zHnhzTHwTVuD`~TAG~^coY5<8!Hk~hUKZ>kzZ=wuN-<;MW_LrH>lN%Jwk1T@#FGB=yG?( zU%i$0^@9!y--jQ?aD7~=GrQ+B%WmA2Clu-Vrcc}p&p7`=TKdTFZor?@N=mfHBtxbV zPlOczy{o|R-3#=)Wy^Lv1Bi3)43K=o@g3fFlh455@A$Y=b!g7hqDx&0Yi|8jeG5t# z@)Q+CnFT|0<)lak6p&syPMJ+AmZKd=_ZtBVc6dXc#;riDKA-$=1~l4dKEZ>9K4^K?6`&E67>m4 zL-g`kuJxri*%^(Nm!h;2CWmM}+c<3YfAeZKHL-GE87Lo(PBxzN`~Z`24`j_+yS}B1 z#3ZL~>q+#lQ8FcNM?*4lgmyRw?8x_P!$)_wEXN0&TfOm8DbFaAw~4aByr+tq3?3T3Cdsv;p!Nf0Q8uH_CY|G zue}feW^nKNQoMfi7`e1@RD!i7QthPK^%uBokJWlx-=Be$WEslq9=rqg6@54iY$r4; zaPh ziqYzQ-U@#t-?*079B#u}uU^szHB4Ys)Cj1WsYA^dh~&-g$GM*XX-4Lt-Vz=2F1MfX zHrZ-nDPTiUChjeTZ?AN9Q&bh`c#g|>a%o#{(`S5E0ufqzHKMBlaf0c0@ZVfb8v-b| zed@EAZPVhp`A(jgj)}glH^s&Y2m4R`e3{nz{2&!LS7M#VfxXK|ibcdh&XdiUVVEY` zZ`mBqV8f4I84E#A+;Q3ID&%BJU(-Kzy?bOs_4+CjhV1l~1{+hpoo&jH`tz{oG_xm@EiUsS5EpNo; z)&W)|!pS;jpaccovu+!^^g$T_77Yxdj1RMAu)P;YkO+enf>ZBIy;sXSL z>1ze!z3!9fmm_!4k4-uWUH@8)M^G-`UuNDoNyE%?O(HL3&&904r`+h&^JUNj-2Dh!FS_VV zpwDd~Z}JONc?Uiv-EH*z6+@)h=om`hh4PYAl{Y%jUSedPef^b#QALp=PfQ||?Do8* z?&CV=i2RrdPEYjk9q4EA7E~TV1i(Y(c0n*U4{_5(u5$>Z z*pmyN_!?O5E(Qg@4Yy=Os{dP4=lYfu?xMj`^;Q2C(oYBzs3wLze$B>)`$!Z@n)yW@ zUp)y{q2=hscjH~drACe0$%DhA7oWq&5C!taX5PrGy3QAFnlI<@=iwKD6L-llul)1^ z7h4b2Vm&!b8@sTR7gFMGruN~!?b;viB*VDZ{lR*W;!{@&z^Kjnkt!;kZ1twWJ1G$z z0ySiyZ9tSxMfZNlaZ{53GNyHUpDWO{3dWcaUZHj_N~t&7_==LdYql_K6RqKU2in9V zDp_Y>zK3VT$!tUj1H)?{m6Ql04Sz(&0HtRn0IHu0(++|8ug+xb>%}^*yDGTKEBz%L zEpO#e<~G?j{i{Pyh)($#!w2R+h4@(>kCBK{DYQmD6=jqls%H`Pi<~)i*FZV}jZ;2KxrMM$Opl*P*f*p?~Oy z1i4yfCIh{xwH-RRl>*w@wd`OOFqWAVo1e^FvUz4BZ|cCqK;M%~`|g}GxhSf4bfA1g z_AqoJkDq~0<;Er|n|T7zGeP|ltCL=9v#cyhTElC5L$Y>~KT4FTla2De^S@C@p}EPA$@N1nJTeq0`^{-c+It^jsqbsE#cN1oQ~bg>VL^ zV|5VY<2)!#5v(3z#3AA{U{Hy}mGE^WUPX=lZBMXM|DDP{C&n0(tjBkeg2|y1LSNC{ zZ=6p911;KPdHh($!9B2l`Qan(`S5SkQ-X2YtixG*V~Je-{Y-rCgL_^oRm#CPdI6+R zY5L2KN#R@4-I?;m8F3%w5sIcL`AC`e1 z3ELr;$y=8cFFzwMp;XnUx6tAAG#A)8B=&F(XPj$af*{-iGy&(KyV-5#y9yGY5OW9M zh#_4^m+kU?ldy9#fg)+|*H_w)7Kn6J3h@h^8>u5)=fMdLfGu*)Al}n!UbW*bFt55g zaNEvKQsPgD93jSq7t2XgOtQO!mua&vs2ATuntc%#lMx@5h=mfQw^9R(K5oxO(;|(K z;#d7`WRk1BR(z+QC3F9swXCw|7f|}N-rw(4pk-`Xj2hLFEfTY<) zAQky)4q>3^hofkaZN8h-0>e->1<0ZA<52b7b}~&)KTqrx8fif>0>kG7fG=kG+X@Oh zA&%8>!(Rj@SJ`n3VuoyrQ!?Vn3e*W9-V?iN$*7AB<@Rl##eY;uT7J9CBoG_0=rtA( z`>P5R9%j&LDp=RbP-+V@0k@5k=g_OhFCx1fFs_-~)!MeG{rL`gZ1;qU{(ct^Pp2=G zZ3;bPdoUacDIdnDUc-Di(K=4xSs>PJd>`qE{;FGBTMJL$2LZ0Ks%mVAu0!Yh(@~nd zPs}QYv>Zz;`ir&KZa%YF?0}DKOYQSpn?P5BunR(Lf)Q`|AHK}5cAMP@3EwtY0p(^9 zZ8w+{=lj8R-HGLqP`-*Z8TBc>xzn^3_V~)s-5k zUj(G$I+Jry>N`>lg2jn?}qrL<%KW8x$heKnX3-n-Q4NGjb-P4o!%OZSUwn zFyxMh0U!!x1#&R0p2J8b<>uz*>YfX*UELUIi?Ukx|56P`bwwwJ{oP)CBHlJC;KpW= znNYWIq3GAWC_4`sq-@c$Yx>l5yz*Cq$lWt+Y=--ta-NtasXOF|`4*daReKD(Hdg8k z);=q%=tW$=mjLA}KNULn*e%@JyW_RG@Jh0HaK&ytVFLNlwR*o@K;^`N1}vD$@<3~J z(EdT*K=Pzv$`{_ce=ic$Z4*nR`2j}U8_dlCY%!(T4TxF;2!IIUb{JtT9Pk_Imm|gH z#NG29`%{+GKH_v7GP`81N`a;=r}6Y-8r1OQl;yhNG`Jway1x^dmyOfCjMw}mef}2N zmz@={c^hr%*!Mx$d+S=+=Lc3ox@rLA9d zk)$vx7p&JRcm*l9CUmh+LraYpaKveL?V~vhL(y7K-w9_J>7pLr^CBmdd*zB?x}ixtzt-S1-Z>g0((r)JNc{rbHhAcCHeD`lokzK~3~_zhaeU(46+ z|C<1vC`(X4UGZAuZf|c#0Ij5=$QcUm z;n4stNbJl4NMjLq>{sY=`$6GjBu~1Nm9q+2)d({FcMP3kDJ0mQkdrw%hGKW_Brhki z2^*6wz4ZvY&|r)4V*b21C-4#3GHl>YSh|HV1RS??p;47p!3;|;-UQLKw|->{t>IAzOwCw=yj1v1JJIN z0seeycao&85AdNy9qF)WwWjZJ%DtS@gs8Y~QRYFX-78X-lB%*S6{SM|ig6N^X;>v# zIEDaHfJo&nD`)&ZL-$y~d2#I+JZySJd+GD`q)F!EgL;h;ijy6pLDQ>SPmls6y$`E~ zZUpqX>RJ~b?yinacuT5XGH=E zrKtC|^fGa=v1TCOUq@w@4BD?s$|)-oA5pAov$T=nKOj1-az&|RYW4P=zd!|);hf3I zv{0AJ@a;I1BByf9tf+M>1M!LC+*OAQlVHmLr%goie^Oz4 zHI)>uylGXw8jvDNNl9n)eO=g9mzK|SQ!_A7mXZ;beLnw%A~=iWlaXR~L|}9qT36Qs zoS|mHC^h*r(#(&IkG}w<32<*Erlx`FyH}VGA%;@+CMjoOcx-)*2cE9XR=JTrrY^64 z^XA8HopB0lyxw95p*umT6<5D8)nH%VF|Az7_(X^806+;gJcjXkXE&&8M3&f19H2Yr zj_SoHUYcI5enO;XsBA6$K$TXNq35R*o(=&3eM`jLQ;%I9pj;S zD0h;ZhFBXlo#{BvJXBy@D=5l!;)QApo+bTZhrvgVnb&g`7l{)JjMKi@y!)*6s^Y|h zm>W~~uZj&w{d^!XU*IOo027X#yFT&<1OTu*C$0YO%OCw>gGJYQ^2L4)+H6l@`w?P0 z^R$+Wi+IVsccDf1p1u}qpYyPDzSs_SM_b!oK1>C3XZnS#h|7{CypuD0))`-(W?tT6 zM%TQ+`A%G-aU0AsX$c!o`A}H5>Sx+j)qmsR_iIm5StJLxx6ey=ofY$P__MVYDPg)+ z-rx84RmoYk-tpl%@pENT-sgQL6bc>UB;3=d;!U5`1SS-;u>O|WelC6XgukTk z5_wjjED%rE)7BPTJuh77@aDpGO>hO4HkJPv%hT(3J*@85p~m&JZ zy&NiNX;_z%-M{C4xzv1ND)xuTu*wU;AoI~`#<9}h9h51(xUN@!qtlr^4m~*5=9aiK zJE*lYL3;09`{u<0@n$#XmbQ=87T?Xdnq9o^1Av#qO66- z*y79AqUX40jax`j&gC--wI@Gm7SKe1=M1RG$5WR_&c{2|EQk>j=_r&!2cZavl7Wsm zW+7lUz4)VPMs;&F308NZBWAz&CoJE%IVxrPKo!ye{3GuE6zkpZ)`I9l{NiPW+|Ox1oc9*Z!vN5gUyXsD3Y*-{Ln|M z9$F8rN{1;;l7jZP?F+j4m$xdk`F*&46(9L5*8%?#RkyWFcyJ6j_EZePr2wS$eFk;Jz;urtJ0Na9kr z-7vPLVsb^4Yk$-$r{vPa@%6^bmm_3vHbSh1+`Lte8_0(+&T=Xfs{>2}9#GCallI+` z&DVutj#A(VT{_|q`NR)^G!#-P$bTYS#{a{rtMmVgJ-cyu3`h_DBf(eJ$3sfe5>-Z+!)>Cln>&;NSp0&u84neJ_Io+c^7=0MPyrL;z_^_QTkOaIp0! zN0KPROJTi%$r!_eI%d zMTH#jj^%Ice+8Wm@Eg#)+MoHR@8y<2XscJA@uXpVh|ujz_SV)#v@{(ej9oYUjr0R4 zGPT=}@Y)$Ln7O^(?p3C~ey-nBo7#2L5WEz6SW{obrYL=*SkfDJU1l^2zfJmy=JwSy zk1b2uEY6$O^gBuzhU5JQSS1c6ezYoQb(=h#G~FVq>SxEX@swqG%Vq2nN)Z>?l5HI~ zu~#)$Te048^ZI&8<52!UTm+`+L~VteAWoJ`W@w?GX-$AS^zvWb@SkYMlJwT^yW4!B z)vYr8>k`d!C>b9x_6M0;NdpfG_4{dIEfjj@#rj3&jmLN3<8t%z%0m@`m@^r{Af1kF{7Y?}%+7qXU+*qW#t7PGLiQ#Z5(St-a+B;Q0b$QXnz4e9Hs?E49Eu{hr z|IoL8zABmQnm8G3J44Z!z>}98f*5vzqr8n+BMBt z961YC{nw#<L}4ontaryarUo7mR4UvJIP67?Y; z>G=GI=+~26f0iEGr|R^kZaI@~>6tg!sSW1SbDEFmV)!Jq;~&^qJg?acCKII8321xV z`qhkmgXNcsZizUTgq20il}O98W-t@%Le z{LI6VO!2Agc{gW6B#qbEs}f>u*Lo`$(Bgc*T^N1JLFiEKFlo#WU1WYvGHcWc)p+K*Qd{yeLi&BR9=cGlwT6*wZalJAB?yTDhNcQF)}CX?zmM zS2jI(9^scl_cmPmxB9p^+1{PRbwKnm{4=g+yDKxt^7i(`h9DCU#nI>s%exEZ`#~>l z*o&v88={l>y<4%vn0@l%NvV~_)1R09&FI{z9EdrQ2cd40>Sb&j9K^9B}_57+Ll6;$H$m`{26 zeQS84nVspuaWmxl(2JyVIaDvNs6;f{m#1ZZ2>+@=s)Ay2jjGhu{$YY2F}Yoq`pHJI zs+%pl1m}5+oZ}49CEI`_C~FFI==q$u&(=#0>4%j0I;uP(T&rx28K{q0_s%svC>D6; zF2GAOaqeE1V)tv;l&_nNT2^RLccv3novt<93NFlr%N`Nj&t2{DlFeFIX7t^i=uF?{ z=KQMMeU=~{xsIEVz}w}9N=R);L{d?UzI(xlcjB`QXq~YU*YFbkr%Py6z-5#!UV}2y z!b@YeH<;b%6iI$;=?%QT1g>Brqs^`t;J>P$YE33qod8%0LQ*MR#JXMoM`s|Gi z7iyWSoL$x})uj~e2Im#2XQ8(!ebHm*2gUxEP=7MmjKQ;0miZDP`wyikyFBjReDFeJ zMkecK`1~U_ud5+Sk46o$W=9LWMi7GzqBn<+9tUO4r%$RWkq64XM6vuK>0&4`f#hC^ zq1Dcsd13Bv(o4Z{s2LyX)Hg@Kpz;IVT*w37PAf;*AC)lRk~59n+mm=U<{5u(wP3lU z5akIWhhh`gM>=h3NFHXLY! zCho?i_M@g)+G+nkzWy>StMv=}MlBRU>6S*iOQn_WZfQgj5Tp?h6zLKq6#?mz z?v^ekq)R#^1*B__iR=Fy`+eShY(K0I9!t3IIp;O6agA}Fzq6xqB7IMrz`pdS%B3{T zifEywP!mR!hyZ_*o9J2X^d_G*>9^kBM~*V_pZfsI^It~{mIsa3;PSBWmB(Cx3(z_e z{MSLDKHlGq*=cSTPc$%8SNDcyao575ch^Cm4A4!0+Rh?qQbxu{E-tH8Hrj<K9{fBycGKa&ux8zWnc|4{<}fu>%-8jf>PWb9HTc+ohc?iuSwW-6fd zXJe(S(FkgXaKxj{*k$YHjX`J_jd+fD=8y`@!e@c<055t1eryyv0*LPbYoZ8*LPf(( z_E*gNiG1;7flm`379LYaaTHm81#y8e40O{JhY>sps~qThJx;cyDv`Ls1Bc*Ze-L7# zfy5O0laR>=Hl?E2Y{PmFdZA~D!mllA>p((Wau=F{n5)iDZ7_~S;fJW>R=0Qwr6|73 z9qCILFza~Vd-TeX-uHxLgbK$J zz5Iz0pAtJv#58Sbxq>8;)KR#Cm=>z+b-qius+(eq{gCi8$4VQfRLysiZwOsLeZSB? zo$XjFNg(fIuCDyjg+KXT2tMaL?z_HIEM@gRqzMoX!m28yS19;_?)zvRt?JDbRnx6* zir;pxew?|-=HBBY5JH}8wxAX-V(zEk%z0*i%CWbAVaJ_6@9S6j?%kuYh}h34nlMT< zR{)WRwrBemr4rFp(0^xk?B8nH722`cXj#F~U?Eh!*(4xUjg~b;fwxnIKf3h#&t<#w z{US?!VtgMA3XeC_x@bpPX0&kuNq;d2iRK+rEjG0C!cf#TN^Nh8dJD98n`05Ci&)H;}JNP=v8SeDE4%Ac8s|{e(5z=JR%q4 zmwc3>{_RO=(!`UNq2Od!DWsznzWIOq2>yHj9myTYCCdE&zE3IPUH$E*sj+&(j zd}(LsfpSRUK0TH$EB&yOoP6uo8+a-u@29IGW zX*M0WD`8}DF9GxfB{oy!ZzG{e5q}`Etzl~$YOSF$S;8_p+Uv0V^5;J45Cdn0>0QlD z=f9WqD%le+Zk@t1m^h^66&%HXI%jxayjYz1ksnP3h zYHaNN^mSx`e8|SV#|5M15j^S+SH?Dk-XwZNceM(+tvG}>Fot85>m|q5Rx}0+@ue+) zB--dNbc(yJQ~WGtdDTX5e1J?XY`d?m_ip*gz51bclhTg6j4B+ofoY@?YM~3st{C<7 zfk|T$%VBuf!Ku<@V;VnNT&tzYW<^U`!oZ^9;{#z#Jr*W_W`gycc@x?=I@hVwl4!Yg1%3C_5-XrToAQ?H-u0mgL9j1%ZfAvc^3^03 zharu{r=gpg`Kw5ubLs{3;u^YlLEAbT4(fFP5k#};2g0E1$kAO^Ht;hHg*v?$mpD8U zxVX6HYoJ4C&MHrE=WHTkRX6dQ@#)2jMtZ!q+EVA%x)z}^Kd);!dfQ$Kt=;R%2GK95%XiO)6UZRpP=I8pAW_txmgTlYwfQvCoo zWVH3az88-^rZZI(A2%*E85vf?wE0U+ORB?G9$wDlA1pUHo%T4z7Bt6Vy7$7%`k`lP z%|1B@4Ls^a;$!uDX&n4cg7@W zWj9|lI!07P-P?z+kLaa3XJRA*GdnDrj>J%iCPRb{;No6sf!4n_G~30}R1p<15wC@M ze2*hYK01;vFZCoE=7z~y;exZzduV<*e&fpDzDW}uWn>!XfCKoqFQ~VDa=*&w-Pzyp z@%Up@-@{#F&$IZIpCJZ@qG8Xa>hK1GpBlrbOcU|s=kR>7zqVajKpOP2Y=rdRq=tR|6Yp!ES&=*!2b-Goi#~K zPd7n`#CuAhQ~`5H3uM?ncA<)@CeqK?V-4@@03$e9^wp}Gr^_e#>|SfQ3jaGXF4sv- zjQjg3!Z?l2f6XU9jUai|l!P>_r$FkO@M@7* z=PRv2zpnr0cz!kM{=-I>e^Fx&s>zg+w=%Q}ZBz8_Z;YyCh-N3nC+l^!1v1-~WM1h| zaZP+bj7he#@Ajdoa28F6gBCp_6wi3P&xA;RqpUqb26Z@DOSOVDv`|Zx$n~yolA1iK zgN2hUCAEpftwZtDI}ZX%t|E0uYl&GG0Uw2)+|^L1k<%?Yn0}nms`@B=MpbAF<@zW) zo*QSl$;&mE5@wNT>hdtTOjb}mUpMoWhhSQfZoX{w==<2?FO=7^YR-4Ytcx$3Y`gsE zh#oDD$b7*M8wK+E?T!~fgdKML3DVJ35LZ`Q2K!=1sPW16QdrTJdBlo zT&#oGi2(J6f4haG)4?BOJ|#uP3sM6J%X&W?#!2Jbb-v^qY-xviLDBMCO+8~t@d6Ts z22BGDwN{l_?$B=z^QhmYxQsd5AgOt3wou$cX7u^_LuL7-_EZogXJo*p%zkE@`yAvVLEC_|!YzqiUGX&fpb(T2~B= zc(pjgwFxBTO-8N#CfOF>P(L8jk2#-ptSS9^>$o)-znaWPsZF8HhHx0`{tiaI{e11| zz3pEwZ(*(kS)b>5F?CJ`X^yy!nQI3Hz2+A_@scq=pACYZE&;pb;#JHe_uKJ{Kg3?v z*BOjs$X63Z2{=8_{9I4b){_&x`|f(u+=NnX=zXgR|E6IBtvTCAZ8qp=qSZ@@Vj~c! zyacr?RpjZG-|(tM9r;5t!uQ?ibvyjOKF7a%7o)%E8e+P5T9?qpd0+2+W)S-xzZA6S zzqm9su57OAI(V>??sNwcP)q;TuW_Aj3?fZz_>?zO@#OP40E{i_qOUx4F3EPD_C4?@2Okr$sarP^7^vUPk0n(lfz%P;#T>Z znMuT@$5wuDSU;DiU`5ZQdeQeW)9J<0IkxVWxDwYb^z8W=;IO?~{|M z`9#1D_1VJSooX-uQ63a&%i^gc9u~WCsP=6UR7GR%(32K6=fI^m^Qg^!tDkmVk^Sz9 z57b4&MVv2`;(NHis+Ht~S**Vml_yiTcS|r&#Ck#SNq6kHz51nvl*UYC><}4?_{X;8 zN2MxEUcGQ=xXLeXV&o_NS1B}1Y$t#R0*$uGkisWHC5-Cd&{Aa%agEs5)-94-K`m}) zpbP!m!;8uM9t>-N8aoXZhZe}DoG(LoKs7ZJys2NLYgKawmj`oV^bD(=Sb@9$#KIfG z7NAyuL_R{ut?rn+XY(rCny;$ZKT|PVwUj;{l5HpX;m28$C(vRn%+)C8XQmCvOJhVZxS({@_+&L z#-PP5zFqah!}jmrBQfLs_BU>)Nxr)(6-rs3dacP%0PUyK#ss6MBBF;{1;B#1wl-0f z2CYY~ZYdfdlpflt;xNg{7C8MNagh7%KjM#yx3FY<+#SC>Udb&wXz>JybEn@f-%?bN z7yy3ghX|Ufsl!nnX#I1{FC;r#v8+krmQnEC3i;v7MwL{AWnn6L-6T@>t|d<|266D! zxz>&uW?Wp{g=ZdvHz+7)*PXHQ)RTKn8ZJl3c=R}+p%ndiO3E9@S+U|g8`k07+eXp! zT+5JkxZGxnZOM4k^HS5%?ud#`fU?gOGTzW%K6h?kFVV^g|Du`l^J!Cer|jS5$;Sji z{x=S0i|L{BU&OU$ctR5zD^G+)ddo~1i%Xtv{&B9I{)i1vGgX76Pa#JlRyDxAlw_1yt8?mdu4WKq@=P0zkvEgv z8Wa}T-tX7GiQ?6(krTB#I}vYZPkFtl1g5CYIw#hFIE`p=x2u_$`y_HCQdI+}b2JRx z*yAx8&`|7Ho<52FPPBYdwYMn+IBJ;p&<*cv5E2T8QooUC#)AKnPLZoBPfA*O3a z%TEwF{ZJPJZF}b>!)ppvxBYh>HW-mK?R(GQgS=BCzbg`a#HgKu z{;=ZgPvR_jI#)N7at__9DwD5_FP*=8F)CB==?BAu67+_`jtZAmf?Jd2m~O$64#O7M zOu_>2?RAf?-w?yayIbE%8YFQhC?!~aW#RB+0SPnq=SU{>0P@j_o1Z8tOIM0%>iqgN z>faA#Zx-&j9hu2+{5mVfT8YI_UKTaZVrcloD)+}z>b~o#Fa{b%^m%kp;){SEfpt|z z!~1{xZY+LzVD?4t{?qk$qB(8PR?j3pBt1M6E>p`1XAl+uJLc>4uYHuVX`%sxfv+WL zWczov+#e}vccB@x@n3&YP5yw_?Y{NF66U;)#o24T=4S>OsG`q82%K;1HjwmhD*H-` z;bXH0QRVBDU7EXHX;A;Ze$mhP2MPLFjj8|1o#n%02D+{Lavu6_`<X3(U8p*zg>hzvi-4arG5?~dIOHTsvPX@4DtO> zeT)th?v5g}_L@x(;Aw@fiKpa{WPcL(_X6=PH6dB1X zGKMdYvt4N*)q!)!FKrK5VNRk+A9dFYO}Gj(b)Ynur>1p1!QILMlD6R^#82Tz?%H1J z0X>%KANR>*?kC5}+$t?LYPh0B<*RL2{4_+b=U4H~QNsyL%je|K<*Pe8vaB@3b4Nu^ zHQ@MPJR1!k4-4A;B7)8I^hj5<;_j2vSXNs^mG%o`=Kz?sS$te!miD&SZCndQ(&<%mp#q3szjDv;w3GRO zhyKn`ji7pJ#@y^eSK;(hqDqa?Whajl&Th}&v@EY@ZAoiatywBJT!V>4enpLJAv77w zQC^QLKYeetM9@f?{LEAtnar=(tA8=ipvP@@*9nu{Yx3%inGfMKTkW(lRlkmrlP|a_t2I+e3NbJ8fpz+`#h^V#xR3g|75e?c75CMPe-;!CP;-_ zj(;vV3i#Q8o|mHg6EFHWD6o#I(B7RS1U6#LwEe`X-1sZ==yJNqNrBe3f2(#5!8HSdg z+3!0&_VKHyCxU{?yQA1opFNvf$X+GbXcr8`JU^0Si<75&#kTbkOGbeBX|X%I3gsM& zJI-oyuadUz>;ZpEhEsny^~C-`@qXR&qd$nH{wQdegfOt+E*+(;I6uaV3)nTg5o*#( zzxuL2DA9Q6Z%4&$)6~{VzFR>>>a~6$VF3#K`MHGeDH}a66z`qI%^f7173>ljcphlh z93EM^9KUy{*>4k`*lp`M$XY3m(AXW-G1OQtD=iYudlpM(xKval$P)XSEZT}cI8C7F zPyc;;j&uF}dglE3h0esJezCUDnRxeo<qfOvo$b?jYk^O1*#prQ ziu$Z$mUIm?KF3)yh%G+bPntC+v2yyd{RhK+zm0Hvm9TkVtmYTr%ef36l#12NV>hO{ z#2e>lKT8w#o6s3_gr-W_JjiF4sP}bpJSdn+r_@x`X+<*dLlg6jr$`Q+o2~8KLtB;} zx|4r4=;>-Qh4*=5icZ`7k_;B@z0nUK2P^Z0^SMfBglE%t0ky?@?NEVABeQ*Cd-lB~-7j^nEuo;6uaAZ9vd%h>sy_feP$ zZ_CfPiS7sgruM{ONd_Sv(IclaYZRVmRbI=Fe#LgXo!SsnG1lxPHay5MR86S^UXaS; zgJap!I9|(WcOqsqG4aB_EnxO}y}f#)Q~g$Wr+Q*ysr2I`Xo%dQ4Y!!b4Jz=QGWaOH zyYR!bSI5SxM4U+lYp=7M`A>(n0Ny8~)a788jql#B&}fwspPsJWH`!(H3Qz7g{*z&E zv^qWxY#Sq!6V0nV``#~R56w4Ky4lW^wF$;*q^U(ET zi2^#2#o>r8gOGzX9eKy5ty!9UC-yL#NI5=!o;cHFRtDvc@0}W)th3WKZtwaN*&hMw zmqJIah;Z`VbjYDVpE*cDR;-Z<3H}yJ(Kk$2OxP zoj&$bW;jnbb$eaO@9Cy6^nB+cfu;Iktih_-q-kSv`6m_1%|cEF+yJrPznA03@WZiZ z+F99JseEv1Z`|_2mt7NjiF#v?;$gnOxw^KMw1@*yinj`lhbd)u~v`#Wu?Pc zkN4NT_^2_YR|3&eh(EfOi{gi0s+GOrW_x)%G+m6i596LAhdlm5CQG}wa2*Yv1*^Q7 z4@cBI_1!FHw0AVdEZuK&ldp~4ZR@?>AmvQm{#4?nRiNJedD5`@$5rj(l?F~$m^?|G zz1}Tf8Zq$jNPYOLgd&enM}ij~4hc7lw~A%m^sTHRYE8ECzU%N-G|Rj2F6L7)>_!~5 zg-Rwc25KkyCa=^g@Ev2Ps?*3&T#xz@SoHO^@@i8OdQgNIQ?yxz7$G^Pg7h`3blJ!y zinmPnP<&K63P=LY9;MwYWOgW^;i4ytN;~wIUb)VRr6*yF@wFAryM?bVZ`&(HdE{-_ zvQztzR0^etSUN{m%7^)SDdS^J8m0$$4OG|#y7CM^1iZrhUChN*Bl_MHHJYLZJ;#jF zMHiDG-W$Q^5@M82@~&h+4=i%O)%$z?jdPlWPSF9IU{$;xdxyxCG}(6r{w^PDG{Wq$ zT5RK$KlhS3qAf1YeSq zuNB&o719;n|L*g#AdY#VzFst+tR7HlP@Ea?cvG@(qIx;HdizRt&1x2s6Of?P3g43A zF&=rrBT5!`C5;VlC=f@SeAM@{1^xgNZ|7}V24;3~cdHwRHm{z`T$WEqYi=ot*wj$} z9HLM-X_RyH3g8fm_vb}zIH{SJI7G#$v9dA!1}z>LG;oUhO3`TN_;siUc+KjKL{yo| zZC?V4t#_cw-vJio;V7jNOTDO>0*6H!V(_1ulhnv?tss;c8`Nxui(ev zo%6&Sic883Fk9}`Z3H!dd+z!Y%h|Z?2sd+>*5dL3b)b9q+PeOCZtHOQjb>p^Z8+-j9SLx@BERiy5j{>Vue(B1| z8g+ENE6pStN~YEhinre{Qs@pc<|YZRnwQF-Yw`TKwlOJ1U~)jcJ^Nf5CO!EtsKc37 zIi2l7u~Ib!=t5fERCD^yqO`uO{~TT2t<0~-kTVeWD$!*qnJv|=WO?6b@g>Zj)oXwx zb9OF?VvS+QzDPs;+Xq8RwKURA)=-tJCY3%ycmz{BTq!7NQPJ6zqosO_V~Z0uPFDAI zjX89ct&19sioQ%B3I;EYs7ig4C6lv9cB{T1RFR9XR7q;)T#pjgJ^9p0>UR%+=o)zJ7ONL<{bIq`jA$u9W;bv^m z=9ukx^Lh5(U&G7uaeYaP2l68%9RW;oU4<;;64~F>+yh--8V6ZZJTJ^=EO}#d1 zc(>$kO^n4I-NqiBzrW5MV`zR`ANhyI3XQ4+BR2KS|DLO@CHGarJAeA`NsX4+4X!LRfmU{ zdQjy-i;5TcJ(g@(&Ua}OBjYBpKTzp9>?=qU!bo2L{*?bW~Q2*YZsm zJ<)MzpLTy89{z%g;Y(L(Es|fA&7q@W&cT;%SNxe!BUWYhGt&=-=q`e`iBjumu>=&Z zzK2;VjPnI?p_M-^^-~$`1e=5sz_>X-I?r?d`B|Q@R4o zV5k;b@AQuOtsOrf=LFvgYdzsp%k|p~jD4=3`?$&HT$?*EV%@LHi4So7F*Mx{XjtB$ z&3k5b7a8XiA>JaYx#nJHlXgDN;KYpo(NTJTQvBh2K3JE=n}v%DM^2qkZxq7-QS?63 z7iYe=D$j;Ck2&hJu85wRdlzbGQma=XlT$L`9udrNwF4+CcJf;~--d4D14=h}e) zL9)thBpbcb8T>7E@>l@I^JIG+@Hnj15srJ5Xv6pyLoDXJ!7uMMup7igo}lX9>BHFB z*<)e(^&z122D-&<1;Q8DV16@p&v$`KcR9aZMbr6V%u*7$uUJ$wO~&u3Nx(;y1%QPGn!7`T6!amfcF0hao!e zt(2C)&p0%8vdjz0;iI&nmD)Y*+*C>gihP0TiUVDRh5pp(%DE)Q{821pxDk3oSy*&K z3r@VErJdQ-jQFD2(PBI>6@RuBc_3tbl^U20jeMzz9A#3UzjgWgzv z{3^JO%0EAjOHo#s2J1TAdu<-0`1T^K%Mpq<9{h6hom~a9d=8 z6D=B3#I9CyJok=hTc`vjuFT{5^==Sx*QYIfQ-%S`@GH90m=K^)Hr zrc3l@9F=~l*ndeAW8kIt>5XDW(`Nm7=!GD`=6vzS)BQod zpYfzq!(Mi{p?Lg(si6d#)0wea_zD4)MArF90FL*+IY>z-9yG$iY9hvC2f*BLAJsl8 zi($LSUxnXIU|@xIm)HltosqZG4ExE0JSq48oVChxtsza&BGOqsgZ~nPcEYK29W2)k zh?N_-wVm^SkzuCO(H$UI=tfXRp@BETcw#}CK3{$uhL%h-W&PE&o3)`&(!YL?75p>A zUg6n+lh2d=P(F!ZUEqA9r>$m}_`pnxp!2<(^-3gGUd!R>xbe?7PDEj(*kOju*w}mp z)V}U3w4010Z9Er@(<1<$w?eC90nZ#@Pvp;q;h;4HmN=~134?koEH;-_3ewK|?N7c6 z)Q<&Al)o3X&@(R`4)Tq6Vy}C1mC)cW&16J^D;oAvqE^Pfbx;7&Xj;^R*k|8nyzEH7 z-=BV2clpLn*Fw$T3b)@!{x|z5h4tv~EaEge_+t)J-y7fz5M^vDl1!Im9O(}SS!N+1 za`Mc!_SyNpeWs8Rwh0mfjjSC*Nf#jpIdSo*_H%lj+aXv@GVh>fy~VW-qh~Z z8~#?CSpmymoD}Qt>T!IDKRNm+C;@OG7;7tLH?HM<9m>M5xrrF8x8H76h%<%!Y!hdC zZ~T{~va;YYOOf-Jdg{Lx?i{Zf(rZdp>4F2j67DSJwIz>BcFc@q*V?G#eVB5&ik2%W znHy1V@{ah}POgwWLtgR5cBfhRSke`-T+WTNyVA50E4^&g;5>R4uB^clMZ^ZOlrCFi zU2~1KUf|;2eWI17a&vYQ*_8S^5JT?rKvutu;XXqAq|OHM`w3uBmo*0^siP&T1|dF| zW+^;8+?=&!vbs7t8&m#ZkU^Yjg^A)<=R(}0et)lCuaj?Onghrne2I;C(Yxw(85F_tK zLqKT3(16Jh*J}Wv_>hX)(X;<+(Qo6XvCH9y%N2(^A$CHbKPs-9^xDrORh`?tw!i+a zOkTWnDVl_=MYYmwBu;-(mtcHVmZyMLiZ3vAOiJ@dN^=p$I?Zm8bYMI_tatYqeZAkh z1U_x>Scu}V0?cm?cH0R(aoE9yf4o5Y~>I) z;;4NuvUJ(U@upB;y^+Yq#{KIVDGU6%>QoUKi@73R^6%Xg=s9JlVy$g~&2t0xwqHy0 z002jm{~DMSfphb^qARP-E%+S%Ckm-QvtMt1Oz&My^7ns}TcMrzxTt7FPn!1g*rz^*4110TSq8gd+jTN3x3ng+X7fA_kJbp(Z<8%hlmAGI$Sgi|R^4u) z_r+f!N^zF&qguwx0l|$nW;o|&G8L={e-cTjfa95HX&Yo0vOvYh$A^CF)ciIC3udnq zj*C+bBC-k%s;E3af$+{xar6SaStcec?!qCmZ=Ioa6EUS55Zs7n6(ZB}ebdRr*aWYt zRg1!)uo9GaU%@K?ce?@VP2+rWVV_h~GB)C|%Y>K;l6H}Gd~6V+^XW3kH=(Ui`|01U zhPM6**#`wYMdBQ^tnWIbsN~A^mX&Uz&N>VwCeERHiRCY8>CVn@@c7A0p6Z3f-m!`7 z%8k0vjTD(P^{YH66)C^Pn_8Tx9INRwaG*T89794Fs95w8{9>H0ANj!^F0Me+l@bAfk65$Ss*yrL|QQ zY#iVTLxdhz2mP^k`Jc&W5>Uuf1P1-mp}l%XHiTe;!&!)6k{=D*mG_ex?0t0xZrTLO z3j<*f*;Jd__Ji9Nn$$Sb-AL7%1N_G*%l7(ZG-5v@NF#A|xD-*F0qS1-X;-bTUsVsd zseEnteC7*L>tQ;J0se=x2NO6`M|A^a$s5C_ZghGWH&1Qu z^tTwtYF0icUH_y#*Wc)ItW@h?32z)VjEhX%o^qCvGTPxQk}+ofIdoB(9^z&st&M2x z6=2k3a}L6kNHyrs99BTqcZ_t@zo-`ew}7&q1~PbCdpHfEvV)-}lG;>Muu|AYF6QF$ z0Ew7B90TG9-|(s>MWX_q8Z44Sb?FNOa(PBZ2I8n>4JdQFuSKO{HkQDjm;tPOQnxi* z??g8?le~KJXZ!1+Zry94AV&MVvswGfWiMmW#Y)wN$ESvircU4UnqjVxo@rdTF0ps< zG2P6zG1*It7@|>PWPNIII;dWcXafM>#l#8f#^vyp-hvJzwC5h??mi~T8CCfC4 zaz=mWlMQGtZb^rV*TeDlL5s8#{H&p*ipE z3lx1DyP9u*o9y6c$>j&JTA^egY3;_(S;u_YY^jLG7^@oir(bk4GzRLAYF7!N|E>&8 zjG*uHd+DBkO%K4Z!5bvwvjX@)joSgnJUsu;sH39)x9gP^h%@!wVP9I%(p!~Mbu~71 zcdk5XwK;y$*`IoO{iS9i$TxH1PR0;Lm4w3#&l60*}pF+W*9zae*(4X z6B;sP*$XE;R|kq?_|Z4g%u;{ZTdRcl_zP-O z8kBvzYXOn(Me>IE&_vD~OG%xE#LfAg9CB}1MjjX)DeL`N6lrJX?ug8c6PbFmX(Mub zo#x%DU~(uC)1Tq^(E}k?sf2R6n=`t4_gGM3Zg0Lx^)HJTgz&36V~tz!kqzd%uFA+u zPaGs{&rju3i^i=A<`=;lg95JRMmqV2VaXm@nrZbcQVV$fb2tAZ-;p$0V-L)#mt4Z|@I-D!ZdGqwCi>VUs}17yVR4 z0=|Gs7kyQ44CAU5lpT-*{#nf<{U}(e`IN(GYNRl4*ZTbAwuY5ee13~{A?5y{mi}rIZYzz` zfwKEZ8tMJ-`tNHsTGYMF+jZ->A1gLkq*+bc4x0 zwv2q#@d9>#iqc)Zj1LlO@6?ik*bpfnhh`?q1lJ@kT7OM3Rrc1>g~49^ezNuooQR^k zF7`vv^?U}j36c%OY4b}EVO1HleNPlI?50=vG;m96q6nY;=vV32-xI`Dm#cgV`GG9Ub95O0sFkheOcium${5XVMW84{(>BzIqR!FLDH`h@klah|%Z& zcs3$X4uCpgLN#3hi2Ur<3X5KsPmb$$jmFb5Q503 zF~&+|)mM!a;{UYn`p1x6ZgYGub}kFvgnJ1-lK%<$SkII=)1#=gGc(EDe~@-CU}YV50IvkLa%yr znX3jAf{;=I}%oW9S{tgq{xoy3)PBBg0$f5iJpAc^vI~-o%Wz)>zNgbcf2HrLY zIaIMJrx|z{l&W9;l|_?6MgI%0l}s=hv!o)M7cL*5r(SlkmK(uM*DkjvLqkW$hA0g5 zR3s;=Qt8%`a}5%>RJRC7#7Go%k`PG(ZK0#(6o`wttIKRDb2C)vZmZF71{_sfXSi3@=wzF4B4efwRR3Z{HSvu^=xtH zln7epYJ*{%R85+d4{}(1FG#%b>hyy&)0#il;?Cdo-8z2ZjN$Psn)XhCIJUaKBucGj zfO0OHYY1ABuG!#3p?(eBJ@Fvxn@PP{uP_nn$ZeYH=4IVb%;9^hr3jj>YbZyHz2zf2=!kX}as%*br1SkIjlx!2Yu&H~UryhAUr2n5LBDFiQoH^L=lAa|V}8Ed;6UoH zhpS>{Y44+sguwm%-G3@<4ZvaKl()UT-3xpqq#L$6oZkj*i$aqYG`{Dhzo7ad`1}9# zVszQYNUSZvg=8pq{{0h$HQwdr;$8AkxD;@H228HW-zq$u0CA>_BRW++Fl9Rm+G4tR zoIBYeFAWPt(lOwMLTlaMiAKNNI&-8*B2Q~$)c&U^(@vqOGCh4$#WPI2q$Jchj=F$6 zFnDEdl~g`51)IuC$XuHNJx!D;WsoR?p?|hLHo>Dl6EytB`jj`swk08+5T4gDQ3T--TbC*2EPOq@$L@rK8r*dp$}o;--^<$8h=&y?oflWqogWBJy8?x!T*TK?|JIu-I zC{5DicK5&HKmBJ4k$z6L9CehNxkz=#yjGF=#;VOFjptdNe_omkku+S~ISozQG)q3t ze;7OQkdM-4%xu1C7>vK+W4sO3;1qrUw5rea^z_3OQ;aPNX_*HkvK><{QzJt>aU8 z|M}~F0$(E){y#CEg-fSDY9m+6OmHHfOi zvO$!I;v(A5AV1&M6Dh=EqN9gaW1}-0=M`1492})D7=#u<;ziG1<7A0nRsV_mUJM(D z+V8O6VPmb|wlcqN(PPSb5({K59`%SBR7LK8$B2yeUq3XYH9kiB_HvqL*QT4Fx#Rta zdk>nhN6iCS=KB-HKVGK6sNNI}`{45je&PWClUI1Ev_%*70kC*n|2{l4^z1U5 zOQ;>(8)srt-ri9ib_MGJ&+lFM$h>85m{a**PG5p$e8S&q#4)fc1h{e~mxS$YJ3dTw z^c;95RN$C=Njd*!#d~z*TNZN;0Un!8us_oQ^5Ope?T?#(jeNdOqKW~1rJu3#AbaAU zO}Avy?GhRaPdxvlcr3#gp1<>%jdDmnN+k``tbON>n_+@~MWXagqZL6mbxd*7(xN~% zn5d+rWXN$m67;$37`!BG82UT_3FxBIO((wKHPHg$3_6yck@*Io=lWJExjZ-E0ge*q5=SBTH9-9=H6r9N_Q%D{&Nh|blwm{W1 z4dNduP^u4D@FF~5kWbET){}{4ADDK3<|~S=D*Ij3#U}Qp^CG7NTO|b-MZFYX)y3932YJ3qkcnHMnvaQ~TGtJe#Zg)F=5&PbZN;>6H zo6^$2K~<5+*+WU)^^56Py;X+J~z4^=!lB)jD2q+%2itRuLj$?2LWO(uOQN^Cg-4iEo?QT;P{-VTl8 z6ebdOuV4w*h0s`pBU*Emol;)^e^T^(CyV!ZYP zt`|Tj%mnhJiGDWPaCC-j+<4;(OjEc4upmlDjm!8+qORp;Tl=UxMckwnIc14?_t*8K zpoZz&$Vm*1>bu%p@2Q@w5iQ@q%sy^GPF@i!qs8<9-s97A-)iZ7_4mXR7{o7ke%zmf z*rt}|0NlQ6XPf$N0f^ufgjhFo)}p}4=TLu9-W!LbrR1EixV-R4 zPvGx#6YuFr^Zwsy8UBDA?OXul*yw6cKUA7t*ff&Uz|Cv|VFcZse8_wCgN?^@zCGM! zIms`(wg+Laf*lnfi712fGKcG5bHF;HLc`#Zo*t?2*@0<^13tX|p>F}dwEaF^kejy1 zBds>74;ldhd1loOmmWW7gemu+0ijbXF)H6W58i75%pmzsUMzvEio(SqIiyup`I4zF32^pN6CYqlJuYu_n0{mF*&NyN z!}T@%sE{;;=$oMBl_B5`_zlf0yEuPkXKUQ!_w;B<&3j;dro4Y&i$IqV4^0qCOzlDw zpW&CDiphjCaHL~1Oc`D^Bdumg#aSAuu%$s9!_j#Yz`j<9`&xY=s(zd{svm3so;ovi zJTF02r5`#vrFEJ~eUwL?=MRT-wBmmSAy=K8Q!w$F)VDI0>gIUqD5C!+cO+_~olzqK zc6bl_E zWDx$Z|26V3K;48$BwW@kkLj%Z$+N4FLfXy?FB_FE`VYN6;_oA64PU^K1JZ3MWMyUN zw=QCNL&<`Ml%??@hXPp~l4%MGj;uj@4E9bKvK~jHrK~0^15c}vMc+D12Q6vm1t7Gxc2}SIPLWX{J;zt=mehBMNldU0q3mf!Rxg$nTbmOh^a?ysQeF;mZaT z4+{2BM{;(5MUUjL6#&^k5*798Swgb4a))D9w%+6xnq7d;{WCims8}WCd4$+;_$HGePL>z%#X*aDTOw@~c4ZAlvm$kUC_U)q zG|Mb2U!J+ADm}mjk<4Yw=g)MtSZP0)<$&`guxaFr*ZSCMM07yL{}t?vM_?o*?Zz#L3B4e`1%|U2fWjQ4is`a!$z3_pim9ED5{+g%+mBR_T+aTbS--uokST zfdg$~G5RNeq=BXSU;8~qJo`wQmQF)1mFZ-~Qrpgc!7fblU94q(?ywtj?wI_?S=_iG zR|jO_(hax8kIxWbcKggea)Mf3UcO!-OUc8dPGCL>0PpRZIs(;FJe?MJK}f)~4=!S6 z;d8eN+$^U3wdWU`vV|;^t5fJ%{|-?q#q_E=U7g|Q4L9;$sKgjzl(zlxL|zjm;N_Yy zT5^SldJAUvFe|(QL244fcj*g5QfHRHOCm;DIKJM_xC<9+7&OUBtu9B88&zR7fOpQ4OUTu=ZQ1?WhzhsVe{T=v2EE$q3{g;^5x6Z zYG8CEr#C_yF#i21cY^!23@~Rg8yXra z=)P@G70FFB9nt!(9$dOO+y#isf1K4-bls@D7`4D>+IS4tBFdoYzdc$p)vjoMA!&N2 zM}HM8qv&Sh;_p7kAs?a#+G_9N^zE=QK^#QD`k{XdNf*dNj=$sfJv|jUwylHaPcmnwjhB7jJra-)s^h;U=7F=a`{?l7&{TF z-2C@C^IZQ`dk0L8LI{oE#dsMKW(paTNNlcq*nKb_>%RZd-u?~2$U|s!(w8rnAS$c^ z=JH#;VwfE~KGEOJ3wh0{qYg(n@m}wiS_*+4>kI15to>I|(Pkb(w5zY6Vya5%Jp&(g zY}RAp3!Xe&<|~hzg1JR=KOjL|7gn#!9WB%N9;dQyaJNtlB@CWER3+)zUkZjtAa{rA+s$BJf0cn63p_ zAX_E1$O-g*Dps;{vTwII_bc2thQtvrGmBp}uMCcd^7NU5(q`x8wjc{?cxtFHG!&1?kzg!4_7W;J()@CQ}u@3;JK_Vvl%Ut=!RlLZ_f7Ope-S1R#LH< z@<5Oa`G`%aDmQJ7NoSEM3G#>JeRd`Ln=7>8ffV@2y?XHb_wR$PHj0I96o9anZvL5R zvP*zL@myi|I;g0&4^NT9^r|Be3X+qEfBbvIEl@8ix=4fD@t^iJk*^7V23$sj5-c>V z9q1Xo%||af3b&g~3XPzr#X%BM0U`{}C+%4Ir@#xh%Y*TwpfD)hy5;2L`c(g`AB>;G zz;=T3Pnvkx@Gp25X(RWhNMSBJ@Q>#4z^Uqcg3$Qj^@ZO7^a&4@m6db*-(d=muoxnz zM_#hlTX314?qB`(FW9+M21!i-B*+Q>(~XHrI4rp29wYe=o@a+QkaTZEO_W~nZ=+r5 zGXJlSDrR(Xq?SdU6afb)hMKk;y!$WjF&UPjPp5_fk`-5E38w zEt=yV?tTxE%lcZIOstfPXj1Ih$Nbnr(g>QjYq?Vh4x%u+V9yJ34 z@WdJ$n`bczq5Ujn`d7IPYD5G6i2CB*w_{qAl0YgN4RUbOkzR0sWQWhdc^D~q9x*|W zmKW*_4{VpXBl0oakd?@Ezmnn-ybF;sf$sk&G)31mUzm!PR^r|hvnNjmppjCGz*4dV zakCsO2vXr(gFUG6|2Tdpf3pJ}cGqtg85tQDNMA!T383h|1f6dPgVM=EPBOaWPVecb zAY9u05|W(dxDVOlb7kY!?+o(WKG4c>yqx}~Ybfx49h2Wj{}VGnmo#U@6lS>>-(RKa zWtj+Zh~)gz(*6AfqTg zS23sIiHU;9GIZNf>7TZ$zkQ;8OYd&L4;Dscz=I?_@__083-3AxwUPDC|jel14`yB>ks;;z7NA&6HrIR5_-_ttSyu3i7=h+^QzAOuN8 zP|}u81w@bG|24RS^F5S=j?C1BsCqL(N&iu25 z&CJ~QeO=dD-}PPJwN|CWf-f4zcV5AMzV9S5aiF_;1WgIXtCnh?_LzqG%prT>4A0M0 z<+}=WXeWU!wwafnF4cCNj`vL30G*yx4@f!9p_(&L2u}^3l$|ymi^Uo))d99I{}lIx z>qe08AJo?aWMoGPOiMj`9?ii-2;9pG8lINJ&>SP@Ess5d-e1(0-O5(-7S$KeKuOJPK4^V@Cl=I(5bqDRm?b<5QpizP!jq&KQ8 z{5zZ2-&WDfN8UbN^&qs}Mh1D?sC3}Fh6r#==&KL1CXf9|P#z~HCXNK%-Z6Uq33l(5 z2q^_BxWeAUYUnU=5rdm46K5x7#2{u02`MC5evjYvPPZ%+B*`Gt`2AJ((01tsnrM&u zi2gms6C)bGNLgZ@~dh-tRFMj%qrE~|AhEcI^y6eR!Z)P_)~WQOO>_mWuj zH*1@4)uAp z@o;kD`z_Ii!FSZ$=G4K#!G_j7pZsZq;^PCGnxr6E%;;QG>r+PtUxNanL>iUInh=UL z8h2&y^vV0j`7)2rh+l`YGx$zhcsh-MF}!@kAO_diw1QvG}%1FQQANRe-7%qs!E z5IsGM>{CTP{|i&Y%at$J$q~WNb;PAf(Jg<2@rDsom};vFXm8aDcv(6_+x4$ceG~E5L=I@hw)}X6Q4*106pq zC)tElP5EF?)`L{m^h)2YfLXtqz4U^=J^SuTi)?=15WO+khmuxjA!zGf5i4$|N+EtS zY}!@Q|CXy&!v?RA&~T4fXa-{T#kl}SrAtl%Gt1=f%d;xpjm>{a*F)v9hvwwWbVHm8 zuS`Gl!23HO5P3G!{pVA|SbtcbuPutrS>~&2Y+DkDuR)}4iJ-;eXE|6vG{JDlx{)4@ z(Mjkro%EmeNfasqmjHJ4K1_;{l9LOsImmSG+!cp;b?Bg-R&tEvhUE~i)8arXkZp-6 znLnkls6&5uuuBt=PjWHaQ86`p2aX-T#ie zUoReTn7TC-9tTruO>7y-nM~ipGo~Sd2V!s)wM{cXgYQo&#u`RAQ(&L}@np!n!1ub4 z(4RW>0y+mDgN|AuUEq6v!l>EUpOI)Up^5_-Gz?1lsexLUIs%iJ_N`8}NBF1!#4zx0 ztotLLB}cO~AlNkuM&p4Mb;vU8{`k(!5c5FsOmXC~PKKQ!DPVw}loi|HTi@PTW>3bJ zRX7WvP062S_~(h#7)4O)v@HLv5G0|4cXJ^b98!bXTn|F!0SBM%J16<-9hjWnZ%Y?^ zb5wIQpF6Go_zukaN8pY9+;_I_LmH2C7qOfuv^c9Ij|SMULTl0D^9Ms9fAu%P7VyV2 ztVgJf1xZo1A)C(6r}$+Ju*q0ayYNRC88I{PyO{#YJgcbbu!N*OdHR%gUPzvAk|JF> z;I;*m|Ll)wtMDW0fIa!(h>s6I@f*0br#cgJCVRU(HgS=W>T_oG`5t8>&K$@jAuTUt z!7gi7!ce9=XT?9#x-libU>8j66c11g$wL0pm5s8hht-SLq>aitoK+whT z5UOtJo|!`$@x&huAi}-J9leYnv}&1zu7J3$N;H8O^)^H))YEK(C1z@Z04okhg1ZZ$ z0AQpEPhlB|-Z5ZvJ|Ny37`AZ`IEJi3)<6D5EM?FH02xU$Ea<+l*x+qlpC$iG;`aw2 zJw7?Am-s^gu{J8p4v3pbzX%9fyt{hdLBN;eODn&R*-vQu4~>VcQPa3%PL1RX4D49~9@Te*nb|QJQyBi1){tkBFym1;Po-A4tRfn40=%comR;N-qxa zE@0}{XI%OC_^#5|K?1SR@?K59J zSU1+JaXOq~w0x~La; z4rpdYY}}TYq?V@(wF@Cqd1Satg*sADFN;(GLJPaTz{W&=)d18dNEeS5e)zX_U z2+58h8Wn`fMpnN>(?XDX^uH!2BO6;6D*yK6s9}2*OnADvyFhN@1LT@+qwiQ6>brP~ z!{`1!7;=BBKU%Ij-gx{goIT$HgT}Gdc9F(Hqn68Ny`239^4mhrfZkCdn$tzad^<=& zHXGEBiYtl=f>r~~;sINd#@R&H@CjI&Kv(~A$>lPcYSkRomQ}DgpWTj9T@1z9ppGr6 z(U!r3;)&z%9o4l5qg!9V7wO?Kt}CA}Om2z~RT>B0TrN7{5#KO6AB8>VLqq->F*Y%d zjc5)T_&Fq^?#6QDiAA2}dUa#I91}a)<&Jn9$oiL|5$=8Hd1>PYMywo$7-rfxJ@hGr zcGiBRJ)$4S?7$Kq={TuC!S7R-QgSlsg}3pw+7t9^f<9AXOzVOi9COKys;a8ZbyG0J zTeyY_yxhqW&V6c9CEy7jxOsF8-UD)MMQj}coo8%HsogVz(7h*w)UZ*6&XP^=CH`pd zfuY7{-UE@Mel#K{gYubUShrlj;r5W2cz{3G2xVsYzgoa$$kPDz{%W1VpR z=5+`uvC2EM#J}fDy(4>mKe1do+1$r2gVFuhdwYNK^LHQBq)8Q$bxe)9JW6Dm1ne>f zyCd|7%6Ma7#9)lA{Fsoq7`39(HBE#JA*D+In?K>^49vUHa_Hkn(IBb`<51IIpY8bt zpb}Z^rk5XU$!=*w+nizGu~*`>hK&7ey^kG%sRJ+Wez5pl2t#|vJ|gZ$(bk@ZVnL_X zjkK0bq7ivk`0iJz+hPyDps(CQqJ=r+B!y$;x4DFC4fMps_CaP_-gzD}PG<+}w-=uM z_WxY_TsWC6CYMURiDyO>M8j$Ma@Zp}>n)^foF6KPBTH)h?g3R`28XD;+^W&^o9!~r%= zkS!`ygBCLr*b7`m+RJ~@u0{peOc&k|PQ6IUdt$dr&yYA56_45?(nAOa( zgaQ(}fz#CfJbU5X?lG&u($XKawe|?m7ARU zOf&X&iCkXL3#!4g+NQ{aPNQjlYT3>*%8TFzafob4&)|KTCZM=h#`=QnxP+k3legpN z8YNDuC@Y7W4ZnFp&B(a4&cCeNc!AmX1&e&*hKuZAeaYcOb?Fn&l@bpn*xGPk^kL#q ze9b}=TW_xmP5xKh8b(zWpG_WLE>r(&)A8z<3&e~&{5*=cE&zD19!4|;`CMm;UrfM- z#R!SntTQjN@jbwI9L(z3+M6Ff%cIq&&(RjvEo+ zUK?1~-qY0URS5_PT#a#%AVgvH~twSC-U$`cJT!krr- z?01RJ-+vw)sZ5b~Gd#7k%Z_=o#Ao8+D9TPT-+i#RiJqTj4Euav@MZoKe+Li z_e1rkzRDQSuL#aqN(-^b3PJ2(c_JaTatBy-9TE9-nCdu{nVGd9ZDE{waUDhtP(B_y zd7JJ#yD@~RttxN%!H~_v-#Yem1F>`>mfeV{=d^Lm_a)EuRAwwDTU4|AO}3nFJEfZT z{iCNi^P!-((w7zn8?xoMIGqh6u$=(xKS>L6SqrjGR^R;|I;g@J1F-^T^zp z0WC^4l^W;m0SFf7SpC!xAFl&}MX>yFIu(cDeo zRR=W!_M5wGNe(yXj>}D@<2~t$ADKD^Jbjh@rQl5zPuUZ**pn=;LJE1VD=jdZg}GZi z6NnIk2czKQM_(J$<5|`9PGT^aM7@-|$x&AfB)yJ3REb_mRWgMUv2lcIQ-+L;jNDGi z%g%E5m{h>74L>9fvsX#TwE#hEd0`&=r7Qx}`@(~Ia8NZlGc@7|CNu*8MtR|=v|*>R zy~y5n5iMI9o6vR5T-K<#aj!!RO+|9_9GUp!lRx%;HHvToZjqIsjeMOVEeA10$*^s^ z(8bNWVM)6?OCE!e_*!W8Uotxv)*Qj%qdw{cqr9`V((3h9`mv|iK~g=KQ#LZ&oH*x} zc{N<}{l*;blKjnZvS^St~FvU8qup!5l?;)g*p0ksfN?gT~)yE(^Y z-1S_!DZ!4xD3H+@{`~pzxR#}5ZkFp?(gQy?IMkn`)Y%Du$`nw5Z7gS0rTplv2yL4?aZ}J?Daq3L1j`<~m&_fhsI7CM z$3klqS-AWksxd|AYW1WjpOj{4t(iG=V@qdADeUYG`fr~v#G3;Z8tFlM9CXxK2b?7$mIIs1H>Ec<%-IWm*{JNG2m$uck)g7kucF6|q#aC?qdsrwFKFrt%^ z;@(7swl-YXT-rsLgEQd4rASY{-!JowN)7OqPD2{->7(&dERgEtdc9p`Guce!FBT}c z;$1~~;XAK7);fP9K^j|0oFtx!%%*SRMv}D7dwwM%WEA2&cAXrLN-C|DKJ55#43H?3 z-f`ABU*$XK1SNbB7=LvQ^m|SX&TSsMtMNo#W{lSLCoeActPN#yrbMcSJ*?ZB<$(S! zRypu#r*hev{FE|G^(^GuOV>OfXWsX(zv@H}_ke0NdSVb@=eWbP&#oE2eFpP|lhrc@ZYN<`L)?pm}V?U{iMx1+QNSY@NGv*C8Y42n*i4I&{SLPN1murL-bWEnKd^7@YsU=ozs6nzcZV6Rg=kE5r^=eQ=&-c)yhAG7Za z)9|B@q|A!Mie;8MRiF;w6c!d9s(K>Lp3kI$6$}&FhY|hE=94ZL%O4&ayAYg&PX)4M z*Dw>v72lag`)VQol8@X{P6&+v~v>p^XHFoJ6W+NJ--Cn>G)eNVC#+umxIo4o+o@FJynfTUjV&!^%-tQb*Tjpfy`;e4GpWRhXVRei zhL`7xMEfz4zOaHcCKYL>t}XcGGxloc zCio}WXzo_vKo&sjQhb}iiTPn-7>JZ6H9)BE;sW)E^AHpj!l<`CIKjpcec&O$)YR0B z5+ckOE!s2nXrdDgFVD+NK_n9Av5M#p&)CgqIB{TEJ_Usy!(e<<*%cw=PTAQdEa9O5 zquel!-+s3IKsuzb+b_ZZc|RXET^qbV&&YIeN~pp*;Q4ZUn0sF~Xj#>|wXtLsWbP)) zq;d=jNrRB4<2Wjw`@phR*^V; z`JQK$MD#mf<^NimPuk~L&)hJOXQHG;A9yn&C^kBcMkS2>_g_3?7r21qo^Seji6=XV zx)%JSerahUt&EvqBSza@4`^yRW|AKP?yopVXkT4(Ei5A)8>f{gs z|9WppD*5KVDxy?;R+K25999d{S4VokuE0!*Q=8Qw?5zA04;{H{8Cp`9PRS#lGjIG(E|%t_hD3 z)~f>Cv+#^uE+3+qA=D*aMpm{2AVoiLkqWbUJoi5wfL11>+hwo^GQ0JVE1C4Oxy5?M zUmUK@HkRMMP)*l~rM(i=Ri`#%pksBVWc>c)f?w+_VHQLw==W&_9I{pNbcctVu|Oc{ zBq^-^Xq9mrG@USoy)+E@4$vr6VMm2e1OO1jU=9z%p-x@TilZ=AA%9qT0P}1SpluoO zwjW1aS7oap?%$pR0HQ2_Pzy$w^w?cFxr-$@$cUyjG%ULj!)^|`e3#+RLNk)8VidST zf7qtJtatg>#;J*k^7Kh3WU|`x*`7g`fz~Ou?T7qaX-RubTeRyM5e`e|{63 zGtk$c2ZWK|lchEYnLX+iJjewMVC@`y)S?(3f#)>J$HBdNUte2H@a%piMDNMwqm0FP zN};hK1JGc09!&+#>!NV^4P1=UM#G*l0~9ERZjha`xV$AA=K%g$R*-&c#x2Y&L%V|* z9cUYAS@{moWPgsDeO6H$e+%rn$<(9D=Q=7NCx}G~DLX_yvHtCE3~8f`l87zKOa3$o?{QG_rlaH`8ei7j`Vm?}M69@ADhl zd3wK&4!X}bSoYHOXm4srk-_bz_I6V1d_8wl0~i|~Y$l*orR<)$q6?OnR>a*^YETP? zcV!*egS<~44E&oNsJmD>o%m#l>p~-Bv3Gcs>1ZBjHgAGtsm^S}yffJ+n%@R2+V_zg zu&$q2d7crWP$G)f!S(d%vm(N&&g(>^9HxB*Cqf=));DT>5e_9fZm!+oPN5=eI#h1$ z0G3t@#-@10ruHc@DHlCxHHYMNu4d?8r?1uv?E=4QtCEYx*FZHnDv7AYjx%BeQ#e0(jZgs;O$BLel{O@93Chu z15g#0Li?INr>R(DP(7rXz4QH*u>-dXcg;Chy0p2W{gfs}kC+Oh;2OQVo@wFw{rPLg zy1mfPrwDc2?Cl@i8`X0MhHfgXu65;nP70*(5aF>X1kZ_cP>B4|8vQcnKoQ8L2w+Kd zTw+*{$XMKiXHf|1Bm!eZo!b~N5x>n*A0eGL3dDy3`CAv z$yK@}$BzMMjXYJ#0XE#xp9toK zW@O2Q#mD7!XvDkW-}L0_42qB~MVaNY{ZwahMk1?=XXjue+4U5I%zIqWM_Ib5v)~t) zi#y4g35XGhs85@CpU57VG5dyWF7%t@SFuAC%py8eC{B{0=+5CxjpS_o*K-y$Lx70Y z;C-eFankIpE?HR2qYp>IU%$QwRBUUARLZlcckhFuG*5KMtE}5w_HeI*VXSQ4SphD0 z5Zu!|yHbV?K!Ef}>hpn29M}}(w40yNXm|e{8&+nhW(Ch}uk>@lpDefgF?r-#GP^3x zl{DOuWK(6Y(8xr|v$BV#!_MKZyP9H}MQ+^tHkTpxUM4XZbQ_1<<{_6Co0b_CoVpT7 z)Lm<)`5xHUh4mVyEmrGU@G*{4zhJZ`*GfTVb}DlwWX0OxfgF!`md2cYt&R613zP7Q z)aPL87MkD2OWN49?gjE%zG9R~q0_~#n=3aNi-28K(_y8cDW{gQ&@_!!qSZ;T9ImYJ z@*ZiGDHmJA8Wn-mcVuT5>Nh6%(m+KRVn5scne+r#b`1pvBQ8>Cs32;3Pa;#Coed+Z z@m1tqPdPTmrt_`xXh-p5Y12++H?PnWVIo27kD^2rJDaCFl7_kJF6vZLX#UV9Rq=xK z1l3xBblj2TPh2&5+iSv4b8fG}@F1M|_OE4pua;Fsv+?scSB-pS!aHwl!AKzjZm!aO zCM81q$T~Dq>Qzx}@yNN2mB_5;{q695pMr<-7PP-q_hnYwRk0~Ba8oaARn)?RI;}(H ztWce_DDlRa!lWZ9o-mu0HxI%qRs|>Nr_?ug^p-|yZVik;qx%)kEMEc~pB$(nVHpq+ zKVEVN0)p0oViWRWS>>YNsx6giH70sk^~X$EQ%8a#q#C;2F^N*e*XKi@Nmg3`othWe zm0k%keYq*t>&W@cpP?T0I=cs1YCZuqV*$EdEw6yG_~#?WtUYBX zbgt_w;d-w$(iuQC_r$<5HJ&>j4 zeRNN1P_xo5BkT17@COKPNlP@GruLb>4s`iO5o}(urHx*WHvrR=?=oZGBEO|XP%$zU z!6-6E(Kz^K=~OuYEHA7Pmh*>f3m+b$yS_It)4ew(HFW?ij0reCj+F`+=THOFk@x_N)kWP#c3@EJitL;{lv z3H0SXaZ)+T6*Pk`ZrZ>m>k#!oaJ-nRsD$kTK(DW%q2T~Q6XmA%a9&{#;s%g$!UI09 zI+!YRsiqqg6;x|{rjgQeat>in*EA6@hX(Sti?xxFV=p1h`0}WI4rqjec)z9;Gt>dT z446%hWaf&(hxwhOlKF#)5`Nj&Q;dm~lYNsAC>Onxx~{Z5wAJ&QUm1ls$rAda*ePwt zLe{|nV*IN2>S#!t%qxV^=+0o=HRAvo;kCOEES$QiJ+0SkTcdF#R6X6u zN|mVfVuI09c?Nq;_zi<6YQwThX3hXcFYTR?e#2**isruTPTmPQzd5*V_o|qywe%ak zt<|iA6^R^|v7ur4BusjOr ztt6XGNJ69}dp)tyM5CSD;`ZR`wEdbh!@^5^W71Cdt}kA1a3z(ZhtO5%=s&Y#rqOuI z&Xp*So?e9hc3?nWK+)|+k-d_nRc3xGP5gt8U!6!D79QK_OmF?1uIwKyNwV-RJ>6WA z!o z+dn`0t;awPWJ_OP-xVT}FGae89e5&iRve)#BVo2TXHG_1dIjMf9x(5Mfr^KJf_Xgk zI2N*!1VrrWK_Khu6f*)6? z{>X9p0u9c00!{i0QCT#RrBG z8IhHybBh~5ca>W}8pXvD;afcPY=6-W!0eXFWUycUuA^2^zU1L2_j2dm-WfL!ZjB~uJg3=lu)IleU7so>6|8hng&;L zu@YaxwZnA)KRRtCu~wF3w8Q1ITe`;cqhmB~_n^?$VE1VHZI7xg+EY8SW?EacR6DI^ z8u9xQ+YfNfUAwvV=5712Kw+h|{;>|@0AUxwt-GDZA=5?DgPq0^(?y*5?RWlV9~4@) z%qiSdXRmc7wPW25zq4-$uHu86YTnS&ohvj(QQpJ z&cpmUI}Zag)JQN1GP|>M~EO@Qm_41VW*3Dg%O-@S83)4mZvL! zl5vUST1Rr}FKOHv{A+SxIas{7y`jRv?rymQ-hJWm5$CtM0=oL4yQ#M-9I7X7x;>a* z7+5AFmx#Zk8y_s3^+ZQN_hzKmza;0xG4_4zUMWkMghcEe$-L`^kSvzDZm)fv086m| z)hD)-m&wV`8Atgc#zC{p(gF;L_2}*6fiwBL_091Uuo~i{-sdA;P z@;s736Ah~Q>_WlAd)eEY5GD23e$(EMQP}$Bog!*@G1#k^|pMZZxLG{xB z3i^3oS_3u1iG9d0g+>Imz5zkDJ?{0-FW1^BTm+)bHsSPetTMAx$bX}oX;??Ox`Q`c zHFD}RMMt#f5d9#qbRq6>VpVMlY^IAr&Cz)RB35oK;aoD+R=^u!FbOM8z-$bkdG~=+ zqp%(ToJua?SyLx4v>UbnQxQ^k1R$in0a<}iMidW0p8o^633y&%$3Zoy)Oe6Fe>{=n zw9FBeN@8wo=6mU$^oRMtKf)_z21S%lF-Mbp=hBBDfAJ_wqbQY2v$(0mBtz3B0pax> zy36-OBk}0AOMG>b0Fh$u+XCi`eh_rbq3YL|->|{;lK5~)H!gUL{2{%;eT0KB$vAqh zH>U`A-3C)!?gCBe@2|w%OcBq3<^WXAGE!29H!d=Lu_M*CZN0I$0VL4?2oxskP@j}i zlxkDFO>YEY)zNWXB8+FDIH2J(rpg)EhT&?HZ=S&`x$*DJNPXRv zJzFFL=u9B}^OXMaH!p6;0%}1rV103T5Tu%IkCvEzUxi{IzI5eQwDOu1WNJ}@p22D2 zB%~Uv7636B7q8!~;NJT0wbM zZ}J`Ts)=A3^WWTXi8MG^6Fg{C2SAFO1DX@n6lqGw!Dx^eXazC|p$JzsO^#jwAh`vU zFijS=hv}G_X1amQ!2uW~P4H2}qsqV&qXak7UJrsLecL`+{Tv5X&I`M}AObN43b(kv zUfc{K7?g6u1G1pl5^U2l?1vh)8xOy}b5kt^WZ%?iv#6lyox((Jt-N+Nz}K=K+W~r% z@1k^+72YX{#|xC#m;y4Xy;DvctH%cU@Um~6xGiyMEZz6M5_5y1@Vlv1;5{~v3Cv%P zzvu(R0ilg=icSyiV#o%pnH!WElWbg6HBF0kh!UXMw34U*Fg|Pm;)mhY-w_6+_%3mC z_wqQ*_t^|?G|z&Fd_6u53T}S22@}j6k7cUmE%u^ZMJ-T>l#dyvxVHa}`fTF8pcBK3 zss#u@7OU$U8wpTaP(;-Jy;>p;xTiif^iXx-Nh?i{^E$F}!R7n?xqKe|jOp8S&sQYr zDEt589u-F5K~Vj95W4k>lph;TQrw0Go&rbYK&6$ZTjL6p!_Pc4oo{F>DS)3no1oT@ zI;_=#{+S@Wv!*l(*cQc`5(l+FrWaeKr$+-){*AnNUhBk5XV1P@1HsHFWS^Q%mN?Ro zrUdmF7#*sxUCoA2wImO!@E5mHDWTKic(ud}1dlyje{2AYS-#qpfB%*qmlUUV$lOy z6Oar+=!UBxfXw{!)e#vANyZ8OL;QhTgw;nQO!2kF_6+bblX{I#ew>h?Ulc@+@oX{{+fc%kKxkwD8KK}WgyXODF}=Vh-v`FoEwMbLOzE8m@w zCG&oW1n~o~VY4B>)eBlmqK$Yc9$1_LF7?!lnp<~%eAtD|*h);5XiWRhF74)E0D(W8 zDZ%u42L?KdfDCs?zD;Yw33Qw0%VQ0Azzl?TwaYOAq3888e4n>&o1rQed)DkM#{~ z$U!$BT%I1<465JU+l+tB_=&DbD8QlK5P-F-0o1rCSc3S0XRD_)>|){}r$ftcmkvZm z+V2w?<(p z+beFFF#Y@SJXL3RK5_eOjCT_IDPcyqPG!3@0lpXOv4=1G`$^No{*$ph(5T3aeGg-E zXWT);Q1bPtoL#}$t@boWIcSa3{L+Kk6<8kplE>blQFb8pl@E1stEZd*a?{fCSjKo% zz^&oq_qH3MT=ECnbaW`QC>zV)1Kk~rz+4Q^jq^FO%GP5k55Z@ zh7VJ>4B@0Oz=A$kC;2YUsbc-J|3p7r*C9AVP8Xkyv!>$rd)ll(qNpEG`aN(mM&d53 zZ0D}Jp`)kGX66J`f9}K-LU@3JAC)_jie3G>1XV?j?jVdOTO8hp3EhXz8z&{)FQAn( z;qV+3JD#eOVAzG>f83;_6birnm`;L;5*htPV^dS$HdVIdno6^&r<8cpCaqw_a);7t z%dMcL56XV84z)Y^X|wOk;B&e$CVF}f#MqAg9Uq+h{z7!Q(SrvApevwTu?36wuNw!W zT?spx8*ZHT?;D3L1@svfZ_dos%c6Ns09H=v)X4C}mb9_G(3N$ks*ktho~thA5#+ zK;*%tM

2+lGdQeI{wK50p~x7Ld#FzDEi9f`S4u#ug{PV5txf}fw>`D3A{#gK!5EecOq3|zN39iQ#rRM-Ls^CS6etzM&Thjs0rI!A>|pm2MLQ}-~tDb zLJ0Kgcp!Ewrr2m9g)M(+V?*)Hbl)6Z6G&>%Ikufp zOr51wmTNIo-htAcS}?d5>OlH|H@FX?T)DU@>k+1|&J-Dox*fc&#vDjv{@S93^*mYt zn?Jx;7`YxW_-6ucI>C?T0MuTpv-6+4t-@K4WtIOoyg?h&$OP`65aaut1iH!h@IX8j zF&y#w^*6Kb^dPII*Jo=XAMwlu$6kXP&V6Z4v~<3kKLwAXRSkmk2sh*dw%nooF~9m{ z^N(7>;@})#m#jv~ruE1NV{o6fknd@6gl$cr;jx^DfMp{kIr$eqH1w;&516@uaF}(i z0-}W$&5cL@0WWotF}9{?215h~8tw_(;RAc~Z1_zlFvf7KI$rYrxhEMW=79eO;V+#Z z68H!DhJSvS{QnQ;|MN;jQ~n=BF8^b^A$s5qV8vmNy`556kVi`pJc@Ro4TrFU_4F7k zNYH}Mg%h!#f>7nm6V|V&5an<&T#2+Ncoj^7AvhZNAwZoZU0o`f$n$VMq(ZL_yMt$W zV+pFUGchQn#7MtDn)8GS?-Wm8X!nH)8+dkvd>)$Llt3>#v#wPC8mQdQU3&o5acBoH zseK(l`pS2f$ST0=90(N5obe9f;hm$R4Dv$)o#r5eyQi%T_ zSZYCmWR}Yp=v_6%kfil(brk9efl!Ht=3D&s^Fy}nC9n}E`rRQXypZtpSRq*I{-E0o zCZHrOLDdr7fhZO)fY?{`^9htN4y7xg@GPWIOVv8$c^CyXeL&P%LH5H@OwPff6eWmz z^Y4Eov#>V4HX zd#Gg1M=~}@kv{~f{QWpWsI@Q@Jp<%g&zl=4$vRq$`X!hF8i+H0OzZ{vV}-c+RY*f< zLdI$yvSnY2+=2E*uyzpA4T^~yuwU|d>JfRta*GaKm<{~UOru2DmmUh%cOF_%S?}|w zGQ0zFpm~tgv378)1ayS}xm@@O6wGs1Kc~sZ>wa5~&~ea+JCnr58SMJ?zlZzaQi^UX5mP6AE1F`R^<)_3cVSZ`2`D^cSIMM^T8Ju{P3;#k?k?7}>GW%GMgM?ebgze!bkpDUI@xl&0 zDry>VOpd++Tm7)exE(KI2^$=75tZ>s$0UY><8ahG4-#dFo~{gzKg94j0h;Am;o*+Z zQNJ~khIBUZaL~#)PG?pGfyCq6)m}#D>9bX!bhUsa1WM>@0ikFCZk!vc_^V*C7s9nH z*;3AdA~WCdzpWn)tRH(02%0PA*P<;eKQF@RgoOQ@qqV>yhFtjC>4tJ3DMF(zOF{xO z$st49pGZL*QB;EjoY^Z#(jK-g2_c@Pfx@_=5r^v~bE-q*RG4Ei(Cj!LIo4VfG;?pw~Yt}?8 z;OafGa~P89hEJa=-%XZ}2SAk;fpe(J`*5+tg63~B9>TC<`0XHS&5tz6*^vF?c3d2K zw8Q{5=Sg|SuSKvcHf9OzM>}fwK#@k|7C1{#rdMO7)?y#=KEOO3@rqx{p21#tA12+~ zLJ!orWxyK^K+rH>F%?$?mMFa0Y}j?RmD{wN6$e5*;LM?m1ZPZvX>z?G2Ct*dX1*U2 z0}X2ipago-y}hli6ySM3aO?|^xGw`obl!a5Urb$~>Usd@>$f%w5qFhP1lS8P^#e#k zP{#UWJW2e^kH7`qynq8g9$_3Sxx!=G-E>Uwrzp7SIc;Mq_Bd4ZisVbt09~>s6hNYd zU#RKm-yfWa_jE^q*-Rn65lL14oThS1}owxsfaQ!BRc zQtb|2iwXem_m`?Cf%R{1ki;}If_;&06+1+)d}h!Y+S&nuMCAe;-t>uQh>J4^_kYnF zADnIofqmKik1zT~_BQ1SAe9)t0W~KQ;zN*2VIn><685rv%64iMBHI#R-XmDk4Ea?^ z)r`8KuKNR0_`3jN=DL{cT6wIPv24i3@3%;yH&4$BG$w^8fye;4YN~6fe)p86X(N^> zWLFZ2;sv0JPbV#({}E(zoz+OkA&X?=<2^YE@o6a(-BGH`t6av2CYQR+xU50?trXcv zw+Pb&H)ydWvrXfBzdKJ^k?qc3qK_|^FnV6KZ#w%@9ZdD`bg8K6!>@;PfvIf1aT#W4 zRC-L96(FfX(J3!I~Q=)X|} z`Rb}2%Z|k7!;i=PfrI1TGXSP{N*8KV7LQc=Bc0{&9=M1pjXaaZ0KOm8St?oKfDEfJ zSN9BvWZcb|hrCaoL(E;SR||*r9#DCe8v{^zfjfhWR2;K=&Yb#!w8`T#0JL(zGcT6U z3O!cpn7G81dr!^Za(2FFJnWq3LHyKtRJBHGbZThAQj< zZngVu617+t7*T&@03R=C9g=9 znD%I!lSw6!uWQo>iUMbCGgos=upQbS=71wqh0Y385lB6{5Fa3Wra^B+`4-^ExUAfa zMl(jgqCE)dtqX$iHQ>M;KC?vh!I8@@XPNNWoTL&M^FBAUj9X8T{58X0B7S5fotMF5+ZuY811C^LE&Evv75Hh7bw43KIuH60$EiI~9&nbqVduwn zJO(15cSNhEws5+P$CV#IB86260Ea`Y7tp1xMeY#mJ3uL1#U&@B*sOC3 zZ+fIJL#aUg(S#?buRnZ^avh6&BoxqUYLUiCS+0t8R*)YWlFX0p?UtEMKZfHiqb;cv zYrr0#!xvF9N>h92g@DsFl0;cKMsa_-m(Mvmq4ZEUZ)6F*(GfpHy z{DMmRk=NkVz0mDs?7XvutY4bvo$l93;^|0k2(5Y5o<>U2K&|{9*VleDt*mmP*kZ8P zKe0=@ha~lheQD;ZLZe=w($}oQC7O!Njweu2lni*yJ+D#GEAC;Y=eC_-O`A9d!yfW5 zFj`qm)JF+SebwAUELrf_$sE*8C9aWO5_>K%;%L$M%_r^~=><}xIyTUcXb{Z9 zl%>}dW%mI&(Dn{1-|(W>FjWtMj81tu>z?zHy*MRr=&NKC#qP7IkEO4K;N_LRz(jJC z=7H)I0mlG-$3?o8-Aj;buZer8N!=ioX__!a(KzurWSGwqiUxK@_jcm_Zi8gXqzbT| zUkLWh zvZ;*W6sw)!tX2jn#v1QbiB5s7@ReNPlz!BiKzJwiqFoz@rju|7GD!FND|CuQz;IH-t5#(v5Yq2(n=;;fIR5 zN@fi@PS?IGMH5WQav&o#rQrc`w~FA;)rdd{`FmlVJkxIx&w-Wqx3T>BP6#ygK8y4r?3`XYasCCs99VyJ@3=dw!> zoUi1e0@+LF&wp~9FKGPX;^n#!$aph3W{+S2*5x?P5D0^Sf0_*4HhBKS^@#BX^ z+5!V8B^O-}ID>B=E-h}oH+7RI)}S4!3VUbh4%NQUEdnOL7V>u#j~BLcp}B1tDgC*n zs3X-5c6!h=$ZzN%WYg1`8f6FYfMVua+S>4WI5U$P-NB?jV8a?v(ZePbT~6rbO~s-N z&oZ!K(H=DcLA^U*4bLtGZd5{n14P9j##@4NKD#*-(Cs-li28m6R(gK)cuhQra8MtK z?ZB3%rk@CG2<-tJTgzhvp@S9`NDly}=zF=bneNrhiSab3Gc;h423z1b1Y)Cegr3rx z1y?{7yX(-HwH66h>8*B-VMOzn-oC^8ymKHKM%34oMtn%fcx{p$ntSI8Yg%OmZBG_m ziXi(yJRq|cJ5X+IfQw698bhI|BkOXZLstKhxxHP&%trZgvV8bU0*4m38Xg+$So z9RCb+neJ$n%PLSBRgLfvx`p;o)KUn+_`&q4c_duxMK(EqZ~xeuA1Wr zZIFl=A0D?KvaDL*9z{vu#XT^5+R;I^j`nwp_xRnN1IDu6l?Fq*;bv$zH9sKS&20lS z7`GU+HljwMPHSw@3=G2(R8!fvn*d3;SI#=pN88}woE;#c_~JMt!wUE6YFk-bNkCFS zT0quVG9&^BAd*)9woFRP&1n;jL{5Zj5bf#lLI>;inp%8GIzAU9cbf)3$5sY5?S!KN zX6(u#=E(8OTGB#N4K5TH?FM?GFc_*xK3Nj_vb9NCSuo9DqJ0i*Z>njk;H> z!C7yUJfr($nw!Eo1$R>^Wdl_x1l*Z}4)GHVd7tt{p*_JvFVRLBPLf_sV%z264dA{` zJ-qA$mfLSVuDd)!WLoKEBL8tp^&e>gW!r&jjd?WoL;`n&5F&KhhRQ#`p+K3770F-C zK2_1%e!0@%3@Rz{r@nMNGdET*>>zqArw&>_Y|p_&ts`Y}^xm60kd7c`&w|+=(N??dujduGybVIwMPd8HKdeLrfLwmED8uTb zjH<)GzT5ajYgLYN)m=5UdRFm-^*Oa~wGic>N&fX0l-7nazt{TQ|Lt;iDyOiQOibTDa*v0*&al=ZYDDUE;`JLMUg*AS`rJvCt9Wuc|IM11rpF zbL%%3idP4qkZ8$+YLnOgf%6fAhRIkfmIE`qKNv9ywn{_d?3W7W0DCS!f9g205$>64 zCTd-r+w*RNfn;fDnq~l8jmHkaCvD@xwxZyzRT13bAIbEP!~MW866TH@#zxokoz+A-#vsY;A#cspZI z9UK|8E8CTdW(YWd@~M(6Dk?->Dd&Kk&HZ#N@ z-uvGedlP7=`#1i3DpX2~ND1QV zg+K^e)D|~3?$tjA*TCKOkB)T3sLNY$0O@rzeBr4jxDI{Ck~+lM3$DA#h6+C}KJ`r} zvth7qdylEqiFTAoaD~K4V}A69_4N-cjS2PbgCS_EcZy5UlB>HGM|R-W&bDH^wPs(~ zrxqS~JDFb4q9i-HAx4gL!k*Miss+Jn?Z2?R<={Cf5&c=g;Pkh`p-r`RjtirVgjTANA?OhOwniFrnBr#b$aW!JQ?NC2V#NIbZx|i z)m!WwR4l2bmU2X+&fw9^bJ?1_bdOHArSzSh9WGe$QdkK3iv0v?JYo7<`BLzD0mw;L zil8r`qS#y>2)!*sJ*?~^{qv1FqnVp>w|u37#=KoCj8*jyJ-eP+r`3J|D|TJMQlD#L zWw32YC3dqdg>XU!^Cnl^;*ju%yVnxxs)5+WO8!_khvS2u{z&~x(U(0tjUr*;dm1-5 z3;XAjr7rN&l^dH3EQjQUDQ({8xqy|MvgV4(^w((j#G$L#&q+M|f^?WQGYf8T>;-F9 z9PSmfC2(_Sbd3MX{rqyPcvECo+jAgNC-q^C#s}S@h*6e(>99RreVp;(!-u5B4dPpk zP)#(JZq$FJN$|n%pmGSV{tmFmX}LMB@9S`aa!CZwM6^SgE#8bj8EY=Cxv8wpz9&pt z7-_mqjYb&m2;m*?um%>gCJn`N|AFTR_B}@yU9I@vZBxcJh0?d0B56uxe*kc;|(N@u9qw z^Jfpw4@N16WEAtyoeN?JdF@{AilTWzGe^UXvm)wl_Wi571y(1R2K53VqT3G*~t zB}|XMT?Z%$Ny_eemtI@s-^(6KzaeI^b9Dt%xA1VsK9Tu=?WhB{EGveD4#uSJ&pivr ztUVMML_S|P=62O_4uD#*8_tDJh%4e{IQ(GDCTacY9*5Sf~y8@QG%sUhquf7Cv6Djftpejp=9Z zk_ktjIzmm#OzdAiL9`s9 z0C;ozYi>-o4#QX7=ko_K<;+RUaUmyGMK(BgrzU^ZQkfro0Un)M&pTUHm%t&?$JEJa z|1&?-x9dvHEmNs7SCFu6){SbbE)e&#=$(&Hogc|7Jcu2GHdt%I&z!J#*4#Ne-o9BB zKK49zd`dyec(2}vCw8RLaeoAidd5&sA`Km7n%BSryF|m{AM3n~u`GS5j$x;~A?jtLnl9u||XF9pUpTkS@WqZ9T zS0%+76U&~G`l(V#@2PLEM>Cf&_U9cDCa78)Fe04tIm?{iQ13DK=uXm=Oa-U z!S_5A7Q?`m$hs9*x1P6=Txnv7#4~vf%V@cF<`vNDS)63Rej)TOk+(P|Z zmusrlCbO>XtTxkBPBnI1N-JDIZ?^8L zj#yA_bi&5#h#t*^Lify@Rs$JyYMZn#6j+k^c$pFrz5uIx_w3TQ&i!RgS()ClqKS(?^J7G`Pk4+jp2t$ zSKL~^2b!<4Kojn>9umN1FEEJ_LjMkDo45mD8S)to6sE7@Ip#2VDBS$($8C zdRQL`@x7!d8V?$XRE)9mO-BVL(fTw0y{W%z$|tr>W#%m6zlM6d;*;&;%7iLP6xX5Y zN|veV1-VTwC6R80E-mU=1%#mUQav&|9lIS0s?4lA%Yj=ffp%Wz6+zBOyEXi^{)5v( zIUcD)hb3m`8Ke^0bIw5(ZXt&1SZV%{;ItQ9<4+#+77O~`x*eI6cS%=YH(L60RpveO zwrX+c*0sWyek$TSbu)D`O=#w^c&Oresd~k(;##cV%MP-jkR~B-2DY!_6W5=0fmrIX zp+4pTVH;bBodE%7wZ?eZ@C2r!cIa+8!q#JM1fL4~ooA%(9o4hX`FE==Y%Obg1J4m` zL1+?`T1tHnJ|ksKt5?U}!Ivcb*7-iLG&pBYVZ;qQ%Y8{Yb(}IE#{UCvQuWE zvFG)=Njt6VI{w&V!^qhFUC@s@6_Y05yv5Z!l1S7(b&HzBBQf4q)e8+?*7Hl$q+yx6 z+$uo?Rj$l}v%lYoexiQ6>gk~e(qf%%gVYj&h!`QaRsd6`b4oh^J!?oszi!BTuE%=3 zOIjq>R5kP-(d*zP|3onz(a&gg+aPLYg>4?1FLv;z{vn@8t~pP##r($F3eWt&!As99Q!Kgz3Zb=XpHw3 zfX9B$LT2sz_wVQXw3AK(^ahot%J0IfAdHRFV~`(lhTiN5KI?zk4{1HGJk3F=OkqOR zg@iDAW~Ua=J%|O&atIigq_?-^>M;sLD7XvAAC=J<-Y_^5#Ej%{Kzkx{Uk85+&2+x2lzPjpR`5VxT@gg-_)&jAo*&qYkK z!3!Pi@75NicCm{alQJXPo`;Ay+4K7G#jy<4l+$21oRZV=%-(*e8ada?%ITWS#`bH+ zNARA@2TMH7I&)C~ty<2|rahFC^3*g~Gp{q^kCy^|x@F>il(KZzxSPIiaZ89imn`9w zR$9$UkE@c=HVvU~)SxA3;hG^LqM=F_ksAFFr(&teBn)d#a;AX~G#4!PV!(WNCBBS4 z3Ju%@2WCQ7m$9tmYsM7RdlaN&uce#vH4A7jWzFaKV|1cCwzi>yzp~6BrxQ|OFZ4lj zC&@-WYV%)8+=jBj@o+Wc6UWo4dsMGv1`-3cHuM^{+a4j`=V;7F-<8?;49V-5E7XZp z^7D1-qos}9m1G8=?#@`A3|8E~yGKEH*w4*=gA3=!~>?4q%r*6K25oPVBLGmA)CR zit%{{C-}aqn%ge`mgzIIm}7*h^EH%^7aeRASziu+EAdg?NACu&adVZ7#^SQ2C*Bd7 z&~EC8^b1Ge*HX8YbMYN`s=GI{N}d~|^F4#b&9w}VVx6aLOX?6gushp2wOKr>s0t2} z=Hw!|v@4S=1~Mmw_B+j5+TpU!jR2PIDq@72dQFjyTFKtjCBlyw*NvhHd3=+ss+YQ%M=uq^FE9jlpfVn(8G zFd3P=-CPf!UYpb6kE3hjukSWM54OqZerSCG=i-!x1G3yI^N)zv#uY>p^uB*=R;WRT z6m=Mn>h4WdJ@08PdP}eOBhm2V>63)p>hsTRUVUorSf>&e+NZLJ&v;`DNLTBwF|+)2 z^nBc=8UoTN zTh*k$2%}MZw@bf|?uXQS8Izc@GgSMusjKIlE=`!~UCDL2$0a`*+ghrV@f-SyL(T8$ z%^jAbm{gJ77Ax4d?UxPHc3CieIfV2@cOQU$44$c{S3>vaD$kTc@7CpAGUWBY%cQIQ zBX%)q?k*;$Z7M5IqosZ2fk;8LrTz=`5Ia&1#5D65MSGo$ViOQiG#reyf39m5V{=(3 z^yz|WNGTWJ)I8eQ=sQmr+ivUEVpYliFxXHA*vUH`!B_Jn-~a@-%p=m3ah#zGi<}iy z-4vF&_1(uNALw3tC1+X8A@s(P<$-nbo4TyCN1eqk3+)gsHICO3kile$z2@{cST${_ z2Hly3%=XG>L{qW%X(DJxJ+{+TlmAzh)e@4h9d;I2Rzt2=Q#*#ajOg7CbQf@O6Oun{&jH5O-8pEUbB9!! zE%Uz&0qr(&KZ*a`PiGBuygLzj;7R%DN^Dptgn|63gb&W-`~_U-M~I{fZd^O@I>BUy2qK7qd~bz5 z!#qnoh-XK_`8y5{J)qxRKXn3Zy!^8kz_#o?pmEy@bW+GmeOURCgT1v^poYqh3MM++Ms0Di zT8RDH#TApwXQ2cu!9vVi$rhieZKz#5HlmSiU|NZYj_;f1W=cea#6J==*rny7R1*pr;-IoVYFo z^k4=!9FXNTAg4M?LWm70!jkp3uR=P5UiMfoFeNWS0D{J?TV!x*_Saz0wQI1|%^*59 z*sm+UGk8#9+yC?I=j_OK$a?~}+eji}=^X;1P6TP#4E!}Dh=}OX*H*AYL?h71KK<9C zsWZy82N6soCaAyC=70Vn)Or15xHc#U3ZN=JyoyYJxhSOPx_jb<8pGkOKS~h z%kATzXfk%-d-SwYvK&^-$kbvdtgpV@sYo z^)F2G=Qb4%Xq_|gH4NLR@+hj53n-XLb={9@cqMii1fRsN7C#W&a8M3CUZ{ep1uE>mB$$2 zn@oX(-5G|AX5Z#8K_igtb_eNAY>X*wiT+!uVlss|Rs2N0gT2NM`F3O2E=0Q)6j^OH z72fmjoO{UT)i^&h^XVU%FO_Df1BburemUmdSNsoyrec7lMZ<%Y_T2aySes2? zCFzw(uNKwi><3%KIwrrP9^me-pnAI72zC!Q{hP?lvN`0YC@{k$nN(2Ok!V<0C{NgVwPOjXLBVe`;N z@bf-DZSY^Oz_hC$>ORNLMHoL%r(ZQM_sZ!BfC;|E5XsUTghMVS#a5Gds6#5u9RCi+ zjX8}u^}HBR2KQ4KDIkv`Lsp}=d2u4W8b*I5L+LBw@7_7W*!Zq&rM3w$b^O9;ZcI3d z8#_B^Z_W9F5Ze%dir&YTOEVWg4uR|95Q6PQdiq0oEwv!h>OnfGs{~ULF~_o!4ynL{ z%~qP4uOuxiy7*TRO$OeKV@^(cr{IHpvjpf@X^@xFFUNw^Oa@Yn`#i1$Y;Ab=fJn3N zvY|>bXu{h4?WZow1z_LJwWd5T|HoryV!0Bh#!s6(a%uR@_4WFPasr49%`D-HPa#-g zkGGIgLU6IkPGimSv-4$!AhRBcxJIg6r!|A8aF(35@naM5Mo8k6(lX(3gy8D(WM&TK zQ*dMX|L(gtre5a_X4ex|@^XQ4-sbWuR`LjLs_s$;POVOR>?}gnnVX@vlENb+??6PM zN1jm!;FC_x-P4=9o@hm{QhdFnxM(F$m*V&K516>*RE^-R{My|1!qhx$xk{zZiuNSz z<@oxzkDNNpMw@e+6}gAzSQYh~dSXW+q70=>W^#jSnX+Du;-h(ItzNZE7H8XDt~zG6 zIp+Q{@2D%4&(OQhgW8LUZaZ5OF?rg=7h8rmF8NH9&2w=RbtB_?&h(VoWKp~Fo?+B8 zZOSjs7RFC`nTBrQjB`imxiaMvwhqtIif_5~^q1OXlncC#6)GLEc!jg4uU>neDKm9U?7w0W-KZEq$*+eYxb@22NbfEWI#gBpfW`be{npbV2 zD7kq2LdHspQrD=(`*JIa6US6vx+MWGh{|{6s^j37>u3f!_KLon>us@n+_>3 zfBwYC$^U(Vh}d3d4P*ED{N%H0>{0Tjn23`jC&}Bat%iq({~#N`%bJ2I!ji6nSxd#I zHRO^tf^BX9e12 z#5m-V*t%OJm?Eo?-hYkmQ;S1j-MPC$!O>judKQ`;Hq9W}FAqilRj}58R-pPbvaD41 zmV3JnfIn^23e@8t=YXB;UOo4}@KW48POFH7u@MqXJsZLP?A@rXxSDF#IZ$8@aH-j{ z6-2#P1rNI#gIEm^Gil&u(4wp$jnVeood<)S%@J5j*Vs6Chplo!@jnNxG52a$Fk0cKzzuNk@50&$8i^D$ zfeZGp%q*e!HVc3w%mX(!w@ti-d%aK<=nuzUgv0Cnzk^H_DqcWvE1r#D^>;z!^(!(? zZC|TrU}nb*yEGVlh4x7Cxk4Ema_V1U0@AjRjAUt1u0ZKSS%*Z^(h-jVDL+?9k+^lB38AQLG`YtY&rN*a?)dDJR6yPm5FAZ+LE}+=;ce2 zQ$D^`0kjAW12$4mEQ*pZdaV_V=}&6!W=Z->R-i<1I4Q zBkz|+zf^bihmQ00Mn!5`Tke|fukr%6`s$V*j_tqOgFRpT!pz0r6-VivWEi@vb_VvF zViQN89yuN+X1kUxk?pqxUpTK7R;H}l&+0=!Vv+On0OWI*9S zMHO%m&#rlU{j65G{%eEJ1AxW{+h<4S2VF^rB4sWtR>CMuMR61<%KfhiRpz3zw1^E` zUPp8r8trM{GTPdh;cjGKvohvJU*rCwgq*2pIPWcT36Nv=v-{xxm(CA|@E$2JB0-gQRp0yEa~HJFwTwV_nR(o zF-cK{G%S}DUthtpErc|WEVq4mzg16}EzyQH)2i2Y`g>iyg8i679Abjd(lGv+}jOtUE zXRSG@`j;oK;0$KKUwTzVObn3qAt463R^deKGLh`ubUseOn@pPRq8Hi}I@d1foRRcsvx2)%?d~`jUu$QfRLstKM#sOyV6{7!$Nv#3=Dn4Ru#$LWe}X&0AXwO!_O&qtO*sg*9Jmj7QTo8~Y>hn4Zl%y>>e6 z_1{Rt(dFVYX?ZtgU&?4;3z;N+o{Tbfo!!fazQ;@zb*K<#SN@pJ&-yquw)fiF>f1>A zIgstL>+kmHkutI+O`=QBwO2+fHeTp-FrLjdr%26`4AbIPzt3d(C2F)w{BzkpQB03O zkLq{md?wM@9d1kQF{FqY*0j-5Otc;6+M9f>`rJk+AW~IFPo6 z27UY;SeUZUMWTOrzjRsqF7cx9Qj4}flcKQl@2u1Nu$dn}EX(hEzhXiC(m6;^s#y6* z=GkDH&RyN=H2jb>w`YUz-;{R?pt!xa<@KZbgf{d5XynwuM1;|gF=dxVd?B+2mNS+h z!mgeMJ)l=Xv5j+0Gx2WPeRF*#zyDHe?m`s3w73`SP=27eM(j|_uGb3|VaKrnOUFm1 z#e6AwVxwFv@}e-mJii9%L25+ zIVY3LNL0hFr<<+i3JMjARkWKSjqL{w^(KS$laf_cSo(lzB6{KN8{>=C-$k2<{WyL65HVy7?(D2D4d|zfbwN>Gbz+7FS4Za4GdVqv`@N7%u5SEf=P9!DjQ9%`S?$eAg8((yq|KQlV9P#wO+1Ay>Ch@2%XrNr&lxg3XZBm94nouez&!e%{e46ma`H-TNMdqlnv5js?~0I z?Zw2p88}U~B}Z?qycZ{z1dcr!THEGtyI;y#VXRFLUn`OeuqVXi!5 zV{?p;NZ9W5@FQnUF4%);o5+b(b|h!Tb)S%r6BA3(i))8&+(`O>et862<&(BiuA95f zd@JbHk*5#;aaAI-@G1D4J=Sq;7rRH7n#e-7FEygwDGvuI_RgeZ zR(Ck|8lKm6cPbTBwv+oIsXReC<(_fv< zB7{m4yiT}h{`jbY!i6?Y!agUY{&m#*+AeaP<~%$Jk|%?(Vs= z0(4wA?u=Bh_n*0pp1FzMrC(!=N!Gj8h4e86QcjEU@8LfsWb~9idYZQ)gQyS0-aOo) zJqF;oqF7*Zgu>Ym3SB;NHe9T5^Ve<^w}r@sSu9vrg5GRr4}G)mSZg}!-#zpjL1F3n zmtY{XF(vJ;-8ebDC)L?Y>Xg;%)?KEdyNt#X)s8NTjm_EhX5A^>F2WiU)PqPTEjr7{ zA6xHFo;FY|ef9qvrQ-T+==$ z-Nr#1+v!(lnx-Qk$90wK$Mh5_NfdL)pYPp}KVLXQm>ccktCl29g*iF!2y{lYK1Ydh z3}#jWDCSB9R*{#$`--9%SR;ZGk&-{}-9@*+^=ZA(vn-6MFP_fuIOlic=64x_)2 z3DZ8`EQ&+FTFPE(myQC4}ek(|XH zw+_F1m_{_hD(NR1y3s#G-bfpk#^mUg8>Nyp!f!s^9x=G`U8o+g{W?_@NvHFj{R1Yy zwkwVA5KY=8F(Ji{m6mH5(wa$@SWx!OS#PfzPV_eZd^_Kh&eQtgyPol7UDgkiXFHZI ziaGg$V!gcW1Ly)?{8-n2$fdGdMvSjaR!;Tk(ek$%2l@;qPQ6i8*$X}WzH3oig8LJo zeHoSc%Ec$P;K9qSl`~B%kxtsdFAMhgkqR%A%M`U9ilH|u!JI^;^V`3EO7Djv2BnnF z7Y+!bA`k!fQ<9d9&Xuo`1C+PKS^q95s(%JxSwv8g;Jk9WH~c0|%_z8d zGs84OVY1Ut>_45wrY}g@s@FKO!i{ZLTDCSUFMPD`_uMUG{v|RZBXa$#HCNI?+s~af z-CzZY$|`gFmqYdjW-m2O1s`~l`kmWsruh28KaaMdu8j#T47*-vsLu9^hc5L%-y_n* zlkz@|-Fl$6DGP=EFy@+(&@xh;BQeRc@g-_gdvN8ZKx)AWHzqzz^PlG=T*Kef8xfmH zNtcs&HM?RdZ+;UJkm`NX88E+49&Ue7D^_!K)?b7Jss8tv!+_Or+!k7(L+G1h`wgY_ z#B_jj9yirB#Co9O^| z!tU#7noH~a(Vy?zkq=geU0Jw^v`r8^r$x$|+H>CQNaM7KJh6Eq@fi1e)%Prhsd|r% z12BgMr-@Z~{N2~ljD|ld7bj_Hn|J>31eo7>>_@&6tAvU=8GSO0Ef=o&83WqUf#t!$->)|Q-jklib&lS4UE&>v?EP@fL@6Ud&RoJ zM?!HV?3Do88gwBV%Ru30^bsA^0@7Lf8_RO=o#e)Aa#OayC2a4hfsd@sAQ)|k$g=*sV0;%>tWyv3Ppagq7lHh`FPN+RHvLjzB%S>p%4UU%MDz1RWGnD(ZSqj2X=)$tIOrybF=h%BqkPdQTLUB5QP?y{`q%}(>*;V9h4CE zAe{ZH*wcD@fPIkyP*#)Gz+Ju zJg&$+?;NFcA7~ z4jjipG`;PikIq&aRjPS9V;dsi0xTC(5ol7#v3(k%b_&#BNIx|9l+HG96&c%_1guET zRGRTjA((G;z^Ye5tN-))b);lNUNV^>r|D^)t11r(_&g_Iq%FLALhys|AmffKSkXZnq_8V z{>tvon5q4(+$NLCzA`p~%RW?>JjPP5EnJZ~1<RaywKrN5D*#p4@7j7C=w?N8(daQY`B?P8wb}b2NxH^1p2*a>(-YYq{ggbgZ>b?H|N$EcKXpTx(8$M1lI^9@sW}Y+oyk4m?BK zTR{TanLQTq?*QK>`df}&Z`pDVVB9NzU>sVCYRlSpf_+$8>fTSXZoxQR?<7Vf%_haf z`Y7YN=@UizeA_8Tk?x@wQ+u!6oSuZ+f?8H~W6O@Zs(!m6@a|H`CH9Z)15dHY4^J1BEFTZLYA~OdqFmm1oL5De{U?tb<}08Ptpiy&!Nv- z7NU;`zCBb&mMiXtybdk6)r6&Yq?xVHph23TT<@gQgV@0NUeI56c%=s6c(b`KE#VCN(l?uSbpjd7_l$w>+XU2$<~?sY6GChO`r`%h2_+YW@M`t8 z5C*ZR-Nb{A(?71DUEcs4q!PsD=u)Os=^$iGg7?IL-osGgPSoo@K&qH8R#;C*nxRj_l4kuJU;odz-Y-WN=5M?5f(% zC+?C_H7nxi7`YeRI$7vCgitX!b7QSkad~z3MZb~RTO2iaLYl65(z=w8Q-0rE&ZHs9 zO?HLU_3lyNew5gL8$w@j3PTcFZ=$=#js`oI7kZUG5tVX6D@931#m8A>&z!CkGMkFc z>C3STjrQS8#|m|dEtPLquK&GZdO32$XW#VD)BvGCAuxcZ7s!F?;-e>h5vKS2-PL7t za>at8hReiq=8VN4w)o0*kRu|0H7otAWiGT)H z{Ajt{lyf2q;&r3B{j*lHZC4=s)4e7jDYEZ5((n-ID+4BOJKO&VT5-u0Mm2 z2!Mq$2tYH&XCRm3VPbmG`V-2inOX#{WF#rkd;$(ISg@623<7iM_T3GS)&@EncS;j2 zv&SgeaW)(BG6mWWIbMxYo5kNkZ=@b#QMLo2)FqHT1@A)+aM>J1&ZE!XU&L)l#1%P( z*$v-$vShABp^**ErgSKe`E|xnIHRn1jRfLbgN*Mhd?+188H?r!1&fQ#8Ax#9z@C2U znFwwy;n<$ai!Qz6=?Aj)h1a((BI&T1Da~)Mg0$!cLY6wwTYexM;Si4|t9ryK1y&&= z!KM?rM13%+K&223kmYs>&^tvrKoq-C&dEK;WV#3@S}$Kp2CNQkP4(0!-js1|@?tt| zvdNci%wBHXmURA97&~Y^P$#u>rD%s;EXIjirLYKJxSo44iZjt>21i*D*7D43|BQ)+ zFQnAs1{>;$T1u>XXNi48PIvTdv_p0mR@lRtYqoDYDl)|pzalsK&Q?P2%}q>gM>mo~ z^H+w1B(AztwouB;lux7CZ>Iib?CpAv@Z^y*dN)7<@KCM-D(0|;x_ZI&eV4!fB{3s_ zM$Db|7}wo$vH6$eq;qfA9sBXAc7xO}S-muL@-On};Vz2(7;jv$eZ&TNO*XE=s}O%{ zpn^7b`s?YgtIPWeo0sLy>U{Yv+6;;&nZ=#?LI~Fv+<|5Vx^o84=he-M^VC zW_wEYRX(h(Uy%$;(|_QCLM5$xLSc+RX0pC-7uO?f%TCReD+^T$$2PPzUG$u=moLWr z(HKIse_eRB(S31+UJbKf2{Lc-6O1%J|63cwYhE`WU!Q)n_Pit!{UN!H3#ANa^Tw(r zVZE87nV=oKcKsygDC|goSs(iVvE~g*fGE2=PcB;dpN2&9|FljZP)rp^PV1)6lxyVA z;Z9;G(Vp3L0iMnc5q&9LR_ksbLwV^b28=`SLX_w2M(n$@my5iN;!PLhHcIEUWuIEz zHo&0umstO~C3DNk8AL7+Ld=G`@%&T6Xpxai(ewxNmoL+Jo(w0x>I3G^VLTlA zSo9|}XW&&H=)@W{VTH{ePpUJEE3?+f^TK)?e^%Jy%6-fEyHaZv=Y-*zAO1+S%RnSK zGtWnqy0pau3PBn4^1*{fUdfsK+UF*DwUyI9YM+~ivJ85olpXhB_1Xt*h{j&IEp3JF z&YNZN#XCS_wD&(-bjS{AX?fO71m&aMI`GhNYTgLeJpFy7X z3%;?-e#j_{;?`K#otG6rTKb@XaahcXH}3JhIO|jyyX3yxK#M3NN`vfEgJbo(3pzi# zaWHME7b#b4YY?g-N`!6l(n-gMBS@TYWEB{6N*0)_u5dBj32*6vNE0eSzYZCqTT#I9 zAP=|`;6Gtq_5fn=V^< zu6$byJVi!5ZK351pK=s%zf&F|YvH#77n&U3BJD`|bWdaUT)F>D?r!hW8^m>W1E_@0 z+@7p;k)``>3=}1@ZQRj5w)}*{S@z9oF$hkEcA(*phA{*YmU)v~2!_&<`wMp-~O;c3`$OfW(DV~T&4*~%FYWaA8~^JGs%Y91aAxgpp;&EXwL8m z0eb4tk|CX%xge~kUb`i?0Q}O$|5t{te#8_sUY#L8ES+f)?@)Me>{dCDI4zE+->O6? zPM`&ecJT9E2fK{}GKP#B9R*m5*Ayn|S9VczA$$=^om>!9q#2-_!}Z%V;edxRMzT6! zfiL^tV|2}W6V z%f~;xoDMxSkT2N0`FVEEzk1oJ^ErHDluMmVC`9VMn;&9JBPKRb$IU_KX$Jv)h2W+e z;Fv_@Tq|-9&;ITNvry%};}rlTk&FPS)Zb>gn7+ymPJ)iI`?db&vSQ8jl^qWY-#{38 zwdfDg2Ka?(;DM1NBNgnnWMp2QZ>RcX3{?KSo~8sn84EwSqGrHBb}!zoVb&5Lj|uq6 zO~{^Q&INAafJ;LG>5XZkqQCj`zd3~vXGWH^s_ONW8y#iSCia00!*2uBFlB|Q4I+Y9 z?%(}UM?5wGCz9S4_^S3Hq)~)BxOheN=pv}V3VaqvpFTJi6k=Z+qQGHhVLE+O!I(EP z($gfdb900uEJev7yiZ%>Md=72eQH$F75vRU>y*u7b~}{#9mfI!azj^fukcOZdQS8m zFAcJA{~^Tq{BZwtR*+S9&hvh(1)mqxrEPpiK;D|U%LEG*_esyHr;j`z?wPK3fj#n!4K0w5iT`+LQLd?$HIqluIu{8GOBP{#2Bison z*71iYfP2k)x@M#)PouG+pl#+0d_v(6zFhU{)f+)Uh?fr{Xx#If{&ArFO@ft>V!zF` zceO(A05U-x99ns`D+E~c{6SB3Js$x~3%D~99(L(jVnMQ6-;uyfWHdSTN?(OVcPz0X zK7<+0K?$^ceVDQS zdJi6ljZU*4?czHW)4g0r7GI`qJK&!z1f4YaMm$`2^ES>F*#cKas~rv*X&0MYect!> zYu61~y8$Qst$Xc^aA{z0owkUj6$)68rsn z%Hs?PNzrfeGuoy>f8F;}t`88jLt@$x#9-kCKQ6l{D0GRCqfULd^Z@hEKu@-9(2Tk7 z0CrBa8Fj5iX>)!*k^_a)*ZV>R2c84sOWL-Bt%M2hqfa@4IoQN*A?8!rZ+XCT^@8Ee zDka4$6KOjH1;YkJLg+P^dObfAk-ASQ*lFH(nPi2$FMg@uL}&e4Q)~RdnY*!}d*k!l z1Qj4yw%%^9wj_m0RUB9tT1hm zh3fFdDE*e(x^dMcf8Qr0#>#V()r&!}J@2REk2)hQ%LlM z+r?CGapu^{p5BG=7aG%rr&fxHyVu)MnbZICgDWf+$Hn+p{I>P;>+d1k)P*Fyo}edX z#nVR(cg2y=XrU;1$!4F+&szz{>_9xo7sK?Hf+a&bY_C17WSX0`YDm{Nvk2A@(=?v zy&~uDk=$Mr2g@7xz$S3&??qpj?_P)Y8_V-{o_cMv+It~D$my1!2ar&LJ?6>VP9R4etcAo`c&+Xs$=eqTBqtF|`#@&nXk2LeEuFM2lS?pd)k$dWkBlgZ+> zT}-6aKWJg6l`jYD&Dgoi)sRpd6d%k^UhqCWyQflnW{=L%rd{tk^KdVr4ph@qrxIhW z$5tF~=V=)aMqBY$cb&PCohCFKH+U4>6mT$AzTNRd3EwB}hwC?E421~p(H!8>7pm3j zlg->XE??aG(d8%@?sQLy_S0oD?H6e?OWJb?N&HEz27>_kn+eOSG1TZ3_FHAF8nx_W zs4vNEqfW}V^FKf0)p>_VvSR|IoaK*h)4yz>)%b2m`6@v zi7E-Pu~?+p4Yfk{w=u>PttG0@fb3`8{Ug}}WYI|8C2e9{OCzN%Nhr-42}Iswpo;ym z;NcX$A}E}OH%{zi)VgZ)c{JlF89j9u47)EszbMsCV>!?*hLuo(y7ZeAxEQKT)

U zuV}?ld?3LttImjWVMVFES^no0oL-K)G2sMw=6W`x)@WR_~#h3Zr z2#z2&&-Q13Ct7a{e!wjQJI!{~eZ3;Rnd-nTnvf1&Q{0#}ADT@UOa8T%&uMZpl&Ql0 z=N(H8@}1kPDxYx}4H@(<-l1%Ve~j&~D9!3j4Z5es?-^O`*bJ7?w;9_b*=SAi@)ToC z>X&m5&sHO(GDa4#8NMOr_AOtIHH1SRXj-lF#CNa3L1S(r@{J+|HzI*=y{x(&TCZP7 ztIzwL36+LJ-0^9|kz{0N4FG39|7qykPeXm`&hH*;qZ-96t>m`3@L`p3Yyzq)d#EAu zak-_nO^Xiz>QJ(;^vk1Ih@>kK<3RG7`ClXIrFH&?WqBo#NjODWg?(!hs%z3KwSHZ&x=^ z;9DskV=A~NCTtS2uBmWxgHLMLvCI`3oj*GEJ%8uMwug=fd3YQU9Uu}PR!NL$S6t$TBeo5%H+xP>}w^TFrRQ(*hNXY3~6pIM@~F8ytu=@3<43s_8#NrQO} zbLl=@Nqv#$5+DnT>%KFvhh|`nbEA9_38&=m^~30kXo5C)&lMxgeuHhFQ;w_egQ{(- z42+V8yE>u7A9e|1E`g6>^gSm}rlx&%*Wgy=^&#Hq4+qwQUq{fZZ+lMl7m_!soL6ww z3O?^WT`bA^?Arl?9!3v^cGNYbJ!s1lMl!OmwwcnWjg?BB_~-K)WnxZDXU58JD}Uw` z@Qev?RYgY}z?GlBCm~F82+Z!aS1_Z2hiRL#K?zc2{H%aQ`#VQ8GPFccstE3I%a%@Y zonxapDfbD*nA;pZdekMU52A?1#T29JP+t%XF~{+}&MOdxz9T&&qX$$o)(9*F;__<$ zimB&n4o;r?ar_f7X-Az_LJUspoyhzYR=yXx08eI~{{n+k2dL;<^e;;HcD$egSWC8L z{=?;ay&abkP{f{+kn1D5+`(7@yQK561y}BxRxfDS^FA=%17l-{qRuXbjs3;E>NSZM z1W+fBx^bZl&UBY{GrE&o%(qjC@mUMKFa!g$MI{8m~_+C_ImyIp4`hvp|LDk|q#l8$}s`o-IgtL$03Er87r__H$o6o0rXYfXz=qNPWZ`X@{HL z4uCGk_NhpnbThZGAV)z2mGP1d`O8u-L6xYS+G-#<$$}*F6v)#WBGM~L5$ZaaOLIf6 z17L9m5|Kq2UWG+PaHg{?`g5Q2lFsEO<0>JE7Y4%o9bE!{oOAJ7P3~?3$U3Z2X&Tit zwL{qlLIoc-oDi0+tFl}pRZ33@nUVcX5}1RwgEP|`E#%uf_ZO{Urk>*kx1mH_u?}#4 zWi3|Ph7x;rlVADBP3|HOCXml0dEeZ8T4|VyZv2jDj8Kdcgh$=h3-HTXa$YmweDt~n zkMqLQ7pw4jt;Uo~=UL?H%O!S`-f#GQ;iRlL507~Lx-Gfq`|>_iE7A)U%Qd&k4K-72 zwtP4LNRmz#Y4LZ%3{Ee3W%O{z=jN-5+9{F16AKktywlpk|3lSRhE=t7;bMZQASEeC z!=|Mhq(OSq4HDAbA)=DfB`FP?l5SACQ@W(PQ}WKm`R;R{>#uVR_S$Q$Ip-Mfc%!gL z`pJ#}l7gM+CQ`Fd0+}6{F+e8Z(CtebL}QnC=fDB@5>oeYFOEm{Nq$tMKEswO!}z#h zu>|zU^(%1J`gH4PGQY+qFE+Ne1<4kxJD5LaG5 zKzcoc_9WM{(LmyB;ase>tOcb%gdgMhAQ}8H3gvE3e-a2sfpNc%fH6b?f(Xd}WHb7g zB8p(_vxvFO{Z;uKp)qj1Ik8Ncc&(Cm2eJuKOvje=r6e9CHJqJ!tg@#1c;Vz5QdjZ} z-Ry? z5QiXCopV4~r(f_KkN4Ny_2n#|6dZy>1h`HfcwFUnNTq`WN+Ep@<-Z>VkZ7Kk3zJSw zXXM^JddQwCb1=^E?b}a~w=n3F@66P3JJj%lNX#}aFm&V{Z%AkC9lU@&_{&@SxR(og z51rn2e6`m6-BbFJLba4F~3pkjoDPK;%IP z2SCV$v7t1xajX~C!JI%exiKe+S_Vd3UyTJZmkf8dt@|MOZ1#{>Id*!mMQcbH4bBWg zb{L=tjgiydoztt3HJwK=mCQph>A8CqgVE|c7mb!$YQunMMd>eJ51*nrYy3&>A_f$s zZd9OxUOfe|S|SR8_i*xn{(?;oNvIn5okDUzka8N)?-d;Y>5OYb9r^DqaPa>~y103c z&^pQ7cS1@Y&-ikC_GQZj4B$c(9>NF=X}9U190@5YWfG>V3E3si)nXR6$lOp1wU@khlJT*00o2qB$ZElc3f^mU6)VHGu&wQ%@0Oi+_8&69C zR=4_}iu*907oR2+|6U+mM{5jqtFasrXXsy5Xwk$tvm87By}s9dTaCKl^5KtT44|C` zffF=LM^(L5{#5+$S;q!wn7_>@^Y50=$$I!(%>$i1hjcnm^#|F+O3Sk!HQ#%xCP@2Q zrqLe#Sj+%o>nUOoeD(=?W!QdTAq$BygT;Z6*Gbr;EhAk?QY2hMYJ)K>U9BJABKC$AmaI9K$>E{m6ybl%0JbnIG zOX#&?9K8TXd;i8)o!WJQ8&0{6%g@=z|a^$UGq z^lApOaY_L+HU(_--vc&l39xIOg_y2T7GE7D6;*6Y$^(xoeup}Kz72y*`>wc02!&aX zi3U|V+A9?;5MqErF#fLeD?6su?CI`~ev# zG8YhRc?h}fGqvK9ldT{}6$nv>Vv6ekbE3yK%y;me4)5r0f^A&l@IVw8t+|}k_dG5q z{tGL<2U?7@y-^W>%-F0JI6wzDlra49H9)h3DUsoshQKol>BST4#(yT`r0R-hwe|Qt zRgW2piJv?^vs}2nPUN8yF}1nNM9j3472xc7%62U@>E_$Wl3xD$-vxt)SY8ez(gJ*mv@ zt`iMav=Ly7@o|6Xm=&@o({Y|OR#hyRNHR%uHok~M|L{^H5Drb}Vm#ssh*G_nt&FQ( zo`4(m7X;SW3WC5E1^tUgVC-1?;RZ^B#G-nKGD4hQs`b;@H~F^hABTBA&C&OP!r**N z({Y!EIq_uXZa@!!NFu>K?Ca6)U)9qFyJ785(grF>K23O zwn4vCu4P@*1`FD_H*!%?6lYkWj7c8}5Tl77yM5O^M%naL+63d^p9BL^)C7Q?KLD~* z4FqeQ0*g^iYwLYs8)&*L2)Y1*V{e;l&?P}n?=Ox<#}#{f|MivjrAN>F&B^8H%)lJa z3In0@K6CK5N!DsGd5doJOB^kqh@Vv@AFa?5=;UU>68yiknQ3yYdAbBA{OwNbG_gmAAzCQnwyXtsNAX9~0AcvfF z-zwzzFiZioqEU4px|bl+35dhS3(?^KSnHNj2IdTBWk8ZIT=hLNa?yKt0z>n}7eKF% z8$fqvmO4)Y^9NP}JHs}NeNmzi*{{L1H`w{iz$FTLPr1S9_0{cO)Jq$1Vu1A#2#SFp zz;ne=%C6*w_8Kf6@I*Le)mSYjn4xS4e(=GnL+{`31r|phsXX{b0XF-eBsZFS-nZ7f zJX%fUvjlK?RIuM8y}*H7hCvLeZb=H530-x#AFnTX?+s@K*FA#b0-!)as6V{~uM8A} z+|}Ssg-tyS&TmDzU_w zXdl)#!K|KjJV9nkn?vZc%O#Ov6^Npldb%3k0 zy9OCD7uXCLo}HZn0jr)?e0@6wy6eI@Vd_!pdWjG z1hXBa<^q#ZSKeGH+1tZbF~XKwuxE-AfO$=B<~e^HGE{>Lxzz%eDFZ9(2yl8`QJPGF z(LSvEl1~tmX}ogbBQG5QOrG%_%^czsDCw#)C(j=dLZu55+#RyL76kJ6!vY{UF1zFg zC()0AqcImiS?E2(h#3IInR6fd_(ZOt#J};;cLg7qNX|;1?=r`K@B`(@i-zg#yqh90 zZ$kP-44MK3-x!SH2iYm$oTjk=h?zeIea63b^-9iKr3^|L;pI$e0WhdR^-GE z3F!)ui_MfTjiaE~vU?nurR;#Xm41gr;9OA01j^Yaxh9E>UqPu6DUy`9M3tr&sdhev zRXB;tcXu8B81f;pY74?~8~bq41f+&-DIfq#ZFcSUX7BcJW75?ce8ykp*3$dbi>yP?>)|0^BL6u?=u}j5H=hT9pi}4!W z&i9PelIZh#W%>KSlP{nE&`WP!e0KCw{#00@pp^^5LHH zD#b*+ z(ow$b(wI${jGDi)0(gw2mS(Ki8;6+W=?t;EV+(Z)a`~@{G=pHkcaNeUQOfaitgt}f z{nb`NCR>T=CBMbaR{7o2RUpWgOE#D1JNOK3wUpU8z_SX7&Mo7#?E__0$FX~ciqy7h zPiN4d&h9D4T2>YS_DUEf1_2UM<8#H_WoW%a8xm4X9a#)qKB&r9C-GmLguM19377FsRZ4&rg> zN<~e_n46pX6ug(_~`ypX^s^0&!AiWOw?1ErZjl>K~^>yNXVe@>a?wL6c!L51_* zMd|8GfYTf4R2EQSS1vxu1Nd`_BiCA@yl_p=+h}q4=Po!Vk-8-`ofCAD;A31Oa^Z5Zo6B9X?{%vG1)iyDDG8xTen_|cXX^5 zpyN>zLmU^({M-e2hLBif%6HDnpxC@?KHQTj<(SPaZVRiv%JTnx?pSE=E9Vs#wW;EK z9}urGQXbaexGkf~kfy1A1`Lshqd`jV@Y3ekY}7~WKKH8GBY|v&dBY8CRdA<8VV@Hu z#r}e0R6+Al?J^`g?|L~N?Rg=nN6tLtiU#Js*YiQU0@BAfrIcR5Iv0Ie9A4Lsx3yTB z$@=7B#2=*Wqzq<3D=)1Drgw$FU*+^{-lvZC>V6-{l7O(je4V<=WV?2JRSZXS)gA

hVY_MxiweC!Wsn%w&e4~9DtYbiG(bb%8Vv3+ zPG7CgmSyL-h~?a>+l|wM>F4Dg7knlMB?osG(ft z0x!YAXaX%;z~|X^(gIF?&)l`j%_78t&L{AA%SThr4hcstuLV8M?#;m`Pw!pLv&jxS z?(zR%Ancx#5yHm7(9_PPu~xL}$zfs?vRg!I{q_+xw|MbwELrJUO4ie*&*@!IOCq_%!dg7I)PpaaP#b4ff-#j7~cNf+Y1BnJhBvU zaMX}8GC~O>PoR!cF>AAjsp)gZ`ispc^2wS7Y6-m)DUtb(lHkh+8Iq}B(a_Pg0DF$l zAh$&*XM~iE4I2=r(1%9~)g0L+loG!ati(`>8G-91v;PwWhc0h~<a*OF5EW%0!pbA~{#*YAr2vR=Wt8>_#b*_dzMm}UBLL_i zUqKQdn_7XS82H3L4+G%@m$I3WGoyl2QwC{0ss>}JI+r=*UXyuWs?TKF}O;>nT zA0F3(+Yd)1VoTsVm`elWlf|)~p8Wnxi^{c?mERG^VI@>bR)%Ca7#J0{?=sEb12y(9 z5U)1~_)kNEZXg&mJ+<_Y2sbaU7ChU$$TFXNB4xYG)FcDL7#Y3UcKt=jhmBkqqaqIl z^z(hYi83ovml2`AQ4aqEIT^(~k*Q2pxcf6QM9y%K(mPGoCxMRw4=ZUKvj1MhZ{d6k z2G2h45i)Y{G+2^<+-`xTOPiHr`APE|i9x}3Jzc?Y#gCtcQU7~sFu@Iq7&2=G;9$vY1cII_TlT@bzA>*JMc%e z!khHUBegcb?z3Vy3YEGZOp^?~W~%LMDXc!tEUaZ?Y?-kBWdE*`2X4TM65Xqh-{U{kkb@maHjUzUiYD{dHm8Jx>`8(gr_&X`$pJ5?HeFqasWHy+{onREKFU zGxs~N-&D>;#9yo3haq}9>FGNVe&4GrWdeo`!1ZGcJ%WEn>|b51mHhi-;x^4-W%HX{ zheVN$5&Zis$Gy2%1AIr%potK0HIl-XULQ<*@aD}MNb(W?j*1U-=6EcI4~YV!qOdO7 z&lk?Ta+XiM$$2#q4&Y~3uz^=i6nFr|Q0qA5O)^p{vrCv(eP@BI3KuJKUZTPSGbA!# zyleGgcEe_^hA_sj-Agl~2t;$UkJoc$;d|X#D}x3srvzTJ++k84hf?vVsA*_uUwLR= zFbWGRAV1a^^YdHSUV1={^RyZIIC!O1ln(o8swB?ew6=<5k)M|GU-6hGqMJqU_Fxk^ zo=^^>t!D+&I+QkqIQ*zg$8N$&?x!Lu7>AqpjMddbJ3G%!ZEUK;@IrrekbokQh2hoL2gt40 z{d2ksH8sLn<031YsMH@8e$#_T=P#8xT*D=9f1-1pu_B7FqOIYIRt!K(Tcaw3-e|=m zW{)$=E8A^kH@?@FNV zTiedAq_A|)e-tiL`o`fMot0Y zy0WJ?Q~1A6i6lIXm>m6b2R@4RN#01l6q zBxA*Uws-?Rr+I#!ArqoI2WDf631M*?r z-Q9yA{1CpnV$czUGq~YsM(1@4hgr@yk@yaqQoq4eF=4IToehp(H+$&+`PbjgplgZH zfB{5R6(v^86kbKLs#q{y9rnDtG#`ys_+$y8-(Y9e_#abW`tcjqckIdmd!GRzrE zLEaQ%;()-QQcq_NtGAAD?B?cX(U3alJ@IJ{tAf;I5&yvo4;NQgx31J=;3M3ehd}M> z2qUMXd+OzWg4xgzM#LXvZ^XR_0tEUR&$efus%>mpj);2-W~Jr~4k{dIu*b$39i{q^ zaqtib-@8Xc4{N^h^B*kH;dOO$4;YYpYe?qqxN*Nw-0!&g?fd1G(TnZntQ>OP3!dIz zz8MmKx0gSk4ttn$7+uaa>>r(q=kMFbXQoPQY!UEa8!Rl)Ffg?3&epXJ6j`|Q05z&# zhE!L8&GcjU!k0OvrQt~_p#sS~A}hC%8EAx3(?rs~1^xzhs;vXAz(7nfBEpxU z=?2wBjo+}PrPacQ-xewD<^}~FMW`l?s17TXl!N}+O-f4c_V6Qru%&=A&o1lRaaom3 z7pCL$=YGM`U$7=MI(z%X2CBIZw4ZF?Ge^f|I5=YWQelNgNB?ped|hraE?18Z3brrB zlh5W`Bos6>0+Ry+1JIrch38NQb$8qDzbBY_gxn%i-0^*pj7ZRnz;CySB*ki)jDbOx zA_)zT8R?PWZ3y{vRmRVPX9orgixudp-5;YhCpc&%>pMK;Qa)G7`NvPFpYz)aa!92n zubUlA(n`y;0K+8Vf+j&vZJh``%^)L<$#rb(hYi6`-vpyZB&3Gzrll=jMvrG8OE!Cp3MGXTx^hGU=;Tq3g{P@TioJNRd_x7qtM@f3)Y3i7o^u_8&YL02 z%kPr}k^_tQ-P3HlS-C15ctcdE%AH)QJOzV;)12(z(y6KS?435>e^_1pI#-8!SvEQF zfrm30*BgSl#(T`$(|$8*DdHizVM$DJzo+w_^^4`S?b#Qq(s_raUe{?~TYxnWMvuZVt=l~xec!%^@UWf(?2SkRqFRDIq4lr!h=w_8U=9_Z8{L$rMId%8b) zGq7wo{;?>Y{ryYrBpqz1dJBlg+R2d!h(pUkeP&osx$#WA!yoqQB{&vFSd}wsz$uw^ zG`I>$ZJp*)!^2IUMS z54mV)ID4v?CYS1M$ zD4arrf+ABd0Vwtcosi=laYuW5!X7YVbSqHG>jgdr*jqr7uvpUw*v2eC4EGIhZ=ysb z1WK@B>F?>mTB23Xj`#BNT3I7twGHPMplH=r(PeB0ZqJ-DdupW#hSOn}AKjRI=v$Pbb=C6!HTJBx|-N$qQK#4^U zFqsdz`1oe7fF?EQ&Y$~eR%6xaY>uuy)p5^SV5YFnO;^1d@0Mt`+cVdS z_sGJiJ-|*wV6kb;BOO=|1|1{C%kA@nrTW@xEcUJ+# zA9NCuA;B;|@K^speWM||eb|HU+x)X9C7!XG9RUZoIxflQH3fDcsrdV}YLmn;nHN*m z-iJOeg+h+`jV1CimyQbXz<%@Y1$dzoalLMb(n|l?da51G{ABK_z9>8Lx#w$GUnQ%> zLcF(Qg{GR{hd){(ld8B;2J6x({WBw-!7{(TLfKSFz$|}yhP&lc847-$T)+yyPK{Xv zo{S(J=7y;RflbgitTVyCTc)Q9dCO}AD+>m@KSsNk7;kP?0XWvR=XYFoxx7;qd|BD5 zDZpF&SYvKvFC)F}Ja`|4B?SlEo=aS&mzUB_teNw#BL7_OafTfozLA*nbiPUmwY{+%U7f<2#MA?HO1qT{mM%A;5e8j~{znJcRli z0j0NTcauwd2+LW>&sJInJAy*D^1gwL{HQ3m$kbGIJzOk{e+Bf~G^ev5kP)v~uTFL5 zR{ZBm89BdPeQav#MAK?b=2}e3?J=Rv7}z=$HH`bu&y~wk%gX*0Z4QKD7awaYT@9;W zxVP>l@7uC??^5>c?T$BHk6azhcl2@cakU*1tNA!*p2+~ZyRMeIVAffM2~oira}@#e zlxNG#^VKZ@ZJV!A-F!dy*cTf|3mF8%hRWqJ&X2A}>=Liaae%+RCV9a(8j>~e{n$-w%=9z2=V8@ijc0KR{pp@pi-X~n zv1lgV{MGjK^V7O|;4`qeN6+1uz^$U3c-W$QP!ki&dCO(#j4im$MeCzumBYxn7NfrU z0o3-TG$&^<2``%lPPcMCr}ic{Zc;FD(%1b?vguTVcFD#0@s}CgKB=kNwORs|M^|B& z(P;hw0g@3-B?XeB0htJC%sj+9oy<*n%)F_dFOo3{n`A1tdAYg2fP9GF#slEcD{Ha6 ziJbCqmb?Q1Tm%Hc!QUJNL*M01!r?-;qP+fv+1jV?s+t~|qWj>%5Vl)|RY5R8r4@=U z;3oA2m_&3BTm#!#U>e8{9GS^rN~7ms^dK5TB4um))0H8QEG83V_*Aq5vx}EY31Fdg^YdSOtxw8lYjKiAMA8_TItm#Hp1GZBn6VL*+yX7DotG_hxmHyg;x)+t z7kApQ0#(ykB5B&7TGMUhKMag@mvJ3K9YjAxLnS-{wzm{Eq?+G0ju8+(H z0q?jWVX}GV?BE|&wxaeMAUqTZ{wFFbu9P%*1c@An;B{$ud^FHA02IDA zxOu~-OJ^&wXIwNWFvSPC7iHK4RInH+?WCkuhvxt%0`GhHaf)tPY+hX?uT80L+bEzrp-%-s2!OueMQ>k>AvRe!A0U zF%FNe1!JV2uz|%QhX1sbG#R(I+D|~1kxQ^$Qhnblqk3A zUrt8ojvR7V)aQ61n)+Xp_2=$fL%B=2FsXpXHA{^`Ro2EONOKLv680O*bOd11Ryh z*gZJ*$2EJnC^E-9F2qDL&poBCWM;n|R5SoE}N!b$zy}$~GK29_`$7M3#$BesMW3xS#sjD4y^o+We+hmaEmg z^2Sy~JIkovTDP(_O6@g6?{oxuj0`?3dD_U@5;<3a(#N@d>Z-`Q^1{X7n+Nal@2k*^ zl1tG>Jmhaa5AJ1g3+ZBBC0pF`ITzfx75t-r?&Q)jo*?PDV;I<$F-@~Rtq!A+rn;^Jvvg~8QDiU&mRrHLFBQyA2?N?t9y@~%#n`jd+F-=^)}m=+_Z>X53)|-O6T)AJnrJ0{oEa*Y*=u(FLXSdHCSo= zNP@CLnw9Vwb4+YAT=lUsaQ%@qE@L|qUx59d;w7(#M@C<4Uqz)9 z0~*$a#?&XBXSZEpr24~|n$x9%N#By>1#zT=$k;`s2g6^|-do$Yl9LP2se9SK8#|WD z9vW7SN#!R@A5o0%B=+sC(PW?x_NGmU;?IcQi_ba?<9;Dd(UkaON|%Jao0SfXwO*gNg$o_fi>`rHmTm~C1;N$&Rw)lQ}k{Uy zNz@8FPEon0DI_s5B9@l45ypSdCT}bS8(yIodZUO(DTZ7-daJXUW03KYN27fU2tXX{ zS@aAhH4S`l%fdU)UU_TSm3>S2Rl#OU>=1zK3=1~&bV}^$03EWc11tcZz2eC^&~3A+ z@g$-?sf@vjCZl|q#m}K3mmV3((G1v5>lCoSXy(PtagnUz3=v;daJLG3su~+rScF3HK z>lS+#ME|9*W-!`dsWh@{D(~wm)uA5^R1{_SmZl`##OKnug0j0#_$6$dGOhc)xTY@t z4Nv43I^I7;#L%EdRFC&Ep)a8BH2@$sNaaFKpqu{hwGOYcB}Kh4wenqe zTKeXsnnRktn)o_KP1ASe(c||o@xxWjer+G3}qMITgNyN{ksKs;Qi z`kb?nb;gO&HqN?DoxwV*qk1xAKrTLVY_d5B*w$1I=VYjWc@^uAm?X&x21;D5sQw@F zAe*BFLKn>K5bso6cT?BGmEy^461mN&YLT7P?nVvfjTe=tEsIvvD?xDF5th#Z0i6-d z5r_!cWF8zK)&32{^)GO7{e}G=ld`kpK*LBOA*4pIlGsdna8;7Qs6_MN;(4d%5rfzoarW_<}q<8Y9<OI=Gs5C^ez+nHcG#c z>jZwaAAdVMUpV(?Av6D+&Tk1@5oKa=3;_aUpoR`ia2ivjI6_n@JlTuuuhBC>!) zZcjitQ?Ind_w@3z1O6{YKuC;&ZZo&SY*BGq#=S5e-5nJfaq~y{V^`$IG_)te=iBIM z!)Dll{<&E&c2zRU9PB((>2TF#ddk`Cm6)ItG7)3H#uWHy*SPEh9~XwJS@wO0kzeu! z77J@dzQxh>E2>d!#!|6{lr&_exRnf1uS&pJsKmf-;-XEpq)~tr*+YJnT%cL&zv@CW zL=VfS?aJ?HG(h&5bPaQ$-o>lGFsPPti#Lx{D5) zj%JOeEc;n6tSgeQN;)Cq{~G<(r`E!k!YQ#%v<9#rJQ~u{A%-zDWcKjNBiMASlf;|X zWAYSoQcvLQDtQ8lgUbKA=Fbnfk@vf^)t2p&bGp=kipR7&#)Ni-~|ILiRX5g}su5tegS7EWSteJK5`RwM z#m*;w+vW1C+Vz==z<4h$&i~e^z50sEimTxcRwt|n)2JefpzLe^+iI}qrhzMrc(pM- zg{w3dJ^5@IJ8sEUm1Xf0Uo;vt+?Be!%$>l7L)0iT7D@cwMwgkQP7AC_tc}#~*Bg(x z!c|{eqnlup@i!IB4P@AI6 z@`=cL2vR9Q)$ecUX;)I8c8AF70UE+)tPVW%!A9(7+6cZF-H*zTPAq!X@{}`V!~M_K za;(C64e!s2HN$f`qq4xe6ZKo2ms@A+S0Fr6G5PHv&tJUAQW??vll4tl2dA67#yH;K)SoOvO>vG(LlKA>VU+} zBIRJY`J-DY^cIWbbt*TV^wso11z(*!w~2^I{~g+z35Pc%D>1m^nj`Z%!Tkm)>qM(x zMva(@lFVG(at*E&84+BzbQ?-M!j8RH%>A{z{tY~#_x!yMX7>D`)PD<@ z=&iW{Eal%Cetd7N5i+SYX*|0l1V7&ua{=4x#j1^q6Dl!XA)vY;;!&aL=LKVXZXPP7 zp(guLb*#K+vf+}(MToaUo9u?O@k!6hO45h)q9i|j|MJ*UrXL3Y?&UqZ6=Vt2W#V53dZN zJ!nUbpb#_>XnEI)R5E0$ah+2;ahH5BOTR-T4hA5c zSg%K*K3yiaes4WTFD(D>CZa++b!mMn_=R_5TomhCG|aleW`Nzb2G%4w-}vT=a%*L z#qY`KI_Nu;eD5Y;C5+lsPX!_~sL-Gt4YtZ;aikp8Qeg6kGB;>|I>xaCB=x;e8I z-1af1X!OV>Nohd55_d!~#KggM)Y^L!_dhM(Mjx=qw2_BcUkXN@ZbN?ZDpG4b%yAsB#w#p zaCU+xkX&-%q4S%80qgUSOADESG|xZNE#RKfOdiJf2}DiFC#l2pcPykW5|bD!PA#O` z&jMQ|e5ivSB9x49R&Cz3kC0FQH;u`Vk(B)P`3cr$!~DTg#}MH6fMBa1$~kg)Y-TT< zwR0B_0r(d*~NR-+t51`zgAYcWpVY=3uC3H$| zxo4k9x4ki1Qq6?Tx;bk#EquFh7k$o^Le&&AMt3O$ zW9I>j)jNr+>s|%Rt0n{2+cE3v26^nMhi=S)K>;YgKHhuKFO?n%*eFj}SXerHdt1S1 zRo_O_9B_AZGurtdq^}&PB`PVH|k-;ouS1{12Z0%d4|Yb zD)|)psaH%owb%eGZ3DF55)e}k$o5A`UVslw8kv>(umEr&0=+7&XM=!H7I$)TGW`wC zWYwp``+Rk~8t??VHZDn$&*P8lAID&!pJ0NsrX>=Qi)MjK6P9)A4VGMJerTe^%W(#6z9WW{DG78%om{DM1}S(%xF3b> z?yY02JouJ?%oP~G7!w>ETs>;W_LH-+YlDFyLi$ab51YV0>L+4frAz3il7b|P0#qP$ zI$fNS(ic!q*A88Qn;`CtDG-s6X~3A7U)VI9#MbK|+PQ7!_!Mx1#5 zVO_MVih{yx%ql>ybMAKtCFDfAJl$ylJ5IMKjJM)=U8#V(M4VV==jyHL0=3RMtblOcLOpgPE{Z3zH2_+xlBEFRvU4)Q-o6XES;X)!x<-w@lI zmEQgRwx(Kw=3irSyr+^GF>}!}aZ}Fh4CadPMrrXfc>I58edg3U1{}VRRrB72$mqnP zQc_RQNCfD|L_|bNz(|pcS5KQv2@q(uW>VtgY2?^oqf^C{ptjt_pOB_dklGWo4#b$Z z=J&mPT%mgR)J=>**5qQP$60OE`^+6BvY2Q*l_*IkmP^S@ug)8yF8)tdm0I~xCr>y0 zJ$UEy&EA2RbNM`TQ`v9vs^m~V8?Tr*_(kHz`@KCD-TA8p&lDHV(rcR{T`tB zX*#u}?$hz*7p6{izsZs{94-a^q_M;>UQJy%OzR^<06} zGL78z-w}4q@=e**7gVL(`eb1EkToLw3iDI9a!xB(IhO>5lGHPtZ&6UK@HtBLse>Cg z-^5rLw(~ihG}c-$(AZ-*LeHFA$gOfh$VGdER-Hnj0{!5x;4@LtZ)SZ2GFy>XSDw&0 z4BDL*Z~`+MwLiN8DZ>r9YvBm>(HtceWtB|xu0WA9bn9T_{RIdiQPI8uu$77hFIu-)@bs;_?&;k#m&F0 zD714GL0ikWM0twoW) zc^2m|OR)dBU0pL+y#rnm1~!&jmlexF@JJLre(Uv@R8tpHb6Q@{GL}TN9D6`Z0eqRZ zCr6fxfk#ImATN+J^c}h!TCz`PScWc0UQW!puv{t}sil0dZ04s3uDEw{CKaz#2z3+&GHY>E-*4mNI4@Jupu z%STx_=O$XP#;N#E1!P-mwO@-99~;$uSy)ZMFeB_8^8zoxB-m(Mt=_$Rw*76#vsK#ilM9bh7y16D=J|HEM8H2?2qpR87L+VS01 z^QExpKmIiP3FjNQsR5cr9KyQ+`%Z0LGjD1d=FbIUMZKYG9xn9=9CX|8I2Ky(9bdQ| z6ybU~TGNk;`kWNdLc(ub&~tlxEC$yoOP$_SVHe-Hzqp<|$FeEpa+kv#4d=KQ@8aQN z>E_L)TVM1bDi>6gzCcrL2n1Tp-_5IZ3c&yu$XrW6MOs~38}!oWwB1>tty@MYI+bvD zmxLiqP}74*L+tS#Qeuu))Nrb4M_2KNk*WCF$I`@v3Rq2|laP=+!($ejV=VM=b0cM8 zDd~r4Ld=TlSN!gd86EO*YhlZR`%Ry3yOy53qSBXaeeoae65H3+^`vF`QP&c?KP%2R zFm28)0WTI~XQk3xH?f_l?{O`bZ%9pdCAip6#3-H-grrwk5N_85QUFwEDZ<9ZpmW-S z#m5L$+6wm3EJ>xhLha!gA3NUD-HmUG$s#`#kJQL86dfYM!#@HOe^MG66kw^t4*Un5 zp-j;9=Y3#Un>J*&KdPn&IP&*T+kcIDuemy^6L}uTs@77r!-f{az$ImYr1UDu5KvV& zgHZM5otT)Ic;MVG$sD&T#>hD4d^}9F0kZvYwm>jDinO%!^5y>zQA%L>u)|5HQRV-R z_7!(8$$2O5i=I(q-#v0kdt+n^veEV>?(O%0jgJ;sc)k9~QhuNPy%?gD-$U`cMslI` zo8K|UD|z=oKYQgw@ietazPWOF#$oqttWN*!Jk87`6Tv-VO)*0Xvh=4>a{CP+1Sz#zqGtH;Y~HP5PS-NK|YrB=ejp5Kk%4ly)m&a#4@}B za_-O&_V^N`jzv&I8S;~pi_C!d^R;x;9104GhY-i8&}(%dH4NH6K@R#as2)Lk#iI`j z4~WqT++Yqsp`Uuh11j@2pjr9(VT20QItE~pDk&@52E>FIfF=t(SxcoFb!*vhRB&8R zQ%x0>45U6exY%`QiOhWpE@L+c1*<`l76=s<(o`~X>oLKQ$dW5XnXU$iEjlrvS448n zuyA-J{QHU+9Ro5d<;7e>+Ub$S&$aB>4_E@}Q7Fao(F_z)Y~r z%E<{9BU72UL+&P1KtIu?_L$Eh*Fd=t-NuF`@fd(PToDl;+o{HyoplSq3sq|Wv z&l(#Wt3jK?;&tKp+25aYQB5k&Vs6fj%W*ZF&pYduE@1y>6RZHLS>iMMiT$P@|2^}H zefn^WsQW#nY2YR@!5VandBqg~i1Kzzsk|??m;Lp$&@F!k!IiX{nq%^pOV&o!*gKDu zdaM@C#=6NTT(5e$&z!=J+d(6_)Q$GrR`o!X9`N}GI9PNnqJGW+_H_RKyv5Yg+#}vr z_1bj1ogw;X79D*%mpO|g}PUr)r;Jk=vQe@lEzy(?G^ zOX%nYk0;lyrqnAuN%CYZf_{irwC#uq`~)FLAEZ+2L{~cRfv$58&u=PF6ejY3QVIWb zM`UGVB-U5@Q5Xg}!LnPPF?Wheh>O~*HWFlnpbSSQ-D_fkVFLbiPnRZIHzr-Zs6yDal`30CmGthw{&{D%gt;Z1P} zX;pVgG6^L>)?Urad%-s;mxFmhhJB}OcWcl(+L*-`Zl#Y^{LP4^0~Z(Oy_PA>tV6FVCKo!n|91L319NPkSQK6M}J z=l+bwZQ8E%#*dxr8b0y>VfV&@)ZCSCKzm-qANt#-ur247;4Hi>R&a1;t zp`i;2#Cu@?@@mJZCFoa5Jv1RxnN1;>e0;HKuH%-4V@M3@DbeMCQt2{gs~$GD8Ok3| zclSJ-?y3z8ozDGJ-@}WD5i!XgIu$lz(ENRE6@~FI#i%}YlKjbsMk_G#8Yi#+!xruf z+sT3dL)ceFMcGE}s@MTa2$D)li%OSDgMbV(G$v)1|V!kK5Dd*6Fs`wFQ5hvHlPUD!Kw^P|(Btpi0jaYEj_3~P58t4^N4Vph#ChH+h(BjK6 z%eX(@P9$;CTY05EW);dMiIF!2){c|x<79*6|DFp=;WprL5)D_+?kOV^D8|qd_i9)~ zlq5#s^oqLKn?e!iuD(tW)5BR7(AoGF#Z|A`A2+f)KbTtTU}6{+P9^FS=Dv!D=l_P8 z+2J~%-np5}jEAPWPgZy5G-B6=dzYrx)(^kt2+}E^fFWL_t!w3z4(2n?>kfwX&xT3t zlm2~@-lkQ%zAZQP;EvDD&k_s;;K#i7-oOlbCYj1L@584)FWv{c{D-7F!aJwbj(2Eu zjfaxWe;ddsoK1Dh^+XHS)=pO|2Jt3!HEG5dy zmftXz^|&+^rn8Mrseh7Aw7FO#b@{jPbCKF5A6W(;kjihnsQp8`*#>@d9a&fwYFv$n z)vG4MdKfY<%;u)CA2T)wrRA$lx$ToJt2n9nCH3sdxMhV7t-R*qy7@`@`8n5YEeq7f zWIxIjkcp;CmZ2 zr%ea88Rmd?5+`r0W#v)Gx}skiB$>>}O|fhG5D=_9)~Da6EfU4hKR||)N#P+Bvp18_ z@Rce7zEkWPKR+R>{HY)CngC$m+m_6%Grj=YIKWrR3x|Vep@|UH!&6GmsLqzEYMu0) zpn4*49N+^$5b|<~=i`O;D^va16^75?4*0~CT)`HDp@1(!()LT?{k=6c7MHFVfBe<# zhFnXg@uoEw-07?J(q6E)Rvc|dZ|zuip8~iN#V3m3V@;x1nVISNw+s^XEtB!vdM`Hb zP{u;NNc^*6)AH)+n1Cjw{k2)2|Ak3JujxDN-*yyaEITVT&~PFKwJAZ~RXyk6&S%=C z$uoC{Ol6b1?N|D4TLv9|lrGWQsBwaoLs*g6?%Z0H=l-{q!xP63y(L@l zM+Gg6THjQkoJdemKk{#3Vrt4C!#%+qZge@a^5mZyk~fcN>fCmuyg_;6-)}|Cz|T*H zCL$%olRt$&A+*J0}eo6)-UVBK>{GWp~_fDkpL7>`)uF9hiSyDNh9mb?pg zmVeo=JLr7%o~5WI$bxdT^$7c96QHEJ_V#N84032b@-i#e{%4M^b!+6X6hBe3cOh7o zW++SNBgcySoAqKV23#@5bf~#9j-b{X6oQ)Xa;QwPy{*~C@3_O|Dc>2Fy}L_F24C7b zQ?h|}vTghK@4i{`mzj&J2gh3pUaUt@0VE&u>hn4{{%l(>t}u5N7~E9&2MzcZK|mfM z5m5qgVk~TJ8(_`x2F$uIhtSK`LZV=x-&G|wzWB<_gX@cFAw&Czg%K;z;|(xC2P@XH zR!EPE>Xy^`>()3$h@PHl(}nC3G661B--mbPelbOt=63bh3(NK;Cigw}?$j!08v-;X z+Agbl4%jS}4bO>-dHB68#owF;nrh<7Skd$3zwXd++EFLNx^LXS#=N>=Ke<5a=k!WrMo;_Qatx1=9!wB z53lu~-(ZziqiH|#9weVh3+H?;+;ugRzoc9GVdAHIaQ)t=kX^|Z#>e?pF((YR-YW!Y z9lG$nc)$d_VoSBy;*Qhy@->Oj6*87=euGbMOA^n>u@P5B%Mnoc|G4#_zBDbi;v>cT zp4_z&Ow&Dsr8PAM58?7mvaH1yAvWMFAUcyNIgQn(#)vpN7+zH(=kXW$dbz=&^>|Br zI>cig&@XOtP-->_#Yh{t(&d z0-Rx;L$K0OxYzwBO~i{~;><|UM+{4&@JdVd3DAk~KeQZPjtl%RbV{S2#2xN&0k4la zrXnG1?_AAUhSV1yLR#MW_+2$jOq|~&XUZ(>nn?PhsHOVeplZzXxrvkdewp#w^oU>= z-LQJsFL>pn%kI+vL-TyZ{7rY=KPH4>_fx{_%#SS3{xqLWwlPG$%9&8*Fx^&R#TO56 zHT)XurOMuNCz3IqMEx;b;@-@Be5ZMz==Cc7+0F z)YI)cN$%lP@;(1|cx4DMnxc=g`)O|91`sAGDXGC!Nl7WB=_PwsAYNF1h?Vp=;fhBm zP95{U-e?B0C%& z7M4(6eh|BFZEf9|rXf}!wlm-LpjCpBeaN(eCm!kO5n#!u$LUW*AI_TGwtCLOK+!no zpS>%~A;&-O>vhKY2Dis3U^KdEEm4!dqN16DQ-iHD#+obzYu9?~M^~+E75;rh)XNab z6zg$xdBUq6rgA|x$_^%(lAVweeFtN)!$Di*7;0u{h_HPzi)9L&beUKc4g3!eWRus^ zqn((X6k~`k9o+6UNtY^4ER|Y1G&(3{$HCC&C9c7o-g}{Q^oN*o@*rvV4R`S^Q6)Vx z9%m;li}<(4hj_esGr#NWoj+FDNQKl(T{DLjZ1GZFW-}{C>(Hml+-swh{bI2hw9vHq z(onq40~Zeb-n4OB-0fNs5w*6-Y%Z|V@4ofl1C1T3C|}c`3RP9RWQ-;)=be=`j4SxJ zxrvwq!#aW@q$vWuI-U6#5LHJZ>n7(2awHgU#tR}B>=6d8#X6ZJdbz1G8vJlEv~ zLBqbFfcBXNA^`r7(D8w_V~kBz#kb7J7em2+T)q<#-5siWPFZHRFlv~iFsbUe`Ve;M z#C~D)9DM*@9kFVz{aWSW;Z>!thQH~bke^T37S39SG$|mDfAaNbO?$hn#tj|-uLy~W z>qbKn45DNwz#Hvvu)fMue@;ziQMgjCh{q7AEj`aFc~;LI$?dCC3ioa>DxCPaERKPe z5K-W11EPs|Lm;sCRS;vw4uz=gk3dimQXNBa^Heu1f4(prI_3Bsnf3JK;iz~4Xy^}z z(q|grWgT@Vr_yrS(lM=HpJwwilP!ck)Ol)WEmJH_O`OsE8sJY##aYhjd0z}4qiK(U~Br~CmUJeTqr)*yW5|CZFCvEce3fWK2jK25##EPl^?Hn;QKt{QtU)fnC zxjl46IflShzwwH^O{+<8y-gh79aeUvS=$>w$miLJq+(+P@`G5dRo=D?K& zGEw5q+J=U6cmk`Z05qlzN3=+g!%^LpB+-?r&P~p1brx*D&MvkLw9JC$tat*zfeBlR zsWo78l~=5%p4?PHhcfJPO`+~zs`UzK4L!>|b^m;h${3b~IqXGp)e>qt4I16g$;qN< zi81I5Q_*6?2Cxe%#>78oVau+Mu8aBy3&55(xm6zJ*gjt8$xPuR(uIkiEjwJ5*5Ezn zBnKT;!vc5g_F76=hBrD3dM+uyo_l>1ZH*q!SKj56>Uel6`HO9DJqD)Lzc2PMFc??b z72;M6LF1dbbmz{UNPhb)6-4K%{g70f-6W(dMOuoj1DBh~+>ZXQZ*62&!un2#d+=)0 zk>bCbYOC$ptLhNrcu_aUN|J?GTZdzx?eUap4gx59iKRXdHF_CUDDx_)5a$+8erMR@ zF-e<9U*UA2#&`}BiqbAgh&l23wY1m@DPapSg~kyAefGN4VHLkVe)PC-bNO{nH~b)3 z7~PtRiY`vz!~*j=_0q340VW|Kt>N&9h{^9!559r95@&C!A0y*E@^`)AyQfaoR7iV1nMzkNykE^nl1*hVO`_h^m3U5@vTr(HGIbpS zGCI^b1#3GGS?4O-rreLujf9EC@NX!zO+r#}X(Iu{ zyo-sL(|M-6yv+UkL9J|X)zr9NDM~8m^OnxGY*Z$2PH;{RL{K>ef}99PGzt|weALnqS8s64`JRMCm{>YPQm06I0l8PnM{uC(E1{*QPfQ~*VMe_|H%D)N5|E2O za0qGW!Y{zJXyWHqXNr=-&-LM$cHbA(zXb2ZmuD!{Tgbf~&uO9*f&8#~a&c_z4v@nR z7TmgZk4!m8uG%hxto%cSmeVEhR(>P*Rm9NnE@IM|{Pin$xAN?-Exo0h@1_r?Ew`(e z9lp#w`1dC2;q`Pd`32wwH3|QF-%hvb57;#h4BUcT-G`rCw^aWw4G|ZOIx#~eujlU< z#_^g)DtrYg)EMzhAM}h&@*r#WLYb}k+u2#GMIe6GXBW0DeSp!{#nGPq@2mE+Jb+{B zt!$)%3U3k|g0J_cevyyIbw<6+7#Zv=B+3<)o)41qLQNK9b?;G<;(G-4s|L@w4cSYh zN6QUfF#kJpxBpGq#mTNC!^8D1OD@)Bc~UJHNM@-EXH~zBkN*||0B_ULokPkT#D35N z_iJP?Sm%|uRjbwN-!b{5ZoRaZb1Ujc{(XAUxgo}R~Y;%qpZrVmoQLKx&5krp5L``#gMAiaa;;4t%&u+6Sb^t>ojj1QXV z)!aR0bahM0)|;em%|214!cF5*yDxvnv)GFg^7rbqlrn3H?KhuPk0Anvi8%Wc%2;+u%93KiHx~=N}*+cM+KRF zV`rs`!SD21TWG^W=$h#kaMqGG{u|#R^U-@64DN`DlGr{x_@; zbJmQEXqv=x8iz}!MldNJ!hH~NP>@ec^yRWZ-tsNIFn#ZwK`DP#5raFAs6L2wa-Mz204B~3q>mjZeRVo99q~sP3Up(2As>Cv(V=?%nCifo`+`44&yshP~t3Bvol*{B*f>WijA*m0I zh=3DMfTI&L(_#%X_`t^9v}^u^5ri`ctE-8ss7lG9&>OuhM~^1D*{a(w7f0xWoem2* zUM)1cxYgrr36uz_vBf7!3L~a~ZhDbQ!yX^$Jp^`sd(1*)TWZz-iJv$Ps5EK%fK|cF zpdgjZi;iH^{AM=7w+P0s3R+`bebZjPLB%s?C@QiYOzFQi~Wv%Dln zOe_s_IZkD}7)gB@CW#|hh9a})4rmj9&(Hfn=oK?XI9xRhC=5@!Wn;Uklyn zIzb$Co8<%3(X)m@Dq`(2PHwm8-3lng&dcD3+2C7_QjA!N4Lv$Jd*>c*JEAQuIUwlh zJz!Mcg&y&)TTvs0)y(NxXBaOrwTAa|j*nZQhV+|E%X$U7FR*I@C~q#)(V)&8a2G+s zyLJIP{0%uI1icx_;ol*EozObDbaA9ic~}~gJ&($Bh3na$BOA_a6C_6D0i+VN`-6J_X{8gk{jMAF(Z- z4p+Bm=j?x!-pfRhSVxbFv)K?iaAZO$Y$&0$y!US|K9aL6AxX!`Med#L5md?@IAY^6t?`|8Q@<$*TyE|R?N6Dqmcl$f9hHO_pkS2I|K9-t&_W z5w@9-a=Tqx_e{e)keK3z2nYzorkN!a4O!|5jN>1v>PL|{8wsq9{@DJFwZr_=bd=eN z^gmnVjbiU}ABcIws9fYB-5_cUAKz^;SL372$1ea}MG86>76&iGL@lPIbQ3iDZ-F|% z6%*nfAUczXEu+Q=;+6;HKBa%~Nu~6Hr#w;X(97W;muUFUGb7CbC#!Rm1O|f{FB~$f zLzo0mM#sS%<(+ua8$ss0S4%T7pb29-+Bc`wvZn~xobwI7Mn>q9H66qxT`pAkVDOxC z^U$Y5H+KB{s%k*4iw=amq8C7H0lqUaCKc(!AvKo3pdD{X4YJ}wpAW`GX6ts7Eg^T0W~gS% z>^(u;!ZTpma8crKUu%+zYqL}LR^^*a3*!|@WYkLhV!*zUiXbh}jpHvI9UTF~1Qi$P zJQHVMf4Ef*hk82BxwOx-7@0#fu@oH`H{BJ*lc(yD#G&Wj9T=z+tB_BVak6i=*mnk19=05uH5 zO%N;E8Y1esNx34=H#3D=+uLl86%xrGzEmki!mb^UF<4+J+P42ahkESv%ld zI%&2-wc)FmmkT&n&?jKoAgkDCgccQcE7Kc2=jxdxTV;0dOY)=Xxi^WzO3?ipN4&jJ z_%!oi9$CILMj|EobLE8;aa$s-y#VRMf;}Bt9a%@@u{Jl};RI6zm(J%}D(`gIJUIF|x8?Fr|Fo3tPvL#yh<8wLashF(! z#A_u76O{~sQCn)NkjjDgK1?BrmQdjv82F^D$<$G8Wi=iblH}usaJj2EqMqJmVIf>F zGXuAa6Y2AU0EW%D&>k#6r6%$(T<9dPw>}oQ5HhgNEnLrf*7`&Hn^Cdse_af2lWh7; z;{4u^gRsE4!O3kGCAo>%f$%u2e&)Fy9vVuUwVpn{xabGIH`=@F(>{%}UMBK8ahgF` z>IYYd=K5L!jJfGijF})IawayB(0cWr#5fkSWAg-0bZ7n1ZIEg;I)++Z<=S9S_P5oV zhS9&jBW{{wXrFN=jd|#W3zZBVUdxz7-rC}}v-W*ecR6@4j=^dpteU0yO3XrO>tAe` z9l7`xC??!Hr_7iC4{p5gDOX4vfiHQ3*T%Vu)Sa>gepARD`HpwS|Vm+OcKiTLp2?bO2SS&;Te)#xW%RCM^?2OO?X8T{t; z;@a4TLf*_(QDMUnU)%Oh@a5$I<88-s$f*kgJL zhaa)9Y}f-O32H&eU))22$`xHHFI|Y`Fz#bn1n*Jssd^kpDfpwCh|HH`u2ZU+vF(Pg z0fRv~|6k4e-THs@>L3v^LgG`;x&2kRm7-UNM*o=>qkT2EwwdnUeUz@0+2QOiCUy=I zw__fD3GW;1oXf`W@VaNI*pu;GAB^l>v z2v|_2#76skN($k+{nvA(S$^rF>&oblcb<>OGRZo61kAS0g7OuW6UjJ*48jOTs|uJe z|1od+BHCuJf7D301JH3ZQD2`s{H23GJZ^4o>nhl9jC29}`}@8*`A2YB-X*ys`aVPP zD+_}+7?{>0Uj1xX8SrgQi}7^(4UzE9n}0W5CZ zIu@S(zm&)r+R$@n&;AAzA5j@)ZEW?=g-?xZ2b1fC#QT+AM+t`~#9V7g=@xL>g4_^KdjB-KZ>EnGNlB=)$rKe8 zN{7_gKA9edZnpQKEpxL!pcNh)DFO+Sc3eQ3S6^I)y7ya1+3&-j+&r7LSMH7!f?|JZ z5aR}JW|V#zm%rT29K01TH|yP@t+ifQq)gcWEUXi)WQV-;6F!%nZz>VNVW5oh4x*80 z-6kd`7D+*E-BXdZWqHNxxSsqg{L?2k^N{5tTi{Xu2BeMHG8-&2gpYQTB{lCbjl z$QkxF#U0sFUw?WxRY4l~kA=-d>a+>B~qh{1TEMU#nR&1nq9IA+0 zZg3wwkT`)wywrk+_C;G4pz+Z$F#2?Y-43{lVSy2dr}*V*4LlSMxSMliTTyJ#4#I>~ zPvji$wwKne%|{+5D~8o^2a-<%ponP+MXzKm_a)Iy(|8uTr&4Vx zu!NUwHTNGjSMYufqCIJX+dVuy#B@qbtps=jif`ln zHD2;;mkKxf4YquV2Km`1d!hkzF{U(=T@ZCyfDx_t`k!D>cGcC@;ef|oJr170YXDl5 z=k8JP1z~?MTu+_EN-$JV4$iM!;>=85)Dbw?ltV{-*CC%uYnUN$dYqlp<{|~%DQ&C- z2tkw6@H8H`x|Q&~shSFX~zlk>a>&)JiK39B7( z>n5uAU8m6gRhT0pc&>+k{CFy)g;uS17!0;azyQAc4j-*D@xtSj`yrRv5)!~dH~lbj zhYe<*>894KTTIy1)&4TLpb2&3e5Gj>&N`8Q5a9q1uzH9=x$k+xz0Ggd= z%oY5$6Wb{YG@Ns{e!t&jZ=ST+ws5((h+`PXdg&*Eoj>&P@u@%OYTyjJG_i#Vz;WA# zv>e9IA$HLY5GonNBW-kGHc zIhh~!Ki<(v`12Js#Fgb`#LGK6>4se@8d~0EmBSxaDCa#RPuZu`0rt#ywEWUQ{pip6 zvu7WrplaHDk7~o5tCWH-H0E^pl*wGVXZfNqE={PUG zBliVv*IEV5+VZ@v&Ov)0frEpg-!C~)(PY{qx-^1c?zWANu{d znFux8Hv5B~viyQw&kDt_%l$UE|F&n?%<^tpvpOH^d6NZ+_UE03&s}Vs?RnB!E?9Ji zl32De#8}=-dUS%)4nq07XEw~^;N(P2ODoFKU7EiH&rRA>;&R$O%!~EGmS*No0$t*; zD?-H+IQi>FDJPu`Yj?ThhA1@#ZozM$0d(AljRc3=fx|FpLL>QXv5|EZyMJO3mJOkQ z1ZjU}1zDA^SWzrOKWs=-w1A47 z(5#k~L5%~0SiFE4)r9v*wVQz;*#x}u#t?jRAEe8K+y8=6hRPe55E&_$ zICFX#)BouwxL%Mto!>^EXd=TG8_CS;Vea)s^k=~JFHmuZ0csVRA{wyOmkDR7f%v2A zgoN+Fzg~4%K7hGe#23WxtAJc_XchAkC8@ALJ^-{s;{c7|H`jf;1|3@cfH)W1Rc1#Q&4q<};K_)# zb{iEW&iBA_cnyBz1n8(8{zMtv({Cm8BjI>5x0>oLJ4;N*fHIZi^LY22y8fKwi8!d> zg=LJ|SDf5e{RFZ(6_Y?|Ps|cGNhcH=e!B2=17=lQA{MjKUBRjej?YPcrFe z3uU0aZFVRe`r6RYdicf z(Ls91C7upbCiM0X&7-ltfICAO-ehlIu70vdPA&ZQ?H-g)ZeRluP`V#jxV;1y2(+fP!FxJui4el#U*49LRS^B(IrPm)E^H2v$5N4)=9I3+n)1SB$6&e1i~8n6#&qtSI&iJ0=CYHkoBZ7@>SJ#TS^sXcGoE zjj(Xl#whzpL}cWzWR8NfS!|F}%akr^dlopD_bBB>F>XS!f`UZ;*(o2fZ)CN~)zQMK zb5w_a-1*{v7Q#j`l?`s$?d_qTDsO)}QAPefYalGl;~c9lg-Ypq)L)V(z&29x)te?| zZ+R(c0F$_wQ3dy; zcBNDz3gsxrde_)aZcA!kBpxGw-FecV8;P&JO3w2ve>0@Rau_-UXlO|Qz-A~MgQ5lt zQ52t^(HWqo)G8XhN~Wb#ILv33o%;wM`CML`C>9o--{Xa;8X6jAhLyp>McN%i&}u=y z$fNq`gQWAnp1!`frGC+2;?ldP@wj1H*)MEZo4!wbTC)7y8;G~H7C0C#I6*vBVV&~bwD-3r|< z;ryzT4l<|#R7l8WV0kAVZEIBX_MF2?#r8+&W|y}C><7fl1GZGu4;~O$T3SkbK9zlx ztuXoJQ{Uy>D_mdKi!Z|k-xxe-Y=B_@^s1=FJy4DN!&L_v=nuz(>6JiqTmcQJof!7A z40V`{#k~V6r)M>(YT;D@dOj_>{`=<(Yd;sCI$;Rx*E)vYH~J%oUyyz?dUbU}s1H2O zPd)eA1WsndV;ZGTCZjqyf(U;D>=TAQ#wt=5x~|LPugfs6?)k@!XH0Adg5;|`>VbsT z7-U(gm@JJ|J!dIQuF6L7--6yzpLd6ItzWG-l_wscStH8s?ru?(^R+bpc$-BU8X6pJ zZSD0fNI&@vn+=$S4icUv_w*nzDM;~rHz!pae#P2|qj6w+XJiDgzHQw#?iVn?QyU)w zW|eLG9H4oy3Vr??yp~ZcmEj*HF)yv}e~cDa3Oe2CRwYkagtu0or(A?Vt`{)clVGf~ zTh`c)jF#6Faso+^$tLDti`j+iQ@anja+g4<>HuNdzZVvK zk-gyV4(Qi79sQF1l#a|uhUrfolegWR1h0V;D-NG;ZK6yqpk!^Z+WY$|`HLFUUr{kJ zZ`4u`k4*s+YVDN1;{B-a)ZX(?y{B(~My@7-JJ3o+c)=@7kOnR$whH?@kA7l_{93}9 zS6l5?UQ&m}U0nU4vxehpIrer5GG`bH4=sN`^nv2sWdHYdOrP2DAv_SW3%KOhr6z%b zsWS|*SC;FtdnMT6z}v zZnHIuApD-@Be-8}u1<^Rx5f|)U3UwTM| zXa-$C2lvA;^4a8UaySGC?&^$yQA*6yN%j(6 zCBj6OJ8*-&dx_@vnB%#t3=9k>Tbx8Eiw?<~H%*#6-RZsh+Tr0?B#WjYpglM@Jces6K?wogSWY%_Oem?q zJ55{LEmc8khOaa;tx)3uxb}AuNFF#*KUpiN>n(|-Y8L&DlE{a*D_)z!9eWfSW{z#BNY2Yu@kR9UJ=rQQxIE$O=pN~zdKY`#8HQ2@FR#s5BEw2@Sg1G4O^`9tXF zGFGibLP-4i1cSDDWfL{G?)2*Q=A$3GlD!Oq;sNhwIQwrK&Z7-=b;)?27(MN`z0@Ed za{l(9JiEENH4g#z_W>{H?Zk9xsI3T*)zhB*E&Y|cEEZ## zsWQINloR=msLdSx-@L-@p`d6_ky{ykFw@9-p8VSB*@@ZOsV~H^jVS&5y7Re*i8^m+ z>8r1GYKN zi|D39e}5<;TWfL)Pi3ISW)BY!tF=LZNlc?yJv|$d1-lskBPikb+}+(3YP!M)Od6r}S_5Kve4b`^j;=)E zw)L}DVy{%Jb^HY?({a)jkk@jcjDKHDcV*kTVJ~;&)=rheQbP_A{Kt(ix4qFw&etcm zy;pwV+yQEFpv&GvcxL4g1E*7l3V>V@++nGd$rKNVbLY;jZfuAk@>wuc}8+Z3< zX_)-8L@|w`Sn(i5jesI0@}MT@d=lL=PLy;(x<*+WcO`W1la2?)(*&kJfkPV%G;@db zRxZTPiXme8u|mo8yCbmtvbZEqxLB3a)ercA>MXmkA>+90Y^}H=qc3;Lv-(;TX2GmZKLFr{;dh6-g#5)A- zge$;kB;eT{UW90qk#2=h@H7v2>njWZ=YRst9N%YWvjZdS9e`h#5H26sLao*l$XAaR zm-4iEqoMTkykrx8`^01VT{IA{jh4|k>cC6Ft&{xx z!L`vJ_xxf>(59wx^e*@I`86~)P5^bKIJHSy znU={FqXX%6NOuacjk+*Dsg&BJr9RyR`j>C;I(*VbOurK++&REt% z$AiDkTo?cLFd{p)0b%DD30_iNBW$_bU^`w`)}ZegH|sTn%GkjCCd#@r<>#i!7o`|8 zafa7^(%>L&B#!FfHfnZYn3VcVO{--Wtj@aLySOhpBZ$APc_w#beZargg4*b|{a^1V z?Fp?@vG^G>%}R(}pwa0?`XD1n_;P@52%sB0li^~8Q6a~5JwGa8N+d}RayXgPewP0) zMqu9_%~`zB!JqK6SMIIjYRv@>t0_q|EO3F5?XEnQ=i3J5E*QYp+Q^`6_XZtx;>+(^GYXK^Ru2X1 z&IBGFUeS<|Y9pbs7}jR{)a*mzc*&HNK3T2o@2yW#C_1R-YLh!R>qKVC1>n#*;~MR0liYmv{!hz#2K>4n?F275!iHL++R50#rR8^Gg>JvEPuR>aSbL$VoDQD zzj=0RBkY$eD7v@jJa0Bn0-tVtW&XcyYR{^Z_x;!g@%Qfmo zOq)Sk(rP>R*b6Xs+5zTKSD~poQZ=@2b8y^10xndl0EyE!*Dz(e3;LnDsH34fp`@%U zU%{oLQ?pm;q#8UP_$h#WUghQhJv|aY@Ebe~sBe9zd__%@!;$}>()_{IeH3=M4FX(f_I)_OPbtM`QavWgQ)C2q|#!m_@-+x1R zWUAV2(<(ShAH%|IRkOM67FE`qHdT{Df{tIVPqqB|rj4UkWabZ+z8AM7jjbm-pS6f$ z+vo};Ty`To){6~7@fvOMPI$>y0+Q< zvg5h8y9Ca6nt!SVnRd3gXn-=&rBdh;!jK26pBs#K`ak;0-in!88=+WHLL}r+?py+g z&?$o!0vGrdS*0-W1X#I(q_L97D6R136`&?6-(H*^bzTt!Zlv!%nBuH<@UOf8|0!@| z6V-^_44g}ml#^?OO1V%VNkrK(14v^X0IUEg(qbQjB(RV96QT_%d8p5c&67X41%mus z*K)QSX$CCuWU$=>PsmG5VJE;-kjJZeN5WnuE!DDXF>;DkjKv^=Ws;X~tJZs~JvRE` zYT#luktt5jy!Rb&F;dCw?DZSNo803in@ZZXm-}p!ojB`H*CI zh;{;j)ak?TuLMEbyo1w>&Q-}VdnuwcAhZ?R`A0OR@Y{1b#P%FG&S@fyjh(+8l&khd zg>Qr+6})1E2mB-|N8aa`Z#@46b^%k8>sKAuO9MM3&}xW^&WHu5JC9$y_#A2dYlBzF znW5r1#1xClS^VK0aS-IJB|h*+M_@vxiyf_6sMvpWSwjuWk1Cn{mwRw(e- zr1sPn)RLJr{aY+nZ!S;lN);K_M3N^a@vHND7ivbtzC zOR8qPzp^i>(cgE@tf6xu5E0tB@9rFcfuVmEaj?7UzBI1~F zf;5{GgXqM@4cIa30Pch=;KOj=W7AQB+K}zsNu$d1QR`%GnQ~a9xBkB;;v)MEAxUbC z3kkPbERW+FKu+3I%|RAt`K}S9u_;?R(rF!Tegt*GOrSB}KS?_;7j#3M=hN_&H9M5U z(i2D&QB#{&w_iIIZKfxfj$9tZT^i4@_h{lw`n5dqxr@ZnaYXGgY>zo>Mcr_)f0RlQL+eu!425i zo)Q^4|Jgc{cuSe#nd^?h8KJlrnSWp;>d}En8psf|vs_;(UC1IJ$nf&Ii1@sKi{dtK z8$1?VXI{xGfgec6Mal^r2OXRS*7eMU+{It}Zfi{6w1;`>yz*aknX+)Q~XD0vM z^U=yZtMHut?aI!Y#a6D;oy2h26suY=JXP8M{!BpNq4;-pV{j4+jDh8u2~5 zyAc#1Rq?l5W+JCssJrrCUhvOxL!va7d?BAkWQh>40ES3nC=|ZX(T*;Md(xlBd|$Z+ zzA*J-MWT2@6;hGGbV_9r_H`94okiq7q~zpow(SnXGx)~92U2C0Tf6#^Rb6UP6A5=R zyZEX#PgGc`mS{+;*dt1}z5An>W*wgyC50xH#Ss$^kTfC?1doj3Ol4Z&P+Vljd)c~u zXs%2OWBg_N?Z8G?4>jMu*9dST?niPbIC6Moq^rpMITYLp*r4wKC#L?KL*C2wS^{|i z%MLdn8%tEV9exZ6@c>~G;yeT`-~|va(+_@^^i@jFEC>vJ!D;gAp2(;CRw)^ZP-5dZ^D&`v*yU0uXhwTTZ7>=MfpA}R|GGT6 zkiOLC=sd2|R3oDz$SmCa_?M;_lk=T|zhytx;UU(1Vg@D~_;WnZJC=T((@NXtA!r+E zBbIS8L@CfPX7~1WJ}=c+4p!d>MVO?ppioI3LuExa-U+o)#vijYyCQnkWrO{@hMS5R z4iIEylVGW%0+LFwP|I=JG-3f#>i+f$aqqQcT~Smz31iN@2};bG(c1_v-HEksxBtHf zD3jb->1=k)mt1?3C8W>xZ_&H&{p2D_e;Ww?GIJKQ0+=*PDl@^RLYL!L_pm_UV5l7+i+sti8>+ zHHKt*6<#J_jDK>&26%X*(d>N03lfM=*yj3Cv~KEsRXIEHy?IM(Fo_jGZmd4H<3szd zy)Q3=My^$}3e&N?IDf z2zDU_g)0bk8>qY$9f!$F@J)!}D)fYPxYl1G{8v=YjVBq%Ml9gH=2aXiZ&c$_f=ijP ze}*TGiHOq=mb=%nD4S1>td;@= zFvl0;$dl8_>D-NR-*TFc@3HDEq{e)320l)keDZqvA5t)E^C7zEvUQ6mE%LK0@t-f4 z$u~6|#d+orD@W|k#AnEeDk+o{cLK4ZUtR7+OJN8}KMnaduE~3znDrf~V?4=)Z2vpc zbf$f{eF+aQ+j=Z|DUHiIKxSa(Nr!!+ypJZy-ZFcz|PjE3y0*xps76c)_&kNgEMM$qJava74*dq!~6espKKF=uxAT7)F0 z743b4^7D~TQw3}<>pO2fa{q)S5S=OGc_JNJ3J#~0Lzeg(M{@OPXWCAWONZQ&DU2tS zsp@l!{K0-%+3q5~=^3E7YGWYUGI?!%-D=pXjTLyg5}Es8{M(SJ_1r$?5iG+k)zN4h z)%{=AoJ^Pgy`>^;ot;f^Z0F16rBksb+4Lnop|+Y4&s@IIpO)gKyBGDQ#F$`hxjlSq z07_QGTjXpOG8_X73T*PaJR&<*;xH1^n?MdL#?oZV0Z3?N!=$YIuJR~aC4RR_J2WFN zf3a%-ya>v|=dpQ-w*afa%geC_;72MQ2T3MI zfGm+ZtX{VVcpAN}L`W{r@2T&+w=_wCH3vcPwX%ZL^)-#zTwi|wNPDvDmZsKQmL51> zSA`F_()A!0&{o83s8Jg!oJikOH=m^WDu8Y1=sqH%hm1cwup?F}v7&>Vk4{IwvaMPw>3$SnyW%V@WpZXvTr*2*Pk!cBiA`}0Glp*_v zh~eEAg>(z3y9Yv!x7{34sX$r&9;^dZv5BpI;C}O4c!O)abb#!e&Hkm(0ouy|)PtYm zme=vd0wRIW@0i)!nGEExoks&tS{pP2UjF`>80=L@KRGi=5Xk|A6ojm|3EuXe? z06(6v14s+86|RtpV2daStY~2`qDSo-s-E<^UM!7vcp{NS=_=U|qt^Ob_b{O_V__jf z0!vJ6fXPhnhCYRRj<&mut2gz0(WOW6|A(&kjB2XuqDF%aQ4~cHMG)x{1RjtgU8Q#j z3B4%Nq<5sFfCy3p0qMPk5_&HpA`p5np(qf6(3C3u?%?}#G{4yxow4EIu2%w6#aFn#x#fjtu!?F@b^#qL5pu5 zfEA%oW&NgPkrlo2a)CctvSi_%kM{nzm!MHbc~CA69Jx$Cm+wB6>DCPzC>Q(zj_?jB z@1r|Uz!Yp5;9mipUnR*)ug;lWCY;v2#T%$%a8hQLm6Z_*I_{o8qJmX9Apo?Klu|_R z0j}<*_dgG$JncBB5h^C8V-Mi3SBV8p$m^Y(FYSw=Obrt0*-<>i4 zJl@jx01h6Bs7r}pd#^Gdnz5-++AD>2lD}2vrbQp!uH{1hBF^6K3_{Jw(TIv78HWc6 zXYZZgRBWqT(Xl~t2bI^0Oa+n45Pc`~%N%Y^kzTVbS!5GVt!jDj*+J4=skV+z^FjAF zP$7vK4C_1os;M$mr%fg(!hqQhJ4hc=DW7%k(A>(_;WdQUotbHE-bUb|NS zaJ-2nLuFMD;A4P-r4@`1_yLgbD$r8-0?v4#0TlomEb~A@S7Onfn6L0#jlKlp)E_^6 zYE66YADNI40;cR4A3lTtf*8wcybZ|hI3ub7SL$XIN&z3BRlMPM+Kqeso%G3-KgZ$G zHI;5zMDBYO-A{FEv>@)5pQ%iAMk$}H#D;tvF8-+yiE4y(!C5nRI3Scgf3r@Q`9h$8 z#`*`PEacmW`=oIt_HTi3D42BTUvj!-@G+?O;=D(|CzkU5hYG(=D?|)vvkDmuwrj;1 zWFHI19fKa89|w-~mGnx-fiPM@b?U$;?VpU z>j$FG8Fu;gD)+Mju|2o^|F^LcdCp@l0Ivn)DIw^Ki;F)C*d%DvDyJ~08t#qq{EJ1sJHDBd zuSQyw9Q4w&T`LQLhE7+?GF7!naXY!YT0DXH7A0jXLdg0Ge7`t3|m2))U zDb?N3%*yZ>nyA`+&4?%>=z~%&vR!a5^cm2*`PVWZdXl57clahFQ{5HhA;Y{Np@w~ zs)n`7M7b|8o5h&1RpV=K^?1Su2Jb57i|3iImi#PHT1}*_MbQCm^bvp(D<(Ly!aAE~ zkRe20%VPC{|J>jHD=wqvijFg0$?*-xN6y^k^v6=r$ChcOfx)i9QHg!~8^^cVOn-!lRrn|uGm zk?!mNm3kkSj$~Y}$fRmEjVB~=d>`_a2p}mvnS0MoTw`JfUQ-&)n<+n_o$!fUkeR9a z+oRDP8gr$(uaj=^;AGbg4~b?Y(bzfan7Eho;|2r_aVO_ETp!G<(5=mW`KQh$56bnS zyxudj;f}iG)h*Yy0G|P4c0Tya=>h9P)sFAq=gp;%D9-46L|^(?ci?VvK2?m-Tj~X3 zPmbRC)AomI!_4t90K9~8G*2Y}p(EG8QJsQ+UHRc7?q446&a(P~o{dj)B4Z5R2PSIR z&!&x@jqdi`B_3$jn7;z>GDbvxDW#FEQf&JzQmNM2WYSIIrDr6Vum-b=(q3{}`;s5D6fDH6c~@=A+cr5M|;Ll0GRoqz!y4behknF5`r_LB1UBgw81xi>=1Xpyz|Wl zaHPSxZ+z5}aAB&SGjtJL%~Aug{K#=CO}X*-32zj^S7a#CNS9%Bny>fm%Nu9y@=D^o z{AL}i@IjJ5N7%^w?#{N0513>bPY*ZRYJTtG0sa_NW66i@Q+_;uDD~BZcfMnpkth)X z^s~oflRhDZPw3^lxo&pti41)km&}zJyKE;^i`K|vJ9=9Z^Ra^qk6P?pG1TRjehpr1 zM#jTG!zi2ID>LM`X`t$G!(gHuargcDGfphO21v9$fMmoO|Q%?9{_Kq*7$O$rhEuFN;@?YtaVF@Fwl0q ztr(=n?Qnm69*V36sr^1ya2%dBSV|O!1{y-N%kR-3B(?zk!r(v1H8uy&)JM)&UONKP zaWavsqXXQov~(=qjMDZ>VdG(?TrO8@&7T4F1mevH%5(1O*J~GBa42$HY+J(lw;MfL zCT8goLPF1^FNOdr(_W23*g=An0+RBUr_5o9G;HXKngV*#g^{8jw&k$k%%JqkX-~|+ z;zn1hY}tYhutQq9F&C`z3iciqCpR9l>*Oyp{gx~ejL|L^9XC`e-32^6AUot{-u%d` ze(gfIIaRCmB>?_cA^RVJa7l>SBA_}w-HD-q!#(PmT zB2n1RiF+wp27wKBO*iMi;(ft?(zWLJa=$+4lT8~QHSWf1#H=Y#rU~+qNDVLB5vcrg zor?l&$u||RRbt5i+3Vv~XKF?3$e;L@b3$OW8OaOjk6%Q zqQcE&(jjQ;1Bt$o#5MV6{-t=$tOILrWWx*-aNJ@7onIiZ|1p6cC(xlBC5H!u=HZr> zJW+3&|Kioatl01RjV5rdjGhcEPcOO?zVQ|sL^d?0Rh%PLrTU>JlP*_?q%5K=S5!l2 z89jSeaaF08{bm`9%rLMW<`h-`Jd7j;p9f;?g*STo(Q`10o9&r@9et=~E5 zig)vN(R}iPb>&sh*hQ|KJ6Bd4#>kcTaD9F~FLUm_@RRVBkDraK$w3xH+=6GXd^D?C1*c7P#p&#p{<@f8tsq;iU^RDV;$DT_Yt-%^ z%{SEMC;@d=WD%r7w=Fz@8S!EFJOuJga@s?|ZGU4^f5M1x!!uCvWOuf5bF@;j?d6YC z90Wp{Z(k7%_d(pz{p7!U#obq@3gf-jQEbdyat;Dv_^%?Ba-z5>tN937V=WMTPq;$o z!7U4A#;{ABN?{|>ay=B#DckDXv06(|O_8g^CBAr%rpzx-xS^Q8)7}LXcGZh<%gb`` zGL+lf7pRvb#bIq@D#4Xy!}4 z6t`Nz0mtIb?6&9 zn|VT!vzwVSOZg@=a^dbL=5`Otg6-SpvBLzs)X11cIrDcZf_Q<6DkTOIz-x}uz;aj> zdo#mGAdnvxNxu4Q`(8FFUgbU*+taPBFy1;@w6k(Bnd=w?@?3_IiqO-X@V*g+$bz-L z?biN`w=xna{Pa!x?R^r6-{d}pzcoL<5hKC|UYX<%hCywqAwxD`CU#9u zHShTNO+idj#-`te0r?upa9 zy-Ws)pJq7ujp0OJ{=iXzidpLBU!Cj5ebhHuB?I)u-czQ6`|Vgo`2@pz01T^Uo2#)a z`aA8m??L|fSRu-F?}XFBn?&s}sQS-96eT?!_p$KzF%SBU{&(&porswW9DQYyeS2}j zS!u)UC@#0g!ykptBOHIQUDkYZ_mknKgTaW>QMusq1m_(?e)x+d=^o{#ofI4+%c$|7 zgo3&gvJ^7xrrrEVBSu?!G;Nm@+|5sJ{$56nJ_(tVqxCD(n(_$DagBkkF#D5ngY%FJ zH-q6%(IIdIS9S5=?KgaKin&cSZ;#v8ns#f?3o9JH6|Vpqpp=fompVCT`+^tE4PHVz9JJHj1$J=9x_!vW*Y^}qZmu}Y} zH<*e0e;5;ZoR5YVF+^Fu870w))>ySN5)@|2I|d(+-q-A5{@<;9B&J?dp(&L+eCNKi z=Z3^7dsMQp!W4V%g(6i*!iSa{=|PJc8hceP1yTqTjbH?oa4EW^o1GMUJc(BkvMA`c zL8al7X;CipX219H=$G;WiZhi7!Z9?#Ei}vWWrI%T3HQBhHcWW#ZF}%vD9H9G9y=^5 zf4eB-pOP4sQl&78j=#SnI(YZ_IqLoka#!(DZ5GvDz~?>yCENuA;? zLwKE%L7vhE1EIX!XR%^-+cX%&uT`@UKKndhq4xXnB$0ejpckEcP@)B2zQA$1fCe$m=tYb! zSCJvtA3Y;NUQ>@c`}#q|Gi`3mfL2FyR7yfLHXTNW@ZfdNk=xizck49?uG-yB@E+5*tjSA#9?`RF9uq$d7AJ!CT>1GLtJfM+2R2R!uE^#agpk#Ltwu#DRr(V$}UccW-fnl5BCX&k%GnOhgIPLqQ&UNU?TuL;S z76JS9n^|Y18*vWaBa9-?F3uO`L1!pn8&`(QhRO?B5!2A|;Zqc%jmx02;3v}q0S;7F zr8^yry?KMnoT+q7}Ztoce^dp6RiOOi<_)>*ONS zMV&@84>ctj&^R%tKJUc3GEJxd&<;1=-DvOk0XJx22@WDzpUDL72~z8WAmQlKa@}7l zG*sL4a_1lkVC>N120x#&6`;>PcEYaWg_?GEGI_s!3F?MG_-cs)2W*Ph=*9J$P7bVs z!@@>Z0C4)IcBQy}VbThN0${M=i+JB8lQ2HHM<40htfN*XrXSL! zdceBhylGi~0HH-V#Ov7akl>kMW)U^%L zfVvm5>2^W`NU&kRou`?GRDNFQ3_iawICf`+zQ=l63p&&^!wl9#kQiF|m?-o6AD&QA z9R(G>37*ku7!ve)*xGu|P4 z16@{Rfg?O}p;|jroCwbJ!aKU6|FRpWr|EiS`XaoNC&wDGw#_FRu}P!3$>b}sw5!D= zkOVT~bNXZ?ebLjjOF0YH%$l;lrDAj#^3&kaEE(k46VRSd2g;+0W@b5HRPA>Ojt$T- zz-&kq5cmmjW<)%bhB{fa-N<*FVW6rNa{Rr;?JVLtywtj`aWM44!FrmpG*c#6=!pg! zIp1#F0eJn;3W__TWv^v}Kav+RY8~X5?|!ZUosVE*5j1?-jJV2c<3XU^k` z)Xy?*a1N4&&~EgoOrfCR%NxZtt{}Rput)P*YG}D^sk`KZKmWA1P4P>HNF;LbWXgbo zI%A?dd!dWlG*y%#=Q`bXiQlMC`x0)d7#anQ`&ghbh2SdPUScTCl)Y$eRP{EP_GtA9 zSP_B=yVjfd8U=+cbfHIWMa8dAzBfCM2#P#zWTDlY50ar&^{yk!LT#b6?r#}Kdg+i7 zYs&rA}{pbIj5zdly+5aQM*XX-<-XLA~X6~0X)|bIH zjov<|ck?zv-Ok;>_&iLH9XVkXU152VaX`;8Ao=0KYU}QbaA}eLa&1JDUI1HdvB&+M zL~CjWN(tMfyBQhai$wRHIEc`?cySIfn=SQ!8RHev!^Ux4Yk2MQUQXH3K-o08M&jtX zQs=woyJEmn5C*J8%fnKRcJa6w&YWTR8>e6KBsi_rX%5mwpI@DGTfWxSr@Q>#!6L`b zrOq1<#)G(<`dck8iJmY-6h*(9vgP^qO^(nbNNR0_fHCg(iMz?APRov`>dk!*jT#?# z*NG&RIMq)b3)(ZdK9QO`y`0ZRq3GNl@0=)J-GC!(Crj>WHDbo2Rim;vFqg-hOlCxd ze*fwwtXVGm+oh(sPL`kM-r_noopmgLFZ_D2`pEeR(1T{_jvG%g?x?YUG@x^wvG9cs0@c z3+w5Tm%kF7uQjo*u(-qw<9tI@k6I-+X$E@dCVi zCTI?SZvy#W=z^7cg=| zqtVXRV?btixj)^1#ugB*g-;Zflwy!`FG?3pf@=+O!c!fcoJ@f|D=(PMNcPKk&|FfY z7A|q}ws8Xt*DT*%`21S5Q^9iSD-c*(HcSpkHJ$8^WF}K;F&_Nsp z@B*X={2UjTyC(s7RzRESzyZwn7T~aej?(tizQYg4Do?P9c5coTuo*nm@Hts#EA2_; zri0IY#Bp*ATcMIU*;%7wYSAXq(+pyd?*u=g!9O$2UZv;J|9+j0@y;h{z<$~pHeXaZ zZacZ2tARD(TDGe6Fnk?kKu*ap&6zT8S=7TyCX?yxB;1B~T%J!ioCV~bkZFs^5j=jd z$9Q0m9gRk=T@TtW)n&oHjPyoPA?FJt=h3%ZtL$creZT#eB}`^9UuA<_DN$7Yv{*WZ zTcfo3Pi~Cx)GND4e=?CVThb)!sR5TxgM&Y<+bYk&-2$`)=w1~tPg)BrTlj*Dbri;r z=RFDz{TMkFa-|bfxGm-yJH&u6H65JhW>9N{>+0C%l*oM4-;q5^A| zLVUyCd&2r-YJa(%KAW&lon>H>HEzIlES%@VlAz>Rf>x*!rXB3EMBVj&E|Kg1x-aqx z={3sCfk(i1y$mqiI`u1cH&fkZnom@!^*ppBb}!W%6n?ne`JZR_A~aE1A~#x941rc+ zJx){(1qMuDq`3D9muK0T%i)@~O6 zF~uV#C08|+-^cCZMYWS4wY$pxy5Lqmkbz(Tlg)$qS@2z;k??A<7vowljzKE1vge}* z+q2?E7{%(HqcUoq0>91z(CddAsgC+S)pPYaEF%9c;IG358}&Eyt9DYk*4( z*CtolCWA_~+0RLKf5bLg;8LcuK;q7hMRpHsG9y#=eDo=0eNT>25D>IdbaX6R0AFv- z!8x+{!g;4Q(@=J&A@t@=3Um>@jA7&$hd0R@KTQl zhy7#D$lF?1;Pg%^n%!C>b+}~pGGuwTV zvk?UM{FN%<4+P!fnI{I$=dzdfVh`@;p8U#N2|Vb2aeNK#8-0Yq6Wj@lm8Obc4jq6; z;sH(i$uD+r(L;-Y2Z@*YTo@4<{5*NyAe(sBCHPS6xvh|pM#`aWfo*fWNnGgin4tn=c8NWiQ}C`42Cm=nK(jWbpz0vf5s;GWj1e`P^fSGN>Kf8%Ec>sz zd?qTYzl}B7!e!JB&qJPB!O&2uV7cv^A0C7R2M;*y1g;Oj$mDX9T@x^3pbuZWiRJKK zlQ`Y)bHo_AqykX^Eux}LV__47p5HQtNakr`elTK~0ge9H@oufabfexExUC#hnhIdS z7!<}%tMdgG(7TI**tY>kxPAeUO7|+cYZoZp=YrY1)&NOWea3x}uCb6Zd$nljl24RU%1sjV-Un*4prR#}l00%C}4SqUsrBw2H7id1-0g@xi-uwdQ@cvrW0{ z(;TXJ&-H?-CJ`;OJ5(WE%fp>-(6iuPeYb5cuHjbNbyH(md4P&fPKuaIz5V!q!6HtD zN0BMTxX~xym#!%EiR~pNfg#N%Z;9>i_jyN_hY|0LPk)ov?!7E+c*U#G{qZKH_g6AU zWtHμAD{lJi*&vK056x;PI=DMA$fQ}dp%tp3Eq@Z1MoI2?h1B?tVTonx#O7s2gm zbFXnnHWwd`j5UGzM|{)a`WAfkW>lh+lZj^3qTm#+IBu~EJP{~ zH^ok1UG&6qEDOXRp;hM%z0|Q?V%5!n6Ybb5wD=eOQ?y>f=)p|lB5rSXYjxrE#*70aqi){G_guj;_O_pR!4?I`5L)cDy?BSPu(Ft zZsAR{llkDfzI$$-8g~Kn%}NZW0CQD{ifS{+s(2vS{h;&?*LFkBNZ(4AKg+?%Oln~F zW6`ueX>ONr@dn30OcC@dkRzLKn$y!-n`B9S@OU>V7lH6FA~dGu?R~H;*b7mRNdO}A zKCAwuV(mYsjmEIog!ucm0*?>z?;6CKcKtoK7HW?V#jaH^p2iWx3iI~1I~T*FHwtDW zps(b_K$f5$JC1en^hGT;Bs%?0OFCSLZt^a$3aP7l8$>u-Y--#z+40n>WfIhCz7tXh z%lGy!?xm~Nb98jv-ueqh8+X>My2K(-8b5{Hn%Ms|P^vtS#M?SStRz_ zS%TLl41g~qV{{vpM~4KfcM4x&eXq08e7Q4+vpxm5Aj7W4;eE^usUff)LVB*(wvVO! z_v*dqR`-(;)$$x(+WxyxG&HO5tCN^op4>$U#P65=O1@Z@QM*9Xu8|ogHp8*+6acrJ zS^wdfnz6#G>~7&K?!NZOzY1?tmxdOo;^s8Xn_o0A>L)N!wXybK0I#%@K98 z-lPf8QlFc@mDQEJp(y=4yjZUCcT=fx3DpNfX*9`sXFaW~kdRG)ANMJWvF*IAny$wO zfr#NcF@e<9&1nvuRf97(Y9OH_%!ZKcVL8*?k z*AW1RUD;cZH%WXdWcCFzZP?_HnN?v2bWr1iz~^_05)^%Q4i2@bVC}Ljllf9i2p)Ml z3I7&*ZE5t*%Gk=hGZ3G;1gC;6hL-^cYI#-Fv1kvm;r=PW)g#Z7%SJEe*$l5N7D2NN zhmv)4kcr8htsuu|Q?!{Xd2a1mPwTtcNkWwmmu74qEh=-yE69gM#Br@nDrOHcD2^4Q zLS5IgZM^O$)0Vq-X)aIaXFA^%f?faq{SrKD^5_p+nM)_-y@Uy6iu@W*yNH+=l)Nt@ zxd$J6tybt@YI&$C`2m6ZFh=8CZ!$dqS283H*6v2G?}}B2rtb7gR6gN}G-jyLU9w1=dk_eAs5mt6-M+ zw$HuZ4pZ?dJoXi)k+pbf$u9&BhDy~(*QQv+Zc%21O+3EnT5m!p`ly|i?NggY%(SU# zS#F`yrQ95aZL-!Zi|)^USDq@)xXUVtCvl2ViW;S1W}1yCIqxh_<%EHU&gh$()!rt5 z`1x)rLKD00)Eh;Tn|#N9-TCjY`#kss?98U)1gOsZan(bMy~X}4Fq7&oKi4&981~nB z(r@FXnMp_8LnMnT=F#$-srwT9rhN~V?^3hT;VN#*nr0Vv&a%Y^2P0|em`IaVCg5rh zy3D=V{{YwS&)C^@iUq~7wUQfF9M>KMAt9D044KnddhdMYQ z`7Pnkq|wq_i;8nc`-1{pDp*<-?^DAyoT8U=#qF55&fO!g<Q)9khbH!eSpCjUo@%bjL8;c-RQ6u4Rza+;I-* zIVjA=7mt{TX;chf9z+-yYIbsPA(Qv_9latR07eR6^!u~$*2q$nnz%+M^YDPj7YoGT zzbDsc)%#dSnI;Oz2gTf$S5+Qa91r2tV=32QlUG)Xq)b$z$U=h*I0t0= zoqciB7ccTEonDKGsS)+ zLj`N4&;?+Mwrfytvh?MhaY$$kosdx3tZm%Q+$2hSPKg{VErT`75%77Yl?s@3v2Ck5 zlYPhK{^DfkZqa~#8EZ+Gu=R8?Zm}z$K80Y#-RPAs6@M-$ z;vyuCz9J*yh8LJ9 zKcN0XZRAyY!eaIGl3uh~QH)T5SaW#uVyK~G7yAr}lI{nCi+IPm=oG?rtmha-^v!9F z_mgapXP^}q`Xf=)y{y>M0cgGv(8@tDGH5%UTha-y7R#-`xzZ}3f~-D&qk~DsjYI#6 zuq|?)i;!oEGLJ`u`4$~!^lNmu{?10-bFm1$jThwO1^6hTxMTfZ228$$w~*MhOc+a* zaf^O(P`OoUbl{Wd)l_Bn)E(r;##dZPb%hZJK0xW+<0B>i?T<9VQrB<}L&}1kV9=QB zClqT$e6gu`nWB$|i4-gqwe_j=S^0^qt=Lu*eyLzd;d!3Ny}=yj_;)&J& zP&zz83N58*tab?cDt@EwFF!`Up{8TQ8RuU$|D(P4WnGil1xvVJT60j7qiE>nS!4#H8 z;OBnfHeIDn0;;{MmivgAWmkdSO|vs~=0N(yZADK0qX22WC&W+YTeXlZ@q}HTu6Jw9 z0RYH@s#GAL6pUMo#bOmEn!M{DjBbEo^BACjYv{T<&hQgE7bjJA_u^gjVd6~t~*+&!^D>Cj0E$U+AmPPBPzsRTYsGm zP*6w#>S03o7!6R;N;5JsWdo3t_tn=6`ygG4s7Xdg9_vL<+hSZq5_?!F#Q&ykuTE$b zY8C(=Ky{+{M`uqD${udPz0Ii+bp_mU3o?q5sn0-It>OH{s<0Xt@s)taiJU>^j8GWe zb4|@y>&c3ILtIQ;9Q_{wP|)AIcaIl{I4no)UdWLOtSw<2GAd7z}1c;`h*h2P?I0|fVXeB&PGWo zR4M7{pO2}6bP6n5$sipsd;74sv z5Wb3z^kZ@DSk84q)}P|e0mm2B3;%YVB0}}Egm07R?)d9XFzUU`eQzW#oTwD}fK`Z- zvm$7ACn74Nn+WjrC*&*qg67nMmV-;^{0=bH=};D&W(Mrr>d<>nWDa)MJjbih?@?%5 zLoG1Wo~IH^14clIklw+QN75`-(=KBMb-W`$n2h@sFarV2E8^31+3Jknw6ZPr`eT)1 zt7=rD9KJj()4M#&bM`>beb~Kv4&hu_h%-gMw86_y)vkEIpmWcA`I_A+W1wj- zv5RdV0|F8w(DGkcw#@P(mRMw_yHR4pA;t=#H+K~FrD9+;T92Q zq4}8lUGV0PVv8c^i=SHlF2FP)f%OyI_p=L_Iqw2GOYFc@DQdzN+g~lm57W8shgOjS+FH7WAEVhGF|! zh~KBk|7dIVZ2=_-#Q8XKd20Xkx=Ue+8 zj|q#9kyj&BDO}^gMO}SqZXp4I=!OD4>WPZZCvaHd1`XqPV?>QupdM+! zflwz{J2iTqJJAh}Z7+rP0s~>z1w=wuaY89vyyjKSwx!YD-}{goUgT>*mr6fdHjz7P z);S|`?fI=bxn_ywsY1QLKkiHg`AO$5IACTxY$(#LRG6{_hc7V4svY%KP8B!MLWV>{ zjJ58%fKkqwV|AeIhSOwI&wq8&AndkYxF{qfRB5|tFbi5GWYpkY{S*5)Z5QMwkiVP; z$kz*PU62sM1b+3uNSF^M2Q==z3a2jA2g@^ zYQI`5Es(5K**tuc7>^0W9o3QEIyBB<)%1v*+VbgFidfUbN$P9z)R}@}w198*XrC?N z36U9Bg!>wJ)f8xtdUP~{Oyt@3(0dxW1Oz~3MXZlOSA9X%_aNF3u)dDsd(3KgM)ka_ z^G!G~|KIfID^C=3;Dt04WM>sXKM@Zec@)4sRXlbGESd!fEeHZ7LGQkBlN|_=xB})E zm$10O1JPUzrV$L26d&!u*sBm1Dvcn1v*zelZ@<>O0H*SRhrLj(aX?>n_k6ed_|aGw z)K}pmAGq-_K-vItdQ1o)p#q7@!0}+4+R0LQI8Qxu0hrlP;Yf{jN({~J>@;O`!T3ZAgAy^T}k^uhWA!kaZCkF6XUf?s4JU#<9@=G_WS3f4z@4*OpF}ckXg}9YH+8C=@eO5ao zL3l=wmofz;J%odgrkh3w82Ow^zpD9M=^SUbzW{hxuV284xHLrfPNFX(D*gpXc`MAM zrBl!fpfTeiOnEAW1}bCPKrqOZ$Z`NO1qR0t+1cfLMYoh{cPFh}0t+Vd&}4*@2(jJI zCyZ(=Hu-BruXJKYo|T;~|CHVJ_F?R;vop#sCmT6t2k@g>5V)KfQDZ4$o(rqxeX%GG zEdmanbaA*XOQT*fxbcPI;lS3ZA711t{(E&?*NPS$_nli3O&9#sKvqGcbWPo<-|${+ zF#KZn^?gf&6pVN04PpZ9_Z@Fk8XW>yffcrva?J-YG7U@1k9m2I#I{2dz8a+a6aOl0 zpJ;gHAq)??PPYKK95FYUWI6!1g&9OGiURT@Ae3==|4_%<*%Ym#SiVySvc5R^jiQ)1 zE_!BGCW$ZLB3e=RuH7*e7}a%Pp!KR?)PkZ#>5Z$x$~*EK*;_znK@;bjQ5z@)wxnNX zu^h@mo}8FZK3frZ`2`~8_LLR|w0Bq6A9f@2ABdf5-xEyBw3{AV*~0?3?q*!Ui9{Ws z+i(It%F7cJ)e#3*Kr7~%IcLGW(T$Axq0U`UODT&~(4UbXufZ&Gi5b%6Yd}h1@7ihZ z2bD{NRuIB-xoIBe?&Pzt{~o~VyD#~!(7;>nK>D5UPEyG7SEPb8h~=j-;Mz^380Ew< zF-mcnX@fxD|BWjDjN4}MnNc>%PN+rU?88&Qfv(vH8nYaCKH=RqW-&(1x?^@A3E+M5 z?W$!2%{|5VshqY$kdH3DxbI}vHPi_iHBh18Fb$t3f1fEGwLEL>#2ji`Ij3Z|` zkc^9J)OOa=8SSy+eF)q~k`ZjfG+U@X@mwWXoyCaDu^_3^KRMp;U9p4%&k8bEw;R6# zp@#K@w?bTvzhuvGdp9fR_*A95I)OZtQj5A0_M{?pmO3!KFmH-I^)z7-a8oOs{%vs9 zo%Eo_j2vUOvk*qWjH}c3hgW|iE#f9t6O%yy_vlZA?{F)IsC>@*@{6Ca4LEIy70Zj2 zn^ruXiDTqZiiQS{?{6t=*NypfBabM9j;!V&Jc)UM=O8WL5O=TrGSX@BT}n{f9SG5P z&;;YIlvc44+i$2>xxnVU)8Wuj(th&4+q|+C*kt-?fKfds@81%L*Vfj0JZk>>^+h~@ z5s1M8#BJS=0T$&A-qZgrh1f`4yE0qLx(P(+U?4=GGb(JB6(&dbBBzk+wwX_3SC*-R`v#yE1Ki@ zCvyAW#p{D_(?nK-XNKym6uUa`u8BY z5{;~HzX`n&9aVClNLZ%Krz|9Uz6R2J3o+X3LBz7I%y}6mG#~oJ1Qg1~d@vhY<#uCU z>{c9kOo6QPP7zLBjq8@7A#?MuJEWC%kbas!Vb}k+KRNU%v8z;w&E}DM@3mn6q4$&E zsOm4vA8+j<#Ccr@uXG(p*&z!|=j=UE#-Py(T#ehX{q_0{6JUI_IUq|PJYjv^wYvYx zUGkfo{WVRSaixu3E5pBi;u`9-@7PY$pDpKWfTZ;ELScai{XSa)t0-f=K+LT9KC1l! zxDBin^z<_sIB#6EmL6?$MiWtzgx0Iv4G)3Hbg7|=ynGZ$fon$(#dd}@4qVfC@_#bf ziPzR=eb-&Eqc1{1wsH*}Q8R^9xfXbpF%Q5DO}~tQY<|t&gHCcQ+GW9rub> zHGb25x|fmp;gc%OG`kxuW%Un@(GB|gH9^YO?ghezs9gx6k_nfnxB<+V z({ZbwV|y`_3SnDoGpaCQG#65Fh$m`^&>H?hv@3pI>xo0R; z8opd8pLj#|6<5}X=^CU*kwva{dVMw-w7@}=H6u_Y+fC1XL$#HeM|iM$J*svE574iV zUHI#kvyilGVybh8iZvKckLjke6OD?!%hJk=d_TK^rz-LP(#*y_9TInX!f3(g? z^3|kA<)@-AgjuBaM;pst*6W{+a>L;3ub3_RKI_2S{og{fBwe! zKdB3qs^FvWTdKU(GL(+sWBD$eg*T{43N5#LF<@(usX}=w^3W(?bW1b3ETD?Y{PVwohtPG7- za{*}NmeGmMjA4TYbu9SW2WO{2>+oM+ox2L+M=b5tUN5Sd2|S9{PA^^u5%2xR6br8m!*ezemiK4$(p z8A|GBkqmMJsl$KTnx| z7yJt_)DYTf5!oER#_2D@hFv%uoA%hw{PMLP#L50)&ZsZnWdeQ2Y8`;H&@gUw-k`ZVvVzd~^z; zU;@fl{|w=Ic1$G_;@$Os>!r&s=*{8PM41kJtMK6{6%M-4OQG3SX~z{kEnStiO9A;x zq_6%5QtS^OCxc4Ja*SN>V8h=#HWqH?vXZM@J*zu8=8f{7gq$T%9vl^ih^(?R#0D@z z38cvy?erXd`l8P=diYc2duL;~U#ILp2aH_}eJ->H$(IeD<8?7VCN)ILFtxww2pRlD za{R*3s*xpHJ*-s-=DFPDb9^uYIv9Xt6(2vb6tPH8EgiqVMtlt3R6-$MU1YHV z|4P8iZ1bc!osq@zjvKO8_;$|2ojRCo_S9)7hSz+@)_v~%$sj)q>-h8qzes6Ds&IuD z6*}0Y6j-FlRcy9uP(rDjfBKXTyVKa}ZY3Km>4UJpT7)cO zw)N~diWnUseEPb^6%8(HYWo`@`tiJOjSqgQ>L6X7_5N+9Unu4g!`G=#e_H|u7LaEk zfXU)(ddZN0Rj@u-c%FCn^X~JtzPRRJ`%sfM9VJJZ<{x0X#BcNw2Ztg+$>10pKsjjL z6hHx9-ADm!WS|VJF^r`WY;QFQY1d{cD92fxdnPGE4U=Y5S&e6;@F`Pi(vQm>6M-6R z_Vo#3&8>Djnpv+$8m`U1pGH6~vxh!;pUL}}sk6GlS@zupT#!%eLGqI{f2sYjQ*bmV zv;!NqZ)>h&R>B~?%4Q+EI|+RGh#4~a|7OS#Kl@REUg!|!?qhBvaXE0tH0=~Ph2WDG zePhgyRSiFc0*SWB~b^ z?vsXDWZ@5HSgroLpA?jT21Bz)4Zh;8LvSnRw!m5J8&)mNe3}NP^TF0gb07lKUSj*q zwT^G&{B{6}-WA_>^3XTAQD*l+1!<8l%;xy5t=a3?=b#Cho~+X!GhWrhY4D<{Z~ z#=@gbPFWg8mdV?6|IQXp^};shh$OdAE;XK=@@q}+?^)i?pz!I8JuRBy6igadR^_=f zcm-1N0G?$j0}}WF1af#A=$gtY@;uJWVI0BKw!wboS?`K>8yaeaS;8aWZ#J|-z|>nn zDkuuENEXHInB?T5G5oJz2c!8oNmNk=s8m?6Ku(PKqTZgo>fIF_D}@dRhciSu&sX<- zgKsO@<|L|Jv3mHkU!PFwvFoR#qv1VXt7WeI!YfB4?2rn;pyiKNP88?_0=;?*q!Ql3 z6;U;W#|sK^dYY@~{gMKq7-1-Wt1=~o3LEI3F9RBNHMr8(K{eCsOe%z3d(gN_xLiwI zJmCtX27d9gIvt{Weur*%*&ElX<(BkFqgO%(`MSFaM+9n!VWV)-CuTo!ce&4;B#mWW zFWD^ZosI#BN;$D%Rew?sB5rWTARlV-Jp)ODP%pCeE_zCwsmiU z6cj{FRwf3J-WN?L@LMeaO=;9Em8Z{zT~{B0BYAFq-t;d>Ux4YiqO^3|&FY@c&M2$m zS{Xa@O5JSpzLd>=Ns8cUTZuUVfL#!EC%ug-!zMuyj~Ss7Z+)K51SV#|GWz|x`G&89 zip@D{V?#*CR)|I8v5y4hbVD_~IKEVg%hx;jsG-O&01Dx}GfUv}2bK;7L_wT*Ahe;9 zV;TIxMRAvtdt#;mWN0UI_ts^F_G-e7!%Y4H^yh}CYjX&ERPL({xTgcBbkJ*6-`w2H z0+=_kNp+9ch1o*UE>BSxD3wkOg72SP<#QRgbV;&ac z*}KHKwIQwp!T|G$KNshu=EJzADs9$=2Dv7~I5sY7G6VocnkL>D6V8{N!J9#Vpw88N z^;s9&1VK5c9~j0ZSsi76SM|}GKH$> z|0N3U^0d=35xX3q@3T{K29``T`!=go>DIdz5qr>OZ-!X!0Sj}W=A{Muxp2Qsiptd$3@f%2#jiK^i7(^u#11aR;ZD*yA$Xsq=m0wfB3Nj|6w@1}9 z*P+P!GlpE|N3#^0vv?txI{u2T1W6iCl-$U-VNwHh(g6^ve#|++lRU>Vxw@axuY(`# zg!jDq{*|r?Px9uE2RIKevz`WQ<`Mn^II{v2#4bZa=>Ym=^F3bqe~NqWu%@=|T@)*B z8-gMh5K)SRwo$1HC`fM-5_(aJBE44uw}LbQ=@zQggeJX%N(U(-krIk15eNc`gwQ!- z#eKg0eZO<=bAI=E&V6qFP!c6;uDRwMbCmaehbd(d3Q{bN#J)^bw_*&Or6kkswFHN{ zc8k80OY#Z|9e`it0aR-}5rfTTG4Z@El)GQAsHmu(h;$eNiRuAm!_EYu1jm!ZLcfy@ zbVv4&?`iPa{L^cy`gRZ(#%@R#h448w{1$z@TyDn?CU$8%?xK6KKLn$01u~0^;DmOJ zZ*O_bd`NHAn#%?K?J^DWz>~C#Ci9;KL%Jku$G6Zy8x90})AbioCz&Qpb-}uv-M1lg zA@~)t+*jC894f*uyLQaVtbe~G#SRLwdi?Q?Os^Fp&j5PVc8sE207ZyZ@;+V3DCVwuMc7)oR>$O-oa6Yho5y?Z#Mb>4vI7j9*&i;92R?N+4Ob1Bi&^{ z_0v{y>M}DOxf8P>U=RXQaGl`klgqjc9qPE`f({cp@%ypv?&bH_=PH6WtzUD%uGF3N z#>SyA2WNG(Tbx$AWu0`Yn{2IL7)g~aRIn^8m2G>AvG~+yT+jX@ULtprl2W{s@pxCQtJPhJbW@-Dk9&?R zDrHDMq*X8OY1ftW3JDGTf`WVpq%a8GQyznqdR}E12k*x_4YQE@8VKuiL1l z6po(%8#FLkd^_poq!U~`!rDsa65<)9K2}M7A>nf8O*PTJMmeXr^<2qlbFgZVC+=U0Wi^k71jJ-?S`yd!=3m=K>Gp|0&G{|(QKxE=KD!0} z`jzf;1`^;;?Xo9A{wg`wlpVNvvxp$zDsg;^Ti~x#)pu@rKjd$hOeLxo?|;D=)iLMZ zdHO)f{W+J&*$ccyil!8B&QgU3QCH;{_>gcs)lz)8gJhDf5Z;$!1I-m?$XZC!VdE?N z_EV_T$|B@Hq_$t`4o;86^wnt_T6%&mATr}bNr8r2W60=aqiHc|l&i*~Q@V#iI`-kk z?ApafcPXvR{nv8NMWK}1^-yUl>H>4SE{fEq2t`@1@2HaaGcr#rZxDL{;v3&N8-?<` zfg|7qh&jwyH#4;(%;?hP4pN!$sqkn}?g8%Ir<6m+|DX#Z7No(dG*qP&7hjZ$3=6x0 zxAW^kD@SM%JNCn7;?sI(nt(!-vIq6`l1hZDlH1+%7n&YMf?w* zhAH(}i4VHNsoW6pGkog=T!7I^BCysecbG{`+fAFiN8aXo9|;_aI}MXVPy#tW9n;)%FHw5y}d@C%ft@F&rRE>B`2nee{O}t)<3eU)~)K&u` z5ZXTbTwym}ZwzdkOmNqr()h&?_=R_8t4_`LOgQegE%5-1E&yA&goU+0({9GFF#anz zL9pAZLqDb6=}FF?`q~m6a}#SJv?#!%cl6;fxYdww($2)|)-(pOn^xs2WxY2RYGmKm z5qz;#QcL*-T=+jW8W`y=?}6}l`nNAL=9j9~5Ss`!lR{s@j{>v20zZh*j-YJ)p!iNp zVV$EnT_z^#fp&$6V`-USoE5aQ7O?!yR(T<8j}NoqkEVmF9PQBboyC&ykiP>c8)~wg zXdlzorZjH%-vIhxqZ*+;FH3XMdQagozHUpKbOBBBq3<(U;TQg<# zzmX3sXP#D`8I{&Rh}#-7ai~YtoGKYnk{9VB&ge2-u6}FG)cjHmYn%I2-A6UTJB7Tf zs7wLF|)O<32a!dVcnDZP4w2axM| zdPTBekhs(G2DQE$a?{{AR!MT(l-UzP{%k};zsjj&t4r}wi`gGeLdfVn=qvUuu5AgB ze_wQ2I#YSa+qxD66Sg;^1=bG_KkDO(xZz+U=pW~89lW&UYWy58{p!`LNyvD8(5~by z{SErMrj8~&SlTT$>T`!(|G6<|O8(IKUt)7Hd7C0~&-noMhRA~?$C^u)MQMAN#l?7M zsce_-7Jj*scl_GuQ>8#|;ZmDuu%UGv3p=0Ke&n+N;Z ze6}8KiY9~_2YwOHFo-MZ;8OKq?fv+x7_0Vp-%QGJA-QUAJeH!V`849^r@buucN`gm ztbUAAu&8RGyHl}nVUt*`sT>ndN=HR4jEj%Hcns9Xy-_agowle3UbZCgwV+>tVYiW97WlR1N@Fjg<@pi)@E4pfm! z*GDE!;TG0_ySr1XqTq?U$W~aG0_19JVez&xxh`_3(Lp7uK znXLxRiMxCwGZ(FrnA{T`>#2I5R@9+8yP>b9aPmb|Mz-=fh*F}JgAm;yfpk*sOd)M4MSz5?vPXRsQb~E;}wNTR%yQj-xg*Fd{eta zyw?CHht?-WQ+y7S7fK+~YbPh3Yv(N`RL>RcB6+_k@vd+%4)V-hvS!bY!FyLke(ZdX zD>4&3`QoSk4l6Z_%v9y0xZ1_T!(8hR)FpWo2fWr7MCW6Y_K>{w!@qfueyr)1!U(K1 zmOI`=_tNlN{l-yHcR8dTplo)|oK|%L(nboTfw%5iuhj1zuYQOQ%4_HS6oR#J?#J-& zvFtXe7}vHP_(*-ZXtR7TxVIfscg2$_*SkDC#vUgeoqR0+Xcb+6C{nTMuPl0w6Gm2L z16vd`s_ z53kL@V{ic|Y>~B_dz9^<%gw{X4D3udajsRCAIF%aR<-}oA+ImOanvUuqSJE;pP+5l zwZXn`A0xOV%7&eUmYY+LyTI!Ot8y3EhV z?|HICWBmi-mptwyJ%R|ALf=t>xjeQfJ=3=~PDSV1rE^jH)vfguqH8NG5=>00)47Go z6**2;+YV!QOlus1M0YgqhM@$=$wR&+wNn-{OR4suY|VdHljs7xow2O}7o@!Sba}f~ z5^Tp!@iBN&{3O2iktB?&)OjG;G&63H&$6t*6Z>oXoKQN4>~KZVUQXRiE`=G!B*`Yj zOuN%zdTLK1;C|upWI#G$J-&ot(8421eOp+9No4GMhCZL=hw?Ypm{?&b2u{2)ff9se zIA_Ry7z}J*5oR1yeh97R36mBuWbt%tjyOHj+Y{lzb|GPIHM7{aAsdB_Pfk#|?bYls zU(!K}A366j`FQ7F1`Fwr_hRC5G4JADcmzlGb)r6~lo88{KrUhFNUES`cB(Qi%sFAw zYDoWP0a3N&aNFvxD+KTHGKpsAk5@V=>~w`YX8v%VcVc^)-vCcZu@TG}v*kCIYw(7m zg=7f-K1Ec`!Y`}zxH|#X;`Z|r$tpaim?2ixJuTncM9_ypl3cpInW-VQ`NHQ%Ff~N< zG#8YgaYe_KL&`kkb9uJiL4AYDVwI0rx6{KS?FmX{oVlscDHBY~;?E|qAzJkTLgOrGS!>_TWJ9h1Ee9NG?J`^z)L?A zcfvgWtu@)%)6xXnuEKooC$}EySiEZYj+Yw6!^Rc~o0)E^rRa_%?3$0yf(NyMtHri| zQzcYb)vR>;D!Nb;dd-toa+IW^?gz#{glc>hl5lM#NqBoH>)gV<4MinfmGr24zu}eb zki;Q7xp0>fHLGCxX9hS``-q{KjclldrTYCA+IjG~ggEC6?bSPgp`Qscw=)nm+krT4 zf%sJ(^G0>`h86a2=eZx~$oh6CK0lS4J&aiF@#{g8Y^9yaW;K({M9xCJGCP7@&37p) zmV_F=NW*V=(HFB-M(`)q@q&$~@B#P$auaHyzr{h~lXbMHPE3>jd9F{dzGac!qRfO( zwlQ2&f&{p_J?sw${cuWkVUwWU*Su(`?i!96bEtasbL!&MKCuAI^n7*pZd?T%=Yyj5 zBNspl28Z-KFNTPe7kDz0lNKk1%>qV1{|3Qv|A8eBTsQe`=rn0%s9Ri!545`#NM!h! z%*PDuf}p!y*3{x1&4M(g{C8OyX{fExsfW+Uf=$alj|17N)c0u(DY`MIcJ|gJnv$Px z5?ly^<=D%WLe@*X3x22HSp@&!(rGrpyo%F$)x{lBbb?EvN2=31X-IEWbcW*qVIQNl zZ|u-nE$afJLNo{{gK=ty-KpH_nfqO^e0CX%D1E#fDq9=f`;$)2x~XN0Ufhg)#3k^v zjl~NNvc)9GJ4U~JnJbeaIT3Kwb!G|3WHe6@dTNGj^^(C=etJ_g!wkvQJXoVaplCJk zhi_+GJD`rAnqYeWr@Sds9DI;!i^Zuft}VJ5uMLJl&>5iP zE_g%N!ZMgbhg)-Js4)c_{}y(A@J&=iY^96fOA+1DzHy8A*-kOXu4$m)xzD1k!G4$%{>OJXSDA31 z79^$(3&#?vAR_{~gEINLBE)+H$&12I6a?CiFte*O)}R~M1!#`(G~kq>y!tEg-&mu9 z4lEZvn@Gw@E!vuIBolHe_n|XT+1R{sVKh!Qs&nE-eQ<*{A7850gssS+FUucRyvB~=YsQ+GP^E9k&rE&M zv@2ZJu3$USG_6~wMtw_S(!F;tY5mCUve37-hrd4>Eimn=zF2Gjzx|NuL}mC zPUu7~E;A3zw7Zvl<{&H9EH|%D##Lt>Eq8Ej&F0VG(PJx;CycO6SbZure$*l!CHL8_ z13hy@o2|!2_eQ`mg}wC^=SBoW3Ubzq98aCke0Rdrl2z%_2i&e(54n0CwUk1FwUBu+c-r0y%tm2nE9IDIfygb4kQq^#KfM#xm0o@ zKFW2lI##cw)^q5raXxZ53+_F5BK0T^Aoc=jZQsO7+2cf!6KOv6S88SEx5ky-w90Dg zDyR^s_I&K?qXGG$`Cmg#;lB#feN`@ZS3!6lspfc(LdHKcFYmqX9#t-DNU5u&Nc+0_ z%=BeOgP;p=ZIDt3A~pt@d1Zh!F1hFG4 z;6Y5gEF)ua=S=LQ0+d9{DK4o5!Km<|A<3wTXXX(fA}Q^!k8-xyCkPUh&+>J2W|~`M zltw-|7Szc#``x|$&zD?UT=*w!eDs2eIWi^Mjc31+NbPuF=4f0RXVShAFsUPM>2Dc# zFh?Wf=B9J?XkU$_{@q8Tf}^J1T5Bn8@%Fh>naSFOIS-|o$A^kP-HGYGQ^6+4g-2Co08K5gSS~8 z-B6;Eo0!TLcBgCgb={d+87Wz`QG4R&3zBOWNF>rCiM{aYVVeQ+`Z364)>HpeZspZ0 z*N1PCn{I^o%%6~b)SyloO)Cr$3=9aV8VuV zMdwqW`S|1*?wL@U=E~B-xM@eSze#zmupE1ddHO3(bVC+%w5dLh-LTzRvT5FVww^Ln zd7WpM0snDhis|Z)!L4XeVR|vdkF~GJD<4{N(V)q$;LL9oDV~+iY!xWZ!aE-&L;mj*e(xhPA)G-I(F}cIWDZrWmXHyxt+k=-bOz zmuZ448o-gA7~hU}`w~AtTB8b?d0;@?s89AxgL2&&$9Y!sve2PhPo$03VPs$7_x-XxH4XzhqsvJM53rbe269qLu$~K)7y=VR>nOZY67w;zeQaV0~PlZY8-K13fp`ef%5@@yc?yIJqlwZTe z@(n?oYsA#=FomA1uQZ6xTs86nMivV>#j$LoJ_Bb3A>ChLo=^cg&DiOT6p zuOM8b6ax!P3hKyp0q3yq2KhSFazJOZ_v!6THAe1`@Pixj;EG*yxsYFQ)-^-uwmF`U z9Vg@#si375g%pwNmt%RgC&7_{s@2AnOef?jXKhjdZodf4Fs3bQMg@_ANF1>)B>`8i z;(XJOS13W|&O&r8*;*wffwkYxVKnx6ApK;7QO|{o9mXD`TovPfKI%T}qN~)ucABnt zd^f(BU5)>+g;%L7ZzfX_Y#J6zX7F z$bR9e&vO692^aEM-6Cd7+H`$+v}&#FCf-PJ^Xk^Glichkf`+mB z^lUWKsLmfd*NR+uv1G&HoR>{MhTn1}CMf3ZDV zm^|Rxb!s#L(dSx@w9H&{kK;_=sHjvb=qd10Hyt)T>)%5>dXum%-OySuA*OhTnR&+Y ze@dzN|1G6Ln#$wf92{dWs)q!z^QV4Xy>Ay-2JVFd36C@56}p-x%Q0E!iZLnzLbX$$;fso&y2$J(kr@S zyo(##@=3d_P05Kj`z2lttBU!I9YtNgL1})Ou}Zq$^J~d3G_U)*o1iz&1(TF(l3?uq z{3;@vvW%^9Z_lQen$oxqdP`#mLcJYw%DoFOjOQ68LfJM&VQcB zr#YYNC4;w#rf%jGoPl2&&YaXSz3aE}Q?bWfK>r-e_EP#J1|!o$rlzN_ZLZjBrHw?t z{7R&5=Nofvdw)K2a!^7`!BsPgg}q+8ext=@6I0OnZ$c?#vNWp5S&W~#UdLdoxQFygP#&o!>s3W`L$+0OG&9)HcPEwc6 z<2IS@860&pd|?DsT5P_T8x)e37mbUlqYZ5&IVf>*%tTjzJ-zBksi<#77iN1K*Ub+n z`3#ngRJ){g6Rm8DXMFX4iA@vxoiLG^5xI-LxT$KF3>&Wxkqb(1S$fuo$yd=+F$0Gr zh8AyA+@^YMs`4Ep{cNYk?5dL*?o~D^>6 z1)E19Q43Nf%F^gclP7Dqe`CszbRoY(J3Ev?`oigC;I%NK(vd^|{z^3$+^ z4%6`J9>22iof(3lhHjQh$RU1~gAnbFe?C5wGHe?9UEVDCPgc?UkiNNA`IGxy|FQ&D zUOo`1D%86%T>bH&fS*KxGT`j~izeIa&sGh!vf|7L67R)_i)@ z+}h98(M6@W05yqFqNnkKCPHp1ADMAWRYxbh)UkV5H?MXj5A+*m+IT8F_Or#tscN+! zXw3;IB!^-KIJw#b{}72Y-~LpdwBl23&NJiF7+zmKoKOJW9ulg*L~d85o4O@0 zT{h&zuhnAPRgdy$EX~a>$M!#PnM#?vGQD5iq%b>s7B8(LYm^9Ah*HriBz+gYI~|JY zXq!HHA-+9Lv1uTD2l^n1I)PEtnAFrrvUQm z#D7Vo-7MK=O?a~>>9$rfHN9Hl!q?%_sJDJ{Yq-gGFYT+_2e#HZrn z@(tV3EWz69&4s1zwG9l0>jGtP45c9@aC~&$(AQ2J$QiDtP|>|1wHC0l`y`3#x8dTf z^2TlIEeOEKXDgWpEGcU~6_S3bZEXBHe=u+P0oB|f1MZj^7b{mt^1rfhdG%L1wIi5u z*V1;vw(E$DWoM~H)WWw};=(T2w( zBC%w!)uDd3BP;B&ibCWV6Z2`FILo?bPYSNDym}jSnzolK8uoAs$!u@@f_;&og3@8e z9V;_2b~z;%K-?BO|1N^6`u6-DfPSQIt*oE&#?0JezM8w8VRZjz^CILZ#jMXUx1ItP zn;eLOI416(v%0_Gd#JuWhG(|pIDp==ug>ZI)uMXnrTd6e>kP+Z$QPY)ZXF^y2G>-R zHH=R{?m+ccA=*@L;OFeu;5H@U6)x2a_SxD-exPSa40?7V0?S+E~yDt9uI9Q$%qK4DJlxIteN`UF&=VB~Fx3q96=%i@3(oN$hr1!nNIQoASo+K6hLZ95Q`+X6;hD6$%_#z4SSghYlr+CR zuUf2Sq5+iCMOD2*Igr}2vF9w&&?{B_4IwXmKsmAZ~~L zgtb?ro^1)6l=(&uWq!*1qjrqVyKBCzFWze}Nar+#q|ZH4(G%yqmsTH9%|O;WL-~2N z`gRR=XV}lDobhXtm!q=AeE&QZxmAYwLq3R;nUKqXsv=vhzY%%&k-mVD!!3NDJT3W| zs%2?u*`B4IEc|&Ezai$giL`(;?yfbiaYs5f&lFev9_=;r(|_K2kX)!&^a>FhCj790 zJFtYx1^jckHHfE%mlq;*1!&4H1icp*L40Mcr3}Hz!0QyTE$TdF>L%bIKBhR~{!QG3 zCF9Iax(8XGTpuxj&rGK{0l%MMXkR7&Dm|T4(j$H4`y)QuR{$jFZ`LiPc+Ch8`3Dao z0O14>8EHh>Z!g0c)+Q6=yKc}R$pMK7Q4gVOfZXaLKr&`ccOh4Rm%2JuF$F|D5UiIs zNFJvvM#d)r^$f7RoFXFFG0(5zw>%&Xewl+-96XzW;2x3A;r11V(kq{LfpWUq>l!#l z;q^f09h{LHtG`Hp(Vyv!>yts0K9t-j1l>+RtYpyAOrSKLeU7i_+FoZ1U&LGC$oo#wbt0@2LlT-pA^#An^ez=xPL&N>^(E}Z! zcXRGFk4FEvY-D7Vh{0f#?%cWax@ISc6BXGJMMIuEi3MYM%VUrF6Fp!Mv?zj!IHCwL zAuGY@2J~Mp0F2>E{Ht@=XJH^J$s? zGBo*?o;?;g&!GhCI4J9Mlnt2h0qgVayg_co{Z(f#pyFG;3-|-Xmbrng>Afc9P`{dm zD9DJ8$3V zbf5rX%Qp-l+YYRRS&?1_V9cCJak?FV;Ek+gYZWLxcI=oE7z#>Ru@7~=!OJv)?bLxH zjMce_{W48j6XANO4Ms(d6T#`SEJc_Vj`;niOH+& z0ccjzUg(?Cb;l(I^my#uR9vTf-}-)#XQ_g;B%X-F;pRb3U{6h0{xB4UvLl<*J~~uw zE)yxffIwMVzMJUum}qgaDqJCb9fXnW%j+w23hS0%EQnKPKDPQ!1hSCLkFc<04^ZYu ziFF{hq}+GSaz@pZDrqtP74!9Z5&Yy z6#9}AKY>3Iny!@b5Taq~OpF~QX6K`$na0=deaxDnK+ui8xjrx~1%cl@{Ta_Un;@^< zQwMT570qU=UAJZd?De4{SLW19F^A=R(^9J_qOEAnQ6{Dlz!!J*uYZ~WH7M6{U<1rU z9f>|~yw0b>2N3UTD@NWknL8?+Mr0e2xM2I2R}O-hXHRjT8?NF*q3+L0*r(kctMyuY zmP7sQu*VSYj{86E&J*tLPq+tBGu#h3e&%|(XZGI@&-Gsqk9q4`pdKqwmqrc;HL`&w zmS!dN17&sh+1}cu*dal|&9$J$O_SGd-=5zk6H~mtJ#PeremQZ;4xtVor7|hL5}YKT z$oZNx=5NAU(VM+xmHYLzH^q8fGQ!Xb0o}IZATuNRU@iYvn0Zi(EI+deGWkvz&-3vx z@ml-#!YYE{jRj}EV{yVvpO1$@s|K>brwXjdpk+xwWePV{x1LX-TdM|%(9Deenv&?xq@O+)XWAfwUe-=wXr%_Sf( z_nIh?{5E^*$OSUPbu>EKX*|X1pG@mk<>f;ni&6lF;1y$cpodjTQwx{t6nlyf?kKX94j) zy6*D5_gQeJ!Qp~KX_4Zn`|$7>zFgRS)Ctr1jsgQy4Wx%@mE{k-^>4F$jg{HNh=WxT z%sax__%%H}eLmSSRC4IZR_jL}wXo38$`$&z0CX1I-l8_RwX8vkj<({b5IG>g;(i*K z@5>CU1!)kZ`v$VuraiY-rz}Cd=c0MbL1hh%t^XKVxHzizKSx#yBV+i_k%_|F>oBrp zh6(XdQ2R)IZVRevTPv+Hp~SgQCqIn+jAeWQHJR+lxDJCYU>~@)_aGiomPQhgm($`8 zE`bcQDSHZxQF3r}2i@fz@P)9p&jhE_rMjJs-!BBu@(~>32FOa2Wo4Fj5A;NKAE&6O zPETiNr%j!4T)lQ%x$VhT?TKfi4I?8)#`D^n6Ul$e0_>=O1)PA|foqw>Hi+LmIg2C{ zx+8SN?b9aW4ec4TKk|Oe)-3!b`r8=9KeCZSthoaiv~cnLZxvVx#g4xF)WO_=eS=5C zG5Gg`3xNj*OQ_yZ_G$!Fu9aZPP_FXuWYfA`%v=fB{)u5>z;+7?3rB_dUy^YA`~XoR ziMHwM1#Y}gx}r(@{l>dybbh_3 zyPYQ`FJT;-0o@y~mufv69;!#%S>CX`51Zk7Z?Fd5Iv-)JUnw1M#taw#mpIDx--a3_ zj<-A;vESH5?rH=$#q~@9d^&kK$mWBd&0@&rgN6W1Va?~DBo0?7uG6aDq43 zuns8mAP4OtFrSof+-SN@{aP6q1nmQ)bX$>~c$oQwfZprWPee>Fsjlnv1E+r$cc3_) z1c#tH*eq^176NDGtT^JW^=ZfBv4HJsYiklZp{4cRT;QfWX#PDsM+xgo?ZiPbHmueuY+mA;LWibotCACt#^Z;8K(1y z?cZkXIzM3%jskA57;x?(7Q^+3V%gOut+aMFvQzA7$C^>s^)_{(gp9h@7KLA9lIS>4Scg9#O^Xsu0*J{YFOH{nQn>>m;I3h>kc!tOP+xp>2XW^uVl`I4+`M32e+M#2;68Ip9K7;prU4n+XdR%iDiz=vlJRI%PnT5C~hF3dl zWf*n9pw2Dm(CKf6)Hk~r+Q2Bz*^E22vMqu}jf$&lX`ba6OzO$e5%f^C!AC%Q_(l9t-RuH=xoZg!VD-Cg4 zU=3;_kG1I73z($ozrD2(*cL;KD5rj{Y_qiH3qnF3G9cqFS7P_L>1*3~@pa;&Pb9dd56ldzviD4TDFGxhgYnFvBG z3#`i~E%2+f(%>Z906Pq4ReYe{{x0w_FT=w_JjokioGb#BSZVOse;jDK{A7+ngtY$8 z>%p!6b4>N$a?JnvO8BYNn--=2^V$EMXym^)kh1|uh>WOgKpT_&?z%9FcZZ9PAutmp z)T6u1T6CO-8j$3U4ALnc@gxFSeX1ov>Kjw%)-iVv=rqaY7Wv_vLQ_OrrZEdYG z!m2o;)`W&HJS4{o)P-KhCiJyVAY?qynS%%+nnUh!FvKKaSG*uLaGmJ_4J6-n2vL?6 zVFh~)mRr|V7Y=kMT_FK~ARaQx%Aown$-~nL`i(i%3C%fGyP8{XclvIF7?p~F!Hcm0 z5C%+Nh>40yTS({Lo*RdT10cHhddweOA0xonM<*q*z1{|?F8v6JZzuHY?^h*yehFb> zV-xU)T=?nDG5}qkjYn?i1QOdmcziy7XK$>UdbJ0Dw~^wquz8D;wKe{*GYC_W(t^%J zgGVf4<&U&<$h?C6k-n3M`D`7??n(YYv}X=`Zs5+UN-aNPkj}aEsRtBkhHAY^5VM-> z_3fmxvhoM2HdPBwa1?Tu8o7RuM_M1K`uWvgg=C)OGlMGUOioVDm1X0bH&dzEYm2Qi zTSaes0MqMvO|0u;Sg9|Ui)gvXu1_3jyjk+1vB=;=YJg$K)riWSS+?3D5sgbPB}cy zT$=f)*bPb8y>r@eUzhMXIlcN{L9TY-`6L{q;@`LY*FL7~e3&=Bi0IjLlKO_oV7jR4 zbHs`S@K@U)1G8)1p-6A?g-yY|-d`{SD^mYuebK!Df&yKs3f?xW>(0mP9cle1+EV$!E z&)2v~g`8%6J@_OBB_rnM=BH~NU`1ug@*^AJ&n7KoS19uD`D_I{D;2Avu1a01eUJ=Kufz literal 0 HcmV?d00001 diff --git a/code/global/output/steady_state.png b/code/global/output/steady_state.png new file mode 100644 index 0000000000000000000000000000000000000000..edfd9a70f225dc7ba7185cb4d818c02207e49c6d GIT binary patch literal 140168 zcmeFZWmuI_*ENcQl%#a0bZr_WRJuixmM-b;Mr6|>NJt7wH_|PeZt3ol?oEFSeV+Gw z&;N7&o%_1D}Kd_ilAiZY;SGnZ2jJd+SSz2>AjsT4~Gy3FB`Rmv$MUEFej(Y z|NH`noufG?<)?WI@FHmTvRY0E2t-frzaE&U|9*||00BWx@`ajv%FY~$GyYpLA@j@l{nlu^Z{6aPVItNlLxaMr}ODLX(qa3V4pn{M~NhZZ_FZ zfJFAwlXB*Lr-8Kofq~i7snpcI>V?!REs{uSI_Uqta-Wx0ouK33{;x}Qz%cOB|KoQM z{{J8Rf3qHrMO8L4zj}Jm@fekVp5)_io_|e896o-GjvknrN?|#c|0t;SV6-=UKOIgP zwohjoy(3l?91_yj+WO#afqJlXR(CvqC=@CiS|&GO7$FEVRF-_n?dzEm(e!Q$#Fl7@zcTHfnN>^fDj>IL6$rDT2@8k*MM1@e*}ed2f5bbjPG z=e4QK!|{g<8!#!iiklarb}r~b#}hsxAH|p zueHhN>QiDO)Tlk|n@RU)Zsi{v$KyJ7tur%ZRujeTQx)dyjlz<(SbpDVSrA}yUA>&m zYpMFUvy#q36auy>JNP`#o39i~ zuvCkUt+oiLGl}-wu*G2kh$sfe#&jW!@STc5lag15DzA|Oz%^NBj51!N6EbUZ`e!U& z@6qzc^NU2(8t&iQr$_EJRudd-dJV)ACHkEk>3&fXAy|g}U-<@Sjh)x~l8$^ATLK2h zw&NV{%9O4(!K(;dU+p);FE-Li{GZ^e7Pk|}q@jqTurNW4ToVQC(nN~Sc4q{7SEqg` zB&^=ZQ2E4dKe1MMqfS@-gu(>{o09F>ewvsMX(TCM`3Vc|8fin|C#Ljz&r_Bv>nYkh zLJi)&Bp#Hp{I@*V4(3)?;leI|x(u-f(#4~}X`A_Fr6_4avW!wbU1`<3^piusDZ*{u zLq?fMJfc4lvXV57FV_LKr>CID-J6|Q48Dly(d>QUIHmCAe%(P#NXGHN?7taV^hP+m zG<*H}^{S6qTlDW^yt@g-yWV(q9kZ=5nx40b)bu?YlF!X)J9zFmGxsFTL~{w*wdoVi zkB$QC>+4fWKrF&nV^wf)a1Oe-wTslT1Za!nwQB5_jP;$?dcHV{L8|JuzHc_8r)v~y zs*0V1lh-9|Xe9Wt&Sm>od%IL|I@1oDXM>osqhom?3PY8gbu_WrE8UAS^T9M3y%j?T z2M1ztUlCGX3+joP`FYA8@?Sm&{H&4h8ttecbSB)tNYM8UmH)!hIy|geb#r^Qe`g-e z_C-E;zxhsYToo_4sJ%Qh4Z2B_;-K$Ns`A?Zw&x40V=e}yxIQ^KStO4@LIH{tPOPwC zz}0mDD<#cj$HvC4E!M(4PM*JO(1z>nBrKz6a=feS`X&prR{}dJU18S0%0)x#5SQe% zG0;7z;BC8njF+AT7ir(fS1+K_;d$Y$u7!K`R^0`UQcrKEd9L0qK_Ua{g0p}N5$S77 zPs;1p_q%y3MtF`rVbP_TA+q8ok8eh_Koj!`?} z%6)g5&qQF@>W#I^&E@HuaSU}tWTej@SC|;}XGw}@uGfP!iPzWHtKPwniQ*S%92ArJ ztl~w|#Ly1)!oR74?PNU{IP~0|sqV~wTd+D7ljH%fSZMI5(9O0O&ODlAIBN_Ed33p3 zy)cw1^|;n9?9_U$?i7#W7-V6FkUmIJx~3jmz4O-Z&T+5nm1yyyN-K>E(I=tmhqq8F zh}Y?#r&c#fXT3RdR29dQ@v4JqqH7Jyof8oyGLn*UI$Y9yq{cADHsZr6@a+s_xk9tq zw{e!^P!y*#D*{#}dVL%IEuBi^_(1FI(cyep70inrTCnMPzQ=wpIxMHITM4&+{RY-- z)QrENP?jFgsjuhjCM6eDXmWO8hEG8eNh9$w!*Q+0ajtzk96qK-#8t3HHLoHeA#r%v zN!9D}WLv#hR|tcc3jq{!_muN_FO~T_a8izVXfp43o~SmRfnX2oC0e!F7)Tu)m0oD_ z>9?7jo+cF)5m{|~|1~Hm$f4~cW;C0C#Sry{cb0S{<&5u4mCc$rmCs3xk!Lc$&FTpn z9)n1^BB%_jPT_>?+)XFH6^`tlJ$sf_P*C8}SXfvX!Kz-^b;7qlS8slDcE&%*8qcm< zQ|eI*j?`CuV-V(Hrgn36rY9#Syqs1xHa2Xpze9&RIyyv@3a>`E+3OwGX!jT2WEqF%2r+O_$^G zdVlQk*LB?VbU3+E^!8|;aRWH=biJX2>&<8NK38W+Gaj=?ARaz%Q`;^-i*fML^Sv<5 zQqJMwbX)DhHSYfWbj{rMa;JSILI4sSy7i3De1O~-B;K`!v$M1H{rS`FiM4a3>vy@Y zXp9{l9ph(?!L9XP2)pjU)6-?lW-h2su297N$CUVfl zU4W(lxQ>sPE30hhzHZh1`2L-E!F>f&%4_+!zs8QwW_pyEjO?nb97Nh1HpVC_l4hCg zn{}7N@LhH9-UYuD4$AIS5xNcqDLu{ z(MtQFTc_<}F^7#kQ9i9Fx1r~kpkxSC8*1y34=N3$J~L<2C{Cdr=p{Z6HO)~wPuW78n#1Y1$K~dL50Ep0jm4-{9dC-)lara=tfbC8h^G?ScEr{N#-C&>FVjUB7X> zI@{G6kWm}rdLbJ(7#hnwB7S#Y7yNoutd3T@J3##><9t_VGHO}r_*Jh&K(EPr3JxNm zx9Xj)ZUl&mur=irLBm@aACR&YXPP{YvpsHL;+Hv?x3KOvQ{|?Sch|?!*XEo^j~;bX zB`b+Dh`S#w#;k#qV1!jH>7V;LuaD7U`TOgXj6~&8Dy-Yf^L@@7kno3Vyv}WpyGv`D z9acVKkjq36AI9xFUUtU`%Hg&CKpk(#KFU6S?d_W#YlQ1>Mz1%6j}*Q(QK)6CRxox{ z3t*6OG^J=Cqf=5Us;JdLwMqArW6$$HV+v`CU!izzWthu15z)~CDpZ2@8A4l!`i#Ry zjmM;AMfXK>KfO1BQ)4G+`8e^JTd_#(pan>S>*q%REyX)kscIXf7)Pw?>i#do~nOCc(!+RKeR?De3Iv29!doYud6vYu@B z)$agjza3lrIs^3+&s9Wwv_bZo_|3_i6;LMDPQHJqU%9?G?tc*r=P*0Gf-yV}Qx z)O>tzZR-;`(m=wgHmU_sCdsentB_MRgn^;sTG!e6=kUUU{+<)|-M+`~8%TY$4aq$^ z<6w>I1(>rI^FPO$5FBd)aIBZx!_6-!Mc+H_dYnIc6Zne0Q?idySTE5?8DeK)k$bH} zPoxmXM6*G#P(6~Pk}UpsIKXJO#$kQ(wYRcL9cCuxNLjvSnNde1nZVZx)oUu8R4t|S zeZQxu?cU2ZMwfRqRd>0&;VS7PDkGJWT4oyM8hVQ4Qi9~!jyKM0!_1L;D* z_pqJl2uTXbIU8X`%VmH5T@`yEEMzQKRYHtWnSqDSlQPLy)7M=zjUJBI7{&dVEh{s# zPc%r9I&La@Ku(e@3E1}I3EqLn4O*mZmu{RH}~(^$I2(P zy;dDj6jt*BpaNdOdXvry)Qhx59ba;q?5HvpTYBNP>Z@Np@9=K`+c8LS9h$M~u-`AR z;7zxIC%$YVeZoO77*6>m+kZ_8*)Q9mNh+KWoFZCg3s3f#p`Nh3oI3b5w?b*?hm^e3 z5OHH%w&bYC`%PE2*}LI-OWA8upAyEdARueV#1phO!yN{B2irQ|yg-V}-e^DM%L+ol z?%ARp>dVimM!i-li8(}}kHNn5L#nc zN*gg`9|pzN-%UJZ5PmL#83XTU!Sb3$S~}i&oQ?F2`7q%N*gMUE<9V-rQ=w>Cqi~Nu z<3(4n8S1FK>^nYMG4I4pL2Xbb80B%oR_x6n70l}d6e5bL>fFY4VK)w`xKMP`acX)N zgm^ZsK!=r%?&-(NepSR3x{BKi&CTR9??yS`pfEV>&o`P|>PJX$l8br6V>T{Nw&?by zRM&qVKN5M}E00r&$4#bx)7$_kN3K@II^9Tn4cw@~c?q1|u#MZxZB7r)4t=7MD=vuM z4d4M4-b$)>@*qKR=pjLn>xl>N3^Q+obKe=}cYlf5Dw|waP8BxQ{-ibf8|NNfDHEmUBHF;mq4TgKZ&lC$KmeCu%>avn-#D6iW2N^55 z;xwWn;1fnF+Yc2+NE+j_`u#oBqwkvNMZ|)*^X8CZI?q^+2mI=UO|n0-vr!=X<{Eyu zlC)QlX&XZQ2Mc<$FNNnq^dYC{h5N<;g$JjY${R9lynS;6gN%y`qR#Y?P*3zsTYWRv z-Dy+3l0wTes5V7rQ&6exT%F4T& zrA;CdorvkyQ;Ai{kIU;s-`l`LR$h!8ZfKcqG;vvu<|^idYk$txbbvu|wKzEcP@w}v z;=X)`oxM_!E4~2Wr=sz&VcSPhRh=xE=mB0c{@w7ryu5P4YrppQB;sV5fl>Dn|2zdA z9ypO{P=Zru5=ulE%?Ik zb@rgdj+4#w&u+)2C<;(B25rT}#Oz&MeDe24DsUD`I`aVE7BV~;Q4wcOd*-}}6Brn{ z>ZUV%+Y&v|5f9jTm-9}S<-+0FS-d!o zSvC*>j+#`nq_u2A-lx^n2)3Wl$wVdM1uutm6x!Ci?eqJEC{9<~aRx~p2K{iilO!XF z_Tub0CZAE{X84dqO;4LS3kAV1830)h?<9uUwL|ZyY&b`B8a+?b@{1xIo~u(xJ2~px zJ377$LdN9wQ6~42`uqxq0=+zJ26k)k8RF_iK5}^Ku$}g{;p%&*+ei~!|0-+C$sj^H z?(YV}=H@7@paG@Ekv z!=TcdP=i;p%F4d0eC-6_*Ad>Ryuuq<0ae9(y6sr5rnc7fB8A!At0o~~OOkp8P13A! zRZ%pYlEIU|@pN3L{N14*2$Z)B2$Cu~hr0ImTxl}u8t9_eDX7y_w^t6mVN}yfGjR@6 z<^&`p%=@frxjOE-{}Q^~ zNqrauD>Lp~g?zi`Ja-;fyVZw)?V5Rx1v~{+JjgCK;1{Y)pq($*eb&_!;&ZS_-z9u7|23}p_`&Cw=rP_lCaR?n$T|&9}0bQ z`A)C#+ajU8H%g43vJJ5nCHhu>nv)bugh6(YbS>y(+9`q?kYOn2!xymGiu5y8Kgc z)cMq*@Zxw-e2w?8S}+PV)7(`VN8A;JZGTFsCu#PF@_*!D^(oA@)r5DyeYw$S?$xY0^{ zs2YKLtRDgY&fW2#mWIK5)+@!qk8mNGF?25NB)@s~Cgd&Bhu8=G+AMeO)26|9nys-@f&)saPoveVo&5Tq;D`*B%v%Mb!)%x;{gQs zMLn_W`jTn-mrcG4-Zps=pm~YKGD5G;_hkS$^KKTfnKtp=dmn0WB&TeYzQ$k~Bhv46 z^m9La>lkN0VDh}&=+c|&_FA!VjdB9O800|(SlJ5ZxUmXauKwLbmuVh6q_9=Rc^jGD<tKrKF^ZTmnR8^P(HB`1oFx$I>=CNX%8T2`0!8cUJ$}CC&B>$S={+D&UD)wR=5P-czb%fDybjYVHb$*~?{r zg8+^02p~K$@xu!d+h*R~H`so@QepTp=h0;A>#$HfcU8sCk2{oms@F_UrvY3k-N~~> ze|UAeeXbk4ksdcm1=t?L9ifC-Qk1cZ{OBnUAm5o}12dBYb~@72}_xqe01`@8Mh9h0yT zNfaVl`8FVTi!t6_n4V@iNH^bJ7F_H{NQJjYF9+tbf{K=!p1y#0LnnYqL+2T+*XTJm zt8k+Acu*XBSm$kx!wNFcMbEpAjh-pV1@ws&cgCY$E4J~Q5U|}PfY)LjAtlJHo-8-* zU0pEBZHxdw8i1NV&Y|aU!(~aw-{0kq_O&Z5yJwD{w4sGg(`N1hZAWAzv^npiKe74{ zZ$w`wm0!vR`k=YVpJ67;9H4OSg~kJh6eX0z3y`m3NWoN-PrZ=8SB*?1+@WKXj!YpI!k&;tO``g)D)cqi1$0BCx@C`N5RnB+6`mxUoTzo$FfVSTo_8C*>cxMRD(oUZ{t)*;#-3#{qJUu|W>-lzVya?4AD!jG-4EJizv^KvG0~z^ zJRv2#3Px3yY`J0!-f&9(CK1w;7HG79Bl=Nl=!?Tu+pk|H)VBv{rGjWguqMVPjqEg= zWhdQw*P;u0{gWOaxI90kU+q9|Gz?eNwY|x>?nn_MGD&NhxjxZX5oencA3v_hCfS!7 zrxg~?A;LLN7t{5mQ$m%I!5A|INIK$glCxV@92%XZjD|+9bjYccx}QVTqH-J0*An#2 zl2Y*HxZfDPDlBntRt#Qgce7vm6WV}zH0@Wkw>fCbcRH?n(NhlA#*1=*aFZCrWFVke!)YPX zef5y#W9~0`?(+qF>E`*Ekg6g})Amfb?o85TqO|wV&(CizEgAkS zFMq9QHi)EdxvmGTtLS$LcFi~H!vp5&K4+5` zGe6(cZ6tFU{-PTUop>@>q+Q9B;xe=Tuj?*l6&0~FPj9QLs`vqe zn%&&mVy3zmUc*dzY=aW3Wn*ZiWn^TIc@LWalU{)TRO~I$t^KW;uf}{)4P?mSv9Yo1 zlza6R$b3m0ephEUS<%ta%TD|@vG3mzv1uNR?w_ut;bntNI>X->6&o8n+sv&T9vuwdncr6pEe#=zZ2u)P8312O}Oe23QBTxtE#H1 zR+Bj>2=V`=^Y?Hb2A$fSt#w-8f9~z=9e~?kIp1Y-c(`=>kX%bW>yciN!oqC z@77>hQO6UA`T+Ho-RgPh(gW`CZ14 zH)S=dR(iF>3aTDkrd#f&DT^fyG_Ck0PykI;bcTz!!cxs?JexxG>N5q3nNrw3&f-aB zOJTD^Zk7s66(!#x4zs>GIRTt3uc2J_WC(wosn}oGX&nQgOI8zsLp|LZ`xi~-%c3|i z;cy>?!ec-+`rE=yq<0b+24B023FPb6mR}bF-GbH&BP?wwb0c*!K~kjw@Y7z_JW7V_ z(7}UyVJqmJ5NMg+e@>TS)L8Y zUW3FIFA*0e?6X|<6zW3M_#No2EFm@od`RuL%;T!$GcY$3ph(oXA7<8yg;PfjE~Cp1 zOH_jJJ|ec!mOgnxIP=s~JbcMs4|WSL?BqWbe}l8l9<}sOLxlB#WF{7YOV$4G%oPE= zaKvKmponvIbL^f5P)O(<3M~hoMv&W|QMvIT0*(5x>tr}b_9JGCfvT;m_SYw{{RgFWTM`*ZQ;SaY0i36Xf zcsE)Y&3cb;1!WsDp$CF`)kC8J^J4qh-PK$9bh8~toImuNcsn*`bivJMozSH~2E{~b zKk_GX>^;q}_m%J^1q{5WPkt4?kOe&~KhU@mioj(SZ-*K&nM3*tHXE3Q$%e8dL)2qF z;mb+HeEL~Y(K~+D{%V?5j#Icl%rJ|6%p;yjV42{9q+}9~sQx2WRAI)4&4KbK2D)|5 zdDl889Hxsh*mxSy&=!@GCpUof#}I|iFK6adZ-Kqe?En+e3U&&7xDQ0yC9bJnbucJwlG|m@@GO!(6jb!=m&1_C-RCoPgDcw z!%V_oZWwiiM@S{KJ3D+Cyzl?5ggkD1bB6n9xc%+h_E;)pQw#x?QFj2#JiK%p)wc#&FE}rDqfF*mX_92J_46L8bZ6)Nvuj=Yu^9$ z>0O$j11&mj3^_r1GBNEKFH(i8WWE z2REJzi{$Rtt}aG zl$uj;Sj!+@ev4r!|DV-V`yo%@aa0C+8b~N8bSx}Ax}`e!s6=gQKkXLge-|WihQ=B? z1TuV##0k!CzCKdGAm#1)T_9BpMZJW*N?Ddk0_mf7>AB?Zr<@g)mFOtB)l9o)W;11jN0DSfTUb)W~;p*Vv{NL*lD&VI7`}%+72hgG)8W|bPZ}ag# z$Hp#gZN1-mO9NJ&Hq)CyM1-7!g9927ac|)vjyU{#b4;8a?ut0;T)2XkmD&m=$bSu1 zYo=f6shQD--s2=&N~(q7O4AwiLyR2Bjqf)QoHpA$hv!E=o2STby7Vg%ur6fUNj;?~8Ll_CRtDc`>3?6Y&qH!mr$@9}v$075A;VC3n@y=D2_NTm6RdVLbza!dKes z6ReXPMuPLt7^M}J?PX8(joP|ra)*p_e@uM-xA1AOAf1+Up8$QY<;|pUxU&KahBRG% z97lwXj=AyI1*Mwtttg^U4ICiPfNM?NXm%*v?_qt1#OWf38P%}Cr6J#rwg36EQJI_o z)+N2)oxiOBIK019UM8cHYoU^-Mb|rY6F2EZ@)1?$zJ{T@iReq!R-3oYRU$b){O|tO z4yQW3m*}4r4zQuo#yESkbcskOW~oa&Vm>I5H%`U;M^%zB&w=wr@aJH1_PTqM5xC;y zHU?RB5bhvcA~m5NoxBI486R6w6?fl}EDYRYM!ijDB}9u-gzeU-#j!4bAPPWx`FC$J zXX%AVCwb5W>$N3g9DlcaA>+H0wbn7GXKeY``Zrd1xE9U(FGS73uIFfEWyijm&^#`F z_V@8|nWY{*cqWZ7an>7>nY^)QCG33e*uFzD@~UZsx<$B=*1$VVy~C}UqB&G8l9Cf0 z??Y|Mutl6eW{QES=~KJ=dp3FCNn!8#sML<-$qJQ0&kMeMPfg?J5?&ZLaK4C5Zh~C#0AQ><>_D6HLm+vKc$!QI4el4%TCN9|ZD5v=q&c+hy7qGe3U8`LS}}Oh33A zxWmGtkU@;vTc0{brn?pKd0cT5gqvLGu~g8BWXSP-V%y>F)`y;f;bqQcMfj4buLurmP$U9x?6W6RG_=Y~wV;fXa4k2Pn$^qhNwwLtZ z$1E#nQA9;Wlj!O1gLl|A_2Ko$?ypC_8kNK~O2ImZ(%!%O5Z`RDNgtZ+Gg)?r3m@@Z z2bON>e#j&-_agJ2DrOYrnj@?&2&aOsU&Nr=|SP@-=^7 zix|)AGQ;-b*CcnE1ll_Pa?cBgH1;PvAenxlbH!ORK^cNVQIFpig3Me6yjkE{QwfbZ z3VWfenII;I7{_c9-fZp@C4FP|&~+=da#UDUow6xF%BzHS?VE5=$tY`(>bu5QJ#)C? z4J~iYOg@K0GsK02v~JlS%EXY<_sETpkCy^+R1M5|-8ZT`358lO?jukMcJNt)AMw`s zTXcPB`hex3T`A$rypwdhHYd&IwXB4%?dl@woMzfQq+xGls5U037_!WrV@9 zLpWOU9XHVr=)tn6@`u*ZZeuK8J|IV%Ff2_+-=M|Z+**tHfOY(;roFSgEndz$jPTek zWU+chz_iR0?-?1Z_iW~8a@&sn7zYVkhJhbS4wNP2^a_w&uk4$cLyEsIT3bgp33>Q& zvq015MR-j87*vjLxgN_jAo|w-Z98F?`?|}n?-K?)Ojy(6RhU_r;EbWg2B~{a8Kbph z=`kZ6{=dvb#C-ao)DPL-QHWPKwUDP~J(F-OOxyS>&TZ(l-qhci=6Hve1irDQlekM2 zF}eD)U{G;djWg;{v{Cg(O~E7n5C4)n4jwW*`oYU^bN=jQxQ(c=x)UsrIcw?k zlBtD3^d|oMxZ&aat3p9}4V9S3J0m|IesnI3`ZXXVKK(y+moxAMc8U(-&S5Z( z5b3zOdTDi8;D7-vqT2hQzd?NQ)woPqS5k6WVyO^90^Jk*5mtPc4mjn!g+>KZ$?e~& zN?VTP^2FYq(%u;tL%1^HFic3Ny8uZbYAeH{MO@K?MmCgyY@Pj0@yeJVIPt!W_fZ(+~{vv6y!1xlUBoeyy}9p&k^ zyQ#MKA6EZicQ_eU%uuswr%kZ!G+vHJNU0^sN!06@!!@q+%F@|=O}KyEjYPAS3*`7m zpv~R}I{T_+MpEHLGWWi!8{olUufqC!pV&{O@2fA-#EN#x1BvUmljMaGsjsWJcrTIQ z7RUx07HRK_MvT{%$TWYRv%Kc@lzMX?eKGH>GJn>`f-T%^hWtJIncn+*QZ06ZIKCKH zcHhT7H5P(>Lp!9?=b;w|ngCJ$szx^h8sUoSlt;*t2JMC3qCf9o_>S7s2 z)@-i?4x|MQ@1dGZ87w=b`%Hq1E5}^cGT+^jq;J5xIcy$_ZpHatwIUJIb>JF@qFL+V zWc~M$51xp=&wTNYU4yAre@IBD`p6quyV=E^+e_R2LZTs%1-j?Y6P9~>x~i{(3sI>t zeZ;WFH@@fNBqz?*FBi<7LR^;Q@e3A|Aoa4S%JZbq#uut#TT&^zTtA&K8%8Ga_%pRd zaYb=E-2Sez&57`0AB(|0zQyMi19Nr`oS!e-f2k)P`-)2@Zbhn(h0zHZ1%5)O_ww>mD0z7QY-=tML;pJqY7>-)nI~||5cJBh8G}}< zABR0|D+lsz(Fofobpdjg6omVj?O|;b8Dlxod~}WhK3~;aK1GeGP9|;SNveeZO#`^5 z;oBhy&A89i5UM^}9QI=on7KK_3fLPVrfV1(mMHBz)kbf)Y_164@1oQc-JD9>1tNgf8pyp7so7)<>ZlWEpgU-Kprq}BfSmq_^jv@urQKsL+o^ikcEI=Aomw%5 z+&k|xN|nzTdQ-`$k>jT$%nrAR{rh8l6bGMa(WiRQeAY6?N;K^A4&C!M4c(1VCvF%1 z=-@(C%JJ2Yv6ZVinoX{Ke_A0XdEBPCT#++OtMFTP*0n#Y87-`+-OG|VYunr5SQNsG zpamxb+Ntpz@50{XC@*!z(A}4}fW%gV7NDyRXUSmK&H^#l0GRFf?`>}(!07bTcJ4B1 z+_WQtq`V*eAFY@eCGd#+0{%EoMCn-2!|QfodRNEWbbDz4gi+ak~V)!mg&6JMe#fHtn;ZK)RX59{v`puKd z6-WAP>-s<_N?us@pD&yk3wmO?I!#C184CXacMgi0sH9sY=Y%xtMY);If4+Dm75iT6o@^PLw&UNR#l^)zy6s zlE=*zeGH7!;F*&FR~D6(dgXp`WICGvR^iM5bfO1M4w%;QL}ipJ|&7P#_FN|~U~Um+zf_?)MD+bK4hf^XhUUAcmoSx0aLx52F` zYQKn}U1j~=R;6&eB>>UkWK)&bZvHxH{_%aO?pfRm{`t-9S2WEF8%Y`z)=@UI$N|2` zh9_{j+$kq}ZMWgO@W1I9r%;@L<2a1rtZJNivW^?Ms_3D)*GJ<=TD}p7#0x(Ry@BCa zGOjn&sWHQ*+~fmf5BUk-Cx~7fH$U-EJ_ZB3E@t%fE+)9N&AjH5ya(jDaN0RHZGpUOFiPpV`cv2MeP@8011DaO!3+T)y^_eK! zpx~Ke4}|Eq;ma;GdTAgh7!eb{IbebsTEc_@P``Q$vS$=o7 z>s!mGz+yu)(Y^{a?9YwzQY{n|6gFubojP>T(IbPqtd4aC$G~$oM4u`u+--HqtPpCTEYn*>@Ir_vh>V$!0q6>aHUPm zt<%s$vaCvz7ySFt$V)|*Y*IsfZPkfs0*{=smvkjFm^?lB zEry<-i4jCg*)w>iHUXc;V1rm%T3T3VW=I|mQ35}`8n9e65Vfw3*v3)^iu(Ea(K9mk zP7`P~7U#aDu{6PFRo{u(qP6F*^*qhT$d-B+-UlW|M9r-3^$;cnz!1#{O5MZ5dbwfz$YmC)U9| ziHN3R!soLKE*}ff!6QA6*G+{>m@HK8i?qtt<+q=dKU5zKkwmy}ZJ4Bl^Dds&1w-6g zv2%ykML{)Od3|3Et()WYQuORWIh>%#|7Ld_R@)>fs;X@4eZE=8^%o0yKEOa;{3Q%( z3H628OQREuCR(ZZDB}4d(hfFl>5a}oh7wbRlL8BUMIilvcP zMZE}8>=#~WEPW@Be|0$FhMA@8Sg*dyywOn)%zC#@(R@UEpM-JAMeG`zXJwHLX_>$+ z)70HtpaazlH3Q&qcjx{2Igt|L&p$w?0)BJ8fJ;ePylKb+*2MF{3)obm>S^)0yr2=UHPlvS`k*0YH>^#n!;76bmqin%fp=^w)%! zGs6Y`$nWs)i@=Bdcm#4Zr4lj*d?%MrFIKGn5^0UwSFJGk-o<}{MOiqq5W-}MY55wj zbg>3G7AxP7>*310h5zrial{|_%!E@!_`z~Ihz_E{T9CPVXBx$wwIGHsBA@gD*&<|o z$bu`&0fx#XK4JufgoNy{3)xKbPEJjkl0N6Ro7Y8lBFPCyIP&=OMkEgaqh13gq((4g z@ug8vYH<7kA~KWbz}{pTA|8Wc3uwWwx&C}Vg3Mz+5GSfu5$=u+IzUwSp2z}?k`B-Y zUEJAW3pu{H+%8cCMm=jUb`VIJ-fO=0+NrHpSF3FfSEGrZvO&^o9)&THnn>_T)+!?%lddEf!!ps z_dH<5mHqJWCts;6hf&%)dc-?nAje(b%WCJv7yeN*0I{%Rj`?qEHvuW+6{a0T2Hp4_V;;9cNzw5Im6pkAX{|{^TR9t;1ZlV2P z!S$ZCZ&hv_J4JMYE+&XIDAOGeF*uZ}A-@vwgMpR@^w(hFr$tKw#VD4TUqiLqt(XXf zoOrPC1W*=VOEwtQ<%IBboE}J{lss7{wYpE<;L(D3yUU|QqBvfL$p`<_?t(&X$RW$g z0q#DNnl1O#-JxiG{;T7vw&%DneKtC*SropEN-JJV_t$OQv#arq_Y`?)a zFgIpgnl6vUHJ`G2%M?H_Fq;+%Oabr=6-(Zbv~0P~-Qz_JL- zNKCjxc8v^y-17754KK~4vdo4FEXR(o9UPt)1ZZ=z64sLAMx!MonlKJU$k#tJQgq{V zE_qk?-1XMvKSR(*IWRAvh9?cD#py=nKpt{ zapTXUi4{CZ+bY4m4Vj^hMSRFr#SsBE1uauei`m`id+}fd=YCPXk93g+WsYa)eevgU z!?bukjvU#mY3y*94Iet!sENxf)-=(#$7irZgH>*tv^zs!5q6wZym_lJZDjYt8@OJ} zvjG*^-t+15n^UMPZ%cg*=H;l{i!#r5!=2|En$LbN zCZ3riTN6!D9r~}Xav`&6+hRwL<3teB=He(g+;WAin>>nOx}f7f$Xcb==SwF{5?hUI zr1C1x$kWjL8OE*};y(Z3hsa#@zeQeBQm8irWuWlknQ{QlQ^EpH3y5PiY5TiEdxv>4 zreRakOTwZ|%~cBvUhiroTDV@Ok>}Wx;>4j5vZ8!~!KKKqMgX?%>t|*I=YP-tvruG= zg&!T$3fgMymf=bjDBpH8juDbi4j8qH+LnF3X*z5Q^Lhq()O9^-x_kY|dm>L5{n?C; zh4H^VLa!l{`TY^E(G4w!76ei{bSO&#Q-Cc+oJ#%O6^M9>guwSRB?ebNWZRtuP;|WK zzL8$?m!Li)+a{lf&PM*HP0&-%+uicbqh(miUk|gf6*^w<88^uUc7m;A&~@(D`c3xz z@s4yBZZhX=Ou$MbQe`GHBw`Z=bSsWnF<#93T?Sb_1CDAi;l6l;d~pEOASDg@tcH5b z+q7xy5-c5b)@LZkJO3LJj6CxTd;P(VpDn~N)he?_=kogAdVIe)v^`#u!56)LIgg^y za3_6~Le|%87Ol;L)5YI^u-sRCF@>Cp|CiU@lLVi8Uib5!*B!r%7XD%QE%7$g zQfLCoGvVZ?hM@o6jSem6gP9P-B^gt@-!!)@)>z|)hWk&5MYd3W7M*(jr9VpJ20)#< z_T|?7r%oMeZTG+%YzPErczFLl5^awIt?c77#A}%c4g#fj(%S47y{gE%#M{Bb=^X7= zYspN6XtfEbpb;YYgcNAUfcDVUkHhXu-q*_2JG?A|e=9@w+9<&1O~43iOy;n0hasOC z#c<2iJbE~aTbudF#6ylPQArn1#PKxL&e_yf*;iZmN-jqnp=EQ8OIo8-56OVB^Zu%# zdbmT3_F=bm7YXs>p4w700)-`Eb=v?{lSFC&&tDPfFi+dN`596{^jzH9Q$_2dqJ#e_ zJG*Q@As*QO>1M^%lbp09y=|Fier`E^kP(ZW3ps`&BTm2V7^S*P^iolpwu)?_jqI&L%{ts{)nzMBBa@uVVOdgZ;;FFkrT9jXW*M#6 zZ$>{(g>MSt&4B{QFPY^&%hJ6czdxe)bk#j|xK} zjP2KmPiM*5OKJYC-3KVo`^r^1>G;Q>cAn#{@TX+{9Q%+jOQIN?@8*;jK;9vAIlcV4f^xQ0spr$#l}l%1+wFt z1WKp*I>=ddW0LRP7#~~9-nQ|W^i%?cknL$bhRuD7<(T8i z01Yu=PlArXcJW^Yb2DL5FSDLN9Gs}JLRdls*w!thm)=<}MR$4TY8|~Qk{VP zRqd#2SgC7YGw2iBa%&Ju`K=^y8ZPbaMfu)b(mZ{-2INWI26tTGS_+7b#RCKFAwcIc z0G6lnY*-}OdvSVGXZV33@PSGqe*i*R2N+99&yx<$tCF^`*z_x|E;Z{Xy{H=)7+`w+ z9aX#9HWD-)S=P16P0)ZF%p^nipHeE@#7Ri zGmqJ99>AkJf|DI4-KAI={l+V?2JCb~+n)vEcej!acW>VHGN0(U$j6Yg?O2B}H(D8P zFUBikH6egcR$yB$s-AxdK0ScT=h8Vv)Qb?Pd_3}TOaWj%$gDO3^~3$>GD#OO|DprW zf@5u=XJ(keex$(sb1)5cKlebw8@gBm2A8;@o~qls+uN*&1HeIO3@po~)zzJ$c#QXU zG-qIA8lX!fI(FF{dXVmSTT5@Ab3p8Ihf#dexZrK2)vmC_^ z&w;t)5X=D@fDf2(-)}tU@;bAwZ3GUtK=2s_x@Yr^UKYTh<~?{X?%jWaiLbBkk*J)k z?5Ey)_@foU@<34Vv0xLva4MmA%BaCFU%pY#j2RuOZ_7F(!!3EG9hpQ}x~D*{j$*8idFEgY(9w*O%oq`M>(P)fRykXAvuJ0zsLk(LGt z1?f;yy1PM;6bYq48tE>7vjOk(-21-Y^9R5_XV0uz>r=D%_+~T35My+CqhnX7ybmF+ zRtuyYG@DmxBW!H`fPDTiExqc6yC%cGdWN$D&ArUx733F;bCVEu&)0Y)V9Wxhqn1M0|bV0vUq|51uD|TBC*jZy_ggN z!N4#1!5@OXzf$A@DURFy4JJQT98SpWQAT9eMS3?=DAE>C?<78<`ick+*>2`zpXvPt zh99d~#`l0_42*C54d+|+dv*Y=Txy^)6L3Gs-85iub#u!AW7P>jm-_`By2DA}8+D_A zr7r{IAJ`|N@JeV=1z&@n+sdGhXObDS0?G*<5Klb{6D~SIt*;pnrg-KKB|ov5uG!>H z zgD)3A=DR$nfZfR0$;t!_{vO#*uVW6cK-PgiD={k|2qbps5?+_!ws)9ulK!aYSQLOq zLT>>}v(##mTLNZneVxH0E-}~&TWl_lNo$Nu05!Y$#Uw3e8C3$lZ~4h%etxeLCLFUO zy=GmcMJCM(*X3h>u;W+rB$8pPu#l-_zXmM`EY+tl5P$w%vavF}y0gnG;YY=w5cuJB z>&f}1l>~>ZGdJ;pJ%Q)9e0FeP*n4sWr)mj>0wni(%V)wpcT7Z`K-eThruo=uc^nf# zyinZsRkxG%Pf(gd;Syu01hZ7f*MYPgn=bn7cxRW6RcqZUNp&K>A-%K7Egy-Uif6eDkj&>YC^{h~d^8uVB4)1&VK1SnUd-z*A zCKslT2<9XL#+s~cTHOEh1L4v__}(~X|F3`0B|jDU4Pn(5#QDdu+7JgnHEF6gT zD_YyOI=abISoYn{cm>QJe{dZA#i&D~C)u zLljX1e!<|wi}E;tk$q`lVr7+QQyyraG|11m?+%ut!7H%Xn9(Lpk=cGl(wVTBE5B4r zg8CG>2bOx8QQube13FfB4>1Xx1J;L!Mi8aE69Y|(y)zOnO@OV@rqOR&ZHqp;{_zh3 zvz&F;>JVB>tGMa9(Vd{({GsP780-68eG`3To>7><&j{VM{*_yw zZWZCu#z(9>EMypAZ?>18Ak0cD%M>e4A>z58r8^xi@HODX;ot?h1=ipnlWbfr5oH#& z0js|c#Ig97PfVx-_KC1o0#GPyh8-vxUxdo3jtuEYaQZ^#O$eE_gF((rLQuR$sUadT zL=HE499wD-4Z|NoZVJFP+TPMLyA8ydk_aE5FaCph3kd=_ewwHLa#0ByafwhD+JpDp zJm;|7B<~x1P~fCqCX6pk-hde)^qYaC^(U(9_5(T3@lvGFnu(PY6YNI$|N8C{(*j5* z81PgqA845b3w=`>1&<1kBqoL03Cg7eo_1DPsg?Zxrt{x(p7T`&??V*&L1H0K-m%Xb z{=nL5xHH#kYiHLA6ugf%B!i!@^WZZR9I^wd&mU4o!yOkl9+85M%Xa-ceH^_K5{L|l z7n#ge$eM%B)h`e$HROT{A7fXR{#oMqW~o5{5hi-bwcX$vECacY52ucopKf4+?70*NB1@RWflLiwO2 zKzC+`KDInd6}NniP>MG2nY!5tmUNj z{w@Q;;wmS4jWQb9Q~~<&SKudMe+tSi9q82nq1k8Y0qJoW=ENgDw|K++DSgsrl^pdQ zAVa7@?T4!x)x>8k(|0Dz4i#wHsbF0eK+_5%I~}aVsgnQpnyMV+|KuRy=MxODGZ(q2 z>mzH4IE56NYk7$zU*JOU5GdjS-FN3TGlUX7Ywt*)zEgF)I=~8!dqCYW>Q6Gtq(|J; z=qX?9*-9=*U0ufB^kseW0L5;B836%7U@tk>>Yh6(Dwp|pd2~HdjdMVvm012@r+M(e zSYsk374ujEDoTdOaNpL^XLti@l&QNmRSbTRF!kb>btVt{?~Dz(mGs&&El0|U|M*K!-Aok@YBQ3lci0i($Y+&@ZYJQJO*CtwI+shVs z0A?1DxU<|1$h{LNAgRn)8Yv1|b$s*>2q%Qec@H`CW$yAze&SdtenKwUG=iH&^aqhB zN^$uIn3G=%lSJ}87Q;@L5IfP#I9sw5Jdu!pABC7gRLyxVtyAX)mJkotD-gExVss zSGgcz7Ld(8tacHJ5`wq0n%A$vXEaTn)@NNP=BQ@1kYY8A;R?;xRAGE`N6<^YG62$3 zr4oX9FvoFY48-4Awtz0r_(dW}!~1ft%xPony)GA62$>fw7rX%A#CF4R!Nnjhy%D-< z_EA*-d?fnm$SZo6wB<3DA{#_>HM*(|hZp!WHGyBlD@iS&ktwK-nj!aw($IL@3Zs1i zKOqSFv7=>v7UHem=(M`Fct0(7y#`C@?x`Ih~#p2&GJ{fGZr%_ z1_YP_68LUJJm8V2dPVNI_~_vINPG7#?3YKGv!14tN~#;2nNG94+~Vi0!DokN>D`f5 z3s7y*^E4YX^LR+#x9>1>$+z_nLzqbIo$CXW3N3^;Nmcsl=$U{#37caMD&yd*d5sQC zZ*O7zV$9U++y|8J(%ZtxMJ8OiMTf& z@Nj?es#ojpqe$KCu+ni`rU9RA=X}TvH4cK3N?(qC#npn!MV?Upd z5ntqcaRK6A17M(!7#C|Rbc;>Hr3mjI3F2MPNr*$0m#^AGX0Y0RBlwfa9zf{Udq;8c zNkqD-Pia&Nj{l&%nIGB)oKK3BFdD`?@VGZGKCrNgB!mUyIRVKO=(_=v-99}%{jES8 zF>c}yLa8vj4}fnO?tKq|c3SBJ9}%=a^5jC(iJ?F>i@pjP(RiyTrF$y<<#*8~EqOie zmOCTd=YN)EmTx?22*JRk&+9Y*I%1Dy$82R`ftDhRT&aoi`0>N9^eZ7=a@glo&_c|F zquF{`;(%^z9OlV;oJ+cRTwrW(GcY&K=DJ*5;$qJ>nEH8aKFBNM|l-PZLs#- zIg3h5HF-f+a9E=Rcc3o%Llg%RpqJ|C=$Hc&>RxGB7@Efi#YkN~x)}fLOSw&`NP;k` zXn5w!JK#j6nT~Zr=*0T6!1JrDbE zd46nMRtTaYPEHo1cA@xzy{tfDXCnZ+sMp#*5ET=fUtbReNrNQ7lfSdK*BkA`tXYvO zGnA{$0I{`Fy0LT|k&%)1-q+02<^Z4mx$WH1vyOuBgc`jFl^t=jrY-r}VnBIGeRI~&hAYXi z{N4&4o^BY*l6~cT)z`ge<+lUI#I8x3W+wPHP=M2c`H;S>k-p&>0!r z-vc0Lf)YSNbd=8{k&a~G28!OanzagP zM-yHIA?7KWd0S4;zqXWG8qkJWq7sm~@pS=nOtPodi2N1!^EBVW2pGE{Nfn)NJrT+Y z2C_AoXiQgd9`a~{i#9wDvPHw}Hs0JKV(36xX%A`N50da%xi`1mk&wkryI^|mnIx3? z2eQma?UN|XW?23xvnR&Dxz_}u0hd>RV9yWaUs7>%6N0#pca2hk0LUQ$v+)yvguV0u zFxfK)?6a=BHt7Hnv)5oV`Ln;XyBmEB3kxg9*?i)3{J$vA*+U!pqn&%Y>c>Xe<3qRCpJj zSnpNrO6o@I83GkU+{m?aWyvAWuBB-WR7}_`iu0n4AyXxWt%oGLZiA8u2a8CqaYg#6 zIypT0ze8;Mrb)sP-j(g=&=Igz)$<61@oVXeeZghZZIXdS20?BurGPLGZBf8(|KbUB ziS`L3YyuJVuE`*}v>^@TNAlU?>v~^09juR{ft0ka=08`Alq)o;eGurRP*vj$1MD+X z&2TBLC|VYb_%5Y>Nd;)!Oyr?zLU#*zx6z}Xwf?g5oghe1O`7PK?!laO%8xuwbZr}Z zQ&~oD`YWFJrrE?XMgz^svDVKotn_Tl#Kmz11m^C9?NyzO^3_&yomAazhA&qJ)4zy! z9`ttVyi92w8|5Q&6NzP^c1K1IqD>rB*82@IbIVq8f&Z7xdD$S-C9ebs`7L#optC!Z z=+V~09VtqECl)hm_|mx=QZ7Q;`;yzJ-4q<9jQ21nG`ws`*yJ;gHDkFq`rG_ z&f;b`J5+U2q9O4RnO=&m3sTQhoi*>^waBr+Yc)w)%5WlCNlT1Z(z6m4p%fQ$>mPse z0AOZ^zcRX}3*q*aqjofzqf<%e#Fp;Fu9L+>WVBpQH$(%Vh*JO_GTCeUhAix_pdY*+Yim3tek{4)Qq>84O*_QUYmIb+m{0822TQ)PDjR8+Avw4p8cm1T5{Iw#PfP-H z(#G?XOe>Wd-d~e%efNmFT%ot3JQuKu;2e%%*bYY5wjTqi5-FeMa}3!|WJ%soP-3YJ zyk>zsAOGR)ZpW0Li7CSLFPfOh_P|@ndKUYTPOnJ-1UcS^?|Azb+w)*ps;v4I(7mRs z0?~9L^pC^`S-3F4wgo`?VkJD#(gzO4&|KKJ!f^gUZRZX!_>w%^*CUcL<1^ZAo&G!d zQr$uO1KH-B3T}OqRRmyB+4-K%dhYs+6b zR(Cpyq~aq^%YPTrT7-rPn6IiaRiMDcJ;Jo|=ehgN)c!60o^glw;kwGf?@1yMCfx#J zL*~~Lxwndl{Y*gMWR1g0WO5V8JoN+;m*o{DMMZWi7Ct_WnflsV!Cii6uv96&+2A=Q zL+~qOFdm-1am4?^e@YY$PCT#hhSo4{YRFtdDQ}~EZ4Fx2@s8vq=>vOL>fo!px8DUD z>bLknPYvTw__*Eiq*pF*ju?K%S_j@b%|`xv3{fS@|Ef+%7FC#{b>rXC&FYENyuNfd ztJDMmxt(Mn2m~Z=_nbppY*-1psp3{jvBE6M2 z9!7W?{u4)hXEPB-Tbm_pusaV1ikVMp)saIH2$${vAuVJ?0XU zn5kp7*MEOi3T?g;0rLf2HEML1Cr92 zMtt-NX*1O)0r}2}p%w!vvs_n%_+4}gAqv+^(|h3UuQ!SxXxQPxk0&e8AcxidG#b0> zRI(OED1)h(+D04&{h2+CMoivPboK8ry!&Wy+Z#i>)%Xp&Y7Ebbj-PDbX@+NsQl&vY zMwH0-dK|>s&hj*x?0{JZ#ryRbI@(O$;IDgvT#Q+(n#ow;heii*6dO^*G030`+=wE7 zjO5?y11T2?%Xx4{#93>hUJ{&jK;;SblA!RyfcTHi$!aW?#)EGT#qoJQUaE*&*NUq? z!#0>d!%x0op6)Ee!;?OelLTE}hF6`zLEG&7g6bCm56^r;G-tie;%@6NhKCP9eL2Z> zlkgnPAVuY42-5s`@j$ry?v3S`Zm`}NW`Yej7&Q*-&z)}z{mqK=^M+v`jDqIcGdKWa z`&wuGaP_6kwM%A=d?tWuz?t}GEz-=Z%{zB33dm;Bd-+({XTDV55fwr36TUj39!Tc% z2Xar4HI9*X{qJ$jYIVkeOM4T9Ri4jnwpw9kgxEjxVRWys2tv33R-_> za_cm6Lu7LkSU$Scc>V;aTJ0!|GU&UaX$oQTmCn@rn@IjR-YK<#8jt;OVl#Lfhw?Sq zAhS*WgDJqM$BpcHG_>BnxsQJxo2fRb*`%Q&&`#ren%3xSCweD(lk%nQOiBywe*`5!un{FH)uBZ9@c_OPKUB4lE z|0MOgJA7Y}*AgArGJopBOpvn;>{d}S>~p<)a`O{T>~oq^0`*VFFC2v6MyiqTGbj|A z<-an<0^bq7_}g067x}A|eYi!8NlTk(Xnh8!&P=1V9L={BE)K0msM~7$KSLhf%Wv)R zpOqTT5QdM8T>g=TtDI0ujdFKTk}lD%th_ZnzF;m^_1*k`Y2L(d?afa-dADfqvbsl+ zG7h{_m}!UIH<9&gzuWGEp2Ej69SOV~@pjx7zL|OKTam;g(|4!M;|*8jdR1=~cp8fK z=p`;?k7ThvVr>S)m?jk1tL;SF&y7V9=@6*10)X%0{V3S?dp{7K8%!PZKRiolnN{hqK$87?e48w0o=WQgm@ zMisJALXQ*!)J+$F7!U&`~Fe(B?oh0&8KTxePRf*438N+S(unRpJ3W4TL z9{-^gMqJ=J2d-G($~jhT(YyZ8d=i2=8ibK$Mhc%NRfPedwELCg-eRGeKM7$?q(GlKm z-p8NW>hfFKU>(b1=##m}(&lhaLIW4Jhkqvih(tPH1pq@P0T==~(5^pAkH7>_jRZ{O zPeZy2UE$ySR)hpY&X_j7kz#5KBMWO;Z74y-l3z9V)UsmmM+)BAc6R6rnJHAN15X`+ z_}|BfL$3DGS@iLFQFe0i<_USzWM12EacZOkbKm=4ui&1)=N?S`w^8#*^l{Hg&3j^77IuP}fDkeId|a84iO?$;@z={qT-?_TQp%Do+gSQTxr~IN2>QSK7Lf=t|$Y1f!qE;oU-X?g~Kb zmR<<7&~qZZ3o4QUog_o)6&N$yQ^bLFj&1e+lI=hF8-rFqP3|VtJb(dPoIa6Z5dsVv zhZ>z)uvJ0safFokzgIR4HGdTo0LPZx(JQ(c){?IUcf_DoUg*oaWSV!(S9!+T@nl3(dHJ zqzzHW95hHbvQOd-xoKz%0J;<1QilY7Qq{LyP;U16dFs$af=K6iwp5}~vKR~ot-ZqfO4X&;;RRgXHik1r*fjZ$q1pWBaUBy|Gj!YYmVec> zR2>1IB2e+l#B_0LuF5dmo%3vYdV*PI%z~at|A7f5b12XSy;vZEDyo&;tFtws-- zK`_CWMj8nvAWt(il%uTp{GQ_FO`3!PrXnqD&@x;%A-sD;^1(3y`OIWO=vZa1;rU>5 z)_`tqNc11 zv-ik#_sb+c?7iFB{x&{*y^F*k7BVBhcWMI#Bj6nxlG4qd z(;##MexyF}0i-{R>b~7a<WgCALy&+JVoX)6wV|HK=XblWR`(KtFR1%$p!_4L&yQV= z+P=D=a5sL6J`*9=dGK3g!gD?{lZ43QE<~|hYAOf#7e?IJh;N+J2NjNq71vthhdA@Q zNa)jfZF;`^uJ4?>2VO`IRUCVOU7dnzS2_3HN)3DWp31PY*Wk79f`17Wz%MsXgVMux zV*nOM2r{a6JozRUSnh(cdZg&&*}NcEbMl?*TAa7A;{+Yv%7wsg0d$&%;=pd9LDyJL zr(pfJ#*Cm$W1K`F7#1bc9?m9*#=7{p88vsrFRru#dDn%u{7X1b9^{3IN@59)&S^5% ze2W(cMQVg&V{3x)H~WY#Xw|7@?OjU~y$^$~>~rP2!&W+|p>_3!U+Yd+ceiK(kXBzT zZm>7@B%nK+E-_zc?7AB^2&Cd`A_3Nb+a*Jb7guic%QAS3tslmRHd|>cx>-3WI4xy3gkB*(erBp5g8}Sr5BBFcD}zKmpzoyX|G3Erj}L zhpG;L|H>6=q>W{x%)Dj(pN%b0gSbhGj9?jq zPX1M??Xk**{Q;6A3BUJGb*&7W^3anyF+d)CilP#WQv(+=K*c3)=j{Ffh?UrnQ0yT!WTxz`X^U;^*i2_SF>420ihQspx>04LPG%k zR`@XqEMhv3+s8R5`63NkswXE3`yV6JDiR!I{8?t!{GNr4X9i21srX0lGRPd)`*{z} zDsV0u+W5ErF38Xe3$-nIAxHCfZ#!S1|3p2BLZS#rqCKaOH`;!o@4M}CVt9Z)@iRL| zdLXddVB!4}?V7_U!^*W(vXD|9xY*lzL)$|XXS!~-cJwo#x_3bZ{ycroInR$2@mp)d zAo%7Coz(APli-|6b+nuFJJl%Ay)FVW;^{EZZZ@hmKazAT42cQ+W}$lXvnXKJQ6{UJ z?)((ekbb>LKn_fK8E*aG68KC}OwJl2aX&r(U>fyY*J){R_) zbz*S|#HT;%i3L|^Cu(aFOtI?I0-kBX+1>lqlu3d`X%7w~a zC-5yoH48Nx7{-fsTb7#pV<~dOi5t}-2oD?CygSo!HAf~dv@@TFNqC5r?(I0OqD$^O zMx$SpXdrryH1SWR{DyR0*B_LYzy7=BXQtch#`dlDrlPmQ`d-G}rw^_YicyhcVUw`h!k>Dk{;1#tKHH&UgMWva`cR$6Vx+RZQXW z!dIB%v`wM*@A4B(F`@5mEq5Q13TvGb%M*eF-^I|{>XlQ3|?k}%xAxvf-Vuak`|Hxn5HR~;3_}?D&Vv9 zDCJ>cS9&g2?z4`|m++F?3PzoLObqxgokRsk8S-u{4&k)sJvL3Y$k4pRF7nAN->#Di zm`gN`M>cSAmt1a_%aw~T0vK~qCaEdUg4TC{4hqsORXZs4X1O_w9RKYn} zfrr+ZEqQnCLQS3%*mvYwa}5_*lYpHB7j$zT^fm*jfbWNv5K3780RcBJA56@^(1n)m zf}uJimwiW&2&ES_(*pEZ(Ydl|daJrQoM5boQNy|^F42e`y2uw&@U4^mvEm^oojFcs z=Zb=T=cjVY`I<~#M=vp!x6r$`Y1@}PA0Ba!om4m(d+yppR)`m(h^|@JbL?Hr2_%BIz~1C+jaE;H0^?`MNAKFY(jVFL{ps zeEiD)R9w!US5Cj8nBfl0r({|6+l07G9BsvSxi9TJ=k)REwSEZH%i6Tm#m*s= zraVpzN`9RM_;(>hB-td zJ=&avDLfnKIQ;ZtZZ&}d1!*cI2#EfY8*mEDvX$>kg|DmPHLkz+Re3Wn^|zTKJ^;R=@+rnnPEuxwg>2y@35t1xypRvpz>kc?R(5I3;IZd&x<^@`ZOiVuGy?1t7fw42A$Ecd@)@~d zf!sGbk6fRyW(-JKccmrNK$_LHghng3jwmnW<@(+z)UMqG=h& zIgsPKGB+4%KHg@f0L=ki@$G%9_QF{>C^y33DQ-FE3dDhKK7%%wOE%9v0bV}74{47H z38_8ekR%=g{PtgBrn)=O1^^KW-kJoqmN>R%6zMP6(k#tIXBUCuB#U9Rx| zV+Z=Ti~rgWguP^Z`-Zj%-T16>KO_Y$wR?>_5!cH_Gz;hZDB8$&@7^hH?c%Rhi#FDc#Kanm>uOY8JS}b@@l}4CDrX zLWUWk%Bj>hNzj>Y|UVqq{oyR2}RBy>17zsopR8+$%ZiX7H=T`6j zK3TJMl%ZTRzCqkN0GuqL^iO)40dy3k-QqD{|0+zSh0sHW7dOZm9E;aNS)^n@&M~}! zaXbZQSB`n9J{T+9H{X2LFfg>R3VhAPU}$@8PWLOpIq(`T09`}Lr0F+)Z^`6nXgXo- z&zKwspns~oLBOBuh)i;M2aqX;j~O>@*71`rc82Jxxl=WH6sTr|GjPHKd4Jxc;>48RVpcM|^INV|@lj)R2#^0a@b*6mX-9-VK}U6K6zz4Bq{d!IQ%x7z zs!MiU&BolmND2KyL)~<;gCUdHOePWfaU4?sc&(eX;B0rD8#3vE11@}HJ_1wsK_Ax* z!yHV93`7?_OD$I`R`sI-IlOjHtFw%C+>}-Zy$7W9LBQGHl(e^% zzbxJ}yo`QvpMmc7)J`TKPkc{|KK+2w{6G@0w;7^&nYl{!tQW6O2%OeOc?K!orN;n~ z@@-nge-l+VrxVJP30=q#O#WyC-5djC(X830p3EJZCsdgcfV4>K)XRn6$dHf3w4IVJ zkzQ-!4v0w0uqadnB{&ZL?{j1T2jc)zBnu+CX2(ZjR_ipu2{O=L5V$#%f!R7U8RU*Z-D;yc61?r29Pr`Z zTcbEYqJGU^(3#L^PV=g8XPW3TV}low8xwO`;s{X!n$k6Lg{X_a1Vnm-5OE&bP%)*;L;?MI9O2RvfOhs%s$osF*P-WmT4JkmB`Va7|(0V&RHuB<_ z!Zzcy{Epo-@cS8D)l9Rf{SkIri>KpQ_)ld1X#NLNqC2g(c?82U|Nnk#Y3(DT5)g5L z`Lu?KHl;R8>ez`sSQfEq{g2SYzAMd!hpA;*fC2}bPZO0a(#A-GtdIlLlWn*J1V ze>2nP^4}4Gy7|BEd&GqrNZgzRU*I4M@CaZRV6tG3V=;&ur6R};+!3z0$*fbPjHZcs z_BZoL1>aMkw3opf$Zw!8uc@lGJdXJKHQFGwE;~N{9@W>sKTCJzrO)isxhsOxAz|g9 zVwYTOGNYEqfd2!)z*b&1NzVKLF)q{7Xww$)Z{7t{-JVOPko{K= zN;LY;3x>-_f}c81E*UQ?i*)-so(@>) zbfxC;1K$J|lxeZr$OL0)!829fur2-!P{@tX6YDR<*2B3^%7W%}RT~E~O!TT?7e!%0F z0lStZjJ^3r8u;fm>a7Dk7~9P=m?QNVPjZR?)&O9)FVHf1gPtP%8$>fbE9eEDE0r34 zRz#6Ft=&l|GD=)d3T%QC8_hXlO6H)L)0+V$!_>b_{bI6tRkO<1Fq%4JcxM{d=dH)H z(6!O$HHM-P_^kRUi0393F_;E;;jiFrZJE0g9|1%2|FI5!h808MWVL=i5d#J zHJ^imZco!?YumsPdp!|=8S)(rSu!G-9xAcp({IGl>D-wz_a8SL4xH2dF|qR8>r^W# zEq#`xQ_v048B1Ml=oBj<(Y3CHJRyjh6%P@Og#MH}#ci**+!h57n6hZ1rFJD8cPMJw zy@8+&^Wx6=5!?t^t{LK=pcrEX98bBsq$+bu>D~$;zP}% z!jb;Xu8PCm`iK}2e-($76?0v*-mO&T%723wF49DeKWiN6LPOlSC*gluFS;|io5V^y zlJwso^efuy5*#ftB?J$dpa#AK5mAkp_fR%~G3Cun!qL8FdO*C}(NHdp%umu_|NF<_ zD*;72(E0C}wC_O&Hr7r&DfBx(&r*U1JPh&*+|QSM^b!{0zd!Xn9nv&v`?AgIIrS3E zC_O9S>>Nu&?Ir_RVV}$Zu&JHJ3u&L17K;8gW7DHf{60gJ=N4kEZdL^lJfaI^%>7D_qBvJ8O!Z_1!zzjju z8HD%1+KuYM_g2h+lFhH8Ft^UJw*|#Z=2PH^{a>Fr5T|D8@^3^(}^w50^EHr~;uieHrMq`op z#A#oF6trATd?;^O^BE6x%r(;8tQ~@v!)m_d)V@R0d-D+KyQMfk2t$6A%7s#lq3(11 zc_efM?f(X%|4EQpO|2ki;TIPYaF(XE=+SGk>8KR*&XO5X+ckdkWAI-vw9G#+bYd|p zP3FZ7-<9e2^q(yuzZRb0q3r)EXtboXvU`doc6dJtr{zzqKY$S!G4;B!&P72k28iY< ze610_s)_dijZgERpp=OXpFhO+T$4PBKfG)So;Ls7lrHhkzO&SzU5;+#fM9=9A%vmt zCr&u-pv9WXZ!0FMSUn$O zOemd`0@ldqvOJ^_6AlkhW2+GtOk_ec^L`pngXxKfRCVXeZvQp4Nks(%TC5>~xmBts z#qVP~-exkXX5VA)BvqmfZ>^^8ZX0GR6w8q`Z7G(r(QgI02#Oo{FAj5+NVDqPGA!pS z>h{Isnr}jFl;y?$W9S83O0chCzDiNBf>LvfslQ# z`{VJJq=??HhKEq?0x|8rntM>*P$WF=$nz(lo{N>+bKB;BBm3&@i7}ewBms=I{b9iq z13+6+G%csfsZt(y7eZ`DdL+^9Y-*|HdWFSxJ*+e=k~VxRBfLuV zfa8KBSG4&R>J>crj97X>1NxkDAlL&SPAxp-`R%vc0eT$ex#>m}Kr^*P^#<`!5bqQ= zqbB(|steQ!BORXK`OWbiqk$wA#8?&UAHVYmH4)e5sZer)BntqHgIcYZ8aFF=Ne#4G z_Iw~>8f=I5z^m69K?e28!7^}7Cm&fJ_9tUq2=ke-5|{33Bbb~Z-sh*cG9!&8R0)I5 zPGy!glZ=SJc}$?;YE<&!C;$!DjqT8A87G2F)N#IH?Zk422U>tV<^Yq!>Xbt+7R%yE zNWy_{Z8(mhZQ4+Wm(bp1su2PA>D@k+lz|6qbMzC(61Q(4+j(Xdkiak2+qQE?ftD}w zv=*(JLceVpCb=0fGq+m;4KOpU3}RT2_Z@tC@4M@0D$3I!tE{n2c3hhG31y4?!J28S zs0~wSaJA#pam1YVoX5 zJQB=1GLrZk6pM-L&?)@Mx8tR9igfyWn$APa!F~v69+*<)Tr@L{VEL)qnN!HvK0MF` z)vlYDr6U~2oouG;gtKy$LRaPj-t6)!Euy;4s72C>S@?UQg?qG;gnIsK3y)(lFq`~bNL!e0!QxtA`bn- z(!1)wYKJ?@FQGKO5!VIW0O)kt;tCOq8c;5bz=9rK3IY7SnGG`HudnmBka4MgeFTO)=-`AVm&xk3 zW8tE>9+6mZx)?`&E4ig)Vx5qZROl1`F>e8V)vyG}Qv&)bNJXJDWY}TNm8s-gEc6E* zFM9yNa>N{Y{eQgIt2&DB!*k`ocZ%K>0~e0nG!||uza9Xy%DszRw-{hzzkBz)Et4Xg zR`o@|mY{n=8rokKzpLDEczrB+(}ptnHrH-IVT4np^>yGg4h}emfW=^5nA8>9I@NPG z6_)a{K=k~5M5*l$mYt&`ZebVBN2EaXLYT zT9RS2j`v|M#&g6U(iJ&JYYcIIWL2nYxB!vpbsK?m{}2`&l(vQ>`rpM&Sq<0dnYy;s z#EeTs;%k%SGbF7zum1dRTovkdSe1@>dO%VZz&nHYD8QwcpoiS^yBh?}xs6}<<|}z2 zd}V%CpWJ*rH{jB{SQK1W(^rl>6%sGj|G(HY+p*AfFcCikQbzrv~{T9R-{&&sbb5z-_l z+b1L`{4U(WMD{E=5UfMPDLE{|riL88sfkxS%7SKL0U)c)oNm4Ae%hWIO1?w^sar#z zdMUCpkt9H3u%l-q%frJzNQ-1)>8`sKnfw0>NFQ8hRQ)#~Egg;kx=!Yzc&t%=>L%f$ zR%8sc49nyBiK!gg=N_tljb55wt~ldf&DSYa0YYNia=_{0IaP*>R9%3|BlS4U3KM6^ z>$dDZ1i)1V5J6xF)X>u+&WtaD;DBpwpc;Rc(e{HcysmwvCB#3nIO*7Lx50ou*apwU zp^^)Vt^q%nttY*$EqD5}nAoa7v)zbh(u`6h{H3SE*q z_Bsc<)|garBC^btl3{Tk!n(=I4tiJdJPRGLBPGQ5YJf-A#m?T%qjsG)i96oe`lU7H_QjO z92a7exSvyCJeo>`MORTwH3UzV zGCMoVCUDkaOhwdbyCakTD$!h|DSQn_w!6eWaysW`TP%pb_nhFu8f(O;4dk1wQbO#) z^H0O{Na#Iol31Ad7D+Z}Ub(y9#L>_wkoxgJdqlb491-USqu&rJtV9_;PHkhYV&8)Y z57=CH^hHHQ!?Wr(G4!_^_GRColk6oe60zvY01L$(6cpAb5Kh$%QhHNf&w~G~h)zQp zSWM|(Ou-%M&xgo8GnF8{x+(LTZ@tU+EGt7&T57?SjVZ|2d$wE5p(-)dUx>mQeGRuQ ze|&O?zM&!1pUPfKDH4)GODs>gpOmP~BMnU{516XEovm1EdM0*`F&guYUc8`FX^&nc z=k*E<5}xX9**;1N^?xUQw$rM&{VRzmDUX+qN}{XNq})ZGDK#@I3q)+8_4M?BH!NSQ zEBNi(x8R6~C6JAekB^_w1+q0#KvX@si;D}t^JZ{-JQ29+mg@22BygE32=s;`-WpKpU+^a!#}v|@n*X2Buqvf_v^0o zhwNRji^H=_62GI{|NI>mJk=Ms2(=$0OUAA5*Gi0$@PF~PF*6rgz9<&0rZ{wS`6&b0 zQ9AaSRGTK8{az^qJsV`R(XOl3MU-?#%QAmkJQ9B&yl`?he(Nb-5N^`}E;>J#3(3*} zmj=iboAxF6b{ftDiv+iq84(#-9|(X6Psu$3*U?f@HLq<7om8M*oMbAH^?fO46JQ8> z<8AIwr{XAVQV&LL$ZS6UhhywN+2b52j z4WX8NR8-Z*zC$zVGXqyqhahs@<{+nL2HZb#3a(FZ1-I@-jH}^w16*DqQ#@ogx%~M% zhAunqBZM*#O2^}g0ev03$5k1&Qm+(~vrbkP7DPi(ot_>w^zqC#v>QbUnsjVprpi9B zw?iMMWlqY@!(Z*s-I{kS&}(G_mn&X<_w}5o12#9odca8{1=g>Vv4*6YsxS!{J-{aeVJr3M+?Q7L}o@*_80g&uh>*W!R z?eXR%-WsRRB8SNr;}e>j6xj}Ra#~M~*;$P@@6}av#+Bxbp4^|9R?B}1cOS{{ifwwL z77}!;(%oklxzvI;3&sKYfemt{Ca~owamvRG453G}-sxbl<1SbY`hbW?3~Uze?&@Og zI6b+zApNZp^X{}@sCzu#zg-gJEDX+0H0=d3T)b!Er^u%K3of4#O|+-WQrpYnOhytC-7kE;veVvW ztv5AV6wT!w(#4daN_1<>lYVnkdS%vQPg>h`iLmp9`)l`8+(EEq`l0v5dTwFJt_%pw zi*cXhF*S^v;?~3*+-mc)?*JhY3ie9u~DF=aBt^VVm6)=pI%ItV)TsN&5BLlvnKN zRh2^Dq`&K4=+Csv3*xm& z>X@BO9p%Rws&#b~n9>Y2qzCrLhxSj%KyGhJbzBDVrX^8@2$c$_*LAtZd*}Q30$`Ov z1ht4WrL5l6canvH%*3n`;v1cZx3WD=t05ccO3ws01?)35^neWv&~kH+BATa_=;`UR zv@&TV<2dh2xKp2kVQ7CKS9=UxEouM8R~{a!TKp<<{PKA8(@Zm-D0t zgv<4irAEU;(cK9C?Iz<=-iUKuqT3iRUAbFl0lWsiyfXIY z1&;B|ZT^*q?alD`{HaSTBCW=rmJCe_0gc0Jg)IwT&yT_~Zw?IVt-Bc)yk#Z4xs#_H zt)p$#&5!ruz1@4!McJ=tOTDn*H7l|w=rh}XhHEXsI5$wOwy%-)T6B+j(NE}~0{`~r z6c6muQHuFAda{cgp5wXHST*h{3YPNx{DrSU1Z2!y?g2_To?t`J*+HRqCUA~ zuJ46IA$|IRtUxnZhJ1Xs2Mf7G=&?1 zaSeFfwoFQ?b57wdN5Lm`e;XSS{65oBp`=iv*3tSl%j{kJuA%AZJ<96bU%=N&6f0S# zKPoF_v6`uK?Mvk!9V0cjVV(T?>bkbu8ciD5zcPE42z&t9+aL9^?IYCY0k{n1%v%h?NYAvi!w@#JLyu@6LlApT69pJ8efW( zR+xUE9>FHq5-xTC%rC#{KOcwNNYn2pqG(4_+_Zb%11qo3U{ewgF?@`gn}=7$#l^t- ze%@HNk>+%}uf4Rq-on9TPEwC`-BTc?0jM8LmdV;{Ub=9|BSGmX@3+}_NUnX2L9A&B zF81t2r`Y0&rtwx!hSb_4TG_-71zEwR<0!3Q`mj@)fH4QNtej2EX7EN;bY*o=(us8c zV#AYukqkT>!M9HWV<>#fgd3nz(hw=}zsBI87We*T2G+r`?sjKHMn}Ff+3XH){~{qm zv!{zgdgPZ3X~zLhHkdcvu=nH%_jL-k=inA2#kJmjBy63R_(J_*?vuZaj&V%%mdNGB zbrOE&=%%KZ@Cno~`HVJYC(CJH|C*#r&0yhctu!mO>nukt#!%t}o&`hkzXZ!J@wogV zI-4-KKTS1Q-L*F#7~?wcPi*VJ&^LLQRwuQ%pn8~Iqo0)Zj-Cpxm@qKE6h3rYRMeUn zT|7!4;$pDK=vohXb(vU2;!CZ*ARXxzXNXs$9DSNw#qo)Vlz9YZTj-J_Iozl5OOT9N zIh2-)iF!u?@aMsP=GCL$sues(_zb`SQPZB_2uqg9&`UTtEBOox; zY)nXNT_g-V7CI?2duW5FqHU!TNIW^*GCA`T_#UndjBilFA*;+leoUks>Dw17_Kbe0 zC+J1>S;{{M{!s}BL~8Rd<`QiMeP6*bO#`-r;IwsciV@S&oiXeI0O z_`1LG;6@{cA80TSUzrwQsd&6 zCElGoKVoy7_yCr7>1j&Bx=sF0P9+Oz?fdGj+%wqc=@XG=zuS(-RYbh$NS{^-RuRH(o|RxyTJ4KLhte-?;$+D7dw4q6om)TI%SuPHcX*# zktEkWlbDB~{2-JO&20Lv8VnLP*{e5skx3pHn=$uW{y+X|#q`}ZS>cgnN*K-uP04pK zBV>pRkytvHG<<8mhhb}dx00)T_t4g1{XRHd@GMeA*gO}I+1C$4u+Qon{2dT@!o6r< zcVIYmY&UE<+`)D>%8?_M-mDcIFFmAqYuDKDh}Dhj=gaQ;>W&pv-UCjwSD(Fdx-`KfJ1mZ zNw3ZWLz5RXxASG6Y}Ri5>|*hp#g|wP|D65QIlC!d?Q7x+ zMzPpAFnHXzh0i}V5MLYoAvL7Adlp5vOi z(I4D};+!Umi9KaDvolr7y2Z$mh!`m?$DlIuy=541air6NDdw4;S1IKaG+wv3?0oTv zk?am#CEtmZ$DYNt*WZyjTG`BG+4)~9Lb1!SsU9vG+7ms9?zkvzaUmk8kRUvR(Rt-}9f^nTT~U)+EvP(y~V9h*~33TmS64Dz26U zx3p5NoQ!QP520ux6w z71qr_l;(b@b`s&y%XZcmYdNF-u=9`aiqz1td-YJGfmWKe+;u#w3j%yp`W33mO(pqC zPwuxid0+LyEvhQr`GLripQ_);XFWJlJ1O1!O!;hNpA%E@m^_VG8M2pBcu^qNGbX2A zXs?UUcy(<>yBaXaXqzLdbxV0#9KJKv)o>8Z{nFwRTbmIx+__oTiKQ^c|>F` z%KOuuB4@PxA8U!wZ|p2V^$GzUIudpD)7Wt^QehE$z0g+AXPPADGSvmLS`T6>+ZxC8Dkh~v5S~hb46%X z{3$XrNprD(>J{061XcXP`ntL0g(j&(TCn6oSoY1r(IWG=Pu$;-%)Sv_j8wRCbhtU$ zdLtx3s6qS*`;w1d49IcsZy){Ub>ta%zO49S7itak8LY3r_Na$HRh}$26PVUSX(X%f zo2ew<*FI6Y!kK?aq)goI4QQ(N?2ydu?;t+iBdw_Tr%~RBcmKlq%LnI`96o2VQ9K<9 z6A%C@$lr2FD|Em@=Z^xAx*0uHHRgNSppiNPM2PkyQHkm9pvQ;*%4r+a?? z)NBy=ESswSLaod+PL8~cp-2_ww(>TsfKSTYsln`*4B7<3)-xpuy5+AvlePQWirxVa z&u%_7f#+Y5SwdG!;aJ%~_X!S%zo%8z#9o|Mu#GHn3ghOA2OYQBRjO-ib~SU8_G`0o zFJ>qqm^W%c3hn9WA99hkvG@RA*Au+a!o}xhK!XS&v#EcP=5zh#!jjUMhW_NBp@F#B z8S2B3VtG#->j_T9UFHqwW1}BWZWCt9^y_4^{&hIw$5KhL#_7g#Ir=mWmtI(SLdRbR zv0AioG^)UDj z8;2=fovG&U^TqD|Qq0lUEImqdu}~wI#7p6jhOaCQe2j2({pETWpcJW4mIyt?>3B7F zlyL&TWOVdm;z9l?|`C$*M)Y3Q@Z?-P6q4P zmroCbv~PEc0jcM zQ`==rL>+gBF>)ol-UOJmOV!{}8lS-IFWMPO>j7>5pf0O!Gt8E{I)Mu7>7IbQWMIrS z)!VmkHLLCY!7>f`3QM(zT$U05i#G$a%D`64ff^=o0(MxrgomED<=~GuqVaorDiH(A znzS|yO`W5Wi5%I`io8y?3&IsZGl`m1 zwdS)-!I(7mEid9eTjOMSt-ONVDn#8F&F>FEm6}b|*`AtM`}oUp)3IAvVW_$KC%-AI zDb%A0m?eAH{xTh1zi+Jv5Z{8s;i%nKJQT8*rP>2kg7BHOK>=tttg~aQdl97T?Xwq$ z++cc|_5K!wvWGjF(=k%rF*!9^0PdaYF)xVXdreDxZIDE(5?KjKMO?l;BWaM1Ziore%e4d zR>wPGJh`Z%OEU3iT-_}wOWAE#;~Q%SY4g8}A54J}K^^78n;yoZG`pw@(G0b7rR@S{ zfyolAbG~7krb%@2XP;XYg$l5*o6Uj>duy^o>Ga-!mbKJ~-+x+BexrF({dBYrT}(Ka zAO8VE@(nfln!WS?-bvp-}0A`lWJQLOU$CjW9%K=)^rW6HE718RFu=2EAn zp`urwPFv$EraU&~6RIShA>9nPQ7MTgNx!6R)iPdr0)hJdvOzHRvfL9Sv9^A`uVvszLFf;w-2HCqsSela*Eu zBARcUK#o*GZiS3{GiWeg+btL@U6BL(u`f?dG|J75Jl=?jvAZG{$?UzM?CJW`ya2-1 z$EYC0AM;WKs=*_mu=I1ZRp#Z#QBr=VybK-^&nNy}Z;HtUV$74W)IS%>k!;>m9)Qr> zkhm)o2&18L>i2cDd2OvX5Eo?v&zG*2n%4f9?)?vy7f%eawo7v{Y7dyNi^&^-w``>V9){giJ#Lnl2C(jR@9&6>^xKbw(;%0HHRe)o>Tv26g={19xYLCf8EAZck; z(D5--pmSU&SvUN^LttvvU6vxvM(KeK9yfF&fAG9-nD;ob0a#S~pW=fe50aD@}_i88M566Lwo1wPV-TiK1?AyN2jqN3TK%}_4tFsce^Pm<+OIr5zo5} z5a#k6>x2N%4(P|)AzrrWm)yO|F?>GTW|2N47D}pJTG?3?jq(tdKHBR(=401`n{VIe!braX502-w#OCDdubE6XZCoeu)8^jYhd1R~?m97gTj__4OO7O+PEH}+(JBt-NosGIqlM;GKWXC zXVkZn@+a(jL%t$8;9K9&cY=<5daPt&1Fugj#q56Tmm}mpP&QL7vO1^7K7Jig zVzT3v!DCv9u)VL9ZL=7jX_vL2E8*(h;8jddb#}lEFuy-kHl<-6GXBA@sCHwgts%hl(auxf6@qHN zg*9GOpSruBEH@Q*8gpGw|A;@De(Kc4@DvPzuw7&)`n9XuwSwq3ITEV62^uz)Bff3G z2ERMHXUV3^*xDB13rD4Nx6#;%wrjD5A z!k{yPhMoOS(D~)!U_)uyD_<51*mEN*f&AI!iymtu9Rv!WHw7-jMWd9ebsUqt1S90evf9uuC0ngZGoY4Y#2=y^xq zx8vlE!Ig{8m#$|tafm@1K`xqz+sd=MX#{bW`_ZsIra9jO@naXb9g^{iSOSFu$uO_q z*ZXO2svdztH3!=dY>6r@o?J9vICcjMnft+@*;Gdj@VdxCat*xRz}NCAh(9xuw%zj> z+L0yp`|`{*ZjL{^a~>+5b?$m*7qD8(;hf59u}Ldb|G+bF^Bbi3{wYcqK;HSXylEf` zGIf~ytfGnz?{>+siJ*wUH1B2n>cLO#Bfd93x&7|zO*bDo3qRY5_cVcWBH}-N$->FQ zxAZj&ZJ#{Hga|@FozUM)<8=rCJCAl|BTBtZ6XN2QzqkWET-2-`>PFtfyLO@E-&(tg zu<}ZZ&TMg=ryc1ui0c8t)*o&`Jmip2w{UM({Qs|TPuQVwc6FTCyv2lZc>a4gDZY(} zW4lr_*5{%VB&=HkN|yIkh2tQU_ZCnb%{X&@i7Ea@SSY<2hhX z*$WE!KSTwG8t^*N4{<7)VtQx5boVAQ(G~3i`PJ*{qj-z9JjXZ*k)>hpof^WsHISx5 z4G*?Xz1mJaCBgcW(Arvl6cK}twQzIUNZ{LEHk*r0a_zLi>DC*fyMQc+RK;7M?yk6BsSSr~^oC5HI=2vJJav3#OASBI!?d$RXv9~)zR z5vGGh#pz%EARG`9^~c#&9xW@&Y+eSG8q>+IUcP~I{^M?E+Z!eMA-y^n5o#da-psdj zcD#V1_1GVtevTUwMDSY1{ZkQ`V~#_?_XTW#Q2;89^}qQRDGK#q#Orny99Q4KpjeeL z?BR}?;^qnH;_R06A4%eWIuO0a?Bu-bZ7jKT69(i z?ZW6_`i@fYbqsGS^;DnKPxMMHrMHN|JobSfP$soV8}oq#)C?IGQuT|c1!X0CkCVa1 zWaM<7S_l0ihM*~&tIYO>{U6=dr(=l6s)hWN z!|mY?O76CgUWCAePCsU+x%Kdn(n6Hhfp|hKf}|RAl*_xOs>#4v#mF}P9>|sn`_)B7 z+srgA4`t6oUdqeg4-O7i%~uBHl?WI>T}=V52Fa{}zJACF$buG~KG^d!l3}c13%8X| zy8Zfi^j+{68OpL-FdGA=eRz0ygjw8_(RmDLUUwO7;K}HJCr3@Uu4*@aN0b38o+1xn zpM4*Z4IOeypSl0}>`TR`hT4ocY0}hy;^&V{mW`qZEH(fsre=#u(20z3 zNFG|tCiDI?_T)@pxWZ%MA$%_z4d$riJXOu9aD#a)T=JmGN^W4a*cGX|Zo_(i3N2)8 zMqhl#$fyaFdm+)k2BIK%LPR^>1UJ1Bk;Co`KBegshrc zH+leOz5hSxT>r;@G$7pV;FoO!GZ;R>&=jl-bHaz#W&sRfaZw|ocAmt6i~KR&<;1dV zZQ#zYmfr=Bzw8|`fAR-uML!z#iP9KPG*#sy7}#|3+r)esA3cKDK6@G)dDeo*VPX%M zO|nt_fh=|}IY#~m3INIl@U&>i=gMtZ^?^MD%}wh31N4wu>i>fqQyo3 z-S^eKQc0Y_XnJ_*CJfQ>hX=mayfH0T{xnLk)l4y85e+n2W1?Y}sStK%kztpyqfF{A zP*IuTAKK9DQSQOOBv~e~b zw!C&$ZXQue76v%s(OS$)2;mL}S!SRA-Izz*{kPPtxP)foVY_!fN2@hi6i)k&^kci5 z+zuV-P~n#$X>Tq|X=(6NalFF4y7!|<|1!3u_Q_t)95s|wC?CZ9lAvakD6OGFpZ0t^$G5Rk9G@BJ;jIa|m4I?d zYP7Z07dZ8h!3=SQeD(704Kz@>(HZrkNS{ZOZ}V2FWF@jGvWS{35NuXscUYDJAWE2p zMx>_x*}@fKtpE_~j)SnTpdQZ#Q!hlVSs3Q#vSc75r5=YsWW}AgcAWgf!&XGQ+;c_YLL+w z-GredYqoc1jllGGEK2Lc6>O+FU_=Wq8t0E`RfTYvI}#k~N}3g|xIgZ4V5_pbvIFf6 zXTjDxDJcH1nP5ZWUNJMBj{RFJX}|5)X}~?Lbn@9fM!-RUJKULt0kx`HqnD@LEErlU zG5B90*I*EU#veQ zpBbIkqs3+Rr&lR(DnqvJ0l=5L@oQ473^v6W21%F3$m?}R0&3onz~o;qRqNdx;qLuOFRCvuPD4+epqLQQkJWhb zPj08k1gW?Ss4LIEYoZRMaPb6eS(JC8FG;L9(um50U$`v4t1$Zoz&n89ed7N`6hR0; zKw9+Cz4UUL?~pyk`u(XTrQgeNHQRlUv>IQ&lnICY_!)|ecvFMV0m1yr>uQr-WX2!4-t7a`PW(#IN0jo!b1ZA*IH3V8Hof-zg? zqqh133(h-8lOAeiP=N(zXVa;aAYE(@^ctqTFmkshI3e>I{gq`m6_y@9v;1$kZELS; z5%TTi<`GGB<$Cw!x$K;06sPoLOO^Xhrg$W!*kuzn@epYvPn`7ac&VW;%=6u!nI8+- zJ1b3i=_5!vI>sx(f~f_MxKAzLzt;04 zIJDzy+w-JfvzT5TZNxk04_k`G1s|^*(SG;qOe;cp!3u9=^Bfg@GKtfi#(C`OA2~6?sPO4uA%%hsNH#>#g{rVBQ_+#6DhFjr zG{rn90#_UVhF6u2Lq*`1T=iPps*#SCRRt++yP(BBfg#2G^)GswFx&OGBWH9Wc{pE& zp{J-w02V?D`1tsQk@Nl;)2jh{W|s$2dx7IafmMfL6xpC?TPmpCw*PAdAYz``ScM`t zoVGK=JC}kp;WNU9_wT9xaq%u|w5h4CwmW>s1?XLaZXbs%1dYJ7OFmj`@j%vDg^mR} zTxay*Ud%7zstOSdY;pLX7VhaTcW{Db*qswBCVv_{-Ki-(7uey%owjoyewlfUJfaeF z#>;kaaUC)y11^}Xx&2UMrE%(?!+_dcl|ouRz9jzbDqoXy&p1qMBHdyFA#$mQ($m&rgiW(zg~L((k?a_>E{sp&*^qdU+>Y)}frLD_5jHy?a~Db>Sz9)zUs zl0=pLAN(2bnHCF+>9>1~ux)2!x%U3>wIMRnH?4$?X~3q7QyMf?CF*{?ihT*v8C#Qb z`8P#iZc>y5lz?|x=fcb>)h-e4km#o+ccM`MO){_bD>6l48G^0(=xb1Vq#d3im~nxiDYk>OddNuB~HuUT3>$$ z)*%Bqe|V`ih)RncH6(Zp0nNn+5%1qsgcm1kC6o$69qw#KEiQ1u-`l(XAK*U|nZyeY z@`@PXE8V{fz6p4k5Nc{U4u=6`K@f7Q_8NS3R~%Es`!kx;Wm!Ds&P=i$_j;XGcvS`0 z1;zuS&gg^`qa@lq{eR%@Z+`zU{*1Plwl&O_JA}_aM@KKXXcg~t|xl>K(Xcqkgk%-3LbnZezj~WkM$tF zt>of;w3!FJ4jz{C?Rk*;&Syaa1bcphTdx6diFKe-ZaUcFvhRgl?Dja_suY7y1HoGf z=r@=Z{4NF#G|QyCrGe$KxqzXTyV6W%oecybqMf0PTNyp z@s0HA?{C-dKIEJOi*T~R^7#(yA!tES?nuwaYu8iIh@E$u0>2*WJx}fERWT{%+%OT- z9I-`RU2DnO8i>bTuq6Ys3_G00w6`463QS$2&v2Y>F_?H%0r8hT2V{Lu+8g4}e0Bid z)Wx%NOFX^&w@v;{fGWLpF-Wm{(&u;kL6N$Z2bE_MfcYKhsYf}L_S7>;M;TI}rW>?1 zJYl{!K~;!{i<{+vLUvf3W*mYY3>zAfqQ)nwGDUpzt&IHtCRW;!bo+yB#!gko$*xqh(VlyohqtFgpMvpTi0k@IB33VRwBafSn-=GD>^rOn~Rr z4QFu)rL~`W0@aiCv`4F%?7a(yLvF=WuqmaB3BLPWl@}vAl1HSIMdS9*`!Z|d*=$u> zl&>{4O(!ez2P5g9A7TI%iTxAaUQX%y9zYd_&22w?^`;24@yH`+e2k=&F9%Sku>IABXDI2P(e;ZKX?$iLh1 z1y{(Smi{Wq3R}!5(!cRE?}J|n(n@M#zGtz&x^7>dBTL2RRqeX}XoO~;ZcDT(7o`ll z^aI6~7$CfG+28&pR_X=}2|V-|;-k$W^PdTJt_sx*x>>Po#85q+gLlq)$KZt12A&qD zeVxJE#4IxrhGPgI2l^bR6G*dkL0mBP=#YA3VPWCoH~FDBEtTi~pzu~2^iL+$YejD4 zeb(#Ga3|7n&K?(hEQ-`ynBYv?Jm<_1i{vWETw2^){-atWGi&-BcOujWR*~0S^J>eb z?(GYRnfSMDASAYzZf-}mh4_d+#J3u>h|MgEtOlYOdB1cncE_y!%}=&tLdesycr=8X z2u*_49jp@<*7=58ar+jTK#UAd+IsAP-(l9i@Y(M5dbb01r?IhFDM3NO%PrW_(o(St zLZ66HRhP4hMd{aYc+DH|iM(MmzmA%mi`*CuQ@FKG&ZMN%&bR*oi8egYsI&?|0N%XU7~D)!jJN&t|( z%OF$w26{*gmx)-d-Uy^y^2wyNuaj8KZlFYm%yBje^i%EuW3bPy*28Q8BnJX42iyfL z%w8``Ug-mbFRkz*2fqzao1%($7@-O<=!_Sjh|aZSt7oV^4`er8hl;+w{=Ci! z8C8UKeR$Ff3uFs6%kGo|Os>JhH6%GG=vL<-A2^Dbp8qgU&d(qnq^j+`fWpf8WjGAL zp%K}%%vnpUX6=uG@{p;!Z12!^Db~sH7$|aF+^f*?gvG;!Fb`mOP=igBMw5R+OMiNB zGWoYY0WzEIUETF8&h6UEYYnH{{GH{#*$Tm$;sk?2N1^rfhfd2;8!p0g_CJN zs?)Z#WCU&U3r#-P!AiZT;bBFL>(@bl5xMh}-AxedXc-y97Noi}fHbf#8n0H!$I<51 zc6s(c&XA9<$D4e>JXmZXZak1;R;RN>sEV@ewF7$mM;tKyq@Fa^h*cu7rD!3vi?7z= z<^LBBUCfG$l~0)6y0~EpDwzr4JLm3s;msRbJn_-p;AZr-1wXib=gyP=>Bi}}9*71p z<=D_*R;4 zQgJ$k6ofW*0Yw0L?T38Ocm-$(kdOV&i%S{`4U#~hVOr~V%Gjc?KmZIlg{=KSZUzWk zoYSh8o3}@+m70zfJqAc-y0y?i+0WJ=E1-eWIrtesK;{+iA{`<850vayU}SjI>Fm9?u@$GByBku|Lu5^MxFpa0A`x4$L$$t z!fvULFe%&!1?goq0zv_!CKa@afZb|jy?zxzT_-f?mGYCgF-_uIi=R+JotabxdbPS@&bU}MVo04=yXV%a>&9Ie5eNar5^PSbcl;jEN*!k>c&ZE} zuo+kn>p&Fh&N$iIwhQ^=Vcm6l@ngkbvCYYnyVKj)egVoxBfo6Sz(i{0-0<|nwE0v* z1c+@(S?0*z)i_<(xbDqfZ=VX8QYxt`q|csl4vN19Cnn%$@LsNcjwmEPm;Sc zK=Y+j7Q__4Srx$nI=#+mysl-2wdjIogRtX; z>nkqqYUC^Og{e;fTX7%{;_O$i*q3NY&W~rnR1a^=gFaU4;bHJaE>rUeR4=(UPi|O_ z3bkoQ>t-0&(gnG!YZ(iqgn=$zLI!2dGy)IjK`%(5Yd?@%H-&CwgZFk0<(vKme?eXp zopef5ud(Kl;EIp(h#V^4+w(eDNPKJqAKK(qE}N27RVA63nQ2RrHUj!jv`UelYOX!V zy|oI6oFLU@)a1ZjhI7qz+2t6}* zy9ufM*4nrreuL_RA3M^ru6QW$h%4(JP+NlK#E@z?+=2{`j?K#s2S^m8hk&x1BO=L6 zE<0=pjA}$bhyCfwW0x;P--sATQ>VDJ_k3l#IIB7(KUg2p-CDs8*G6IjPI8N<+dUUA zCMgy4of)XQf@(QKEb;zTW}6%cB{%YQKmKjOgXma7>r@x#X=2cb9eDczE$Ap{3;rL~ zy35B(eqX-8-O((d);(QGle~J9g&WzFD`-1dKR*hqsTa|9HuCzoZ)9n@$V#uO-OW#ai=?f%TVb~R!YM@Bq)`qZy)3S&l# z6xK-=8Dmn+*D*OMBQW#hp)Y^nJ;jz%Gzx5QfhI?}rA{ybD01kzbZdOpz)QxKW^P-2 zZFe1q)_9TCNnHrgZSegi#u;s=kC``Npuu_%P>TQn9jU`g+nzWB?qOjcLr+_q613%- zY)zCmCbDWvKlI*70%@yFNu$kwI$$DfK_Sb=e|Bs;g!xN}|ILnB=B|_7V;-IaokMh& zZHjC!oAgMP7(IjoYcDlDQ3Y|MEB_5T3%G~rTI9f-mwv+){NgallHG}{f4<|U!R^>r z=jLDM$O}P)0;OXD??z;_hOO0J_H>4`ik?LL`<@ebLa6u_wYHX-%_YjrbwO#Zgp5A=VEPqQZ68=pCGBwm6>-9wH#|UX5K@F>#%E3LHjve{{-QylM&hKrj&Y&nRW#(}c1z>s z`Ht9=!-G*BvbW!JE%dDZw6?bLtXpl46^lL4JB>d-aDWLOWnY|wN_F8+rsSp56sRx5 z9C|%HJXWWwIe2+_p@wR}=^;QXvcu*1(PhpNP`6zArf`n&kXFF1Su~edg_5Z`3rH`D z&;HI+;s6}97+3}TcA#sip9&8NqzYGAKs6Gy8n|S7T6qYL#HoPG-H7hVz2Kzp)LnYU zQ`Sd*#QfIdppo5To#5nf%fwRXHV)3y3y-8h6WhsR!=YWkfrM3Yf4TWksbgfMz1jkb zmhC)Sw_8A(Ny5*y6`X+}zNB_7Q)P@`gWB7`%4BE@wDS6{02hT0e;s9fRwQ+J8EldYk||vQcw*p$<}j&PsuWtE;M%ZqOS(KV!)o@CM;89 zt+AXlQ8vHh@|)S_cx^oB<$PbNY_)8|D|1S}filjn<{6`}6Ue<;p$B+t^hW@DDxgq@ z*W3T`#RFX@1qD0SlayGyz?Ql4dQR6vCa~OA&+?yg*JIZ0c%3yNhteFL)%oQXNh|YW zYg-MKnJjR`mH633BOTBx?9-gaN%^rwLaWZr<14wPCE3T!rl~llG zw~_o5Ak?I@01&nku(MYJx)@iWTMchRfFc|g!EGvGNA3|x;s#>V)RV#~#|T)gdYYf>5xDHHbnk4wX- zgwvo}1mH6d#i!45pE>~l(veQ(bv*A~FXxn0k#`l zFjd@?{5W)AC|{gq0S-4CEuetZ5vWkp3qG5hfh>kR9jETq99iN#3|M8~q;1Ear~}kY z0&#Ue=~fO~O^#Y3Szex~Q5&)x_Y2J#>_=aExJMTkx6Z)+UfehyfYa}%lqU`fU-m5J zd318Vcz23i<8bD<9H$+Ae!5Z*gD^7Y6sIh8upRptLWTnlw6(Rx6pq0dkLlH$^IKb1 zPvb%18iiP%1^x2EOFLzB-$8rki-_=IDyx`;gyUL+Eq?W_BW0j90;&h3>?kzaq%#GQ z6#VN87foe$?s*A^!m1bWN&0IXXG#5=HwCbFK(pr5U2*CDH7(7#!P;N!KP-XmaOqRhrv;9Lvr?jF$YHXdVfSF39*3UW~b zEy7@$O>Zfa#oW^N@|h#}X#@{s814z}|9;J=(=cUv!$uZ&t5y=28iN`O6A2t4XcTQw!%Jms&qV17Pb&Xs; z;~Nptsowd8`P06DP1?L-lhDB;ggBe<*4FI>uohg3?Q{Uv5l%)HzOeOH&DNF`FxW{s z+-;ZUAdqZMl!pREOCL|f+-!H(Gl77C(a|I4&anp)?vWvvPhG*SI_^IP>_CoI?O|g* z6-dS8k#efMcW_yp{Whr>FIkjAl$&sKtG}bct{8Jj1*LRXQM= zsyO9cH|X(bwEqh9zXj`=A-ed$x3tFqL0jVri#IA1abQxiLr|WsG*e-vk>Dg*tGL(KhSx=c?JueQs!!A_Q4+6 zU5{M#{rYTNFYPxMM3Asm10B__rpKgI!!~*zr0s{>&v&hH2!P2J5c>`$Swz`BP6hgi zagRlS?ylWW1DtnHtFzJ|RQW~;s}-N^?~kbH(6F$;Ow}*bk$Vi5#woF}2ek(5930_0 zm)(OL0KK~$DNq0>8{Llur1U@SK3jWX_^dN^n<6-ih3l*Z=y!(|WJ%kbSSo1P9S`&h zG)KYgh5rs6xIdr;ZDE^v%YS!LLLKO?>kl*pDLD~kjd`u59xq=45o zIYUj_G-iT9$BMbi{Vtw>Rh*S3-x7yGGg0g5fr;V(&x6#nH4nUk9J7Vlp&{)y0(qgdFmPRzaCeYOSRdDi1`$}0 zzAbXX*hICYz2R%Ah1LFX&JgBbkO9s2B#CNjVme=cFk;(yHU?(i?2{##h`i^S|C%Dr zOb#$!qv&ieh$jXDOOl6CI@sLaX=9e_G?zyiG$eI=kq+@P>{VWRQ-B>>eLxRzqouh- z#Uaj-PadG_E(~-Y+5OvF$*o>|COUE<*=6HNA?!6(Wo`4w`-L(z9cvj5Zv~w-24}AG zE&i9BD_}T@jmOS-X|}>BNc$~Sk$RIiSvQ!+N}GVr8#V|flVfB+CgFF~f-CH;&6GY~X5?6xUGfHX zHuA~#&)I?@PvP3W0HSg^-#K+L9pF;1``y_Z4xmxh12-~0aYbkvf@s^@{hW12pvN-x z@P>zTOGrrAwpCh9c7QS{oZ2eQ`vUc(?lr)Zh6l@8vuX0kuT}d8e?7l0&=WAfHw7bS z{@FKHY!D1)NBP{O)NLagS|N;IO$b5O z)z=s?i}nl>9O*WbZgOpule1JY2*Ft9JqoyWJP!!TxUHY&X?qH(dixzJ{M{Rx;mFF_ zwovUJJ42bD@KQO5$smQ$Ht+S5_24xye*Em(x)Yz|j@WC4=n*U~WXCaKv z>PY^+@C)3|`;?pO6a!5x@fL%QJDt(aVczaLv6mwn@u2;(FxmDz#y3Mnh9;#s1FwU5M7|v8tzqW)bnND z1+;+@b(YNAUh!N3A8s!dcPQCG!{6|uo-FBeq+Jr5fEMdu7rV$I) zp|vtJMdiV2F2ut<;eOrljSdW`TZGwdznqujpsP%3`Fo$xZi^lNsQ<0NV#Bnlj1Hg@ zooKS|f#{Y{YLBgU=eJ=0aQ{G{;; zrHMR$GslTRl(zqY@rrm$yyv4;5YT#|e`loz1s}1yQT}UIy!ACp>3%8d@JQhE6yHS)_{kq9_ls%kO4rrMem}8u zX)$_i(+C>2dSVTxzlBO^lJ^#aDR2;JMCOT=Jj$$v4vbNk{^*0)fyTg*0*VA00|zzR zT&Vlr4MvILxkn+>(iqiB{D4XY9^4G8q?Ok#=j{MJU9#MTuP!4%e5V(P14J_Pq%a=f z*gm`U;8%b-SJA48QIZ2uX|6$_qgB6H6)>@4m%-f(@aiv7 zpjAyN6&mI!GmX4J!&T7bcziW$DNE#{oai3tJf>~?YuHhu`stIV%R(%d*p!#w>ZwKK z?%FaKXpk3yIP`_OTKK?FHHBY(=wXf^;UGbqOQlwj&r<6nyBPYEEv2}>I_kj@;o0s- z+fvC~;Q-FOk5-w8JD^o2vGPLDD94puWL4EF&;O(9Dg&bGwkV2mOC@4rb2uL?W$57JU-CaX>!#j7z_}=_yICJmWwf5R;%RmPK4r=h&PM0X1 z^h(GL$q;-cWXEj4jfV*}9f0J2|G@*G!Z?vsmSktI%nb%MB&Q>KaG$~&-4(G>bX?#y z;vqh6=!b+~h{88})Ne`(1Xx(xy?*VUtm`p{O6i$g<4mFCfo)H`Q^Ciq7_RrRZ$mRf zf~@oHvL*jALm<0-(RB|L5#E`sU*yzXE-!ONO?0>~nj>!kSx8U>U4V$-n`s%#oOZP= z<+4B7AC$OvmN~tIa8fYagu1OSKyShMlEL`LAy{Z2GK7&0dMBjvTk#L>R6q~D(e#83a_Nv z_-f$p&WF?E1EACLVPFqypW!fY;*;b5wnATBZWtf2e1&TzCbT@ePGa%2_KHy5sNzqAW6eb^mg{e?z_Z+5I8Q zDa`HZ2BIQ0$GYD3hfFL~fi@Ky;s9G$E;K#|it=eC*-PzC1p!v>{u0&V2fy1SDQ*#E zs%0qq1yrb%$Sj9PKSm9(RWVaLky{Sdq#O-kp0pl-|H6PYIuPg!#KlU0BdBKKEh?p$ zh6#1#ND-lZ(2tj32HbC0#)-`^@*>b#aWNF;0OTR5h3Y`W89ic+Q?OTiuN%w^=inHE z!31yVLQ?8+^ObZWd-^P!Tx97OO{jl))XTg(=*v2+HpKbW<1^-Xc-6dl>7#M0CzoYF31NHlf5cX6dNc@oJ-X?}n)b+JI}qplebbIRLsMU?Tm& zWK$!UNOwZK^7eOeZw&{%`$KgCK1%_w<790*Vg=xsuN= zY6GX|!1ywRYqrp5F|F7>F*&|oe-txon;2P`eZWiy|42heu+f>QHFHaeY#P+`{~>-u z6JtQ479uH+Ffi2J+9l#Hue|Bqe0e6n^_<|!*BHI-#L`1#nvlbP*b&ooA=^x_S0ez_ zXV`uGdr<$!+PWAp8(HtLs%8dx?O!>#*S&?efObJwv4xJ7+(PV$4rINM8pHHjriMp@ z#1zF%%AMPJ%v04v=FYgya|~HF1UwNq3FJ z7A$i0h?HC#j!gm1=qY{0nRnE`;ZJh|a2z^CNYes0=~3-s@Ov%;B{H+8hG$pP54=sa z%jp`eDB^~+Otuyl#RH8y!U#1T{CY@n9yoI~us@R~9;M3M{6D&P96#`f!$D3sSFh1{ z|B(%ov$cWQ9cGw4IIS`EE?089D=jVLMO=F*Kp2+wo)Jj@N=^$j9f6po+QTr7da97! zRQ`vGUqC@}xaVng=eSuJd7(b{G)%T9L)9ZZUw;b_G)oH)pXhPUwMIu)H8UgZ@AQg? zJVO1PhE_qybfzAz7{2YYU4JIWLDL@Lhh?zY*cJIYLnX*=YfdKhHI27s(~K9G!Twee z{C?;nU^66JJdtKWsqvt=Ezrwx>+Z~QxcuWA+ZTfO!PEJ88ioV;Az;>t$??$gLHTS0 zWgVmUhCTj*9#B7A!W}AmC@2JBMhvxiTZ8h}?5H|meGeYDe=yUJn#oLmvj+)`qt7D|e z`y4jq+q?irnuj_j8}$_P+p~rmhws>dazLmX0QpezBl;Sc(l#g$?`MEA-5}7=B!!gz zs%EDX#fNhZy7p%WZF>zHaeXL?mNH9{6*9Q^R(P~-Vg+=e!__;$1qB0(Ty_wE0zU5^ zHMmAvyghl?Aa3&+!2Zv8R69%8+HdFAm+@ZelX`KfCtnJ66ynxA9Cm9n?oYyR>jzs-))H#I2ZZj@TnFlTSqosg zKzwhb3@KCLfl5>x)*$hb8HIoVY!a$!Z-Po##-xMPCptOl-uaBRI5VrV7NB?A3{*tx zYPU(L;2br3ok^12(4@5N@X+ruC3M~mZmX}uO$Be4>SoEwt%FnCaZRuG5aT#-L5}yQ zeR`5*Ts7O{i5)N5P-?INZQ%dwPy+soICM=_zw}}smAkM`-(zlc$k0~3l*w56RF?nGLJh;b873z4u^GIpVL_E{VHQOyBxJN#Z06J_aP8%E#-ml~K6 zDjM4G3b?;*=8P)x0gSV!Kq~qvj@q4rZvOI7tEL4uvmh{_#F&Q~PZM zK@N{Cwc;`>Fu6=F$8oQM+fMT6M(5Tow*sEZ0LI<8m!7xNA(8@|&3suF(B;8|bKMSh z(#w^nQW_s2%l-QG&T?-V<~V;3hX|hzB9DssGZkZNO@$OQ@8g5JK;AOx-7Kv`Hi*2S zIFHgAJ04_8=LX}oGG&%mQ0!mO$N+IYw04TYx1XRt zIo$J%C};iZe4EM7-(>`#JdQzAjDI4pBH_M4Hxe+5BvQaI^1sOYhZ(11?QL4n8_-Bi zvfegcI5lha?RSgR^eLz3H*qaxGW$xj1~Av9t=C&fTV0^30BYWIsHb9x4|f5Y3A4$Z z8s)qZF68syDz;gz{Tb8?Tn=|g)~lnsB~-HA#fgd&CnXKQd?KAomdClaGG~+vxkmH8 z(LIu)`q1Vsd#?k1I`!Hro$${v}J+t?m0sH+n{d>ecEg+=^snvE4tu0Xi-6V1`cNp<)c1l!+bY#V{y;V3=LJE-H3I$mxcpLf`qJ zvL~MamsF<0O#Bif_Zx0bTOLLm#lyU`R?u&no?cWyF~mw^7jBEepV2}J9pIHLLOzv-7Shmr zCg|mgYoYng=;F>o$-+~6{LDnqER_KI<-dM*ITTGk%I$mT8>YkLwFZVS{Wuh8W|wAO zLYEYL?lIl~G;>v*PqRaE9~DR(!ukT=jxSR$c5#7;iTWb5X^=i>H8lwwJMPgK_77d3 zT3l@!SLp|$*TL&u!ZzS(nrkf-;sO-Gl!`bQ?+bHD>&CCtnL?kLjU%jS(0HG!^<3D$KZUwklDW0bq*Q&a@UdNRkuAjDh?^6FC(e-!j+ez+4-P_fT0 zD)QaWGJF|KkCOyE;tx^AiLiESG^trslOT@sWZEljYIP3z_i0RyoCDKDQpT)b7>k2v z(^~pUcbBO@7a^{bk+qW9j8rCm7PpKn_lpA*$qh(~H-Zn6;zeZ+4J%^6Xs_u&A&R2X zP1H9+8<9s_!E{m~{qS`C`uR1DS|Fw(*{fm|hM)oz$cM6T_)$My z!|oi)NbbMOg5Bi8}h1* zbfp2bA=DN38;*p^7qeJVwv}VVq_oci>hY${*=9(;pKtA1_3(K5{5x2tp6xVo z0$_P8$>UqDoNrI?&Z9~hb9*asek?`ppnyaXp8{@mLf z9m99rO4DTg(PV}=Z+Kd7^jbSu_r3O0J3R(G_$&g zg~>02H0-ZKd7rT;wr9%gVbhZ<)r2LC>!(AyP=UMgTdZ<=5oET|hvxim*#rEGoQdMi zk28qYKagG#CY-!6^r`V=nPP?{hA|8ziU$D5!vbe|pTL;=83IvgbgKp;|X zSaOoZXY)HBX;lkc@fGOP&$UOlLsCDlmoJVf5sO3WDGo}t7?7f=az@wxMc9F8Aett^ z^8sO5!|c-EU!40;p9T!vy$4Nj7UQ8Gb`jo84`@GakDTw`&%AaE(wa%-FGq*QhKfXx zl{QfLfp)0GM;-ui&EywF5au~}btjZOO|m2T<93>?rU%>O25mVSStOwe1S|YW2+y(T zm0?ETPXH83OwP5}NX`{$+AVNISv~6-Ku%}E9ZpwfR`y5hOE5aknRgZmfz48923 zGeK1??t)SXRMq~qRJx0OWfx2M&PDVBrIezry^(^4_IWeCZa!OTjyME7o0s(z7*BU= z$@^+FStM^vtU&ZZ?!j~}kSw6T#P94oWF==AaZ7K&Y7Y-9jY4(Xa{57+b+ZEavvy4L5nQgD0TsQNK?+@vi+o^jrbBfAAMN z`GJ&kWnyC8pIAx=q*L@}tT^_sIG#0g8nJK_rJE`ajWQq}j~BW+4f}FKhNc6R_*^>} zhaCCl+RTTlIcRZ&Ql|6;^@+BzqCAS~DsrC=Fnk4uMm$UwJ-(}uwoCsRRr&`=p~e>= zYspr`kOnZ{)vY+r)kQ2LcYn>JYvl&E)PNhL4bo!Ll6RAnprp0m^w6_Scy1zOi4!O6 z>VYb0?-!Cn_Uh!*su22Q=tez@MfO^r38tITp3d^L^YbIfwn|UvXf8s+Y>r1FPn?cd2#?4Dja7w%+IKC>}cG zeTl0tPq0}VgWAdx?0iv&medi~8Nw1leo^Vm5X0S9L~ESMrH=-1!A_I!bR)-h4z8RU zv5&hMS+qv-YG>x>na!pIr$suAsJm?K4u!ncn^^D&Xx7?6Uo7SUE74%d3gY(F%+-$V4{`kynW#H~;%4ZG?ASLM(W>z_+W(%-DnjGl2X)3GyZ zg84IV0ZZ+rsUC=$1l&oR&GRS|5v+NW|7(o#P3wgQF*bEET-~~#`5j3w?YUrBPff+Y_O+`!yqec{E( zJ5PB$Eavgo50~I18$8r-h!q}6JmH9)6Z5-QiEWkFD?Kxe@9=|1ix@B(-%PKLqc z%H-VNoa^4o<8*Q?tP19O93gu= z_=251)UsEoSC8M?_JYVEUT{TDfEr&LQGd{Yc6`8N)z7sHG`|HilMW-6G#T$N+<0eT zN|XKZfIu-;GD6I7An(h8>J2;XCfL(0Am_9qjRVF9fS~g4xGK^K;cy;>|G3YTE}7(p zI18fV7wrdg_iliOf{?yGZIbL3G`id>4D9p@EqZhr21b{cN0EO#xIcci$E2yH7meW# z{#5gbH*Q4U=lJ-@?WKMKFc^1OEh?dQy?#S{jv80sPs3*g9fn3op$9S5_)nZlGqu-m zKBW5CWL~bs+UPb|54D7^4sk{=4okhs9wOCRsRdiF`hSAyeqe9^5ZWKF2c8gFJ6!H_ z0e}K~Td$pqyY|ZaVQ)JyGQtB2f*Wh*y>_cxqv?E*eqlUV+r@Qp%Qis2sHB(+jcT%A z0Zu%A|BnQf@BD6avD%*q!XYyo7X=vM`;1w8LvQ%#WJl7a^| zIr(FC#P_wmA9GPDB%o~t;%d5ruh|b>kiDUe6%7TB9D+MO{CHrSd`2gXfl^_i?N@^0 zzFwm{=xh1_A^Nri?XBG$+uJLA2EcYwhfQO)u zNDM&ld9m2aaqNiZ40gz;5)$kjSk44oTwRsR9XM?YbnBndO&yrEJ9WPT((_TWrf_M<+seou3sak9v1hbKJ zSl0*Xew3tQasIgyQ>M1JKs`buEIB#y^YRFy%*#HqpywI8XXB*J`evHrf%&}hB}rvz z?I<81zIW*s(I9<`Eew)X2i-@I=>g&HKPt*44A(5Js57esN_Y1zh^=ZgRs}P5q$@MK z=nJhM#{Sj{Mtr6gC~;d~SC==-wfU_$e{H_R@&cHb_$)tHBux`+H(LL3FQj{XMNuQ8 zI~eGA`urN%OI*K ze9TPkB=qXlRWKMk1;oF*`yZ|Ym|H2GtzHSDSQyK=_5;OHAUBT=L*Q=DBodNSB33Z7 z<}^J^ip=vi1#tH_Sj2+W_cKLwpGt?kh4$9zA;j#8G2e^{yeU&%ww2fqdL!E67Z zgFhI^cbqu`RuzzfTXXLq9~~bbhcfHn`|LQm4L-hYDTr|n14C4h|MikLlk=evcINQ0 z9^)n6!8~I0p1q{4e#lzQJ)u0O)0T%o)1RbNVllHid3vhNm4N(-cX;D^ z%C7(%{Ok4%z*u2GRm74YNkEBHO)W3(ud#k}`yepcVk|ds`_-4g#Og<*vFxovE+*fHtvS6BupCDsk}F6cjCcD-t|zl% zj$}2->nkkY;b2V!&HPve+GVI|wXXmzcSbU{`sObdtC=w@3mfb}q8ISEp9r}5Oq862 z6zL!@L3#Y-fAV;A?kU>>S<6d0>I_r%MF7^Tn9{FK)rF}z$Ba1o*^~9Dzgn@ZwjG#}4M}uvOj;*j>2- z)jLm3AA%ip?|{5%k;3K4FJG8yqNTymkIWDl%pbdoaARDX^3*_-=XlbMlG7u8@ zU3gag1kgU#Vc7l9vszia9+GUYNJ0*`aQi5muz1*ULqf$aG{Lh)R2y)*ryt22`Ne7g ziBm|m5Bl7B%2*kGB}?bi$+ap2Gj>L)9y^bZ_Lp9zI^4R1kNa-o*QX0T`XUvzOvf;) zRu)N!2C_;n74mfxU8+vKhdiiPP!=(u8qGXN%*s%^Fn-%oyWy){ARVDFP|@Z8K<6u; zMi!Z5a-Vu!pZE-{a3UIG4KzPD_7BqeFAkDpVD-V=@a34DF`MI|lHFR_-sOt4i$IBd zdR~55Ld%hR#5Uj2NJCfsuZ_}Rqt4(58d2Xrehz{n68>vPHz$hB57UVodB&sRVaz%? z%M*5ED!1!_7Fyz@&Fd@g)xdJ4bcH7L^#-@HpHyA3=zy691mz(7Ii0;IT+%%vAVA2y zH)4T%hvhTijo;B!21_&d|0#U@#wyKuK!P>X9u$F3lJu)!@J(yQZ5y*MCD9LJkea$Z z$waqkUr&HeW$fFMRcH~?u9iKBMH^f1fL01QA{RUJzcs4V=b*T|PTx3J0To^N316M- zyoRkGV4`oW6powM!nvfKZq+viKJH%sD=d8Xx1TwtLY?AmepMxqdf;f_FhRUe8S?j{ zq)2q!n@`4RiRO#8jx5>gEXe&Hl~1%}MNbD-p@F@%l82*mSk5K{=n@m833mY|FYQjx z{L9}S7--iEis@Ii?z$uC%s#9QRioXWRAlxWbvd?dl!6iem&2pc-V0|V5!Z4 zDMwj1DM4~ofw^>-&CuR5SYXG*)Os2J$``JTMNUxG=49GeUDc(K4F=LZDe`^oe_zKJ zL$>|jD?LIPl<8RKnwJZ3?N2aAMuN?c_surdxj7UzrS9t(Rho~kO4~E7r_A*db=(Vm zu8Gd}iU%RbeOB__6C}^eCrwc=!PJAj1@AJa+lhn!MNRK`fXF{q$@@wdHM_uMz`QZv zE!o}kPeW*{|2Km1rEP@~zy{`NoU4kTc;urK*1o?q9A*m!Lo3Wihq$*_IqL)E-NDQw zGoFcj67Dq2O|G!C0yWqNfIcngYC6Ng`rbA$HF=M{6?C2m3#4u=b{@P118BB@?Q6SS z%v)Nx%h5cy@&s>Z%beZ&dOigWa&Z;;FXe1`uk`F%?0f{fGs5o0mZB)OWB6^Wsz+cD zeB5!5taq*4-Op3&8>f92i{w8!AT>41AIZSP(Xx66YmnmjgS(uSq>Lm&uYWyYSzc%P z;L@}X25qh@rQ&+}%yv5N;+x~7Cr%QK%YTVc7kD(k_MZj`Sjn?baPbM7Q*aQj=s7FC{p!@dileY<+tLpwW zz9Dyt-VpMgaEE4Vb%SBNnNl4}jUxu{+!Rj8Na;o2x0@2n)6M=(wG6y7T|ozsW{i~a zMoZn#dOM@d^Sf)B4G5JTy}lgQE9Ciip6IX-9^CF;KO#{8V-#;+Uq%hogD&$o5H&X8 zvx(JpHh}YuHd4wG4HK%D_RNdlzS-K_GXs&jp^?1{K8|^jk*HCOojqWrufQ5ABD8`( z@DulCAO2~Zk?vtnNYpgr9?abxcX;-7V&Y9Oy{eE@*j29b!cW~HxsuhO6L2?2=YmcD zWplLGZitRmVmoPSYMPplcAkR-R31L8d88KjNX7*cX#(S>zJ7iiC4A3-Bvv?!o(mAw z;pgYKT{>1nh(w`l_D!u0zE29;wjOA%J=50WI0trBp(R`I7Y!;Yv(oD!vzuGa+v-bk#k)HoFZcz7X4f}1Vl3v{W@cuxv|EXn&%TwP zo!YNfoSpJHn1E1Nw-*{3%EukjfM>x)3@D#a8}B1+O|RWI!9Ac=o)NKe)^Li61&EQ= z+hDwZ7WCl8(YxGIloLOrt!;))9=&wd`QwRJwCi{^Xy!TfLCw5i`<>W2b+Yegg+Pi> z$h$YlMC<83*vXmR@Az&8h!n~F6(vLCw}5n3gV)0DF?%hJoSgi4#l)G}pZ|!lND-`4 z@CL(9xi)JW(?ejYYo+M+ty>=+dMAj7XoFoBfSc3*b@*%~Q~g<#(N4#hj`llsAr7whTZrB&al(NkGEWlx~wX-97CIy5^yoydj!vKwj*=UI6L3XJI zkdO8n)0az>c-!%l-+5uJe^s`a1}%>>soo&YuAbwzJLTEzb%)LADOg(#02v>hLwxPI z#9Wky`p%v?yLA9BN+hv%^MV$5t$q(^DUs95lc3>39pjB$#{@&(bGg!KM-{o#s3Cm*HUa)WZ zIO`4TK|ImRzJ^0*^Z8L9wA)rWOLU}n2X)$7=+3eQ)_AvCnDsjjWO+`Oh%*MKi3k+D zuH?s6g?7Gma0q2MkAd-Pc=PGlBkPKZ2u|AXH8p|}%TK_ZW(<%M>mH+E(e2P%;%Ig{ zJ9>{9UP} zN>k-2CGC>~j2B-;&fLYoz$kUQLtq*Z{M{bQ&^FW`iaxoOQZ@Mw~-$Mz{=b^x#e9 zyBp&`pnU%1@{b`;#u;45m__l1LuAI9qUJqlRoTwp|h8cT?&_q z#t9q!z)D&K7%pC)&`*^~iaH%3`^irMe_5! zFxRSu-n0-hm%0J4pHf}>HV5(Bw{NXAJb;GGA{bcl`1+>B@#ecL!U;s};uhnl^Z7-6T7^hfiV4O-Y9ECEk-m07ib`;XUy^9V}>^e%mB`X=##orw1;>2@3#k@$UH5 z>6n3MMKXtud3ke+?VCSE$n>4yb*H7GCSx}4C){dQz4C{SJN#ZpMn1cwJIlv|>E?ke z$Ad-Jx6Hxc-^8k=<=&u3#6Qnba`BkB~f;#c0NO8fg(Td!D6K>kdsyhVCq3YF)6Nn)u0^kK|78e()Z! z^Pj0d9gfrUt$-V^DGAtEr^om{=UZFF1dn+FT#H(jDYM13ka$kGc#5d;EnYDLSS0U= zh9rUJR=PJS=ezkKQq$>I>RX-f=*M6K;O7Tk?n)lBV*M$4k;@r6{)1X7{vz0pJ|59i zL40=s#?o~cgDC8uN&^MY4~;(OS!Cjx$up9aNgLd9=zbd<>=((`jaP=8q+K~C@ibZqS|!{|wE2E8pZ@GDePZ>J z0&s(3>Z6F@6l@%pJJij~sZyU#%MtkktfRdtiB&Eht?dHfTR2jb956?+wcN>!>u@Qy zeqjpYJECW}&; z6qH9r74b*xCQ(;K>W5q@+``pR$_?Dx1Ea(*vC&>9nkqT%(jeF91=~9k>@|<1;vgBDroQWzrWdcxSn5yw?Z)r{6 zc61uCSR#37u)?b-$9(ns;xXq*r4%C5NZ#KS9Nu)pER<-}GZct34O^@N5z1F!V4p>= z>){tSZNALYKpMH8GJBiani}1-3B6q86M zUs2T@Mb>H$;gCQ#K~u(y9lVfq(g9yy+V4bUb?T<^QhAOdj1td&&e5x;nv9oo!Iq0o z3mtqk!Vof8Q~uoHg&E{=*@XRyqdAnNB`4rp8d&X!Ol~&b69Qs{RwNw6U`s@ox(&D= z&zKdzq_Bfljy83H^G>f~Z@lOY5)mp&%0(Z((=5>-AKt@-QcU5yiU|p#;(P&#yfn3u z$b0(oB_hqVwa-iFd4_og=Q!Rx*E(LSWDCLKQY5sRNl>huBAgP2+S`#X}n-f6y!o~Fns0Uw3O zM6tq0eRA!WJ}e$z0pBqdbeG$5!FQ>g`D;tdv=84ZHo2FghF$F?Z>oJ3eyulee&W$m z*e^5X13|O)LXw8;jIH=_A(4_`Sg^2sU#E2(>tS~oY%7tUR<$6V_jpE*>~Xg&k49KZ zO(2WU9A<=kM}vlye4=2MO(ArPIdE!hA8khRg*ehn+Np7^4#vm@4eO~#mauF$_Ose; zrs)snBxr!Or=TthA#2-iKLQe{T=jVsdOA4+oHjjJaQBGe)~NxpX-*nWzu%N zlHLJ()a56AViR!}Fxx?8rZzj|q^(%HaQI?_K zpj-{fSiOPwR*sh6_R?j&`WNk+*KZ||ON-%M(yy%gVqM-s9R*X_kmp*6tYD|TH<-IM z|B-5zHOEFh$P;&Cbr(3NyV6WGk6w*XQj1uO`)Zdyq^!5=Q#TEp6V!;+(R9$%bh1kV z`RVp(U@M@&gvS% z@77Um((N+{*lBwfk^&M^*wQ>F%nV7jGLSVEXIG8w2RoiR)YqUKDj6mKfB(Yg!o63% zVK@%`$KkyECo!#2Rv$?8ZMOV~4-Rb(x4X}!YLp>@PfMgK40pQwaS8|JBhQ~WSXsF1 zBT6hJzhG@-gOmj0ql2IS!&ZCM!zDYK#ZVpfGSB>?mLTH82Ig>P9d9n1)%2Gn)=LL8 z5iBO%{62{ZRhX=?I_h}(8h)tv!a@BW`=g8gIPqekUss)8)NBlsY)Jh!vZWf9zQigf zDcO5Nnm3`#h_O4e!mu~H=BCfw=u$}M0`Jc2{MVuA3xZ+VG}PF&Mj(#G4L|?d(6QFa zL5<*ez5@8_(^RGGSl;7poD3X;pOtHjvf--p?HGI%qqc8Z@m6HzC|xj1%WEN>%( zw-~Inc67&KsTy1(==X8|8+aeD;uFNgPo={T<8M0u%FTpP8)foIRM(d!q)ngodw$+wD{3tju*dcYKzMtyi z89p*qW{)q8v0^y|MBJr8g0r+F>dZbuIqsy8WDPC}pPe*#o7KCr3UD(p9_2s}qq48K z3r-+f5@H9;w)2~}7VByK2V!S?=ZhCFrs#}2!L+An9l7mu{pF;+ShW%(i&Eu3;H}9B zcfS2JHV$14jE{lAR3}>rEVE`b#&8|=pxvR)nR7hbRal3c%}%Kx;W4uaF;lM}?{V_sb3If&ldSqXl_!`y1Y(TOc{4;BwJclDI| zE`_2Vg@dV~&EYNAxh8&_(@NQ+qo6+RWCz#k1U>kYzxbu(?)<&QvX}1pHo^1UuJoya zJ=28j9T#uiN4uRz*5HMV&6tX$Ir}+L(36c#DJy1XdlyioQS%e|>d*78c)ka-?q=*}p#!cLMc=?>k;1PU#SVXT| z6BuhkVBdkv$vC#&`+kEw=QF2KR?Fm5<`4N}QQ0r4{pEPV_e|6@2=+45Hf8Spc^xTj z(V*&ca#rFh>deh-x#&@%n;vyC#aL!*#12Fa(^S_^iBDVk4xKiveG>UW0(=sSM8wJA zeYv!G0}uRyQg@T+=NiwWY`?I;4r3y+@hA;G=IWtB$v76$BK7 zQ&f%1Bw6jp(`^V>6h{==XWopv%`VHk2E_`X-v`E(7k3E~62v_D{1SO7BRLk772`bp z5!@-iu<>pM1SbM9j>Xi7wXWr!aANlQTq6mkpX5koVOQ1H{?UvAt zWzC3&e)e^-54>mdHf26Z=C9^IT9~>i(%h2!U|vNdCN8O zJ8M#mc4VfyS!Ex}Pcg4?l6x15u7h@)0OaRK}3!&1X zVt6pMcE%A#n#SI|_qre5_LAf1R2BRyqTzdc(9<*(aJkwbDO?~PKoOvD+MmmPR|2uX ze-bqEsydTA6F?#0jx%)noGtfJH}@?ye0%RRM`jSxr*(o%(>5&n>%|KW79;s=7&d^J zrsu6nBda-&c1(}NEoU08jk0c&^PEmUb^voM>6IK6z? zx3F|g9;?h7bQBbBH!gy2`RHI~8N%`*J#X2d-TdroN;Rg3iFq!U)tV9f&hlVx2x4sC zw_GV8R=^W*OHDL$1M9Zo@SzX)(K+5PBHz0g+T5$}og4*hAH{X~WkgxcM$l$AcgrRO zV`F2>f(91r;WwSU5?P%xL>(?AtfiNQyFqi$G9!g#NM7zI-{1Qo8aP*auhmvY%zG={ z;K{4{q3^E*6JXduJt=b3c;&O?l3wt1_TV2LYu}qXV_hUtXafRvAwpj(-?IDxot~1ECF-Q7E+G9TFQ21CS)+PQ^8wcy8N^Qo-u zep|iM5?Pg|6mtr`l->;4BpKp^eGAy>K}7FQeoUU7)&B4zJ|WLwdwiauBH6&YT{v7j zNF3%2<*^?v1c8-&Yh~_szcHFymCKDlkn%+JMm12;sv>?clxD>nm+n{}uv5gJsai;I znd>JLF&9o4k)h>LFs(#CSj(+j}uOuh&{-_SOh(KS;aFV6=H#&Q!U zNT_0A+ksmAFGPwtsKm%%V7us6o{0&ou$Ja5y>XIeR1 z!ORfeltp+vv?;1(Pa*q|rw6iT5RispPITqPugGdYr~IGA`s)pB?TF3RcwWYZH@a56 z8LEoz1QdN4HseWgR!2*o8QSd8aGUV|7hI zm9qLg`YSL#Zns6JQHuo#i@USg_XPorCaPUeKrCJVrNXNc>jW0deUX)nk(c9!tl#)8 z#0jr0n@pD&3@gZBpkn%~&%j?z5r{eS($gP`n8!!b?&0|kjiij`!@C=@rI)&;-F1=C z-FeUQ5am3budsHh?YJdMh?$7HE6S0<^pk&F#c+vAIE(BM$7n7$ix84a$de3@WjowH zA3$jI>cuM2jGR9;kB4Xmsj#{a_C*x)Q`09$4d|%28_=$MhUxm}N9D3|T>&msoS~)z zxtR(Cls0yOfYxDkgz2*Y7N@F|I2Q|ddr%52liuVZ2@LZ2b{{fOj4_+3 zES3{aV4(gFc0fRl?m4txF){TN;yWn?02!5hx{^#k&-Ql)N9Pp$8cKU^i8TFAYleeM zvk%Zu@?(haUBQh!J-6-QM|x{w?VAh4vhex%As)+}iONH;P_xJ6*9Y3c10l&F8a?OO zG(C}z(W|R!0)>{znG+a%|Ke%TZD-n(gFti!uR}6TDWo75K+K9tkWBY5hh&P~ow(mz zcTMtly%qan+cX-19L^#|zm6F{a0YW-PnH9j#qntFX3Pii-VgGMB^WQdhrCi2wZSV< zWTHP>Y>&$p8(L)BPLBU1NvA6aN6?ZGp+RB{3^2Ij4cX`3)Amh&`Glx;{mRZReT~!t z7Y9ey!$z}ktN72SBh!q7Gj9+R7@82cXk79Y9XNPJuEyGmc!X$6`D&jDi;1qM8$1=H z9^#3gA3qdq=jm4xaKn{ZdseT1+;U-sQ>DpL*oxIijiw2owL9Hh6ut(aQvkrmKNwEn zs}rFs-+N4OIe>vUlPu_xZp{MIA<&TAo}`XOz8X}3>1feVCV~yQrNGB}Ez2v;#=Ss? z(C54l_`k-sPWmdGDu#0laUn)?Ghg6v!o2zka0-y06e9DimK5-c-hD0TFA^kh*zytX z9{76K3#qP%8UETA{x8Aez1Qlb9)6G$NV=xnziv*cvHg|GVLd;}36V`kwT-YBUdm`q z$hYZ*`6PPL3JtYIx26f&qan}pAeHdQRBwjD4t*|nb9J-GXp}FpdW=c=i2?vS62jR` zVyp{!;s%A|;;WOwFC88s3-Eibbdrd%ai*qGkKv&2U~4=&p)oF@T9s$1iPaCr$0%={ zakN@02V_fZ^?Sq!)5xDUJ7pu@f&&vyZg5b2$#*$9=IoE1i5*T^h<$QH31FM@xGJ z(EuT_2yNS7_h5g7SFhXG0x@rCd390tF*6_hAcKtJjf8|a=m#7igYkPU73agTleVbA zTYTRWXKnx_wcp}iZ1NTHoJ zY`&eRnWtG?Apqu;8&`b{WMzT((WW0xuH2C}cqv9h)0v_7f5~;f4dmD!;R2g972{@T zbx;mJK?EXiUuhE7hk40zdH03zN%!_o_r>ooQ*mw!P55Nx(3AC2Rh@Irs8urkCnil)Bp(2y z<@dtdqGM1Y8zTp#_g}EB9vSgt)2v$6W_U{jsvxE9-sR9dF)MXp zTiuhu=zBz#Sbx~7n8cO+r|6>*HS0=O>^7mf5@qF;`C27}>2SSde0x2C@DT%p)O2}N z48d)(j?Nkna>-|X3`Sa|;o0c_Z4L|(+lR~(Ac$hagto!LfdL4=$<5SoPC1eb=gbCeg!@T;pbluefE8vMyEP1 zjbktmmwqt+{4YE|s@MftBgUg`@EqRYv*ud+L*+oFSCm!uP69AQN0p1CHlt6VYQT}P zM2u!-tph;l4-04BBrII@T9wlT5qe`m4;y+7>#>0kL3HlYoKC2Ti?Xsh6yb^L>q&^P zl3v0G>jr{?*Ccx!&fK`}%rh3NlMWzM@<2QBr$hX2Gth^QFjy!Q<Fno}^oiZeTphk?3+0JF#N7Y@3#;Dm!KiMSVu4(QHj51dJKq~1|ffYC0{@on1y#Ds_xUG5h zNc{!imQlbv#>~^}pBprRKbw+{2mWk|z_t&x0FbO2&Aaishey$R3mtE{6jDDS$B+E1 zFV@+z7%6ayX&te@niiA!Ylv5b<13;<=E~?35`NI`Z3Kmn-xW?0-maj@z4=5hhs$ay zC9BU>o;JqM`hqab@|1KS&qZiEH7&;vZI7Ghue_LvZe>mw7ZSA(wg=FlAB;Cm;KPs} z5KDw@wX(}2B$Oq^GSlRb{3*dT7`$EyE0RWGNWTkPC+kzS!(x^lQ5xfD2o#iEo6kEm z{kU3Y_?1dJDqSJce(_rZ|B(G-rjyjFfbiE(Bl(un^k)6h#o*67_xLZbJu8(DKMu`9 z3b${5e6kojDQ0Bn&U;+JxZUu;3W5t*S8O7871jcxNV#bm#uW=~(qo=+DJbgRb!UR! zz)VDj1^59XC8rS9@EAaX!gSVsa?*SmAhf@{<|m_X-uh@IvqOrPiqlp$>^eX3U|QxS zmXy1inyBtDhMiXnb}2QDM)-~g%aS||Wo2$=XOL`Rw(Pe}F)p(g?c7`GVv1?->-3qu zr`Km^miqEc;>;oHf&614&ve0iK2CuAr)7dWQkLED;Bx znTeUt*cUTl+HV>QHG(q)tVRb@+^Ssmzd`xM`t5SZ2Wz04?6KC3Ss(X|d#Q;%05p?V zKQ_UTlS$j#-O>hgeK%-%W?rsH;&0wz%i}+|ZXH#r&>QkG$&NpF>TMoOY+Uv?kdfc`%lxQ(jMLc{`u zL{;ZOeo-qnO0H;r!hhJl1Yi0_9reoG+#~p)x2K?E$lqnQ1uqzzphtjbN%z&(zvx^6 zYM0Xx-VZe>oP=1stj@q$iQbExNd+`oqu#x1)`vZ(TM@?zI{!dTaw&)zx(kv!a$C(1 z?6l4RPC!z>C+f^gLx0M5<7E`>9!}uU2&L7XOd95S1El~el`>d9GH)fOC{a;QRaZ~3 z`~SiJO&v5U+O>KMczyP>6=iY-6;QqCd1ZDDuazeWQ>>&-o0+8!FL9B+mQlb*IkH@#DJ9^YGogyalaBKkL+yeocKx_a3v)H%VzI+yy9{ z51lEHd(<{h%Xtwo>hgF?D&l_k&^C#E9y|x0|MzQ4%8|*YF?Bu^a3GaS zS0Jj`O5dq`>N?aoX-~_lM8~G4b=a@yu9B4b5c2OBw#xcBc@vm=hq7+~AWn|!w{M1q%eI?|rFY4I4JsW#U?GFWJ#Qc>*gM2Po65tK^?%I0cTiJn*FKCD zv4DsTr6~$1DkZc~MUR3ChzcS|krqVhy< zZS*|v`~3Br`M#NNzT=E%kd(deec!8G*R|IAeCH2kp@L8r>&OideBctIN3X3QJg;M7 z-VpFN6?ds86A20V9v%9E<{z)H$X6G>An1vZ za{vsp_wcRDYH3l^O`KI3QU?`N+sq?`-Cs|Qb*Mu5{g~Z&k;2@g|yfp(Kv&&+<0lgTGJqvK3G zp`Ge@e`sKT>ebI$se&@yg)z}@zu|DWf8*8Zwc-hW)B3^YS(zC7kZ#3t9RH=9P!LQf z|9YnyQa*CCyfiuI1-;yK#HDX>TcMKbGGxyLwysmkto#=@+YzD3*vzw5i}0ftAAZtj zmqcj#^Y;piG39bsE9CVvhaN$&Gc~Dot0Vt&+GWqbD4ScZ`$r^q#hpfo)x@dW^-YiB zDrk!jWtJa#PIhKr9VY4VY#RETvzXYt+e%q44_te3u)kHDMWg)RWU zoLTVD4X8IaJM@zmt)9E7nQ)gq^S;&FTf-+0PCV)Uc>kPCci2bF)=ty=FAlhk^PNZA zBQ;~d3utUj_5LLFl1(eGR+R0JKlYXn0-)b^r5cK0ZzDGsFECv^YtPOt${t_25tBL) zmfy;5$UBnTc22T=^psc0fphnI_lve<#YlGO|ILIt{tG8EATIJ?c{qDw$s_EXCjlVrcDYTxF^+$A%$CEOdZ@~-tVczq=Whg zS{jF(u2*X}?7yU{N|Vg|`u@+|#Xaj>wS=6Gs?*Q?-&3m4Y}WdH>Fo|}nrM2?Qy#qx#rhb}OVBIEchp zrDIY)m8$TiRg0c)8WW~1(?d*ER{798B=OHJ8@=bckjZEf?BS`Vg@W8HCmeh<=;YEqBu*%Em7Rv+^0Z%og?rn5&fLC1uEu0 zxNX03*!s=^F9lqwmZWz?F_J_r3;oTaBCke%`Vn|zb7M%86@yR|110Ml?Hy zPNa?>KNp_?${6lm`;Ok5Az3#a%GH^bSL<;C>&bL~;+%{>JrsP?QznpS37IiZ@0#lx((}%UkMp=Dqs6{vm5hgsV~3y!Y5U73sS~;Vpz;D z(7mOuiHl34s@fkoPKqgQuA9cE^=^d1m}T|(XA0WNPlLs%``bZzwuKtaH3ifl#AJbU z%WbD2Lw9C@}G)knA~PpVQ78BSZybr9c-}DLwRy@YxC0WX80D*hnA0^mdr0T z+_i}^(OKWNm{!$#=*R+8Av_sM@wSJU*sJh(oH_mPp_9kWBCoyPIRb7^B=Nw%Y?EZc zL-1FBa@@;XIqnY0i0~v{1;3yCSrzB|ni-6a85;N$yA5HlEOSdzVUj_Vs@b&HQ6|>BQl? zeGzz$>DEKcA6AGXBtj?ar9!~@Gw0N&H_NY)xX04xjmwYRO$bR>Fin>Hv?aG-ZTM^S zv!dlT*EJ*7QtX`Gn&^zW+|9DS>rapbk>ZDdGb7Pwt#9)6s~N8@+

d2JBX2o#36n zaLBJWJN5M)-D2k{_aGBfKJ2^|vw@+pe)VC!dSUF9h6s6iW{Y`H?S|rCRYJ zpUzWqp`(L^aK~UYiEp z01!$wLAW&JM$Ou=UZ`lb7d3*-XLI1m+2swz9$WqkB`^!Fmra1rwV z@|b58#()9TORZ5BLyzq7Z!hfk%qo4|n;iT>{fPZ+^&J1bTW9NAbCe_1d~ zdm=(w2sIWnRXC4es?~^dTrD));MTGGXOj{~yLe-pimZuzIo0-ipSJ4_94IY`&}~jE zLqr2dZZ&VETVK1l9L_%*lQoSXwjeX zWhMHsT6%Q4>D%*X7amux6tM6immS4_=+&;am2=IB3W@7ar5h5`Z^^{GADa(#bDQtF z?Kx6%%e8@5;)+oDwEBbU%(>ZFb*FcgQ)Aj!#dRJiS8W@ue*4F?$v?RXhkcZIlb6vM zpbNfos!7+r3F()GjGPHfx*j*TWw)u3BWCwzc77F0Kg=HQ?b>l~dSCtD1#pH1_&K2$ zD@$L~eIsW0IrCkdcVfFEU!(YCPvXP)*h1Q9sTc3zt^uuUBBN*fe_7F1wumNBNZn7J ziCgRbi@ppH=ZlAyJ}+Jn&=iF$=&kU58`v?Ay|pmw>(q$8qtnzot>zS@3*fBJBhTKJ zPobGlHLl5T{4wtDq!bSxJ&@)hDzld_nucT zT6d>;g{T=t$`=^px<4%_#=4~46Fc4S@F&d}{knV9v4utzC(eDf8jbf(6mzT}IF{ll zyP?g^;iC9v8}oVB-iA4WJ6g<*EA_+Ey ztp0(sY1$`sS1aeCS77V>iPQ#gtbQ1xUdE}yo>b)q3V+HK_ZquO5cfRpD9HvmN2bm1 z0I{$a*&3>O@44`|BR5nypJ0eK#JRe9NPQDm!s9Ahix*a9P%zI?U-C_8ZLA4 zROJ`Gn)#ENutCjizUUSoLik*r2f1nR3yN4S@_r@xbo$XGN6<<(73T zr}$IZy;%;ota5m@Tk!VBsfL_mP8{eRAITvfnfw`#-08ax#e*d`6cts&F6oFkPH98c zzeR-=%Up?LO{_|6{4YD{G?+@QjI^2WvI=O5r`Rw)1mFP_eICD^MBSFHdhJ@2BV^rOE=dQmO3WwvUIbA6lW$|b`{o@T$4U2yjRmO> zfr0Uoy^mjBPq@|l0HOHo>#q-eqayqNaucQeI8ix)zgf!j`u(4Np6%mFrPD31HCcu^ zHLri74+!;Cy0&YXHWJaUE|PIex#ku9@YJ7oGA>K7F&3Js&xQmCCw6u|U`N~h^Y)m) zeVDBwGX}#|GGKI_DNLn#(pN@9%iV^7I*IP-`8GzA^B*xM7~9{ooO3a$-Rk2DL0iKT$(rwZ+{k_AHeIhe zGM;|d&Ig=;#`=lSAOTxd7|LZI^0*`kRX8D~2NaW4rx1#Z_D} zp2=~07t|Khtml{l@0($B5RvZz(|elo8+iFG-?dAo_7i~{-@A&}b z!$xeeHGvFl(QgEt(6#Az&Q^-=k~rW=uc~To7SA=f{EvL?tmfLzA$>oE7c+@7GghU3 zfq}$4S)^nK85%zC#IKQwFX%A6zi`>(_twkbyzn(-*~NO-p6~!x$y&k~Kj!B!<#YyDRrmKC_*mB#4?Y=u9tMpFv&?{b1i=NI)#{_hvOv`i2 z?j&5-?cLfL5LVP82l83QdoJlwnBJ{sWR%EW?FcZ|f0Zsn!x~0T;%6Ke6zt zJnkv7MpOiO<|MIe3JUOubzPttzFM|H&03!{@;zTQ|6XGYNY9Xe*!s_xnfzZr9ohrI7^s>w2P)P=drH#x2^ zbV$J*jfrdxu$31`)^6V-k6zLq@p1*&gIm6_a`-VcGRHxILWL}7dZ9ROa0;B~B;F2bVoZ8eioLkr7 zsTcIP>1&lbd`@?R;?uJ{OnMTm2#^#uu!Z>gv_e6OwTrVhZin7U)K zK~_*uAeBJXEj5ob_-R|(a3_fS6u6$$KW)!2Hzu)TiX&p9(%`f&KL$9*pz@ESN;n?S#ER)m+iqqUDmM)h~=I0s&NF^VydXuX{ zGWUOs zJ6&AEs4PmhZbt2wa zpaw7K6_{Z)3ik=(!|bcwG{sqjPr|xqS0!Rw3k+&}lRsArz3ssS!0vJR;UO>1B4FY@ z$kLZ*HE@Mp$^pMYXI5dixk$2f`SqOMu0zLhG(GtyQ;hkrpymBT5^*o+7n_%?gL&<#eEt!fSU8|~H*bPM- zjirwD@3QXsyR2N?u&k%#!*Nlbio&ZO26Zhq!s+@J$I?OD4B@D@j5@&%9}#mO1^`P! z_1C(=uBgDgJds|ThKq~gpn#&P)u zqYu}yyM?O$WFFi)m&hiNzy5c}{!aklxbb}87Gv2Y^blwRO#6{5R(hfJ9$FT2AmHIp zXos$=GUz>i?h`2cb%N)V6kF^J50(4XEbvRF&51WaAugT=EeOo}$~2i|m*d#u$7~DRauM*jRZAm4?a?3XJuYmtJ)IEp_gl)bfRoT-;zX zF8ghnj9u(tQg<+1i93!EkuWvsw0E_BWu6ozm9Ff^yn_oE<+f}?g7k4|+SOcoaxd_OGBi-0YYrO&bNZ(*>~_~7r~6T+uYe@iEM{eB9r!=Z?^ z#kSrr-vc3A+k~E4^_&*(fu?^@&72d|PC1O@E&JhI#)?%on7)9LS?eL$x_vv@{`sMa zt3;&pZAAQXQZYg|KL1Huv($81g2(zwmu@nQ6#i&VbiB*uIHP$*9_kHWyg1IZixtj{ z@NcQ1V=()dY!DBA#Gx3$F?*sTZM;Y*bog|XXazf8%=HvgXG zGjQcTaOJB1Ub(){9Ra0FPh`Ks+)*oKn{g1ZR^&Eptglk4+dmcw>bYGi9y-(*E2jsJ zI?qsRl}0n}7K64a0jB0|tCNMvZMOK+h2gY2!llqv{zrR9%AIO;^284?fR@5lakOUh zfGWJlkX0yphjIdxzjEN>;gk&Vv<&t=D6-N|T7Z-G0!!=lU@-T?zHCFBTd(Y4 z^K<9U1>6!SZ{>K(E@@}=y{0DGI$CaHxw;_m%^T|sI11{ru8U?0s;cj}&!4Zi9{KrE zy3gjY1{4_N;Pk+l2!Ez5zY=!`Q?=;ywDZeHLy)Uy&Oyn!1dJ0k?}gT(gXi_&m{!AJ zdXBfyaNIg1(y)y!cVlBCz<#{l*Wtyn{W3m5S1Slgr)7`$cD z0NZZ3GuI?AeGRlkg~QN{+3pN4udBV>vu{3d7X6Pic^!be-V2sj?Ym>0fGpXQ78Y}X z1OJ5qz%V`?iHM^)zpjz|oMiwW0DCS|Db8}O6{K!yt5c=KzDh9VxonGH@#^|K z-|U6Q!|fY8CwfaA3t9X(ksu>EWYkOe(O0^x$sN97jgjJ{7}X@lQRu&pGOvbbS2FPW zlAKF>_}KmiAe|6^aQzKH+}>NJ{xIN?mF=7DGz{$OyZik4 z^8jeRA!$)Gup3ukPMULVQT^`EF4f+)<7{cA|kDeo#PM*8p1GX zS`H_lETOfu^$qx>522xCCrD*aGah|!;*6A^FfnMoE#h$mgEhXWk1n2 zd3|;U;;jytGR>sHME-F0mL!z0PC$GkkW)7HU3<6{$~+XTtiI@2LK+_c*o})j>G?N5 zc)D-?KHWp`bYV}=h-Lm)!qLCh_n$-pwCbR!{Lhd7my~1Ae_nW)8|v@={pp#(tDUF* z{ee!^4`lrR`N^00|7m`+hwfS8!gvQiJa#6T`JbAfDT9LvP+gzK=dw17o7`}RznvG^ zcI@7-iWN4tgO+~Mu8hd{)-Vb6jUt)e6p}x2jRNshP&Meo%suCNZ*LhG$F4XVs4l0h zKQOHIYl3cq3u{y&rM_S*wU+k_vxQVuRT@dz4!Z)H)9%>so(zo!G@%6$VY>00;A=+q zG-wN{0RfX~jEn+gL9Mwax69!yNLu~693zA1P@esMd0YVbAcUIQMpx7K(6Zb(48kWJ z56hd+@4{#rXp_XCZ|*On3Q14G_L!5g=T(KB)6$y{s*6Uc&=Rc%>%dRY}K zX>5!N1%Wc~`vV-(NS2;^m81rR2!%a1Zvh;MnAARqk!cwe27@)Fw=~r&>NwSNA(jI1 zOTPKd&QIavTv_ z$2~q>OGQ%dncbdQ(g2kg*X?Jq{r#Om2VzEO&hj4Gro_{zl&A9}O%amzX`t1+ASYMJ zCKJl=N{P;ttX+qPu-+`r__h8ysOv|Z%Sk2K*w`0?5B~^vs|lkjXCBL0^hn6e)p5@) zEongDVjlPta4b{Uek-uiCR}(F?S!e8WpGZR#IE_;F&!h)bYD3lOwJ|R!^t-(zCAE7 zP_nTyE(#qaCcAPKkhJe&!$`!_%Y*kCoc{n6l#sVsd=9l_C zpF;i)#qchnBxIk&jhw2L@;c5$_e&IZ{$4DU-Zn9Fa!%iIm>;>LQ0cL**QWA($j7u&~M72g z16CTzyeB1*n@eAVK>z`9aa;;88PO2#fP)wt6*U3S#q}f-#GRbZ!HyH1O`(?Ed82t9 z4bquZQ9ybs6{4Lt0FICnMghiK+6Sq5xGAE}dKUloxB$Bl2i@1m;|7Q6R7b)&VlYLA z9Fuql0Zo`#-&~*eM0;FlNpBXsT3~ee*fA4|(#*m_8*~7&@V;XWu2DTyMkv;G3jHZ5 zX$-n9%9<;lY(YWeD}jQmc$gMDlxLr=miqg|V$CQ)YFZ8QTBLu3JFG*dO|G)3scFHn z^i)S-83pBNXMZK87Qm#t<{)6`9&m=;sqYTCj_(^6{wT^uMj|6Dx&z5aF)l&2T#*x5oX*H8R01M)%V0k30Fgg{{Xe2kra zlGzPGp7?ZqW)U;gSHt!Sx-`@%eYGIJ7L>V>8*zC9piQ)gG^)@m*YU}9!dKkEIyq;5NEBdO*W2 zf1HELg>mSWaUc4Zn9-TOMxE0s$=kktdvI{Dk+<%T^C@^GBcmZr`lv3Ua&&Z1PGn4c zm+8;A=W`!c4~nV4NYHGSwoSkR98Rx{wj@J^nOwejZaL;RjcV-j&0)N~k#UEJ^PCZY zL3%EfXvc|37{C4#0>5D6gPcc>*!K|;TOT*WRoj3zkfytAaaw*49iJNBZC@?e`%lm& z+od(S!RkCl@#r9&hU7Nz(u7NeW?dgfORynf0ICqGACdExM-l>s5B$opO`@FFt0+sy z_w?tP27OM~tK2XhH|s0wy2P)RVx@JP0D)I`pC1&JKY={VVd^bdSatW6`6in1w)KmJ zJqDIM;jl^j?Q4!`bbjsMe=JzYN){62r(2rF8hRlPI5b?Ig7l~8;#947vtnYr{3>YV z$Xt&ykkgTdBSfF7q@#j}kvD!dN*}@QN&-Puo2fCz;tk?7fBc4jG#RhlT+f0MxLmhP zXq6NzMyv^gHX&NBDB??oPNkWMws!(55Ts{ctv=3wKSN?rOwzmU#*G_yRKm@XR$=^| zdc`)wrsf;#OM0bdkm&|+AOhaYO_%)?z~I+$ZXE(&?D$tCN|Jev6$&bjWN0CVqUaue zc~oU;kJ-b@qK18B0XE!0*frT}-?1@xIK+7&g+LB-*DwU$ac~Or!>=7dl0V#F@zNf5 zfLf&M@f8FtN&zOa&mM0kLv&d{q{eQp;Vc4!SYP9c%XiQ-=3QMl4tPczL}VF2R0ra2 zD=Pkk&iNDWCZq`o*CNbOD*?@%@4LTZPWE8?N+E=3m_x%%Bg-&M=2mgXzbhOo0Y~Q_ z2>k~4?AwRBlYnHS0no>_$z-OvDjG{mXMi#Z$U!c*{FWc&GL%^ zcj8|mOi}>ENL_t?uhJI%<>N?g(g-}m64mz7L6yNpZgZ1XJvGyvj*DO&ky2RZ?M zGy5r_|81v&wsw@lYQ_tN>OAqPQq)0*MKt6tVS}T|u@1l*)ZEE7xK=6y+ssJ?w3lgfD|guY0^&eWR-FT?s*?tp9uOs4FO>h! z3;&zrczt|JfP=h-?*C0ju=N4(*Z(K!$Nw+COf}QK|IBa z%ODXh3^cW<(MeW`o*18j>8zBcBs#Av(5!621OqgfN%<7?bJd}6*m6>)L9JDgWdgB~ z?WBiHMIB_p69dZXDhDbkbz;HG&qf_Xx*>its3 z>H9zo)(4+U0MOalUW%(6-Xg}Q4POr|XxY~&L;R}FagRN9lT>{bBx;L5+a@907Qls+ z?!ov+?12P|8zSL_WPE`_Pj^`EyH6MQ^$QX{%x}Zu4{~E1P|^E|HQ&7UEx@4Nkr(?e zePKT;ENL_3Bcp-E^%s~&Ub%X8vbes-%HvygHKIUZ2PpiuEX~NnlJS@QMxW!$mx~mL^Hb zkX&{w8k`U^c$Hc7dv7@m`Zn1gfu{nCVxk$VqoeBtp@`i?rx1YHEsMW1R6oAZh^;IV zD(^<#H>?rReT%YrJ@%H z`L-kNR!J?*J3WMigieTCM3u)vsJ25cSNqGO=g_jyxDvm%RJo}R)LT-Z@e9co+vXmn zpC;|S#kR@iCCDf+F)opEXk}yJR;xb&8;atRfw#a&mC4xoS^qd2c5vCbDmm684W>_! z1hg*e2S-w>C&FHfemt`BsrF^gSI3I9bg3-6OjQW%91 zQcqlcB6MSUxF$%2S$FT=ReK<@Lzn8!Nk_P*8^r`&5zR|QC^m=PF~>D+?Z3mxpHx2y zwPRui-=ny%?|j!EH5k|D>ierlV0QZ=OtgPEy?5b8cCmsF_flN=2GJ8KN6BWcT(&&B<^EyBXzmvp@ zn{ad-`FYBp6;+#Gj;Z6OmLOc=l%<7U*PuBBYPng@Su4KbPd!>}DoYaYwF_c@Az0W~ z?T5R4KPooFkIG}D-Oo0TaKZpEVdQpT`gYi^MG&uJ0=tTBO*$%375!5z!BT-tlpYkh zXsjv0JDO74(b3T!`RhlXP;B(8SFbKC)!?VeJ+Wmca(KiE-buWh2>wynQuew_h`QPX zjV%}5^ktXGRLg)^fVC(i#Y%zg-XO0#a6&}&!GrK}>?-ELmkfd)YriYC3kmZk(YUfD zU=+W2dP5_5?eTDDKP4;Uf|fiX90f8Jb5}zQPI?145|hFoJ1fX2eHUDWp*d_k%}kSk zQj$e!BO%?WU5IEJL)uTF)o}+pv4xq~N=Fc%pF@os)l3v(T@itVL&^o)0O9Gkv_jT+ zp4^VMyyA(RhKY4Ry~If!JE2#Myq_ZcdEms%eRS&htQnRXgyn570;`m}hL_OuwDoAT zAXM9~OP9v3E>88P({ng|J)&nUHZ%%ZzqyiOW|))>uVCNOMg9B7a zpYF^~_>5}kS%h>!3ihNn=_E7r!0a?M$l9XGvsIAj775%SZtfI=KE{jv1QN)lY}VE) z*B2vkROkpM1v8)~*AeA`?Apv#4INDA1k8+OgJ@nrS}$(&fQRRp{MivfYXBCJDG&niZD4wO`g}UL zwon`l@-fsgMjHOQH%8;k-yAwC-M7!ywi`T90$fBUMQ}X$f@X6ChLBSxQCoKE?AaP? zm``1_SnaGGv5v~hqB$BF8D*AxPvHWKV~)8ho>y{!7_wil<=PV@~zT1_H;UY*5Q5IiGs8^A{7y2~J) z;X4e&DR!F|BHV8S!-w-mUy8zgp*o2b@@E#EZdhLhizFRz*0z5gsA($X8S8lq0P{^` z$DoO&(C}Y|c6X9~9N$yCW(%t3EYuLD2 z)~`B9-T>mIt~n5gtbVHBsh5-qo-qK9>M-AN^huuMLV9j^Ll}KkCj?+_2Q^Y4g8Vjo zm8pewvZuIZ;gd5o2M&P5xSO-p9|$!G4rqX?ad`nK5F;7~Hty>>t);nnf{8-7{NqC7 zF(XHEKSY7K3&0dwDMd*%SrqD5WJC&B5d{2LE6e17w9fq6d}hxfU^-XJ4RMS(@ULS< zV4sL%3xxA^brWwj;r;BO8nJfQ6l(yQN2nISbt^0*h;*|!3Z&PtpYh;qx~CEdcPZ2 zA?nk8Xc8brtvKn}W9|L963j`C+XX5sjObVxr_>Rt16YZd=>QsuHHeYR9XH(~6T@je zCt)t419W=-I@l95D?VUZw43a1%eEQ5sbTf_ zP!xgFh{JvUj;n_-E|Xd=O!*C{XDLTD8$T;{UFdl%dJ{{0?W~gPNiKlCLI~EW^PB?V zg><_p#r0kXkT%E+SDj(9yWqAtmfONv<;ECT|aYO#D{qwr@{C5Bi!Ux*ihlaPlqm*4s{|s)^-y= zZuYx6^Bp~T}XF<^^g|rrLXhd&%Zmz~&6khaSEeA9?wr;cY?6XNSpdlk#Z5mU99h=39wF=86m`tF^;2VEs!DWgG1*;PkY*BK-QMYF(`}gJ;Y(HTlMKzYo zjiC~V@m-l~(ws4GsC)B(^A~Cm1KQVdy)!g>sWcnV-$0Hc)_y~iFJ|501zqc3wvc?9 zOZY#DR2-)xPw}W(3$%dNm}`z^a6*-SsM;9~N`xHyX}I6etSZPN%t$?8UNVJkIqYP! zXx0~#ZMfw}_WL!`md#u^OKmsm^fS*xZeDnh+vPMo^$YvqvETL!?>dr6h`ccpd(k9H z#BSs#_2$7CV)mn=X}#K|`f}S;Q$tBnqa!=8_R-vJ;pqvsIZU`fPRE)AO&!{l2b)nC zmk7g8yyT;Z#2o5*e1@d;37#L5ZZSx3)y2qnGyEkOBStofUnQ2cT1z}j6e*{fM@i;* zw|f}2l?)4$K{%|Lvx|*vS{Sx@KRA$SkhFBKB5k{Ef@l>(Y%hE8PaoBm5|I=M5TKXB zI{khxH{otOSGg4e@N>?YrBjlnYaj#5ar4h{*?<8e#W94?&a=_asr=;+e_5hqxcCIo;JPV87f$yP*M8P35C`vMRNl^R%r)&?w@ZB9?R3_Ir zYvF_u$I*E0LtXAFcN-4_{TD-`u;I5iOK`RR77?D;*E>I?!uVUUFH_;7?maR%IU z(KhBi*0v_Svj$e8{}eSd+^~<0zfbmdxHElptU_)j#v{&FpRlbKOWw7e?w8qr zKg-AD{rIu9kS@1mliz;3a^BLHJ73TSji4ss;VR&Pou9ClqUp!$GRC9ygwxR|6aw0e z=bw~uu?xjLz)+U`@EeiN6DZ-VEb4OI@t_ivbt;SX3EzFdug)nUMAXmVuEw3VCWZj` zILIUbwx?Mz4sxJzrV83z*h7eohW6St4%KS@NV8~)zn1wUr}0%cSz!fl0THMbsLc{( zl8FPll!{Hv3k>C0TvV9@kDyRi;XUAyCJ>;#LtR7X6WH!Mt4q@wy0);5w`~-VEOJ?H z#x@+AdVzr~FtdZC)EC1-S>+(}pl&!}*4Ec`byaY(8YvY;8Vk4_kY(p__y(c8d5Vw}{xc!F`WGBKG1@0yJ zje8(mnz=2+`+;*vI{k@36`0Q0;o@09J!K}#9(%kR>CR6t?}sLYkP%#m_@64lG4~Df zc^ygzfQPZvL5Y4Uz>>q=K>C7^i#z6upVOib^X*gD@v+C7&YZ}V^hQ7hlXWM;gI{iv zc0=TBE8s^k#T6(A)SGV^1P!tngo zkUg0Ev1o=keSpqtDoTS>V)H9{v(x$IDyQ1gWF0@EtnAHF{dgsGGD;>IA2@|M+3Q)f zNT!+$^-=kfWef)g2T>P^Q?zLNsa4C+)YMdy{K=_RR{&+7ZmiJ|l+Y~eo(b_Dbyx4s z97t>K1yrO&Uu@aeIsZXaL#`b1nMF*dlJK^i8@t2P{Q(`Yy&s6}YP(lFfB?{iQHTyx z=RPE7xIoW}X?+V}J$$ag_RE8Ao=)g{zf80d<*u(7_3C$UB6>PNL>8=pF>ZZ*;H25l zu-+r4y9V4w*5n}ssF$V~N(K2)Y^Km*wqcsQxnD1&FZIzXD2d9ohSkm%cDl+|JRnz| zSuSLl7pPb^aLsfAVr!yt{;t@Y`dgQZzi-gJBWc{0kgGOXMHA|}8oJj3ME~Umk0#*w z`{P1V-+d^7$7g%chR-v8%Ug2p>nbM@Y(sCGQxEMd53gt9TPaGID21s+uhn&mtrRFn0}4;&4jdae$15YLOzb`hf|0yaM)<&;uhA;GD?&+eQB1v_5)Dqq zI@VI+5iv1nw18yRA)v;TlmqXzxUR*xPG!segXPFha!hBvf?HEG_JEeQ#S5$s8$M(H zoT?xvpcgV2kl)&}Z?nD~ilT=Q)n$uiNyq%D^c{#@aN6TX9q4;&3aWk00^4?MjC`?ko6jm0awhHu z2hFzPrJMLs!F?vHY*Sl1ks#oVcAm@`6UBHu_FNwDUI1#fTYnD)k4@ONV|Ql>WIpPM zqGj#{_SRQ?wV?f&n$*NQ*394WZ!hKOsYJ^t+MZ8l@X0Ng*;pP#_wA-+TZ!SSXcgX5 z8cxO?`z~dX6YKa97XHBLq}#C;o_>Nd*PzK z5u?qU72QBN#Z7hmiMM`CxQbc6P1ORVi6Z9wc)NK^wT!qod*f$nF;zycpLQLZ1S|MC zNgDiP@O|PS3uTFYKD{}v2M8h*JqJp0&+k8{7rJ5|Ho!c7*Kny6-zOdv znWa-Ih8xF}?Hx1=citUE-6!E{GMo>id5X8|u^%b?4BBXII{MoFw>vL@O2B-57v+E`jWxP9e zb?5-F{kC2{vrr;pBpF&fdUhE|PtqzTe!j=ZB@R0~;g8T^6KKu z@CS(cDWD@=vU(2XxEvP`RM}f9EejCVKuJpHYEs|+$`4&(pCMT3>zig}%3j{TcA zZ!|65j(#HVf6`m|EA|aFEp_6B|3pm3P7eZyjS01E*K=CcceBz_eJO{DPPQyZ+3Any z4MYVnEhWdzDgzU$>iG#vq`FDE7Bq7?YsGi8pC6Vw-nRp?2o`SgHRYUB6h^f4};GO z02ENNK1{%J9F}zeN*x}Al0;##q1Q3M+!6q*PZ+{5mhag}4jBWGKC&Cwfu?XMufRs@ zf{JOh`+DvgY%g}8QtjKOdrLcJflNii+`3;Q0OjFIrl#)zqX2SDDy1}L6RIby;O1)D zHGrVChIR#k3yoCJRyDS3WISk^MQmM1qhOX{sl%k14oEx=fg9x*7lWcEA%MDZz!`r5V-4p6L*&)ueap%B4HC$|JMav0HWtpo4;wB2Z z%96;_pQ%x+5Qvt_-XqV33g}Ha0f{r|EQ4i62!cM~wmL6DHyre0aP+@57`nN^lO`6@rI9*0gDJUu7 zO|zUGR%h@iVo^a=hUc%Z+d4e3m3m}bogAYd7dnVz5>k;JKSzKzF-IAE_c7C>kK;qY ze`c~mA808`HsT^8X*tFoGoqe~(Z=M!WeA{$X9v?)!=k~3bqe&7?*nJmaZGGU;64-t zd>#+X7k?>N?RL(gpC`Axs;Vl}Gx96Wt*YLllpwl{LPYMXUiCCLp5yzW{%WZqgrBku zRcg8GAA8ImPIl+(nB!dy`?!PHtURMj?Z?U4F{`ddx;sn`o&bHp$~OxOo@Kk;pIEV+ z8OlGW%%19sQ&-TN=8NMb%gV;Nx1rQ!A>w;$S17^S$aN{-Tp1TweLyg*FLNL}_jvj^ zdb&}<GZGvp8&T;Is!T``D&WdKnR!*E7ty=9oQ9hVWoGo1$Kvg8CH$NlhUGk|;H z=WZ=Pa{ih@Fgj2_K$B|t##GL2d4{@oqi4rM+wqR9*g?0UMcRzgAr_WN!$@4Dw6jg< zB%ac}jyp>|5n)*C%puH>Hqn=WK#7L+UA1nnd*yKCY!iI=`^oY58Vpw`f#C#d1T|bl z{?jV0y(b26KBd?WcXpGUCK?B(Ko5=PAm%la>2E9%6VM!ZYH4h8j-*1qIjOz95~6&~ z3JmlW5P*09B7$!(k7rwb_bHC%vg7G#G^~VMq=fWH#u}bVX1Kyq*#|WySdQc=PYwJ< zLrTlUV$~UY`U5BIx+$cnefY}8{_ye4QbHeCa}xE8{NqEj1nLdU4YjzG^V%IfJL*2L z#+!lLgrOq>_Kl()ou^3n?=zSfZj`_kFnOXb^N-gdCvv#tn?cr0o1uVtdVM@O^(%I}B@*x>3wJ#w9?p9Rn%} zuop9LOTi$?$WttMX-=q-Yy%-NLBcVts{nx*2iEX{dqF#ulT}QU#q{NZ{DE{z5HBdB zX$o*Wd(2eHcxG<(mBhrv6ewFSK}ZSdhZv6xeE5I|{;3JiE%B@{=U)7{iLL@_r+4bf zt(l4-8q!$I7o|#uucKC@qM|a83c00fKWobX|6KO#)p+Kn&kx0=MBbtM@J`gJAf4MW z(p@S}{UDB!(tE~w`iEdvi@$hZ3NrDbVs+}CzR_zjA!;5Tqz@jtjcn{>QBmA;?8{#e zpcCxE!))D|4xsSC&J_spP!WjHvE>k~zR`$9;Y82e&~=7Nnh6JSv(BFzq^Mi?)#xr& zMUw_Mizpl0-_Dj%OpPbNJrp6ONdu{K6MaprH0YPRr zbrTSgN!jp$i;c6GDLGJ-77*Op=Ng`E+mYw7?jn`=q3f7!S;WWJc-1?Y3a;FKxwV;> z9AHcW!T1)>ftWDw>Q@P3GTAbg=H_=?2!QT-|G_4Fh(6+npO8sHwww`_#TemLL~v#J z9c>mag{t(S;jyPw8=cwco|Rqs%f*0c3pg$$AWLkcZpgfR>TyDcpN*Sq$9?+IiPq0;*A4QgyEntUN7eG{ww1A7z!Gs*?_r)9djEVe2}>`L(+ zf*OkuhFt21CGBr`A`M$?m+Z&=Lt>lSp8tS~RodvqZj?42j@+cW<7@1W8vOeG2UzDP zN?CQFH18gWS0Y|JEJIe<%i?ylz1SMQ`2Vo>)=^chQQzn`kRu8zp%P*dDkv)57>IOt zDM)vB2r7ai3L?^obZxp*N~OC)kWDvR8tz<+<9Xk2jC=pN>n{0I0DmgB))USk_bFZ z1JXDK(ZXjxNaw+1Vpu@RcR|1)aRB?_d#ihgk(o&Uy9j{$|M=hkzmkBRRhJ`v;ShDc z0nL~Zxa6WF@(7t4)1hKTNXo%n2nT@BXWc_9vSV%|T@9x67%U~EqyU@`-^9oq4OOYG zp;@8@h!cEX-l-cWy3b^S$4dZ1Q3z%|8M8Vtq3k7Uq?Ux z_evQ4-tqqqV2Q}h%MwC&&OnQ7P-=jT5fBg%_+y_za)Bof!pq74DU=0^RC5@zw>*GL z$uQ?cR!~W{d;$6C=o3)=%QERSH4HD^1DHjmlKZWaJQ$V)x=;x;u`=kUu2|s{=^91t z!^S-s>D)HBYdkz#rTGQ0U*IjEC@*XIrvaQq>aNP=%_tC%>PXdjz-Zg;G9?IIVnH_ULN(=PzCZF% zPu8pDXxD)`r0W2{K(ZRZnb%WW{98ARe96-OV1<`l_6(9(I2toqUs1kdU~OIj55&=~ z1im4&@%h(j`F0vHQM+r`*(I;zKH+4t%PeNvKSqS@S>)JarER!AhB3q^gvLKeCh=*% zJ1kfv!L7QW!RY3c(%5N`pJC==?bgve*!a&odnfB{&Ha4w5@ma%nYs4#NL8kr^S|4b zBAl5gb-C{D3~Z`o;Q99=hi~oX*}7;>sD85Q6kUIMEplDMj5RG>mD=D(vYnL2Tn8;@ z(4_6KRm8Y`WI&Tk05ajok9Uk0miwE5Oprb?^6jLY^pLdun~A%1F3tAMpBpJ=`}D$? zZi(*~*}ZKVC3%b;RIpTlVW^ zKHZ$go|ZI8r8`>rcf!WkCL3?m@ea)MoKs)9U3V24kAU|*kIdWc*vibjUBU+roT~qc z@HU7gDnNz3&9D@#>RHoKvE$d1VPtLBaT{@|is;V~#$q^O&HT{Ys)($LW;!%=mM-iG@%K~0Ca*TV$VMm;Y$aycAW&;r^g2gA>zycn zeBjVHcMDe&WkKYPhVwn~A@urkxVOuu)+`SWx0({lbm~C6Qn+Y|r(@vOWp2rO(m*@r zJHqv3a<+W%gH+CR391M}^<1s}NHx|YC$7_0ec5({3t;*ouSp$!e7zk*Id7QdOgU@S zWZ#Dl)&(1u0zJlo@ZT3aOYfomHP*!0kbrJFcw{*ZuV25W;U5M!51EP!u#kGI5eBAsTAK>aH5gIUCUZEOgx;Rtz`T)lHQ!(%3^DQqDiM73niBff`w^I2g~ zmcP3Ewqs~lkziEL+8T#ID4!OtWqR3A)hwi5;P1U|O|d4yb%gHZ{?B$K$zxsfP}#8bD0^`U_WG@O=ksw7=zKK{!Kuz zasB%(>AW0K{*kq#(IT-eJ*>YbqcA6z@OA`7LRz=$nGl&Fw5D%SUr=vOxt#nH?N*%2d@Xag8lwVZ-G`}q3SrpQU{X;9+Ijno!zG%`g z_RmFWKFUi8EKTz=uGZyRO$LWkppDo|!fhtzR&5wS&M|u8_>z2*>anc>y)8FEWg)`` za3R}A=F6kKc6a~j^wfb?69Bel*H3D4tC!RW89g(3!E$!W$eaPks(cv77-X7rM@z;z z;vCg`+VP@#69#wIJJG3pqidg=;btpc z7S=k}1~s0qK&$d&9%` zSxbp9zX=iPEYliVv>CBH^zOm*9nGzqRi8qCYm3p^EwIVos(C?BRO|ShqmGI4Y*Nzu z%9D|NlJ{>5{d&Ilw&pN7*-L?YTvlTb`4P~D2O~XwpB^A*HS_}b|9sy1GjFoEI8vbv z%^%vJT3^Z~4e2nCOi>0~77JxZNVWqCVIZ%O1lcTS2*#9UOM@mgY*{auHT24#3+VXd zz1(koNlX1yU7wIu{ZpF>ZJi7OZJ6$>BYu>Lv1#w)7U1$G1IpN^G`}sy$#Gyts9X#>s|4pP`~mx{;HQk|IPB% z*Z6No=365$?}P7qK*^CHRH-?r6}f~O1(6}Migz|w@qiVXg<6eHz}Uy!)%!UGd6PCk zT6{@LO41mD#$htHldpR1uMHo2txi&7lp}dKVPwR9=|!Mi%RZ41>;C^$45qRo%o~3RG{zr+D zx`m+65&Ks1Xq4Z2V}vW_J+&*1{uOFRNoWo;*S@9!mh{VkUmJAkBQ86gW*}D9Ncp4B z=rjtuCWshrCOuKSMj2NAmPmH<`|02SqOA3%!BZOZgqN<45igFj6T+_F77SVx2pc^! zo<88BLA_oZx&D|1dRpE%M=^J{Bh+#6_io^Hme%7-zJi!xf6gg6;8e?3m#kXaYJv`p z-3dRcQ7~D^F~^&+Ti@YR&U-Fl(ja(BM$z}UeDG%(ML&UH3^O%l8U}|)jUSDWK;uWQ z++XZm+Q)gxX3cIFxLMhkSK2&Hm}B)VztJ|mtT_Q!+uHwu5zKN=?={X6##BPA3!W+G zD(9!PD3GAY8!$7XDUxU0Bbz>Aqfy^{`LXS((@ga*Qc{RXr3v0VJ-Le%cVe7oDwiMb zmdt);DrXh95gsj)*gieA=#m{|;B2a#r{HXAz~dGst0lpYA!pdR3)|>7ulT(AQrj^< z-T>o{=Y=^@yjQODYQw}A#T?s0&F&jy8OHJ7j-N){gi3Lk$OwblZt>UI%q|&?mFMW; zwpgtqS?f~InCO(=<|RI=eCY5sF{JRp^Gfu|L7<0E=7MPx1E-?r*qtQ^#{-+4AIp$k z>KAIedD-x1=n1qox|H+bR6l=xp6$TX^j@bLhNC@m0tsY4lDXYal6?zJ-5(6{v{&WP z_;C42`B=Gl|dc-zk_EX}&>uo@y$wSnsL=jJ5A#(f&4!rKG<1 z9xbZgZ2e`k{e_I9j&(WGclFaESL}=55vR^jk)1Wv<)t1EX8HnS9Tm!KKg0Nt72APB zF)>$>%_mBK@mnE%&}r@2mD~I(gVvV>>Ssf4o^ib^^Hyfp`oo{{P zpIyn6>iY)}ev=zW06RXJ=MIZ;VvymZyq(c!!(9L1J6ke&d|k78?PKAWfhVEx z07e^1{A1qhFj(i<95AT1p-y<_Rw;X!ff{s@mQOn$vd5801p`;oW zJDT9I@m6Q;9OnBG#ETUw@AKf3&FNn3d7HOAZ@u2ex=wqOf;LX6ODsjP%P=pA%FO4( z@ugM#Pqg7BCVOG6$jBpNPpe>L7-z;8qw9O!Km+TXd^<1YteHhq>PH3O7arHcp65_v z&w*TQ2kN>HT(VZ)X)=6Uoj7RmB#C79Bl)79rqE`xo9xx2NF=*9v@> zyXSk}RWoLrS==uw))VhNkJW>Pq$$Z|ovG3q(1K~g=)$A^T+%F=EF?Z_kTzi96D;~z zs=+>zDqeE2?em&vmRhbP&^mlpwB^LYKAG}=bPpcg+EvTn&7^skHn6?DmGrds`x;5K zKKbVxZGI<-mv9V6FxfD13!H=$-Qz#(zqcl*t{dWtcl%)u%D^9(#M=d}Ge8?6{d|? z=${Ww-d||DnEJ5ozLymF-b=`BgCLSRH12mn1Ycj_!lu_1Wjaa9R#fqd z;)>?+Dj0lu1Eyi64xnvUzpLqprNEgPl6f)I4LH$VZpeq(zV_c`4Bi@KoR8~Z#E=W1 zJNu4~F44HT?g)npTXn;nK$u;n%3U_0$l1CSn@E)b@7!l9G0FuL$m_o^t6mlj%h$)2 z$t_{?w!vkfZ^&2Fx!;wpMQN9!`-<3K$Q`YK8PFb+x8M>~mLsLCn2&*B34PQgy;^Y<*g@SR z-y2^X+nSD5i#Mz6Es`>-J$)M?EurT&%$NI`84Y>dn^g*=eAEyfS+X1j(kr1z$KTZyVN5R`N?eRcQ$nXEhCe)KK6K1?lReDCI% zWPOW~$)CQqr+3My&%_B3Hza$ETN|q-$GL{m9>)05BkmeGtI0Y|hr^rvSs!}d+(i|g z1T(K4x2`;`v@H&13!hKNkJDX8a@VWMyV=gdCg11?64Uxq z<2pNhLYCL@SwXGgo>?y5cW<1bdCxL{k~`OGsaM6V5k1{fkimNW44~)usXxa(;fDUQ z9}hn!I()#Ho>%mn7IZVB;^2x^tsjuARIv8_Yq&-8t~H; zdn`~ko=ABsSAM>oqi$A?Md{w2#F;}FE>?5{57m>&1F4MM8vHbIIyU~BH(RDEfo6AF z?9501r7aFrbXe<8{vz8l-ct%cKM3s7!I9VS!o+e_tT@idsrJ!P7dMC$zobTZkSMFG zBw!||NPVq(?1G;-kK5fXm~w*<_8+>kBqbwU1hO>G!>)OwK&E&Pn*zg`3YU+#@2*-^ z;#46$F}PscdoM|$>)?+7NdgS{A%qXFJp;p$-Kf^wO~4@)XCd&}i2BuOBv9%3?H4q% z2bRlmQqP?b+A+ePyiA1oegeISF8`um&$-q7nedkbx%-ZC2|;b)zaC&s6-DhDK79`{ zLys{cwE9fj36d0cX^Lv)<>)G@cl`dr2pu91`K*H_-!bggH{*BQI?W1_Xr?ErG{a0?*q`aq z-9>bn+6qpcUq+A6P=Iw>pu)N$Nw~Z9Dc)CJBR#1BXXWX_o8p; z%nOw9>VCzU@>#06K?~noW@=S+j!2i?1|T9top`!Nd~~lX-p%Kn9+~}PK*grjmmO$c zn0e#hn;Z%O6?rjau63K0(`a{iJh4j|I)_Wnr}lk$Cu)6I5pGu$bZ^%)D2PFR|K7=S!2Y(Dh15vQ;#o6ZGm_0q9@@k(mW{Ijn*ehvR?UEwxX2LB{Qco7V<3Jx zt!c}$o55v76FR{k<4Au(=FP4f`Vs9CLsZcRi^*-^~3ZyS8TGH;RiMumk^24*X?v*maIO8x`@q zA1|{ct-!wK$P*Kr%ZgSVa^KAJg(N9-Z-tcw?$(pd?|AKV8sv-x%JI*9^WL$0P?A!d z?8YwaLxB75b|9mi-TIw(vrQ9eC`pl-^|s21VZ~{x!o2rSf_p@ah>YeA2v<0-WBs>G zhd*^NB(T6#;2lMa3(7fR=a*mdtr0C*jg1E!+-D|`gqEi_#3IN*hYd+{#kX1SD zp^x4tX26E+9#%>-(c-MByx1$zW3!+9TU&3jcWrxFudL#K6aY(JDp+oy$6&Z22%KPX z&b%j8zQ?$w?O{OiXBdlcXO}%a^%~uK-|2(<@sdWT73}rL1Q=NGm=*sMbiNd;ZiY|N zepHUET=+D)TbFJ&eerE@n4S>qHtyKt3MhVk$z#Y~){eL3k}^8AL+ICe3f$Me@U=f{ z#5`f|$7Gecc@(N$I+gYxO^Cub{Mg)oz`{;I87C_kn5#ibCKJtHY3-1)ZsleaL!0|^ zLxhgx6MyQLwVfF+)2$6(7V;#2_;5}~m|TY0Hccg??2_}%o{DuMjf+6qIQIbvrKibb&znKV3zn#%QPV z%gx(%;lmr|k$?1pH7FQ3t~_4X>{Q`o=whFRmK8U6sDRo&>io#B-m1VV8ST}1(~s31 zZP)U(vmL3GRA9YPlfQ%936m(WX`^3vi+JDOKcD9Nb*o#THy!Ma;`SnEnAqNjh#kTV zdwW1T=M8Vo(E7CzY>(mA*aZ)62ff)D=cRUiV-5DfPlM>4GE__P@1wSL;?G*}Yh0oI zy_o7AIWwDa%b%R$XUc61X7PbHNV1Xj7{|p#V&}zre;y?ng)xmuhC9x`SM-hLd4K-9 zBm;rX4(OB*@6zUWj$Ic2{mH<}5lp2y8^|23reBsbtMR<}m7df*Cf0(QqOYp^joB&;Y_(T?9r_(WGasj<2{@(fJR zAIcp#mRVK?4*fGfZn(IqG6W?SfybT(_| znfQG!n5+Ib#FnIpiJ^_l%luuLcTu~p@wl}9)sT37+M?1>XS&RTfgz55olS)7wp-Te zeL;#>nvttCkVBBUJaLYPZ7amzdA!7q{u3E~9p6^jOw9KCTOBhyqK7fGW0h@uWouo= zlIpo-5BYeQWdgLC1-vnsLce3s0p(k0#ju?Oqh9JK9`cpJT);1Cwa&6UrP{ya52!}QJ*7f*i-$b*4 zo+jm5KSFKPV{5S1<79$vF6Ff!CuIMJpEq4?o?hycGr5rxs2bTjA;LMJlmD; z+5xs^3F(Mb-t}>6mZ#@lbWcTV=xL25gMX30OobJ z?Uzd*vdMi|sy+~g)ACll`dJpWcB3k%ZH<8wofqB&^F+u$sXemm#t!FKyu0&?h-4{; zT?B-{6@;(?te7hvU)9I9eb(j(?)8aPQh2>*<7Q%K^z$uB@Jr;pr!IB`b%ad&uy2di z1kH-f?wcE?zsmZRKXD8rB#iFy8Jq=QOugWbR@@P85G1&@0DCiYbEQ+r%UF7l_{htj zHSgMY%Tp6sB}#XMpeAiaY*CTk3l?=yLNW=C>=c;M?riH5jj>iMUg;ivK4TNq{nv`_ z<(d^8uxyT#M?UQ~-GdVx7nsioRv4x2b(94gE0g3Q`(An+$i;IYtVAChwPhP>ua8r^ zqf0tHx=KBx9m1w}5#5oc)50W)b$i*O)dpBu^XJum%0z#9GEK&8A8f0wEdSC3;{A9I z*^mO)p3w?}jLux)0r8q}<$zWXmt5KKep%#ok=JRea}&Y0){Eyk&i*1@&j587F! zP@OSF&c=}my~TjZ(wBfw#`V8+m6$7AL>}BSjV-xl7$@YRt&Wa2z(i0bb|j0!pmV_- zrD>IWA1DGq(%1~CP5H*JD_vv`Ei?pf;-er!5_)cm)czD6{YEpPOH*s=(4WKX4i zYJgG-=G+{2V5&%>-c)@aoK8tWYW!2>0)>f8p#f{)RD@8>pG-7g-RkM2SIc2+@Y~$n zn%a94KDBUy>c>cc12M`*iJybB#HE{55~{fgl$e2=_os)|G}RRg*kN}qX^`Db5AUql z?|9_@x$F^vFyk;s?==ny>~Qa=GF0L2>s=hHSL0Rbag3C zpN;`&)KqrEtqy>5G|)L#RX zo^gM$F}T=i&5N|D>V-Wj@IIi!F}~Y-0*9LcbaW?#$rZmoBDPx? zR0XJQH&nnhU1@y{XsD%+8Tft^?6mg&R>)>tJk*w^$S1x7BufG`8)$O9w+$GaPH3y+ z_-M1Pwl=gLk^&KJb^s)}@Uyw+YxFb2KR@~D$ATt!nv=c8w>Y`!GI1OVoY6aR@$tb`4u>%m${(tel?||6 zO#h9vgpq+cy)xuS8GFqqrOSsHLf1Yvm@Z?<BHMZBIm0y%y#cw&x{Pg*Agmj$kaU zZIl+;DBzwi!%5V#oE7ime`rn`%S^sn(PIITwnT_Lc$lgB?T&k-xvNLHz5XW+N?htT zZ>mj@3MywZRznB}itSRJp!(H5dGgJ;dcG<5bRNL(+4URG4?%TU@zS>wc7S;ai>`3n zwub^^=|~>Cb}02GKY!lwQ^g5PlFACvo3-|k)Zn`oHolGp`Rq-*$F6!EF+~(?Aso)oKKDpZGXcFd#3j;@of_Y&a1ad+6*aywT6V}!fI#5DUc1DOFZ z3&o0Z(AlDK!pslp*ELvE4!N;g7Brg%(98ICNaG<-Kg03zuf^-$wQ5&^ z)_L_yzN_ms8juN-T@J^L`7pzV3o~WTQ{(YGpCc`5K`XR{mklIq4*f(Z6jSQx+m>Ut zvGl9>cc6HqHp^nQtG$#BZ~?m1MZDh5*;&x~?`#&bTmif8mDc z)=vuj1x!Tw&V3)Ph&;Jnc{y=5LaedogPes~2^+9hi7LO2+&d4gWod1!t;P7GBO}dd zuU@$V1Pr~p?m_k}vj|%YRL!hCGG5jJO>#`2;9U%ov(WsYkunJe9n^|q3v_CULi8gK zdnW$8d9580qCUw3Vl~^gX^D(j(<%nAt}Dl9d5^HuVl1+lRzEL9MP$^fpHk0)z&yW= zWQ4hx&I%)N*GrKK?@TDh#kKW{#In^cLrG}lni`b0umvEEgZ=7t0F&N5C#U#ka$tz5P&?u&cUVf<^$^}Ewdq(%tB zUlgz5LARlMFJD4pB0|l>&|51=E48qfjU$Ie(rO2$9VAAwU=-*^u*16M3LbO?t$umw zWbp;&Mp!l3%jl`_or0b1tp{s;{$jVu#$28?k93WNMA}ymhc~+D>%14?{D&7>ADp$X zOIU)|X|mAbQ9Oe4)4Mww&_bTEObHNGO}c7oYRQxG^72i(yIc5{`#8*K5!icWJTB>v znl-giO@j-B!P>ODI9m+n2^kP@Co*cG@2VL0DcOn&j z)aTD9U4HQD^TiRUjqZ*je|6^SqtDc>FfT}>0uuQqXtNOiUG>oIR5)b z5Yp1y{#S-NV@BPg2K5qya^=JYrJcaD zitMGz>NXZmV6bI5?WprZzYgeQtHBRigNT^83!3;e9?phgrZ`#(C_&(hpFJPW4AMjq z=>!a!eJo(tO`ug&f2mUr()%BQGCQyQU~JggK7?^#rjd6ailZLcPJd{#l)}N@=s$yO zf@jXlEfUO6E%SRHXd^#;sTb7LXD4)XixVl~=-4ixZ%0(O_y(81GL%9=BbUZ;XqY}H zMSzjI!B`;^;Fc}H8nTe91H-hzHs0m!;?#BpqiDg~`RmRyE8(bUKJE9m3qfa%x-zaR zF*ESVSge^R+ECZ_hJlxIZN#~HJOrAnee^dYw6Wc;34E!WCw9+y7WYRi?V_0p7<=&P z*(r9fSO^WwKrN>)T6#HkskD@)$X{c2+Q9JHskNXcnOgYq#&+t|OWpccifg#0d2%w^ zX9qX%ls90ec)lF!1_ipNassgE~yQ(H{d?& z`F^u%bs}EdplF(q{)I;1Y4b)#ry2)PZ;>D_*7xRzSg4=^U0{k$O_5#|wCZ*u?C^09y>y^2Qd+A*5 zMPgrw@rN~NWGK@eyD~2jXj0qXRjM=(2Vff*)?qf4FvjkhUg6qvU^nP zv-(W9#o4b0SpSan$<7>PZKLkswRxVqVapJ zbbc~JI&qdOt#`B;p`e?MWgv#&6IBI*IM||RYW^aJN*1^&#EX;`5CRaU>aE_^HM0_mH!usVq<|qwe*FA@6u9_7gE;DaLi_-0x(X;9st!(H{t3|W z4RC-$1h!m^BZEoq5T{Z~Gq+8u`y;L}o^s~Dz55v{!e;22f_Q#ewl6;0NdQm zr`Nr!$ruC`eQ|-a(qnSFVH2W1E+%jfHDDZ#@BY40i{56u%^}T2| zT?KsPN5g|!=Qyy&s4&q%qyR-_&8GTVqXDCS@G+R07aX-b)1ChbeFTJVWMqD~jz=?h zFisDos{oHu^$3dIG*&=)RK^Nn#8c6VTNM`F2d4~pwK4~FuVyoH-M^>!Y~n14s_8AK zA$O+kT(v^EEyh%Rh1coefOz`7M3^xNOd-S%?>M)3?M^D2o={+zvVjtyEWxY}z3oOyK}q@qUH{k@SU)XK>RN{A^p@VPuWEc#ity*ADyk2SH;{xA zMmpQw;?veU$*ykXiBLs^LI9UE0Wcly&T(UbY=I+loHso%Fb~q{l8^H0tfT*;OHbq(mM>6b0e{FP%;fKV|$xECmT6 zW$_lXb?S!veQ?u;Q#gLEk;0lMAOPYl_$_}kJK`$WY0#2^=Az!Lge!kEZZFj|w= zZh-ZbAF-Joh@?VZypljFQ6Af@n#ftMZcuyW!C|&yi`c*!kpt$TMPx^lq;{$1DUXOR zSA06c6u0Um&ARd^av=7K$Q>>kDI_i^8BtN|*X2jlwsA-lqOPbe_jEvZ-Q0AJC46o0 z98#s~*+1%8`F62v8QOt2AQFwTz7)Fp z^Wwe&yM5M@s+oB;fvk0D?MUzlG0hRN_hrr%FD=>?Xy%iyg2Z~)yN?1+tepmf$s}yK z58;E~6lbTalEv9g`h4jP!jw+*yf}10f&XsrU%u;$3{4%VV{R=j6G`t?G&44Qh;<~` z-0njcfCPmrC7(@F%ruMAheQ}DuWtmh%6?(yM)h9QGsM3zYrGFZ)keP|XDQ9y?S3W5 zFl3>64Uzc_;>~N~qFwER%-kLEMrp%Ed1d#HY|5jmC+gYeI|=-S5*fnmRY;zk<7}(A zWpw4@MZ}7(bT6GbiQyOky{Rt3(%R3@_$i`@p*-Q$`dejC!2j)D{$|WpfViTP))xvF zT-c|Xk>uy;PO!5(XMk=pKV)?_-u#L)S$$5!)SalykEljhRcmzX_M+<$I<;DgkA3kC9>~Jw@1?Zzm_m0 zGF7xbedkZ{bhs$K?3TG~6MFHU7cfijlc6Z)9PUC+rWQXWM7~fSlZ7q~4xN)pH)LVo zGK=Vflno8(XkwciSr-J@QAzX>2?6(Js=9&AYqxYNyi3qUQXH=NsoG^R?sC z%=&Q56|Wuk439w4SrL>KDo0{r%@c-ToX&8Xf62BEuu!r{quW}+n{_fJzxylZ*p>^L zoVRi)y18z!36T-n%FOnE$j5;UvAhe19TU+wVCkB(6|plO-n9+Et}g0W70`E_@G-3#MO@(xoB$Yf^$V?+KP2Hg)xDGALqmXP^;o%xwZ*W*31xzB*~tt3NKWKQYD+Hgn#z3wwX6;K#kl6TP> z7;^9ho8M>2rxD%aj@M$cm#Q}OUkHC@ei=U!ebl-fXyw>hI#L=cNv$hcRWHh zAHyjgk*qVO2NCS3ZW@~Rfhn8Rr_{1c#!P0qw3b)aSMv}V0S*dKd&E`a^Cwh$A6a$`_XbF2cJKg{(qO0c zZEo{0xBeAvKYren*|$m-8?gTFr#VvoX|Lz1mV3DXDmyLnkuC2qB@x)(B=X-kdqeRi z)v1*GJcStYR**ya@nP^Vp1MS48VMCd9nZsiaOR>xjT>}`V7uZ|o%}SKr6+C^NNHB> zLl|48_mu}n{0ejYl#cn;Wg*>0v0Sfx)jGO_7ZDKULAEVsF>`V=`gpr)^2 zCnYWRzEs=t;V0a7NI9UR3)&@svUe~i*>k~(q1rePOCA65(7zXT&@?eAR9GdeWzSps z>IwU3Hf6`cy|xjit1u!dzikpMhcnC9F`Ip8w1fFXabOe$MJgxljh{6JnW!|^Z8MWS zc?pEK8Y1XE;zmJ^bwk=lHH#zfY7_3sLrc&9^a^O@z3a(hL$0vLyhRyNCHWLc6niue zL`-EgQ2VaF|M0%iR|*rw)IbK3shIDl|DM3RiE~Cz#KO*+^BC5eLqz5r)`VVL8&zs9 z2+awy5&C$PC@t<}R{7f#hGef%B{lv7Kub5MyJ;mpH~v1J<=xYs#)g1T^{`76C%hlRzCDT?b$;u zXpytGnA+7{Xw_vYNjrEC6?X@=PuLY7`Telshc-&{c{)h0?iYGii0Z6Nb^BN>+2UIM zjj9JrnsVKn=h~}(pGXdjQ!ntN^&DkMT5tT#)M?N#KSu&WDq%T$uMI>nrQMU_4l`NO ztZ7#1Nv9=gUEifB5#=K~2E>6$gj6E;rcHnBywcoGLKc;i4KD#o!5b%{E+1MF8_DQ3 zyTvklXj6z-@zEY_*~iE5Dy~TOa?(2fb%5x_!4qno)wVp^;A~PT=rb}{5b&} zaPD{Dn!Pym!&vU-H`ElsU~W0@Lp~mI%O-=dkLdjuqtliyd8n_#!B{!~9Xk5c50ID$ ziKSSdc*jZj;K=UBc0pAn;{UI8D_j>CyT56j$8Jteo#1;Uh=P*piHR00E2?sw`o;fO zLZ{bCt!DrtMMzgecEeVod-v}7`TEMj*YPkVV=--#BxWaV(CMLD9kYzR`v-0kh~j-N z7ZpoV?n9Ri!Er~7R4wH2@M~`;x(mT)L_|91d{mc>UU!YClWL~D!O5u%gHXP}@B=LL z-A{s&4gKW;n9m8pwXkIw-HeyM;=B~tjH78QYtG(Orz(>- zuIMHr865W!B`x-=-t72y?^4Jv7^4Sfps0$ZNiJ=}2+JpDU)RmGAkEofw2{6mRKz$> zeer2jO+Yd@*ELNb2#T~qr#N%w3q<~A2W?vNnz?C6;vq8Iw|*#Q&XoIoy80uQ%rI|I z9L_loE-06qzlJsZXjCl{Iptbd1EjThK7uXj2+G42PJ~b0-OHivT1sQ1L{6p&6rcO9 zLf_V(-@nViq;2R{D&<^;%-o33DvOvYuM)^wcgClFd<}Y-mIln*+6i1D+Us*0_YDWK8 zZ<6fS3i5ZybSWvT8$(&8oztP4$~5%5zY~&jz2Vy#q0PSVV3Ts2%tCHM$7C0igZJ`- z_YxExa+LvVLA9w05ImCSTc0DG>sfH~POx-`!WuTVu=%VcS zp$*;!hiAr>g$k%0e)*6);&-Q;h@cGMS-;YI>=y+7n{=Uzh&aDC%?K1=i(>Pj7b>j8 zq78ar+qAq9L}n4O!Pq>cL#@GO8;ss{2w?8YpEbrWFf;x6KGDQBT6zp6T~Cik{)91X z)aw@%N3u=?2P+%CLvs7zGyb)sJw;aGnb1?UN!QSDI$Xx*<;&3ev@|kFuL`8I-zs#! zFwF6cfZ#t3C9LcV(Vl9J3~CQ9 z$7fQM%z8a4p8QsKu$G{&bw_0S%U25gRg(~U?;Yt8SsZNFilnOVMfl&dezlkXpB4t) zX-e;`wMM9;PJ|M*@P;OMR|UvSD9FhL7M-hu45gVv3TE#S&FUTkd zXbm@Zmx8d6*-Ki+v^d=6)h_VAUWDCY;vcuOdSi^PLPpkcj`tsb)tFb%5ks!Uz~JJD z3-gsLV;`}4G%kPUHbYinaFVjIaf&-M{*rHQ`RqIMGff_o*a9)F>6T9>N_DrG>=nbQ zoXgdjVB`bp`T~50oz_za;0j$9gZIsP9dxueJ+!G4XZs`jdELPZka<3GPo=D7-yScI!O+!!o2OzZSQb7Nix(B@5N*kH+L)VzCr^+ER8Y zrcz-uI}54*-mr5J-N*?MozGONVEoE#WInO`%$zBpphOgjXF%Cs}^(Cgh0#!ukzNY7TAX6|~|9RvxW zpb!VGZDoJ__%X3D1R`Ozdyz9F(uyk}K=-N_o6!?O#dLC0RJd~;>2&mTaNhPIrrK>_ zFo_tvqy|nV79$uC*+T*v;t$_es1i)WzFS~rST_o-n1&R_K(`XZ`TKV?Xo`=%AQd?p2N!yv=wpL zn6GkMBCJAqe;ZdLS82u0WMn(?kbrQb?|?og$Jx5Ago=FMykQN1-U6At6qVzMYKvOj zOL)LdfaBt-R1PAvleu4Jh~D{Eh&b3QNI1Sn zCA`9hb2Vg4|EY8_75w-W0wcL$-iO=;kf6`M*5`9~l^o?$QkpvKgeDJs|Kmc2;vbtt#TFPJVHrKG>-VnFZ2O^IVm2zuCPX-bRrTL#< zP}hHy9SmPflRl@HRN%__Z|9`HpJ{UChBrP){f3e`@I ztl<6(Hg5}>wIX?saT{vIw%6dg;^Pr}91O6y%z#tKx1eM$NcaJ*&^7*1L& zR4@~?ZS*T^XVseI^gq}UjOVPs zm}cU?J{6B{!#`iq7#_{`)?-A_)NHk(4B=S;@h+hvYo18wy*I&OusIa7!x^7w7yAeQ zKAQKFcHHxE`8+F+e2Q)9_3q+aJ$95}D+Rya`zJ=je^W0A@mcHY+`W;UV|g99JaWdK zBIzVHBR+2cA@`K&&uF})oxHW?!G%A-fs`>X|K6K?PCq@n4)BvRk?Q?Sl>vgn$d#i; zsnt6!IHD%4qI$of!7mvLWl0A?$I;HE6h2SutS94#Y(}bdAY^7JQStC4!R7!Wu7D5h z7WZ>y$=nBY{8`Mk6k9Z@A-SjI1A1raD0nF!i|{TvY>*sSc|(Ik0*B*-bg9MH6D1|xA*X1J# z}T>wOtfO+*QDkCipol1WZAyp-Y$ejWK8hBQ_1?GoYxtgZh|UsYfI}6tWE%}ao)o~_u<(}45wcap@Y{2QPDftw+SQi5| zXYn+$0?x2Usq28!3|<|(?3pt&4yJ)!vzkeS3O4)EnlY2MHsn{4Fc_w=oTrg2`c?;; zCa8*}PSR#Pc%m)xM0UjDI~!0-RrktBA^JZpguEoSNj1^vO}_egYJX=dnQ|twvqHka zah0lBItB>~VY!cAk|c7!JASHtWA8@_%~`|0J1Z$Ul+sh7Zr~q8{m{g*#Swj8(j^cy zT)N<-$U|i9XEV#)dcP;(b$XKb6o&=RUdzp^%RofM-MI)LN2`;%1toC zY5(Pq{zRxQq>>@m?UI*#LGSNVZ;pIOhD=ZD)Oy!bovU7mH!S-ljRY(*nGe9~mi>UI z0yF^|l%99`m04;sY4(<;pi&p&>QJ`-ZYweF#w1(}l_Zc$Ax*7@-BS=kcIhlc9()9w zTfHQK(aX-UD=q#m{!gs_ig3kK7szby=A898l+cZC(%CbE~8yWQk> zG?G`hIXIJB1u>5cq*daGx3klG#fQ{qz4Q-ODhq5nnk~9oFjaPuQ*C^es(xxe!{REM zF8}X1uXfzcg`j5F(}jvcUuXKREL8q~ya_T%W2HK*%o(>z(On4P&@vkn7lN(TNwB$q zI-4LoES^Tv0WdDw!Z_b_Zs5_Fxn(~APx*Z7$umQ&+?s&Y<<3yL{&;+g@%k06%D;gK z$Q_3Rs>i~gmpvmkB*UU<<~H0=%M(aXK6~7qN{F5X5GxwJNZH^!yk4b)6tMg8yO>4XGS+JPD?|rEf)gPU_w(mX zw)(0=<&f9@A4;2e4NLb}+d0RCADSuab{usg?o8Bm`+GbKD()YSNj~oQ^Ps*lE30c1 zVN}=11Dzzq4I+5d!hiQ)5gd}2mH=q6rzTR!CS!U4V83fJLwa+_zSoc6PVa;!t@04M zq$j7~e8lkcknVEt7Bd-4u#eL!NlxE+OAIg%_|Q(oW0j_7DN53p%_cipm0!beJslu3 zGsOTC;pT+cSD#T!~U)1$Sw6h~U z^2A>EYa#%DK)}urWLoI~?Tmn?|6bgrnWNm|3fj2^xaL=Av#a)EA$LnCh0!vQH^$h>3{HjWGwF~LCT0!Uk$|Z9L{R{vG+~D2cn7RG*xjUp(oas=e1G>V9LT*;N zMI|G;XTCtkLA&)aRGs{%)w5TLnsB2FSN{@?OJ17l50qF(89-FDmI#`60+cUpxy#IW z>Q_Tp-+>sdw0kK`GPDi!GH6%hw_BqAKO&&A3`9if;5kaPfF<4Pe%VhXtLN%}Jpnlz zOn}-2ToH@X%r*c}5DbSc1jr9ExeGqWE|1opHRFzhY_75Ytm5&mBmrMEM{*E&oUPj{ zRz1*<0jUX$8HNX>Vkr%hg(VN<&N1o7^6p=%X+&NfHPiqKhaB$T5MGY*LfeTPCAD$D zpF7Xj*`q?jQx1egX)9zIY^t{a6oKPvZBbqv`4j`$-T$&}evzSIHq|1TaTDQ}hPESg zFOQ0E1eL)S3tl{bx658qeR;ictF zA0D_7mHx=1-)24CMZ&)0E-c1r3-iVUf48@OT`Vh@p05CjRyaQmgAS65ufXf|y5v2fy0oUNM z5Ps;(JV?p4f;dCUG`b5d>YY&w8WCufHpoF%h7GCW%Dvx>aHe4?Dbr-#$o{(uY-@I? z0WmQxHCdWYRX8Hi!*F48q*`~{v%h?1#{XWo+i_Ev#72Xj zSw?q@Az9AS6SaaWt6|AEt^o)IDddQ~>66wgubkgXe=`;J2oYT@Vts<`iE9dp{$%*L zkf3(i2;;r~v?eXcGMRh+fD+JW?lbatVeDzET&4QgayTG)knFSTw0Sy+t#}+Cdtf<4 z0Wd@6klTkW`Dz1fUfm=|O6+8DUSL30-8F>bE59oFY_2y3qnH%?5n+Ej%@VZ(rPl%_ z5=l>>nk>#o07#y??!|PZIw^VOD*(*Gc343zPnz#)feLCl90_5|V`qflI|}d|6C%hS zU%EpamHF7|Wj$wFz{%sSpWQ{F8mRi9{D#e@XRgaeJ1w_v82}BXz<_S3Ify~b9GQEF zMOXPzVO}uI;N7m98;YL%Uyeq4Uu3*XBJ-zeCsz&DyDAX>y#{gM1B3k#vd5l~cUbqx zR7rI(P|e!6wMc*GpYoqkS69=5pvWG|JotZ_`^u;)*RI`VVgV`}kwv+6BPfcSk`4<4 zB$Q48C8eYmjf$Wkpj!~65eWh5ZbiBqX+=VM(ao9nv%q(M-#5-Uf6f@^tTA}Eud?cS z?t9*IUh}%Hi6}sex9_w*zlT){VtXKAkHu-MJn}14EojI0HAY2Xr!M+dP-CDQzv{|u zad7`rAD=n#DQiRJw12QW8@>|~Q9|~agPDeN7lIOq51?t0QXTYrzd(%har`G@?vwi` z?3S0Fk0agkvy6CG7qV5Wbtm%GhdDgn@ya)odRpF+kD-&Icvk_j$IdbHehklKh??TA zdZT;T;S(DGv5(Pky*Dz~qUj{{Mxl;l>+=)P?u~5_bKb6}Mvz8tG&rMesasqgYw;Zi zRyMq<9xeIh;$27zQA~tGQ@Bve(aa?Yx*7jE%%jjP7uERBE$=U{z6j4{r4wr2EhGs| zY8y$F59i4qC3M6WbjCp$_dD3HM$n@=D`EbSY{U+H^M#wQBX*Edr2`3;MIY0*q(mxmS#v=tvoOnV1J=?9W_X1FCJ+9#t~yo!9J_yqMr@xWak9+p@t! z_>nD_dgE5*jng|g&yZi)n12}&#@4UT)|$J7`Scd>TmmT<28iRRgUfmMSy)(DHD05^ z@K?ZVTfw`x_2XSMukJ38dDG1Y%9;xoCbIex^Yio50xy`I?p-yA6wyfU!D~V{7zi1! zvFwTTb~m96ToqADXw6GY=JGIzz--qLpy)sWt2l&meJ^o0 z0;kv^$>wGb&;+c5=`~)6Fs)oq%g3O3q3@G_k%qAs5`9IDsitXxbQE{?(5N8c&89=n zEg&@+c}a>C%Q1DQpK*x7bXdM&t(6 z?;NOBQE1lj=glJRXqx}3=cpOf-ADjG`XO^b!`RgqVKN@?VgX@wBCB2{vG`Xf@P(kN zwMq!DpufI*)#zc-)!%2GS({AXXVtY-lOjM0zTnGS6F#(?pfUz40K}GwBYVHYfYMqW zu#`rSY<)!q-=Gn=hx-q4UX&JD{(z>ZzUAB_B`xI?0jXPS=&AC>Tg2JetCaXS`2$Umu^u3w%fbL70xf(cpz^m0fjxa*%WNqT+%SWi=2O$s z(0HtPn9XB1o>E??9qlqkPZ5)oFfzk%J)xs0$?NX$=@e3U^o9PtoF!S>Y;CI+Nb73N zVIPSv-n;{PS!@@&(9DdSF@)>H;{(GAqe3ZRpt_H;vT}@NLBtjJz|x`a91H5>1l?+0 zkSY1$`(Vy%&V33q&dpV8uR`UNa{S_yoMu+&-TJm*SQR+agsDR<1U@R;XV-Eqew;Ms zf2C8Zk(csGrQJInD+~x1e4NO>fAf9&Hk!AWa0PsG8~4zt-Lnyxe#8W0&k=E>jOL{P!L^h+Ue&pBoyqF>55(J85}+KTaYa0Fg>JuMJ6+16C@bL^U`m#8{^ z^5lkXQRbH~m&qB0lw@UP?FRi6>u#BV!m7h;O%5~`rN43L2VA~?5nLW*c~a4!Ha~Sj3~TvEe%8l zi~+e{@$K;l*y9iJMx-L72wL$=MPmLBsslUrNN+r>@7rcnrHA zFIXN{(k^igTzjDv_NgIhj8Z9ujNRb9(ST7WhWf~a#!kt|{`^T}2mt5=|37-(Uh6;m za+oi9#WVKlA2F?ZfEA;p|!>iP2RDm&s0C9G#=02 z{Bb&S2vx5gk5?!{>!8E&n9^UrwCItt_DTG8v`;W5MzJyE-Brq<#xMy>zJWD*#S4}|(9n`^$Gm;C>qDCqL*YHVFEoJC zAr_U1j6X?bBF6FaopqasL|aZGlOa?4N|ND6&p^erEq=rpr^Ny8znSITEe_O}15vVf z>P>8?-xe~nbe((O<41FyHe=jCK;$Wmx}rWmCD%s^K)6IU)<+|Wm7eJcue677c z^HIS)O{*bbqU&^o2pfL&BF^wc2cUEiCsbu*(Pr3|s@}E)^ryJKoO$C~Rlv@mp_n+g(-M0)Y7patX4u8ARt2adzOfI&}tE4syBR`b|NYHhK-cU z2V*jycRD(pzl?Y@?Pjk;q5=4*@a?ew_4nqG_{GS;tEdDEg2mlCnA+Asi6- zCQ73P<$=5l`X3ngXOJ#5>p{3w(41aRMv^%rwW3>)nekkDys@T&ssNXmcS~KmQ8ir; zy^{3h)hRXGYOw9-l6skC7aw^Zr;hF$tkDmPB-~4b%*am9TGraLU_JfQ@ zLL>QmLHpRg9cHT8&NYRAYOL$C&QM4Bb-Rh^Ohb9LCTs_}a00%-ca15^q#20D>18y$ zNpUQR|AOU0q=bpTIH88WUka@jHUDxD*&~1^JRd4Usn19(_K~(a0ny{^3Ty%_Gb=}Vlv4*89S%g{sI{CS3G_54>@4-t?j4OLSXoKUK;hrIdD)hw;`(^t08FQKh(VH0^o~HBAR%Nn4;w z>oMtk@`Vng*cnJ&88-+YK|GeJEv?q5rkI|n^?M5p{SoVY>pxh7v+Db0ip%Jo>EQ0% z;s93!01c|~i&BZzTNVG~mo|rbInLasTYR9JvN`|oKT)p(meVu~QE|bnBP-w(}Fz{pCs6 zxj_hg4RmKLTb!RT{8i{lN2;m&DMsvYMXE)-5~vWp><&|_T*=@+p)k0CIv9FS7CkY?7xeA}z$Z%Al zV76qhkps}=Q49NG0vY;3lmkG){mN3EZq|H>ZJ{CRUpEGE9tlyJ;Iah5N7VsPZO^sU zPW+rIPr{lEgg_9}BuUdic;rn_`t&CaPbTSH4rwHR+^(F*B4KlT>&A%hM!V(OZZeFp z1k3AMGmU9`yhWSJfCXM0D|@j!Gfk;~_)GkFbrse(s$%0N^^JIMX1u>lD(Qh!t#h-n zJozl9k~9m>6f$`16y!D2%KtCINr_82X)3-P2Bh$&QRVY!f_jA z3-k=fVtt(|0ghEl3}_#DcydNx%~xHG_L|v6q7{PWs8H z!u||W%6$smSOPSGRt%R-j2r+n=AK4uGVD^Bse?9jx$bij9*j%#XD$OdK4<{xT9|=f%TNbw8eIU-wj(+aU{$hMB+#b0)JSaA zigVp#h~ROz-J}K6pQ3;G{pB-|dV;zETvVS^+pomE&Hb>|Du>Re35aXw>HOnP<&-PB z?+2o4Y~hRFTu+zlk#Lof+InPeaK(H=gzX~S8@$#vHGxdBS}rs^LHXj47`C5MQVy}9R-jqxgX3L#S(K0 z^7ka5m30^QzV9iSOjWX*zH$$SY2FPqh>3cQ`F{Y#Ea?P+r-4*wz6G&+Z?>8h!1?fm z)U?O(b-{a{VyLTB1ipQzzaOBq4vupR=O>VE?ROl7j0QO=yzf(V|7e&%!F57;8w{*QPh~&rbms2c?q~g>_y)`WT~Z+=Vof*$ASN1hbN#DL z3a(zJRsol&n$pcRfs#=~*Es?la#CVX*adHTT*vv_2U$_m)H3nLB>)nt%gf8R8<89f zc!&NHW;oDc!%jCvkG_`aBK?~w92|__<3RwqmH??3=z{E4-!2@O9ykUR2vFb5eo63K zu`7kparX{GkG-r8DXi!3@1f2vNUtTE2i+v<@OXKR-i40szxFGBeOBCS)hdmrFj+Z4 zBRi2AYpmf=-?O{Z&GCAz@R^wd3`5x{Go-w6r}ft7-!~Waz`i5^%MfLT`8fq!_m)*r zXqS5zlA{*_TL*ntJWO* zjq!B6vN<#=$+tQYxc=PVBoXf<3g~Ty`)*WvcKN8z1-+6xivgE-xeF1axOr8HOOS z`4y7^WDHuc$eEuabxmCapxO=``uzjdxA%K}|oSJGq!R5bA%>pvm)}YuLUyE38&Qn398~-KPx>oEg#?S8Ocz zeDC)ZopNrN^vMJCU>MREr<@iIqeR0Q1g(Q-cqo`e63*O>j)%qK|%Z574#vVFF|>pTet1K=M*enGu25J>`X{X;^zm$H#4prg=n6|uOzl%w^u zq5m?J0`56_mJBz}6qz*Hi!yB5;spRg*z9TO?50C|$7S(-;cQK-7UP=!i#xnbsoIok zO;@o#2&#Tu4)mVr{_n<1kx8>)Iu%q7xPC~ZRen! zbvt3DYL6?-{P2&TgWj@2f}*x@kFz-y>G&}-TpRrC%_wZhc;(8KO=}&yD+&s22F_kE znM~1Qs76*!&X>`7&D_8J*?Jlfh67*>V-QM}9huH45WW3DZsOM@ia431U|UR{ZQ-aF zUdCqdb8(Dk$e+rd4=>?S*b#!AM>y%vs#zPKVVX<=%+KsQXIAFT5Dz0*J461teyhu& z0pdbYg_bwlo>jI7VyV!`*aA>Ao~%rcbKsw1H4d?%QJ&R|8OlYFWbRnNhC(_7jZ82pk}WX_!J9cFx~@=20TIUdxOY` zJJC5cP{Cq-6CoZ^c_Z7AwMFMiErS2;?Tk zax<p>8CwOrcP7U7Dk@vb138R8HYE_OEzgz57cUP0KyVc@POKJgc({oHz zjQSUW6z#TEsF)fZw%+3JWBy40=I{F^>ea9otj$!{j?GW?X(oY&NxA+T9f>N;4Rcd4 z>Y0)H?+OArc`as;JTlI*Y%aAO(JE^@vi^^J(yai}m!2-$t99>QxZ5FGZ6^m9hpl9P z=eYRG;}I`k9zuq4|I-$Ub0H@iruw8m{Kg3)B?&NE_B>DgI&z^KOF95qml7TtBkP6e?&0Hx{OF9S1Cr261X)MfS> z3;U3K!bh_J^_Ot@9x25QV6)c{EYGswR|HyNH}#%>umCIbx#7fDgH?kG5#crB9V0bf zffhA|bYPrpLkf#vt2g)g%O}JJZ=L&MHO@u}uZSE@%~jqS*SDy@Irg!^+~Hs~u))!W z{@vh!^xt=n>U?Lg6?3LH0g&kBRbnavBxQ~eC7IY&b7r3-1u|-m@NqZPcUvT1lH6L! zIJLRT_Y-!YUdEDuvbo#$HNn*i7rqnlQU8QatE*WrLzBSJ36nFK_BCk$3uL>*fBy>x z6MQ$27!)exdkN8w^Ir%G?g8YrQr}Ps`(SP-!%6}F$naPss@e_)%9CpGE#QaJ(ege2 zaVo|Sl}`m?p?5>tVyrswwQsdeR5pRB;TXn5pMQ;dsELMmOSaP|MC9Dyv3h}ogB;%< zuiP^u<4MGmKzLl$4)V*vAR^Ei<+6^TSc~9a8U-(s+YYz4L52~3t5xu@i!9m6W81)Q zc*l!$4xZOP{DTQV=U7OcCaP+9kbZAkhlo5H65?6ZKwuA`4?X&p_Tv%jOM!L+hLUYc|!u@&E2sKRtdBGajAKSOf_Sax*F_`8y5 z1>qP6;M$|;|3HxQQ%8t8{#T~O=oSeI6wp^+*icY}q$FImNS6O}3vY!A&uurV{uM#8 z)tBnw7+;%?YHXyjI%w;et57}+B9En~&$<%KAFYD}f`9C-{|F;)liF&rS1rsCUE5Cn zAnCvaNU+>%!#1)vFz+*-r6oSWcLxv!ejdXhWVZ@w)!`I1(Aco^ijEHH5cu(aNPfPL(O5|V^%KSRbMGtYKn_;*zLwPd!9#rD zriOIk8UL#2ptJt>Cdqs$H>Y6d8tXdx3cg?Q|7eWn$=h4PK{rS(fRX)>@sQJh_N$u0 zTr05b$`-*G34n2Y5ARJF>_@VYDHd)#F?ShZb|p-DN>lWQfS(Tl%fIm(GYIm)MGoY1 z_Qbp;Ft36ynD_(H5F4srhGl&_$LI-=xe;ifLYk93w&T-XxV%r9&bNZ1TP5YRd`Ye2 zU_2%s!?}R!qrp=W`CnTkgEuwQuR|eEv8(jB+CzljQ5l>Qy98wM?H6wPmFzUe+{#>5 zBQK3GJE`SK1K~=`3sE9}6yN80Nlt140E_FTFq!DBE)%bMBl^fi6A2lm!w*E@MP1%R zKAPUCO8Il7_sp-h(5JBT(H8tI=YqHb&eIa64n@6qsH-k!dGA9mG23oU7%+%1y{ zjaAEX@E7|>Ro@$o2myn6hi!i7{P+p_jC}Wz22wT@MdI_AO9Yr^K+3(d3)nJHX&cst zf)3T0H(j^>5_-*Pv4a;G8^ZS+2n5Nep_~ML@T%R)75?Vl#}-Sz>K#`f@C*qb zsOg}%v@%ltt!i1dE;S)vzLx^tf*9kvmrcW~LlH45k>8!t4gfioz;4s(Z^1qf&`F)b zdWqYen-YK|f6l>NMO9lc`WXcwkfpMk)mGjVp?_H6@7asv?cqd&s>%zg+VyJ9ch&`Y zNTd9Jv2&L6UsqipAd`-AXU-Wxd*6{*5?|2e5ndPX+ ztYx4gB|TY&qv1pX4s}6R^gk{z)pB7rKEwYJ!+~{VGc>T2>?suD|M+kd4h!oCqz-)@ zMPHgFLkE&@m5bIkfULs5!(#f2uKe$yTYE{zDxg5QoW*7D%5tFn)&@7bNTj$-`4%Ln z_M%D&;7F#Fg2+^uUmmtvT*eUU7RGT9ll|C_dkmqiM9MDYgt+$!w*cc8(b_OqT0juz zZzU0a%VuHK7%kral?etO!xcmu3j9ils^%_%)~)qTO+|j0GM$$Vsc8(6>*6@_m7Ez` z*fw*4m2QL@uRG2dR0%2ED@A_wz;io7C5FX{{diZ#?4LEYk;nAMK7%C^Xb6y%;mo3r z#7*or{RKjjm@by+Gg-x2D5nNHc%+gb6*}mEAGx&fVDML4BXiJG8%qOZ5w>k#764}W za5U}$?Vk8lCSay+q{STDuYcHXAJAfvJ9RCY6gOE|ergVYqp!Dvgx)z8yY>&3@%+b+ zjl=-S*UBVvJB!}~&wXAG?KdApR0iA1NRsxBQDDd1W}!+Ha04-#10s5YD;Mcdbn(B} zEI>Iun5sUMN9GB(?~!@ATP`QkfCLX$%dgz~ev!?3v-CDb%fPWJ=09zybqvnD4PoU^ z_t_=r>uZALzK1X(H{ps){@@RW6^J5IHQtXq+v3XB;&s$NoSWjS$@}ZuJ8%b+(|0Lb zlvXG>tF_G)xDCJ(rBk{bR=FdI=ogp!7B8?JfjW;pmX+_ah7#LB-<+WhsTuN+!TgkX z(amr%LI`U8PfN!v@L82eP*m1gWkGafl1a%ln1l5+AT^-ZRJ-Z`7{ekzkkRt#I#Ps5 zAL@rK3|?^?M^~$gz^U%?t}~UgcUM3eeX{4~)k|{dt93jz{0hF#18$JWx^ZYB$00*; zuw5jzAm_nn1xscLwrV^Djd>xz4{8@1SPJ4piQnS=3ssCUDz)$DVb;?^6{MApOQhSF zP^neUD93BkY2H0$S6bcYL%NzBo^fBj78o3mBZgYit=ODl%u54egbx>q0PEcX-UzZkq|yLO#u{_KWU&JCyIXD`U_2Hp z)m=Oi#%uT$kh`TPx+!P^#&!#uZA;gUSQ;JRbAR@)SELV@4eE}IK`8`xjev|QJg*0N zGtjfP-p5q}8(v zxzCG8_UK~Dx}*g|Hjq7$7LW@F0rlU&(gebqZtsbzG2Zk(30`UGa5b|O5NevUtC#wEL zo}+unlBZ6fmt6!x(azoq{X^{C4h?uwXZ2uYf3U4nL#Ft#eGvJUR}JAr&-6x5>2D*D zMUZ>+@#~MYWk%aEx25oaDLHJ9U)7atsUl7boqRk=8kKDh*e^rNtuvhP?Hv>z$QgNg zDS4|Cp9}0E%ezNO5h+^~uD9Jts;&o&%@qWv3wSwN&LKw;i`(?1-yi@adhsJYK76dJ zJ$36hl+Co#;?QW6Np9@Dd>c-rrw|UoiM{;`4LpgtJ(#Ux@fmc9v8#d>JPgoji9c#+ zgY}=ys^CKw_tjtLiF`64g}Q6tWVN)s9BcO%m_@^*{?gNiy}&tc;Y#uDF+hKVt9t_b zB;hFf<>E--9AJNm{wIGQ$Og9`#h(8c{^`au{lkoc){=ml-fe~vUG($`02~<>yX@#p z(Q&VC7qA?8HCeRj)EciS4e;#{@i7@=0}zstT|YdE_k+PXJY*Jx3NHl&1;u-^=iWf2 zH9%i+0HS@A3K2j9ggY~gV!Y^hLuWD;ruve8{=8p6ETfxQTx_>$SC4uAd|v_hql`90 zJf#3mXmlU8vXwP2&>-zusq9Rf;&1We6!orf`(|iB zCQ%O`A4PF-x2C2h-&e1uecIuZ`vXp3Tz3wTsd)HZ-H__)YA(G`crHns$!-Iu+Gc7R z8a+nE1SRv%cJzT=JQPd`+K$7&L@m(&o&0nTy#9e!3+S`qF&{5H&x!<59~{@Wt?cIk z8n6CthI|c}J+41ZQ7q+YSBrfA{-h&tmi36(MZg`8okA}bzDBToYWAxsYA6LfEuum% zc+;gxR?n)Sf5WxQ^r7X4QJ^DI$dJMI}9YhKpVcX5S8fq<#UQAZA;Jy_5@0!GD1W z1-pdAuU>^WIs{GxW(IB8#vpLnBqGW&ddAtKhztDR;605T4N@GzZ{2KB#&wD>OG|{= zfY4Bl(d8cmnBZJBud;c6tnJfTR@QrO(d)XT)pbx_IB`NOw1SFcx(rm1NwN`L6x^TYvu;0jtrG z{?AW4!R_URMIvXS=~@L0jc|f{S~cV26(JibZXL68ZO`-8|Y_FL=^<~vm-W#_wL(%Glrhi&q9HrJ#y;ixpm_BPX76U5n+7VNhv zTSq@H(kdhAT-~5QVhBbZ;NuY*$ZhZ*HBqYHo>xh}_ZXTeJl5kKM}7K=oQv8Lly&M@ z*VEcH>~GHw*SE8TIcV2yNJ>g_J2%1xGtuZ`#Vk&751xVvI6SJ{lZ4d38e^y~Bt9n0 zNd;)@fgCz+%3=#+?e*P???Jf{nEp{aXgrxWCBntXz@Yv= z_kRId1j~=}#b$clVc3sicM$ro*tdbjLz6F8sU|(_sj&aTpY`69|9hDhe+0k?eK71n zIPwvXvSfx>8 ztAtYY;RXZw{{0+NIj}m$K(=vap2($FCkIPQ?&BTi*`kB&*#~XP6iNNfh3}&@}FKiJCJ}YIW$!X$V_&SXOJN`Fs-qe~ue3)PX2a9#g^8*iC zVr7R4bl2M7UpuH*A1WN;uj!P-b@Jqwmpq^(&4Irva_C)O>@y9e+`r%4daSK>jUJHT zn6R)g3vi^*bhr?^1T);n=7>a&sRDSH^@)LOQQ1(4)4?#A0=_r&@rJ#4dq?trc(4Tn zn$0zpBWe!@_O+mbxDSl+TKkr4MXwHUr!uHsbn!5NDGx)!`rWD%U0J+gIvUVX94gTJ zY69auj{00Ud-jp_RBw+E4K?*({&zRJU@*Vo*qn58a}%IBr*yAtaVO#Es9=W|o-))G zPdVwfLNV#FLSYSMF-54|=$ToX4CwsTB_y0<6LDBF<1$(MvGV~lGjpjeC|m>;6V;SY z7FrBT=|Lc>u}_9(DRW(IsBqUyI_UrqlNgo4xcA{hhYoRXHu77J$mBRuPE$bBh+|4{ zz-c|^$Lm$9L4&E@f;!knvz7CDVU7#_v(8Rm3hd?$XZJh80B&39W2ykH+t%Zy$J;-t zDzfK9YO}Gjn!)gn)-`)R+nJw7hhI9+Mn*=~TEJ+NbxjQ!6M*S07AJd{56GVC%{JGW z&Bbld&NGaIlNEA6vbo|&W^>JvEU)3jIUQSD+q#O18?*kL!ouqphP%=YbV9jwlW>Ez zghx8v&Sg{#0~+UbYl1m{l%4x1nm1LrT4Rv}^91x5Ko2qHf96@LAT$tB!jxR#I_bXm;R{YVc`z`gs&^nsN0nNWnVFx%AKLrrk zsZJ?$o?A0YDrghZ`{NDRug^1dGw?8W?}48q@Mo9`GX90p^}%?%rZYMUz&h@O^+f0T zpo0&ia6(4H;~Cv?S*;2<$`|*r_DT;zISd{es>1Rpa-di|S9fth125u`y}Y}pgRmTC2G=YqD(VayTeZWE4nosTU8TeM`e=x>$JLM<2A$PXfN9&M^Ik1d6o#V?5U%0@<*Tg z9xoirY@>qNkWO|y+p9$@Xg3#Ydx)I;{JaBE^lzccOSY>pwg?T|v3OfKTQBNdwV3dD z^_X5YM2e3sEpqGdfz#gJTw+1Nw#@Ue8>F{tn$GJPy>?^gCwlS}(P$|YfZ1i6vrIM5 zE2n;SpkHLEQU$@z9Eg7c({6(~7?0JcTy2P;VqsxnE;OTR2^^tscIuH8Nx+&Qn1K_b znIg7UY?60@xbom9jRF;r=EXxov;3ie5OyG3h&C<64Z3RV>bbGtGi4pK%U59an~aiD z>~N@kotj;l(n>4@-l4v_P+|MH@bKcbdM z7NyjKI2kxcJ~PM+d=8QA``iOR6x{o&l}K`}$}{s0*4CM}FP}eWZO`v7fRsRsomU4o zb(>1j=Ekaet{!=J79Sl9CSn{M9u5j>3p)!NBId_xtxV_Xg;F^5`!yh+)4N`3&&)(Lp^@8_&6O8ol)|v&!IY3qHDR5>Uy2v z)VU+|wY3=Z`y0>+`4>p7xrxuOTD!r#&T}fZNL<++_A(wg4BUv<~8=uJ?I*4T}N~+ z+0k_*rZS4V7jeVKO_L2jM{6ok1SeC??RQWI*g+L=3wRmMwrX%D*VK+LPIT#s*d{^R z`eL>_jW6ish`^20dPyT_A`Vq!M!PFqnFG^)4*^3)%Pl6+sD)Wfi&xwEa zD5)3_f0PPL<&oshXL?=H9#b!%|JF{J&-ft82eB zcZ7}lEUoSjhSWQo3O329e{U3K@oFs#*#R0vr)fK#<&2CEX9=}D?H#We@}CjE#;9hQ zAj%DU(b^-6+eV_IMR%C!jvm!cG%#LT8t~bC6l#A=&of2zmcb?n;wpvuVr5UUDG0ss zfv-}@y4Q=Pr%YI>)04h5<;Ue_vsQUUzigV#=O}&S80dZV_N~I`GVHxsrM1PhOHcP2 zHokwh{GJp6s8$4oM(2!2=bSY!#R*~oR$uK*lP;eku`YCHG5pp$nT(KU@#6{9f|ZwSQ>4O z4}lmpRImgYzOwm%2G682kM(x?v;)dY4)y5Ma&8CZ6?jD-FjvjY&|Hz!ib$~36U z#Is9Z)^muoD+R4LNnGIJ#fJ&oXYw~!1w4BMr?OwyB5o?xc2?Kov@b`VB}{LM5^+Oo z5dsj3!qB>hwAHAt0YYFXzlHO3Ka8bWUz8BqfQ{V>Wj8B)_1O)`WLmQNoQ#}$!Ll;~ z)#RB|d#I~V`3PxG@Idh#1JjZWhwDNx27no#C0v>yx=tD{ZDG5)22>n4ItRu^w#zd4o6^tl+ng=QB`G-cg?cN0k(+@XGiyGIxJ#grd zr9zTM4^sFILp2BhE_F9oy^v0=*mBI~OIkr?h7nDGsAzy?SxS zlzHm6s8UEYHRvhaXCXh$n&j0P(twON%) zz{4(t30Ua3^5yOQFSQG1UcFts$e^h?+y4irfxP#&AN|+<-~Q7-ZAqF*#l>R6GZm-U z9YcYFhnQv_BQSJ}r>Mx+*Ozk@kB)rEFrp)``@H>z!6yBVvBVAVhQ&>Tf^Y}!)!bJq z>44IKNFNbHo3kcUPY$yG?O6rW@4|=~AkvHm88RFe_9Mr6kYeb>!=3BcyoFpi8ApnVtf}S-k2?N{gWd{7vtB^i| z#uP9GIeb6`ogzq`b(D%K62z%&oSdnWMRhP0F%FiP7n5kAWD!>RPKkLb3c#ehVkXeO zfEWn$-v$7qq0j|mEbG_PWer*iY!W*U9y=!kH4~a;X<-lqh?G%7_@zz=DWa2mv>ZY?1~|{2tyfm@VqbL zJ37KnpP8K%j54Bq_DWU^Z_n;K1cT{xBOaV9xdVc2;6m1(I}PkeEsN&WBAq{^5n!;K zB6}50^aRJ##sChTIWWcz(rjmS+I$FPquc>+!4{}q)Xht&huQ-`Pwcevi={T0|E|Bf zwepMdBFMdv`6hyXAd?Eo zDF7n4oB^p$XwiD;my(iJNc0@x_H?G{`IuyX-^;b2m(~5qGbdR3>Q&ceN4h=JVBs(a z)}H~QV?mIiUTD7!0|gO353<@gz(AaXFdWXq9Y3`j){Iz=IZQ9s0(#Q-n##&EM+Cu0 zb#^p9>0-3KK)DZ-b5%RC5~!)SMbTB*8B*lHVJHqpYwGGUf57T6b8~kO8iun+@rQ-e z>I5$;o6}&m*QKYY9~~C=VKjDn(Vuq?}=nbLOdym8_%>x^-yWbuW*Ma!2Eb z%O8vJlBZHnJ-2%E-HXQhL#!lg<&5^}{9ib0+?ko^T@NlYlzCqMtAgT{)*CmN-cYwf zfDu;0Y2{`0B8yp$&Z2~#p!v|Y8nt!h>fv_^JcSRxr3x{YgA>DDQ&ZEv9I){I(}x$l zyw|%K7#IxTWVU6b<24zlo0^=I`t<43awuG=kEUyIktBhKb_)W7RG;Ibdh03_r9mPq zBawj?hNh-Tc$7uWEP-7!*;?U`9KXJsii(P@IfP9J3U|(X(p;!p%*e=S6{5u9=yUGp z7gZ8)^isE8?rbpAcGvS|`=&X5UoA!Z+Q!C0mnr44a!Nes)ylUUkOsW5KYfTvBws1# z(F69#JLcf|L7q!B3xi4gf?YVRh}FuXlG(o{|H8^WS0Lvy%m5DSQg(7+{dU=|ezR1uePUNu z;GTx=)Eos9z$ZOKl((DDC zO0fwKY63XO82BjvvbInNOf8pc-1Sbt%8*!;?z}(9gO*G8r}#Wn&edKcqD%cxwIPBo zKVKFfSiV9nsHImI{H_`Y!*>gpz=y@tU&HPU6Q$6!Sc=2)%%v-95bP+I*8zkdctv!R zg3Hr)|1r8e>&L#papwX4?}{O-rd1!W-h7)35Ydj|z@VVnVE}h!W_wizSVAoCOqLuL zKHu5ZWwbYY*K&+a$fK$=jygm~_&m$qzGv?4?M7+HaQ$6rVXI=Py5-s7;ZqMGPt-Ry zt}VFG3z1zLK-4z`g@;dlS%V9xSQ8@1OSZ{M(;~_@)t!?z1$>ooq5DF#lrAd` z==R;&Pur*$Q3NIYdUzPn1%8kEV7wa=*je$5?7fO_&C`L#I*o2b5YhwLc=I zOFttHum|)Z$X619Hft#s=BcP*`=EHfarn=js;=#yZxzZRKZ+b~$t7S_Z<>4ir3#>U z%kc+?J5*1J7sj2z7I|0Vw|rZR4WZ4oS@$_f=mA!o7=T^d;=boH4u5M-U|+mqQsP-I zs@m>It(DJlyOSYAI27lw{z(;a_DeThjIf|@itsixrB!}xMj{r_9kI3f#bDF@x;k%; zW}U6GvokI{j@xX*b53p2qU-4ae`{TqzZnERAcKoS;G=CTLM`hS!`TO<)t`5S zzoU7u8sHi81KHu&Ioa6Q5W%8mc*@U+@@-0IF|!0n{%WFT=0j!1JOBooDqf#zhGtnaK*&wcXfy)Kb;bcDVwetv zZzvSzvs@F*&$TIXS}Hi8ybdVO=**f^@A~%bUAd3&LWd=QZ85OKVjywnfLuq!N7L-{ zo{xEX_Qk=lMB{sN0PhiU`m&$p1E3}R2*_LBXIi+dE5ZwBnI)jFF{-8oJb-NqdNB$M z_`vd-=h@s_PH!P8FdS^*wq^szp!Xq84IWsDC8az6#F6WPmV@7wn}=ucL`zfCKajYG z)H6IJ96HkRqYy4JoO`*sO)$y386tD=Q%%=ky-?fJ^9u`%(~d{lOp4YQ95$d3lY+ff z8!-*Fza-koi|q#tVPJ8^XSSV+ySu=HlqKwtz$QUKjq36FUaCuk%72&*Ag7maM}mp- znuS8J<=H|Yb>L5i1DoBw3h>nidhKn#C3O#GH-LHwGSITzj)1Mj@-ho?k{u}V(_c1Xs zz0Is42$br*3%Q=R7^j9~1p()Eg>tJU&3c4c9BY5$!mpm&s!haRKuE^w5|^VXVq1+E zX6?sYeZ*T|;g(|$-{-K~u?OoB_?*~7=HxMw-{t>rFIY7oii~Va?*>WC=`lZTI!w> z3UyK+g(4a`K@6XyO%|Vne+k%2s@p5uJh68&ur)@>8`wX!w6V7|dwki^*w)U>#`-3w z0Ou|C%cl1BPwfP`xUByD3!FB#CR_}GW2W$slTW2I>`*A0Gsr(jOyawhP)ATG>3ern zoMYxkNn%tc2RBzNo-bAAvug#8X&(>0a;3qC=${5^`IjGsFE*Tc@BQYTe$ux$i_jY=l$dP|Mfw_hZFj+=>PpAPWw;o(f|Dsh4SLN z;zjhIAE2!6p~(L0gP}Jl>i_zn=$|9(|Mh|C)&DOpWZAVRU4iD(rAsE1$Np|t*MCmM z>p2`)?bY7Cz9x&jD(17ya)D)Kw@;Fi2KV-=Y~%AhIimS(76X^gi+OzRVHQez+yCVi ziPqi)ye|I29MhF6ce}g0jaDa`R_n;PqznuaB^t?5D9sN28{bJTxm{LHlMAvQt&rB& zPq_EsLCc^?Jcokt%Ik^gX`$zDtYzDLdt=(exP6bIYO9`oz#6a7&NcK+o1jq6H*=K* z{DoRyeQZ3E^Jlzy^O#qQB7XvB&7SS{!R`_hE30&xd^Gc?PoIK=f*6;oHskh|JP$+C z<6LDH%cmJu?!O`?@AhPtad9c96LnKp&Cz6v_t?p7jpkRN=F(-4&~?o$^gW3}-AhSH zxtCi$b#eQ*AJ=+AwsZ)ybO3|M`qFZ-#XxiX;hxF%@c6;E1`ZPEy_Lqv7bFa))xOYC zpirCjyx8y#o9xVOP88~?h<3UygIe`N;}0%$`hH+ur3zz*IJKH?nv&j~@xz1C*RNlH z3|Jbg(&DU`xw738e{hYJmDM$A#)_Ddf!%Jpy?9VfON;GrZ(7W_KU>4L52MyZDYkdn zB+ezR=i3Ps>b>>nmHY0Bvilw$_&zlzG`e}J3>)>dg`@Pgm0O&gNJA#|zt&a9i!Euh z0Y{koEf}^|R_}mDedv05+DmJ9Z>4M)lK_gdXhgwp1=vXO;H#c=Ph|P8nj^ zpC~t37LQk0rlFHF(G(v`bwgD& zt-q|*;k4Lc*D%g4ZOOXhUgP+<-jnv2%q$ia45nP=9Rmtwlger84u8d!VPz`XhOZ(c z^%T9Jim!Ec=J@2=bjQFN1Mi`flvF4Gdar!7Pw(fu99Y5nC67&^w*dhWot=-$f4(G} zuRctV_Xuv`tdvuG`e?zf*ZUG)7vtSzG}#=+w|2!MO(57+p?))~{C$+(hYL6F*VWZs z6A;i48%@tB@6kKlQJWnsOh1V1?Z~ly!iQg*n3$OHdx>pRaCR>9xB}-N`=MG{aokKK z{$_E9nYoAK`i$cC{*ouxXrKn^ zcjpUtI<$4Rls!fJDd=t|)M)L?m5#Y)9SG2HXr^#6du;vk?f&Ou{Snkp*RuPN1cXlx zWR^W7N+3Ul`uPpQ)G1tif&1=8?5exnRBP8#w2)J-%XrzdKkL)cf_Z}0@>SNj_%=yD z>RCfkQBj}1EVby(h=>SUpHml-Hf;tA8a^ME*6dq_Oc(HT_Uuy9-PSi{g(w&aVRIvN ziB`xl+hcrnvPH|y8io2<=F$cprnMKTrt2c<`DCcb%x7oEb?u--bXRWa68GcRqf2FQ zg{O*(i>LMxFWA-d+;>>37CTr8a2@Z@(V}&?v9`{2dEW4jYRBDNw6~W|x+?wGZr^GH zjo!yi6B85Km#caj(&HjRoqi38SSM`raYHB89hv0O$ zedY9r#V$2N15f>(u-k*={38jQRm*i`e7j4POQKbJIkfFny#-Hy-Cgs*Z`yt#qM6y& zS5*y96lnbg;Y~q1+1$e;dCECJcvUJnH6bCPVH&b-?5jBy?eqds%C*}G#-?{p)aKBO zd1&WhGTW57>G`Y16;&>D6?TXY?`eibtDSV=4RGCEC~ct1RLYTotrhwnMR}>+Y*Uu2 ziPrl4fq$%X@+(H#ey(5w+x+GA$+JeI6%GX^9dY;Ll@->vx3?qp4yX8Qy5`!V`T1S* z(EDS7=)^JI>3Gj-`99yTU+?YT(|z{rQ)`^aa2mF!PfagpV7Sz#^f@PnSyx2EGzl$o z>iiAya84a;yqqN&hDT52S!SHfUj3y5eLKtc7=eoQgSot#u^GFn=bPcf)|G90n)_85 z1#9M_WyUSkv)2g+!2t&mXYQGd7P+OZLIeoyr)Js11_!i7>mRRhMgSi4-*@W5n`XL_ z@vg%qR==C)@xkz0x${(JrZQ1 zv#4+4t|-DE-wz@pB2`_Y_`)ATPlobja`;m;_hMY8A|0po!DI@+3;8~9UA-!yH=;EC zsrp1lhV7z+FGbk=yk%2U(jBK@NLyTeicEGQAREe zWD0KzQXzK-pz5gwwy`kD^=|w-vol7nHObemUAr|jkSa@4rON7}mcnc#mB&Q(5QDguCPWGF*1dEFHve!AN@7L#Q;mX~odP$qh~r~l-mdZTjN-(TO}525Rs zXc^mdnv%?4U9}x7dzQ=)AkzqVXPO&x?M9oH=G``~D04l-A)BSUva+!dZC4jc$7MV` zs@>qoD!%SKxa6L~WnJF({(Kn7 zM5&*yJ^HiXV}DL>4v$#-gg3fRd1rU2CSqd3DDw8RBo|M6`=YqzTXN1OPo6*$xs$y1 zk=bg4`608|!IojNyV(AENO@~I-f8%`Z9;Z-tM%brxSNxf@ZsLtju6JiV(y?1g-Tf4 z-zcr=n*&7fZSkx+ZziX7jAuzX?{Ga`Y;ZsK$B!RRWScy}H27%B&}j5T zp67m6+&vx7eLUZI&faFtA@7p1Va!bv|LWSl@Fi<<2gXNXrtT2m5eUNIxwk?gz=cAY zRsvj`U7ZC_3!%07+_k9eV%~n!abA7R0x!zz>%9Dh1V1#9l~)~XQ@wrv{`>Qc5dE!s zoO`_)=lo351YWx~KN@wu*_PEyl#ri)!(}?wDFusdo+AEpc(5rpdkVp2<@%{pPC`zL zp-mwyvVa!aVz8-etPYB@BGN9)Kkv9~PexezXKw2N^4-2~VPZf*f*`MpR4CN)HSKhB zA)9hYK-`9}6i`othVt^qf1|aT z5g-7^X%^gLHNeXDEdu!Un2NVgDrSwNIzI1j4DTd$L+-;Xd*Ym`Tyx@xEuHVA@y0p- zKDoVExwP(<3_;y>tk`Ya*8P{&&id@svkWqEB$3Bu#W?h95VDWo19OHH>w6vmx_?WV zkEKoV;wt;-+LMH(UX93jFFSG{f5=ht@`#@A=+52Hm3Quo9B4dwiawWEj;!CWXzPS zK*54{lWhCcE;z?t4_1{ut=r!_2cb=`Bczwk^hdsdPpWK0)U*_vYW9s9H7kDJ2*vR{ z|4f(nRV%zP@sS>JXIUQ3e8O^LKk3?cN%|}dm+}iX{;KyShq|!Kjtgqh=p`O^Ckb

P4y4Fwdtgk*A?48rx6;Pd~g^-(`_ z|DQR9Rqperd`djd|JUFAe1Yo!6?P)XQ%mebqpX}ni?7!XQ-)} z8~y3+HXd~nx26M76P;3C{ICs_QG26X^FDX_QB>{Pw6aRAt%3O!Rb}Nr^9BG}`g)fh z7zu7YD*?U_PBey1T!;*vB2Tt>ZhjzODN^Z2;OqX+L}@9VWcD z;&^g(Gt5QpRQUIwC=*H`C;}d!T3UK(PZb>KzZm1p&2S+6x^TPQ_}-QAdC|nu4%|O% zYUB!z8-ef`;cxB94LnOdv98aRRbRM08z}F-D%v_&4}j$<>!EtNg6O!1oDHS)+wkab zF;nx5P%5oB(~Y-WyH6Zl>W4N@DM)M2`~$daF+C~iE`!IG!R&Bp-f0Vt&X26C@Tvj}MLh^?`2FXP@%l{HD&(-YLPKFXmQJ*=n1xMz`=6PnL2v7dP~SmWuL)xHV!a7)6SR})a(M|^E)XbMx)Wv%xi?@7## z(DTq3`S3+QP+~jWPt%vQtQG=BXHa$94b_Xh8cvt>boD}Ji&o&9%AzVZ z{u#gT#pC(!chT1bd(~s%CQ(+(I$B)ghrt#e?z)GZJfA#iG)fjS+{*uyjcsn3R2sR} z9GCV_q+*0Xsw)(51~}x}3n%nl7?Cx2m3(jJQ9UR1@L|dGks`61Hwy>t7DmdI^Dwma z0Joc?cr7gb#kPLFS_LMu1uE6Au||`FC-td9TV|USA6}pY@-6+x(;Ip+S>|}hz?!_6 zQ8rySH#egLs#nGfFWgw!^MVR+f-XN_#ZTg+AIuoSddJ=%Cw-d)4Dd9^i6ba+t#CuMwzatGein^y3PAmm9K8>NIbl7 z6;@q18Lry&HW+(9>>is1E{2a8h5Af%gqO0VMPDecLwluaQfgNIe!pmYfNH6Rc**pD zlugjV4Y33D{pQzb;Qa~S_o$xwH6BmAf=^`^f{%FnI3KZcm0r4zW@GD_d-LWEb-Zj& zUiA*{expsrEVD+9M^(0FF;>!W7&6u9K9*F~L{Z`F6B^#?E~o49(Vp=9g=d#b$TuO3=%Al#v95vKL0{ zySNZ#54paY2*U_D2m_)JLBKQ~0L%gd|AK<7ti^_Q+Gd8`P$g@B@&n2$(Pwo^v$cJh zXfnLdw_SqEc8ZQ`Vrp;$ymx~QFP45))KTdOcxF1I z*QEEAm&dNLcXp(a!mWwhA>7kIG@n%gVu`>A4BC+ccbZ2=N;=Qb(a}+w89HgbK$3IQ z*q01#-=GNlEvnhqug9tNr;#g!+qpJI>v4@b_Wm*iJoFU2c@l{#Pj7*HW3w<+*@lwH z&*E#}-e0&1tV4m#xaeSkv2606rJBQPU4=N|@;_l?0bG&C##G(z6Le}BJGK8Hm9 z4u=z&KKa9U1!nRel{F_*kXpTrzwhDGg%`qWb8E!6=xde2FnN~@vWKwj<1X6fI2^s` zeOR8_BMoJiU0Zgm=wyjwt-~sa=dFO}W!}pY?P$y(M7hg1ZahMOe5p;%z9TDJp?0T6 zzWxi#;S%|8A81+XeJS~u^va$lt}g-A+!1GG#|g{>5(&?rB6iRH{kWyar6qzZqQQ@5 zj@Gnv#MZCOPeS!b_L0#m*S_JdMBurRQyQVHnUIo?{S;-YBIR{T{;lZbZsVqGpt$EB zrjFmun%idaOrMcx+_XW*6;u&|`O)V#b1KW_EE2QmI%>{VAIoK^B|sk2L;<_08L!>| zv4iXGJL^FZB4nOCNz2x$R6TR%%tY~kZlQ}O@Ljo2!(wAcXS6rRp66w&f4Uo=XER>S zW8Qmx|A5z`UrI+Oc5jl)Gw7eI_uo{0Z!UbDtz;;{#KfmU+U&PJCqXo6`y@b}#UH?I zt%!T!+-v!==JBQ8ju0QB?Ur))Ge+CFe|GRRLjFzV?q}&lA0kI>F7`u=`|e|jV{LgS zp>6il8Ip+-AIFKmx#T*O(!Oa7t}~1$$;rc*WW#4r{p+(m#yx5B-GwF{)bVVWFN*`| zdK01hnM6f(tM^uAw;}5~P~`yXCp1T%vGjACz-*zJn0xnj9(j0&AhhexYTO)o!Ii6x zd#;ZU(}pQEqi@~r`n=!g|7S|(G^{Jma#~RqWuadx_4ZUE5rp8_x2W)lf?O|$7^E?0 z>Kzz(I+EWeZho{<4U5IvJ7fVTD{X6Qo4g6NidyLf6%L|kQiD;aad)6|9WygB0-+Y$ z2AXrCpoR0SQ0+ZqUweBmg_5$(n+&en3$TNn45r5nIozO zoc@TzfDZ7^%)rdJx^Ip;$wI&li61O92^g{Ok>9Dp8r$Ey_Zo?AtF1RXq<|?eD_nTs zcdfKY#%6ct&3g+2rmQs*`+q#~M=(LfMKXNGRJZM8V57PEYL0JRo{N`CjaCn)d_*=! z0dP*%Ar;xRxG$0jg0Y&z)UWxd@{uCs=Ua$##z157cpkX1a&UyPDyK=w$h@OSrv$Ix zSQ;PyZI`Lqirrsb>k#8QI5@Cvpi-N;(Fesl%hd<|p?iCK_v00<68knY@}H}#e>`YK z8g9_82n4#m$(u&^dU0{Mz&oMROLTG6SG!cL>>o;2+1TgZUYp}8xn%c!3l^=NX*=^U zi~9Ak5X)ogf8Fyl+N=24pZhMq-?B_Jv*q83l)gB(oV5hhpth(eJTysuwO?*wMzy%mAU0*pYJ-L*5o6pxoTjG6~gm zM68fgGt$Hw7R*}9=@L|bWt8@<>s`!VspxP37acj*hvJ?) zIXE~}xX#R`$LF%uuw1+L9cY)Um5vK%oDy?I!Zbzbz-wM2mlfNZbyOW^_7zN_1u31= zc$S%=pEEw`Lu=hhQwN=vb#HF`>)!}sY2d+6Bv))ra&nxIMO&nP9&(TOIS!$=L@o;z zPO6(3=qd9)mB_$R(Yv>|Psq$3-1r}r=QZABG`IyCJvYU=`ues;6?L?Thz?0Y@39wc ziX5>>-_1RF8Or@`s(iUPR~)PR*<~Qg1JAOszqp2ZX0bNacIM(mtd^jbGCv3Awk(nl z%aYGBg%vD34cf-L>{piH)@Uf^(P`710q0b|Z(I$32Oe0vhJIIqAJcJnuhMqM_qiT8 zZY>*>uXoEDcP88+fSO2?IM%uToU=~)I{Px1CjloYE<FN0D~N92 z)B6F4#JtBvMcT>1U=9FTfmsi;3y}Piz&{4Xdmd;gDJxI za|gn3XKMV%C&N?=wc&)I3J+eFRU@6qUx2o6E>WI1aawPDiff^vK|^z;JS42ru61}c zSt7+oO|~|Nsp1}S%X05|uyjWwrx~zxFAK~$tGTMm0o~{FG}bdc=>Z6;BFO_d&x$mZ zDJ(M|MFylYt@5x?R?N`DS#ks9Yix?E9 zF`Hl;5|iRI?TrsSs)J7(oJ{EQfg?{?C|J!trv9z})tv*6Zr(a2hxJFING=h6i%m-a znyGd1Sg5JVtM}??z-5T1z++;;wkK@gzTmJ}WCrmI13Tsdl9#F9hS*>}>X`hyRAcJqFV| z(C*FDTF;>M2U;rBBDefLFj?22YeN0%%Vpq=L#o|(-&JN}b3&Fpo6PKX?}YdfYaCVI zwQV+9dK*k};SzX7)w?9Cawn&swMPbvEh3;fKGXlof9Ie*e=UQ_i#I{*4@wndRi)D3 zD`0Gi6p5ZDl(jt{T@_2y>r_po-;;*t`1-AHd`d!_Py$k`1-S+*-;wM`8lL$HiqQy= zJCKo;#S7l*U9GG!pruPqN%?9pjT^{|w@$z5fKwSYe=sVpqEei3@+|Xr0Lz+tYq#)Y zKn~AFC&ilWzc|X8#W!~O6vc8_SJhDeeZ{s0VyA%6g$M82F!@g&zMtv*1i@q=d%@<)n%SZemG9D<*);mwoI*3i_`BA*^JRufKpbQ zp1>Oo1bMtja=xXIoEdP8( z*_+v!)2 z6FuCsn;omF+!jHanhS2a&cBGZcgGJoAzm028iMZvPS43q>Qm&&nUXh&r5?_;lfM{q z-Sf~qU~KGJk$Oi{_OvP7=}m}HlVh=0tH&>F?qv^CVsUENi`ci=mto3}=U+-7nx4j_ zqpBo`9Aq-Lal0EgB$ z2QYG2#Cj+`JbWt9jzgk+CnK{QD0g*w8>o9S zoHSK$F#(BEV|DaPqs0yFmRTs(((BQN{Z=Ok5DPLU4h4P30b|*$tgK0-XP>E@4pF$> zwJs1zeJFaZL`J6mGi5y0CBk<`-;z8{C*Au1PTe7hphh#_D30~dAtjfk}Pcs zB0p}>oICfXl9j!5gzi0sl^&`^g=nsUYIl8uW;mc}>Dz|orofTEA+4oK=z&|!&Bg77 z0#beQnI}T3NEv3&fYvYWfkyfS=yZB%3a8p**$~}88QC25YZe)7N_(#mxDtxv$*AUy z;yXgoxj|pf{4`4PjDh-sL67$Ahl}+Bm#PQEDCWW^W{hY06j>B zO#R!lESCNl45kU_fd-(kIm;)`tQ|miwrcFNOCdsj3r<7NA7AkxfWc~OHYTeRH>EF^ zF_~tdr6XJx?eg4E^+n9jJNLkja|w1VfZG*Pme;A;vmN;%)7AU3^CA*81u2hYJAQ9( z4=3X+6VJn(>_K!CBX1pu*r}?UIe0g`V7=lk#*@7CwX19Rpv5jm$9hdLswyI4;^x-K zoA?cA`dQ}&Ca^=yEU_A9MidTE)|SGGd4LFVX1i1U3kW8B%Fz7q1jYxM0Y4ktiH@I_f<{u-o7k1fi(huS4G}&6xh!U+(S-(V)8+Q(e zy5u{c+{wmEo}r@dJKv!cVN1UMA^7=X`Gfx|O(-5@(!L}Y0f7ZV@oYn=v6q^wn9B|6 zQ4rH9ZB?9BP0aO4%&9rtUjnM{DpWdU9s#6U@*i*98J81wUC+q<$m_mi|BPj_&p}Dq zWTun)HLgRdnDN%O?d~VX__I(Os!Dd1j8qB%Of;(XI-;bg$Ox&zVslfdzCAg7<5ks} zI;PI?jef5Gxm{&bT;{z#vYl>Ot}p~hAQ~-~$uPBbZ#pNVQJ>}WlLJt4YU(6@5mX7I zrsP>d4axlOC_9L<4R;?q~LlAnt6SGu&`nJ9Vm+& z=K5!;1+ZuO0oq*ycFPvpf4F^6lzk9QMn8226hlDQbC^q>d!G)*faz)mO~v(ix7D{> zyIKs@cBLDy$Zal`_&T8G()V5XFeQK~cGU=&0^oLJmcM4{%Y)^5O1w7KX=#kdxcS^O zUzBS7#w(d?gk64kuJgi-B}_M7%(HsCxZ-1K2FqFbUZR`4cZB+s-$J7=VQ;-h;rH}( zI@jY-x3xCM8k?2h4ZkU@PHe{A*zkcG4`N^fhP1^ZB?Vj3MEzPuCL2RmcNC3O5@vVr z-ZcWBb2V!pDYwISpQ9>JqRQ*-R)QwtfM5Fc>xIXY>t5|K)#e$-U7`=acy;C1onS3Z zCI>vRM)lllIoh}p#%b`Pg{=WDvxRKcS?Fjqajpy9RhUuCeOiob*0={RlB-dC{UNVzD zshpad`w*C{Hs;HWC)Vx3JLefHO&QNp5 zzynUYUd)(wbgG``Cy{6buXCy>suZiJZjPu(DpJX)QAuJHy(cEZ_p7bvs)Y3P^jrYe zO_Wc^^%&$Si0!L@Mr;C`l^YEAuR=^gcRg1-ay0Fz>dhnUgcDTP?R)u1zQ5OKLdDND z;H~Qw#RZGo7nJj&g#t83?mSirbcrSebSp~z)UQ5699DT7(R7>{PorI~fFayGr(R4v z{~Z<1V-}c7{%f;lLdK;yHJBB^kB4Lzpd}d$CM?&w*tF|PBGU1`JBH%W*>f?@4W-RDu7!W5LQD zzMqOIuCRjQ5;b2K$0q4}+;OJ-0sei+o6CRXzhj47w|_;9P>6^@a5NbTv&!@cJ4eON zZSZ0GArfd3)=t;1+P+l`r+rBPA%Oja=TPCP-C@N)^2JY*uivZ@RS_qk_jc<$8OL{a zo69^-P7UZS36CXCL-LrB>t7&Qz|<%uWlt#+h$eO*n%YZ_s)i{y0%H0(MwLHLlCPpw z2UzEm#5Pqd4<9VmN#)U08TIRCqGOd&r8T4OcX2o~N)VBb^yJQ;S=*>hVLqx37J#v%^}fC1*BH+d9A8}i+l+GDWR8w)8FGhA=^(PhZBD)W{fGey^+_t=c<49 zNOmAf?BsIc2njng0y*lDr&m{tb~Du}5zRRh+`%gx0a$7NyrQC4Mw2-hvwSQyPg8fk zM5+Z=?@1t5gsP8*17RvlTbJjAP}HbJlg&?OxMMzZQc=$gOuTvlGBv(LM}oE9KAwMB zC;4R?fl))5y6=8T$vAy-ebY*jtgarpU6iQ-S)G5LSKkeMc%$ zTFRL-8WrBB4Ue`5`wF}VWX0g$X9Z<{4kyX4d%4HN*J^ug<6M6?3UQ{&w^SjEA@6P*_!Uy^) zkDfpgxctQ60d34VCY`IV)=Qe*+$(oq*Aq4viTI)TG=NKp2!A=u&ZLdozM5SxA4!+E zQWD_)#lmXF^Z(3q9eDUx+Rx6-U7e;(KG{O0Hnc5&R|2F!G*I{(eH!wyM>Mn&A+(FkT0WL!kV;+V3Ox)_w;?1~eb0 z2_MsqPWMMDq}U-!AB2S=vp${nRa5gcfp1Ru^wL(AtZpVy+}Cq_uw8V}#%rDz~I zty)XEx*1}(%luK#vnqrSAaCv3BVe})tNMV|r{_XE0@c}#L@nbiWyL!jA@jdwGILWG zZo(sq>v*zA)f2%`Q?>Q`e4cLSo>j_gt_S+s`7{B?LTjqC;yx6V1f#xPOo&lT>JDh> zt}l<#p~g3WNXUNLy9}!zsW}?n_u=k?sLnjUt?i2}W&%u@0{l~hbT<$WiJKsLJ;JSV zaPxfs?4DiLoM{LS{xlE1pTqiDPa76DlX!Fp=}M`yfi0eXKl_^c_0IFUqm!*gKqO37 zni8sSelLuo@&k+`_|j_>^{u~cEN;Yxes?fwc^6YmDkJQEt)@XaFhD9S#g{FA_$<%G zBKPh!O`LxYizMvx$MX|Frwz_;+^hU@P?9PKl0XV_?*vl3(}=9D9ZT@uY3EF6pE?XV z!J*vNG);)#inlD!CS(Tg6Z&`lRz59o3W2V#o((rXNO^8A#=>QZF$)ba7?q#n zMK${NNOn=#joBWik-aFS|56*WwYBv_;!HhDD-uG9JprhS76SXnH_XuN*i{xV^Axo9B(rEGT)9(KQ@St<1d&> zZCvSV|6|)0$r>vNVG{L#z;^pTK#@h|>lY5uinLw95-H)7qb%KRNroUcQWo1t6>23Dw_`&b@F8 z>|sVuQgwkO?7RNq!x}z5v!`k@cO#FMzMLAn?f=WLLw7-y z|ES?z4*nBDm7250v!fH^VsxE(5!P+tdI5{SLO;Pb*o&s?_8)m(1dF~E7#5Z~ z0LogVDGe&l65MBK6vVg9A#!bG^5r{p6uzEK{p-L%S0IEbKE!&Z$}t2RQRwFgedGd% z?&d&$HABP|Gbae(NLcf9T{atH%C)Tj>h>~;8*#9+%jL<+$Y|PELt|ZAbZ=RL{s)?i zD*~Wl3s@^C6lFX%E61ClsvG(JDYIE)gBqjJ(wEY3;V>q&b&n_AAI`BBb1qcN`uKgV z(aZCe(3R%nNEREFL9pA;gDW{xY7K-HoH(kUuXSWjUGXnI6CQ`QLf$ZWT3VWnl++sv zIT7IFR|7ovBw%`L#Pe|f_gkeL>lu2E>`>56{7o!W=fZ_*h!T;q5U~R|NFiJi;SkRe zaEUYIHGZ!!HPwbTamb`sg@r*NUW|U;-2A>T2GkNmcoFC!6xz>rOY-pWOaO<>vD4~L zFEq~y{O=jH5W2HhL`AjaTYOm3&7=IN-R(@~Z}+Sf?zGBrWfH$wYwvje6# zvXq=S=q!`U7l_@|SfwyqAh;_V<#&6!yWtCgs+jtz;#e|W>;@hbVy*gRvsHNa1(I8B zl+=G+*4u2r1iyBlzXDA*J=-A@zc-}~x{smh4F zSi2>VQDWT{nrDcKq^GBs?p7l0dO^5O;VN>2Cj_<^HAkw>ZO~4x&l=)uf76DLNu8C5 z*u9YeY%dJg(bb2&Sika^;H{5=a5+Oo#RLuZ5MZco2_68~HHV*Q3K_y?7#m-;6 zh~D4bq)`aHXmL{Cc(SV-;!HyRNX5}`J3u>@5@nDqK;JS?2)soP)FD}H?|jrd3t=GP z&p)*Zcdh(CZH=Bk5(gM7OKIa(|MqB|KWsj0L5KG>l?ke@ox)(;J7UVy1J;gzpve^c4z z!tsJ*RQ3(f0?@wiTXFw{9_GuCYnhY{*Ju=nXGMN|wF@Y&e)_AR*lJpDPa}y?yb)rJ z< zVwhdKzyn>ENhg^p?ruNV%(-DM+teS5*5GH^Dxe z*s{X6mM^FK!%k{7-ZgXZqcA_4cBT?;GTkR?fv9Rc`ohhQw(h1pv}_sjR6sFgK`SUyiId16!gu)j>KLnbHe+KF6WgXkxy zTpE|LsQ$g}HDfry)vgDZnm_|PM{_i%Y+n>8Y;7T*dw7iQq49r^J7({6=ed zc8cU}!*Ad7R%ak4U(iSv|3N|_wRwoSXPP6 zI48oH0)_P8Nm~@t;`Q(23Kf#X(R#`s;DNQ=N`FlEPa#Q669e*xyE@X^&Vlb|>1{l6 z2sU4K6#-2@)pea#BsI=IaR)O4vn04^_BYr9IT6doOgZak4*J zZN>s8p*uG2tnT4yt?>7k8W6zlWvFuo!yy*w7$fo{=ZnDM-+>_n-H3=` zn+`SAC!bxY|LH0qC59HiDaM%nfgkW|z|}N~X~>A&%+s#8Pb_2%PL7IWa2t=eh!J`+ z*|T#29>g+7dP`5jo&tY z&Vq*!BE0%fP_7P84ay@oFWz{?tB+X1=Tz66d^F!6(us+S<6K0yL8!pO`2kw?9gDOB zw^~Xb+R_-`^Rs>w`K528usvuVLbSuUWsrzeb!&LfZs=llW z4tay6PH#xx@SW)^8`q4UBt(~@OmarVSOuw%WGk!xhyJLrMyu4?7UrE~K^ijRIBcb) zrDY-N2(eQ7;Y0rs=RY5yO^|x6yi&T*+E;=MFc`{Ljb23i5vN1H(XB>$KC!=^N2;IMtb$BOa1p=y zROf{k#m9xifCR#=Hz9(JSK61{*MrVcQ8mGQMCNAWw{P!+WQ$4lslo@Og8CW&OQG!F zgCKDh8W|q3VGow+!8jx8Y-NeEKX!^gBl%?T!dAnTe{F7Hz(^2r0ThQM!XeQ$Ld1q? z_JPDKWe%JOGfe9A?ZRNU<)$0jC>5=1Y-}tI{Hm_l=7`-HdU}pUPDWv=yT|rh^KP!0 zAw~Po6DkZ~WMi#6-{SG)cholL6wRg<97tA~{;0nt0J_dI1c*!rdrX`}OpLc5_vG^5 z0C~k*07}i}$3({Ar0QLXi)U*EuQF*O{T;oUJ5;c~pTva7c>8#MXOj6TjlXvGrs&6w zwGBq3VytaBr#ZT17eW0x&6g%8OW(a{`%S27pP+*rY_Sa?CJ;jMZU~f`{KqJ#x(PMZ z3df1R4xyyJ7zY@&r9&$CjlO49PMw-8-=${+Z2XYm1pd$-l`k;6BgKo9oxW1V6#A24 zeqZ#T;=qqlg31Qv&w&?(^DrJ-Hj3tZ-8u_r*$?Vy~_%+9nU ze4VhOwtB*AqMbnk8egU+0{AnUb2Pl0jZwROk;=qle@7wmAw4C0Q|3LwPJwFjW_Iu+ zrS{ajv^FP!G^|lGPl4ig@>q)CK0jQb9v^hK`P1T3Q-`UIMlgQdu3C zM~^N$jXFdC9F+bK)Z*OS5f&caoE#vEo}HaN!@$4^l1CorW&LshyLfIjRimDlo%?8g z)|wLROZ@hRrGT#X?BD&R991>Lp)@C+4 z9K;gw7L2ND^Vhd$jSu#=1!rzS?*~>G1Uj~+&O{00!-IVeQ8n<|_vg9qBA&Ujxf#Kh zTXq+bk3)kZ1Yi&{k~rEyfBUI8NgmRy1(UV~{YwkXD}xC2zOL(R>XUk{+}@WrhHyR! zLIR%pYO@zFq?B6VPOS!n?Z${)gR8Dgx~M|Ok(=!>1dCd}bv*y63}4^*vI~d@oX%9u zi7zDNs$u7U3EX*H*G&=O+vl}*p#sMRO&6GnWP)a=TAev7X4V|$fo!1%Cv*S4kLbY0 z!tkWO&|>Fw9Iu6HatDn2&(^}O!}r$W4?{sq(e!+&3Wl;)--H09 zu^IV9lz8UnG+SxRaLe9Kn}ddJ_8US0>m-=_1lvsX$tYV z;{&}(Q|fWoggf=JqsBXdg!H1A7!P83H9h+e4SGCwWa`OfFk{0M0AI~1IIu?R#Q&7b zkM4Bb^G-d0)y+hE(ehh|LcceDRT~P3)qX7-YpyPxs$PHZiM~3H!2Dy!vrCFwY#~B z$L5{IAq>s^lz~Q6Miu0UiliBMcZP#x7t(!NT7rZ2Yfse_fNB0>`9EbdNq*P3xj%Is zfJ3}xfkFA8?|~>*c7EbKB$jkOJE8w8^zY3%n<3n1?LxM0mFBy5?+_SMF{&>CwPy$@ zh>+HMG`T(^mQvEw<1%dY56ji59HIlwa4-xpwjgi$Knl0qZv9nI^UV}vgD3q-AC7G) zt1$Vzxx87Wa)ItAOR2&P2c_p{i5idRqm1Ro+FY{?^uOm5fvDu8lC0UmvDsy~e|bq{ z3#yhG8FewR{k{5-Xo+iM$x1kb0AIaTsVEW z65u&g-dp?Pm_wK%@4zYmPB3%ev=cQlRUr@Beu21E%czu%4tUaqh13>7!@!U`Fgdy5 zX%i84Aukb%#M=|vX&u-3N9~o9J3emYfCwAS7M*8WZP-Xr>1Tej_rj5E(=6H-i;LJ_ zO~Gd@vR`7xVcO_xw?itv(i%{ToG=Scgi>rDo33+jiELYVv5N_LKwNJmt zCM}TaYqT{&nBRpYUD!?V%Fc2-P6Fe#3Ytg!4vak$h@jL|Q1tZ=Ll`q%ztB@%Q6UI3 z8#HPYZ=rp=kh&D}qj7JiUfy?7oRg9luKOL~wI1?C3h|;)`dvwnb&Rr>*aEwkMdH)D z)FI*LX9(YN9!xizVdWdksdF-EP!!tVCA)if35@74K1iA=rzrDlVAbAaFMc$*Bz*Sbx=lex$`ns2gIU%%yMIP03XuN{?u^ zTi3~`*#nXA2?^F{Yx&pIKGypKR-5zxv=yO3F^)G$V)FFUbkUZt10Nd>RV-DqDS z*qb5SJHeNk&8F%fC!;>FBp_W2f9Tw`$@mq^vBO-}we-;F7m&p1IfTFK{jOuraYEPy zueoTLi+@iipajFw4|EuRAu~{l$@iO}LvZ!v$&;(_^WbMOettFJQ|DC`oi_$e94w-c zxpChj(8&oRZ}LHeX#)gRPrK-@B?6=z9Nr)b7epftM8T)($a2p^k17$1e)eaWxRIBg zAUETL)0Zu!4-;vWjS*x9roTAJ^no%)DuP2Rt<;yCmO1WXEgEU=i6a2+iKI9254mpz zgfjobK4OqzC`z&tKC(!^ zIl*HSq0%Q~7M8k$hJLxwp`#nMZps7g3`-I1l+v4P&6BMR8(cdO|2ov5g@wqVka725 z=ndEqKZKgIv>K$3&!anbfw`XvUJVNivju5;6TB3rVR(J<3}iDhDuxq`o2 z1&0 zBH1+Qg&gHP)}qJ1>o|=vA=7~e)zE8dhQS&T$=(C;{Upp)P{CmK$~B!UFRyiut6?qX z+Au#zm?Q*Nw!1#kWe5UV6>i4jVSr; zWGA8hqBb&9%gTQ29ROX^3@hW<-@%64>Q$Ge z+K||`uRelGxC+tPFGWNCD^KJbNVdfc&*!;Ehn@gk3h$L+KwkH--jD^amC}qbiRU)F zAKktS9CmYa7~3SgfuhQ}(BmyA%3x*;!15Q;<}54hJ;~!P)`=b%-ant%h-Cw7R+KEE zHWuEVv?ee=5ulZFTGHKr8i&o?F7qlafP&hV6l7$&t)e}5 zW?A-Q(b=hJy-ZSdoHjQ%_wP(lh-244{$MHwAvUP@HuGaBhlL#eG5=NP?Yavy`Jv0p zPuD(yx@>mi1q?fM(`0J?frr|Hu&J4dM*A`hKU6v2geC*RmOXk>&W-Td(xf?Peb53q zYg@$o0P!)AySDpLucn+&mXGfLF!t7AQLSy?KcJ|fz!n9QQUPf}NeM+%LVAWyDQTn| zL=hDg>5%Rjx;so7BnG5Wx|Obh-?_%U?`J>v?>*k*ons&S54W3Pt#z&II^+BOoUJa1 zC)angkjy~Y6$GS|umN#(eZrC&(w>3>L3P*v^zG6HDFkkSq_IY?($PGt-J?&}MFylw z)!-ZWxmYQ6SO)bX4G-cp_$e9e#aQ3TCNQSqy{!~yZPC9SHv%N&oUCh98(_<>R*gab zAVq4S-;l!{cd7vCrsfulros7M{pr6ApkJo<7K%C?FMN_X*zV}6ad?ycd z3Zq`bif}?eR3gH{{fPL!wY8NtE-yR#0V4IB1+6&J@uCw_+3d~8N-G(M*H(cJ(&1ZX zhw~pw9{QqvLNPG-!oBnueTjobJ>e+rsUBYVwq={!u7t99W;5i{9@}NRCw|3PoUq_C ztt89r`t`KxKH)V=JQW^sewsKbDdPBz-j5$&a|O!DYN?f&1Bgf@y9AVrWGAdf<;wu= z)IVGYuDoHs&bYNA3%StSNcm$dsTxwKCa=()7WJENkt1!`@7D)riIxk3K|Ot=NREF8 z%5S;N=GR{I;4Vx&GW3V`U>-znzNJYdv|60}y?no$Tc?|#qVmld7#HsVqohaJ7GO2S ziCv8B$_TVRF8Q<)v3!mhl5r#u2) zTZ6kW2m5-mc}EMX%{gxNvRSEe&nErO!s5&*VSG*bJC|Ie`ODK&lhymW6Yqyv*XA1{ z7DIl6aoQ@%#lI8&+G=LIAhgn_+69evg*>ESs}h8_sNlvt5iQbV{Uw6IVBEd_rxB>& zM!#MQZ8hnK50eoaj6ehV@#E|6pah<3MiQFSw$x|nul(J~*IyP^lO<>yVo5;G(5ct! zr=+}igdvVk+IuLHm4@FbL|=`|2X7U*ho56=r#}y0m`v0=aA@28JyKjOW`M;P<+MDF z7e*HCFM&JH&)Ocb9w=@?mfqUh8g1M%Y~St$_el#7SmB5)`UBt5{I`*f!d^e)kX+uE z5^aowWWNPkpHO#b7CWf1D41jpldEdyD&90fGXukJBbQAlIu`hIkr?3)LI9rD64xBX>I}C}^z+ z`!#=8qkxcIij2kzI~arSZ)H44LN-C%YvTVFI$gPWbI{z#xsUc4OWj4DNfKCzzpj=z zt*fYLT6!1S@C=VlXGKF0L*8f|sC|E% zLZ2q&G>w&aO@JFx>10Io9#D-VqbeNQS~Ycv>%0GTQ|?;b{@|Ia$%dK=LW%mJo@5D`^bvAH49+ z;jS9~-D?Vov>g%_`3A_ASlo#s2pOG!gX77>B<(@0kTMdaLt6-uV?#-x4 z&9dt3P3VJVfs)NGFQxb9Ycn5-qjLAneV`3Dmvt81qRI!Xv!e2$~PAT1w6QE2EdQog)4?=(ad3C z$e|-Vkh@YE#32BPx-F{glVx$DDu{*v_8^MK+N!sBIxW9c!KADs%D%qxsn*jK-cql& z*L{4N!w409D0kPdrXs2tU&?WQVor1%EEz;3^uL0QqG=VHT&+vo+zO&%zTt!ukSJhg zm|4fu^Yvgj|GNDPzF%+LnL(XQDPm4 z^SJ#?kU}J~$sOHllCZhRJ)G>6 ziQXZi%;LB@*Oy&gDm9c8B@ZrT14(MuNk*o{#!463|0@Vv1zfQygBN0??%g9~g^)Pe z{{H@--l=1YlzR7N?gxH*UPuR2e{FkG>SZeH{hLUVKt!LR|F9f^+}N636l^zx-)&4+ zA&@Fm&__@u7vFcji6<)pBt1j$zHU#yskI;R&26WL6z!L=ViHH^KZT-*Y=b z!9#Gp7wPK<613U*ouGXOtqr1BGXs^>YVXx!FOTsF@Mq>`MILx~J>4tj>FB=$jOO-jlTY%ge25W9M-eIiliWl_IXdF1}7ZskYgBk zmS?kbDe3G-k19qyh4ta-1W(x4Od)`e!Y;DK31FC-is-_PLQOI{e^g0Kh3dyhKj%+5s1*}ui~?A z);pTi7Y(Y5we@sa1#2tSBCK1S30v|7*^=mQXL%ThC3D}*DRA2N}SK?Ilf zc{=r+L*Y=T7WBv^*v25o8y=tU%j=t`7ju7+F*n+#Ld*e5e*6D4)lG(XgvW(GxWHY0 z@p|qYYu?_}1E@6etADJ-x5=+8tcX3lX4ich);x5bu=U>A0P@2oSd|tI3lK^~ASU=S78Dn`7~0eI9l(QP^a;wYdfpx9lEe8MN^bDw zM67QXLm2}z5}1{lh^M?dxDW1{)|P0pW9MT<1mMw$LpzR{=~CmK$n_3;DnM!#$Mzl0 z5Bq=iaQ)K&Ch7%75f@@{e+YX<9CM$J!yCOUBSiTDPyr;bapfbZQ5@}w<_{x9Tq2Fw z;hTC9L*n<`zXn8aV$`1X?a#9ct4XbcuI?M3xf(hjBC9wNSR+H>ASL;`fV1-L4wcOR zF5w{dZvZVrTVovbfXmgZZ>ymlU=MEQW(Ykdq4O4kXlxNs5OT`yZFl(4+FAq@al9uK zy53S6(6w@fHlN3WMuDjQXYtmx?U_gZdk)eI|so+nce%Mny^ihnP%|s~rFH*G5Apz$Hurpf?|{ zDlHNXk0_90eB*9?Ge*(AM8_j%!*Ka&2q(<~LKUDm(Bp83*y9BMivk@3m@Ih{Rzxuc z5xZVr!b?<83X^ckYqjX>=m3m88zL4@PlX6AKe^e2vrM}VuVuD|8(vLzt=SMt&yRe! z!vaY26%d_rqFB_058AP`4C!|?cyhk8@_6nB_Viv@+ZXILZo-NQuk;i%@nOwk5s0#B{e3|4hSfB4haIR^&?OVqga?^a0C)p~8 zk;Z&IGjf%9xWxf09GS45Zr!{l=qE__Yc;&8eL3Z+#@ub$dErT?1Fg#r68%h*xK=J= zqMT2j&8wN$;;b)xv?r94T_&iBvw_{xc)dgPasU;BGcfsN@!8x%nFMW#aFn83=|t3D zWMsdt*X>VAVUYFUn`LBk`1|=GFkdo%mpDSJur#H-gY|ehr%W zkDkrD8b{$pBot92h&hqU`E1^mDp4GS7wn z%+n9wv40Vb4Z|;=P%M=7Cy}=6AnJt%=9QyV^kJYu)`5@^DbI$5g`GwO^?URB2O%Kf zGow10k@+>zUGlv#AwE&EDVsbm4-D4`ogsas7K(&fMl;$3n0F#KT|?fE7*HE)71zDp@PDz*pm?Pv#gxxKBED&({l}6ce+S z&l}BL-iH6iQ(L|Ds>MgemKlWajl$&*;N4m!i0fP6YdtZbwL#1=*c^hE!cK7iryqh% zfrCG{8g76+upz*YAE}R@Q->!X^Q{9*(rZI#5K+TJi)U);oLcl-Ip5UG6L}-63!>yi zbGUTX3*Wd@Xh&~IdJnzl2=b}b2CZA_W>zas{btZS;s=Z7ow`xMDb|$k#6(no^BVjn z!$y*y!E*@*w>zaBn7`baHP{Pb?-3+3upTN`Mp70rx0|q^D75PX*w%irq>r%A15~G- z{!+}hI_*>t9j)Jp4RRW`hhv2ec_A{t5zU@sVA*wOS(|Uu^hYWqLU4y}&BwukBwp+F z+H=RDRCITpesxH--#b;qw5AFjzvWrBXX+RO9ru{e&q+m`s-gbyP<&Ds>VM2^A{vyF z>QRG#5r}SWBpM(hM~)m>2%L31e}7q-2vuwb{WSB99+f%j+$^8uG!ixCyKgRX}fCE<%_22Ru(%wIoSaJbdIrP&-C`9y<02Y@`eo(8bYlb{EvFn0{8h^NCoq zMzF(kPf8|y51%Er^0CZgGWqsPKBq@6!WoLywdTi(eqC(Ms4k;OV)G4;HY0oZ=dMv) z6jGx1T2qeZGkGN?rSVbJj;b^1NyyoA*-zEe1)d@IVnzr4zfa%79&OuopRB3bR!S;D zO71Q2>Nr05%ktqtu6w^%wgv$x*>8`6LjC%0#N=r_uUwNUs}b5=HGA}MyY$O+5pg0{ zqAAr8HhzHzYJt42aZY?MATt@d>RSR13^OX?#U38sk5F>q4h9sVkO!1dTxsC%|9BmC zc6FmY<*o`qyFL(m6Yfv$i;9!q3Lp+_9!;kR-ep;jg#CDqxvY4=Q?xEyYkyTWUC;fq ze{X}N!{Oi4IjWmyq=Xx**)@U9N zUhI`00SwK*#~b3rq+%rxY$7rk_}>1>D_uF@_u`!YsS!ixl%>yo z3TG;Ac=?~kkE8I)UeffpA?gI{He(&fx9hP9fC`X8#2eUzx5vPsXK`Ho7EA-JDmpL3c= zgV{{HRL924D+PHo`@_(TG`M2U!$fAVL&PCyJps;ddof4?T&{Us634Mq+^t}_;tCY{ zp59*Pxu|bDh2sc|V7$BlKh|;oRcZoSTM|Mr4R{k9l(Hh8V$wX9~DNldgbR~ zKVNlvb0T8?UMgZwR_LW`jHIb@FuwbhO!(d4){nO8LW}K(4ZJm1=QKM;bU(uGdi7lF z>9oE>p_iCN8=1My5BWA2iBHxbv^RKtY!B>{rS8Sj&J*CQv^qcQ)I7j51xJm=$`%42 z1?bK~4{y@?^HizmLmG2zCFY+(2?gTnBdcu7pta;AJ9#_Aq403E#T{2dUJVftw?C{s zcPiVri=l~;0jyME3(4ekjaoj}^m`euUei}CRdpsSl#xS~JAab;0KJ32o0sYiyb-%aEJ)?u z6L#+EL5m+bmq9`yxes%Mggv4jeMQnkmrsT@HEf~ILZDh;fn|LMUlSip%>_2{Tz%Vi zg6rZ=>l>oJUM_a2^w>^a9$7=T5-TB%#6y}F_>LP-cRs{qnnO$4;4fVO#g z%;K1J6atTjL#&`%^QrjRck?I5fFB7zejZA5D|IEB?jF0r4>gM<^zUuHa@c_F)y|Fk zWIa$Ws*IueK+x|331?(8OCK)_-3rI;E^V8NyEe7kre;*9xQ-b_XJ+)i;;_8JCsdDA zTSh0)vdX`F&);t zk#cwE>+;6syM^PA3c}R*hv(*`4gK|MiyR)H-^mA3ghOaKOG}rW)y#6wU%D*QK;{)? zaMU*TlnnCKgc_>rgXHjVrM#-k!ozj*5QE=)KrkVAq6vG*I$X*+GPqLWLet>K&*gAJQSQ>zIgEtx|lMGijk?QS3pn!Nq^~# zlDe^RDqtW4?Wdy6+;OA&a`hxV@6fBtJF6AZ;w-j zfvlK}GBJkMmq5s_7>u1AneMNIxcA5L+pF?iH>N_)t0b@bm<^IG&MG%kT ze;$5LcxOHR57yN6yQeHVM};Y?Gv$5@yq9T*P|bfMfrDWvP$P7a--{`NW2n|w5JaiJ zH_IN#%+JjkLxXSS^C`tf@OrRAP|wmV1;a<$qWj^~m?}u))g{WWXaBb;j=z#VX+p$|^$RLMyy>8b{ z^++HqMCfr}*X5z4_{pT>w+em-$3WRQuAUyqip|N2gpCYeXNVilC{Ny)(5exzQCI7e zk*~R3r{2b8F*lc1+Rj+AzOJHe`h=K;r!FR(A zvTO75ecJQ#*JeV2%G8JWE;tFPG2m-EfC$@w1=$6*74#m61P(e-HkT_>!VXs_bf?Gl*?*-Z!o}?#V-xs^njWx(? zCiY%4hnLgghL`+S0KJm~f)}+YdeKyG$mOVp@`Vz_d=u54hA$VDJ6t*L`kTY-HhY`EE zJXqFYXe8NiHlEgKWF!QgnQ$zL4w^3Uya`%0?j@8`7?sukxOEGy2L&{t^u5aGomqDQNy+SROy|qvPw#&uuQ8Ju7aa9{>U%wpak))>Tbkp%5@+J z4l5t!l%+h_o3g*N{3VVgur=e`JM!s$XU7;({5m~Iiuj{f0)E#xM)lET`V=`|!^PEsO^qRUsK=CsMh-OuO^@SZ3mQn z7Y-;;NlNek!jPLLWH)AD7s6w+OZ(#MGeyd<-}-gJ71lj?!H7bg;^$N%-Xik3{Oye+ zC8(|Q;1?mz29eNUwH~-i^=kKbEQO(ZYV{W5g_Lh??jGvPXx7FC(%dWxZ2OxucWxDi z?fe_fScIOg0axK9C^%Gfbh1Bb&own6f@Fl+<@~0v8-ffJ{V?Q<%~_VrH{Pj?gIMKr z2@BENbQ=NIdl=^OFA2&&jyqlc?r4OoOw+{R_6ymK!s(q(mG%_pXeFX?TI{<*OjagA z?{$-l8syO`?>?v?1zUgi=#r4(T!K7 zLQpf(wiiC#w@AQVoqb?2lDHp>8%o@o@J!0AzDM5t=7%laVIKAQP{Ut^7Vrx9HfRsf z<0|hKX_cN$ShSKd;YCYx%o;N*ubZWo%I8%zJezTTv2>b`)VF<%+T{^~XhTGX=jF%3 z5Q-cCr`cdETrePag-kpjd0mhM_E8Z*!4ldYr3lUuz_CBd93KCJV}EEB*rx)T@+a~Y zJ0LQ58-F9{IJ+Bx%*hzW&sAehl3C}VL-~;epPF5%AThhQ`s32M{_E|?EQWe!yBsoK z4kbZmC(<`#L^QjPHszh)Wpkda% z-^v2^mgQCe2#%ib*Bkl#h|vzI(Oypy1_E0a00 z%#(E6TjYU>XHZ@4o?nEaI4Ls=rxF4O7wkRD(7LxtwL4i^q#}~zAGd=q+g5tQ30~*~ zd~D7ji6E;mixlMfGkzzr(w}{8PIf`RPu<9I-Rmi>xc%d_eTSQRI+$rnt|SuOx$74F_Kn_?UhB(Pa$}5yt?E?{VHQ-KS|GXI z$!{cM);gDb*FiBUt)tT|7}&$>E?uQgBp1$vMnK9QkR`a1y2L9YR@^)X2L}@n=&S&t zZjO2*uX^Rx9soL=Hh$?uR6dH0hdE+9sy$x_pqTQ5p<{3c3068qNug^fA{DA;b**hK zn++@Ewh#F!ltpKiSoPhoQgkT{k29~c@KWziEQ!N@HgvBm5R1of>D5fS)}T3RLKoLs z2U*VyFE#bp=iTNIckrkUZNR-T!N-R#7uhUNb5~qft&&o0fq(9*&P%P2zD`$r-D|0O z;r={rPS+&veR_d6KD2oJM zl*tLTXuS)4-p^8WPOP#;4S6akZjTOd;%%q*WBD|LA4|4h_=Yna&DiG}fofQU%}w@} zQWJ1Orfbh(d=emxoLJEN%~z=&JE9goSN)Pa!s+F)UO5MpQ=8GLit+(K!)HhPgGYvg;3nb~cC4ekBhN`yM(UI*2Jqs?_Bm<|2y!3fViB{R3b}CxEUU z@yl-Tz#T<%8u8yF^EA7^2K1gY#ZgY*vWez?at?wYL;^Q9D`F&WB4P{G{^4=(PPo&Z z>@3~*1iD`P&bw!tU?kB5VC^C$_I^LA`J75qQ4ESio7>)0#Ifp5;@RP1#zM1 z{?<6+#hH(21OE`8dDoTUkPL9u=ZGNS?pA~bzANm!5Osxxr5VN_tk=%W%$Q2(>*{iu z#Xf$%Fi;`~NbJ5LKNk1+L5$REv?tZy`J&Tb}T(?0Z0 zQa|!L*wlt|zCO&Y+w-y({vh2TT<}2 z;_RmtCM)}2KdsD`rr2NW*k8ZD-Z471q|8@o7E7rUhr6s+cIs|R<||(;wpJ@9nf)O> z54P(=S&BbRh%Sl=<{+$>nk&<6zfZwhEVZz82Z~`yt?aEbQ@=i+F;KIJB{#$LeP!2T zk2+4Y^p)BBG9nh?0~zM*XkFa{pbxljE?Fz|Av0Km0qxfU1|^U^Ci|o8g~dgsBg;?% zCbW&)hv*`oL9Hw9dV;Jxe<{ts47`gw@em_Ni@FseLe7=-KK&NxkqtPBD>~nMHFTh5dZTo=7mr8jFpo;iw3FiTu ztY`Zl-JYX451K*_z%b^U6{7v;FcCx%>=$yIpRwNK*SEO{NY_5izL=gjGC*JafB~RS zvku+Ee{55X+mIw`JLUpWR68Iqw|YeuktBZ}9d+$rLK-SCSnoE189>BYE34SKaO~%G zL^X*NQ%ZKtpo~0{bA5T(3H8S{SJ4Nx%C%&YrMcU;Y2HbMgVn@se|y>q&BM>2>QIR! z=ZN+3(2)}}z6sy(anZDw@2Di3%r}uQV99$O`Y5bdw~?7eIqRA$BhNog`B759;=LD; z%PlTeaCdS2o7?6SqmLx(U8rMbud8OMGlqMM8-XzcN%Ibb{Ei5+GA}})oOtgz? z9V$<_{d@(tWVm`qYirM-g-WVyjt7ToOKiHA)s677z~*Ie!$4qe($}OSOVz?A^<ecx`Oa>EQx|Uv7*L!~TbJ`Y8aXqvmY5-hNZ1{N&2ebM^*d3j=9 zYn}HiCsz?l=2LAjhQ|T&y73hahw9myH47doL|x+m z+yBv0NMB{+<_-%js6gg)m6kH^DYwUzBL5AyHvVcY&HFKN}fP`Kzd^cOVtnAy09dvuCefH){{8gXt9ZQ0AIvr-BG*ik)ii$T(PK ztZc4v12UHflqonxz8FpbDFYXO0tDNo+izh4rL2)E-CU3)Y$)wR zkAU+o;t8OXyn8=5?hZGquBL!RU*JOh4l;_nO~UC@r-*0TVN%lBjaKZXr&!4(Eh7Fk z5M;F=ld+M3Gw}pSN=r@E+w80Eh9nb=#4U-p;}5GJe1CQzrTh`1EqIae_4cE z-%5gI6d#hJO*wG9Rma_35J4vBng{mU$IIjKU;U5HPQE@Z=j3zBNl~~eAL<7!j<@7^ z+#*UWbY0o!4!#0taW;*K=Hi35WZ;cMS>92-mUZ7VD)sa8nTm{4KrBWqC!LJvoq!lt&`!4{(nl1mB)B={@q2>xU-*g0rsraxNK!Z=VgoiJLoT zU=1luf{fzKoRglo}>odV{ZPALKpa#``DACjwR2*`&xjOfQ9aR9Rv1~4`P$xJ}9oBOda4M zjQggVg|1)0U0m-0#9z^6IH&XorQ1B+W;suPsp~q0>v(TZB9@#+_{FpJJ#P9)=tn

w*G{HEXPbo7E`gerMPjifbbtkWZvDZrQ2jU3;aSg_ z=bfE>ll?rg_TgF!=nnR)dBLrsMnZDvHWfKF?`XY*s|BO&T~ee+Q=$0skW9$%|tdcQml%PYZdAd=ae^JtGW1iPTfaWdnOwUK5foo5v~a zysZfm`VzX|sbL&s8uYVgSdXL6%K7J_G&e!M0=D0wO9=jj`)1ITrUcstMD&=PtiCg! z;Vv0r)Sk$yt}=9ct5(t9kCQ`RMdx@R9&NSPAZx4N7AXXp^+hmqtCy+trE=G-sZ@>T z!Qlgy*mJrrYOkuR_TL#_q9nQQ_rA@{k1YESfRgLdzdHN`+=KvZ3WNs=94DYE6M5Ve6g#M zGzYUl7`GBp>oJ25N!G{YiL;>wX5W3*EC>u2j)$F;E&fFcR&sZ*LVexwFW|X=H=j0diG^B{19HtqTTn@!1QTd5xOGZbSo3kH zztb2r(<1G&E2LYu$kBLS;&68-r^vlYJp8%?#15j<63m_0kK_vCF?RmzR#w-BD4Zi0)9xDb1I#E!LD3+Uj&ofmT zX6t4WR+HW>{{qxH2TgU1m`5qnq9q#Zd6m4hgxtRUxC`|m6T}yn))vO5>ga0$ zBlA`WAsS2^wfen{(D`+}QT~-Ea_^o@<&eloR*8eXqUybsmc#j7oM{X=o*YOOLeA1a zj|udFt`-+0Rf*)poLoOO-uN?*@C?08wVl46p3SDKre?HWR>1nj$GM0z2Vtyf0<;Tu zY#EQm?*%F7aD)S=gGtWJ@~jh7qd`+0jfcKIkMB)#M4gvsk(r*W7m$wY9HC48v8aQ)IVVd8b6j8F2VW8kldYx@ zWgMbg`|S)|*EA2u7r;LRpWZ}BHv|n3`nrU9vFGgx2;~(9@TJ)m~G0{PlIMU#f6Jzc)a| zWklY3OQ-*MonR~eUeQy+<|LNImTu(lf4CT|Me}VLu+724mO$Sn zUVe-%eNF==2+(02wQrvA#m0miVdBHJwY-{FX(Rd?r?Mq$ZiJnt$>uPLCegB}Qui!( z3?fUyv5vLDRpxe&Ji99vyb1o`bH<$t@EY0^g!`*P|2NIJq3lK8d>HtZG<9;eEkX<7b_#j<An|D z0KbX1kF~Y+1s@UE0p5ssh~t83?hgOnC3fXTW3WCCQ}xcPP<6y;onKNX=29iiiREL< z5n1>7glX2YKtv>_K8Xo=sbBohKh-4rQ5l|ujZZ5zl03BLbD8AV7?B##Hztum*+bAL zYpYeqKm9i1bGp=K7~2P9FN3(>&(x> zlCC?xe)?1Q!?wYjZ)s(Ky0AnFD|yIPoU|`(-{yWSEeD|qn>X@a4(`~PlZc1S>HiA< zkX{!v1b{o=q1&~_pAL(wUvwq|Ta;yj2si-P_kPpmZ@2Vt?=(UcC>!fGQQin;duZwku<=XkdTmwtiugs#Z@qZIE<$k%muk`0a)EL z)TN~Ut(hydwb~w&n`OHaJNF&HTI-h{R^PKqrXRu?W`}Z7y!nxBNB8|_t=JgkLwA$N3sRBGrlj?Ol;fUiaBrNPomCJGjGW(27-{ZdkWNq=5)9rCnU8x8` z117Y6zvzPu6jyzFisi<_;d!qem%w+hNewrYrnfUf(%KX)#+d{cH735wR$dC8ENF_r z1RTP^)?cqGIuY~H_R8cx=A$*IOHS72VAc(J_l_~IW+SF;7phy#Fg&8LsAy3wLa8qb z30RRdKh|w3y3+qmB~xa5jwB!{h~jS~%r(LYoyP;_8BM;#haBa;fD*llMqQ^9GfG9& zi^}AOsk1znJ-+r|%*qmM?EFaHAE}Bxq}hurdV5+ecBL9?=~ol+oc+%Gi>Dr1nVe-{ zsZmgCRbk5c+-N{Qb7Y`iJ(e)(>o&{!=LGg5?_sCDS(SL7txpUSkVAHv^q~uN!bR}# zwAZ`67nQZJUGbN|%>=}QcqP;%`n{@>fmPZjVnJ5q8~rn%KWsU9+q45(N7)UK^ zKh@qTIgWxca4?87Z4PZwmo}|piMAbMgyYlA#qw=+UGt&`@PTZ1)lyd*dKG z-G#G!pl46Egvrwb&xGFzAUJ2L&p2F$tKE!Jgj`-6YL1oTud!FLlC#+48K6@#3s+3` z37|^2X@&~Z*#otfIk-zHPGw^CkTGGRU`p9BLnd`N^~Gl8R>~rxzH>r$21ob8h{j^) zzdZhN+g&&-!20gwK&3l0kU{kP=0moZFzX$y3X>336{qG^dM<2FpgYlbElACUUz^mZ zaZ#eBPy9NfKw$s{)F#lFV(us-1OC9FrX7h=KXnnkq8fe@t+=NdNo$=2Oup5L7W(e} z;^Jb{4ionvk&GHfowSh_$M({$61(^X%cAZsuj(NPHeH&CJeejm8#~IPo?|9pC zgLA^S4+M0ub$PF1-eRu~=#+G(Z*6bSCbadu{fkI64p3KXFQ~d(KJS;poNLPo-`ofj zag3;AlPcMa_O6*_rP8Z6$TmH;D zU3~b7B+bk-YJ2@F=Xe5mYwYs2PwR|E>HU7Vvy5I(Rr&gx8VOZ>6+S@+fGE)`N|Bi8 z($ho82!aQ6F`Sz^-xc*L4a+?rZ&nmqj$GN^uO%zoN-vuD(Q>5uO^OQis~>mq^JVrs z(6eQ~gB?8liv)dGK!jDKeodj;y}k7dG6}ok4>Y%)wz|6nR+m4_^DMweEr=;!!etzD zC(dncNTd&RA;IY>rj6qcD=3>h@B!Y!jByRSrA0D}19B=MBk^EnWwL-z0z2+-pC?|9^j4kzSCsfBviccQ1OaathlVczv~dza%P`#kn+Mo{Irh9 zEpNfn420H>x7o$aUNk{2uBc4nzjWk2T4naM?qA4qrG`ah$lWd@e(F#6JdF zqxi~)5e4>Ywz|R`I*_DZT)AGqN-_6r^oxS!?M$uxVb}awx49G*Qg{Nv&Oi(yJWKf&O0MtYE zBbkt(b-e;X6#-qhry+tzpBu`ey^4p10LQg>zKHc4NEnd%u_9tZ9(pWJx@iZ3CkLYW zDt!WXr1}W*iu-Or1B~cV;EORL8O67~fh7o6nl1C4S4CRk@-uQ%%)5xkaoSqAiS3?g zl&c(9i%UO!AQcr~={3=FjVfFeIA;~qwZ#)Z92JfmU{+GG_tyYe%`76Kbu!S1_(C9!jf*F)vz#ZYL}OxoS90P8n_5?FrItTkh7=j(>dyGXUA?!kG= z2_&Ec2L~Z*5xV$4mpdbk4MD?raXpr``G*eK&^+CklhyosD*|J$W~x^~X~2J%w5+Fa z!`Heq@*A)iF@BotVNQh)BeVb(0R)gfLy5~{)If4iRt)C?%8Y}Pdo3WxR=^G?^C)v% zQWzY~A~({onl&(k6)`l6J&qOksziz@V6f5l%=Psp+J=JQ1Wc9eOw*`?F|xWQa_CMK z6Lp@d1-=0M#U*Kcio2F)6t`F=a%2j=e(*=2X9fe-(KxM*or7g>=b5Q=hDMS9?pn~k@6|e#Y2-Ffc#pua zfxJh)8_ER{ActkuJ{J*x;!*hQdcn_Tfw=3ueBX80pY zKdXwy^pJE2=IZ-Fvg>Y}tB|L%Lq!xp#g?4L8uA99fxo21TigP zsLT1X>o<-RJqWZhi&DjE(+Z^JSKkvbrk(i$hnaqgQiK*N@Q#lJc?VQVM3ey)fjIQC zHqK`8W)C^|5%nGvxiI`vM)x0TP;_Tyy5cBFfv#kHaV$C6@7SdoEwzHhN&yWu-2%o- z`&=AXUs>xu(XC2OPE1aG;g|-m4M|dJ*|twR#r5c>2L>vv(c4}U2@M{rOVAm#m8HBs z9S3=z5;3*qec8D4O!jMz5i`%m16}AN=S6xq*FXaL=aYF_pWP)w*r;O`# z)F1-6Y1GR$EBs|6GB}iVo+_R%@Du*b( zumOiTQCM-VaceS-3E}rNSGHo*BQh%}9JaTsDh)&014iT%)v4r+4<447hlRuKN5|X6j1U zqv-eCDz-i^N&TNEdX*vRHu02)tM0<&#AM6J_$&*PT$YJBjx#RVPF)3SJUMk{aP&jy zInz#2a*y@oJJ#Wa5A4XrN<+zv_Bs`uB-K`RzKYB z*5r2FZoB(X?e>@KtJ)7~X^KRVB~uySW+`LmTW35y*%IBVqgiv1>S#$lKZ~$yTun%N z>wE@HN+TCVxce)%sV_?Zy!p?UhI7@AD#m=i8#UQn>SVRMy2_Qlg4-yfDI%vXRxxSl zydDzawf~t@jCfEF4}Y31qsi65VtV)4yGy8Gp~_6ho%E&N;&jfD$3r^%PV>BkyT|VP zRU^hO?WOuoLNRVvWg^+ML==HR|M}QO)YT>2cts&7SJ&(RQj<3DF09lr>-)#CCbI)t zyd3--W#9ML1S(I*HRl_)<@*_Div)KSI^TX6q4tgZIA!LyhG`DzdcXVdBnKmW(``;j8`l&z zpq%u-9wp!BgPGChZrH*9{!G4$!j(xsjEHXj787QYew|agtGV%UT$D+{iv{6$8|38> zc@X*i2KUD+Cgy3`YEGBiDj(+@>po(3#o)u`;@5HQH?f-0b7j^|eA$+F6cKKC{k%@d zS=N<1A2uovu}-4g-%14>b#sSW+n-Nj?5U#jy(a`)OmdE&Dq2}RPF++=-zlp}N~T#^ zbD0fY=&-805guvFt4cSwGQ9Jt6t6{-b=UKDIOp!?>?6E}Q~O@$$TWT(^BP|_M{A33 zdnNqyGBoSIujEfvlghuS*b?Cx*ccMmWG}z#PPhSozB}{qKA5<#3Jh_o7w3$>GcH+{ zs+9RU;O%_=llA@f#@q>1Q&Um-g50T60XlJOT906+C#E|edwV}~=ER}}Wq)3}^I83! zFKN(hH{VEvZ;72ph?|2d7Br-Zdk02o==@quhen-!YY%F&|6J~t(EN?3l{#LxbqDuj zVUG)qWzp_XFv^tC6iNFLKW;uHWvi}IWN)%w8){l`=FBg_+Db`x9UMrtV=AM3k7h%c%66ySL+g>J6D7ucnNhiK$ z|JwiD=fqZvSgzXclE;Q0=mOKmp4nOQMX0q5*sr~|l5oG*RkW(J5gHe1%WgS)$zIxK zK9~_l2CFEuHs|MLny_{&VXeAaMD)Bm@tbf~R=1bX%?(oLPgx0F8qc$Axbf^i3wUUI zdt1g61&50arzn%C#OOnIeQRO+0aQzuF^7EDKzJ;R+cxds`uWe#qBU{YQ`Grm%3311 z3UB@rb{CQ3qz#6%%0znl$e%Z}ucAeqH1>9~iBIH`2zSk?ak2l|hQ==M;qh0J z)XFt?%Cam66T;uK-V91>>P^XzS5bRp;ENKBU7-vz6WcJ?-1?NDfbhP3U$GH;VswZ2 zJr)gv5N!OP&n^GMN2CXL_$PMSDNI`VttXb%OT*@#ALew`Nr(W2L7lZ6AM-T)U8Vip zMWv2XF|+ZLO-(JEGp_&q1fptUxM@Oh%h&5iJFFPsHN)*Hx+CZQ*hK&^8ZUe==qfak zBcq@Qhq<>c2z3vPvyN3k09yMxSjq2p2vut1$>#9sMN1f_Ltr2pb9lUb`EmtB1~H3>ta^yykkn2|E?3fR(#L(s z0bCqc4tMd-Cy`USUNZbII{GVKi|L=XP1~3Vt4By1a00O+PO>qAwl?V(I4!>C8=3+v zGn;Q%5zItwJKvaoK6&-mN$RVSWw}OGt^f0?5SiQ|p~b#tin}26ARsij%~1&AHD&~% zKMxqua;vf>{W2Tf=q0nhTwPOLm_N+7{`4os0<1lJcX~=l^qnCHwEldQw5}K=$;tE=0Wq$~=6ilpi}UfD^=46wx9SBaEVrt})e(~%3prOi z-+qO*=J3%{X19N@DOw0D`H;P8?l?Jzu4mCsBH{L5gz9{Z6ChGrHeS3qRwNGORLt@? zQ2i0G6cj05BT_w^QD{jyb4LPmA;O(=gp*U~7Pqac5XBFxbS6ku{(P?TLv)pPVipB| z?q@9%5)!f4`5WHQ<|4ZvlIMi&Q=#(I1Y*84;9$qohp1opUJGl_?R_)?+WGTIEGD1w zID~zHL{6~lT#0K68S)JDEZff?>P;yi#G+DH`1rcAySuw}OEc2aqMcAE)XUuw&!1>siJ5gqc`<}N%oP8z4$j5B01eakvEK>qY&adv`3omP>G=^ zQ~YmsOX&Ca+MNHm{s7sL{(O|q!!v9?*Hv+ics@vs&7Sd#Q~3W!+Iv7XnRR>P=*TG6 z5k&_S7?EC;DqTfHI?_8Tz4ul@r2Gp|J9b~~%J~Af8%03aKe-R!SXuO_<;ZZi1 z3Y6zkr$NKdPv}S6^#E>g5V+8|LNfueA)nU>t)2b%iv&mvhEy8 zIU^q4X^-i#*|%#%ovZtX{9ri~cQZ8X$^VL^%p*%Fe$!m>V$Lauv@A$bXMp!|F{pAP zt!a>I!@$U5EJ#gC;sutPh}OY=f553&f{`~ZsF2;hecKIu-KuNqp|hYjIeJ@7PG7yw z*z>J}J$0T?yf`f#!`OCSzH=?wr;rI~M|<)NPY>A3SJ= z>M;Q{b)SJ&tUiJ;M^LP{KJN1cNtARbp*yL|W&zhwMfTH~UAf;@?2dArhvgg+nqs#l z%kk;E#EBHuf8^gGc6Yhp-+atepS!*J?~t3mRbP! zF8lZQXo|rV1py^H;L)l`PXc-0h_*>&WTXmP9@Yo+nh2md7Y(qE@_~lgnFk(rH}Igf zf3xXJ>&|*!(>>><^Cqa@L;G-Q^Y?!&@i_-l72hIY!T)iIVVho??08L_d>zpY0=`_Z z61i7K;Coh_^%O4eK46;3(dlmS4$Z0#rD>|SJSMm*wD3b3Aw1+o>lW)W+duyKG7P`R z??LDwm~1UnI)qoNs&}s*96Du%17Ak&>-9(@;K`GNzR2S3n8$gXj)4`j1oP^RPmd`~ zh}cv}`Tp-~QTSO9%HALc<$#R*&!gg9lrpzPZnaU>@wi1yeCEW^9S|t`=KA-((D2SdAqR}U> z(y6?}zPWi*FhY8J*{SLVf@=aIql(SZ;aLZWvO)M+`Rjp(pOs50_LVpmJybC&#_!~@ zN<~NCxjBaVp)t(7gQMGL+_`A&y)scD(FqB5xUXNo^5f64?57O?66L6Fu)6fh3q_nC zU}jk_T&Penheb3QV+lQ5%jq7YA7pYuRtk?dklmNF(Oq$k}o8l9eQ%WE(58D^J_- zv^##5@zhm3|CV#MKi#drqf88e}D0-eFjf1hmEB`8z zQRT40ESLsdk3pefS+ixeXK?6Y#)0XD#^-0PT;-1yo#55im@ax>=GSqP{p&O=IW2^> zlXL8d^=;bjd%sgRJ{@Fesy^Tqk74G=^LU#MT!AcJ3ejTQIYPJ_tTX?b?aqxw1oyur zOR7@mz5?XVP2Anz4@p?dswM~@4g2)XEnYUbuJQ{Z*31bXO7x61A>5Dl*Rp8cypv-N zU&LuvZqlW{@nzg-PE9RJUNTa8+X!X*6d~MAF#aLYCcN9rEQ~)Q^*sbEV*2_44aSP} zj%5N#*C6e3B%@T~SpdKr1Aq`8ixwAL_Vp(oj>er{zSFM;f6g$wHfZsb-U|)Iw8RTp z0z?E^UXa9E*dEu?DR=J_sXYJep>Y$tK`vrg&nf3CeMJwgwd7FYKsUI zG=hE}8iXI(yJJ8KHwyGOFaT_3<>A3-mnVoiCI~z9wog?7n&?>ACl+vD@&*CAKyt!N zTcTLu?yHA$-%?~`gM-sy!We_5W4`*kv^ z9EMcow|BfNij2-a+IRRI9wq2DhKb%Qwf)ixJ>1u>(w?Nhg%)xr5?a?KcYeI{1i0@* z?MzwZ@?90$nC`Am{;XybpI{zdtlSUNX)nLMQ}xE;0(tDBQk4RrmoDl=0{s#}a=_+9 z33yyxg=S`m*8FMcie#ka>h+aJ#)Sn)vrkWLD5QUJ#5s%jcT07^C8>JNB|Iqp)H>Z3V8D3F-r zh=I$T2T|vo{CkF9vGtBO-*=P4ymo@;5I5_&i%eBg9bW5U(R)>PLqM%_g4K9j;S%Mg zGeEP5K*dG`@Ktxe&`rZOADYKDA0(N7MO=_z%4HTFIwjf&39t}wQn-N52@Ta*LEjG>E*!NqB$z+SQpm#?TgmGlQJ*TKWiC&IocBGm~uy5l1PuM0Ty?yR)- z%N4IQ(nlZ$p6Z7G+Gg^@rpn8X8CbyvZ3!YXHG!@1LJGvCB_zMqM5)~@bEX3*2ZYMV zni{EfSkVU=i~tti8u4Tr9=4MV3!J7|Cwi0%{)~j}L3aX};YkH~+T#g2WVPm`Lkdzm z>yD>M!|##$)guntt-isY{5nunud|1ufYl&0s*z0&ytmx0x-QUYWMgCdpS^zp@G4s8?Lk`4Nd|h#A-f?@?yt@=;H!M4`nB(%Kx-e1xbOPU@POnh z9eU_ela!Lhhk3Ft&9BX&+olD8Fl!u)4i89E7QP%0A@s$1?oWljsqJuGEfh?;{yry+ zm$<1vhCxTj7i_H3q3S>c%b~b9IP+V^`^WzJt-E)$r!*284AD>mO&OgcHbG!m{gqi4 zy6aR|VGDu|_4!PeR!X_f>&6FJdn3~?L@D+kc?U}i2|{die-B9FTfz{xB4@;!(t4NK z_k1C2!pljpy$uhc=6nh^`v|yzaKXbDQZz({EsT>($gTv**dSHWHw|3TDQ7-*9z-7N zzo;(qr0iJ8@9GE^@ zG1AfLa0S|mY#SaS_zlotK)8HdkC4r@aaI>Rhyb<}Zo{T&!0iZW{js^R)b@@47B~F( zB-E@J%3fb43=Oq9#10!(D2FFC?<@&5&r?&)>GBBG>QJa|*iM7EjV@ol#5Vu0T*vkP zS_WO=^4|9ls}#XUjQ}C;cyMstDwK$HjC3BOj5~7lr@?*4X&3|%fAH5mx~*5ahPC>8 z8R@RX|5{yK!!A@-`10WivB3k}>+6U=-H&is@e<0|r7LeC+wRc)bIQ4b10J2KM92!8 zfdj#dpcUfzVNa5llgquR?W(D%*&@Nh@_r7P0>kpVPt+(u!R$%ZtC1&rRz5XT{+`Tl zjVK=q_TyliNa&PZ-437HnGn+zL3L(!pi&ET-A21)z{IYEN!X^{ms|zXnW~?10MpwH z`m(3{!K=UmJ_GyI8<~HWqO_=nk&QY4D3nlY$zwnw8{K;uUaq6|eV?2Qey5)1Ysd1N z3_B);wZJw$K1zasOXjaiS^`h8|mOi`x_fSbP` zGPIoj|DPjWSk@^tm9B*DKO2-*5U`q7ck^^n15*?zyj0Gi@#&y_bGZBzcvi+cX^=Mn zkPd{+%-ziAAvSn_(lSCjJO_W8x2#5eoim$=+AernCpH@JWab0ooxR0TYia zd^#XdqXE{)oFLb7E^sE|*=pVT_$goT`)u`ss_Gc{(Rhk~hEndpGK>lhI$yyQ3AeF_ z13~0DNM^3No7C2WhDH-`{Rj|0V1FdH4zuA?TU+7#2pF_kn;@oJ=wXSMz{1LaC!DMs zW?45pj8q^AIbKgS=-pLM;R(#55Jtq97Ln8`ix;4lh{VvgJ0FOXL1Do*=-g>=OwKbb zQvwB^aW69vR3`%M34^Sy++qQ6wQkd0T!_<8>t=7Se&`G;J-rtVn4^(>P}CRrqK3Uw&&AGk?R#GNntbhP z`(NrHhps{WeXYW86rA$jF8%N~rYxQYA1q367|1OacC)?wIXL?a632R{RJNDJx2M)& zcXvPzuk3Q;U9doS%9qI435T5~1za2)+|&?d>?9b~u0WMj1}f(f!DXhLBp6N_n+XjH z&2q72Z%j5_`efG4)i25a-E5?Cz6$f#@O7_?uiFhHJOP%BC;?rj@7oD$O}80YI9Av5 zl|wA^mRmCPkbONMn*KcKo-j1hQ?h&Xa$MT>h<<(3NMrJ`p>^Dw~>(l)jpcV(%rm zDYUVpG0v1KIW(j6&USX^_qAD}opNmYYG`xCOn)E0z>ur4jA305vW%uxA>P#xK9pcB zW)l|H{%2L^3I%~&r2szx;EHLmD@jbBVprXN%v&Q++D(Tio0-uL?M~zs9M4^0t^d0M zUkD*C;PXoIscT!%b&lOyAoCS-h_~*X4Y4vn(pAK%8ziiebk(*i>p7CH{w(siRTn~{ z-3-h@gq0TyZI1nnV%&NVGNr`EcH;@Kmm90SKVL8U>%mfOcZ-Bdb3QNil*jfyyx}f> zmP4Is{Z{_i^{4P`%KuBp0nxo#a|pKkN2Zd=($vYxWni`$CV`$SyD1i3f0h z*iQR*&c&$*ZM_MIzUl=t`omu9(W&UB)z{YB%YV+@|E2;&d^@Q@XG`cPcb;KEIzw|8 zQeyam$u7~zi1f4hbstm&Xk-0IU%ZQtx6|jA6f?UTh`uUVynY6>_T3Gtz48sbr?8)3 z8@~lvKDVcFR=PD;UvM>)D6Gd(t=ZLCxsubF1P>vD4OpV3(S zT%@Kur@S~>0TKBz^qg8Hq@@Qf{h{Jzpdc@=Xt`bExj(I^qkK2+obTxPmv0MtF6)3< zc(UQ~bnN`0zY8B(pW)fIg&jT+nSHiOvGx03?&9#eHi`AoZr?2)phADAx)=>4fg8BJ zhflj8?EsiO$ODcw<@+}FJl7DWd~XbE)VR~iKQ`9QclPjJ4CXALJF_r=)48*IfAe`( zlvUGU=hm~*JYyv8hH$@z{jgOn@Trijo>9UhtIPEzY27Gw+)bxi0z%D~VXiHM3Jfx} ze;%BCv~NO6`0aF+Q<$+sUcQ*;LMo3yk~I0h%VVDHy&5|Gv8_$rlz!;CVHbZKPE+-V zQ%S%Zhe8F!9AW1`x*)r}oMv&?Hr;H6@C4t)h-QpC+6RfA+H}a?3%1`0bBNd_7K?Ro zjAB=f1WP_e;P$3N)elY4%2as{wo#b4^X5{5R@x@lUtS`<8yw8I?hxlV+$kj1_$0aj zRq=O?BP(aOv@|4e>l5AimU@~AiglU`n`mVKGmM#5BY3HR?*DbtfEdfh$rHxL;>t`5 zL6jLiQv+(mu=$SNA8h>n2~@?1i_+3#pS@$7ziP!>4Vw$$lt3Bn;I#g5=ZC+etIGkp zHz6#$Hz*dwR0+kIs_W=LK(81K{qU_up4Ua4KNuLemnPa`5l@8oA3j))x5TsWvIiP@ zF}z1xTnptM0g~mok9;P}Xl}5uL953?rABY$^(CElPEli-stJ~;?*$&K6ZOI9t3mxf zA3G8Ift|x8tc~r4rG-T!;8imq3mD(~@qg@Dcd0ZSqoZRxY2xu*B}J-F_uZ~;y(`z! zR2Vx{X>WaSB(r}RWG~$Kxr+d2C=|F%G=V&kCA3JvG|w_e7p&h^8AKf>JqDps7GIwX zmnwjsGgcn13*?!){w^xvlR^S(Uc%Q)<8DyCV%qR2 z+s?^=g{Oy(g|J;$4@=8_4%$7dyeQf7MnCk|D%fOPJ~7WBDR2(Y>=W1=oCa<%+SLB-HhP%2A|SK z^9X5n#4NIt@Sj`Y-DZQ68tbzH(iu9*@2B>`M#(5;-Rt$(%m#FfsW`jL+I+1K3Qh^q*a2G45BQ>;!m^)OVy|I}a8I#Q6 zt+pkHq#pkmQ{U(Q`&LFL#bdU&Bku^Kgmz|-M%l-!doT@atIyP)-f|$z7!{dqFY@CA ze-8(^qoGWN5<}w9+({*UjZ192s(!)5nL~m%SJ`PtAPd;J<>*nk<^Q>tp2H!*drhXI zy4u5DvlG2A)4i8acSNsws^xS9;wtbKZ`?0vT31ns>dHQP3%`~sU#&VDosvA$QS|F9 zR6i_setbT8ZJrO`#SVB5(HTHm#AY*Vt0M7dxVlOiP)ZKuaYq^UyC~im zM};<@om&|P9FXABZBs1T^s9EeyBK5isbLdt1$Aq`?SJ378=2UDYkO>$AFv!9h&8vM z^NBw3H+5h!~Ru;h*=Wy<1n_2yB25gHh1VK~$MF>w^NJTt>$!%z1H> zGEpEn{rC#LZh2P9_xor!al<>2RZL*`^(cgq@9am<+$PSa3cRTH6DgaMbd%6}P+y`Q zq#xY-+WhY@v+p?bQF?k?9N(eY)-eXyZlnPPyJj{RzVxl4JTtdCOW09GD?w-i#{pep=hWP!tc zZ&`Gv>sLW19D5U{JZ{gA`oK~prUyR?c|LIr3PvOijc+Fo?p zTpxszNAU4e6yixo?+v11$5jw<{qgfds z&+)Ei>h!+8ui{fQ2u2@2nu^ubUh!hj@bi24>#4p~_@S~}7dU%TKSu6ak#Fy1dRAI0 zY=lo&jItQ4^2DOlnWy^8avisTr=AXLxu8|cpP_Zw&Q8mbx3`OfD1McTpHC5L*M^SC z*56?c9KkPpx>y%x5*KTw(=VySIc|MP7uI_Ibt?YUn2J@nLIYG$NBHjS-XI_dE`j+O zuBZ0*@vDWBE=s@G)oDPtJ7UaM7oyhAagloeab{wdmU>f-?~+ARBwJ)$947*lf&ZGJ zTFoXSV+vK=X%`CU!}L{o=EmP@ABs|!;R4U%=#UU9nu7;lK*a@5ps1$G)Lv#jl2b6R zYED4an{jWETyrZ@9jZ0gH4{v)0k1Sf+&FK zX1;^Xht~Y&!v0FMoU|{gAFYYHnuQ95D(MJaQBRnd$Frb(d-yb{ciZ+^ZGDzQ5s+jh zEqsNrXm2Q~&GmP>#(5;@L|B>Vk(>mm>%_0X9F>+n{^B}xm})39YVm@gNi#-QN4R`< zgAg8;*f>lQj*a_%BO?4L)d)jyM6Oth?6AkIN~YA-pcR6s3Z*dk5B8ucH&(FYv?orF z6fN!wDcmU>?4=5sSX`?hjf#UC33=-TSx>)>%Hhs$6w5KXM^<)81yAh(sNQv9fxnln z)+|B5d85VzTkIw+p#~4oSwoUrn|&+0qy@rxsq-ImfB4q|WW-ozQ7^u1^_dGl-1^SJ zc?9y$XwD+F7XgbsDahMG%7yq~b{;(>hY8#iG0Wr|;xnl9Wn$!jHz-!|Mlmm7>7sz| z`h&x?Ck^x+m)o;dd?%y21;4Mz6Dgz*k*Vq|e~&g!X~>$D#-W;9zGA~$FOAbosqdmND<=)@@jc^-d*!aGI)HF;IjcreRUp@*zchC-> zA$&U-{9G?+U}vGpgVS`IXxlt{Lf?Khh~&>)S?tIdicVIvB}NF~luk3m2U!q#Ln(wt1ZQdaHwsk zRoOF~JhLzu^fF-WJDcE}K*I>fO$Mu5|58x|Y)WiCp z3od>RN7h2$wen=*`s3dslM|i$iUgrN6(JMgfUa@p*#^h1_E2$oT^X z1GiFeZ*`%%d*wuACUNxhYeC0FCxarfPq-inAw2+a*Bby*wrNCdg#f@*Rv4EG4G1cX zQRMNVSQ@#0%m)fBAy|2>*_vln85_@QM22YS>$J-5Yy0Jdkmt+KAd>J=QG-hU{;#Cv zyC?E!jbR6y?U8{Y{f4sWj~pa^Af1hTu62R>D>6cGg_5mE8^nE(r@!m@$NuNvCKN1r zVYEl89H2Dn&@BtShSL!m_fSoyD$ zQVjz_Oyg;(^PhZCPP43ShrUFoiZVVg)MJj6$49XaYx*QV4OL4d5X6m;otp7bRk-m` zCfc~GG-l6U;_JWQ+>)im-Q6dl~dg})b1+;L{14%+J zsMqBqFZUVI0hQ>^5?{Wlj=Y$zF!nQ0^BplRO_&T>U1@{+HoKcKEO09HMt-*MaXWNJlA)$HSHj=0dXo zKpER$OZ|$mDKcJ5|K+7Q$>&ZnZF;BA#U^%Pp=gXPb5)({;=Br_QQ{~(_KW9VUA}7p zcx)?isGUN7D$=AMhUQQ%7~E(-5w?wVBlg$KWlF@TUC-*2Gwb5jg?`+nmR}cfQAA<) ziII%yu6@w}Y>8t#E2$ncs}hr1emaUxG7_}KJy0_G#G6xO7W_sw_8k7p+zA-itp$8UUzY=#^I+@^Nk{`YT)5hvLR7+Ja$B1y{ zI#L)QN-n%q_!SVTlNZBK{1jW^G6GXb_Ic<^f8GFLe@urg8 z@C-HU@#dP=h8Cc}QV4f`r&j#VcuGEz)hAh^UwuOG?^=TnFdssW2UHK$WGWreBtzcyI z<5Jlz&x482&qL=opa&rN(?B~sSZzby0wjIL&B}7wuO~Q z;P7&+mFrQgl#C{fjBA}g_SqWqTn4I*vcr{gIv|D=(w2RcVN4@hScIpC)sS!4bAZ(% zU(!X>jhIw2;@N*WKP@9n2TJgYcLefO4CQENt|X9uT?$on7_fZQV1h!>FtjR?@RQII zWt8AcJe(@3HRT`^VCcn`QI+>11K2OED;`|jf~@V@jyA?T#lmk&i_PE`v?l0-uC6g~6#N47S z$?Ikkey)5{4`NRu*Fh2b+|9lWQ;&C)83`HgE0BZoe7gyw&_~5aBg8{3%l1UB)|5CH zOI^~@e3~pg-|sF}+baW23qcGFKE~CaeTQDKyHe9BCfe-|$lvE)sR)~lU!zz<7eWbV zXT-g^Q&$r|nO^lunN`ABxHXO;I%`tDWgB2r4z0HQ+rV z9-ng7Up+nXUS42BXX|R+u_?E6Y;xgAUtZLF8yEJNR2b`x=Ph|yp@uDHx9dK5MD&q< z8Ovo>e(sMB4e}3qS3h0-UD?6sje)}bJ(?64=Gwjqe{@4wD(P)7U{JR8#4=hR8)+r! z=OkUj$XEv_{1sdX2a;eKji4 z?&_Yz46WCHqE-iL0>u5;F|6hg6K-Y-N~JwAKnae0&sG64ik2qUeV|TOwF}F9`aqC} ziuA(D-Q621?_<W@-|~=@(zkoCH-0YR=p`@m9aZ`Ls{O<@4sdy*@{#$1W#uyA zcQnxFQ?}(Tl@wQ9Wwn>b3istJey>tfbI+B?W^bQ)6^(xUdQt1$i|#X;YqMS5Xr&C1 z{oF6?Xr(%@{n{fV^iHargUS4FV6GZGjsmU{5_@JE`_g&sMe@v1LTp?j%8_c;P4Rh4 zTXFi@YAK%zievnR*K)4vKl^x(TG0cZrG4~6h@sg>4d003t$VC!de9dirGJC8G;2@7 z>5Q)QScP zKTMw`Wzc+g&9t+9=aZfDE81uA0|iPWA0U3$Mh%ZziN`*fl@j-2*NW6Cfu&kG9FH@& zPyVv05f_;P3up9$FL5^c`_D9VDtj2I4n5xAJ(hRT=zR?T^_RD;>I+0%F?p@8@Rw+9 z`wyH5irX_nLr+gn1bweYsCaUdSLdRjdszvsaRQ<*3wE?GvI@a@j$X_u2@w?tKP}Dz zyUA$i)(_2wJ#sCG*_XCq!%`t4o~wMv+%2ZLP2ifEtVd3o#MIZQ`1^o2BeW|%EgMib zE?4jB_gil9EzFI++Un6^n7llD!M-0Y=b)q18Wc6cbj4EhXXpx10Q|&Tp8M3*u~`{J zA`sAO|2nFCtZZ4~lIj=k-dOY1RH~o2K!QVJ4w~nZjo8l@zTmQ zIt_`Ws@Q5>HfcWz3@Gizo`suq@VxGwDZ#76lQ3P})5_}IT8q8Mn}0Q@Q~snjXSY%Y zjw|B;PfR~|R7$=!!$oB9Y#(U{;hm2xE$FfE<*H5IJU3fkObo8C%b)=c|;1C@Xs9BnE&JDAqAR+ah4K z&zkm6{XY^wAL0WM;6RN4#I^*w0UgPQ>V>LHTD@={y;F>Phi!U(lYW(&gcgkB^H0pn zu(O(~Y>ui<<+{DJiaZ^TBG(6lnr-qm>~f+Eb6@nEpmkZwbOpMvc+*0YXZv_tzHY#4 zng8|9V{CzmWC>2`52_jkHPgE6-L51DP2ALzfs&a!7@TN{sPem2`s%XHxBA_|Ps-TM z<>M^!@65SW2Yv;M4qF`cBO~-m-weKR#f#PRofh=HIRQFh*Hgd;V2(%x+XAh5#(t2+ z=0uRG082)Yscn5dUq6IqK;4mjN(ps7PYV^E&j+?c@F-Y0)RLY7&LE!5%aVUM@&i_FOjL(^%q@nxyxJo=sz{#5uu#~81@XUDafQY;R0hB|hRC)!TM!mLb~ z<-#+r<6p~aC<{d>l=T-3CJNBQCJ3G_5-)OTvlzo41?^Q`Ad+c~H%!d6F~dS~yL?M} zb=9Z&WQrVQZB@-bj8s5_Bg-P;+VN9I9b&NUH3s>wqH{WlHs4|to4I?{gvf5HM$>&| z@pT+RJ*5U~1}F2!bdGvRu$U|>bP!*4^4Vc^Kla_ryF-{=?Hl7u_S3{4z){axNwQvi ze%+(Te%eS49dQNhNkM-86f;>~O2v94kpP8&as!mbcgi^*aK@ z9Z^}%!>2E_FAD*bXB{NZEH*dRu~1jq@3#y?$Wj4;0@eUNfl8y*rdZ?M5vSWwh_T5g zcU&oeGQJUuo*}SZV->rKjj*%0)t>XR!u@5|gmGng?9_>p&ZX8PSp-GY!Tm?DtOX@B zMpc2L;+TXnOSB{TH^%-I5;gv|JpbNnHaDoEc}H~qiS;j3)F*VrvfrreJV`a28EpUh z!`&9FHp@U=GP_fT)kJf)$AsT6I`50!JinE=EyxNt4o~e>$)CM0Y><0{ zJiBsdqfJH(hoSCqFFU;d`|CR=xu3>_=t+2{@!%+ka(q`ERa)sIBLEJ!V6$GFzo&ar zMA6V1^%#e-E_VAeFKYzvHBl}DTN14(ve};VlJ1jXbZO18-uxz$RoBOX0mtmZbT$s4 z8CR#g-UZFvs#hiA-So4f+|-;3BImpk8*sU64CEUG-PH_45_5h? z475c)Y9Uo$gj~dV5)=t&88ekNDp#^)|FlX{ZRF0{cm3HLOWJBTBJ1d^<)Cx^pEp%W z#lt8(HRZU2-ql;N(Yo}YzfkaIvy61*Yed3**?av#d!)&CvsU)1l9?4h%sL&@OdlkT zqC8V&>Mz>amR6gLQ{%Cy^`NwL@_lsH%CIuy|6Nvsd`F? z`W?uDx5gLCPZ?BfZf+KXJ&(|#+qDpNuR%i6x{4Hx#q^H(9hjOay#D&?xQKK9hE*X1 zV_N8r0^3(ui?{c{zJ1RC;v6>zkk&Y^i*sN?t!!BCVZMwC~eG3f`| zd6*x1tDvJX`m#Zuve>{pe!$n?Pimzi6NSP6ZZ(;Nn~+OA8K^?)ot;|BYC}OJ2asO+ zh0>Xsi)Kgcpc0;WD@!_%acV{}^+2H7-I_$}Z}%JH3~77PijtHtFJw1=41VN~7iVZ2Q`S{WxzeTh2hG#SQN<=Vmj(W& zmR3psSs~9Yd_n z8BG0LF-Zq$#1l*DPXGB=QQ5cj>rvyhv(C?Jjc^J0ICRLJnbK=2FRDDJuk}wI7k}d) zI#qsSQD`R2e|4m!CP@afLNOc|-k5Ib>=c@@PGYjA40L3>84rSgSJqnF5vUoaakHdA z1RS~t+mVaW8M2X6ueA0@qd))wvOsByY1+4>2KcN*>jHaQn;(&NMx89~9(=HI)6pOOCxd0BP=h&uJLYH2Z>-c?6k4fvnF2e#}t5wbZ!sbp) zihQ=U$qC9=qhp3$shi6jsC%gjOuiqJ;_wl4fOSMD>oFB-(khsTHhSN97!HA1&(|!ep}bV7upg5V`U*5iXWMbmYr<>qlbC-+ zV<-{*9@;pzDb{*x`Ay0R2eT+LKt@+nJV7Gq1aFatEkOud&1_Hb;-`~VcpZq}YxM-s zxe`%S0Z6L1U5`M5$aOih*NF3s7VUj@1+GBT$LtYJvT3Q!b461G6WZ!pf};5g zXCWAvKXYod@FcsE%>k_psvwu zeS?6yQ)>#T0#(AHs3?x5j0wKGNhDl@P(Y*0Qqu5`SpTGck$9D3e)Dx?rg%~Z`LJU? z-aC>jwe@_{q_>%SDl4QZ0EVrK$#ukP#*`HD`lu}>$tn^{;rZqYu;76X~_u`YT?+CPF-xnfNlex%djN@J2 z`Sx39p3m_|Ejuc+P0dUsGp~JMR`hwQuSi~FZ+MOem??8I zW4}OM&^=@S1@p)lk83IQqx(^H4Yiaaj|I?!4x$v*kXp{F%~eNgsLlArizj#qfXl`Ui#`Jv=Qq+s)>$#b8HT%1Tfc<6No0ns^`tIe2K&G~Nm^@d= zeCjB#Ty;eX?y#jf-*1IFyv9ZBA~q`t&9lmtIrHZE=U&nI3Dc+BQ1j*j1rcva&1+Bh zm+H8S&MxICgXn^O5t{dg{AR& zOQ+&31Qa}1cw&y2cvmzUj%u2U0U zZ0x4w!0hjaYv$1loT2}ybgag>Y{p(?${c%z!=Kb&juUCR_I(Z zS}irF={b0rxJZolPeV|M$lo0ky>ifz65!EYuFI&I@Pm*uNUD^uY8iFwG4Ylt^K{T^ z-#RiJO#PbIyixFz_8w9Sh0zj&PGsc~GXv_iN*OlKIE>Y6((YEw z+@R^RQI7D$3O32Y8zi~>Bsv{|ceD7VkCKtkleNx?ys|BS82q&V7R`}p?X45E0S)yn zGXA&ETSq8|^01XY8l_(h+I2rwvWT(}yW!BV7Ie|)opp-Yjb~kzE0(ARy5sn_d2$S8 z?tT34jL=DW_6O-E7X;i( zrkjZ6u@BieOh{WxCPs<6YTXxn`bh^L_12nVXN+0B>z8j=s@@10!l~c6vzs7$z&k!$ z9J5q1pu>_W_I-I7=-UTjk0<#JNJJy5i11C?X@FuY0_e+t`_AScd0L#)5w{a6EOT2d zz55axEmdcmOHI*bQ>70l>;&SP8@1dVb!wHo$)C&KE56P73wMP!|H{~7&-%@WX7Pc^ z5h?F%lAD|8X0)vfCXcjCROPe3P0BSnb|X*mTtQN9KplHlQm(UVND=$Hid(87T;ZN} zl6u0s#*NqVY0lwT|J2K)Iagq_yQPwZ+Y{$rX21K12+il{Z(lshkne$CQBM!bex;6_y&oD5zA!5j=VOtPMfD@9!AIB7JH=HcFx6E z8Lb&OJF6ZSDtBaZj;!0?f1rOqZ6lG5hx4l7hy77pVf%9o+IT>=!Vk7f#ggNG`6R{ z?#Qx8dSS+^WyH zJy9}Xgz{KL`zRgN@?9XeKaP7G*Szz;HsbW6L5eB;;tOgWUEmnD?xwnEiNRpX(=7@5 z;V3JE!_Q17u`<_BOng?_y|c zjO(z|8>vl#Atp;*&uEiWPZuMZ$zMLOJWq@9)H-2M;OBXpfQ=0<@(2zp$oNpf%*U6y zF@*h*T7vQHAakm3Nb@g)g~a3*kry_*-hDD)NYuFrFrK9HLZI;{%$R~D3O{}UD5sq= zj{T*APva+!P>qCdYy=>QGQs02nr&Sr^HDH#Jq*#OWgHEvWj_%c=0{_oRmxlMAY-?F zlX4T&(!G~Ab*re2_zo)_vvzX6xQ1eXiFdhB+=#a=xV(wjBF1nezvRiL!&Ab`2)+2Y z$uG9`Mo#25LCkmW82#VX4J<0h%Z7RNR5rS?69h9}=`H7!j2U-#zPZ$Rb6ZN+kr*rU zI-a#Zvsobn2;%A)oyiZ5fP}*ISfSMVSQJ0L+QErohFKki;HJUb8==~Y18UJNVTvZbS4#>0RsVYBVd)xb_F#sddHyw zv568jL8M(fH-Us&?H~(=ZnpJcWtrTMsGw9YfF{s|GFf8d*-S@}R^caaLRCU2IE+GLOk>t>21Wr_9awdr;_#iVNVoL|S&Wt0%-5 zsTg^AK@`m|TAJ-EF+nq5aCuuAaatUk-MT5AgO|szh$@j?y32=9*z*;3J?D7>56a`0 ziwvjEbM_ZBC`}c?qQ_*P(NJfTD5IcD8lZFRndoiS8747YCnbPUYT@~e!pc{Ur2bND zk3)x7pd(LUe)Y7`@F}HXo`U|B+KlB|-BQ)zC|!~%U%wMY77yYM#;dh99W0%jAiN?` zc0V)}PhvWEBSeb~`m&C5pi81Q2N>t`jsX4j_8o>X4<-yC*PvwtjAh3_GoP>b)#gB% zHNsX|QPH(;4N^|0e$Tla6K7Ou>MKXTU|osuS921k8Kd6abrqy zpbQ4D>mo%!ZD~6^KB2p4${!%_{}VTDOdZX?ObYKl z=V2ntuJRdTrAi5QWWn`}^aq8uuJ#hUyzDFJOTnW5gTwR z=ozHw#7mOL0ZnbBJ@)r%=D_9W11=v9A-*pOA%;^Y%}CU}jNSuh5O8&;kMAbnG^Ly= z{u}-Fh4IV>LXR->rv`6UYLUH%137w3{T+*BC;Jet-g?n@*qTKnwW(|hJc}2Itk2hm zrg$Oigt`@FQ$1PX$wi#NydS=FC($Ze4i=N*gUIenG5XUEgXEOcDgl&V5|3@^! zW^)nRWe>Zn@w`*g=C6SxGS15&ee7ymd)zhwFW>szap_YnB(}s9x|i0B)A85*a8G}) z^ZOjWIyc>-07b)`_kK7hSJ}@7YesEx{0nt?ZL7GFo67$G$k!j$qkx77=ay5J zX#)vjWRI$qqtkZ0Yfm09^y36FN|%eOORwamnEXI?p~O~0c2i|``*@$hLdzKF8s>2G zeJCWXmR+e=yMMh4%n)1r+S4vg%+0{m6xGt;HLhrf-c)krO{g?m89o7v-QLxiJT7mCQ-49Rjg9rElkv68_@Vk`AMAD>PR)t*r5E{nO) zy>1Vuu8ID_C<_aRP-u0~?Tl>^AFFX|0Yt^eDv0zgleUYMC)r73Vhva4kx%&M`Nb7^ zF1!~m^s1P@X7U-?rKqf2QfAB61}(7osd&)TvBMW?3E30cK*{1?EKOBS62wP=%F0SK zx`D}G1tfA>mRkWv>{T%cofF5NqOF(LQIqRnyqO^%b^=&jBl~^+QM`Vnu6ijhK7O0w ztTkx!SLUuxAOW`=c~D~ACQ2U6?@#NTTAuCe-1weVP7i*G@q!A$=Qdz}VF5Zz+K%vc z;P`$kB_+i`of|1-sj^+KZABD4=FPzzk==F^sfoV5m`Wl6&Q)7zyY6Z9Lz^1G)yKV6 zK88X&;@QDiC!S`6T`kE*a9E_#EmpS*JC+NS-GqzkWGs~$s=cEwpML4*$<&aOq59Tg z58mQ!WJoYkKWAJYREx}$;#;5gS5CePYhFJJRn`{*+sQ582CoFilOydHl`)U)(lT}# z{c6Y9D8<{Ivove&qHE3R;qnq5H9?zQ&{?`+D3sJ+2Fnpgq*kdRsn(xg65)4dPw%5RHq5cn$V0viB)oW zA(+5j3ks^^x@(sjka79)WeXUN;M`=bga|YI>-cU8LOEq#bA~1J*o-hxehbRKqZ1$a zs}H{Y!K^v}1uLWWYwy&w6rbDk5-|M zodF73Xl^6<1r)$Q0YGb$dC3@9vs(2~@qGczTJfNb3WvVpPubA?n5R8{++uzU;-^|f zK6nLM`GY85WPJQoyXij*!1f>{H>F1w$N}84RsJY;u`etvt9t?y>#{SW7-_oI#W;NXeEF6iI z!B_GJgcu3H3$y^o3$?j_m4Ol3Zz%fBn{!6prAY4$T=nGqA$v%N7XU;RHXhwPm+5LC ztI(c0#RJ@)Xhun|3uSAweOiD@M|i`LNlCNqy05PuLc|IBOhI~TYd;H7^J_KU%3Ycz=Hk`;@7N&rzY zb`DZku{mH(KqPPGDHQGrgtXQe-IgrbQ#!LU(1CU|idBX_Awa)}mM!Z+A79JYL39#M%R&- zr$)Piz-}-OJv-|VyImJ8ZA+2HTas`8wpWD=lPI}=xe^`BcigcD&pG!&XV@=QsbM6? zq#x?%44pA9QZjW^(#abliq`fhOGpW#ji?$Jvicl?V8O@@$ zd?aY{1fPIUF(SdS4raRiKqYju>v_y?c<2hx+lrjko6eK%FSl)w!FG%QYnGSY7Vv#7 z8lRmRM-oBH?1ntLtEHFQwv#@|Ca4~Jx>yG7vRw)X4sq$H* zyy&ajIoIh;COv1VU`Uaf00b|KbjxeDoQps(tQIWn*w^hqM1#0Kkt$gh3>t)Pb7eiv zupJTry@_sd@($R+4{T%!uugi}1%q=6qGH7>+J`aOWB&6?0a*`sAMu~>UO?u(q4Xym z&*NkYu303y`)@Eb68J-=G1lBSri5=uBuA;$W^@J_piGO^bFkUr9)3JCU6!9ZZ1X&t zd6K8Ep)O*B@;&T$lW(b@1h8BmXYoo(D8WuHtVfP8RE6jzT3@M6=zAnw(>{W%+dfQ- z+ACL~k{U8`R9z?dqc;2~Sc)J-hv;y%mWE9KVumED9m{IcL$+ww>Jt79MD{&qj(z?TSf0#kSt`GW5JsD5SWsPIPV^uk0vF*z-4lz9a(Kfn@{z|55keQBh^v*C;CH zjABGE5ClX7MI{I-q7noIiX_1X0VOIya%e?S6i|suMoC4IB$7=?4k|&i5(Ok@5O{N! z-M9PxzVVIkjW^yHZ@l{B-Ug|vbN1PLg}LUMtIrEfL!`Mrv)xnp3dkty9N9QHq>07f z6j$F%b3WYWaP!1-&bWJc_)_oLMm6L6))y8#GI9TXg@vm+@7MivKOLf;zV^puadY7! z!*=9u$lqQMA{YAmjXoS&a^&9sS%~k)Z3~k}5MgjZ0idfgl=okfVHXzHf(~&7<;Zo# zD&3p5ZskDcoh>p}B_734i`tM$)G=c7%IB%i9R52}_(kvTweq)2SYW3^+G4+h1)o^s z@Rnwf#{czl@RFkdDg4*VW#kNamj8bF{7W93_Ir6Lw3rkqH0(r3_Rq_O_xb<%jUY_^ z^A2#CIU#=X?_bbG{@?g^*2~k6P!8TMd^0}HxLyHDYebHgAj!q(@hhl9>!bqdl-v&b z5oFQi;ke4oMUi3o!}Lo-9N@#pk0mgQq_?OofwVQDfKIeUrBP@u>to-$_cXAL{g~M9 zUtL|TW7cO>X!!YsWY@S4=o7#(5M}K}RmTtNSDH{p)&AM<(0`oK>etkbSk3HLwhJg| zw*}RW7wFbxm}xh5d&lWP6Y-!~_k%(yh%y!L^Agb!O@b(BneHHhL6Mms&krVv8Py&j zX(P%5?+@7J&P#xj)PgLd5b&9k$QmjC{z>9zgKshrX&(vg3x&!ZU~Te}#qp zh`E*5M_)gWh$zcLfA=NK6Y8}T))rC56wnmcyLX>W*1$%Z`9aj}S^EZrm`??a{Jxkt z3}dYTE~%2oa^otp&g8n$8r>Qc0!Lq)z5c$;BHsNV!ZwR6k0jNJ$Wciyx-@whyv5GkG2Y#UN%(Ad(jmh zHYJW96l2zWQ1rYP-6SqT;s2n-c22~~6pOnr)7oag}bsg4#BD|+)P z76Q9x?!0txy5H8hC*61R} z;gbfbf(`zD1ZcqVbY2+0?;}`xnMYBfb9Tn@m_=#m8NBQoBCRB!XJfeb6)XgT{BrF(VJkvN4$f!irDn5p$OEf=chTeKrsbY@9_-**?ZAJw40z^FCKOGIT4Ea2VAU7=RMj@;2e=hEhL8L zyf1`ndmHCD+Tr@^Q4awl4jO%L3a#F#Im|;!o}yYTy)xtWz9G+f!6|wIDrV&zm0ahg zr4M^u+FJfdg^9Z^%`#4O0BK=hDHF$o&d`Nl(VP-gFUoC?Hqqw}bE>|qyYWaPy)Koq zLR>w-#F~|rH9$8RV%pKKFnsyxQ69ZjU-$ieMa1PDrtRBc7M$hPf8QE8&AtI5n%_?3*3cO5c|Nir2 zFxJt+l6s&=mp!E+z@+Ar#KPgIv57<0hbSdNE}I7xe9noiJg4}0GINzAj?izFhGvZ( z;x1@ke7n924+&}5BNW7oFx_=8SUWUzL!1PR*80K}hmz(+8n!$S{7w%70{Yn?B*@MQ z7*?NeYiqj#CZPGu*tJ*?bRtEWl{7yn>b1v(UhEbpXOhzZ&k&DNq>WQ)OIvKpa7|^* zD6xoqzWaVZNM_n%Db-K)8dOhjki((J9wzH!>ntL%*l+#IydjO;Q~i^D57x7ZIYF%i z5^Vc8IXSz;_dwO(;Vq)ca@dk2zekTYq?RW$OhcxPZRaslA9Tgm=h&v!*NQC*br`u% zPfvd@i6t6k0q~pc%i8ThqN}ub)D~5qV3!H_c72-2PJ#;EZ~&`y)TH4`EpVHofA)eU z)?PLLVUt{d%LF;*$7NAwIa2t6@x)!6?XE!`FqOD}@rBv#4&mCVJ@-(ErH!+WX0?d% zaz7_>r{uH<5{VUf&U`nTdWovU48yk=oy_djA)ug^qwxp+YoW7bl}@H1K3R zQSa!R8o6NyU(o@P>_exF64cWxXgA!8*~S6}N<dl}eL%#H>8Xv2ou1h)0RM;Q$Ja*bH{HtjPvZ$dyn={8n_q1HqO@j(dXZcEPDL-v3^3mPvX`3Go3u0 zJzTR>)1Uc7Y=*)(Pf`E;_~Vn|;-y1$2%03%oKkwO2Mxh>{Y-JE)n_ht3)iMDKe9&n~_q+IeB#{QI4)&!#>6d9??=6qd}q_N_Rq0b1z-7v=pBko>|ndtIzsd!9fxlR~5 zU#yNRSEL#DVU{W@D40G7%O$~AbeK3^j%=Df7U5Xv`+0=&m)^7@j7psYVlIW|bjJzR zO!3Mz)nuJJOrj27H706w_2wEQ+0F`V%8NnKARyh3GhIw}hDG`Wdt%p}uED(;WW(T8 zak*_A>;lWqF9chpDFFFM>)*nO!^T!Qop24GmP{P7IHZT4XU7 zmZYQLm0H{0)>kOGWU-OH`NbUpYK`%yp7FA9TX&y+wQMsU9u&EZYt^3#K;M_?cErNd zv`W{yh0Ryxqu9&=zw!8MnBRXt4t^ysHX!@9IAS-8B69`gxyd3dGn^;b$f<7y;;jFq6*B#Iuk)7q9)PD2pawC#>aIjOh%7B<=9$; zipmPfy^7OAq3~6^ZnHtT4xXNs=nhLB;P%Eu@+#WPcbUpe!Rj2>lsuB0&Z^=6cMU6%r-eZ7-ak*P1P|C@gChQ!gBCWEn1!|+${!RvZ`>j8}~T5-xerNFa| zXZ;VT>6q@^v`H77&SaZFNS&6F;Fj4Qu8Rknm8y&}0d9-rA5=`;Z#Yv$QCLh88o`-D`$XFerU1zG65)}al#DvEm55GEQXSi$?SMQPx9(DYtoDX zbAD!7P8FqvvE~SU5x?o-yzZdINaS#ncvyfOdPQI6VIQNvg5W(NLdR&1rwQKH?t^0D zM!9Ka;y}HMii%!pSbmb|l2C(~WU0romaExJC>hshT0|H4m*K#QIQ6xGM&Nm8PBhmu z7b{ueQwrzUGG+ zy(OR>W~k0g4w|(XWr-a*QcYBf1)D4CJQc6e!ok`)3hV$6_(g}3xYvFd8-FninH2*; zyRn!r{SL#boO1_07bcvFW=<+Sm|Zpz+K;IMbv-k*sL{@NY+FzA=Y=CZqH}VazpeT^ zd_w3;HNF>3Of#ZFrDcCdh6pk!JqRk#l8oVB`9mib7lwf>Zblm?!%K(r0Bk#9qK>lI zK@zL$f+&=}O96iKFn-I09r!ouWF_-*-x~0kU~uK(SmTpNc$3+f-%+icT|fow9c5 z&i>b{-Z`12Q%7-|VXNqMD@A7yyxm5CkBu07 zOy{$9cr$*e->A4K4;qzpL(pMKTh&j7E1#Z4rF7^BDR{Z0>>=#r{ld`bB?~BO@F_y# zbHb%#WN=kid0;8jPK-+aoSB|}I)Um%KtF&4;b!H@KC1+xrAmM~uV@2-5U3^&Qg$M-7Fi$%WT$a6qIisGx#*>_RYILy+t}p++3pUWU#4bKY7wTQk6^o`G60vaf30$bs zUHd_a$l7BD5Ems>eQz}v2S?$R7a8cBxDrbyKJ?6xa*Fuwo3w$=Xs!yBnSwe_qXJqN zFaXMDdbC?uLkuAbIM@{mCy1b?_3y9yX9vc(L+tjA!Bx&mKt-F1Hb;&Mf;v6PHlR3b zk0_E%C^H}WcFOyjnHAJ(ArcT&Ina>jnB7xZV?&XbfAqc+kQ!~k3u6`@zxO`p@t@{?klx26Bc@vpV zHT0{_ojvQ9Wric*{y&dY7(bxHHwHu*)kPw;>_G#vT$615@@M*vBOQ`%eLsGcNt6fh zi4VMPU-uN4jfpk}uYEC*-tK~R%qhK-)>%oJv5D`A+)|ii{dT=(4_Y}3orhTpd)6s(a_wF6)i&)eI z!pemOocQ=Jjz9E*?RVgt`2Uv!LR<9ctCx{GGxaoQXOM|3PXYstZjr)mG2}mu>#~t- zc7T+1qz^QbDtdjK1B>24FhqQbFtg7dkna-?Xl5zF6mS50Gq;x^=eCcCo%-&|1h%L+D>C1kgDpJT&U5Jmk3#Bd`y$A54b~dq5B3KFNZ7J4M2qNd z0&Y9R2^GpiPY22HHX>R}Y=+BCQk=ak(?|ySWPYj?ThK^E(0iEI0-E%@W&$xQuUK^f z%?2{tKSPD`>1CPJf#w=mE;3`O1DOinpAi*H5*GRE7sCp)M{VxG**5$bi;4;>IQ{;8 ziP?!vUjLhGa`KB17@&Kz98he9UcDSH`85D6TrwOvxqi=pRW8v}_CG`Z3vv#7w(tM! zPsCPxhyE8Telk}6|NO51-@7ub+J9dE?>W^lpD4M|gHBFP`p|!IvH0@(8ZG07vXF~P zn>`o4adP+sql^1Vln-pG&{JY~OQf%DaF7@1&?DmB)*frx4Y%YCiF>*Ao6u(0=Mu_Wyr(QYfk=od?y>*04l zFvv#ZMggO~FlJqmQ?Po=fk)ttNqzKU;SF_~i6z1A?~kh62K{9XaQHB)s{CWECYdb!>J~G{_=dnKOt5Is z)rEdZ3|Ffn_H~$fe)MAbxkF3$&;>>R%yRlZ1{{Vo*0S;QkL1*Mk<1#|=C#i+?qk^H z^Xf^KEnD=d+H>$dzQFhb&K$r~pfa<}fxi}I9{rIY8HP8a_Ye!IPEZYsfh8XxE&Irc zu6>HQ(<6p_R8R7zrjaid7Ev0rTF7`zqEJ~}S;;%v{^d#>98o2!HqO5^?4bw$Bt~U? zTT8oGC4rWCJm_8wR;D}dMYQt)=I_JQHQGoBx&e*4eheU+){)K-IK&+XR#ZT`?70}l z4y=k&&D0One-D$oc)PI@G@5H!+C+5>j}?dg|vpN!C0+w?0~QtnAcWTijWX9Vrge=0HwfrhEKV z`5tAfCDE2!;~X%#6cht8r`jHv%n?FMl+06|ydjKv(tXMj>Z!s~Nak`P5^%7^Vfx11 zs(3BF({R=4C^l>m%Hv}Wa*_A{AWfZLw{F@bzIUs9IWb}t;;FTjlaaL~hC#*0Pc~TS z>67SAUk5!3*gmPUva$#3oUpY@xJ5swuAS^}QYf9#^Yy#2fNDsPY4n#R>iN|Q>Zj-LbA%i+*y99^fX*%_ zzVXkCCbLuS7rGzBqorNwc(Zk@V1ZUr?!>dkjOFqIvLBDR}x`&gQ7Tr>i6YfC4 zP2lY%iBELIX>)x%hbqxwqgmeIc=V)prVS#{J=TZOFj?_YVcM!D%~>_q^5f>m%TCYo z`Ea!HVu_4e73UxY?6XzdA$iSna*RY;hECMrb9o5f)L4hFCKbu<8+obp>6>Hy_-m%a zxVfgu%nJQoTeqt0nL|e7BOoe-HdZxM&a$oMAaId7U+Fxr_;ZeQf%XflEY$M(P(Wg4 z5_8f32PqOkN#|z{Y>EsAIg%In+RRQO99kc>o8EQc~uIkR1s4k{1)RKb176ah47kl6KTD{S`I&R%`Uq zEqfG-%gcRHggGxSFP}N~zaBqGFVXGa$0JO&5H~9Rj4)pn;8d=8{{n2%p+4vr@GV7r z7?W~DO_E0~eU@iPoT+o=tjTk<=UiXQX>FP_{L#DzkK@gyJlfe= zm(xQ#JEVnvUD}ITf;2Ba68wDn_IlC%7NKM;3J_vjtac=FkrBW|ex<6|ZCs~`(ZY;b z(fKJ|g1L*^PAq^%Zjm*QV1+hb30(1d%m^(#yrO?LnP>>S_rC-%Im-Eh5^3u$9ovSj z%tqqiOfTX#maG?%j{>2VE~k@t8JFGPpL<36zdz#~kFt6DnJu7l zK&Wg)7&E$e4RIf2W+u_pCm2PYutnEH7+-PHY))#*C=48L8bB!jggf!|yRjt6Uc`Pr z0C~tf2v)GyEbVhZ*7QRaHozoTu3r}#1y7zmlPqQ!&jQS6;bp-BGpC?&qDdR-YbD6&0Po@8#u>Ich~=r*3yd1)B2z z3C`n5O0>4@we&mDA$C&m)22Gl(PtLOIhV#Dev#yNgoT!7v}PQv&+{Mv9=Z1UOo&a} z%1kizgG`?%e?k|$X$5;VZyM&?8ATa8f%7GGq;@cGw&DQVvDxQ8r!oml5FwEJt&5jT z5Aq|aEgy^}Pvw-=Ovox1>!DQ0|H-YVgPN!{I55DD@CgFU%&ak$2<71Nb>&~dEw8<+&x@c0I=A2 zcrIYuiJSle;6OBsS;0GiL{3{zc-UqV!49F6)9XPZ+RA+))Op0=bVKtt?yFQO0n2n~ zp$@FR=!O3fX#RJ69*L##TxHLe=HF;PE4LnZ9a;l*6uI?_^hggSjrn8tA2JZI3Q%4P z6_wunSNQuc5N7{55>vo{UL<3V&@jIlLuONhD#v=hL=r0L_U*Jes+Zr;sij;g7Bow> z8P>5lk(<{dYqkJPDv62ZFBQNFH{`gqXW(K!hSa?nRWikh#)mruq%aTp0BVS2PA?JL zf%z{1D~9#3ei>}R-Q&$&DAf7{nr2C2#8xK?QN9Rqrs1PIfhj&hxKd08IeL0w<;V-; zTmJ%(_Mb!Y<3mg%QAQwHI_9x*P0YY+A1+ow>huI$R4G(J>ysJ@&VdT(mv~(r9pena z8*Q#lV22ZGFGF%#gP#!4Mh){~HZoZ$6BM!X{@-6FpB!)tj5hMxe~!d$dZ;GzU#;35 zdnseUHc9N-XVz3Fv|1Y%tJI!JM3^Th8zRDqnOu4AO^8Z;5wIJ*p>Jb?K(ZKgPX(z09la!?FBo6Z`QK+EeOQHqc?x+d4xUw4FJC3s(SU;c?(nc zYgn~Sa@{A^c(ijoqmPF-Yc#2_63+EVfl`qixbYeTt-zL#AXQHg{r#m5jEw)$PpADR z%;!Ic*ycP~J!;X>(Od0K5Y!X$QfUv-HMm|y;W!$CNCE^ zQjH(SVrjfG+UqMTrkbjM;S#?uyN1NMbD`uOkXk0v?!m?l2h^Q5Krl$qLgd65e9qec zq=Z9{;L8xi-2(RLDyR+eaePVDE3RmVm@Mz2c_&O@#msF6nCx_DSDo8p z2HnOS2MgQl9C@x-=E$vEwy zV&yodeb>_TveP8KG(;TLfqLh?;sXmY-^z-iXhutdrGET~m6>j=x8Y)~3Prd}gvj&cQBIpm&o+q>S9^FzVrebcY|fS?U6!-4XPI zKkavh#*_kM@cGhL1&W5}qnEYOq#Ni=rAte!?nNi&g(aSG9ZE%*7t%5K$^KmA>NWd+ z@pDfa*T78Dz2@B?cF47s8iD_oKrUcS+;=1R)+Q5;gJ1eoHIWUHiBQDlTlQ8<-d(eO zFTD37Y2I8Vz?FIczt~rnY8e~H%LEHi2(ui=F3P^>{1gs3O=2@i_%r?`L~BcF9Xd8dvR;=>UPex(UuKNQaM;gd!iJAL?C)@ zVs=buf)q)-9Rx7X<|%sj=$9`K{16XXf%?I`DINbwi}!1f1zcN^ZeEM5LMB25XOyr_ zeS`;Ii&Lq)w~B33cnN!9(}0A@jG;~7Z7xYyI7Ja$Q6nxhG%TWvF`qJgUr1k1c@v?7 zZ$DYz#%UzO_>BeX&bV%>0bH+s?88Rnf~7Lrs+!zs+r#IRY4mE$yK?)5x zq^w`5Dys3M`g8#n*(D|Pj{tw(@KZFF-)ruo{Hr1ozOWwO1cmxXPfRxHQax+qROBX5 zwuCJ|v{DT-PJDs&1oT8Se}Yr;pS4D%5o{XX48G-CrcJzvhZqtmG!y>j&A1a!PJV{d zV(KmIYuJ@3Czy97HWE;QXnFx`0(Q6z{h7BMAvUMU(5;3-kDVNd4;6edvtICnQa#Pd zF)_aUP*-{A#qaxOcsX?oHZ6?RU0FAp{3^yBg(qIZ|BP2nKJCY=eGm`z#GnaAjHyPH zsOoL}F~}=uYpWkfbIZ@M`B$!@eOAo288)8~N0lai;CTKSff#*hIoP6!(KDFusZ6qb zX}<%Hdli!R2>X)*;qs*FYn79_0oPHo;#_=u7`4D6dVn%5%q6=4dfKBgE}gnT-LCAr zaaEmtcXyf|DpryZxLWn*T5HV3=T|U5G%6=MghE<PAR*^)D{@cYZ@-Jx z$RR{6ct`J1Ei-T@AmRn{mV(5har>RDgqk#>>~zlR zXh;bgsZ~(qFx`oCW09(^1oh_PEd=5Y`MB9tl>JSs+|LJ>`8bXsp7BA?$kTDmbJ3H| zQy|Xy-yTs(!pCMg{AeKwdJyx9lXeFZE9c~-MhaFoVAd*`M1VQ=?BIR|6RIawN8%#4 ze$FB+APnd!A6DFch##iQ=KmUbP|pmkKr!2@HC`?C1=p%|2OdVp?8h#W|2P~m$;-Fl7sVP!0Nh44zZkYM8RYT1n z|7!#*C$7~@J-ruGR0w=UI&JVg&YwPg>%A4|cU)>}YUr~a99eVPNNo~fiX>&>?IU+$ zk@VufK9VCqxk!7&gM&}<=lacvIhbzu5`#;T*t>V{3%FHUTIo*>q>1-@o=yjeOrI%WP^aL|uq77PYuzdh$la`Ot6 z(Py8PUHs<;zn4XO1KnLBeD-khOZuKN;E?AV}w{*C)decgg(%NJpM!3s}{3c+)xdDHp? z)VX3{P)f#ELO(`mPahKOR8|g-QcdgTNR~?q3K4Y(U&fi`*#2N-WE9|9B<*{9QV%pL z1ZW)PPpc|i_uz3Z9AM{i$8MxI%1d%53!RqLkY9Fh+sBZqwB5-5-;NBuUtfx_jst*s zO=LY~_AS~sy~q(hF^~R3Nn6{@pfw@`heTQ$$an=01HPS?x@g6MsnXt0w;sU?xYV9q z8o%Po&y^P>p+Q;Ibk>WR2evxPVYWYmbgym4`ews(k@g*U7cFzaZT(s{C_@| z?$Q!N_V8VFbh|g`i!VL!`&zfi@wK8^v+wz0WqN*PrGvZ1=@1W3!o`)UJmtoFX`iFY*pMlTee_Li^|2L;={6{k1^&2FeBcmnmnZz? zOZlWt3=Csgn0-UQ)9LBy6th;*q(Trq)RSh%`#8cws$|JV{3gbg2FpYL@!i^6babwp z8D8$)K;o~H(h8EwFlC<_Q49OrhroNgp!tiI5Fny;7MOaRP)BUKd`JDQN2@ikWb^zx z6Xhs^ugOJiIDcAr)DcfX6rc9!j_{gAbSEG2kitdT(|t8HHO#Zl7z39%_F39W`YpP9 zRL#xJn{*K&*7gAjEkV@UJPXA#c9I8xw2|2p706n>sUhwst(x!t4>Ee1 z%r0KD?a;{z;X#q)dgBz7N^b}S^ILSuI0jY8UF}{cj+=9Sdg1P>{&i$4(@j0_#MBbk zE#%f}_APqg0zXebKt_dJGRvhX$E)rkdDFXhrx7Ps-KVH%+_=N6XtKP|4iMS6ZixMW zV6cr|y1JE4EkR09T=ACgytwGWO7cDVY9i`F7LG7gZ?r98+;*oua$)Q_5&o1s)l*gA zjMx&I*H{x;Fn_QaZ))h5*@kUDRjQ-&4$gA-)m>%*gBaOXQ!}r3_R56ot4)t^#q>&l ze>`o~=&0^+H;GQ zU$p*l9(t0vLac;)1kLZ$<4=iqd}D_9O5hXO@QG8dIHx3h|E1r#7|kswhq%%dNjibQ z5=VYYjG6kbm8u(yq)Lf)b=ZY%%JrFV$fl9MbCb5f^qb}CiIKhvwR{Pir3V%RN}yZd z`vatd4ck)BiAbErL2=0^nKQWw$rp0l7-+Z2AXiW06buE{e7#k5itO&cKeVcsoDZ@; z=>AMrAlGxm8vtSn)Dt)X$ZW+ZbuuXt74xTp7E#B8TaJCEsHl{Z`D4IKs2>UnBvDm# zng@E%=D>CIw`F+nuiYn-I=oJt>oQqwpWg@T7A@!n&qa!Z0=c~N99o^--K9_1q&rah zdO|?)K(o9ar=0iS9|0aZO(?g)F#;ICLKx;?+FT-H3`CHMWQh#!W$f*1Z_)PuqVh>W za`RVBlyFa1KG06DP82EwbSn;KHy_Msz8NT+hto^S4c0BT=b+rcO7u%nR+)x*eeO-w z(6a4h40fmiPEL4hvN?1!ZM+be|`Pn(j~8(Z>1vc^qvuNZo|3U)6v`9HT$L z--g@#b(TDa6-RwfTK%Zn(^H=stzl6vz^sctp(m*3RWwIbkyzSp4k5iHBG=|wXhN71 zh+7XojUbLazn?$Qu$}X_F;oo?o<{yh+ZxP|D33P+YW#LA4hr^LUt<@2gJ;_@{u=e_ z+!myZ$n!3OPHfrtHVcKEq&gB9$>)noknn_V;l}OVbadf&=;K#(+;9v3Yk%wOo))Hk z)CSA`1_~g1(tes%6ZTjtZ3Z#~gxY1^6Y1aU2r_IX^<3py#~b~v=|=vpP6f+;+n7?ru4Sj_=uQ_)`nv$G z;G%WO`D@;Mnpd@%kulPLhV%vv6i%r-p|P=Uwh6^F^@BM>ryaj@V|p0TEW>ZIT!kJK zS8aMm4*2djTU^QR*?qi1sPBLpQ2E5W=}_hXBx>D(Tx_d%F)RKvq-7AJlxaR!)C!d{9zLV^~XvC3LCc30^_bB#9+l>juNW5-6gh@{jK@h z+6H#0y;bvHYV zmh=Dd&h>2Y^RTd!0Itu*CMMS2SEV-i5n6-b?9bHkrO6KtW}QF#aNC~nqS1BYG!x^j z7V{M!f6EwWW#(gEeEfl@$IL$Sp) zJ+H`zqYs^>FsNE+ho%VA+~7A}@-R23%VuqHT`w}^A_8G#0*YUQLrM*5$KdiO@S(*Z z(zoHfHoP+7_Bt^U(4s7<8tzVfCf#CPJ_|Q;tP@cPyF{f@J8rE$c%4IKyQXcYuiYGa z5PScQ66(5#HD-madRg~qC%>&2hn00X3BDR~z^jfNnv0dCrw{>++X17}(k0{okPFo? zU`$C#3CD2f5`K2_eSe>6m3K;L3az|RHxDjSrwdM&;8-kI+ zFthdbiXtrdV(M2?5vE(QScPp8pnGli6b8ADXW@a`k!klmsNwqf`)8YNIr0o(h86~X z<0k(ku4nhlo%By>4)3vxRTG*Ih`fgX_QU!`#J7^@Eb%%;L7{E26WQAF!b z)|j>KZ(s|*M|`f!ZuT@{++22(qHPn;GDsUb+dbsb&eI^%;*;Ee(&7@N*xG=2I$9P` zLn$UYiaoUnfwQl}KN?3w0>?S|QmBP^g{qk{hVRvPVn}l8H2gsOJU}wb?bcS|(weXB zNHHWUOP!ed$###GYKUw_y`KJk%IwyHmO7m2{8w=MR{v;??6%JYWZYfldNcZXux|>j&CE|`fMk>)k z=I+0MRQkVnTVL8%@PG}}mU+ybeo)W!%zxqh)#Z2wEe_i0?>X1GDazbM02iirr;9OQ zi)FJ_oh_T3CJg*YYUrGd?1^zDLif)ry^iB*p7JsPW1?}E$)Z z1BE`mzLMtVDRmao($eGVn!Al20=%B0Z0DBWLc?I_6LfAHeSSG00R+h<>NTR!@z1Jg zYsz8_kK6Uf>q9%+INegC48@Po0-|ToysH^!zxx!$UiG!9XV(5`$<5XxVnw-fY#Na- zUi|2Xvcil*h*7@bwQcSP?)I)}qJ=7|5*P0Tknl)MImtcZcjWqwX1U;*mTlg$r8k|c z*|ZwacP2((5d}EvW<7oDLdhX`BH#K~*U&&oOm6Mk@-938nI!@?Rfd)rNN*q4{ zHm@oa?!1$TSTyY*!^__9mO`D-6R9KN`pCAOhxH$7k#6-qu#d~V-Q8CakWlvBD)=!0 zwU`!eynjb!8|ANK6y{fPlh_Kn#qQCkJC>d5+O>%8F;~Q{vA??hcADbTrcSp>VpKciW*UkoPh z4BmTQq_`E44dU|h!qwl@v?+VXl9=@DmY5H5{(e6nX*tiIsj#sO7dd1fT-G||?Nk3V zVI~Xr%L$!X(gq6+DzDf1uj-BMPFyM4Fqwz%mRCvU;^FZn$Qbd6rP~dH_F(F!5T|td z77@&We-Rbc%cX+z#@u~gPR^fL@x+`FsqJD;IS;q-o#yA~zw-Uv9R(#NR&v^B-^J?O z7R;*ALRIRdssbOSb69F^xH&P!Ctrs}>?cnoA|k@FZf8w@RQjd{O0t{Ve8#C3)jVlu z&5IPyL5J*fyvnJP6(yyQLMIKm#Rh%Z>N#&DObEdo%fcM{NreHC>E$0#^t!0@xf%t` z-DFrcfP^CA>kvU?S@!G&hWf~UAoHA%<&STpBBX-pfGPpMRj<;VgYS=;Y-_XNDl1@w z=h1y;b(~eU=nZm#A-9@GE_$hl$sYV>R&Y0FKZP^A%ZL_b9ZBf(NKjWFUv`S}y|*Hx zw&!hJ=udj5pCI zQ2P?6(Im6gU|*>B-uxX6YI$EB&Tla*_HSENxy7}0P?$Yq!5}@Th4Vs`M)jp-mNF*8 zb!+KPS{(0Tcv@eRl1J5?>Wwb^8~& zK5Z=uxn(|?cQLo2cV4A4C0~Cp}GQQVYY(tXDl@KZO*!oPK6xuhMH8)h>O# zsK0Z5wd~`G*fe%}I@e<)zbjCRUc6-!pfb8e6h@OCTB6HwLE_@2K8i7ZdHiXjT>2G} z^pV2FnbYZJomXvr?OVsig^RU6PJBv;@<=jM%{XdjI~1XnGJQE&WVmOG+Uw!4Sw4%r zMqU=@1@B)oL5ci5O(K%_{+QW8w;@F2@Qn={Wuo?beRKPLVhkvo?iT(|bt{cuOFnW1 z#_IAWt0hh8?H#XLmPba~>fawbj7P7wFV^OrkjbNRgX|}J&Hn&z^YyG>-Cc(xRc@x( z$A2DpWU$r5d!wAgp27Ggo*K)+MRcxvaDzU{dY-}Lu67rZnH|8bX5LAmWVXZMC*j@_ z*2sqsXC!fAKuDDLJE+O^U8ya%ga#aJI4@E_i!;deoLOA^=kreUO5s*Kjr2oSx7$YR z);zgX(L+sIa@3D{Phc>^pj7Oj7^Q*rQUvp1R-R%S^)3~$IW zq#Pfv@iiaM_bbiHl{jTThqRD`wk)jIq$gZ~baUi`)+s~6c~S@8;m`!qFqvub1V|%E z6jrK`c0qD18FuYT*p%w-?*6WA@m5-#y69Ee4O*f|f3&sk*Kl>OW_n%k!QNJ_iXTDD zLwuWb%gkG+Pw0Je+}H8>&s`!P{Ra0xdJsCR?lfDwqu%kwfo~QFCxVc2$t#h~Rvw;{ zqv13f*m@oWiq5>*0i&U0kmbJgaVkcgN25*itQ+dIXny!ytU~1XT*W0W{ zS0ht*XBb2zAV_G;L~^GHfX)6b4oLx2A{|Xuf@E+AL&C@`1iLATAu6`ik;_w4Pohid#}hsp_VX zsx=j^p(t%eD6#@A^4JSg9~4{rV@<>YwwOIWXu|Mp+@X7{sJiB5NKQn4n%w>In6dGS z4Ern9X$9t^My(|oVq1>%_2Fid$*Sw?mNG4;rHq%6#5BRr5po7X^sg&_=WP?d4v{D< z{?KLIvPJ5~jbvA+`_?3zA$ZL1u;pdfEcIDt#1B0B{^iTNq9m1+Op@KY;0T|T%9TEr znDltWscR>h-O+I7fZq3yxB2G_^vtKl{7a&DIx#G#P!`c86g55LICoEGTf@q;#O)9* z3$;x1NHS&zUQ2dr>dBL54BrVxDteZNk@nEXLrZ>{R~`E7Lua8BdC=C@_A}FOcy^n* z5DhK>ruovgJ+>x$bztt?Wk(&JPv)aTiRN=v6(jlB2Mdmwny;ARO-{WwR0;ppEiIPFif|x^ffiUg?}hg5l8p z(mSg3#&CcZX?vdtF|2UCNl`2Ldh5Ep>9e$DarLb2SclSOHy`o7VtTUufpw94a5~S5 zNk-q&!=WZMrQa%g%%`vI>Uf7KMx3>uxstZhZBQc*ho^oM4tr3OY5v?zz;>iZ|LBY$ z;y*i0Wo6|-T`9zVGiPd&eEy34w$Z4MC5E`>WG=XU8qk{*IJWg8VOx?#i|y`xOK_ z7hc9CWcVjZv4egQUN(V|kp6hh0j{>o1?Ea{VKh7RXG9`q0^vI@zXhHCzd=U@uT?&jJG!yjECm_kO5ivkT(5^X2TWlpo`Mv8N z+F821vj3)TCJr>+6pNeyz^FLuI`+$JOH7)3RmQIcGI4uXlzX=ZeWBD23Uj5Z^!qL( zH1kU6Q4o+THFQ277wci;qDJ|)6)C24bmykLFlc1GuON(oXs_Xcv-@?I#&@kjHu$Ev zvd3~p1C5BWg`*=QO|xKE#ZLl+D2CwD_yh_%Ii7-GgXWTU5!b?{2z`I=GS;-l-|+CC z9~8^g7a3VUw!g&2bBEveObsswg*opSDmz{sQzvM#whcP{eTP9(Ta5!mPaJK5L75^zBMJrN{AwZ@MC)d76E zFF=K{TR?sG%EyP>vl70OF4yo9otXVnt54(a7t~}vu2QAh!Gvho@A-+0{9?jFBy+8S z$C0rbg!wy6ZDOV#TcIrQWjyw|9wnu`>a&@Abd~HIes{ierAr-C#}!QD{309Iml_1F zD~|AR>xkAB@SV|I=dY9%r^_kHr!Sam@hs>Qo2Klb&%xYAj=n93TB_ah88((QEG9LV zlj7h=uz}9zFB2-NN$QjY?)hqbl+2({^b-K!9qh`;y!AEB+79^_7{`BoL;QOc27_~1 zMox5=`b&fBNVqT~Sb$z5MPwFVW>*W_cAb>#H z7D})to7!45;u47+VhWr(|L z4eY4VTqSLif^aU^VN^HVA-~dk^}Ap<>G6^8SxW{S`=97WUQ&+e&|^PeCzCGgllXMy zlVApW1qWq1mydu0PhTYC>5pVo1Pk{`DRTgWqR(~R`Ip5tyxJo}5NY+>)Q^d#Ym`Eq z=WVS=eEDrhuGm6l>I%4*q(~vypZ0~-r+e0TqAPZPmcI2Ls;^ka&Z*y`oSnQDy)`oJ z^FgDVTZ@kgdlelM+fsDbvCvZDfVL91c)CM+|I5Pps9P710|nwn$XVhfof=pNDXQt`4!1Lc+SX96>Qpx_h_ zI!y`#^?B%e;WQ1Uy}A^^nN^`&-s_1t)_D0V*Sg#zF0J={%`Fxy5;iaR_%Up~)UDno z_EWsNGkSfF)RBeoHmQ1>Zvj7xczt)dNne=_SEvusj+hKRe=7C@Z{OsugoT`od#?>E z2gmDa@H50`8LCJH6&Oi+rk;;y6N(vRp{qKMa>f4C;;)F+9`25dS~P+liS?6tQBgJ3DsXx zWw&2ls;7N1^CvHt+1RAih40Lq={*H3eEQz31?sGvwFg%6eR{G@*Y86>LsHEoUyDNO zE&~rgo7@1eq~}(P{f#arY}Vyrzb|l3(PK58tV{lrjU_sXb4!YMkpyX;)@^g-TxT3< z8Z0R-?F0n(h)DP8{z9Q+c*+ju!o1aSvgbt{X&(ZqkUELRW(hz5YN z`vRA91N&1>6O0eLz&t2`h7lH(UHOTr6++TU$4nYtm>oPMB`NtBrGZ|P@x1EU!wNOp z!j?b6#MW!q6cRQGtWAnRC9^d{@PjPA@2Ch?H=-M|?%3gN>DRuTwS&6KIyZ3;!Md!B z8HJ%5q`Yd?sw)s+)M_V4rDwkN&#!yH-5T1a=xao&IpX_EDL7v|rKA+fmmNnaN4PJs z3TJy;_BKgOFo|-l&evdflje#XvSEvVm%^3Ik>UT`b6b7(pv&g&!EqsL88<~%t`b(t zxryGv%*UMUTJcYV$0w4TcXr0SA3ZF-Glq zHq~DBKEB#o+LDs#*;%ue(J=T?5Z87=P1cW0JWGXZdmOn0s~jAHYr?%>q!%;TOC&SZ zr;1lQFN;b35wrjPQ3=V6mN{0@gR-lf8uk@>GCjHKEh%=^qp+Y*DoJs9Z@Y2pSgsPq zM_S3}M|Z62q4C__&unr`H8-c{vnfJ+vM1zf_Fim^9utw^Z%li9Yu|K=W25pMllPfS zukiVuT);*M#lmf!ZQdXti5@hPv}hq9{%Ra@|2oo>>V?G8l9HyHdmaR(M6tyW{bi*U z!6BiU#aGN1CIhqTqh7u|T7QvbwE*6F`auQlbvHVT=*$<5{LQDw0m4lRWy}aFiFt9^ zS`KPehO&*1#yNRvQs^}EmuprVQ0I4j?Z2r8Ws17wntYb=!5?6U&E!k=DJ;xa=f4N{ zkB|dTe5}sDG(M<8Q}9hm`OMvu4sORx{(a~=^jjH^+l$Z5%P&k+=bzd)KBv5}D?Y|K zySMe_MRoOO1kE*zbDr(BcDd+(P;+eX#JI5d+(3+UdMw46yLymm90Q&6OYO&g>;ch! z#yWB)SWP7?zqH0)akj=r^YK`APCQh6@+VKVpDF4Ehd}Gu`}R;{^P6^;L$sWZ^=0b| z0#@X>-Sx}<;n4iN<507xwTRWQg|1; zT6g?0Vr_r1lvNzD{foP zK)`ToY(t{(fO}&NpTn+8!HTH2s5p3vrG8u9jY-_ch7AigVhwmED=4fw@J3*8QU0HQwy@9(q^OEu2Q?7IjCrvRt2FzJ9lE zYPvnq{9uD+)%2&{U7}ud-5m2hzwC_iM$Hx+%Dbn2nH>|(pBuh6>aN|~AyE6=TD!a9 z#GMK&d-tHs;A}gOR+~Pv;J|fE7ETl8cA>+x3Q?_NHDkw*4R9o)RP0U`bnS*I|UC&0T7gb-_^T zsC!La_nhlNj3Iv>m)q3niFhjY3Ccdp(hWQLmE}i=es|8`RRZeUbGc)$nmc(#P@4^#dEu z`F2-cvpoGw^>a#rnu&kOl1n+2!H@TSx>2m4c~gnHYQi=&+fnX5zeFG3__lrC0Yr@J<~wQb{a8rkjp;aFg80A+hzT#FmuzFf+r0XbJp?ICx7}c;*16DoD%0>F(#mDw3lIf@6Uf;`aWu=s^v9&s!lt?Z5XR zSC;5=xcG%J)cR)eWNB#3QWWgNl=k{?a~5w^i94*V^x%kgWx4kACoB^^^xi&AO?F

oW)Od3yZ58Rr<~>KVrMk?|Q><7V6O=qC^}#xE ziGEbWXl9k!{+iquCE>NQbLl%$v-4%^x|Qy0Cpy|?H|(jAjaG^tOH>m!+>-rA{~@&x z)yWyvDt2etGGZb{xfY$}8Nb0+Hx7q}~i!>lih> zHdI(pnin74FVJ(?L)!1hvX7LMaP{7HDdjO+)713u793GMku4SmK|c;BELhLo7yI&T zORQC9m*%bU?40_>^-o+FGnKrww$ds?i!Z#1hygUYqHMj`uWyOwhtTgg{q6R8k&g>g zg<>UWEIL36(DMzx4j^$v#ciE>;_dx>@S%#N>RQd0#yq588fi&`%4-+HT zuuq)&#h3B*luLSg@}|_v4(_0)hzl2=Y|yQhetkWQAuUMu;-t-MhF1?~DNp>k@0Wg` zQBSI=Yimgg8T5JPDBvZW*MDdrPHks~k(bJ-on`Ccy061^KkPJ`=LEFtg6ir<$G;9A zI2`@`#>Z~t)X#<+3VXCS-fhpU{9|fnYC$Aq&@Lgdz1QkQVU>N*xQA`%`%%^O!Hh1x z^g{C!83V&j-G@c$rmXraCEgc~9@h@m&D7FuuB>ou${LIwn0(jgC@>_rBj1^0=3zmh zu47PP-3{3>CvgcM*U+J9ll%H{E|Dp2KrpJtR%`UGBPH6Co43@;Tn9y9;S1wBf>>l? z#LtWPco{-tBpOB?;Hy`eNGinzpfhMw%iA8QGHPQ`Ry9J}fyY6%zK>>$F$XXt&$lmaI=JFS#Y z@E}zR0oiQ|1pV3l#!ryOvpPh$2>nnSv-5~^iZgyV<~CNM0q^g>YCAk(ZrcGQz1B zYX8&=%}MDmfsoW9{|JuS$=Z6RSDo}Xtw3A{UZeclk_t|{XV`61$}&iyxMpGQ>Y%$NT|*d)10lPix@oemB8@HmHI3i*nQ3&us?+X z*v{FZCTx727=XCrB)BQ)1O)}55lx5fwQElS82~Qt6RLJla1_&(L8FX_j`F22ya~L0 z1N#s(VD^JCwZVzairwa0`3}U!1hhkvK_^P1q^Pk(Zv7e%7ha+#&UM0G9Gh|zP?(g(WDcfc_BEui@q=Afnm7Q6I_4qgyrlRhng5Uj;DShp?sV_InWqAAqk638Zc z*HMy~2ZUoFnIwovX8Ow@me>vq;;SC=P1ZcWE+pZOMz)Pf7c_A9{b^ToInvRHTx_T@ z=b(~rOMCpN zVB{#;hP}SCK)OVD7{U-wh&a9v2ePMLuEru)>n{{-iU>;2S-s}g0-hnlFU3xeL^u57 zyI*F`E!*65!&|frJ+pw(ue=Sytx{@eUub;;7;rB`q(v1j^C&mq>SA|5U&CS;VIBAx z40aH9>f!0>hX6M-=U=A^z&ELw&IOR!aRxTRt6XqEE|~>;iH^1$PDp{K4D&qjo6t+8 zkR0~Mvp&FEABFnd0?md8deon*U0-7gJ?HFT;fo?%4*>XcyC(NISz6WjK`J=UOfaUh z@il%0@R~l_=Sthb#H**}xk7fz1a|{a=iqSXk+E3KP6~QI+x|S?S52oGrQl1RE7*OP zMhZ?G3OSN1c|PsRmZQ2mChgaEsxXJ5>&9B;HGfdhE1}WN`*OruI&K+JMCp-?c8*R= z-;dPfZuq~h302A;3a@2kr1I@xHHvz!=b_CistTJsSCSI{{3aIg=40#UAP7K)*+81R`{PrtE0+&o(C{ykX#8L z&FfRU@H;y)_Z6*A=GdaKh7miOMbGUFBD#*?toz}2HUj&WOhZ*RJA|9UJ%DrXr&rgJ zURz>Paoy15WyC;kG(sQKt3Y0C^czOOrl(ZHR1kYp_5L7pv#DVKnUwUcO1XOJ8L{N9 z8?Qk05HJ;`5b{1Mz}R2#5l9{7E-Lz(hUCjsrhE|bQ7Ou9NJ!XOmr&lgt4_GJe$Cnu zJ`;v}n!IrkhsolNb6)(mXl6=rE>rJWrinZuViGP=iY+Aq(G9;NrTq2|8AkYwWV}4& zz=vS6psbw8*NAGha+mwpg$&5FAmD-TwtaR-F$9j84^)jne0y~neKQi(EH=m5dm$l~ z%*DZXo03;r&gf$DPJNUE0nY`^uIto%a4~xD!Nev5k;LfL0_!v;dEPs(iW5S%8KCZv2KPVY5d$Kr=0Br;ft%xG zO>#}jSn^oe*4iZ1!B#^(J~C5%uf`$RO}j8}C3@SrfWE_O5>0MOpyxsV;oINY;rHn5 zh7Y5XWW)X|=p&JLUl5X62;1iXGWiLdTknA(0A_sxt~MQ({Z7l;$Z@ms`{PeQe>e}` zX>Sob0qIHA^B+YBAy)0dC&vqSW{UlRIP)G0`_!-@%FnQ>p+B!-4s&9xo_}_`WxPTh z|I1y#F6Kp^WdNdun^&K(N9JWX6_xh&72u~&45t9!S{=fK5CL{pwdefJ*KgoLo3Msd z`gQ)@yLTgRd1-6wT=`0+w!8pfi`0RGBL}h$LAE+;b}^BMbrtc{uezqll(hQ0}|2Lv+ALSWQ)9%Y9kq|~9xLQZ}#0$e!7FJe2 zphI;H@n0-i@H;JNBg4vAqQwOc19r5 z>MM9KPj#YLSyL>`5}Dti1NjE?oz-_RpaZUaTz-x3nOWYM?knI7>bnUl+q)$VXu1Gt z0uyx_M8e(9oX>YLy+!nM8}w8uwiOkDZa{C9u0V5K<4{7i=1u`1w7z8m1|6^-Q+ufI z{})MN!5&!Bbn8F@p5%IT?|)EY>pnAK`^~L``z1zo+JPqzXW$L$my&+W;eecZs~)>T zlqIZownNUrVz=r%0o=LiVe+h`rgi~lrS?(A!nOb(fiRa8d@r3=%!}r%GuN zc!&FK?ff7%14=y-n^|lwcGQEv%%oHQ|B3VCifPwAn1ty6yE7j^MtnTwS&B`Hag`3_J93!+*Yf3;d3QEhJb0PldBu`NIVk#FkdV;ZAy(B4@q z+g=xC0rnn{<7>gGnYe7)nQ;wytl7cTd<5la!zkp_5I-RG{Zr#mH^x5D06kb^qw}TiBaF3~hlRcWqHrBpsXy`IGu@68X#I)Z5n1{62R3u# z<7?O729^^&<~ampk1Vif;R-VmZ~PxyAw4`kX&MsjJ^@#KQ3KC#z<0kEc7ns3`u%WJ zVoKme-F;xXQ$?d#A~x387q~3)?6I1MhlR{zyG08Ce3^LIod>nhMgtruU?3EN9{1~BAWK85FV z*%yo*ucOV9qE*1EolIh5V*^WhWl)9{NAiHJLWvF>anHFie$B3G;{=Rk1TXqe_~dU~ z3AZhIp!PK?ekEV->E(r-ttO_yY1+;kNU)kDFbJQ_bb%snX6@W!oMlQmQQC7L8BMqL(4N=n28a(8!E($Zr8 z{?-*!B#F_}WK3bgsFg2+ii$a~2A*>5@In>N3I`knDA96qu{Bv4MU~BL0#Vx^4wZ3_ zGVr&k9pVR5jO<4eD=^#s1k%&7eiu;HV3p?*qQb>|)vsv$it(yXBj1@~aH0@UcLMcT zAxE}!sQ^rkdws#^zG-r0WyRk2onJ;kHN%}nwB53t)@>fhABB#GT&3^Oj>>x5h^pMi zMHUCJfmVD#a1i8pis~4KX6g(iJ3@JZ4w+rXS-IZpt@>KHcw*5^p$r;fBB{LjP<1U> z2weaQo)64HP>n8M!7crpS1Zfp4`{rOYLgNT9Zm)fLw>}i!;XD&^A(L24@2u=#rvuqrA#_j3$dQ(|{$BuuZRxJn>4#fCwkzbp)fL9tz7 zQf3}y|GEhrLDT$6s2fEz2DLPmh~P!IQR_19esqi+1J{fWYEPm?`}|0~b*w7l%^~BE zQ;*(Xt9V*9>XbmKbl^Ss*mZ$Oz4*W`FgA#idq3 zs8ZYEg^}q(zzGLk5n?9$L^9{iSYjNd2?R@~Y)#9ULYNyaFrAXvF!evVt)dp;0PBNbYfAjK7K!;jKkw= z0qbT%may`LN@;tw!CNM^1MW8he96M)i<)_|NH!0bq(zI|6)6U+BDu2w1xqdJvcURG z=9#Z1wBP84L40s9wiEa+U3CxT&Z7Na$qv_M0LUM0iSq1}=+fWE*%@|R0m9^}?!M0< z$Ri@$#!dKqJo3l8rOM&D-Eo@|SGH1hF%C9?#fRO;K7k1b8@R}pBI5}puFup+E98}j z)2XjPk>(_lqxR_-mb>oY2C%A+^A_GDqh}gPQGuCLhv_pusOac$maTp3qsy$|TWMay)36B(Ydb?QOC8h*Baqp*VBe2+9d#ObvBhhS zBu%KYa>?LLZ&RwreANrSl!MvNxP2qJY^ESHER@fbsm6YQ$rzIPB+bC1x8I~U*KDS@ zfU54Z@r&837!I>{>dy}eD&?$~81BSZYgXNR)dYKgDEQRs)$r%3)q(srNd4fNF!?)1 zNzQ%-ScVH!OLIpM@<1=f`gRjmLTlO|C`cC^M)ga4`@mZaYFTN}Z32P-gDY^ecl!ME z0hxx=2yqQ>L0#BJf~~WU^^U|PjJbjJqdT;}8S7yHaut-?;Eb56c}aAsQ=i#!y zXkR?J+M=T4h`_3ioyixvTWyKaIdJ*XnH{K`gyyvQ_6{~Tz({tS;IM=>qmhVI-mde= zvk*j1)()euU%#dnDUFM}K^htwnsj*5fW=b_chz`(^6rw~#mO(P54T8nE0?dbo z{*p>|%s27dtas3p7E{K4!E2ffax>fbJjOflE9?#r9B&FRgFiv=$Em!ukgz&1OPwOO zK1oAzEo^t%nC`?gH=@%jAsQC#JM_WCe$HHCKx7~}Ybd?EzTEdm26{~m;4IuaiFEB} zF!YL<=p6sBGvfoPPVI}ilP6YmJpH=N%Mr=So??0}_$Jy4q0a%OxnKP84Lb2Cw%%ct z%?psDNJ0A`8+y4`-$g>gU1zo@UjcM@F;I4`n}RYf0;1(6NWf=?laOF68XOqdp0q3V z_A4@nxly1aaUaz2%~S}RiDrR@^^OX;Mb+bL>%VLC!;UJ@1fHPE7<>R^Br)aI zEs2|zT&JY{x-oM4q1*e#<2{Lhutigb%O^G%jD3x;7rJ<{p@f7aYq}86^!hZvl37D2 zZ~bI27`(MlA{*WJT2bdvR_xwOky(OnZ%|AjJR~=GNH;MhG<4(A7;+K4TP{*aodm)$ zR>*d>p4Ej84B;>Nyv0ZeAE1aC0KEF;B$w`rAL+-0zjD{%6=omj84O{E{$}rm$bG|{ z0EoFJ90%DC^0TrS{&e^RWSPH)JUk>6Mf~~7NBQ+I{I;f2>C7SDVq&hpoVIksl<+G1 zWw0hA8a-h0sR5_Hor}o{ z3^_r#?y5&g;e?}Rl-(wPy7&cauJ4Xs4EElkPA%gH593BwA*CAfOs5b#gfL1~hO2Ke zZTo3;S@I7`-KQp3z=v1U=yATpD$T&?gX)w}w|#1dKkHqX!%8BGZHchhg+cKi;2}_K z2Opq*K}G{szC4sPGLsg+3MiqJ0S(E1iqf0pfA*1BjdtMEP16f|y znT!7{<&XnUj-Q-84!hWO8&D(6#XzGSEHOs@c3F9Ln%@Gs)V$NA(gEK#J?@YSp2+!k z-^8KYT>lWKmaV!Bd!W%47ROZ@$u8P9`>_8yeZ_tbvV>ul4LPZZ^i(z8s;oeOu38n1 z8<;KKZ2+C>{u|sxj>BUM=bUpEDu6c*AASpa9%9gLVcrQj0YI`@?VELGMxr#qrU}R; z@JFm4M88A$)s}{EdM3zZhAg+iordw^MTJnWObfg}bq02B(;q?~u)*^H21*zuW6w7U z`>LUX$mS31*wz-qA%;*Jr0l8rK#EH`_6~ZwIvCk8imrDxphU-c$gmw>dj1{kB51dA z=r603XYJaw{taV3j(l=lX%JO4x<|8P3=Wb96g*+CX(X_!_M{=4+F4-pUKR-@zD-39 zlz7NUG#VP9%?0H$stgpdt7?!s8a(@pIi@6f$~NruVW~v4E_MJQSz*_E*T|3z0(r*T zkT;P5F9X@^;&Pzz-yzVD zAUdYlMWxWx4E6YPH}p1<*qY({QH8EeND71Bik;+l;B-~TNAI}8p5MJX8*eq8Z; zOCsMfu;3ZW1Ok2?n8^(T6aygfg#xg>XY7UY5pn<{$$Oms(5P#qcZMO?6%OoJIFNES z64H0!fT+(!r0u5(c^%umW4xdto`?k=X&v+Q3eM z6WMv8=~;z41e{^D&!jtFp{VS5j1)RALlD=14!4Rh82#V|rrktq<;ULxseg?r}C zKvC%1&yxH0T`%C8`KmUEN#vrFG22E&am$CWXhu{SA?T4=X%o5X=l9#o9KEQ+4)_jT zLlL-JA(BD5OvE1<$QLAcNYmghFW`yN*F37EP; z-lP<><_U;ey3MR>aPQtHARJil=2#3zy*6aX7z|c=Af;=J4=NNXt`&D-i#k?8ryoW&#CU00HD1rh&XnW(A{W|n>)M05$6<>i~zhDpAA7r|{ zyanptI+%=E&9|Wmd(785)SJS=F-opoqn|HTs~q8Fy2GgUjR?iGRX!!!DSSw1yN|Bs93(QaDK)cf0Pd~o^((j1(_G4SX_ zW%17c`Jtkjb=gK^=ky-<>8ra^ksUz4-5Bk1hv1W$0OTD=A^^TqzrF6VO)Aj+bK)+a z{BZ$Pl8ar~w`_|N>Sp-djm2*(rcvU>Retv*!iJ5}*X&DxVowQPH*j*TRh*4nLl!N% zE#Pb#+rx!&tB@BESU}I~FD4AfxaTXu7SepP@C4eR>#YcVou} z`t)_ydxm!()1n9ePkZPw`|PIyn^bJ0Q&Lur2cs##+ZY|GGB7_L33%|y_ZD@x*E&!z zo;Id-zdM7dfoj4L`F{xtH=SH$nvS3w1I50kY+(2j=^D>TWeT1(X5bqj1|EBia40MVJMx@x+nK}e z)wPXIyqwg>-ibKE3q)puwgL@ykuNU)2@FwcJeEEe;Kk;n#1Rg;GXE}qO?-G#J-MC! z=gHA2L`)$tiV0jFvFnOQhNlR;ErtQ>Ab=Bqwi_-tLQy!zn^6XdchXG31v1NaZqU94 zHO_r}iRtKA)v^7)Tl-``&?CT#cR~9lGOBufhfAeA7Bz}zS+oXO-hpM}k4~xFL*Wqr zftn1uAvlQ10z57xz#~IuJ>cctuu0sfVu|&DF&9q{vAhGO_INIlH55#6^6@6(gg1nc zkRRD;zmVS!nvYOTe3nD6hC)N-eU0+l*13dC%4uwm+^1ASs18J5*}?dU0ciTn?72Xu z_k|xR3&vy|2*7{t3CB;b@L8i)NNylG{nzBcDzI#yz%g*>28l(-tO=w(Qcb*vai?qJ zu}5<_;3@ccgK-!mG7tj~kBUhy1fG`w+F}o{x;`9Yn{G1RaR>7%q^Lg>TEP)Xl+kuk z!nqZNPT?S7BCVs;kEAsn-sXU(oFm3tUZ}xUPb&Xj9=?h4HgW~mGNN-}sx15zm7~uc z!42TPJy4Y=X_fsS!78O^KbFXp-#=FUJO8_Casq$O>HYDL7G-4Q+ zxbjtaBjv{xc$e02is}F!6Ht-3D^((lOE5y7-)j7gPHW!8?g&4>*o*Y;VF;hfHNG+~ zU8;nfq>)1TSbl3&=50e$aRlUPC|syfChqoOy|oQk%O@bAT>Oo>uREp^=>@>7wFFYk z*h%Kr9dXD%hce~&6g-AAU?{l|SltZB7ScHaLHG-^1G@4z5u@giP6bP(=aFjLx|zq9 z0Zj($bc*K?az}JU37|+B0CfM7B-7kkJwwfp>ncfY(k#Z^1w}kTXn?U zUI-sVq#|k@Bp!8ReN+e=yqHz)f`Ng-BHi^|Yn78N&W~Yti3E+NJCERFu##UaX%}4OBm!*s==JC0j`L7j=d037~9i$_4GmQ!!fsf>Fl>bcRJ#-y&i?wjR+AP z_b9v_O;n+UX2EbWr2|>rLsY<`?Q8pKV#W2}mk+J}zn2ect+30-9>w)wHX>-c4D_kA zewzkkEo2@NDTyZeg0a*UM~lO^-pnHAxA{w)Nn@KDM&+8arvZLWy<(LRGUVI++*emf|;qJTVS?}PxQ{r1yrnf@T zx_W+XXFFKRo`=E;PAR<~V6j}&2>|6V*h)1$nM;EVc=4yi@;g>gLDVAl(y<`$P@K#q z4YQ$wQQ;_2@Q9a}*Y&k?s>=4_KwCl=dw{2d7NS*RHd?#be~S!RpUN=mBooyE(5%w= zec(ATX?D@d`yPiGRZc0K?^@E*(hI{0#7pMD5vT>9or_VmeaO%h?2REBrK2qi*>!N0 zg11)549wM^3uM=B2ZE{symXX>XgoGde`w)UM&_(Fgs=taxL+Jk)WVA;oqEk;Jb*+O zXIi>(cUG!TnSFxZ$@+&4Arl2Fd3HSv6a{ahLhfgC;=y1qsyjP}qY8|r%8-vkDB-b1 zjnJ?z#7(1!;XFOaLQsom6^$@L6a5ZL6bXew0n?kKsR~+Y{lEJ;;+J;@=_L{`z*)`= ze_R5urAotCx;lD_AIiHC_#A%Y<3>@w-v775G?+(Cfy-JZ^5MFmHJB`E5I$@2IXBs@ zxqIoEd7zO;?urjTEQ7tkt^%lASBU6q%rnHMX7T`)Us$L@u8FD>n0d-?g39lL<>+T~ z1=hfzp!(WsJ-*+xC~?L2mCA34k9GzcUk9{BT;^(}GDy5oK2CkCgqPK1B-(?jdrV&u zxxW*~OM!W{yaf)(wP*{vFQF}1ksQy1*<*%6VdZ(RdbPA#D-1A7xpHZ*{L4KEp!K`# z0B=B8n!rd4BmPGh^>aWkVh81s^<>CxUi$?jeu&Jm#=n4k`cJP8pJxDjHQ!`u^+iRR z(*e~vXHsW<`cE2t}Q~Uatleb6WJ{c3u zLkWzH&Tv@))S^-9Sdatv9`cv#lTHAr(7vpYO`*@b15$KLRJ>#WS-4eS*4_?vd4#Jq zkm)x6F$)7r(UjQOb5b%gCb*p{CRxEOz>mP_AmZ36Ld&Afv#W~mYQ&4! zJR@DM)OsT^bQhjdi*-&K|3%3|9YwQu=2++1=Fl?$v`K%2{AmHUm?gthhv3ki81$`& zPCo6}yT!?l$*K#jJGnb{XFPwtt7HcFYn7Fik}%Sn0u&hSo4J1{R;?kz+=Hmw5xXFO zeXuSZ^NxCXC8$1Pn8*t}Fd_KTT{j(hKppXzIp-ly@_Y7-&AW*&=VrL0h=7^spHo52 ze}RX?fDDjTp6?^v!?K{)i~(N0<*deXBrRHJE5k zE*!`;R!&N4CjOTn>hL`_gsx$C=Ue$IT;5Q}kq2R@lsy-@Z!_?#H)a`{_R^MhVJMkZ z5cStrS*SBu+1N&|l&yM0n#(B|z*bomk3`hL$DDP!h-@E=!3;|;H?RN9GO{89iUaa2 zD6At$M?w!7giO3(IDw8ERAST*@k1VxADRpY$3_xqucH;P6Z5B=fPPq=TAs88bB6 z>kknics%#LPjBpNUV@XFO;HKCx7>9PYjljxMGCCWa06Dz;bqI3qwbS`$$~LECGRGH zlsWKx6(pLfwW7FH%B8`^h=Kx`3f2%oRF9CJ*%f$1=b#OF1ie$W?9pyCIKaNL2obv9 zL*1_u#lxzm`0sv_bAxqGH<$Z67Tle;mXR(y}YJN(R@@H2gqA7a>?|7{%l+~0cprpuQf`5*fRh~Ma z6(yjdGNQ=O=JOc@BjP+eF#l;)u@^s++HZrZf<6S4-4to?bT_b3S);fRO=)I;k^z#; zN9V!{u@h<&lNnRe%ykev%M?1x1E*7{>mqnc?_PgnRO_1B&7#8d6drz>2mB0-YG!mc za^J;K0bq+9Ky{aE;58u{9gq>kdfq9hNP0olUC(Ng5>Tc2Oa%WIg;?eM=hADHbF6b@ zr|SXs(e8}yo0^7z+?1)wMh0B~7g&TOjsJPA=-ty@sqk~*)Orl`*6*LK0Z+uv0dT~9 z(<+a~OfVmjRV*}qwx3MDlPS;YI%Rwf^Y#seaG}FrHQCc6kqilDlsAYbP5= zBja?6lgfh;2<@bBQU45v)ntp=X7pF;z-FJ^N+9YcCo5}RcBLTpIXCX}wjg>az1kJZ z-=Ai}zU9tt_Anqu{DuC=Uhrf7rkME@`^noOEJ!rax2?Bmq$ z7?l=jODrbxtxALJ0KLFF5c8wQ6?GL`eVdqPtA;WdxNm@QXCNY#L3T$DsQQufYgfKm z)bssh8!Ua^QLjCXMy{}MNPhq6BJ9Q1)w4f3B@!|OOx6sobI=)48 zBQrR$%^IM%!r!oMO9ErTK-5 z2JavT&t%t&Lcc@VK=Z-es9MlHj55Yhe2R`1hgta77eikEU^WO5t|X&2_W+eeamJL# zRD3C~V?);Z4Dd<y}^Y2OihT2rcQ7HNf7#C_CKnMb8OdC%w?=jn4arERLf;q$zg zUhRHLnk$VNt=f(ulkxCZ#ttoDjD*Z57Mcqz z4U<7=zdX197-ws(Duj?^J?!HC0S=-4q`X_;E(|bI>m@E!solEmETm zX2B|Iz#4rnFJztcUR-C(II+0EF+boq6Z9^{WQNq|!A2h0H)Y(~rgT#F zX=Wbv*Qq&?r|s3Ir5sV8+v=#Dt;_f_S*^o3J$Z0MW5LvE0m39%K+?RDtVE;OxN6V@ZO!_Vk;1 z)|W?AaZ={)+N5U@hAKMvw4nBAPqSo`*GHA*dL(Ghq%?>#8nvaGl(zR6v$IO8PI0HV zseQ?7eVD@a(b@Xo;#I;_e7;<653etNz&CU(Y1Yf6?6hvmt3KX@ym!4l>0|aYDNem} zL#GqZSUQ%CnfuJ*Qo7}qGaaW%c76^c^7nq(hM&z(8~q%){+g7sWZvJvD^j+^U!7T4 zWAg2k&e)0mu?OGOnXf+n*dY9Y&G=67jKlI|CG#Q4wY$px$HI64C^t;?o0(Mkp=~48=bIDt)h^T39Lbi7#CbP1Hp^z64_M#(!;t5JzX$1h{1O4nQ(2 z^v+d;A{nN>oc3iWmlt*L1iZDDwd1=pe+SihX32)+KPtn~^@IF2cb4tLNseO?O>#F6 z3d?mTR@Xk|m}Fq&>wavh@1DN12k_KQJ6lyc9;xlEXN=GdGdGxal`5;o+?GlH!bxa5 z$1l7-Ih^W!itp3gigPU4UGX_e!quGeCVr9{dssM00nd8cC&%!om{d6)bWugTNzk=y zdmGx&d?7*mi3hi49C4gEnUm$`aW8I7#suvvCEBYkeCFN4DdrN)30{o35m%@2N~>wZ?XCvQqhf4WcxKD-SN-#ghmSh>|ugC*}- zPsxDleP*sIhF2wVVp9^LcJWMAqpgk!_=#nC_pw=J<%h9qJ;Uwc4g)vM1tuL6SWK(J z17ciC+ZqZw3VLj4{25iIY7TmAs`WLrvU7C_FXXK)$8SZPNN$jEdc?dTTkreBWxzUF zKgBX>V1HY@d*p0ARf=wphs$i|!uyVOWB(MN0I89Zuw=7dcS|YH^jCOr86;^AgFizDrAp~nVV5mO{@LPR zR$1F<%1fG)|NC=_-GIy%he4Pyb%S#!k95L7p_p0X!Q;%geC?S^g@B$oUo@~O&rDbq zSO$fq)qE%ToyD+yXpabJXds1rIx|;(0O6;;b0+BpH~Srb$t4qsR4I!>B>nAymW!WO zgM8k)M>(?py)o)cn%gs=#m>cII_z?>lw;uYdVYGEK*Lgpqv;!}83N1)J}N z7p1DwL!(Xwtwb71jlLHhHzixCNS%EARQ^5x!oZ%AYC&0hb>^~w)Ky6zr(cmZ;Hsi2 zu(76Vzy9-MnTGZGhhiQEQyZDDKs!H#ZMup$s zP}gI4GGAn0H2zC9&aoz+ZpggvrDVGSuV~MnfwdCJi06e1znG+&qrI&Bnm^=kja$zw z<$pe;Y`=#^XWiqpW`gsnR!{k_e&L$BO+B|noK+rL`|Y4!_YQom+sh)aS1gy{Lbk@F|Av&08hamd@)5@{Iy{7C!KD8udT>wMld< zII5VP*%}aCC^LUI{2@rHyWvr*hi2*52(ZSX>M)27Fx5P}J4$4%V|Kn%{*Soni-{A{ z&$749eXUdSMcz7$BcJQziFZh|4OSIA0(Pkj>k>*i!v}o1z>LSL_?M{tdVblaX-Qc< z-Ytt%MvbDx5UX%F6$y#-Q=iO+#&JiB5a)+IM%QtA`7+>M2rVS>f?;0|;~{RXCv=ZG zGh);me)%f!)ryuS6|jv>e4rgM3tBX3{{@wmyvD@rTz`g9>0b-%R?z~*8?ZQ&^WR6%$#0C~U7 zEPcI+425e&SPS}88I!7=w{aD|p@$xQ-KVy_{wS(AFO})X*k{fk-0KY;`9`}_34;5F zXQLj@Cx!aqozys|-d-46`gA0vAVK^FWH+E>V6&9v`2Hxxk2D#^el@t=sW7B?uPIp= z{N+CXug^S-OTg!Trovk_0+m7#3@^Ge^Ld zS9OKWTJt#edh#dRy@*$XPCmIBVIjTUVoe(t(UT{A z_rqaD%L;jOPw4i7S8mkSP9`s#t~%%9x{SMdCd?BhrSIp(-M=qm_rPPSD^KdQ@eOZ5 zljVED5I_j|Ca}Q_>$2;De zDa%;|3Z&B=OJpz{mDG|x#d49KY4`eTLaedQb^9@8hVF+1{nhpO)C?xO^wCavkGx9B zn`6ogL;GyZ4i}|;-Yi@wP;O@A;<-hfwqfa9eReD2g7r*XUPNW+n{$LJcO}Q9yx5S* zGE#HVLX(Fe>apTt0r!KNg|61s9FXHojC!d?p*_uZc)UHA_rLnM@4HV!d z-k{1Z@?_+@#_B^Cn00MgRmGGmhW&@|AE_j%luT{B9Kfm?v`~V+bnlGNqrvAp)bD6A9 znka=<=GPv+m8DOt z!x?f($woF08}6H*CbbU>*gTX@s7%QBpCC%J^iOIxv1e)@3Go9&Yiot0lkILUKS@iFys0TKYTOqfj3?-m0Il@s}P{P35f6g~Dj4Eef z1dWW-T_pg|(>TZEtiQ@>_xS6wy=K)#Qs33iNiK!21MCf)L@AxJ!JN}}8=hNVmh1tN z%qgpi|3gRUj{h9j)+@~;>bjQ(Pw#5b^D7Wgijn0xpMyebzif$5mZHq2?ly$ z#q3V<5>*xlsw@*DY&4!-*>%}!1|^yqjD1(Kv)fXgsZ0saxoY>#O&OUZpOPh>;l3wX zqR;AVI^K(H@MN68|E|@(MwUtWS&h7cg4yF@IA4$j%i;Q<=PQurHW+SSQ)Iwc`_q}1 zl13z=S>>eF{Vxqxo5obLjwuhmtCX#Yww(M!;nEh*gTm0t!nFfIqeI$vYg&>he4JuC zw6`0izkL<{u+k7V*Lz<6lCv{gWD&nF?WYFMT`RLG{Gu&lTc%ec%F6124ZS_Keo}D< z+lvQ!06f)NChR?2n{9BAtR(bEJc9Acsc^iE@ui-z>6-@_CEfLe3`{<#5y9`&zh8~9 zieR&8?Z&I26Jh(}_4GibX9D2IZ=0Ky`UX{VopCgb__ka4yE8E_jWulfZZM`PgkM!u zZNgFZ5|zeux8{$LSi=xabdu%$?fWSnb^*3*R}jrr7GC9NZ!CnyO)6Jgv93<2=TvLS zJc4j6*T6fu8c5sL*n^OF z!Kd0G|9Y78{@JtO?%AHyxHYdTXwVN%wj>wJN2+}`fbKp&W4$>J3Gsf&zygR#@zhBt z^(&rJ3am~5KotNJyL!mRJ|A|)ED(|05D>H4GqA<*_ARD1}K7zK}p{u{QGope?}Qbk*|f71@~@GP>_G!|;cKLm!~2!Y^N83Wl=8E!yy^ zwLmk%Yn{jcV?SC?83jC=fpLZu2ya3<@ZR+=_p>*CMQyj%*aGjg@)1c+V_z5$LIfjOd2{Gx zU!lZs*E{ES?6;n|6sHY))yNq*iVf_2Yd;B}&sf1aJ4J%CiYCSCC~Q``aXM3Vtn^H3Vb#0-8az!0bq@4vOKf$%y+{HAzP1ZR1=v7W4_eXHUURZ+2+Trw~89zujuLqaa z;o@IkTN;o~1mcT?A0+u@RJa$`XA~3fhhbd&t;}G}1o8FrzntIj7J2pRiaJW2`7yxd zs3#8?st~wGwok74wF6XU-Sik{6&=(H7~($qQCGzlS00PKF(Rxn%!|``NM!N&J2c&5 z)&<6hrNCUBaqdJVJ0Xb|p`gszpu|YPD<;CyvPubjlv&`eSTP0 zd!0bCZlBhDbsZ%2QO+zvQ!s4?st z`v=Km?ht}2g_IOqm#)O>U5~z>);-xjLx^DiTYJjE4QjufVzieaX?VQDQpaV}8oVVQ zXO|>A*5`^?>Bb=>{Vtgh4eAAYmET4Q23(am>>5gc-`alxRu3X<3k8R9MIL%D| zTF)U=9-ol`vP1JCw8bRkj%wMPTZU+AHXRA451I=I>l0zXR?{(u`vd-1l4QAfb`a*~ zvFnR4a*cV+%>&`1U9=xcMDVwb{V(#Jv2bzfiVlyagW=utL@xd6@x|T(%Xlzh)t>^T zYZh450QtcmWcDcpox3eVECdJq^v_}uyo$Q!4z}utRN3D(VEVK7w2OMv6eDt8Iky9Q zrPqj^x2G3~orhi(m@Ip3uI##}qVzhvElsY`2NXUP9Y{uN(S{Ly(VPr2NXHbMs?U~fm)2M! z&mN=#MOzR6NTd|avKWy@_5EL`_++bk8rDd()BU zmOFwDOdJXrf~jUB_-&m;+foMr{dddBs{$P&ygI5tA70#oUJQ{wd4MIyHdYty|Z_*o|f|~$?_iwu&KSq&k65T*w`nXMZjh%X>3V8&okDV zq(JDx)~Y2q{Zk~!M-cr}aSxgi0RpbRcRb^e@FEq#NMgO~t|`@Q{8ITe!(Dg+ATBd9 zxQ=Fot}g7OFPQ%d6W1QHA82&`X+hM58~K&A(emp=X*P^SZFz1-ipN8~Fr;(0Uq^vj z5UaD?30VU1u*?xGqn>L_c&_d7WDj|Am<2W>&Fx5GEqY)1BZp<^VAz@c_rSZmU!`FF z*s|G~pIq=6#0d~s(L{G;uYI*LIvb+x-nzEk&~@3&JuPgHLm8LAxz-ti==6@s0?RS! zPR2XC0ojM{lOgk@=5LRTP>mR2HJXfm&6181;bMn`dyF-II?BS=dhC%rrNglo)Rw8( z7_d{a+&C894~sz31<&0j5_qIlq%m9dA^I}Kmi9M{ZtUs;!Y;S&=7L@ojx%Z7wVR6u zIcK1FIo$pttgP3tCsp{Bf}tv%@^lZN;*5$8uGy2d^+O9gZ(%M)DY zq6tfrp{$H_>%P7P{Vj9X$H7Riu-a)>)LGBBrohLrcb4n^)x>2(%`p?-^D8IK)+|pc zE$8ws^itF2eIMi7m=gQi)B4Ard1rasZVDZ1OQ)k>mYjDA%y{HV<l{0FEb6D)zu|vi1<*tLABXdQ1_1xV;d}{pCpQnz2x$2cr(S!Gs5krf z%-p=^_j6L2Ya61Q3;rGJCxMgGuLBF~KSt*u$BA7|G1vWH+|Ao2l}HU2z!GL{X*wmp zZCz=$Aqj?S%^23dLKJET9y&;X0}z5hJ+1||$>A_AvUdPq4~wZzF5fwz;7Wqg29iqy zQ@PluFHQ5xgPy#!BS(&Wg2>#GYOuJs0&e(Q=uAF6{bP?%HF&u6(g2LF!vQWMbVHEU z)(l%2hG_vP0WyN(9o5J~o0r3*)G4W4G zRgb88;Nd@C;h4<6A=Qe@?k3%k2w_P0$YS3Uxc%-bYdv7_R$6{)+yWD(Lk&@0jJ%q$ z)Gd{Yis$k^Mh;v=a|sw##Q!s@0MG6BBFi6W2b1PE_`T&Nsbw_suBJ>Y+1>G4F5t0X z5a3iN*851~KZwj)=FhSyCKw4P(COdHs`lVb@#2X92e-x zF(Am_+RdjR8+aVX3JgnwA0x>Fg5^-lGu>woYW6i;#Fz ztJE1@6_jI;2HRGV7UD;TxWbBG%gHQ!im9d+CSOa;-l@)q{J)#W=FxVu91I&PbNbgl zHd#QaN9CggSZ=?Ap$;zSsD0^0nN(-fHK)xSxp14XF|viM{H*Gb&TNWY`qo3D%)WTy zr(aJ(snFaY#b@g6#JoV%1KHv&{tmFQMK-bwG>av;ffjqwmQ>EQfsjqLQk~bW4R6-e zCn5pNIOFRCW4_c zfdzH4-eu`$)4}KHtH}C85<{ZXx-r#p#8y);v`Vg7fvj74DCvpvZp3x1k%8^4d@YCR zTvU6YA9Fc}|# zTG}6u@j7fIV6JNM5rv$Bpls>*Dd1ZTOl9kRvsUwgBukQd z_DyEr60j)a29rsW)au4YsK>?tq|yA(kwC=4$Ykd~Xes3;LQ72?!8S#Je+r0pZ=s@a zcx_!bdA7yyqM!Z<-eS8=wN{}>NVH|8XBc@Ro6G$Y!wYJ+rX%;mYi?fX&+K6Uk=R{ZMMeU`D2d5W~E1bT1ll$fXk@_tl z+`pPdr5t@@lu9jgPlqhWb=cN8+?NU9)$rk5Cj~ffhtvd1zcGH&H)JokU6QO*dzZyf znS^uFU}lEOzRz6H@2*uW&9qH9j~`+&%bf)OGCI~O;LYfQs)i+ zJjc9%)G)Z_Ogpr*%xtC2#u`?`aAyRExg!66jRkxL1gx~WKWQ?ozLv3yeM7P|99Suq zr(jdh7xQu63{>?~{yI&XoOWBiIKJ!RyXU2%L45i%hZ9Tc#xi+03stGsH)L6tAHLaG zKC^hTVRbr8;M!zo9lui6=yhK%u)u#HI;URZwBoXT$RWm%3P1m?o?+VX%0wlH?%3DD z`NJNQ+_&rU(%!f!MEyEByHGAv!l^ND#?mtta^rN*1J|6*5nPGW<96Ljb@d6ZNCCar zxNy4y7{@MdhREB(1|T?>NqPnrf9JaIDHG`PWGhkKIcRxC+aLGmVd>&-fwa{?B zqb5vf6xpQ7FDVkd9r13)f8jYG)Kywx?-j${>?1)ufl_UA`!%DshwlGqJL|nq%{w(T zc8fCi!GKikpt3^UWMd^(MR9y#vJ5^(sVxcTej3k`*e`u~{mP`YAYGJ2_fDRv%xMeK& z(z=boce(A;KDCMafAy4lgCCgCkK7wlvN!W884O>&8??<$xX`4ZkQ7>U#ZO@2h~;ZM z>wafH;^28>HPM@CUjmOy)lMh-Di^z?TzyE$9a21(DFqHmZP!#2S$lJV62Hkic|pp! zM@r9MsZvFYq3Y#QYw?+nb3Mb0H2y3azWAdZxT6+*%6cB&K?}WfYm*-ZU49bV1A?36 zFSZeAEbcAechdQo$n=2Uj*2?!LcIH`l3&@tPrLTwJe$7tNz(r1={I%F`nk+19vqY` zev!(`PLqioCf56}E}nSLU}*j3d5&x{J6oWeaF4e_TUOx`RW`4R&&X_0^Uj7@!<;Hs z)1r2U=;7J!b4vmP4bKCtq)da@e2j}Wa;Db>ME5KD1@=h|`nAMusKp!s9J`dzmqrSX z{i3wc@`y!vA|&U&x3~Z2ufit%9ZLt4wDId9#N{^uO)7poJu-we@q^Pza(Wg{d8QUc zQnHSI0Y^sbo1R&-N=QgZy^Q27Yw?-tJ4?gJ>k*OOz{JF*-aXu!lhs2KQMRsm^Zl93 zmkD~0AL>f#D(6;8=> z@cA2N734qJrhQeNxGLMmzt5%ne7rMB-dm&$e9aY#Hc1HJ?@#Bf1F6VFZm;6b$E&F7m!!XXdG+V&B zgY;xzGU-xzP3psLL zw4S(EV|Oazv#XYOgZ8h(^3a+V;il%``tnL**zjtD$zYgnWRv2Svt8MJQD)#JM>B5I zoi8qPG29vu=l{Q0`^tc*+OA#f2UPR{K~zGdq)U)4MWv-XrMtT<5TqofQR$AMQ#vFh zWhhAn9FT4}Ymd(x=R4oeGk-*wnZ5TN>t1oK>vGLqsFyRAxyfMSC~MPir=wB)Al+My zaG+c+AgX|zgr4>R7o5e7fS}&B$XcC#(OxdVIGO zk`?6yhh~?vfki+x$a*!aNfSI%k#g)t_siO7ev+vY z^Pk^E-z0N7Tvpr^8lo6C2qY*$hlKPW46ZSM{qY1fsaqmv6aO&;2*~ezO^<8KejDdb zrp!s6H~f{WL%9{gdEvGv8@xZx&QhJ`aXu3E=Q^LP=jn>`Lx{Oy&EXbFjzp5#zN^d& z(}7EW+1Aj$D>mD|z@u}rO7@PBMt}3-Z%$!4-)A1jJsZyZxUFY+x3f+i-pY~CvA~Py z82mmJ090b3cIYb~i?Hy|t0YcdpJ~E~U#%8oWsw#H-TL-?juBE#?1SU7UTcvS}?R(5dJJA?E$a+eGe!eq7I0l@r5L-=%!a)^H}Hj{r?JLl5t)Ujkay z*CO3jQt^nRu0@fW0I$`?TB&$=h)s@@LXbMaph2E`TiZjaj~bevwuDMUN{6X6Z_s%n z{b_8+g8A5RRYZh2G5)5U)p3>@_pgFHKaU$okGIHKv!DA)W@!_(WdGzwW(zC`vpPw# zX_Cx8fsXrB<=A@Vmw&hg8K*^*`Ruc@2k@5+KcdgA(BF-rz$cBO*rtepek&?^!NxkAY}jTz=}Vw=1@zDWkVacv0tC0eMGij!-w zWR?!{Ln!TEGP%B6kP9;3I&x4*)l^Y)K;xS7mptEvnRL-Sti=|-*4BrNlW|%l7S1^ErRk2V&g((KzZlxS5!V=@ll6nz2htY^ZB%uJgB1KtWvTVf z-G(Z@(8-|#wyXjDbv}ryFhVAQsBb?ecY6p7l+;?UmS0VW`b!}it?To{O~vl2A0qD+ zS1D%%xh^Iq=EmrZcJwnd%G>wPG+XW>F#E&2_lsf(&e}J+z_(QYMBeLV5`j%{^va)H zNUQla6hWtLCEcR_#Wg|ZqkZNY@I@dUaypH%ElNHT{Z9=FoZGXtU$2j*Kj2$4f9$=8 zn;a1nd;6vRK&p-ZwZ#b`bx?bzmMDrQcB=&!#a`{pbF4D;8%J^?bo^C0ukwuc+PwTT z;)>mHR}XE^F+Bt16JS1bq1N%@?OCSX_iUH)0xw5xo2#SN!aR$o>Glj9>Oig6oUQQJ z0HyeO7ukpb{*120#C~ef6BXBGg(9f*a}`T?#y5`7RZMlOcQQRZMA`;ZY9YJkYYT{U z^NnA(Bd#`GV+Z_vtFEf4vEok|5^G*HCJ>*dTFsked~LKvr@dtvPfg`v)bZQe%232Q zyIGMNXqZs@=UA|;D8Oiv#qjf3!y?mnNZ21?F1R|qHn=wYMN%CB<|g9J)tDkPNDNy>~5=Hbc1oK%L4Sc+J~E)@#{oWwO>|9?_hHB!&`5wo%`| zubN-CZ@$+=aeNzS^9f`Jxsua?PWWzV$I1(ioslE{^s`%~ke_>{Enu{x4eZtC(Leyt z1sG9So}!$$P8X+->yup zg(;uvg!eKfdu_++0PQce*8AYK$fkN%bIc_A)*hN#BMo@bu{>8IyDf^4d-ba<-+lj+ z*awW!!_&oT-^s#CzrUJ3{%G%J{+Za#WlM*>`MI?hKXsR~$y{g4(U_EELRt&b_$G22 z7t@LrFPEK*RFr*Je}u8IZy{23RmzKFf940gZxT>C5JE_TK;&#})(y5oD&F^49ir8#D&FaG52Pa0o~Z#D7uteBk3t-@T$T7qSF=beeT{DX73ooGBY(6;mk znkW2wyRVHer!;#gUW-Q0eZArnhG?CasJPMU(zL|Q?|(=%oV4kUcB?02slVADfIWIt zhzc$9zF}E&lA+JO6Y1$Hh<=WcD>}|7L!;mq;BR~O3$zR071SSV0Q=osQ5(6#>izic zbx4paG7-NO02~b;B_jbtVE&lLVC_WxGxMjq$RO~vDz=8)2ao_Za|$}aGncw8InEk- zN4xGkj_qbC@GMm{W}2jI@w4u_7(y()W{^(#Q2GIdH zJ~|NG_JNWiwmGqY`z6fr5azGz%o^0XURzN?B;BK^?$|DvvXsX z9`dbckuBB7w0rSlw}CbK9E%UjtJ+%oPyt?_x1=mtnVk+%O^(hD^;=ee4V8Bcp>#A}+)=@So zwOR9pGh(;;%tU_<5{Z2JXdW>RJNHn(e9Q>yc?#<4^s3rUQHYig`7BZzB%`2M-Pi*P zV0UdTt?|qa;MmQWq%MSFwAtdaBDXLD3eLbUAC7K=ebfT(0qZvzoeYpezZ(d{P=XA@ z=+l`AlCX0IkG%uNH__$A8@5>XY~nEoq(Iz z6Veo9{)^DyJ^6Xitp0tSSZIaqQqAe%V$5U5`J7Ml$}3(_wz=(7{Yxo;JwhF*6U`Cu zI;4m0BMjV5jEVoHRQwJEqMz`{k}x>?KggYljf8TGtSmx;19I#KTL}nBWE;8Hru-hL zwbH{fjY6zu+hZphT3T+`g#P}xTQi8eyygB6zt+>NzI=kxVMyjR-0WjdQ^)!>70RYN z(A8x6Ph;8zXoIrz*ViK|JU+mX?+qL*I}KfkIe4@aHs&)N%q;*O(*xj=L|7L=go6c= z$SLbLqhxmcYIk&F9aie5P9BG`v?>DW8KpNz04 zJOSlGPazht^2536Q~*!d;$1-k20Glfz{|=Zt=h?G_soZ#Ipz#}592?k%6VXS+uZt_ zj7zwfG;AGa)J{q$&eLCC8{c2?c&&rB(r25`WnDRFl)6`&^XUr7Xd+^Qz9Y@Od>!V% zIt*EJVx&RRuqBXa9hj*kp*%M3IvY$7;zDvh2kFv(!<2pJCm3=TUzbN_VZ-6tqa zi4`O$j8i$aOp$CI?&DoFapV;LFsJ5dnbKaqep}Qn7;bRRpk#Co7(@rgK(e)_IyeN8 z8WB{l_*cQ|Oi2glXiXMU8okZo1+pgynfzN|D%hFw(e2?|&OJ{dZ2X0!HVLSifQ!{V zh)lrsX*84i9WygCIsw_tR>kc{Dh(Rd<=m%oeA)($-Qc{znI2thRXddf3@M)_5f2LK zOYF0Ic&d+6s)by zwpG&+H9a?Xd&aaYf$W9fbSw%*p#zJ9M54f(;nn!#61jjHohlih;}de%@27UWt0!0x z*-3;HZ+C6 zJBkp=6na)xQ2VTEPvCXfR2Tt75HrFG-d2aO0;u#L`Viu8i6tN(i!?hIKWoFk!EO2e za2k#~TLbpJ7++=32=Bh|c0yMXsZ2er+UJ{$ys2@OwiydnKy0uM&7_s{iN;lO9t{@o z#%7QAhm)PC_MnqIp%3A<<%8by9~BlS`P$XgT56YZ)s9=0aK3+J_|6l0f@e28x>~z> zQsP4KTV8qeOq*P|#A+0^$WL+izppXrwY>`Iey!Ld5$kp>$O+lqyFWsuYXqkrtT@J z9Nj3D3w9myl=eNJldgOO=aQ$hxA*>tzP>*BSW{C|x08;JjPgq~BBwpO4y=Z8FT?oq zLO-RX+(sx>Y8#`M9jIw&V$Z|@N!~iRa@AI#M_dQY<9EUCL;*3^x0sk;S~M*!^8^X~ zBbV$KH(>V1Fiw|maeo;<27;=zii&Spf?QlfO7(s7=#|CN*5>B-n5Av)MWu(5J5oEn z@{W!uKu5KY509i3^vXm1Jq=2G>B0iaf}wd3>7U3KsP8x$TN zzdQ)y%eSG#7tz@%xd)bKybOx9Hj9H<5eQoYlJ|i^|b>EVZ6+%PxOKWaO3_OEqRRC{^9&6@x!AdBVZ!~I`a8J zVj?0MkPzW6KiQqV!ikBXIy!X1pocsst(2!oFW=yJc!!D#q1r`EWk#8=FAnc!--B)p zz=FG^M;Gr33F!bgppA8J{QX{1-(fYKUfZGN!TbptS|N$;m9A*69M#y~R6h(+m28@_ z`xXV$@pDl`S-4|7!kS#ytc;RC4>yv<>q4xN=5Su*$40R%lt%aL79SI>jsc%j6krWU zi>zO(%bnz){hnBwyWj@Ci9Ss53Qw#X5_l8?MzfY|$|2>AuJ5m@TIE3d9pzH>9Q2tN z4`{L-Y)U34nRC*LYX(uVv&$;)QrTduCU5GApO9V{b^22WT~lMDi?Q2x%3onpbW8;VNK-q zc`3UE;vZI;;x8gR-sEMAsMwtQhX2ic_CTa+X&JyJRHIP>|f2ue_m>YH zT3*kI-^h-_)PTmpy)6IT3$G>JtkWxb`g_B?qpO@Yw9f?b^)&2a=bnE-r%>qDx#9e`w78qSw^ z6z9w3weN_ynu%yY3(z6xh044GOk9c+5U6x%yG+mk&BM;_cyHZ7l;h%kwaKF?hxL_` zD}1Hr{KY$oSWhg|RrhD6M%q_~E+(R%T_B@0GnrzhF^Vc)I3}DZBXenVBH*=ram`BS z&W+Wn+2P5(?338Jm|qmS@5awj@Rq6@D5h>ivBr*7>m2z<8u6$;3n1|>Zl>@ll{k-H z`$A(qCY#_G^J%0u6eVjJ2`Jpm#ZSSH2 zs)Lu371jG8-0{(niN|tI`5PrZI{H>Jm#Bv!JmP^axeP4OxxhW=yb5A6XGcA}{!lZcEM-n8oMR8yfqx_7LfW1CO<98GUw8>T=4ce&#mui& z@+Y475QK9-3>@x`@p90ywcflEW?k-SZf$(B9(OIzMONc7#;uS#QLuY_a$|DBfSJ+G zJXCNWFW{zAoWwPlb$TC<9%|ot)?}&!AFnp%j;yZIg7~q6I{qp$B{mg%?+PY4G$zc4tGz5PR+-qL{g~+>*k)$A-bBY9@W9!%@a`W{9@M!F=hco`CC4u(RN6>RYy6;|3A1iW9dO%; zAM+5ZkjtexCyQa9WsUmzpeM4}i(g=8F)~ADUG1~3@ROeL`w8g72cJ@xFWZ#2yHt9s zp4q~6%=De-0k}mpCNsduj~xl{d=6Nt;Vh zFrit>zhr0IvsFD#s5#kT*AZONEm7^vsaHvqAEReb^RAn}i;GKbdj`jmG;h!2mxXoJ zA}v-~69~ZW1xV(jG{R^POpU1IqEc$SPC7x_K3fb0-8?yyyNVneQqn__E(LsA;nemRUSP`zZMxSWi1~&7ZoXGB$D~iWszp|Tw1Z^n%JoBQv>uxsjjq?RI$#cnY4R6 zD)#mpsir%$?EB}@)=3_{v{_6J#|h_OwMpt(2C6Z3 zrcrw(9yqkQyaOS`{TwdLnAxyhYLpvFEVSQh*@36{Ag`(J3`6L z%A8}$`>v9W>CvYB956wBo4Rq+Ox;-~8NE%JYoUB7pY5fgwBfZx=RHF~z(Of8(d|vV zqQd^~u{J@0kjtxn6VVUpc|WqO>sSj*5&_eNtatUskYjK-Tue-to!X z72rv!+trjl$K-lfVoCd4ZcvSZ+d*jD8D7=kW$|@Lvi^>f?KaM_oC~mWgHShaQ{egY z=i8~&a*lWv=%3eYodi`fd>t(1cJrjyi}8M$+}K_klXBn6<`Y@%0KA!n)kR#~tc}Wu zh=}eq?y{^!^Jnqs9>PR|g8U!DSs|MWx5gqplT7a@YL5*+%&bCly^~}uEut}S`-rPm zYNZ(=CU$3*I=$`oXq2L*!9>tFK}S2|J5{bCmVzZ`74HBkaeS|Os}A#bL)XU$1K%uE z5Jd;5iTzRxbTc5(yF+MhRW5U1B_3U7wYC${&~6$KaKj*-Yy?^uxhN3Rr#i}HC zsVw$Rr`*UbOPhhYt1@BP*4aATHR-i{&pUH7l4%?cY#o?oyRr!-vSR|ej^eC>vSgpw zndQhTtwm%n32mn)EMdOc@LQJeoBb-c8fDw|%A9nT>2mdN8_Lz3RrBx}6i7D~CZ+gM z^Tn7o^6=d+^)y#<2WP=RU#Z4Q!X0KmOFBmUCWYTe1#d^Yu*|rsF0arywT!v z5pE;m_Tk#s#he*WosF-@zRF4D6%W^)(K5?QGc{0jEfl}=f|-E$+IMG7IrG1Uq{4i* zN?WupXdcRFK1r#M-R9@%(-q4uD3u}1y_Y{?Aye2tM*9^P$Jd!eDoQ1kcgT1&B+lFi z_ixdDURsp+4kYYSYFiZNHJC>dt}lf9inBv_tpOD0nNG4lt4z=N-apBVwVz#myq&ra z3)l2&UeE`{nZm7sKE8i3H$Xj%>gB_Tg)#iID!W56@}8 zN=Bsd8PkwM;sIJQwoWynnZ|S?e7R6km+JFi{8-i^nLpI_O~2wKi?GcNhZ~N&f~&^1 zd1=p&l*!X=g_fQr2bFB(I!!4Pe-kRzyGao3RjyPmBarYg&>B*yJKEO7y>jKg6(p!C zm~`v3!0A!_anI{QoXFdz+c7c9;$K~(qEtAy`m#5*)Jqvhv>tuhJmEb~iU^$O*N;&X;h%GX?v%Hx()Rmn#c#a?E5!_(Ycfvi|Gbfe26eC2>j zeMc3-BUWZT!QgYa<_{W};8fP!!AOKXQ~v6n-Ywd+ci`uGB_Z}lK!ackkMXwDErm-DakDLO2UcKe16 zDWl$NJAo@WzQh2m9`9w#{h>E4<#1n0e#`Ug+O(+s*AGhd5Mu`u5)fcP;7A9~`u3Pj z7s6$R^z8t+Ef%T_AkHa{wi!AP`7H*j0ySc*j_7moI~^bM4y2=U+Doe!1>;A&2Wt+8vRb2`T$Q7v*Yg^V7-RjXg-{iYBDobppby1 z`%HOZm+9a(r00fw(%`Gu+PBgCwNt=Tobl@18ar}}*KgirfP~HcnC+0Wx#?M%gXYA% z>$7o}_Vi`^a>t{Vhi316HW!hMy!0fvSa(_Jxl%E}w~s&pso)u&Snzersy2TKX|<udy!v9(zR!&(-DA|Rhho=ZVcWAKN=7&Wtx9Q5r1u^M zABh`rxOk;YRP4K1rT=ouG@h0)d?Sv(Ld-|s;_lF*oJ$$vpT0Ugog??jYB@N1{d^9r!^CbNSw_$7vyF~;6*_}^SZ5^59b{yFr zxtlV9*L?h!NWcI*qhNCkDzJ>UnzE@EL@^@iFdS|qFSgTWvkn#x+IL!l!+}z>ENoAy7}L%LK(Hs?Hbrw57U7EwK*bH-B!jDl~cRf(-{tnr-*l0Eb3 zrCNydYJo(9WB|U^(4RYcsZ}R%?!%CIl(=Ted`t5EPL~``f&5P$hjf=^qa~WE+kcYV zSr@?-e`D}4W%LeOn06M_)XL3U43y86?BI?wL6&In*xd9yBm=41w=OjWO5*-@ z!EbcN6lAG|5s>wpAHd%1Bfn6IYOXH$N`1_m3jve zcS*W|Vav9m@Y7e0?Sk&9JFLgW^2O^?6X>?hpcuxst;jVm!tNXI;hj!jT}9h6JF{mq z6|Uf77V(J+6%eF8&UZV{?26p}ByjuxFqN6|!a8&a*pd8kiX~@g$D}AV5P*^+_VJ+yb=1<+ z!+%QM_fo{Fv9xz60^qRYJ=JC8YvCgwE2ycH#)gyuedT0DE1X@DzU+ENeHjazM+s2( zO-XaQj!p}vYI?fHW1z{&bPBZ0<<&83k&3UKu?6%&-es9euqRj<#k}Xb>LjO*y+4*T zs^xK=iv6UGKR>bcT|-(;&tmlsluu5v_VwEl7H$(ocFsBscaHg3Mo8v3wB%g8vPgTv zQx@9%xZ;aGoYs9oxEN(3v_)$sw$bK$<;Tom*~MQlvrKOgtOx2EJi@&OWepupI@G6$ zO1R9Y2Q6|Xr7%iQmrphI+wagjpg#pl=G1WeycO7+;Ok({=Y6I?Hpwr#!fWKaDHUQ_ zf~$oa2nXKj>Cf2BlEx-^>Ic0;c2`*R5PL(EG*U$nd71IW%%)E3 z9ZR`S#dy||G8oo$@`1-JHwGpaEhS;X76|6lw)2Ec1XPW&b#{QJDTRKfXNc^ z2+Gi6pL>V{NW)Zq%sM7kv9z>stv&V-5M|5g<2}VF@E%S2XQxjWQD-gR z@T>mwR4Fjm1J_=GFSEBgF0=lv+#-?T13rd;SuXLuRu$c=^2K8?98-V)Wb3xe>e?M- zXJ~PURG$wDNsC0zB(!o9e#w%FV^C)y`40Z9BQkz>;ES zej0j7h0!IiZv{3Q;yn9)YHxp1`K!3ga4=JTVZG(LZae6lEDt>ckHZj{&imzgLgsmB z;XqWER%V5T!~wmCwM4h>@h~#v6i%7=LfIwdIPh{vR4Tp_I1uwToYy=1F}$)vvJ}E5 z?}HYJrqU5B>#3-XiwU9o3*j@w;Q_2$OwHkX*Y*L@4ZfR=CMaxtF{aA}7eEurn)pN9 zIuG9SOHGZqd$df2Lx0@nC|6cy;X+0|t0~pHCPyZ9it9r~7ic@BJ;(P<1JGbg zzdl`Cv$qgmnqLmpBBbqr2J{ld#Kcme%7Dm6O4!+TyUHHq_33a5DT5F!>{wj21Vq8@ z`@5D8A2ThG0$iW{Zkl+l2}$~@%iUFSLP<9T7^(9r*97}=!7oj(sd~z>A^EF|h3}rs zvD4CRTw9V<*uhrMAhW;TaOTQ;c9NXZ?d-T)+?HX`iupuEB^qhh4&j;kOC4qU4uO5t zog8~Oz#5AVxhiKi{!v1WI}6!L|ixxP`PE#G0s`re1pCKA6wy>Z{LqmuP_ zCDPMrW6jH`)eKqvwzvCU<$zDC0LYj*fO6+KOd!e4xg10%RwY-BaSB*aMo@SLLh{jI+;riQHXInt6!7S^2J{@5g6&Xp5pvb%` zTMXWv9R3hqhYW5Ph;~}x(=HWnL!O#L2#K@4Aq8(RSgSL&cBv^N$-R?_ z5o(wYJ-m;FKug5IZyMBrQU!~6lo}XZNWP<^X4x9V-CSTsG|ur-qxi|$1g;+1Od7|o z?J*o2g^BpM9XI}M?|33>Riz8h*9s-c`C(0)sAnO(k3hAsk&m#@lIRAlY{E}x0zF79 zf9ZH3eE9n%uPF~GI+(=}iIXwv%?~OyKtn%{Z^&at*Ltfx&E%LsF~`~tJPRJ8hmdoTKkVmzVUwV*#@SHHUvBKn z$jgTs+Cg@>AT5et<(icRr;n3tUEt-5ktZ@YXsG%P#vJS6(KrRa7G!3&2eA&Bj20U$ zxP!b3`Iu8Btsb*(T}>j5o?Cuj(h8iIerrI7%NGa39G?|=>tkVIVaqmX*CAxwT>zS( zPD@K8xOQ!BXjUa(Yc!=(jhV8YGO2i&bS0wjk9Qhw9&+`w#bkp1`@}@69yUuk(hC)x z7B{|2&R=?f9bygh#%0NzC$EnSg1t)bXdlmqc@FW?Dj91FMkDwGd^UHnsS6aIw-NLe zNV8{5Zk1b&8Ru6!F3uE9lzD=rMZXRscce03h}=-Z?e<*&=#cdSXG4p5DfiF*aES#z zvJb=teRDar9y^jA!5O5lFO(;LM4X(*hy5x8yosw7v-pG$X{f2+)8ir<1jqQwm-vov z_^p7+^p$Y|Co%REpl@&r2+&sS%Dz5hiP{ymT|%+@N%(rhq`3)BTO<-6vSFAmARMWc z3vm`$!v!tXZbu|U#Ka3lf&v2LT0iFJ-AZfG*&LRju>)ubnmn3zaNfr4@Wdc3+ROn{ z_F5UFV6Cn56}5)QLw9)poR)9T=9+^H%>C<+q3{?DTc!__+Z5vA_u*NR*vAoUS{P^I%&xQVTAsup}n|Ic}PZ|j@=&Xu8$ zVMtN;^G!&imFa{_%Yw!YVh=kZ{i|dwcgbTh{AHS4MmzT?xQ%>Ou==`yll0c+`^H8w zhgz^72(S6v%v;uI&{;w+w>^INkjJVmikwa9-Mh)7;{vV9V$oTZJdShvTf31uCUa5W zVlu*?;zJhXGpGPsgfs=;0ewsZr%t|4fH$y!+q=dzDn z5%}$koU$DHjb|&5w#T}QQK)0~Z@4aPB)>n&xx^*R{Y)ty}FjVbkBYlVTd)XX8<4i_5VkOKMds>M! zN!E;IbEAlZr}Nz`igG1;OIfQA=1$qKhefJPK)BmiBcT=dCM^c6<5T6qtgFit)_d zyr*cLhysDc+optA{UQyeYPkk;iJO*Lzkl0i%)9%2Sh6)C+2BCwdE;^7U$Uj?EzR^d4S6qY7(xs^9#+s!rd{>+>EK;jtLC%|HP`Mf5E4ma$n)u+~F+WQoRi!w|)Pu?F0Yqpoq4f8w|(zxZ7aEX5B#gga&{PN$yMZ4tKr- zV2lpIXM_svKWPmiV{{p}^LKou#xMa@5C*!xz<(N~&x}5sYm1FHK6(nBT1MEWF=OTY z%mW;$puzf40H)jcW-OGU(LZ|Fn98GE5u;2Kh*)J`zhnDGJzjq;iyP4pUz+dqJCz{`H_i3%9N)M zkLEfy{G=RB5BT6P!*S&X33i7psgZo`ZXV^w(?~A6O<@uIcdQ@v-AQ2nZUQ#?y_>tk*Wzv13SPMp)_7^FX5>z@^by_zP$wI{{-z! z$#M-<&>Y;I#?FVn;U|iJDi$;&dh>Xz+1h7Vi}aPi;m274{LOrE7Yk>=vilyK57Oc8 zUXIzMGIjl_E_n>*@%?)s$i&`(K}2C{iBR~PqAbB#8i6V=Dwb!7whzEPLZUB?UwiOf z=#OF^cIvNxG2bm^{i9?$3t7f<;l7|RAhmvY@lx^}AOLHM75d52DF#0?3k&z&*nl)e zIUpUSbiDVB_CNu|I^7BXQm(L>W(LC{U}9jk013n`@QQg<2`5VH)5Sa#3~lp>$Y3m<9$b4h{aHwu z9+Fcmn?H%iGC~LxiDI~PPY)++?sRl*aF4C( zx~sYH7TXw3imDbp*%`%nOX)@q4R5Oq6hhuU38l3u zDeh+&FexaWT8+3ySuXQ)Y7PTikhE`rH)a|41s6Df&#=y43gud)jOkTuKxMW<-}hUc z*x%Pu*E{Q4w92~HLjD}id{YgXWX99xu1|X67`JL`B6_w6^(v2Yh(E1L>*bGV-&qP$d zhj|hiuEkXRPT@vFP|)``coc(LtU5K{o>g1z0V`W-EpM*Ni?hmY%9Sni#jcvN5oM}s z8Z0Nr?!iaL9WNCP9YZTjWkB^Qql1#yE?SDAtt_1zYj4F-@sJe3=@-iMwb*CGZ4AP3S8U7|Edn_rWRP?N^lgwqD5;G)!L}1kVL%Dq9 zR}W`^QZN9&V60=^ADgxKTeR+&uV{m_V0!&Oi>RpWpz$S=aubmPYr0h6OVo`6_9k;I zoJML6996a$IP>F;0Hu#&9dK5KnjU*s1_0h;V`FJWyQZde!R064gQ$Xrr{YMKeSni) ztg3B9DSN-@Z{IFiphcoyLbS{G3wcyy4p~($-l0likTc54#trbCkoxHz$xlJQaaD*jaIDI3{KzAz>j~57C`&9B`ex~!w=qZ*~RgM`T(PQ-%tV$k&)0N z%TX5fV&*pQlO)CSPehmp*5FOMjk`(=vx@MO&f3O?!`rMsF%@VbY$faeF)ZhAO;E{- zoyqB8VPeP$g9@SBR>jADyuBS&i(fl(B;O=jHv?^ojsRWG6E+y_*n`BszB^1TZHJAB zj(18692NXhc|?UI-(ho`W`;H3AUoygIMjl(#(f+rmmT>>EemByd9FVsE|h)~LQY}- zz{?C=SavI%V8Mm!e-Ony1ciFnNxmQ3{e|}%yAzS%%y)Yw^EhC8OgKMF_>{%YvB6%i z+%3$`iL=_3MPiCcg9EVkeLAg~SVPj_;qKWhuXYuwo}um$6h8g&UB%w<4etg-m@r^U z^)O(}9v6)qR74Y(g%7uGa%z7FymBSnB6MY9<^aw#+mxaT=?5g1(`QBD(&?rmo|2tal^;wSndtf-vj}+jno4EL-mgHSBO&#Z5eI{9sXc`{0b? zr@6zgW_iUvaMu@ZzO7&0RWgmoz+w`4{G3ono@MXnA@wAE3v;T2GuRh0Dyt;HXLU!>IJRWgR%?XN+#E*EXs5*=}dyDAu&Z7WW#S-|0*Hmy><3 z>Z7S^*z3gv$QhkOtWS7qhdj2>ch4#C2h{;BdiA7&dm!uRL&5M99VcZbnZYVd^Ko8X zDDsf&yV!IZ&Q03B4>QVYwj%K>@VIp^&NA`fI>s*1a`|yy0*e#3ljCm>;TNmgQK}5ui<;rgI+NUOX z?=RAxT830-WU}{u2ZkMlK;fJ>b=zOe0I7@jTW|=^BU<$j@Od8sl${PEpCWZG7t4!^ zgUk&H_FN!3ocRc&kmo($D=Gqu!r0ESo?5-EHk+X3mr=E$iSxT!Ozt~vL&Ne4?r|8l zzTtGaWR`-O^+0JLlSMf70AL`ioPTe$GfJfj;Wa=~(d&~QtFhEfPKW{kXRiQOt$tL+ zgFG4U3SL;|RX;YEx|?EenV=yMPK|l6VotQsiw8_?q(j1Ay)_wi=c6Ly$KFvAR~|JV zTUD6ApYkx@iXM=A{uTv_LNNa!9jXk_qYP^DvnVD(L2X2K92XBS90YtO0SZL!HupE@ z>uCt0=s|u4iMCoZ6b!^#dbwjD?8m9WHX& zTANx!TNFr`nPq8t@68k3T$*t7KuF|b%YMJ13JH@<<XqHl%~&|D`EA2vP!>w$j>IrS>Z z`JHpKEVn?`v{PACn+EB*kw6t|ldCV)oBERX12p*!! zN*+0~U%33N@$~$ccS(d_;EGSHIUhMjztMXekReOax&}!UjWa+IJlFi0o}*0l#j6V! zo(3c9EB2h`>A47tR!M95%7^}T+mvv#Ey(_b2Bk+&U3qlIN=`swJG;;SYUxRm%6QI@b9&y(_pVH%tGvKKa$z zVWlQ`XlAHvR!r;N24md?M=sSQ9FZ?aYurk5wX4fSL+P2AP*Wjq-mobbB79jM>rvgn zBZw5*iXrQ`2hu~oaMkunMld`Kcki-|ynFXf2h$6#wIIs~gPIj3!z))@crJPrHn`_Ky3fhSCRDhIk|{^Z(0T>e`OWYA-m0iN#a8Dv#y4M-Z~#r*c>u$HG#*_bFjwkfs{+(RrdUFIv zrq7oMu(0II=)h42FW&+zN&)%N7Uw4EMw7INxJbN#-Yr>hI58Au6ZmlU#)Q`ea5Eue|*3x$Tbj8O~@3;J{R1fGZ1d=g1~+!|7Ha8wQxz~ z!@3`M*WA|0z2@O-|M?5{f_(|Q0&byo$Y03zJN|t^Q(4a>_z?0J{G&PSXB69hGGar! z{|uf871o#g&o6L-A0qqnzh3ts>-k^*1#A5T>l^*&cOq1K>Uk;_&nUnj`>$7B(7)E& zKfnB6KKTFT>i^4^L|;L~eh?)zV8mpkq@)CbZ3dr%u_;4nK%22RKHQLVgHrQc5NA&q zG;F>}PEM{IGy#AB;+O=yc@u_Y(qZo#9v|-Y%R`~66Y-{Ew4Gx&mq&U%ts9$~jG*h6 z1KM6dGrVA{sBTyl0fX)dKs)(kD84u?t6cnauT?vihh)n{6 zAOS_DN>d*nXwb>4n=;jXlVN^U&JE<+J(?AR9|$PlpULv0OcJxWos=yqFi(d3FWsDH zCadaiIj&1<*EG(oDP5kSU%iU_PpRo|xuIa!PX8HtMo%$$`FDu$Y3Y(?Qh2z5;Ex1{ zT;z<|AtWTsyLI0hwI2>NO5HWiBU0Kxvui$#d-*a~sPObF!d&9FEjO=T)jNW-Igjis zuwxsp#=pepYj+2m5qRdB~QwHq8fOVqd0);7I$D24IGCiqmJ# z zcJenXPQsQa#f}Z^TD-jHuAMj5B3<5O-1K{&J^dzQ>5;&^;0!(L{0zMbyqPk|oCL;` zDk=NcTI6c8BDLP$8<0=#@@#Jny78>LtE+_72UzRB*dKk`p}8Ws8Mc+=u~Os>gD?yo z_!hRdwuz@kp*dK(`nU+lcYEqQVSNn)yx2loI*5y(e+>QgO9JnJI}m7SsOD+RqdXz@ z9-eOUj~7Bz-4oF;3?Of1ay&h|1YO;nM_-|<-9lYGc4*|FhS$kk&GCwQ_fB8t$&*6A*RR!~H%qa6TljdlY4I3jrliBtr;gC~xq^v(Xgq;a4QBX>E@ijRqFM4 z&s~>``u4_9UH?d)Q8?0RY-N)Q-cH?n@If@evmKL;kLEZ>3fuF~jUuN<2zJSCQNiX0 zjR^|u?&Z3~9>s}$DtIF@56Dzqek?G=)~wj`CIg-d<{?tNZyNZpzi5S$us0%ELEz%n zbgD>wTo*?1G`HuN?!2LkELOIBhzdC7$#Lr zVCY`mKAAEF4AbApx<;&F2!-4`-}y6^6asQSDIr~3P5yp6x~{Gjh@L%YChaM_2@1NV z`y1dyvC9bExvgzsrHpO>(x!=!u_K~%;8nzr?LYesLK{y+M0|JMg#n|`3I3C{&J?T` zt<`{SYZjYDE&q~YlyfQbxd9l;meJB0y^&+4EY@DZiU z349g$(%H#T{}x~k)@via+s|FI%{;gGvxS-!ldu*$+wT8)xGePFibJqf|JT3B{qq+- zZ-MzKhTa$4QU8iu%EpAk_Adnb?Ig2qPsiwbo7H{4cgq{>+4lTqR6E=^RYC1`9Kxk@ z)&PhPIG=xm9wN;0uD!4zu;(C@V1%gFwziS#I@X;iJ{X?v8$@teL;@TkV(S>-V{ZV& z1Sj#x@f9lF6~@bKLkL+sa@-+g>fvCTdQg20rNF|A$Z0`1x3%akHUMYUWgm|f>_h%@ zqQQdyGt0lrf52l}H!GFGRQ|C+A4=gk`||-X*~-YWfCYlxxj2^*g@ekUAKyBjw%Yi_ z=lHX)Y%=XkC)Qd%@}Y)^`MTl@$MFigg~x*OUdD5j`U#XZfk`BBI#i z7!6NDx~6pWCIf&q&5B!8>Fa-%Cs@gIDS~&9y&MVW3vAnejOOh-cN(=UxYOt~xaFN9 z*B$dFeCh`MA^&@i=E?uz?mferYP)q&>>n!jih_kEU8)kAs7ME;cT{=_NLMg`4MYW$ z-itIT0coLw6{PnB2u0}-0@4B`1ooJ|@A|%PueHy0_Bq$ue|CPoFH&YQ=kq*c+~Y1{ z?Pb#$aTZ`bqE24cVdAHMSYjdd?TBT}PiGT5IuJ#h1~358yFdE%;$)T2n|z86CRld! zp#+F6S~lts5s1cW1Q}dmtOp{{eL(b>?V>-=fX|Y7lY*2IP`}*~E;~^;=G18uLo_WZZ z8jxf53RP>PKn-;*aT=CcVoP>Sn>jfEB$XN50nE616AsSG@%goq1z1mb`xLn|hF^yzR$V=(G_-o%p7Fgr)1?|zp zS~_shn|{;Md70U6_-{V~jl%IiKhje$!hPi9V?V6L*vR+S((opMwUMT?no~VR$WPk2 zXO9Y4;;nz48YnMnTS;n7@fr!_Y6LO$pL!EXd;ops06sNeN<~reGNE6vW=MniP_J;9 ziS1o0`=VX8ux|{tzHr!g3C?>k+H=e+5w(YHdU^-JR~2RTg(SOI^itQ6a+OHFcd*e6 z*wSGHzd0w1K8{kmXz&f6)Dl2;u8tYm_xH1EMh`B8 z+l2b_w*LL0ghL=4%te>ZXFzMF{!L5l-|nIVyz4y+#}>3*FDB0TErcJJ`2}G&g)>BH zk6C?t@a(~?!|`Sn8FJ=I>rB(rXV27Ry8$|s$dCrJ{|d)J6m1dgyyEJ?q(i@`s0hoV z2D#<#Fg2PeIS0of{^`@FUgt(%W5k?>q)3qHZHfKx;eny4p`jfH&`vB+EAZ{@w@Glk zKdwl!D|)nK)pZgogvDNnKVAV$#}EoL^Ci!zezO1;kLc+z7&G-`wdm1iu%$!7`QE>O ze#QffsV_>$&=RoL%v%W+9F^*`a8C7S54XgF%@OJ-@4xuD)I^+I#t(~G+<09(mGl@IZITU*=mi&0^d`i|}_trirRso;^u0a*pHvWE0j zykJZ46ig6B^9=-5{k$ZTX@x zj#+H}fSKL>L7)=$5PEB-W-6voiL6&3s%l9-({~v#sLKX9EImL#)^n1bmiN7lDbGiW zvFfE($(E4x-FHFdiwDNj(ldk(ONcQbsnEN_{hCkG>>wZ-k5GV6_rLvP>7X^!S?EBq z;J)`kB2KBc%YM`zlg`7-i$zc*a=y{l?t17}5+q!$up#TSMQzP}GKUWzo_Jph^W4E~ zS`g*W4v%y}P(AjD{<;5uI!7q6{^}ebg+ymByv&sPbN+G{pBTwv{?i{U!VWnK9Raim zMhoFdoB8fs(f}I&>*W{xx%QtZ*WX|6=lwr}0{yo@N&kDl#Ks(0s8QhHsHCp0{s(Eg zR(TT;C5CqR^x$o(DX8vR6Nk6?li?qAn=ARghmi~((IbESVY?1#ApgT6bo?g~{+yq< z=Iz_tZd=4FC0QZZ<5R}R<<3b`pV>T$r+Jkw zl|umsiAE2)H1S&{uu6MHyDyKdtwH&xbu0L+E$$o`n_|TtKLAw!q?L$?S zF~aWxKO(JETq)F%TI$+6*qQP~UsA!I(fnzuPL6I&@{f10um@I$yz?9UWr6Hw2{kBk zqWn0jA$0-XyOp$wg7oiCJN{7CBqrn8poA?)@Ku=Rm~YSHP>EE8-Wo%M)^bc23gy8+Nw(tEkLpw&az43Gh9-~ot1BZNYoDX9+AkTC#DG`>trU=D>N zBme|*1@E~6tvIV9Utk9x0SLrQ9M<5`Hwn=GbXI$NW~zEK9fMdWpUh^8NbA)bh;1%> z;#FOX;KkGgsS)RWw`jv>Rt({as^tF56@3L$ipRIW8t*Z1A~~lDcHN&zes&=AsQT5E zJH+lIDdG?l#>rL<$S|i02Bs8_Bs1sl$JL+co5)b7*dUEXH0C#q+bAk7E`;Z{8IWXLdD1Jpxo}YHsVEB7FJ(Gufmfwe6i$*-LkMUL{5e*U zDYlCjnXA$8>mvX2!b%nc0PvGF@=*E77d4xJRU6(%Qd}HwJUKRY6`Q&wQVBpuq-vs+ z0ju|{5Up@zd(7r|~MS~zXT?)`6pyQqeQlLv-`ta>FO%QtVnf+V0- zr#<-yO1Ae6{NJtjYGzDN-QwYTfGFwADhr(eRSsvpBLZ?rFk-Gn;V;eaT>zrVR}`0p2rnZGjV7B&FQ9q=_z9!*Zq%#_?^-X9qgW7`-6k|3H3a^gRV z2@4I4$E_l*^qUnJe>8<(kcN6v^>T*6>JT&7r~y0lhUvp!yN+I(k!ojtU>mssvkyn* zcQOdMRaOTuuyMGsC@6+XPJ919T$GP4f!UMc=d%p8xQT*UE@V0VbTmKw){QbrM9gzHg0aO2fN811I`z6VPq@n7{a#8m) zaH=@rFvuYn@-hcLSVKIbqVe(Z5eE9M#G$F0LzU11A!L$v>hhI`(4lv-9ui^runz_l z%oe~^;{5oaYUj?Kim*A2myHI8!BnesMPP*CfFe5T=g+%JaL6GXE!nMg4TGA;kBkuI zEH=1oZ_8o5a?@p3m;iYrV_rZ8O2P2&HgZ<-@u>nO@dBX#g$VScX=vDwuY7oI4JO~J zNp3UU4R3PNGBO$wt4DC<1IhP;AZb|j>Chy(KneTf#zARzNWpaMm^1@^zcJXFc0#mP z*FiYBT)=L#3G5`0kk3xTmkluBMXM_N>H(RK8&O`sQCp4mJlt|EtpBBoAKub-d~!#- z+*EQeh$$@{qrl)5DHR~1iXTp=Vg1<0&|SNfBRP4unC{&DKO_12CGklBCDXlK!NII&A^}_u|0I3 z&2{Vb#U{ZPVYR{q9bo&pfCElLu0avIfPgv(xokr7MXkD@V@!O&{;LUwkG*!ZG_L&! z5n;l^*|{JsGqVZNh!CA>BVbHvmiTDm^iqvj=^uZ4h+1OYzdsy~@;e7mQ|B9il>(&p zFK6#QMvThd8{WM6y!ga@VIU8k4(lwNh4c0;auR(z1s`w3*w`3uNS?t)4~nMtO9-PH z3NQ)&uDcFvzke(rhvhLG zKDlk2xfUpJkKp82>v_{8P^mgHIyw&992z(I7#QMXkw%U}ZLpb}#qFpQkKIXun0XT< zFBGw=x1yw$917dUfa;76*A0^*7k7<;eCA3)AE-Y2-L~MW?g4$P#R{CvtsYo{X{377 zKCpj2Z3h!fk-o)Pi!P`x%76KvX*RGciTI8Ge3V7MqloHu{zltNWc#e@Kgo1tMOA7r zI57~f?^Tz6;4I?CIO_>j4Ps1N=)2`ZLk;_GRX<{7d9W}BPp}x->qH`(^lPm>ZOd=2 z@)!7mIb1fmbEZVU79G%lAU()EmNeOcai zg{~Ol&1gDi&vy4ED!;Si2u6J6&hEsoq^72}82Ily!S{CJGL%Rn4>B;xZ8x~J!5KFP zS?<4?aKoQK#o1|2gJfD;A0m4BijlAR3Ka^=cVL$s*Ly3IZv#F;viPn9@|AV?y$FTI z6cH3at)ae$S&Xyu=7peYHzTj}n!IlI*Wapnt28xL?HVLgO+d5=MXt|rsRV}GEG&P0Q^~1D%dQXtXvI_v! zKLFCI@~NUM!SLnJOuogQ&|6fVv6GLNH_Uf)DQN=7sVBi{#!VhZdaVh1JG&S*5w8zZ z0mN80PTy#>1OaTmy##*ac0kHSdh*F*kM#JDUlHpDe5g5)jw>jD*&-j}_KOgXAU>MAnt)RBm&$dk4L9p$btV&RFylDpYNU zZB->fClwZ+=Zw|m>}gERbB+r>IdiN<*(?B^twdm_UKx6L0}-9T(Vy{Z>afDu3&W3> zf(SJGGv4g}+q1!BBDoK#InJ_o`!#kz)zf_b?QS`!Y%(OS_140rPr1PcCb{couv_~C z)W$*~67y9{svkW;*w)mVAW32g@A#y$$JFgN8GLhWx=9ArUM1l0+`01-(wX)-vHa{P z5(z=gTsmsp_jDiArDatq4YzBzp9O)?b+AVDVEoMKS6;t0_7pGQO=ErB9lBW2mN@lU znkjd=TCS## zx7X0p76?C_dF|ZO%jeC|5jWhuWP(yem5d08H7Z8Z>urO_LiCB2Ma+`fWPEYB(B{f40P8p!-m)4R%xrzv9j;g7VaGi**pHX^-08x+l6ORNk zOX4S(SdMn@Ydhb6h!F85>Y4I zVN@3Ly2kO$U}T+?mu4nstjXhr?}g5zw_FR8gFlPy6gqahWiJEMN2or7jB^qAW(dyT z2^WHirvaP=_w6k#(n`SW84pJ)N}3ZWJF)NH{lZ}(CY|VD51WyCQ#}lZBT4RZa!)Yp zi1InIEn4WSEghKwzvcnbQleX@k}i;CNU%cB<(oyxm?pJ#Kpunx)4qL0Ks^3tyAGwL zC1n)8RxDVF?5TyILb{5r1gF{}K{zw=FreRP%0=KifCur@E7LV0ERuM;0{vWKsZ*(7 z5(vfnJYYzGg|ps+=!ppi%iT=Niottd{bN?Yc0iwZ7ne?4UN)VQA2yGJ0SKP2YqfVl zVp+oc{{7rNEN*X!uHN^(HD14ZWoI%8En$+u}%F;7k>I+N-t8(*= zwAzr0noWEj44!R`nD;#o2rvRiYYS*Z^D#h(DPPGgY84KFV|;kF2?UeU!S=?F#v>*k zvG}eMat+Myhi}`l`)W0-2`e+R9S)oSoj6QK0*hN$5J68-;+)EjzL67z`c2sTk_>zx zbGQQQ`{P1Dcez@SwY&6S{={AHbMvrsq6kD_UmE1Y)St=cfRPz#Qc+ez9EQ~*^Yb9A zlgiUj-OXUrX091*&X@iE{TDAdp5t&xpNu_ng% z#Kgrbfvq%H7r0D!#thCtfl;t~=T3d_%9tn~5I-={KRkn+%xHwlQIeb6T=N)`#PI;R z7>tgL#G9JAe!qQ90on+^Te~b11yFu&^(>y<61OK5cLYpL*_0+XQp~a2p0P2FEuZf{ zZ5{t>;o3dWF+a-9TbhV-5}$G}XT4-x_8_G%0NLfyCQJ{&UO)b+KOD`2qsiS+Kw3ol z0(eZZWEPz+gqb6O`L`6XsvNgP+*n?y9j19-S$TXXK)bpa%3sfaDpIZ2FeA0>}n|)_`9f0#-4I&@6;3@PFbI! z{g9zympIdF^-LsJ$f??t;8@P7x82mT-P?s=kIpx)6H% z_U+Bfm!Yh7B!M+{jAklyC?Xu4ESc%HZxE5AJ%7nG>Re$b0p~$Bq+$3Mvc^ep(=l2V z+LH~shVQi|OYtu0!9;w0rMh`)u~Yac4sD!yzOaMa+il1JGiX?7Hfi9IM$Z|aiiq^l zP*M`=8ZGOb_Je;sGZ;KH59Jy()?d*WVnszh%G1-epv962sCwt~F6P;(8mG@|0i>=+ z+7A91U0K`U*|0-Yfe7Qt%4-OA+t?>4-^5O^!3wb#jI;RSBy|`FHrtQ2G$gl4o z3o2cfFv+|}Ii^qr|Btw45WXp)#tYD|&JeCP;|?ZLv@#1H0Zl2L!-lW#6muJNN+|w) zKmdSku>sWjcSo{e+|C)aV6)#oPP5|-iYZuKusfE1n_x`>I9qt$zSf5x-;cCLWMFOc zwtVjZHtrZeh~Yne{Ll@IWB!6WhsdAng*b!Ej8|`r2EqujUbQx}AVdB1hpsURL+~dl z$1hxV60^v~rnt@AV%f1|(Gn~6$JQk46yRp)dEXvjaO_@5uj@q}m}nH2E~AWp3p{EB zMG%SyAY0KG)?*|XcVCMNF~V?;%v_OhbgakGlq-yKE^0K&zi^B%9|OR0C@#q(8YK)}^oeK_;jgBP@?_ zm}6ux6;hO5b9Iup^6oyNEC!e^j`K?V2usswA%N2#H^C+*a@%!2( z4e6C|t@yjdfC@_28LvYcqsa-9p7=-Yc7W1M7>@p^`@mn`SfFtx)NkDOjWv*!7lHQh zsG3R5rWDqg>?LwQg=on+=A}Mpyx8K8eK0>km81dbEN0j~=&KBHy#Kzf0I3z~(eAk- zblshN>495$qH?O`oN0vg3+8Ah9iyrb&cA?Zs@B=V$Q6Gc{3{}1G!nL)$0Z<|W_Ct0 zL-)-6OUZiy8Hj})aO%l*X~_~+Jn{oDeA)>-78b8Qxy z_-SF@+FVp%NdZHVI11qx`N?%`Yx17GEB&|ucOs~vHGzecKh@sbcsn*FZx3UB*~ng3 z&L?bSPwCrJ)B&8`p*&EP58z8IH`-!E6b$>}ZQRdW@dG>xS$5k&95Ma5LeKQkckS=- zpf@$D%pAT;oX^jEb}!L~hOTXsgk1Y8_Y@AQPj6Ggx+UuG=_Y{e$RZ^Z0{~eDxieSl&N`)z30l zKveOk^;~oB+P)@;#z=B<-s(3*i+o3ppo!<#u#xkI&dM4;yBp(~rkmE*9(tGLWL$*) z;i2qxCI+S-GM;=Z)evxg8lud*aau1=6l4xx#$FY+Re#VXVAk=66cJ1*;0_up7}!)d z4fC{Bp3S&P@!LWP!FhH6{{5oXHvm5RyP7wGgaoYh5NTN`Zg0p>945UwYDd)2AQ2{D_Y)@wRxgKE3qRuxl6Rw8sCCx<_nD<**FnuzbpF z9~%&9+>C}j3g2f=$CL<)S>^B_u1$HF;>QCRtc|$g^+h0uir>yLyog zw?UBhh|^>c2Z#*KZ*V|!z?S(K`%G&n-r$Nu?`4D-B9&;8Nt>U(mZWRh3k4wJ#5o~m zzphKL+mBRQErozg;d4pKDuv$O;%O9YX0lG3dbG4RuAbg?t*IpK!-3%P)+IFW_iP%H zrq#9IqCBy~CdaVnnAfMZZ$~vmcX)5D)yslXAKO6O32+A6U(f$Zo1TXLBU$JkX=$A+ zYk>VH99W!+e#)xdWw$NwxQKmf7ZXWNE`j<8s9>yNp*V-W`;PY8Lv@wfpD9wI*=?9% z&M8A7o#vbOI-*NUOXMcldF)4W{MpmZ+rijqGp*HK=$^(f zf9+ZpiEU0(S{veYx2?5Fe}jMxRR_KNif}VbOzB*+)1u+jvxb2;%%v1!-{!aFG|U;I zziM5tE?uU(NfpSM-=J49os+3jiU2b_8$q{zbCzcvR~0h39Hz^%E~v}W-CUmz(N~9^ z>T-;2h2NRSC)bWLYt1lXyf&s|?em1pjSXLrpv!xp>_x!f$#$BdY|=uv!y1`YG={pe zc9eK|Yfbk?)L94T-{F0XFc__5O@Q;#g9<7N)|z$;WWMpJ{|ffU@LLQgtS>M;^SjmY z$qMj^3Eb}6UXk&v(VcwuOcrj9%x73Gb>=P{X1f(Vj=F?6^~cDi_%~Kx{h_MJwkYiT;?f}si58Zi-BLE3@sr&=x(o&_+!oqlpt0rkTZ$*2NMYL+&l zOq1JDgHVyRuSZ|L_mnZVc)r*SBFrqdclA#^^l*b(KYB+jDmilxRrF5GQ0{3ia?~UBKwyy zPKw9(Fqy8rQDy1vI)4XNx^w?9)z~l+Ky$Mj#`4BJ(PiLj68saGm!gb4NDn0uW|qTm zd;B)NrF&*4wKA{gq;UceP&hN%AIjx00DXjOVikGht}tF5YVTF@trt7cF)0hDb~-ee zR#n#zhT1+Q7&%pQKudH*=e$7ehKmS)i0;1jvRl5DvY@Jwm@6A?p08yKVsivGr|Ws; zoNKnB;kt{R*@?`a2Cc=14T>i|Hl%`)x{G2^J&Uf&w7EFsL>1Kb3eU&w;kMc2B*-x;| z6Qgw4GV0~(-vi$U!LUrE50_eVPlBKs+E7hE6r>RwUSsr5x?SO=#lLijey{WOU_b#=E={ z13C)xsZ%!O&h*zS(dNMgGQeBJgPj%43$eW+cGavRsscRHjl}}1lj-2W(%{n~byOg9 z8ku2Mbe76MIMZQE2T2(T)N+<;s;b5Q{{DtA$Gc5JXo%HNWi?Z26tb&fE%duon9atJ zZ{4~TyZR9*8F*+$h$yHUbFMhdXOZ7yvht0RXdpEz6aaWGhYaBTyF+@1^~5;xczAdg z?JxihSb$?v0Wu=Qrv{@MW5IFY{!Tg*D>OSRzz{06vb2=II?>edBm_o5*|eVrZu-YS zSvN_l5oro$hf8Z`=B8j(79*TI{MeYqQH80MdjTv~h~FI+WapF>C`7TyqlSPp6A}Pfc=XEafJ3m=N|ul;tt7toI{{7E2<9$ zo0(dqt?t*aU4=;tqOZ=ZZJ5 zj&TRl6#Yan%p#8`LO02U3+^T1uLzSvgv zm!j_y41g`{L2cLPG`NL$3{Kk5>c^q^vhyBxYdPq~Aq{qwE{kk80h@mhDTsv<>y|-= z>KM6a=gx<--Yg-VLdDV1r@W1^<_`T%L*JsAf>Qi~TQ#<hRlaHTbHhPiFrZ)VcXMZuzB;p%bqn!D%JKMyNem%$Zro4Q~iU80^ z)&P4<7dpkwbuA)x9i-zj_aHFJvB|0>mILt_@0o=OV9$OItcV$IakeFASmgoOwRH(*J?kSQ8B!~vJVAoCK4 zEWd>ifUOFwEf)d5RW>zEgo%3evw*G)wJ^YTN>=9JZ55wEx|n2s0MZ-4V&PY@Dz6*{ z_VX1P%5s;+m7Y}8D-euG4z@KkyhCPnL-n@jY|N0qaZPL8Am;5+u3rYaoFKT=xUhJ0Ic)_>vsEIQ5ZfD8H8>l5L>Wfp6|OuJTG z1uG0;louBl9MuUt7zK8mn9(4ZZ_u>Hr+`oi>{!EFQ4n(eu4bYcTKYND4O zOkNBCc6A_`Tsw*|k=iLQGy8%5_qQ+(m`e=T>O=mrbNJW0|No~G`!Ao^m@A{cyu7>% zOv{u3RRg2Jo0?i$fBeg4q38ZgZ2Vsf)#Fd}{fAcauWQ7YY_^1yTNkqWN~y7!}8>($Zy29+Mz zK0H!;!>p{jkD_*Eu={=f|3o9WMqG;7nTrs$K_JvZhGn3B@#4i)?rh#a+SYDV185Mno|yQ_3c;Vrc%UQUM{>+jdkR7QP7#{tGfZVSDRh%_6o zI|x=etng(Y$h!6>hDtFq?f`4K+Qv{s;bf*j6~Qj_2Y>a^Rm_%myG8k&K?V*zEFLK~s#XgG#Y8R5US5`vOz6SK8iFJT=$drD6rZZNKq|Z7D z{y*;6*=55V=PTc;+ix8+L~PZU=Q!Mh9B6s&wQgHyQ}9m0%VY}<2wTLb9MAh$(be0C}Q;su7f5ep&wj@(~u$POapLkTF^v*A;i1x%xEj+ z=;eFZ%OZsPor0IIRjMB^ah+6$ z0mW-ABcNHI^_ofn%Sw?B?rSU{vr|FPS2UpwkiqMa_`<=(r2r$(X*`0QBGf^|KN32} z_k5}eoJ zEwIt0roB_<19|CZXD082DIkVO-5E({^s~HlzTTQW=;ft+-#ygRoo%7KbXrF(8-4$b zbw|V|eR?>>$A6`0R&mR5WnOQ3*l&}+jI-K5gR(~W?k5IUqbQWF)waT#Sw>gATJEN^c@)Wcu^VCzwld@G$l#!Hj9hk&GycY>9BF)}wu4W|Kj$ z-J<8$d&CvBqME>g!aSs;R6#+ZvIP7ZzGk35Ki$PzS`F&)E+3#PRF=R_+gmE_IB>O; zvQMDwQ*lIOq}KoiPDHzY*{u!N5HeiQWioi9x!ZTG_AeCu?9U6iQ^BM`#7}Bh98BzY zPl~A7O%TUm#ryZ7p? zh|?YVr>yZMCbC3jSyCbn>w89dOINCdG#uGkD+d1@SKLvnL!i*+D%*+?<^*f0G*Icg zHUUv6D*^YezETVfbjwSUJo;@Z9#y^CM579LZsbucaq}5SWo}-Ho##}G)t<*W)CQIg zxV*0SAP&z$tJ0f>!F@-n7V>ZJftm$yN^hsENjq*mJh%-6=C$8_sIm*&Q2eb3krjsV zsZyJ3qmE?54*8rXHoKy!87&f{Z$!oreh>IED@Aq_)fGbXaAPx{%h&zbtd0pSaLut5Aw~23<6ZIQ*KcjZ8We|NzI6#hp{$uF z)U$FahdZ;p&5DL7B~5AX-^1}G@LE~%-G}EE+}u2GrH$~hmK2Y}O0#&qr#HT`JI%8; zG06q#1464JrRcYzUt-&`1oj2Jc~{md-Are(AK;by?lyDrmk^_-_FFER zNB3i02QvdD=>tbpZhsuJm<@kAt2((Va#=Cd>%a%EIez=}+;8RC`VUpBZhFq}jE`G} z7Iais=&C{zXyD7p_X7?MN}fr|#ac<9N9NC+m#8dN>hPI-rSv4_*O4TA{lHL>bGmk9 z6vP64^9%(ibJPZN4AK(m;D33hPw6e$hPb(iH{DyvJ-;w%8Hx1IM-)UXRW73CNez z;$>7Kf8Gw%AlW-wqfI%E_h9UZ9uFNtZ?dN;KC&&C#m;HfC*gWqtJyE}UEk|k$S&)e z*)^#T(V3n)NfWdEeCfN&)vHe&W99DP*1^PB^eoj%ip|97v)6bdKi_-aQvQW{^^voudEX^eAmOAGN0-g`8SgVO-95Ql zp|xf%3Djb$yXU#PR8c|mfbg+7cOThj*EurmdnQG_Nz7T7ad*hT1Rwlb~!5pH-7ejNXqSj5y-bwxa zH_XF0u5!vXzTU?nTRr16ydb?#gUk{6^D=j9>)ZFUvBQ^4zPHI8^cxx+cj}$1#A?w)4rkU+ ziL=ZR_;Sk4rauPL`#0y$OxiDPM2NMesPK^V+bCWR`E5|dyGqRYs+}vFuO$~)#oX}i zn&moa3(asNaj;kw;Q3e?b29u?U@L;y&MDOFX|o&fCyMMH)Gq7NJ@3uqIqrTRCe;AV1eo_YSwE7k67& zVWZjvZZce+&W0U5=$0qrq zdGCi+=-A3jtGM_h@0j504_7J2W4rIE%ql)`GP9taVa%$z+HM>sjq^D8EIrOWwmhMv zG`~5+j6S+s7P^HsG1)QmrwwTu37)yJNh2I0zCm%W9q@zj= z89J|H!81|>b%Mww(uTwzPQbKF5lbL z-P^CpoQyG*W=c=bFLUFJcR#$l;+ii!uVUudwTxhRVF)cPlnBXa4A z^D=aHh~Z_sA)aey9og5v!u}$KO(Ae?*N=>#Gwi$~UlSSLlb-v_E4KOUXfBLUiBeRu zRnobynC?jAS!l=#eLK*ndbmI?xoBZl=h#=|8s~cb&6?8c zwy!Ndv;Bo~i>|Kmp?99Ly=O157c8o7$vIKUF?W;8J@RApPVVM55w6o$InPe}F4)q) z%U;%}SnF~WOLA_k8EoeeC!a6k^QL)m^9#5s_d*JESo`zV3=LOYUY=u)(i=vlDV}Zj z?r6r=n(Y-S?P2HCnfJAs^tr#X6g5&^na1avbmMyHOS8K#cXF^QnEA%44-q}GG$br<=p)IOEa-nqtP|KwoBAw|FotUlZOG`BkPjKrp(I0z_-cI zvxhfA6Lj*Mrpr?$1383Ec~thMi@Lt1xq8lUZ$7!8nHYCryu8)d*Js-aM)fS^Zo!sb zzKHZzU)r{N9Y;DcYjQm^f&vcRhAG?$w3^chMFt<(6Vp_DyP9H9|rJO=AtCimy8C!&n$p{{x-x;_TB44x2M`urr~1JSgx4jvg?AR`Cylc>_|ybv7#b~B70{(2z{y3!c1 z_BZmrb<1C=y;J!fGIKEE%VOl8sak4vRarwkSH^*PD#WOpm`FqD%A>)O>Q-QJ3gg;z zqe}krUjT-_1Aeqb35)k^fv|P!SyyiWG94MEVF34W0F+alkD9t%5p5=WNHsVnxd1*^ z$r9eyoQrx3c1(mO_?xrcKA(4;`~g!2%g`Drv{6otdW`V*v+c3~_e&*?B{$rI8m~Aj ze!>QsIhY2t)?t~ zzKhCMn`=>771U4edJT;)T9tHf1YeaCC|gqE&i@czso^|K>4+NUr-#I{8%5DM&7>YD z7}1`eE@EdFFwlLxoa?Z$X_AS1yVfc^GC@!qCme6vFnQ%6$+`0SW{8x*k>J`}%8RZR z^&%%L(2~xbCP(m|Z^rUB?ofO-qm8@?XRHH#Z@;v_EIdE9u~MyN^x&U?%xxwYkFU2L zS4cf5X?FiWEs{E}Oywn1pewsx9E=-_7R0+ z>$I~xO4iyKT&aw(d|Ic3lF(dP4!g@YZp|um`Rt=f_0mc8>Jj;{?l5{&Tquo5Q%7&R zqtrP*qt{GcCd@^34w4f3+5FE6E^rNe?#a^`=^PyM4-YY}{cV z*a~}Fd_l9=v1T@I>8Wd~ub*4xxR$F3EsBt4Q|;PmXVdnKV|vzhf|9s8cfvl#%82mt zzbBNrlp;W=wLDVna?(VywBla(!4yTB<{Qn0+3pUd z^o=w-=^|CT$hgxz*^x20?9=#J(loObhDErdiP%%qOz0VuMst0OT#h*GXZME~?QBUG%QEAz-yv7fto zN<#AJ-EZ46jm{CAOH=6hw-~qaHRB*U(T&dfyI4$vBq>w7BrIr_6cBe$?!@WAuQzs8 z+?x4NTVEr(>w*@F@Ox#iet+iDmi~FjQ0JWM-Ydvqc|5ceTe|awLPP8FVBw=K|5&>Z zJtq_LaJij4l{m*xdY`wNjA}($#ylCeQwo zgQr7#qGPXgUh%6S-6HO`rqD%ttCEn9&*qVV^6W1WoS8OaXB4)jygPErOlMcY!vNRL zn7wb5-ub=M4{O(o9pCWD`Z+c8?|7A=p|&Ees~MT7_q?Lxft6O`*|kP4|+F<%t}> z^sU_;zIx9+Sng7B4)fv#Cnog>8(6BR{9XOD=k7C(c>dlRLpPtVvA(a@loTHqKEK7K zEU)zB7&(u6CTso5nluUy|A zRLY%#BVhw-x%j;e{cBMeV8Kp)%Ww<(J$044we4NJiP723YMtXcX_V9KCsdNhrbPQ_ zt0-ylOYF_sMqQSb;<@3Cg`G3Lk~(DXGcBxp5@1ka>*Q!Wc(Y+VCc3pyTGrp67N$l=( z7a0>Y)6za(!{wTxeFYt8Lq)^a825#|%^j5F$f*$ih_2niw@vu&6rhaz-~N7TqB{L% zhcN5r-42g(OI@}2BS)qLYX@Ay+2zZ%A=5T_heW&2pZ=vQiKY|TZ*{GG;eGqH7++e) z5!P7|chF$U*qqloBz{i%hsU5hp<%~Csq=`UDeLUrxc*xQ&(Cb*l=dDJTAx9LEGMyc zzXAF|v3x>ovsw(25czWzfJzg7VHGQQqn4O0L$MU@20a#~JGL(#*II=&!na5YY;m9R ziJdGO7!iE3E%>CYD>hKOqDEfuK%lOg5@j1W)z^o&vbZW+Yq%*_RU{f|Ty0lDJ7_J6 z)r%&JdkwwhT-;NiuBfV5H7m#$7XMyY>#$gUvawHWQQnM)v)|b9J#p?!D%tR$@b&Sn zbC6n!yA9PETejC*oJ=@Ccp=?UHzL*Ibgd`6kdmd7T0y94oq0|7x%~m7ETo~jT{SHr z%Oj^R<4lekv~k&r?jQS7D&aO(2 z9dW*b6(cZXIB)}Ghsk2$xgOp9_O^sAWrw|JTVI9q|C z>^gvNs=J=)nP$Zv_;M@rATrY-o~9^gUhc@=X82C-pyTxE^;qsznDE4)y|efJQYpUp zNsuHwUSwH0R@vj-{8_+h`tz;?d(?%C`bRE4Wxtcp_N=Aw!&?ZgE>TP~4bW%j5xsqJ z1;yUZuJVon25n9El&wX%)IK!w^F%@Hy9j-G=;@KZ90F>WyBuVrw+Zlx?RX;b!ZI-| zv;TtDamBE$oTD3JS+zXnwWz+~+L?a5RUX|wFNY@&RcgB*Te_pTnmWediZ&|sVj=Kg zTbr_3bChmO^tWLWVlA0VX?k4ed)RMF=y{b4<|!p!G~L4>s&gxk7Kris^!pW?monjM zC>=h!nZ-f7$r+-!V9Ox;$&hKz&nKjbFc%W_*8ooE@h1rQs$Jv-DD*fY1hTe-l2#su zaQ8BfFGofWnt>6FYMgkBgFK6>z~*`P#0 znU}{>!!hu2p+#oAWOgD?CDbyyFjGtiiK!)6t4(O0Q_JgAL5n^jff`%|{P{%S0VdItM()~1I@;(iVg=vDZx?@|XV|$ioGfcP`1o*RfS-?Ed5D4kNkl;sn(35_MUYbF3EGsdfJLg*tD)J=ftd zv2}-e^v^iR7pK#In1)8w_J89>X)PQb#)eG_oa)Fxtn?owQ!Pz0!Xx!`*pNgw$f*ux zKFTxCKj3&jOU+={VoT$oOXtsU+z?>${Ty(~7G`;hQ3184_O-H?#gy};3Nw{zI`@KK zN%H7jW$R(3GQ)CSJs)B*K~)H`Yv<;}+Whu#L)8?QU7kLB#*Qc1H|?mlg%= z>3(Z_+m~Rzj>v2+f%Q4TE;pzSd>kZOLI5mqv`VBeU%rjno8Z_!F}0~Qv9olJ@t4cU z&tpdwvbHvgJDRP&>N7z|fP%;VSc_@O+46Dnj+{8b$@0sh@*Li(cmCX!^L;+F8Ru#+jOO*2i+XgXaO|Z_AR38}D7FT- z`wowZV-4f^{GmqzT+&gW_MbSRieo&P$thzBoHIH-n@gv`Lkil1WI_8i7J(MjS z5)B}GSeTGte*OD!5<5(<+ZTmhUMJi?%JQu9>R3|!Y8bDnnMLH*N6s6Y1F`&UmEUo{ zfEnQzDGI9R)4+^+1G#s`6`u0~92{ag!qvxFSp^WUW@8|kfsrA2KEPN{0%rY|V7%f2 zL$szX`tX*Lcj*Ha+}N?m7^2*^P2oHOUN+04Da zH>FgMSl%?ueZF&dF|`Kt@x2x(RoPRpMnXS!Lu71BnLKYvjzWzA+q|*!rHHFAUP=aj zBF?I>72(P_KLWYySV{n^aMz(4@DgGHmm+8CXQAp5j*GqPhZe*}z59-SoNyYjAK?kg z2O7aDWa6eahMR7+9`UyIT=|qH2hQZz`#B8j zmX$9n=|M1k#K-fS&LYb|>CR^gG1kVtiIG0`C2H&Ps_$^IhR@(fy#w=K_2HJ< z#3RFX5;$5Zd0UqlTDHy%QfT!|Q)9T+3(_MnWYZXdL{NYD zxM(lkEH^R!xZ1vo_2lgypGLZ*vRq~xb{+BQ*Pp>wr;+ehhAYvXq1V-dR?8}z(%M@J|3M-0zjy#;A>=R@JszRG}5+|O=glaZ(y z@5cj(=mxk7NJcKO6A}4p1HfA8sSQ7Pal`7bau4N}Z)z&*_`{Wc4^w;8dEwy=|WUH>NcC_HCU%1dKRpK+w>@QqvGGMFxAs^rtwk_)py22Me8V^MLtCZo= zQ|@YAYKgGBo40)dJ2MFwT&9}r3c@u)QW-JsbM_8FQu z4paW2STv70T#M*8#QX*+xj+K+12l(2$@K^Rr*J9$W`pX?))_rhfv2<-{hJ zibo?7RMIMxg8j2wctSvhy|2R%F9a;RNFg9MX8F!EsI99>(@*5oYF>qeM8Wl|Azr7EBs4a|9e84~ z_R@9!`lb74e(yc{5pOwG+FyeNd)=EGC>M60(bmE&N9dWp>+aUJ{U4OQ zcRba7{6BoSQlTj$MN&p~X2?hq4w7-~aWb-HmhD_hWp7eeWD}VY(U84Ywg}nTS#`hO zr|WzF?%(6S@5kf5|G56>!a1MMd%WiJ^?bH>ZP7KZGVU&Jg`K^<6&-l$Nk)_D*z2f7 zF}dky;}%K}8ha;)*c^|5KFni|!{17e9)sZ_kvr-}>98FitizeQ6h5vH`UNNrV9 z?W1axy8w6g$7v`<@(E7iQGw7YS;$KLVfh=NFb#_<%kkM5$$37f4Zb2FbihWpOmx7Ch#Sr+c6 z6XodF0jshK1J2KeED~K~_21bIAPqx*L z{u*#<8bFz}>!jCwIKg#TCQ;eEV25Gpvj5sgp50e(^HS?JkHeBbO1u2?43U%kP{zKT zxxjV|SrHu7_qLXCmLQ0JGLn|5k0{c8=2ouO;>bJgz5RW}Fik{y0sBF>IFKF3!2GA8 zvT`)9p>+=R*I=N$~1W>a$}rIbawG@zb zL!dICE*V;CuPTMsD@E%J7sA7%>1!k=)GR) z10q3gF0H(9BwB3*NSlI;A2xi`qRP^4phIm!{?jL~!ST#T5StR-g%j6t5;R+&E0~MF{U9dqt{|N4_+(*UevM*@bOni`ezTpu*oe%o~mO07#Kzx z;huB6eED+12EvQQ@+j+UAM;7>>;~j&I+}9 zZ=7pTY2Zg;?cEW1klr~15==26@xl*r(RL8?T6)T*(>RR`1*N~L>KYCMJt+1z#=AK& z5Z8nv&ZH}fIzhBNkpR?}{SwB+2Q+`1O-D-sYI?bUR9X71levm|`Ne!c*A!fBj@^#T zi}%N93j3}mXo4t<_+ucVKXt0Y$sl9iO>jRYCo3x)O#t_844RVmF}>JSG;*w{V6@&Dv~7u=X#j$uKFVDZwYt7?9*X_$wq}XSXwxn{e!s zltg?Hh9BJRbyxq(sx$h}-LQSjI=#jcEO7{4H6p-t$!DqIILs6|G}iB0qfevV4^;^e>%u{R#WA+mB1=q zo9E2Uik=_Uz2MF|HSKewP%cT#_t62}!}FkqwLIOP%!de%H8p#JKV%`cFvQm%?FWdY z9FT3P8@&$_Iq~y|*au`z?-~Dwz8PX7yu{DX?=)Z@rzoFGh|y6cjh&lxAbn|S{s7kA zxNwY_go{n6P;KbP89KI+37g2lY(FS|jGPhq#^)6|c^ z_`r!*hZPQ;JgIr@QC;Obipt%I!Q0&L=Jig@zc#Nco6|dCcsA?&g{L~0Y-|DZqvsfo za3l69?>6%I>!d94EP@ZFcioeAhXvwaJSMk9~8qL zOO`OWER2{OlYB?u>7c=d##s`_n$H+*X6!rjL1Go z0BSqBW^anGtJ2kG8-m9vm_R}KNKliCj+{BVPFG^tLed9P~a=EL__As(u($jSSt&XHSK zwGc!u=}W2=_w~e@qtngz;Q0gNGUcN}K$y2SKCs_8C|~cyL6ItqjYFow%Fh#OuG%^0 zr8p$Vrp3U~>Vy;gJy`LsWqOiek_gigxZ9jT<&1*KW6rvM2X=~LA;^3C#3gr~OZuKF z`O4Kl&bPf>3Vn35qN8yQof`fsVmDv~Gb6OsGbl68TqMbr3olr=WVDw0!^+V;`V9)S zMVH{!Scgi^zrw)xrBt+zxoFmiYNq;mPc>2Mi+L!(-?x6r*vDrjyJ>1`CCU;C;!5mJ zT1shK!Ii@-ov+W?PobI^`$@`ep5=P><32U4qJs!E{#%t5B|9ayb%waCLcX?p^7CQO z+V8!6oA}FP?KI3`3=6_F_*~OArd0U6elRY!|HmY}j>AncxmeP?6TbJ}4P8E7%vn3= z@H6^ZQ!Xtz{7}ED;)58gg^t#t$2*=%E``am^-vNUx7T7dZ;0sTaqjUN2>EB|zo+Ju zEcoiha7ulvBKr}n{)!Lr;GLz#`yU#N^GRR7$He^J2v-ojrqpESi7gx1I)JIAQ6u;} z5BlER{ZM(M3O*WIZf}iA$o-LBT9uO%m&5M#cE6B7(%XNE@>V>y&I6|7&tRP z(Q+fkL-)1m-d!mmVCz-9Z83zPl8!XHaIWr`=?SBAld-k(<)XU#j^v&Ctn>6|?!_;l zg{Gs|BG=(({_XnP$V)kV`|Yc|>*_^0gW~Z0Z;QU%s#SmdS^jxgIYVotUu4nE3O=}* zpY?}7!`)&=n0`%7%?2>kES#KJpI_e&RZPX|o52j%>PJexp4}ugkq$uhp0`pC+Bi);-s}O~DNPD8 z%zlvk~6Y(~GO{WtU+loJUGE7Pmy$SC&(`navg$5^ML#H$3Dae)?P4G zCc3h<6-UFRrQrC6wT+={@z7N{vH&_BSUAk{y0#vM0WTIX|Medh8O33q31gv91A{0L zG8?M3IT^$teK?!!j@w1vA7`oVanufaUQiWS<7u~ocUPe(1fXBWmiO*v{z`m7K>@;> zRQ|Gtc@|h`m^t|lyh7378=9e=z{{omnp!*37Du2_-Iam2y}Q=wp|pcbF8k(m=f&kG zq3}wzYH+_O+0y*>=c=z21_6VL$9ql9giufPx(`Y7z9`oQh8>HHeffKOt+bK-?BXb# z57IckTJ`)x&Q4D2js^w>`O+HR`#hXdjQ5m~KKk*&PewdOxqDd!fGGER!ym z5*9q)+tewG?(DN8U;&;Tq{fqNyDJ$4Vb<&ZJ9n@@>dVt$p=lZMdUvWemjBnLH zf(K^yBA9pa@Fub)<7)|Q^zC2Ym~LqC2nlJjDL*@ppfiXU83p6pzz{eEG^*5ToLmLr zvhn`Y&VMhhk-2%g%I=4GUVEEDP(os)zNDmZi)IXA* zEyr+)^cYX1J!BC}v;J*u=af+rWZR!r16CnpNGTSEf}NS(BzG4Jkq4{k75nHyFz=Z zz0B922e{|O3IYS0ZqlQJTip1DvzFp0P~zkKdmD%bzMs`%4iCFB&PJvKpsDU%Am{1% zwEz^j5fJIoOL~;>ek|$-tN#*Rahi*1#6ZE_PVza=Hmmv0*ZyXW$GZ7!U?m;2>9_mH zb&kGxSDZA&DG;M%eP1mt7{Y|wc*??$|C}k`$Ct_^UZ|UNG1R{6lf7{94$SCnan$Z~ zC;xt>f09?PMB)A3s?yp9h(#fJvh@|96EL3i9I15e)`tmv zde$D*wRXnb3pS$7fI!oCgG}e!zwf)!T(h>l?1~nzYQP`GfJc$LaialEx+Ntg379ug z(*s>Es)%>NwCq5QZw<&*A%bw-Nl!S-9vXGHzH>gm?gRt{MInwKn1aYURR)qP=fT%> zap)n!vJ$KAo55H)e>Qzqd>zVTpwX+I_yy{$gPZgTb@ds}a)omLAkZp|BS2HYgJDtU^5z3p3f~ zkWC@S*EeqzCb{_x$~@OY&q?NJlp-E%#5{_uGO(zcuYPYdF`IQ~o!-3iH$hb4%Ishn z3cdTH{Rw-A4-C>?YgNNhZfw!|CO>9>bU)xIKNGtedidd&WOq{TMeQejSc#aPJbLIB zR@YrFYM&RJ)<^x}hK`rA=CfVt8(6=b5NqKdO`H1Z#&Tr4iaPjiTm!sT3TICB#Cn>A zS$KiVJ@WuZWa72ywa9e%U;e&7l3R@Xc$EiTAkUKXww3+0|A^mwTAukN8i&kF{DMR* zm-pb~D+9~_U}_H!vq52l3#|F6?;USo8Q(ta-4$%Dm%S<4L>JHN9Bhg1BPgQtkDlyMlSA3b|Jn;B76va-+CswGB-krkg*j`TMrq z0=5)|3%)mtZhgRF+lzO7{yXf0pUDB??H^R5+M7EaSwu15CHhX$7aqPXl$5*2?Q(I_ za4H!yGAeqB(Nw8&xYlBR8Ce#|Vg(mi;3 zIk`Zoti=(9J(p_zLd+m9p=mugbT~>MzBwx~yXtx$YqC)C@nx#14y17|YD4+)2Yz=Z ziz+X2G^&d@bo@F~jIuDAc_p?YPgu*@0KVWNMIqkQw=x~WB_*yPAtAv)j8L@87A?!5 zi8pI%%%@8KwX?_?cBwWIr>QwucHZ`poZpI;;_@K@idQg*zvKzaF4}#)=3|%Bn!6jm zn+|_;k-MWl9v%UF1X4=W&}Td9qrR=K&~UH_e3$vx7RDygA^MEFuJc=(HA zX|-O%@ub|ah-@25>W{bG+^(0%*26=MD-%zo&uG##o#*D^hTp_!M;Kai z)UIx>hhH-;fDbcDIwz17#Y$$g=+kT>3pej5nNDGZwynEp5Jj2vAT+Bn7RE7eH!RDx zz1*%x|2+cA5K7Hzb~5`+eEZgUX={<%T=7FHvXO7CNn_z9b=p;D1X)INFkIVa#e28l zO;3{PWOXuCJ};pA;{hKj6yw?$uRem4XXSUfM>F?>8G>=qkaYNv);!) zuif-X8=-?=PF8s#(g;qQ9kH{5hNPjTnd%!gm^elh=b#kF2W-|JzG&L{ewq2Vgp=R( zXz>0bvb!IwWv}Yf(kw|Oap{t!w{E_J9FlF*;=!l%XgxZ-y9UurYHG3SUKn`d8cLm#ePyQ2+Ts!^-uoS$4snRU9r5I?$f`(WukYp$UY2J6> zp^1PXq7gI*&A==b0j(&gU7onuz*|Hu;TJP0T4z3;O8qZVhsc6)p`V3ZN4_}Fmi8hXD$`#;waZO|hY6Hop7ocQJ4v5KxDx&;SziZ^U8FfPd zgVJ*VI4t>5oc(%fRkJ zvwJbR1)Mp3?YW3i(=hn_yR4o&WIA>iNz?5fP0`!%pa9waDSY&Kv^yzQF%T(<=U&?U zX2{KT0FLu*R>}Uz=O-Au+{kq-%9tg>bvv@QAfWu>_`Ts|by!eP?#9~OJgD-~UTi4E z)>7C_qgn@!qVM^B3G)HWTP7tz)A1J4zaq8g$|oc{LrzQp)N3UFLP z`J2#v5-rW}J-3}_XhDayeSCVIui^3d{NzJqWV7Co3?wcOfas`R0XsXpl9yMNkT&JV zpVy47>>o~Eoa#0Xo0~-UQCAlxad7#`a7_+reyx4nm_zn5jPAG`T-Xr1UMZymo=}Kj z>4+1CDifx!YmIyHkfh{|3lCphCgCD(<@lLA%EIUG$X*J3<|LWdx%GxYT=(mODOk`5 zu-v+Ga_Qh>pSKgG#4YrI6Wsu~rBF{6IN;C}ar~8gM%BxLFjY`Zx@0?a^vdx0*Ve9J z*`94ags&7f*P+2@PjK-;l=)O#PCH%qQptJnsNUHXJn4mno7Z3Z`Q^+!3J(t#A9DtZ zf>L{xM4xmsI`J}$QfbH%l?{iBacR52p@WcW;VUFtu=!mD)F>GtlmaEs_187OJ+h%5 z4-`)dz;6l@zQDLCM_W;NU3rG4ig4fY;Z9CfKMS2*+}7(xg66bz*WU+pT1=-z@*%?^dZ)8MIjjO5`K`HLr3Aw_*#_7qPKM`%^x!#Ku0Mv} zx0wpAI;c}9HNzq3?}RA(n*UK)#d9sxD7m~=3Y;$TFv4#>@i}06f{Rr|L<`CC%$njJ z`8yBA#ac)$b&4xXoUKSCUUP+VYnd#)0U?pz)xnITcW&Z{5h2*XDS}LA|7uA%6!g|S>xFk!#myMNsF2c#hv`I53gB1AH3>3()Xdok8K-#U#%F@+(zO8 z+d!CP?_3SxuoHCOu+a5IY^Ca2S}~#766+nZAi2ow`|A@E2&UXjFhn^Rh0f2d6{iMK zuPO=i8*!Fv71qeKyi56b@Ge^}?W1$&E*P;0z}f5m|6AuM;~R-2#GE)aK@3P|?yr7V z+%Hm17j|75-cFDP23P~c3yAdcfa(91l@8!5Psdo}H1fq|UcPY+ETjI!)BxuTa#0>? z1|iFEcMUSlz>U(mx<^fI{siT^$e)Z6WDmq>X0)nvo_y#5k8TbfvAk{Vqq*K7y`805)T= z@Q!TAd|!W0dygF4ZrkCvYUrv^((y(E!4S4x5tRj2VIp1u^h!_5+_itE=4P zsxzqG~q3l9aubRgwuGhOIRlh4-&6(Lbd;cwB}CW_7q zPg#*W=~+Um3?Te>?f2e{bn@DaCZ#3Li-w*)`;=0z(kIBq8i4}hb91v2RKnN>1_n%o z(x+N)6KKpi9!t9v-EQJixC^KE5(rA?&{ZbdXh>_Qw8~`La0-`*aLQ=18ZnAF-N0h8 z>qe5M^y$CmT4*1I^410(*^H91Vj8XWgc24blOGjoco+2J?-D3?x2eV6ezQs zEx=+Fcj%nkc!IS42-OS>hIR5d66QVr2ryajd}Peeg-79@ihcnt16F?iH%4O|+}u68piA&KOh!iL!9-;hbUEpYqJl5xr5)ud6 zoZi-d|2gEsnH3h+E=EbrYP}`Qo1)Jo=h|>K-T^x1%EXru(L%DMTBvP7emuNqA?IW6 z_ZBZJI$h5}_zc2Jib7IGV}h$y`Dyn%+fv?D5I-lh@O3uAO1>4yvBlrzEWeSD0#Jg@ z04SQfrwz0ZL&l$7lS}a$m|NBzVRQ1g3f_;t_x5~Oua`LL03n|v>uw%Jp?JMc;2;>S z96N|hjJ#IlM#LRJn`35A7iUnQ_x*;vyb_cuL)xKgZu34ff{uJg_!|Q*(^83|@894Y zG6?uEH>2mx$aO~a{jtrreZ38IIaZxb9;1@>xX&<&#olJ}LL zKRYCo7hh_fFaoiao%C0OJxWbrU!SVQ#Kye{^R|?MxH)fqX+(ed%Dqv8Nx`T_7BEEA zAjU8v)nNoQN*+wqX70?>);x#*dVkdTmfoLLr2;_UpPl}iw%s-PP=?Ok!N7u?ME!`NnSXHB0`Bb7~0aotjgqwctCfrq^OeBq;dCTFes@*Nz7Rw>g~*W(kI z@f}Dp@2IOs4g)$e#G@T`>EJ|3t!!?_!d073eLdA2*9_MWkr(yaIUgDE3rD^WkNAR% zKtLE-kiBomeG9k$r&6`g@G7uyVFUc^*H4}=S88QPTtKT}n1Jl) zySuNHbj{qrh08w|?AA0-Y5_L}Z>_j-?vLV9dr+`Lw$$oYdTWQ@?O96=f*g`aatNV) z^zGJdLb^d8&rPWTol;AD?|aam);@uOAk4~s#aK-EgUgUzc_S3gI8d7u)OUtlrD;}> z2Jnwmu8j*$DB}Q~w~;}4m2l`oLQ)dc8LbDUU7{rG4KVW3wn{oHzWHHcLtK)BM>kn9`!hec8=J+NP#u?nbXM4 z6YQS%0#Qyd_7Q}1tyv_#IPDyxX`IK=O_D$W4$_;#gr#afu}o-BxiX8YdEHxQcp9?L}Y z1Iz~&Tv-)hT%6F93UnV_=FM?#?@j6`5_3|Le0xdRZge}B@~YI^mLCEydl{kZJOgWw6sYZ78DdY zfq`w%&|4TR%{6M&c^%$0$}t#8aJ^8jLxF8#X=vxkP=XQ?7ad-#u}T9POh*F3qLWVv zu5YV)cAG1BMW;Q1?t9eBrVNr;Zr#+axv9zcoB|%BDxKhr3N1R6Q0WJ*?>;R+@@G}rt04=6s>k-^j$0I zXV*~6!{K0Wm#!q`2$b9_v#LFi-op9}r%K7rAh@3O()){pv))kW(G4oleCY+~l{rnq zRH(CN{wLDUWA#qH>S$xt#7s$I1#rwQaLv(Ynq}d?rU!|6NY@nBxFqE&*fXa{I(8n6E;nvV7$J zi_-xcd(jj9pMwrumD8uyIR{=4o+4%8y3$TeF%6z0t+EcvdRTNB$19I?`Z@gH`!#2y zxS$R7w;VSq8mj?gQ0CKX2hj3dyNHVOsIr2Pr;zPLfZZ^_Dk)6?=%Qb49Enr#eiVqL z%H7VMRrn5@U~?{0ETfjMCX-1oBI2;>Fy%2WC5Uf<6kBHaF?Stu!y>z zsfD}4-{#yoZe`e~FWj{h$6emLX9f*Ep4a3RzSYaQ68B2EV&Y?L=sB>(OS*(o7*BGf zT^ua!-p$M7HnhZz*mvk*v4Nw&Wp5NL{$Xlfj@Z*PRVd;tJ5~n43fPEY{mOHi3pQ#@&2=DT}haP z#GAoq<(0Io`gMe%PsF@q(Jf`NWt`^w;4^xR5n>QRC+%U8nLSN*=oTsKoj{>M10P2e z%yxZZ!E%#iJ0?+n>rW%YEW z!=I%IeDU@FMj+??-3woBnMeDGxb8hF0UKbGz5YFY^1DrU(ag2GGR6D$@GXy9O@kfj9oLfE0j3vca3yLRq@WknTZLUEIbB}77RRBhH)n!INt4HYOYF@!g2 z=Ktgu6{#!t@f@zM`r9wMSnPABpG~5cAsdynoVj@T9w?CZ#e;ktddQnf;BthtNczHm zDFje#cGqXDVz=+tlOqd(d3T#YmYaEBqBxPsw><(IF;G(zfA(n~X`& z4{#;XDuxaqbsMn0G-Q(wj>G?+i8ZtwCe0HZI$G>nag^WmIJ$^s` zIuz+wYRQH0Qj@7o2Zbj_X#V;;He{ic;r_M#JxsdUIPAM*)wOn2R-IDjVT%}RsN|0? zkm=mCmB}&~fg|a*wPFxBPJ($X7jx9QP`UzE4h)_{WG%HWP7p3gyrSJXsKY|W0 zzPVOtqohQtb%E~>>|#_iFQ>2M_JBXdW5FMZB-JO(41+I8?2e+7KP zIOnE*Y0fNMw|~)f+i6wX7Q=@&RT89fJ6PhkV>RJ`U@VXUb4QWcPmD2t4tceHQ2p$* z1mOJ|zdXz&y4<-C?AabmnIDq`w+HYS+7UQQcg22jiQ2z6=8Sx1iTuG`Ma{x{`HSh0 zuB=_+Uj|-r@hET2*5L>be3zv>=?La1rQen_0;*pY)qN1ngK;tl|K9-YMCSw{zgX+Xhq)kyrz(6-cN zR^6~=U?Op?yJi#)V4DFe`Z<#6>eS3Xr}J(sK2J8BLErQ059sPEM8pVT=vVAJ(_THsW|c<9(C?XVG!mrNlS!(5L^s@CjyW6hp8~XN+2w z3Q6-~;m&Ozqlfd8We6o^RD9m~1$DK!alF#Ebr@nx*Df$Bf-$230DQPFrw5inoyg^R zA8VX}$=U$mCe46oAN6r+_BUTZB|J7_=(yI}>Uy~TL4EZ?Ko8vr#PYO1hS$D2diaAC zF%5m|XA)&@(lGXf=~ecTQN~l7gttRDo4iSGnC^~w*a*- zwOpwYM9OHjN2avoYi()G(bU*g+mUW?t}m%!9scDwlKOfIE&Rq)6~K%m!JQ51T)4MWc@ zD>pC@3sn{Is%Mhi9r7l!JOQUsldaaB0qUU3lM~!>;(a*A4eSwV?Q&vIS_ZXha-EcV~NNC$O6x5A-l3u};k=l+J$qTq2-__o5uE zSVeLvtzuv+J8rXT@f_ZF1LAp%gGi9`@xk43 z`QN*COIO@i&2%eWvJF5KptAZ-DGUa}5KJ)aeumNHJSrde!&If~(C07F^L{*sVTA94 zK&nzn-eBBI;Q91ac=p7>wF$F+*nuBCoCC$sOVhdGNm0O268P!ov6}^T~T|5NUZa1)B ztV?Jg@oGVrLH_DZvc?ZBq}h4Y+xSIhcQdaC^2Y(Q>I^1VnC-1s^{;+d{uE|xQ$nU4 z^3~rc3mHV^WAMNipm#z`Q&Zd)Vzb&*=;M1NM@Fj8&-C^KJ+<$sClY7ykOr+T=M|hV zA0MCd4x(TFOs@;G2JdtLYnSp?!ih9iAl+A=d^6X2IvVbgvArU(zbDFGo>ijen3fZWQjAzAVF!Y7a&vQQ z04|3!E;T^lFt?m+hblv#JIHldgu!13DFAZ1RgCypuG)G_5^EaFzooh$pz%tIPMSe* z8Um;6lViR9YK@JJMZh@@Mex3Vn}8Cc&9eX!)wz9CwVIznbLpcGpm>rHkxK_X+FYC6 zdg8@Y;N-y6W>?gmefjDY3y}>D#Fe5#&Y~8YRT^$!3|g$hWlFpiH-d+G>pM@u>BHP4 zhC75Wil^jNh$;6gI_$~gI<&tj0+W1eZ2%t$N0>(h|a+HH@siGCIFspHZmPG<7X8Z_9L7D-vgU3~--qvYbMmx=ueW0S5bLAp{+M}MG zkZ$A^n6-$;OZR(EUCncjb_peq-Jiwryd2w|N|A7H^(R(+gx@athP?RjKY;S#VFRmy zqI6&_z;4Becu-zvVt+Oc%W^rDD#|%s1P_QhHC2RIgfPQ4we2{EzJGBL`7z z**T$ipy`Wma9@30hLfSfbWTsQ-CX#w)rOCWJ9~h#X!sNIg9Q*IjFLhajIF2-+o#(1 z+tX*=K{)I?U@QA*Hva>WKR@l^&-~X`sN2R6rc{Y*^6AuIVhV<|zFrPjSz`560wA~`hC4AB;vh*v7N zA2FM1iV^VHbK~GEukyN@d`{A%rxJ+lm7DJOM8MSe+Y7~> ze9InR<*V4Ibo6Z&RH*HNTMv z!?9NO04s57m)vRq-7i)ciZ@K8EQCThZr(MsIDsnNKO{1GP3N+ydKq>M9ERZC2L zcw;!V874Bnw)`Noar&ZeSGggj{BeN%wu;>qXfvJf%ZDIKx4;g@K914bKvXiyB<*7m zd}BlD^|UN;vmR1+qs#O48}!K|A!wc7287CG5a)>kI7xHsl`(GMf)}0Nnr=(XjT-Nr zsD1kMaK&6st{GGxO@!J;{Q&;06+~WC9cA@QXtBq_N3Y(7{RZNdj#k6glvwR)RK}8ASCHaF3CS7DDF3xqESD ztO7bJ<}i#`1X2e?NcLx91^^ug)KTKMzG`6+pFBs+$dYs}_R(uu_7~Ew(8|Z)0519c z!<&(onfc=hk-duXM3>i_V9NH%9}Fd($5{D2V}2@&U^loGm!DJ3gBwiBhFiT4_&Ei> zB_verwLKN*p#-zyF!W=n%oepbtLhsGf;MaI1)u|gO3xsnj)aN~9ZGEn1awQSW1yBe z`!Nq@+W9FgxNFtz(y!joqtzqkqqT1yv)*XzXoC`tuXnjNC#S*(QjBPlbBmd1Hptz~ z@(2s}PVaz););(i>+P|oT0m3UP~KMp5JA$o0KgW*`e)5ib0I6v1l+bSu@o+Olu=xh z-6(MurlP~4eW$3T6a^jZp%QM*bMD_yj+7M+f`r5rh(Gn$X+vN6^=U_+ruacI{c>hv zE39@UPooejXiZ&-?8JHj^-O%DrAZWQCb&77j%-c(5pIOMmI;vws2dNgJ+DG9}*PMgp6ER?DQI496L6? z_uVaEaQFb@Sm4^f|NQnzq+hcL=URz_pnTq|0OgH<>&^@+7P&Xd9TO8%dEN$S9h~55 zAw`%vOI!B03O2BYh2?g~ZLq0gl};dkv600imINYw8wrPzoI-0_##s zh9lYLW+ox@AlD+-2_PP1QU60cD}>IM)2S$Bl1i|DjY z7Iah2gffVwZk5s)bHEKk%@w%Pk#TYC%*?SDE?7+Ek?R=h>-`>JY~l`&EOJ^&8v(0L zkaZ0uk7)Fsbomw|F!O|^Vmei%rI7@~@C6Gf4^-*cUf=%;V9*%BK?J=U|3|KfeOP?WJJT&Phu+j!9; z0lZl4$cltHH|NopXR@JPpYl>GPv>RS0L}x&vo1p)P!NXV3P)@Rdo$`vnQx5A%p4{8+GsiSk1!Z;Ro(?Yuj=9>QkAY zU7m!JW5$yfk{V9~@@fylO{saa;|a-))=tgz>~CS$TyMH1sq_5=DPGwMU`#Ye_`#W( zOl|JA=fcgecXQ~T<|N(%D9tEc4@a1~4ejz(c`PmVXVXxm|P?z*k zjF+uHfN_w%-)@HbGB5_ihb#-4DSeY>8C~Wz%It_EDhnq5+IVEC6OMoA*JAPY6xX^B4`gbfqgH zd2Yycv;u;IqhYgDHk`Ge|ooum+$R+ZTIQlQv^Vn&tpL|(%2}T6Sg$dd}<%wPp>g%ixCS|Z4HN=4IB(P z->ip5B(~11^)beV98wi?Q3iNr*Cj!EhzFv8tKhcJ{)YB`s&aLBb5m0!Tp&Zea(f=Y zO3In;oe?@2g(Ho{)~GMS(_LG0+MMBtDf#)oNBgGz`^c+KO-=2zUvc}1U)$g{tZYD~ zG!8DVZfR2?bYBUNVcr}ua|673&Y}i{owo!)L&s|DeYKDKEM#728kmniHv~!pNDmBH z!J`2L)_?|IQJjxvfuvQdK@X<#aPw1+#kM}b+pTdMwx)_71{Klud7LomXY+2d%WanG ztgY0aA-bXTuV%WJ$w5)hdObe)R-$+#eCY!uYiDGYsWt5l(7up;n-0yDrCVi{5Kuk% zeym*sE=cDtC(qn!Q@=K1|amD0QT+e^%p~tF8&{eg7wN3ZD1>`yM)ctJF&C-6UqI}y*8~6 zT}1+XKi)x-)%$|06@VH1o_r9NpyrT+&ugiAvKbe(ceGCP9igjje1|r+^>dFk;3V}M z{k7ViAbGVT=|dR}1U%ix)j&$Jmu6wIJ}eFph(`5EyW88sUkYSSK|ySgScPnDPfW_p z2`T_xcV=*frN5O83=+)l-krOU2R<{9AJW`0C0X$67Sk833p)6iqU7d4jN6CqYBuujYS+hF^ zC=3z4qyN>@elf(Hde0DabR!NjKE-yf&Y7zk&SsidQ18H@UyN6@;F|G0cY7z*irK`Y zJ6_Nk*j}Fd$Em)dE%e+OOs)TbA)&Y0^MU(B0J|B$mI2w?C7O1xVQnNwo5aEv)YG zMCOFX@HRE4Pjz-goc&syHcR8PC7yI=<-)Wl2Qdz6_v!Ee3VQ2!lk^D(Pa1-9g!aHU z0wg6GIJ`W(GYbm~6~DktzSrn39X0h2Z$tKZl*AO60QEL19DKy*6dhz0?1bYXR z&B2(>8Phz=$8Ha#(TTCVzqBaHk=@)TKS6;#*u?lKbyH{to}T) zGF1V6qPVoc!QQp!r6@K-g$lqox@233$hWC-wC!-a-Z`s85{p$=XyzeKXgRPgiPV#s zo*t3Z$}sL~^m=c$>xhmIG3XJ7CtWG8*;gs|0-!RqEef+wZ?3LJf?a9=bhFJeTrmON z{LAYrn+|9K(?NdemxM6P%vS?-t4v6HPJzm^%QC^QUgZt5?KjEd?mz+;x7s0l9CaY> zc2DcFUd^f&aGq9n2am=mdVNw{P^CxrR3WtCUb#N2+4j1MMo=8)#duGDdT3hNM9h9> zVnMZUR}kGv!aVYm;L3<2K>+49npes|HXmH}!oIL&2F>ID_H(+aUE_aE!$N)eM?g7p zk%)ZeeNuEZ3ldLo^Yolg=8r_lk@P)2BHT+T1R6IeO=+2dYth{<#M$c%wXB`=RFC&E z7g_LPRwS`q-IYi0V9U^08bQ!;3Lf?a5E@i@m>OH1I+xPU#VNP6<5v92j7HqU5`&>L z*;0NyH!%Fhw7%HKULgJzNZ5`VKb+>Qb(%n2^#(C&Ntv3JpFmY`eG_t{A2vWE6}tU= zXHS_pDeHUC1`4<_Gs(V2&=@fTSgcboPMgTff~X^4eymA}ubKtuTxc({ql~%V%RVT$ zBridm{Ma0sDd57TwCkaG^? zzh&*du0LDFewvE9ZBffC{Pg&}ckg*FK&`C^*fWZK-UBKfxbWUp*Bn&I>Zv3n7IlQK zrb(z~n?3pefD9?$B;rCU>_TgZwd1#`TKRfN&2W7MU@zvN<|<$}D6}>Mx||cUjl+S} zYZ`5%ZQCD3ppPV0Ssf`DMh6zmBpRNXZ-Zg0D#KQTeQCpMVlEgOlwVuRn;B zG`Byo;Dmp6YN$%G0@`pDz|C_rWr)xiM2CNKgAb z|H3u(qQLd-&}bNVh2nfD<-yg4UrR;1Zm;(|E-59q8&&C7mL7>AVZ6uy6+J{gl-;xe zAyW++8~UM`0TpkUC+gr&1G0fc2#r!9CS@a*heGStqHQj56yK_KT0)6x${k35rhsm6 zOjlY7L@aA-Ywt3}<+iHQGpm$n8$5$4RblzpZwq6HZsikyJz3h%mDT8e-!W~rs-2)$ zQ?UxVa_PgrJK44u{v1@iZu>Ot$szE6q72}kXqUrOe?clc84u19o&G#R`{d1&Y+d{A-kLR%Wk;@-Yr#l#{OguBkKz5f0 zDFZ_f%L)Kmqdjjb%b@Tif4p6oYOp#Oh-y?=@!5U=M!=j_f=RRQ{x+ULPvm>uJ&Xc| z*T*)T)-_)eeddz`d7+F~=MH!7+?GyGNH7>P_P?ucQi%3EICO@i-#0_x`Va$j7JWf6 zG?uHNO1-=*9^|iqTT9MZ6>3feHNW+M=b97E6G8$0sz=rr-dcg$;vWBe9x|N)f3}g( zLc0Tt>?D{af4Dk+0p5cO0om7)lai8-)v&$w(VBjG8NIDR1!f*1&g_-T8;GO}G(TU# zkQkcdU;7-TcM>>D=&(R~cIMva6AzM63jV__$!VXI+m{GYa*%^*&16K?397-U3oeq( zDCwbz9202mM1#(4?;TU29H>6XwyOG*?I=8HxF+UO)G_=R!PW`y9KSPY19lBHpjUz7 zx(Ti>%Y_TqU%#E8a0eNibeNmncs~aA{(HqtQWWxeux@P3%*>JD;pfpXEhH9&YG01b zxQ^7>rN7NHIHzBtl*}ZCyL6?x*hp%(<^vVu#0>F0OEESu_AfGnivL4fW4?TdUa!?+ zZqT*gFq7#%KKL)zr(+%7Po++T=P>Ff)Y4?f11x^iLgWD{L zapX6kWa_b{@6Y{vuJv2~53=4n9_#-7|36b2;3+IexHxOt}8d3=j(jEp3mcX9FNESVM9(y*$l@?1IQ1r zxL72~piHQQ1g8jgyyc2U4elc0`q+U7*~z(Ja&3ZrU}B;%6xH=E^Est((kJ63T}%wk zkKc0)6czzhu$Q~jbN7{L(N3C%zHgrnW6pUG7oJDh0(2hWzLXk<)U9wNV0VGx4B8g& z94v5xvgJonwLP|l494{I^hUc?Pzm+^WR26!S|<>FE-1BbPG%}R0)-a-#4Tf|B`dTZ z#$JGw?7s#}WreqYarHx|v+D0pf8-7W?6KLqrDCTgop-k6XV221;m+v8OLz&Z8AtU0pSEkAQv}%(Zl?^@U@f zVRiPWMfRBx9R|)nuLA=Ei_H5KREt3KUGZT<8$5oCRu`$Lu%&1V3Za+!Z2ZtLqVekG z=LniLCi&*KpzkURkGc-k7WD*bFl1g1FFHj{%>p+IdW@T4D!#^J{{8x{txPK~Fjizx zS74Rr@t2eLNe558iDGa~0;wYCSS|<;2S6d~)N{d3%d8g{44ClUO7@DGWSDNMJnLEd z-8K#A9Hcmyy`c>jRmZo)xsy+CXit_ry4X6c8DP*HwvP)BV~yW{T6UW4?WxtQOHYtO z1r5c3s1ln!1Tn>jQM(v=%>K0Kxct=z%k11!$_LLUS_pML*25RYc0pj!SiLc#ndb(K zl4jINeO+2wT2l*?+W^{&Fay+C$kbPQo9u&@?+Z=G+Z8jXrS9SnN)LKWhc@|(5Qb|~a)T=TG+F+g9gkE+`=ARA*E*&L741@CHi!%f-cs#0u+B&?K}S~; z&6Gje>^b7L3ynJv8zqJ2fqn^9{oUe`IicIV1uOkZD9W$9Yvhy~k&Oz1;v9O^~Q07NXtR}(-t{xVgFdIXhAH7szlCBLsg z>e?>6jYGf_7)e;w>_LyD5y}g8ggZx$KEQ?RWNgzjFw8AwCTBz3)`N{G6{Q4|^`La3 zLbSN8K2J+xC_|F6#QyU?T!^Ms^$Sczjf3!8sb@Da|IN1w*@Vx;T_fgt7+SQvi)raK za~FpPkT9h#ZMqDMR?IH|r}7^QHj=ZbJx?aMpj441DNy_ec~GDvNEG;0C*+_1MAsw9 zhwyL1yohpfTT2T^@cgI_BZsb6u$Ijusr#g-F=M8LYJSW*3WH#^Xd%4`GT*-U^KE)C z=`Gd)N_a)?6V*m#?9V;u%I9_Mc4NhqcXJhT;lK@`+=`i(H+ncUcHc;TFcT~j{kW>9OvWt9G z9v-=V{d#_H`}1edTG&Y28Ch7QmXI=9+h-k3zP)|lyg7+>he_zZh9Cho+O%M&aG=sf z-h}G-xH#M(!$&pHzn~#cs>i(`!JVFepc5UYujsoo=@mP%r0N(dLb{YnTnxvrA}BKE zel!|$oPeD#hd4jrVs^~6EPrr^eP0VMNzct9VC~TyxJIZ}LZ(&43M*9bo4kpQ((1e}YkQxRp#LgPro!exsP zz`TJ<#ARx*+IGeusimgkr8BkhA&{}nmhF0x04_Um!@=WtOIsVy*H|6Ao|2lL?@8Za!PB)bP?iX)@m`5F95B+w&+U|1`WwL4-klcue&udlc!brBwe1%CP; zfQh$VyH3)WNr*SyIaDi)nv`E?z?t5MK77mzNvr~hj?8we5)tTC#sEHiVg~x+i zI05j@BY9w(X&YBGQ7^Vg)jdDj(I&srbMY=+u9)}cz*p*khMdbg3@-HyF9V=TzVL=C z_S+Gqp=mh4r3AtZevaQGqvEfma;@susDUosf%E4aoi2xFfmO+yZ?<m;7vZ0&e3F8zIf;yo)WYL5)_p@co@QoTkEI%us0QxjMkI+K0J$ zWZwDV4obApET^#<^X3kJHBYzSUUdP#vzYRsE%sERC;Ndq83ZG)+ zMS%4zz5nYZ(VME<`KqzkH*M{(!blY5oz87PBX@$Bxa;as=%s|i3i;i=M(+bbb;9*W zACa_gnwFM!66W*_(^U~7gY0~ zo==;1lghp>m3;@SvDZJFBTwx5b6Jm)!gWs-G}zgMh8ryew!3z+($FLWA+N8rwkTy= zR8>WV3Iqo*-Qp@~U0(eW4UH1jL2!&krC7un1g;XQ(`i+KXxlK-Kgs}Fk4iA;vIhcI}gcNUc0?C)3v$I#ZIF^#T z@-=)nWAhMxAlKQYn03%)C~5pKrtCIB1or4Yw2T?Eva%3Uc6g+HB-{)8tonTwjJQ`C zXai^FoQs*?L*trcjxFb>M9*DqhsqBx{zN+7RDPR-EqKchrxb4%Q@?4Zo7HDRC)1Kd z&;Y&6Co}WfZf9$Zt7s`ym+D2v@^9xQVSzNhYivXi@oj+5$%>pA=s6X?u?4`y9D za||=_TWoWiP1c4x_O>as?opu)=lnk3-$+tAGh^%m)sVTTFc+8NOWq42I)zuJ5XTVh zaie))o~{@!+S&vilN6XD|8cmb`t_ooT!w@?|Kl>W1|#jb2U|u1U8z@*y5)rql}i^# zwFx{(zJ(EC^fJVEb+@6&lv=Fa_l)K=^jrLZ8#o$3%_{?lb3TvZZ^FB4irOD8fr1D6 z=UJzvG$?=k_)&3u9@r6xBX}NSMp~y)vzYV=7J6shn~WL{Rc2NFNRc1L`kIkf{KtOs zgL9(`7!ys;>Lr<|F&0thU|8zFqSn@`@ zRj2kNXbLHoLOyn4T|Ub4TnYy$SYv3P)-@n;@wrAp9mZPM)ZCn1Ts;Ei3oF7aOY2Ue zL4Zintu5aqSLX`29~|OlAlfs{?H+HK>un{rlggK8xrp=Ux-slFp`N-8%eTXED!cxo0Mzg%^u}{lidw+A;#&3P31{4iTqy1OL-HJo*e{qP6 zP+1@X-%grn;dyX%L!qQOz5o4|YuXqDz1aI!`#swSy3gDNE9{nNwzOD^M(lyJYS!@u z^Rprin9v2#D2wUL8J$%3Hmt)RgBy&YQXJ(9uf>0O7%DCJhCW~p(+9Q=Gq$oZ!3XL9 zNUainP)$A?eA%EY{>6X2c5&2PggvtQZ(&t6bGi6S8jLqJYyfMFJ!G5EatVULP<r=+0Q~CQ`>Sxv6lW(eW3w9L=!CPx!Iq22op=xX3&709CbIMU zInt?8aOkkYEnIre#vuu~a?Y*=E3mds=@mgqr`dS^gqr6F{ymi`5`WCUeR+0-xtoCy zjf(0nP+B_*jenfTu00t%&9D(h@l<$f{=w{~ZI>5sgHVEb@M9!zr^VELIH88ror18y zp$_0~eyYa0(C@m#E5LwN77x6{)|Q$SJUYD{Kc{IiTsiRSO%3YY({gpLo^8uXZ3*#C z3n6u{x@HP1wsi)DPg7k$aKXGbQZ2?a2r9dYE^9zBsgCU>+E;va@n; z(_sAcyBMr{^+FUZ!gtUjv{+SC_}OPmz_kp65%?|?`z>9XdJ~r9jKL)~^@4k))7x;y z{Z-8t^E#_V!C>lfU5_t(Vg&ISS}cc}M+c<@mB?WxfT3CwG=%*PP>wBMssD*L|D7xc z8@V4J``9myBb!ZJp{`9I1zt$zm0gd`e?tmm(jWU<58}6;lp0J$-m;H_*`?WU^Dl68 zoPfP0547_4k*XijMd}5fM`Fs10dT^H3h*mV~r@hS-!Tn*K z2kkK0P&GN(DK~sLFLdQd)_0!G!c~T;Q8f8Se={4;Guk>xcaK= zg=1I`sW$U|*&M;^<15@=Dsmd^dHZ3kclS3_hBo?D`teN^0!&*TJeWX9;%6_4;a6el zfo|24RXgnp`)@+=pg`?<0i$laDgm@V-@FX>PZ?p-Q{8i*in-+B4f;}MpNIUz|w=WngCpk zaLt1h0zKJ75lLkE0F-?B(lq;82)Ond&l7g?F?>hjl*C3*W!#Jh?@{?<1EbCH#6K(F zwrDx6hOhF)KQ<99Tkc@0t}i|wly#TC0h|pFh_kz)&G#7HN9dj|0x2iz4I&H=tUJmm zM*z)EMef{W!nXuY!Zne45KRw{6~5(-ak3CS2OH?H#)}v{*$ltvzvcKmdVi0TQ%6O} zKC8-gT+CL!(mJ+XvA}O4i{8y(j7y~9;{G6=2z`2}A0Y;mW~Rdx#W+yqfpLF5oq6B~ z0ioK__#rq-8$;uXt)2n>Z44F6OVaMUxKRnDdB|lP@3#}|)AC+_DY|eGS%YIv+G*YH z0ts>$xR~ZGR)bkxyjrhrI=)OJOwwrhy%0d=3NzeL5LQhLc-nL_#T-$=SRbimwuG1& zmmu3D@)vU=KcP?V0?(=|ETCtLL6P)5ykjv5n}5%dsU`RY8Y<+$HJiSQU@-=#s9%QFi-aVFpS{dWLtMT$!1KMylbVtNg z7Mp`MYttJ1giI7bC}Q39gSA?y#5fY6yH(fC=^wj&mNdw>_(p+Av^no3e)w&K_aqTs zf*K&F55kfJys+dDoC8svWDCd-B&l=oyC)|Xgt#h*jRaF3qh092+8IKMIij=!tQ|k! z0=igzQ2j6V>mofg4XQAcxRGFW%Ng%RfiWO*3^udB3WsnpSsp?6XhHs(%2uw-WDWAY z0ujYad!{c>I!_(j{3Jj^)tQV}5`5LCF4u#({ySwyZ#&B*BcI|6+z7MM_lLi?f#o*sv*y{KZ~P#e5@ zGek!n3M_z$1rIZMxVgh%en5f!7wgTb#YdbL#>Y&~&w2IgjG z$xW3t-|PFLeSwCS))3AhP@5;_Wsk#-G53+R7<`G8MpJ$#7GM!+7%B!#FjPwGQf%<= zcy%#`{h^SR&Uc1~R)GQY?6H9cjUgAm8qNlg+0vAL-?77L{>Xb-u=c0j(2X7r=7{Ga zu6-(d&}4i2dhH1e$qyWe#dli;4WYudur-Gefor#b7ESQ4)v^4mUd|ZY|j%=yq*u5!gT)_HRV1o z=k|oe4qdCuIeA;#5)iSlJ0vi+M$t#pamb4ZWBt!ry>h{IRL6Em!f7%6~e2tT3wX4AXLeS+_S9 zu;w(!xQL-|oR5TpNYxY5cOm{%4)3Ia=WCp_Fq7FIqd9wA_pfBCQTg^V)9tmsl&vb| zsMf@4;wU1nxj!)k7c4)Dfv27Pe8Y<%%N z!<4OoAH=UxC71g&*R@QR%B;&XJ<8TB`tEEMkwmHuP!L$l!v!kuERbBiad1CF_Xw|y zKb@MMJ{R#Eq^Kwq5JF~n5{?1)X9aj9@#~imFibb{&vQ_;U{yI)9@|b5K7;!yia4L= zf*{j>?;!B5KW& zI@iC@^^5O=%v#yPJr|%p%Xv>uX}O?2Tw-~c8ol1;D!R#k+0S(|A$#8iVRtnIcH^k} zedle0Nnh?<+WmjwJ=-5B*fhG~feXfIp4uw^PVnLBJDSPo4lmc=OA^a99-5m3*Cs~K z9&9G-e=0p~wgf??#dEnWqV;}(0c=YB!7-0lak>llEZ@g{S7seukmS}1#i#@t_CWCd|sIL*XvM02YpIWL4k&hQ6>yk|A4E*tG+q%2K*}z zgKwxiQ@#Mq#nB>OHA4Vm=SkpD%fG$E2x7Fh+-_fBYCqde1*!Zmsb<~`J-j6a<1_|1 zYLCI7=o#hLXPb?M7@GpSmjYEk0Zf~Wm z9F!KI>AM-qNnD5j;Gt{S#+d{sFYk&Bp1-J}f}u*Rs?q`|oj6kN8-i?Q1@t<8d3j5G zwOpV8()NaAH`&HAn{m3US4ICX5Xj84shB7~4318%glh1NHoZNJ9k>tc0%kEPgu9}0 zK!3g5^jnUCerbk5b3oXI;S#yDCzFxgtW63?;qYX*=DJ|VeRkvX%)=VK4TL#&)@|;X zUtcZ4#9V~OxGh{2Ut^~dUPUVI#6vo63*4I^@mR9~!3@G+7Vix~?qs5fDmr;0kp{wg zp?p->$Ra^Ayb0#+b^kZ8{p(Ub;uBC!NT7m%rl}kOT%an$lNpgbmOVaLCA_y38kL!^ zs#uV1D-|o}Wy*=E6Bewh<^Fou`ps9pGGMYhCJb<{F$Tj=LtV3{CeVlI7Xz3IGs88`^w2uLS{f-PSWh|!y66{oU!U<%C%kt=YB zFJP}xAk@M_hSL*Zr`^Boq4eV9c^Yv!Q*M#8fv~ceA1CCu&dZZ8Oam;H$A`A!o&aK? zV7wJq*4Bam5A9l6;bi6^&364kD|_dxT4mtm+;04M`Id%76QomnxdOni-*d=a2mtJ* z$Ct2Y3N=*74YPjYC`6}aE+6%-NW?U^s%LQLufUd)xYyLgl8Q_2bYHw*1nx{@jIvr< z$%`n&6?)UuLzYkAQ4M$?KcU&Jo$X;0JV61oo~V!wwzD}4IG<0=_xq-SC!_tfV85Hz zf}O**)qL$^HN_kf-ph@>vBfi|M6_R}X;5N4A|tMb0CE|;p3-|phsXHQV^qR&Qril@ zB=IF=ob^J69n4()MT+AAvm>1_L(`XyX^H>-yq6KZ|92k`dl*VgU#nKy;LiLnttDulEhD?Mm2=Yun-f z8WVY}Iq?A>!q{1~GMhs0ZN(LFrD|ovw1(w4{r>0}7HElFKt@JP-K4Xdiwe0^{fj8g zbIj%S3=brmL#s203$Pn}V(En=Qsfp{7Lzq^3Mrq|`$$ZG*Ymg}sZTN1i{4-ECD62E zI=TUNZ_)OE9G<$&1cmHrCJ{>QE9c06G42i&2aWz?G`kA66FKxyVs6qZU+Bs6@EO5^ znR#v^8%*BA71jyNO_I%Bz8ZvSC95?KOf9D8=kf;6Vf9(7#Z>8^h#lGFnN1+^wnJGt zu(vgVlfVEfP$W?R3vF;ig9M629zrcn_rJeghpnKXhZaszp(iN3`|u)6@!LrE@9iOp zJCk1}DCCTMnVF1E>Q|gyHq`76^=?#lP3+y!#viOObvaojV~}g$N*#a7qDDtvK8qL^ z(y4qyrG~u?E6mRMuKM~sEcPQRjQu5?N)RV?PKwb`Op~Yx8tPp!7p$*B)C0Hcn(+f^ z>2Sbio(CZ8JxHfm9Il+q&Z`OWC8oqn=D;atRlPI_{$yy)%f6jQU`SfI{U6uERb$#c zT%aj{k+Z7reNDo?^AS2tiCJM~@`rj0r8eds_bg6;Mbd=&^vn#>IAU%+2g|glit1pd z<$EKx7D>sxfjm<(Zk+ZP=0AHbGb$#f_-fxRgE`L`OJTK2|Zmg;1>$D3Iu$E5r1N=PRpukBHm9E@5in zSC<13^dF-MsRH&UEOVP=GaKLhE(D#@qn)uppmnM+>Qrr)uexXkaUs?ZKGdywf*Y8C zaSB4UX*1ePA{fUCFf3M8bX;VB_8cbW&WVF+h~rQ*2~qyus$GHb-g*a9BlERz3Q?G~ zqE4JQ19OWX;A#fP{z}BQ1LeBniWIqe)-%>(dZUTvAEAT_@-Bbe(x<~`EMO((={xLR zg|r4TBRwYSU=D-r{ed?J@yn-EV}A(MKp5Mx4R*5L?MlS2L{1JFu!+8JU7GRk46yOu6{R8N6Ai4y$=l=B z0B4$*Fn}R~)!H%~dbO99vX@l6bfbmb0Q0#84SG5rV50Y^M>jE>t%bm6>3tELG`YL@ z$API#5t0#3aBe+QLmgC)z1_u|NK}}~sC*&q&S})C%+V+g0Rcf1l52rAr*CPYdma1` z+%qHl8(#a2pg^)BK8lf+W?PYdMe;3{yQl59GZ5BqX}>WX=_H=GXSvL`6dj|TS_*k0 z=DEkVT~yPah$+(>WsT2ugWimS`tbT>389*yp&^P^-aX|2%Z(Ih)8=-x*RS)M^|J2X z&bTzD6u$fAE%hW!tV6)PgR;%mKfQ}o9p1a21*0J&wp0Ef;!ju5(6Qty7js~Nr#Tj~ zm5?Q9ohxvl#B!J!Seh#^?wOxX-ii@$%6%Bivw%y;p5`ik_U$-Y(RT0v*v!%p+YO-g zf3Yt;tCuqrc@-?vI+n1AkRlb8Cn>V1pl1j)N%Qj@)oUR`Ui-VYpz&&I7unZZ+O7d( z&XybY_&f+lET07*zWpi7X*1{3;WZxCt4&jdRr;Y9T5zU@YTAiuf93|Fwcq0=Bf}d1 zgk7la-(wH%eROE*84gR6qX&ajl=;<7juB_Ci@9kM0@2DDI-@|Mk3V_t`WOx}5djMg zBc&MvEboYOiq6I*0Y}^j*7wB=L_wGU0Z)&w7Q(H#?`=*aHn<-b%liPQ4clELSia&| zpErtNw4S3Q)oxNy6PapVzGaf_WatsUKD?XuK zD5A(-S-07>293<4rlMnD&`J-4rk{h|aySx&Z;m}DwO9KHr-V83nxKdX27pB;qL|FB z-AizCenSU6KG%BR`Bn{xPXKxKoIa~|T(~;iG`GcTpkp4t?in5)(TxN5E>A*MZL>X2 zrlr$O%2XvN;cAntH^bvs`=qy=eP2Ln&UN_|_eWN89SqNCZ+OuPNb*T<*9qI8%6b%sWO;X#AscfOCl*bfVjcf$P);auN&Vksd|jgj~P;xjAHr1WnXK=R}V7 z)XIva#;$+uO;PX6^B7NE1NmJO-YsimV}mF?yWrwpSpm6o@2xaY5hwFuPPm*jc98Q2vvc zUqcFm&k}wX8mCq$@4!b;39?7JG$Fwb)uZ;Wf*g)vRbFt6z7t0d6o;UMJe?8$e>5|t z9d6%20U3)K6;qk|IcO5^?y(^I2*POph3kA2#GF$HtP?uN^O}MC$d)x@#$H}USu{ej zMPV9{c8jJv*?|c9408)(K89 z&+&WYf$+n1AA;Q_;LWu3C7?n1YQjD%_&ul07(yQ)3qLxc9yxNP0kn;u`_)yGwpI$D zyt-9~5n%E4%+aHNyEbwC$Ky|J%nnbV8yD{b$N=K8U8~ z-MH~hOUrNpRQC~#@y>S;$OI&aJ+DcS?gbQquWM^-QCbWSp{BS*-HJmBS~$m?(S8V~ z_eKN=16}2ox&`FO;r%fnmR?^S+*+TnZ*Fd$Ri)IREGBz>5*84Mi{*a3C_^@h#hCr! ztz3VvalS@GNSFWX5&W_3R`J*vc3*`88a>5ni7?Z$ZQBkD51-BV{r%e-Q8=h9+)>}B zZ-^YEO&DDc)M&8kRdNn6{i>rJ=TLE-B?T)t|GSX@taI_JOTn*ATwDbd!N(*ms`9eS zt(b7LT+HR0EK!4EiElUKf;PXHiZXsE{+Z z?@`;Jqfl8clH-B9V{ZK&$RQd5f)!&R2v?{_6M>(i0nn=DItz&kx+2vC^7N4-&f{-? z{_>MP7?6wKs7#10x{T}Pp^<|TKyWqVN(+-3Iwe|L!ou9O9TpabS3`kt+HU0jZXA)S zF`!P6470dKb*RWBl<)jJIA<)wmJegFnf1I-dAU+|d@H%=rtab^Q`5;tB1>tkOm9pM z$FiSxBfOwPLCueahwqa-r}S~y9BkU7%2&7N=sT_rC1a$>CAg;jgkL<5F_jTT=pUtlbKIk(89WXcRFKeS>5zuV1x>x8sh{9 zZ7feU{Cmp$aAoO8wztoK5&|TtzFTyH^vmiVadQ2vR7WtsuE4b8Pq3(K<=UHeNGh3- zdy;ek8VMO>g;-6hsrZz%q!4IAmBNs+Uu$D6Q{zesSs~5y)UMf*7jtC}&fd;hmoanb zmbTub@Da8wLFJoMiptt~vHxN|5GL+HyonzpY?~ zgoZESEz~Kvl>_CRBy1pbjDP^i{eLPi)d%y_o$$TFqc<{(Si(C!A=kUnkDP_dW(|XJ zBiwRLC{BB%l9Yvg_hWVfcb8u%+q6(e!Xq76a-SH9VHth28ckGS;#F}gI|ZusB0A%& zHTLicLZuW;^V{-mL?yD8IpFS}r4yG^P+9EFB7xWYmPcgr#fb$-6_Xr%h2e9>N^}d1 zaqwm!PrW0;D_CoQ=8~Up7j5(${AYfe&(UTl4LBN}}&FR{U;k8<4IO2=ooHK?)e2#YyfqSm;E)}qD)=0Z% z{ykqbIVEKh22+Kbn=LI5few80;Q&93*v+F^%+kSldHoRg8@T$8Ipy$9;Mh|Fuvx{g3Q5nOHI`ky(I-~L+`M|Rq7AUe`oU}&V*_YB}lHT#o5 z&l`?n_#t9h73Q$7Ab0pUL`Sa%mp?er_(ahSHU+y1+3+$U_XnU=& zBQC;W%>G$#Qm8MOcGluHTA4T-OHu%y6e>A4C#N0dz|Sp3X$&7j{$+YW@h|)9Ls6A^ z*I@{-o}xdHtx5z{??b$d_gm8}h+Jdr1 zM0w7m#F1n2ag%F*YlEoq`J&xl;UH=sS6STMtyIl~aLlXKm)w5cPzS;~d&dBn&f~ik zvdNH{3>k9}GWt#OcLR`vjN^->4<^Zrf+7yu`?sJAQz{V9Oh)F`fzs09)hnqb2Ckhr z87XY>$s#w~=_0mlE>Rhb<=+%?NR;2J1ov$F3_#sIvjC`QZejlu%Un>9QW_VlKF~O8 z#7DB1H}(Z}^vh<6z-cY#1Zr>WhErRKh8#fji3X3b;`?z_HMcuFy6$l-U1DJ)@Sw}? zNKN3V9dUiQcRh2F7#vUV6)+7(fZz|FuJfZ;lfP_-tW||zKe8$(I`E516(_6uYSc3{ zj|Q5h)MS^ueJRuPmzDpsY$;bap}LB%y6NSQKjWX=MUNhUd2Oc=H3Rcj8O@ezI7m?Y z!Xv`AkHV)X2bWi#`L?QdDyaMbqY00kM!oub)Q0MAELm`esISyX^7)^v+ChfliD_*) z`-@t!Z>r8z_o&2?lmE&lE5!L1*oUYX1j(%28Bc;W>js+Ub~J!*4BziEo*@u*k|nF& z`&k09*r0DOn0C(9>R>*gpg^0o%quk1@cv?a@Yvq=P?J4d z>2^q^Gy=)ZA5!WnXXptDC%r_vPWZC6wo!@wjj1M?bzl6+svKr0^NnBKdSn8JyAx_a z{-n7#rJq}vczHF#F{7&h`{(JDe$Mw(UPA2OA$Rv}c!YW<6@KcQSqsoMro8;K#oPGq zf5#XBE5&g?t-jhEu#;u8Wv!I5RLV~P85#;*IU5xR%}=xySF*9Uvt4!7OtUDnq)|*M ze?)dER~Dht0*#VrMB26NcfzEdk8S#DM-{y%zd(YgdsU_kJinQFT&jV;ZJ|6$^t@JA z;NXzZyV4yptN_8&zwU&(*9u5~*)Ux==*(fXGIJh5iE1zrIg^U}WOVikg5SVwW3`6* zfnF-&I|y9`hIRjezf!rp5{R|Izpwx9Se!^u9anX@4am=B$jdzjbQ%L zNvM$_vm{F)`NXr4Z%JF8DI`QWwUpbo;L9`sG&*^bE3UCfEM=1wfw*Js`x`%K`+q^t zjyHtN|HMD8!chs>Ss;_bP$WB)la9MmqSP8)Q%oWs2!SLo7Z;P{Evo|-#nL3Rv*4cu z{%R5JI0x1Y31+sp-%Z1#b+M&v_Y2J2rJvTEIed3K(Li52*vswpc~ z`p$2ahELZ@bx*JIUJS3i??53dOCfi#3PmQAMt;Vx`*2PPiC5KEj7sw>zy=V*T4Aud z3UME6yKj8pQayR+U5`f>dT^ZFb$_!?37-@ugQbo}y8D+CsBr2hUn|Hnxvu}NILv5h zJbmq;KZ@}_x(tJlh)3|8e`kVE0$T8P@(}}7N=G6-z)gMKpj` z4UH=j$(g<}K{XI|w-XkX)Go5U4u%l?b%`XOTcV{bInfm>egP+KJtxm$5#$d{v zGi8bg4#FhUKY|awvo3b1cXQX!pepX44R2sB(ey!#mQ3(m9*F5uBKfS~9zl$4B@ zE?sgJNC01U6P$eLZrxI&p`n?;X6J?!oAvn>DiYr7wNom(f^#G4jiBu>VBbu|r_sWn zV6Dmt_#ysqGJ@O|G?1=vcX(fXO@cob2?%XYgccZoOLq16-@$PpXPbmflSq^a1sN|W z#`QF9i!9Swy?2Aqd1(NkLIGsI+Y$8w!Q-Ga4h_9S%^v+Y_5<3!sN_QxaJoK%b>P=c zXjr>hbaf)&JN&i$y9Us%n;$5yz5%0}@qm+)(qhmu#sbGF=|hm1Tboq$k@7IY~zr){7*WpzdW=FsycvwFy~ z84?%AiX3y_ejzKsNcJiWsO400wU~fc>-cUdGAimCvKz{DD%5L(@1p1Q5;jpvtY#D% zn+0CAc6q{u@ngx_FfhFRDaadE-wR@`vf*K25|C^QY`&hqRe(5|GH^~gtGDUwa?6}m z+}5P;98z+-?`^fW2_e8gychv%|E5@I;?Ke%`TcwT0qrcE8)}qb(+pl&vAsB%OEaA9 z0H*Z4AXVfBOM^R^wo?Zm5*J{9-0BZPzqCS_n~_&`S| z{#Huoif!yBQ~09Ks^Wg!VpVs@v-O35@sPOSrH&g1&)(wD9$PRNZAZ4Zwt6=O!1!^) zzN%8cK;uyZK3Zp+EPuxXztXlcf{tm7;rjL8I1wPzg;ku@TSuB#>9i(ZshDdE$L1&f zh&3fMX0ZF)Dk^?E)6?nOwPfgBVs43)#`nvyV00mj&O^|0sEpr(Q9xj@>f`TZe|GG$ z3$#1wnVBX3xh%|DUS9qacd)YSzJ6WO20#7w+q0m}tgU6BlnaP!DCE}+#nDs-@-1l{ ztfjeannMy`aS#OkFIZHs0yY@&bE`GviqGr-n)p z?N;pfjN+=jE>FHF^(*AzG=W~#ioyxPQ7QokZ`fNC+HT3&&~vYw8W7qTqD4Y02rcEO z8`;$&Q)m7uBrL2GBM83=%1cZoSw4Q9jZwsNa_H$NarLUqjoNH9IKa)&26-D^F3wy5 z#Wwh;Yv)#VNevs3KG}Bk7M?i@zoGQ;D+1jAX%3m^l2G$J1m9#Ds7DypJzzaRXRro@ zFat|Dv=@%qP(DPXweNYK`49Qy-~Jl}#)80XWJyP!DG=+&wnJziZll_D{mk7KS=iS^ z#U0-UzZcS@~M_21;Gy|=f!E9~Nar>m6(U+s9uoV-!^9+sPTTFMEC z#p)o_ry!<`s1BW-o%~-bjSLN=iF4YNF7A#%j-rhU1HMrBl^oB9KZypUQcyCZaT6Uq zeRLjlV`WBM=h={Y56Sc6$WJW1rOg#f9O5e|+;ktQ_=Jui2f{eSUf7N%K>;MMUIhWR zR9neG*z_m1;4@37Sdx*_xAp|=!Eo=*rO4h%taIiKfz zlRhG`yc$e*{ z^NuNCAkoeGF(>Okb52+(cUVTk{o7pmHKsj5r$7}3*#CWbAK9Mppv+9* z=f7QB7*vIAl^@9okfIuGV%4^C^#vlp(6|a zKxW`DvHc&EkkJ=ZN#B0Vs27YppmLXsu9&+AzFo51Gm4JZrQz^Om_Q=b4mvcne*g7S zDA>MVLDL(Vr!1@2d#RF$rje@mve(Z0A?WF%E;@>)3JN-P9%h9gQ_T2+GXA{nZ2Y51 z!X+I(k?_tadjl-s9Rlj2@i)YwmAAYTP(=Y1fS}9m6Iz0pDFn_k{5|%ez;)ep6Z-F| zUi*6m&dd4;L0R9gUV4QNkLTY*V_AqP{@mR43BSQ#r-~(f)*{R8S91EP?Q~j%Z_;I) z0G*rc1V6$O-Yog~Q~x}Q1Ax*hBq4zVsqCPtCKm}KJ)XL`Y+>Zwq>uLJK?S>Gv=gLKMP*f_K7s!}j z!3P`FNH(z&F2HMw->3fepyqoV4d_87VYVq)Vjbzu;}PWSoDYw{7*#yP=Bd|uR{!%r z4aUcO2BHp`<-j9!IMWM*V_>Mk9@==9REQ4Yeu~Wc+T4Yhm>wYA8sc&6X}1fmJ`4qo zCIqwUJ^3X49)A{D);ZV72`@EqFDk*53KE7^{`xFVtwR5#k&!y^;G#r;&JEuaU4{bC zvg^kape=TS`cb{yHqCsXXtru&3v7<;Xntzpmlx*aa|D&XYs=8J>zwg|Q_EOPRKK-i zx~)`2Bq5U84Rtlsc*6*0F7OC0eSIyJm>%#bJHK234JRe~AR2FD z)rOBWeFO41a!^n`pcG5zH{i3qy#%RdCjaku_T#Rvt>6J?m^Yc<4NOEEh+$E|2!N!^ z*vcL{LMxpP%Ci?GZN;AoRv2fX_0zlhf6o#fcjG}aII|0-@v@1YE*g`1Bba z=rNV}u(dj2dKKa$yYBE25YbkCL6*g4FHf1?XtWHHIBBHx=`0!z~ zUBPSDB$ifJR;YFaM%-7W)xiMZ^YUf$=-!`FSC3KE&ibiHkNLhfpnh~HQ_?Q=O z(ID^1bR1)uJN__-PD&s&JGiN15|R_EXF0!!&MC+*m1q)E0ivC;cwtB(K^OOZP{e- zZw`;Q_6fDUDs)iG=Q!j)oyE|@%z9g=A~$f;U00(%58inEMFJ+qEiv3uiy*cTKL9I1YV85%`yY@^;;c)UrY2aCKz#)OtxZ{f5 zJ|trYgU(K15Je?rtK@_L#ry$Ok8$%~AuSeY9pL~8?9D-2cR6q_4A z$GB&h>`^U-$)Ch$3Ne^#tzcGU084WOAu)wSiul`9!2Kc>qdGV~z~Ix2gx{rKFZ0_= z@K_ERr=PP&Km#=V1yaTXI414mkW_f`1`x@bRk@#tk(-S2S=i1ooOZ#YdfgGEz;1L|pnug$Ak@pA`w z2`A*@AjKlW^CC+ew^{F++m>i}WTep+A0>LwyhYH*tyBk<;&JIGGLHnwDO)ciO)EkD zde(0$d+EC_iBdw-`;-hf!OEDX3{yTtELZ_=f)w2US(XUdn9!&STKs2f z5tHE(Y5RTaV(>6v_QslCkW5Z%hJ>n~yS_pKE0uQ#vtIqzQ?jsl`*5alk&8*`fOYU> z<@@NNfs)^(J%xQsks_YuOLrI-I(D;ZgbFO8>OX9&q?_STM>L6g1_I&Vaddd`YcD-yV3|$v`#7_>&=ln zp>9#n^6g&*_=UZ9Dh_U2XW{F6yZxy0&E``3qhWUZk2jskHUA!DM4)n#vJC#=4%WN= ztPlLh)5g})&&73TPN@ALzf4BIG%_fo(JNt-OU-Q-T2-s(rdA5q3EpeLGZt!|u+V88 z;W+%w{n^j~N7XLCjDoT~g$k#`*@0zpoc#$F<{CV(|cwDPL|7sqQ zn*8ZUgGlRIz})OWbcdM-zULF+EhebUtRxu_O6uhn-JKaUrnS+uM#2e?Wurqh=Na@1 zFlFe{8BR}@dsJShf#f0h9k&fD9*Rm3qW>Jre$9L)Uovox{LIAoF^XB60es<6QSmM|4~9ZA22K8F0z0z)I_cr}5F`^g(<{(J>p!0gxvI$e?~E%qGc z59O9^U!L1;3lL96jz>QlMlThEk)(x-C|4p?ZjE2u@8k{K#=n3%xuohfDXD^Z9z+e3OEfnbs;@a!w!m({?AibXHHTlmz{8?@pg=Y95Z|HbS@`#sX2& z%-&jt2m)Xua2&%jvyhNBIQ?au*mho++PAkHx2s z#i&P2{<;6j`QUc+nO8JKckjvS_vKaHmA?1lciE@2a9ZVFJAo^Cn_w>;CE8$sOAbr1 zqtU^&cRyFFNDlj0%+ zq+(>OPKS{la`b~{Q8G<3d;)9~k+2h~OJ>IjO0h=Pa>=7g_sZXSMH@Q3@Dm%3xhzY= zgF0vQiE7+~BCQ%OY^4_0PE%B+F=Bk&6S^ zw}s`#;`%-*J6I~YN&$JcYHEjz1VSbMf7I(PuewnIsiKUD5D(9N%)R!;* zjlOVxt&zxOXEofGhKmt5h+jT#-?Dc2v`C4kXko4nR5)apgW?9O8z|!VE9%!!dDoB? zoRL)B;2)6#-0|1U@QN!jpb%DfO!`@yU!-XS_ozRLD8azlJ^!SpG<~^VUR) zX_CHl*v4PRYbNmNCS6Fs0uO$@0i2GJ`qXY8WVCmwHoZzYYKVm z2)h^-er5@6Y;~6)eS;RFl}Ts#088-{_~XlQ3n<4BC7Zx4G6{+=ipsqY*V)spo8X0F zjs$y)-EoViyhzRzxQ7k4Hw$6+`sojRk$_HWIY!i?V*)E`aYL`FVCZsh_38z7!GK#TQ?Aqm>k^AkV=fLgqV zU1h|@zwiyBJjYFR{!zv%;e+mhGO)YI%g0yVum_Ls-{;chwv#(^b|6#|?lU9D5npBb zN`J`T8Kj9elCC}|tMD2X+-hC4p9v`?_4d0ls(iZ*K5MQm36W@0V;#iKyo;0Qy)A}wea3vatl zzjHG9p8x2Hv_N#8KH_Kz}X6V>DFoc7O^b3q-R_w%wT??vjBx(2=vXPC$d*2 zo25)9{xa111wO$)??Gqs%Y+@|u`y>jQY>qgYA$s{lpz}~Di#*CZDOYea<9!i!QFb< z0+;>9d`m|38Zh-AdR2Lg=02i>>f$J@?T19Xz@mas2hDK%u|k`JSMhuVA>TtZ;e7TR z(ux|8nfu$fZ=4|bfz@BC!-G#h57g?e#eBU`drC>N;FoqXdpOp`i= zI4xST6f2Uwo3~qG-PG^5EVo<}bkW-hEHIvbr1%=TCU6Z4u4$MXfGBPSWY$vPRsA(u ztBZiVfcp$Voy3*c(H(}dJ!t)yQoKZIZ-}%Ai+g$WNPbZ=DsGQ=13>Ny-0)mpr_gu< zY#_sV)7%yV-9w%r*O{frSn4btTD5i~O*vvdtnITR zyqK%n34STbGh!SFqgtC9qcvPq;9h3^a%XkhV#UAttPekT5_^tWdc$2+o}f!?Sc#kA z-_V=D7{1CmlYet|4vqp&nn=8U{)WEKJ@Ku5Vh>2*ngE(a5E6i+hZ6cZXye=*zr1gZ z)f=|&l7{&d&1=7vrJwR>b;%tC!}?9*j@y2kjygYh8>60`!eIX19jAWlGS`#M2+I(U ze+El#*_}&(qA7KNT#lQie0+SC^RYKgX!5H6#0V~bCPSKB6m`%N)&ba)nJ zN8#sW1P}K_ZwYtWqsfVUOD_C7rj#a70X;iHwH^|heoiVJ1XdH*U3s}|o{7oHMmVXq zcJrYe$%hHT!#nTu-~{8jJ`Crj!dqb+x+4#R?Vw*R1-G^V+)(M9p8*%fly(}`qPG(f z_`|Z)V(>|X-SWe_|F5^Nj*5EU+E;8nwunlpbO_QRVIeZqfgz<-N>Twq8uS>5N;fiy z(kb0wfI|A?$78w$9wNt>%Hr@-uvSZ7k+1$`Q~Tu{p{y?wsrmaGYydD!JVq(VU>TH#~pE502&8 z>i4-DrsT4L@Y$seD?WMRHR@P{?i0-^_V!7B$7tIpQHy=kD+RXeV~i_wZHHHb{rznk zgwqt_+Gun7AF%56Iup}oN8k8D-i5u`)c_I@tcpeBs|&NRwvwVefnnC=$!SPEvW+Lc z4)$Zck$G{?jD+INj<{5t=m&m3qGbg(Q|(s%LhHlc6A6*V^|*of)9pEJWMY4EAvLMFi+dKFy4$>j+cg}-HMVRyaL;1z0up+aq;mH9J@d; z&IxPxt|aNnOJD{d7tTJ@Ac9aTs*o|^ZKwK5;BR)Oy=L!7_?`WXn3B7TjAV9dx68#c z-7=l^Y+GMC@!(0EYLY1y65Lm= zxvsu-osQ@Ev-S+wL@l@ur5l>85bG49eh= z0C-Sj3XnqB>y+CJTp8;tFV_lT0p(l9Wtl=YreD{Sy(1)W!;;zl+p(`9Z_$8Uibe+- zV2_xYnX3>UU<`XkDaIaZWuAIW+2@Bro-&Udj+|oUY*x!}X5kW1J_^NWcE@Dsbo+E@ zDPEwL!R5PAh=N`&ft&M;n1;CdMU7HSbBuMeV0r*|(}W$`S8sEW zj)MU{tl!K57|4Q2Y1ITmC^&>RGpjo_Z#^Sx8=P|L(&H+i(6a;IEfiqjD&_iPePcH(l_RWtpxlAL7i z3s6Y8%goBCqXRns9g>R$xWcv23t)m?Y%sKF+jWDQ_oyJ>^d6^?C`#UK z6uUv=jOKYEj6tsP8lby!qsiLY@e-@wKO)7EJWT0`6&#=(My2Lv`3+CV9}n=rCa3%;&z*)m<3>FD-Qe&*-r9Q%0?#hpv<%W!m6VB4J7daeB$WI*e~4L#BX7_EN_VuH zWXVvq-*%s)&EASRoZ{Lvu4$~juMeS3>}4D~lRP2g{BR5URjk#?;7f?)JaS4(!bBvA zLybfUWaI_#iZaFyp#k2{SQYd<qQOHbXZB%%Bk1Aor(bgN<1q*MxNuyOeleGCRm}_DJih>WtIhem*@mScs zO+GGG3mZj253VyZU+?rIiu`7{9FJ;NFYE!;Oju1G;!z}rSyG|U8AGy&k}mQBkqseW zFjpg}Y%6yM$Ie2jM~7Z#x8iswXQ~$8TX9(z?4IynzEc7j z#n0c$vi)(9reP)uKg6NDh0ZCh3^}bhv6v_#c4tDx=UZl)Y;D~8LX4~5W3{(cYmt>} z;@1+4$T2Nh1K-U$@14e`+Nssq_Lk?+ROR4kST#tHObya?EWdd( zHEA$7-~f|~zgior3_P2?w{p|k7Qd_fxwHmjk11DHB0$OW`)7Ct3)A3!Cph3q&}+SF z7N~gf9ELAS>aDF$YCO*4BR7Gq0ye@vEjb5G7??dbq;i7Xt*VW7_qyfemXkP zt49*1;dN-2!$hc#G`*#ASgFF-dQ}$cAsnT}>K58&nGzV#x}~E7rVC&cQr%+!gXqJv zTGRpWh=qRUaM_4d`dw!N4D+jj`6+0S=#(Mr>2k9^B^dT=4AB#<_?f9jfu7(}67sU@ zpj>*B)*@YPkWh+yz!q8G>ZTSkTR|pSCF3ECnySpxBrV0%65Z;q{A!{pUM-nJW!Df3 z??AyIU8iCDSaH^drHk$Y>!E|kjl1Ug(oY*6X3n#fv@;;LM<1l6zLD(4^6844`OtWP ze%j*-xpbq6Zp(NR1+N$U<=xB`S1<$dXPDDeWyI_zUeWcIlP5^*PoMb&R21AAYtMbQ zax-Jf6uL9^`e{5c1xfl%D>)q(Fl8^P&Q?*ngL~dJ85i>JJ zHfcJSwanpg0`ooJ-NXQYL6?Q>2)voOy05S z>&=T2_TNB-dR~Aw6{I30OEmP)>~?FSf8TWA2r%YM;}Q}g-E~8uEI1eK0&bcvxl7Z1 zeK`oXNKp*U(N~Vp;w&Zn9z6*v?6U+UJk`{Jj3G+87;*;vNyL4Csd-kBgffWrW?j%x z){CCp2O0gGc`vh1_Fv?Ltn|^wqfVz<8A~8H=kxH*M&4`?3nPfd54t->4Zf0 ze6INDF~>Zj76<(~12>`;7rhHX*v*%2zI4vAW9jpI&AuXE_FhxhT%uI`-RL>X7PpoQ zOWK4*7e>dbRi2r#4Gr={@xmHYY94QWPP2>CQkr}ko-M+VeJsGhDYvri*6^wu3#V9{ zi*8YmSy!^;_Ygu^%IdIsi~dZ+1H0$MLkAC=c8yD)tH)S>exFi09H)70f7!;Hg;BQJ zw5r5g8H+AB*Cvu{wUWi4i^1}0r(Ni((WJD^C+AsTf@5x55*9BO4{6nsIdV@_>~4A^ z>$WHfqYYcq^UJyxoG|1W%a-VoSKZ$_rHrZ$wB!NdG1`p~E`ZO0#%z1a8FBHhPH?I& zE$zBp@NK)P3)KIbWMEw>b^|5GZG8gT8exkt+JJ^!xC(ad{F9L4%yu>umN*iIDzH=h z{QRlFv}>pkJCUK5qETN2jqa%Ed-p|wFeU^wXbJ>y4Oo8*+Xzy4rx?)#hqpjl$14GQM~3q5eWG-4-Q#_9y8k%!Joy@AF|ks^1E#ih zYe~NPr$Sq=azSz_klbC_*_Ob!qsK(koGt7-i7HI+}A3kN*<%3-d; zaS?0XOm)R3BKDqg4~xoEj(Do}&p37FG~JiohKUgux1~1gZL?d)A2De#WFn z*>tFSY(m#Al7lZmGbZn4O+#>9={D-Ox-y~4fupm*EYfV)tfgX2L)FYNdlr5k6qnn_PGo=T`z$!exxRiQd+~N~RS)-5J}Z9{y+;@` z)m#m|r#^27zlI2zbjj%5jc5r=+?mw<%0H<+$Z0llHOns2$tS&|Y+*4=DO66?_2tBu zOm!ayarP#j7Gc%R&vt8llM00=vv=5y8OQmGTnXj_R$rvoo{s}FW#zZVxumJUB7N!n z)tG2`l2}}{0f}y{q}8TfX354adR~A>t)c?*Dor`pqk*2r+4Nr(9B_gz?vLj-r1zC- zG)wXTKg{!@E*(cVPb;~@==0s`nllBXlEfLVIF(@A$}Y4%9qv!sGXf64XR9?~$Wo+2 z*xC0fZXj&3ZBZ1FLq0!1?&K7j?i)+hHRD?a^WxBb#Tv~4g;Lv&1-rq$_+ z`D@?lW44VdjrNZ+^^q$zxTEuZN`26G(9HKYxGq-uTL{6%!aU=P9IwuB3`r|haaKIy3E50=g{jSj zBb&Rwud--+WJ2lSxiL@P<1@$wT`=l)#GGCnO8nHH(&bHbPCK9$amQ^p`*DB&gQ`4I zpSV?jlX0<|on>L-Qf|0q+uj+=U7BG)I#XcqmPdfifS-^qGU-F^3LzHMht_3p+>_Ag z2##mYC~>VVVbJ?fsj|7we|1QqQ9N>WC?a(zoPAjL=IaKj2F=sjHx7lns2-EM&Rz<$ zf{npket2EycIl8pnXTFmG#t|y>|^3>BipsA)Fh35->L+t4&zvY@3| zpg--BA8sFCUtB8KKpk+qy0Zhv*v@>Ga5>FRoytz}2$#ZEu3NtM5?bW1Yq8>HSS zu<*YNRyv-7tr-_g7q5&@fFS_kNlXa};i^Vd?SS0Z-SrcH6r;2@ib4PHS5}!)ZWsI$^nav-D&AGF&7K>)yGwan68X{vmh=w~&)8WUDrjIe4OSo+!k?S&bl^tM9UGs6y7M;F@^88*WXmRu)uAxf>4);^a*eZ5FNe4~Yws1i$ehg> zDN@?&YgERmLC=dvAJpSn-inefo$?QyiX|A zNCx%_c&TqbS#y^A8AJ=F{noK7a`x02L?!;v(?hVH7>Qp>!h}IH^tDjyAY>z#{|2OuT5b_ zhP**^3SiZk7bk@39G8>Nw-?!4K%pN3UU_1N$LNq0HLI3KU=K+gty+?5Jt{rR>n3yq2lyktvAHAPY0g9p*sD5`K2Lm}k)Vys0!;EWJv5}99l zVy>5!n9*nb_KK38g`|j6?!r6`SDLJ}wzcH^dtZ$W58k|~WjqxzB!5_^qnwXWM9LM@ z7}>Ssxi?8FIN!%bVBz6uz$hz=tQGQrg8!b>5`ce8=F^eaX#{74 zj67g6w0GhH0{Yw5!K5%E4<->UPq;uME#3__ zG?v+)qUU=i4j*9}$q=~DIn_B27N4FRfOtNc_RWnDL?)s0xM(h~pm2B54Vtfxxl2>M z-JJ~d^pkB;FqOMS*tCHzTG|B&0r74ypH1Hi>R6k_UZA>XECPd~F$abTwwm_AfX=g% z&cqbG=!6_bg7BKtz9M?uWKk(5zfZG|$ILs3P1Z*GP|n)z#-WB)ER zX~tD!Srxof!U@S7KD#fKksQ>>ut0|r9CoDDke*1dzE3gt{s>8gtA?VD8t-gdzJ{<0 z0`*bfS6>}V2Ntjc@CD7uvLi&Dl*23L_ar=kG>tsC3v9C& zh$1-jnk>M~wAy#~xAb=9GzWZy;Nzz8&bCC#!0jBT)|h*L;_1N^yEa&t(B|*(rHQK7 zPE~0ZPZ?qiTF&J6^tZ>8%RYj?7J7YxIO3uQj;c$YQ==ZEjWPV!-)+hlhSh{?FFDyW zE~m&Zql})q+8VS=ruNJoyS4VAKHqQ0`72VWLOwzt9({(xsZ1hE`*!n_=3aTa+9DNKuYx-Dp+XCf77K3h*p$( zG@WijUdQex$R=|%w{bs#5wK)mBV)BPFxM&e`AcN&aE*$_gl=CT}|3fQ=a;s7P zVUMG5WI}ljrm}l8%K$cG=|cTwiUjlL+c2fTybt5>&9m%0&83+>Z{ER?Mu+8~9s-`c zNQ)=Rr)|H!+ng^Pb<_g-eJ18igt21yTh~*FJ@oVEd=d>UtwsDcHRQ|vQp&BXGrnbO z`n6mPYD)v?W|z(g4q<{;U24dyFux1}BP$}C8=0D-+XVr9or`$sbrm#C1Slu@0E!k~ z)(1uyd-v;)rI0xNZkmU#Sa#c=?czwe-sE`NSU&@M25=7)6zkJD0^@r#7 zUt4wpE%C4a!EXncDa5<}ASHi?oN6yt_TpN5E>o;Dm!t3a7XM)`Hyu!Q<2_DabH{ivyO6!KHs~<)6=w^;k zzqL%1d3JOdAfDWBUxHNs{5*KC$1yheAaS1f4wi^rT?c3dHO%Oqi8{E@EmD+ znt`N;TA&avYSFUhvN&`3PP#+rKFdH>~2vfad4=CrJ$3}14y6}(}t6x8k&0c!E>vF7D!@180oZ0ZH19; zLrDoZ{*jc254X;N%Z?QnTBQki<#2u(v;aZRT;!S05=g5tuEi>O2b8H#<7W5I1i>x# z;F5&BdjVUaBu73ATC~>{P3Cpi5qUJzUut*cm|-oTOwlJ~4`>0& zpxWElR|KyPz)~H$LIV5v`1sm^eo=xm#M{ia0&c?v*W~%7Keq2}SP?DuQO~#Tts|B# zwCmJsIq^WhWY*b74{r{%l3~%-jxQiSBKn-0dh5J6VTK^v5(i~Stb~2rG7Hy9#Col< zu(2X&{y!WNdNt34vz0~Xg=6EO^0p_G^Rg;SIWI##KhI12&-1e4>$scczGDsjOppiV z>`{yMC(SY8vXmucs6rHfqZeI4R?_I4C9Fgb<2Td%Oxz_o(v=~`}t87;!OcyEfT?%GYk4oBYEzC z8EYWZxG>Znx3+SiPXWqbq=Bmh$jNj}B$CyVZVn9&YB?~nX##~`*zgtk^9mLJIp1KW zlL+rO@XaS`(Tm=Bhmn`hK6LnS8j>aHPlArzfxH2IGzGu-5qEmpClFR5GFwhsLF0lY zYm!~ZE1~D0^2!s(8V|Tbm~fmYD98T0>OZz~o~=-W`|Izvqx%kRck2h~k>yD)u3C(o z9N&&HC_+oSbaKs&y96T})4g|B38tua3;ogD0`Wz$8<<*}wMz5M@fkXJr6s-{pK2nv zpCA=^fXr<~yx13e<;vsZp&&1beD}?Db9fY=` zU@zzZjk=!fXSD8YgOj}W^-j)oS8i>J8w>=^Th(|EHgEOBb{T;|YQv$(5bL&%Quj6I zIgd}(S;*iI>#b&xvYmEBT(;9Km1Lr@L-R5KhvPx23iEv8G7xgwdOlTDTs1dOE+l9p%F2EWp+Q6+eEiy?EFBCJBs0mPL(obF zcHAxfK4gI~I4~)pk$wd_Xp0*OIA;u+&6xe!EfpSXQ)|dcEveejvuHgDgUk3fPXZ}! zOjwDUOGD7G|C!G>=mOS_+yu;qciG~6=XnY06UDeU-WMf?XemoITCx7+=o(&xp-pJ* zx30jlb5vksGKpfz>=oFNE0j&|PvMG|2a>7|t#Is20ortUcSXfzxF6_M_y4|C%H4tP zBRFz?dihn5)*WQX4GYhMtMdEYxzJR0g_ojDTW_M~>3uDvP4dm06MX?RYxmU|*~2*+ zun1M*yPZ;7!E@5AOeGUCDHsoNR(l%x?q8Obtp!wSdbP5u!7y%fJPAovp%R-#!=TZu z7m+i9cO)TBqk($B)HNg8TpWdq_Azth1?3!FI#mX>2G&_acy%&#z>psU6FODw% zpHBp*Ae{?5yMuKNF0$%)n-pcQAf7=&`B&i{U^*HUCUl_Rma$M+2 z(-E0FWqvQ>sRW>}0t~<;H7F2CP;dkaFUwZu7kSoZ`E>&wfUvlc|*$B>>3I0YBLn7gUFQ^G`#wj z#7x@~>$th+F1D$WVA`C9-xb4zPOAPXN+@)KwO* zkvtfo+H(B60o3rr>!5{Wg@P}4QfuW>xmhOAE)YPvQm%)undyvUxgvD<3l=VMeOd^I zJw4l6r7FInqomQ5=K~Gw^ydY-AxOSODTn7xGo!Zo=h6Ip!LdSZ8yx1}y@37kLTe?p zvcISC7M}orpn*UQuhj=d-=W_0tk(cyAZHuEI0YGxLHhT$cX^C184Yj)GkZA*{>Y3l z(93IQ1|A&y!8$u!hn}X`H*ot_pE@Ju6JcUqbQdC`@HKsfe)O9k!F2L}q1mp9N*IfMT1)s3wO@IG_(XPv+c*BKj%@<89fQB?C|9Seukh|7E*w z(CD1C)Ei5fd#ex9py73}d%jUT({flBaus7JADbZ=TmgzXSb@|~;=@S)PW}^ux2_Xy znw_%T&OjWp^YJx|VtUq@Zm51_Z&BICx{F;ofP8rjTzk{!Km0KzQue?NFE0Bv9*s)L z=ga&kdg|_v1+C`3BPaZduE#I;Bf5f+(MotF!S=&{-KT$UPoQjXl{u!Y{q_+ouGdHo z1_0}}gDB%A5xH$=mq8~nBnrlPhuesTq7-cr6U?a%sh4fK0p$Nq)({+0A)UNN6A4d! z3fTV-=fafJHgPx7U4KI6fL499bYBDvfd^?K+_V5@lb}*Ck-2i^8W4xtfhBDvsWg&h z)h#S=6f%_Woz@+hB#`POw~z3FM!S-tyJI&$4T4<8!6KWe<+eJ5AGXi8>}>8TzL#OQ z2I`qqFf(is1}tTS1cieZSU#929EJ9QZiZpJAq_pfl@$QZ6$U#n(v`BKo5%u0l0muz)K)y^dCE$i%wf%8TIfCX5pg|u? zO!NfL5&``sAOTDsLyFD7r|^!*kPs6vp!rw?2wy6I_mPH*9e6xm2NC}}<|c%d-fuT@ zpy$H7tLK`l6CcxBpCt!xzgZ+bMuMJ?7I|rU$r(qF$BY$THOSu{C9aQRU9R1La&&S#djQJCQq;@t;+3%bM`919-;Y}Z1y^)yz?zRTWa*p9U*R1Pwm|rT z7W<18*mbyv7-!VJdnLF+*`p~z9kUTM5&Pty%KAGtUcW6-IQD;TKCdnFUqKcAcbMlt zLI?ibPxQ-wh&P=NI|GebeaCOx!~mEt5CM4UmBML2h&JuqOOf-AdL?agVFGaCgTfz~ zurm5z{|zA=6wL{oZ}dibKI-eY0lPXM2B0G8!D2MDiY`FAXVXr20->AQ_(NCh6SmtE zj>B^jO&1ZrJ9-jLaC>!O%L+h>H)`%*xqLYc!g_ur88Esf=k)Xryf}gkx9_84SBwz! z2iPD4TW{iIbOhhK#n#FV5A zVrwLu$+GC+$zRU^;VE)kN8#BPoCq-RYy%n{`x(CvTJDh40Qd(F4;fjjU=1s68LS*xK%qiW#2CDCpcF7+Nbprb`J}{Bj~zt;52-f$Pyd22Pb8mG zjk&nZv=7QN;kB_ml=59DXQj{}{^jxn3B$>MOTG62cWnS+r=B>_8oD zK9gMv#8G6ndCfMQo38q}Uz)BJF&Mt@pxh|85D{b3MOlSyuX1jH!Yj`| zG?cB+ot(DIneKj=d3$O&95_>=RPN=(OG-*U)mLu=LA6@d0KnRgfgK=6IO-KefVGYV zdW5Wp*BfIlHN+tSF`&n=X!~>z4D|O8LOr>E+#bGX`47rPg5?hW6T=iw_4^4B%6eMe z;vfK-HSGMSz=n}kkbiCMNmyJ$Q*psc$Ov*y)|M~dU^UVA?GTGz%S8>p=!{8qfN88Z zGF09?uK4t6d(Iksh5((&FL_etC)~7g3KU=%gaR0fY{*aG}68 z*UmYarV$GRa6niJzYq=W;=6tDX*7ra^%OA!lKV~=q?3y8UTY8IpmZHAG040rW9KsQ znJ*tdx>|>0ReH(3MqUa9#YAr*4n*klOSRV0r1}txai`b7AXHxcX;ymGFK-#XxD>Jd!gh8Xn__x6D)SA z3YpX$iw>U~2?z=@vXqFlC`^D0jrq5u5JV`ePSZORsDF@d3z_~P z-SF2W0C=;^+c-xCkkkv!2C%e-^7=MaHfoqJ+ynYmH7Jp9)%Xd6f)s*2JMwFQ1ZAaE z5ny3~d}zoD87tW1M)XUa!!4I#)M-sNL5d4LBNP?m2?)W;Df61y?~X; z6A&#p;TL~78+!qGw~T`&jGr4P5G8C4Eg#1w8b>~a71mbML8d{dZ~5-OZrGBxIn< 1 means F-goods are expensive relative to D-goods. + - When p rises, D-country imports become more costly; F-country terms-of-trade improve. + - At the symmetric steady state: p = 1. + +CES price indices (Dixit-Stiglitz): + P_CES_D = [omega_home + (1 − omega_home) · p^(1−eta)]^[1/(1−eta)] + P_CES_F = [omega_home + (1 − omega_home) · (1/p)^(1−eta)]^[1/(1−eta)] + +where omega_home = home-good weight, eta = epsilon_trade = trade elasticity. + +Import demand (in units of the imported good): + IM_D = (1 − omega_home) · (P_CES_D / p)^eta · C_D (D imports F-goods) + IM_F = (1 − omega_home) · (P_CES_F · p)^eta · C_F (F imports D-goods) + +Trade balance (in D-goods): + NX_D = IM_F − p · IM_D (receipts from F imports minus D's import bill) + NX_F = IM_D − IM_F / p (symmetric: Walras identity NX_D + p·NX_F = 0) + +All functions accept scalar or NumPy arrays for p, C, etc. to support +both steady-state evaluation and vectorised transition paths. +""" +import numpy as np + + +def ces_price(p, cal, country="D"): + """CES price index for country's consumption basket. + + p: real exchange rate (price of F-good in D-goods), scalar or array. + """ + omega = cal["omega_home"] + eta = cal["epsilon_trade"] + exp = 1.0 - eta + + if country == "D": + # D uses D-goods (price=1) and F-goods (price=p in D-goods, so 1 per F-good) + # CES over D-good and F-good in D-good units: + # P_D = [omega·1^(1-eta) + (1-omega)·p^(1-eta)]^(1/(1-eta)) + inside = omega + (1.0 - omega) * p ** exp + else: + # F uses F-goods (price=1) and D-goods (price=1/p in F-goods) + # P_F = [omega·1^(1-eta) + (1-omega)·(1/p)^(1-eta)]^(1/(1-eta)) + inside = omega + (1.0 - omega) * (1.0 / p) ** exp + + return inside ** (1.0 / exp) + + +def import_demand(p, C, P_CES, cal, country="D"): + """Volume of imports demanded by `country` (in units of the foreign good). + + D imports F-goods: IM_D = (1−omega)·(P_CES_D/p)^eta · C_D + F imports D-goods: IM_F = (1−omega)·(P_CES_F·p)^eta · C_F + + The relative-price term adjusts for how expensive the import is in + terms of the domestic price index. + """ + omega = cal["omega_home"] + eta = cal["epsilon_trade"] + + if country == "D": + return (1.0 - omega) * (P_CES / p) ** eta * C + else: + return (1.0 - omega) * (P_CES * p) ** eta * C + + +def trade_balance(p, IM_D, IM_F): + """Net exports in D-good units for each country. + + NX_D = value of F-goods imported by F (= IM_F D-goods sold to F) + minus D's import bill (IM_D F-goods × p D-goods/F-good). + NX_F is the symmetric expression in F-good units. + + Walras identity: NX_D + p·NX_F = 0. + """ + NX_D = IM_F - p * IM_D + NX_F = IM_D - IM_F / p + return NX_D, NX_F + + +def external_account(NX_D, Q_bF, b_F_D, Q_bD, b_D_F, + Q_bF_lag, b_F_D_lag, Q_bD_lag, b_D_F_lag, + rb_F, rb_D): + """Current account residual for country D (Walras-redundant diagnostic). + + CA_D = NX_D + interest receipts on F-bonds - interest payments on D-bonds + - change in net foreign assets (NFA). + + This should equal zero to machine precision after the solver converges; + it is NOT imposed as a residual (Walras-redundant). + """ + receipts = (1.0 + rb_F) * Q_bF_lag * b_F_D_lag + payments = (1.0 + rb_D) * Q_bD_lag * b_D_F_lag + nfa_now = Q_bF * b_F_D - Q_bD * b_D_F + nfa_lag = Q_bF_lag * b_F_D_lag - Q_bD_lag * b_D_F_lag + ca_resid = NX_D + receipts - payments - (nfa_now - nfa_lag) + return ca_resid diff --git a/code/global/transition.py b/code/global/transition.py new file mode 100644 index 0000000..c6a4123 --- /dev/null +++ b/code/global/transition.py @@ -0,0 +1,573 @@ +"""Two-country nonlinear transition-path solver. + +Outer unknowns (7T total): + [N_D, N_F, Kap_D, Kap_F, rdep_D, rdep_F, p] + +Seven market-clearing residuals per period: + 1. Capital market D: (n_IC_D − n_ACCUM_D) / n_ss_D → pins Kap_D + 2. Capital market F: (n_IC_F − n_ACCUM_F) / n_ss_F → pins Kap_F + 3. Labour market D: (chi_D·N_D^(1/frisch) − w_D/P_CES_D) / (w_D/P_CES_D) → pins N_D + 4. Labour market F: (chi_F·N_F^(1/frisch) − w_F/P_CES_F) / (w_F/P_CES_F) → pins N_F + 5. Deposit market D: (P_CES_D·A_D − Dep_supply_D) / Kap_D_ss → pins rdep_D + 6. Deposit market F: (P_CES_F·A_F − Dep_supply_F) / Kap_F_ss → pins rdep_F + 7. Goods market D: (Y_D − P_CES_D·C_D − I_D − NX_D − G_D) / Y_D_ss → pins p + +Walras-redundant (diagnostics only, not imposed): + • Goods market F + • Current account (external account D) + +Household income (GHH, all in composite-good units): + y_t(e) = (w_t/P_CES_t)·N_t·e + (Div_t − Tax_t)/P_CES_t + where N_t is aggregate employment and e is the idiosyncratic productiviy. + Dividing by P_CES_t converts nominal D-good flows to composite units consistent + with the real wage w/P_CES and the EGM deposit stock (denominated in real units). + The GHH labour-disutility vN_t = chi·N_t^(1+1/frisch)/(1+1/frisch) is + subtracted from effective income in the EGM composite (passed via vN_path). +""" +import numpy as np +from scipy.optimize import root + +from firms import solve_firm_path, markup_ss +from capital import solve_capital_path +from bank import solve_bank_paths +from household import solve_backward_transition +from distribution import forward_iterate, aggregate_assets, aggregate_consumption +from trade import ces_price, import_demand, trade_balance +from government import govt_transition, ck_default_prob, integrate_b_gov, bd_fundamental_default_prob + + +def _inner_economy(N_D, N_F, Kap_D, Kap_F, rdep_D, rdep_F, p_path, + Z_D_path, Z_F_path, def_D_path, def_F_path, ss, cal, + b_gov_D_path=None, b_gov_F_path=None, + sunspot_D_path=None, sunspot_F_path=None): + """Given the outer guesses, solve all inner blocks for both countries. + + Returns a comprehensive dict of all time paths and residual inputs. + """ + # ── Firms ───────────────────────────────────────────────────────────────── + firm_D = solve_firm_path(N_D, Kap_D, Z_D_path, cal, country="D") + firm_F = solve_firm_path(N_F, Kap_F, Z_F_path, cal, country="F") + + # ── Capital ─────────────────────────────────────────────────────────────── + cap_D = solve_capital_path(Kap_D, ss["Kap_D_ss"], 1.0, firm_D["mpk"], cal, country="D") + cap_F = solve_capital_path(Kap_F, ss["Kap_F_ss"], 1.0, firm_F["mpk"], cal, country="F") + + # ── Trade / CES ─────────────────────────────────────────────────────────── + P_CES_D = np.array([ces_price(p, cal, "D") for p in p_path]) + P_CES_F = np.array([ces_price(p, cal, "F") for p in p_path]) + + T = len(p_path) + if b_gov_D_path is None: + b_gov_D_path = np.full(T, cal["B_gov_D_ss"]) + if b_gov_F_path is None: + b_gov_F_path = np.full(T, cal["B_gov_F_ss"]) + + # ── Bank paths (both banks simultaneously) ───────────────────────────────── + # Bond market clearing uses the SS supply (fixed). The endogenous b_gov + # from the BD/CK outer loop feeds ONLY into govt_transition() (Bohn tax + # rule and coupon accounting), NOT into the bank's IC clearing. This + # decouples the bank Newton problem from outer-loop iteration in b_gov, + # ensuring the BD fixed-point map is contracting (Jacobian ≈ 0.85 < 1). + bk = solve_bank_paths( + Kap_D, Kap_F, cap_D["Q"], cap_F["Q"], + cap_D["rk"], cap_F["rk"], rdep_D, rdep_F, + p_path, cal["B_gov_D_ss"], cal["B_gov_F_ss"], + cal, ss["ss_bank_D"], ss["ss_bank_F"], + def_D_path=def_D_path, def_F_path=def_F_path, + sunspot_D_path=sunspot_D_path, sunspot_F_path=sunspot_F_path, + ) + + # ── Government tax paths (Bohn rule on b_gov_path; CK survival in coupon) ── + gov_D = govt_transition(cal, ss["gs_D"], bk["Q_bD"], def_D_path, b_gov_D_path, "D") + gov_F = govt_transition(cal, ss["gs_F"], bk["Q_bF"], def_F_path, b_gov_F_path, "F", + p_path=p_path) + + # ── Dividends ───────────────────────────────────────────────────────────── + mc_D = markup_ss(cal, "D") + mc_F = markup_ss(cal, "F") + Div_D = (1 - mc_D) * firm_D["Y"] + cap_D["cap_profit"] + bk["div_D"] + Div_F = (1 - mc_F) * firm_F["Y"] + cap_F["cap_profit"] + bk["div_F"] + + # ── GHH income and labour disutility ─────────────────────────────────────── + chi_D = cal["chi_D"]; frisch_D = cal["frisch_D"] + chi_F = cal["chi_F"]; frisch_F = cal["frisch_F"] + sigma_D = cal["sigma_D"]; sigma_F = cal["sigma_F"] + + # Labour disutility v(N) = chi·N^(1+1/frisch)/(1+1/frisch) + vN_D = chi_D * N_D ** (1 + 1 / frisch_D) / (1 + 1 / frisch_D) + vN_F = chi_F * N_F ** (1 + 1 / frisch_F) / (1 + 1 / frisch_F) + + # Effective real income (real wage × idiosyncratic e + non-labour income) + w_real_D = firm_D["w"] / P_CES_D # real wage in D-consumption units + w_real_F = firm_F["w"] / P_CES_F + e_D = ss["e_D"]; e_F = ss["e_F"] + + # y_path shape (T, n_e): individual income in composite-good units. + # Non-labour income (Div_D - Tax_D) is in nominal D-goods; divide by P_CES to match + # the real wage (w/P_CES) and the EGM deposit stock (real composite units). + y_D_path = (w_real_D * N_D)[:, None] * e_D[None, :] + ((Div_D - gov_D["Tax"]) / P_CES_D)[:, None] + y_F_path = (w_real_F * N_F)[:, None] * e_F[None, :] + ((Div_F - gov_F["Tax"]) / P_CES_F)[:, None] + + # ── Household EGM backward ──────────────────────────────────────────────── + r_D_path = np.concatenate(([cal["r_dep_D_target"]], rdep_D)) + r_F_path = np.concatenate(([cal["r_dep_F_target"]], rdep_F)) + + c_D_path, a_pol_D_path = solve_backward_transition( + ss["a_grid_D"], ss["Pi_D"], r_D_path, y_D_path, ss["c_D_ss"], + ss["beta_D_ss"], sigma_D, cal["a_min_D"], vN_path=vN_D, + ) + c_F_path, a_pol_F_path = solve_backward_transition( + ss["a_grid_F"], ss["Pi_F"], r_F_path, y_F_path, ss["c_F_ss"], + ss["beta_F_ss"], sigma_F, cal["a_min_F"], vN_path=vN_F, + ) + + # ── Distribution forward ────────────────────────────────────────────────── + A_D_path = np.empty(T) + C_D_path = np.empty(T) + A_F_path = np.empty(T) + C_F_path = np.empty(T) + + D_D = ss["D_D_ss"] + D_F = ss["D_F_ss"] + + for t in range(T): + C_D_path[t] = aggregate_consumption(D_D, c_D_path[t]) + C_F_path[t] = aggregate_consumption(D_F, c_F_path[t]) + D_D = forward_iterate(D_D, a_pol_D_path[t], ss["a_grid_D"], ss["Pi_D"]) + D_F = forward_iterate(D_F, a_pol_F_path[t], ss["a_grid_F"], ss["Pi_F"]) + A_D_path[t] = aggregate_assets(D_D, ss["a_grid_D"]) + A_F_path[t] = aggregate_assets(D_F, ss["a_grid_F"]) + + # ── Trade ───────────────────────────────────────────────────────────────── + IM_D = np.array([import_demand(p_path[t], C_D_path[t], P_CES_D[t], cal, "D") + for t in range(T)]) + IM_F = np.array([import_demand(p_path[t], C_F_path[t], P_CES_F[t], cal, "F") + for t in range(T)]) + NX_D_path, NX_F_path = trade_balance(p_path, IM_D, IM_F) + + return dict( + firm_D=firm_D, firm_F=firm_F, + cap_D=cap_D, cap_F=cap_F, + bk=bk, + gov_D=gov_D, gov_F=gov_F, + Tax_D=gov_D["Tax"], Tax_F=gov_F["Tax"], + Div_D=Div_D, Div_F=Div_F, + P_CES_D=P_CES_D, P_CES_F=P_CES_F, + y_D=y_D_path, y_F=y_F_path, + vN_D=vN_D, vN_F=vN_F, + c_D=c_D_path, a_pol_D=a_pol_D_path, + c_F=c_F_path, a_pol_F=a_pol_F_path, + A_D=A_D_path, C_D=C_D_path, + A_F=A_F_path, C_F=C_F_path, + NX_D=NX_D_path, NX_F=NX_F_path, + IM_D=IM_D, IM_F=IM_F, + ) + + +def solve_transition(ss, cal, Z_D_path, Z_F_path, + def_D_path=None, def_F_path=None, + b_gov_D_path=None, b_gov_F_path=None, + sunspot_D_path=None, sunspot_F_path=None, + verbose=True, maxiter=300): + """Solve the two-country transition path. + + Arguments + --------- + ss : steady-state dict from solve_steady_state() + cal : calibration dict (mutated in-place with chi, excess_return) + Z_D_path : TFP path for D (length T) + Z_F_path : TFP path for F (length T) + def_D_path : default rate path for D or None (zeros) + def_F_path : default rate path for F or None (zeros) + b_gov_D_path : beginning-of-period debt stock path for D, or None (B_gov_ss) + b_gov_F_path : beginning-of-period debt stock path for F, or None (B_gov_ss) + + Returns + ------- + dict with all 7T outer unknowns and all inner time paths. + """ + T = cal["T"] + assert len(Z_D_path) == T and len(Z_F_path) == T + + if def_D_path is None: + def_D_path = np.zeros(T) + if def_F_path is None: + def_F_path = np.zeros(T) + + Kap_D_ss = ss["Kap_D_ss"] + Kap_F_ss = ss["Kap_F_ss"] + N_ss = 1.0 + + y0 = np.concatenate([ + np.full(T, N_ss), # N_D + np.full(T, N_ss), # N_F + np.full(T, Kap_D_ss), # Kap_D + np.full(T, Kap_F_ss), # Kap_F + np.full(T, cal["r_dep_D_target"]), # rdep_D + np.full(T, cal["r_dep_F_target"]), # rdep_F + np.full(T, ss["p_ss"]), # p + ]) + + ncalls = [0] + chi_D = cal["chi_D"]; frisch_D = cal["frisch_D"] + chi_F = cal["chi_F"]; frisch_F = cal["frisch_F"] + n_ss_D = ss["ss_bank_D"]["n_ss"] + n_ss_F = ss["ss_bank_F"]["n_ss"] + Y_ss_D = ss["ss_firm_D"]["Y_ss"] + G_D = cal["G_D"] + + def residual(y): + ncalls[0] += 1 + N_D, N_F = y[:T], y[T:2*T] + Kap_D, Kap_F = y[2*T:3*T], y[3*T:4*T] + rdep_D, rdep_F = y[4*T:5*T], y[5*T:6*T] + p_path = y[6*T:7*T] + + try: + out = _inner_economy( + N_D, N_F, Kap_D, Kap_F, rdep_D, rdep_F, p_path, + Z_D_path, Z_F_path, def_D_path, def_F_path, ss, cal, + b_gov_D_path=b_gov_D_path, b_gov_F_path=b_gov_F_path, + sunspot_D_path=sunspot_D_path, sunspot_F_path=sunspot_F_path, + ) + except (ValueError, RuntimeError, FloatingPointError) as e: + if verbose: + print(f" call {ncalls[0]:3d}: FAILED ({e}); penalising") + return np.full(7 * T, 10.0) + + bk = out["bk"] + firm_D = out["firm_D"] + firm_F = out["firm_F"] + + # 1. Capital markets + cap_D_resid = (bk["n_IC_D"] - bk["n_D"]) / n_ss_D + cap_F_resid = (bk["n_IC_F"] - bk["n_F"]) / n_ss_F + + # 2. Labour markets (GHH static FOC: chi·N^(1/frisch) = w/P_CES) + lab_D_supply = chi_D * N_D ** (1 / frisch_D) + lab_D_demand = firm_D["w"] / out["P_CES_D"] + lab_D_resid = (lab_D_supply - lab_D_demand) / (lab_D_demand + 1e-12) + + lab_F_supply = chi_F * N_F ** (1 / frisch_F) + lab_F_demand = firm_F["w"] / out["P_CES_F"] + lab_F_resid = (lab_F_supply - lab_F_demand) / (lab_F_demand + 1e-12) + + # 3. Deposit markets: bank supplies Dep_supply in nominal good units (D-goods/F-goods); + # household A is in real composite units. Multiply by P_CES to convert to nominal before + # comparing, so the residual is dimensionally homogeneous and Walras-consistent. + dep_D_resid = (out["P_CES_D"] * out["A_D"] - bk["Dep_supply_D"]) / Kap_D_ss + dep_F_resid = (out["P_CES_F"] * out["A_F"] - bk["Dep_supply_F"]) / Kap_F_ss + + # 4. Goods market D (pins p): Y_D = P_CES_D·C_D + I_D + NX_D + G_D + # C_D is the composite-good index; expenditure in D-goods = P_CES_D·C_D. + goods_D_resid = (firm_D["Y"] - out["P_CES_D"] * out["C_D"] - out["cap_D"]["I"] + - out["NX_D"] - G_D) / Y_ss_D + + resid = np.concatenate([ + cap_D_resid, cap_F_resid, + lab_D_resid, lab_F_resid, + dep_D_resid, dep_F_resid, + goods_D_resid, + ]) + + if verbose: + walras_D = np.max(np.abs(firm_D["Y"] - out["P_CES_D"] * out["C_D"] - out["cap_D"]["I"] - out["NX_D"] - G_D)) + walras_F = np.max(np.abs(firm_F["Y"] - out["P_CES_F"] * out["C_F"] - out["cap_F"]["I"] - out["NX_F"] - cal["G_F"])) + print(f" call {ncalls[0]:3d}: max|resid|={np.max(np.abs(resid)):.3e}" + f" goods_F={walras_F:.3e} goods_D={walras_D:.3e}") + + return resid + + accept_tol = max(cal["tol_mkt"] * 100, 1e-6) + sol = root(residual, y0, method="hybr", + options={"maxfev": max(maxiter * (7 * T + 1), 5000)}) + resid_norm = np.max(np.abs(residual(sol.x))) + + if resid_norm > accept_tol: + sol2 = root(residual, y0, method="krylov", + options={"fatol": cal["tol_mkt"], "maxiter": maxiter}) + resid_norm2 = np.max(np.abs(residual(sol2.x))) + if resid_norm2 < resid_norm: + sol, resid_norm = sol2, resid_norm2 + + if resid_norm > accept_tol: + raise RuntimeError(f"Transition path did not converge: max|resid|={resid_norm:.3e}") + + N_D, N_F = sol.x[:T], sol.x[T:2*T] + Kap_D, Kap_F = sol.x[2*T:3*T], sol.x[3*T:4*T] + rdep_D, rdep_F = sol.x[4*T:5*T], sol.x[5*T:6*T] + p_path = sol.x[6*T:7*T] + + out = _inner_economy(N_D, N_F, Kap_D, Kap_F, rdep_D, rdep_F, p_path, + Z_D_path, Z_F_path, def_D_path, def_F_path, ss, cal, + b_gov_D_path=b_gov_D_path, b_gov_F_path=b_gov_F_path, + sunspot_D_path=sunspot_D_path, sunspot_F_path=sunspot_F_path) + + return dict( + N_D=N_D, N_F=N_F, Kap_D=Kap_D, Kap_F=Kap_F, + rdep_D=rdep_D, rdep_F=rdep_F, p=p_path, + Z_D=Z_D_path, Z_F=Z_F_path, + **{k + "_D": v for k, v in out["firm_D"].items()}, + **{k + "_F": v for k, v in out["firm_F"].items()}, + I_D=out["cap_D"]["I"], Q_D=out["cap_D"]["Q"], rk_D=out["cap_D"]["rk"], + I_F=out["cap_F"]["I"], Q_F=out["cap_F"]["Q"], rk_F=out["cap_F"]["rk"], + A_D=out["A_D"], C_D=out["C_D"], A_F=out["A_F"], C_F=out["C_F"], + NX_D=out["NX_D"], NX_F=out["NX_F"], + Div_D=out["Div_D"], Div_F=out["Div_F"], + Tax_D=out["gov_D"]["Tax"], Tax_F=out["gov_F"]["Tax"], + P_CES_D=out["P_CES_D"], P_CES_F=out["P_CES_F"], + **out["bk"], # all bank paths (alpha_D, mu_D, n_IC_D, n_D, ..., Q_bD, Q_bF, ...) + ) + + +def solve_transition_ck(ss, cal, Z_D_path, Z_F_path, + sunspot_D_path=None, sunspot_F_path=None, verbose=True): + """Cole-Kehoe transition solver: outer fixed-point on (def_rate, b_gov). + + The CK circularity — bond price Q_bD needs def_rate needs b_gov needs Q_bD — + is resolved by iterating the (def_rate, b_gov) pair around the existing 7T + Newton solver until self-consistency is reached. + + Parameters + ---------- + sunspot_D_path : (T,) AR(1) sunspot path ∈ [0,1] for country D. + 0 = good equilibrium, 1 = full default probability. + sunspot_F_path : (T,) same for F (usually zeros; F has no default). + + Returns + ------- + Standard solve_transition dict plus 'b_gov_D', 'b_gov_F', 'def_D', 'def_F'. + """ + T = cal["T"] + b_ss_D = cal["B_gov_D_ss"] + b_ss_F = cal["B_gov_F_ss"] + Y_ss_D = ss["ss_firm_D"]["Y_ss"] + Y_ss_F = ss["ss_firm_F"]["Y_ss"] + + if sunspot_D_path is None: + sunspot_D_path = np.zeros(T) + if sunspot_F_path is None: + sunspot_F_path = np.zeros(T) + + # Initial def_rate: evaluated at SS debt level + def_D = np.array([ck_default_prob(b_ss_D, Y_ss_D, cal, s, "D") for s in sunspot_D_path]) + def_F = np.array([ck_default_prob(b_ss_F, Y_ss_F, cal, s, "F") for s in sunspot_F_path]) + b_gov_D = np.full(T, b_ss_D) + b_gov_F = np.full(T, b_ss_F) + + max_iter = cal.get("ck_max_iter", 25) + tol = cal.get("ck_tol", 1e-5) + damp = cal.get("ck_damping", 0.5) + + for ck_it in range(max_iter): + out = solve_transition( + ss, cal, Z_D_path, Z_F_path, + def_D_path=def_D, def_F_path=def_F, + b_gov_D_path=b_gov_D, b_gov_F_path=b_gov_F, + verbose=False, + ) + + # Forward-integrate new debt stock from budget identity. + # integrate_b_gov returns END-of-period stocks [b1, b2, ..., bT]. + b_gov_D_eop = integrate_b_gov( + b_ss_D, out["Q_bD"], def_D, out["Tax_D"], cal, "D") + b_gov_F_eop = integrate_b_gov( + b_ss_F, out["Q_bF"], def_F, out["Tax_F"], cal, "F") + + # Convert to BEGINNING-of-period stocks (lagged by one) for: + # (a) Bohn rule in govt_transition (b) CK threshold check. + # b_gov_bop[t] = stock at start of period t = end of period t-1. + b_gov_D_new = np.concatenate([[b_ss_D], b_gov_D_eop[:-1]]) + b_gov_F_new = np.concatenate([[b_ss_F], b_gov_F_eop[:-1]]) + + # New default probabilities from Cole-Kehoe crisis-zone function + def_D_new = np.array([ + ck_default_prob(b_gov_D_new[t], Y_ss_D, cal, sunspot_D_path[t], "D") + for t in range(T)]) + def_F_new = np.array([ + ck_default_prob(b_gov_F_new[t], Y_ss_F, cal, sunspot_F_path[t], "F") + for t in range(T)]) + + err_def = max(np.max(np.abs(def_D_new - def_D)), + np.max(np.abs(def_F_new - def_F))) + err_bgov = max(np.max(np.abs(b_gov_D_new - b_gov_D)) / b_ss_D, + np.max(np.abs(b_gov_F_new - b_gov_F)) / b_ss_F) + err = max(err_def, err_bgov) + + if verbose: + print(f" CK iter {ck_it + 1:2d}: err={err:.2e}" + f" (def={err_def:.2e} b_gov={err_bgov:.2e})") + + # Damped update + def_D = (1.0 - damp) * def_D + damp * def_D_new + def_F = (1.0 - damp) * def_F + damp * def_F_new + b_gov_D = (1.0 - damp) * b_gov_D + damp * b_gov_D_new + b_gov_F = (1.0 - damp) * b_gov_F + damp * b_gov_F_new + + if err < tol: + if verbose: + print(f" CK converged in {ck_it + 1} iterations.") + break + else: + if verbose: + print(f" CK warning: did not converge after {max_iter} iterations (err={err:.2e}).") + + # Report end-of-period debt stocks for diagnostics/plotting + out["b_gov_D"] = b_gov_D_eop + out["b_gov_F"] = b_gov_F_eop + out["def_D"] = def_D + out["def_F"] = def_F + return out + + +def _anderson_step(G_hist, F_hist, m=3, reg=1e-10): + """Anderson(m) mixing step for fixed-point iteration g(x) = x. + + G_hist : list of g(x_k) values (most recent last). + F_hist : list of f(x_k) = g(x_k) - x_k residuals (most recent last). + + Returns the Anderson-accelerated next iterate x_{k+1}. + Solves: min_{c, 1'c=1} ||F @ c||_2 → x_next = G @ c. + """ + mk = min(len(G_hist), m) + G = np.column_stack(G_hist[-mk:]) # (n, mk) + F = np.column_stack(F_hist[-mk:]) # (n, mk) + + if mk == 1: + return G[:, 0].copy() + + FtF = F.T @ F + reg * np.eye(mk) + ones = np.ones(mk) + try: + v = np.linalg.solve(FtF, ones) + c = v / (ones @ v) + except np.linalg.LinAlgError: + c = np.ones(mk) / mk + return G @ c + + +def solve_transition_bd(ss, cal, Z_D_path, Z_F_path, + sunspot_D_path=None, sunspot_F_path=None, verbose=True): + """Bocola-Dovis (2019) transition solver. + + Mechanism: + sunspot xi_{t+1} → GK IC tightens (lbD_eff = lbD + psi_bd * xi) + → Q_bD falls (bond price compressed) + → government issues MORE face-value bonds to raise the same revenue + → b_gov rises (beliefs → fundamentals) + → if b_gov/Y_ss >= b_ck_high: fundamental default fires (surv < 1) + → bank NW falls → investment falls → amplification + + Unlike CK, the sunspot does NOT directly cause default — it only lowers + the bond price through the IC denominator. Fundamental default only + triggers if debt crosses b_ck_high (the Bocola-Dovis B̄ boundary). + + The outer loop uses Anderson(m) acceleration to converge the + (b_gov, def_fund) fixed-point while the sunspot path is held fixed. + + Parameters + ---------- + sunspot_D_path : (T,) path xi_t ∈ [0,1] — exogenous rollover-risk shock. + sunspot_F_path : (T,) same for F (default zeros; F has no default). + + Returns + ------- + Standard solve_transition dict plus: + 'b_gov_D', 'b_gov_F' : end-of-period debt paths + 'def_D', 'def_F' : fundamental default rate paths (0/1) + 'sunspot_D', 'sunspot_F' : the exogenous sunspot paths + """ + T = cal["T"] + b_ss_D = cal["B_gov_D_ss"] + b_ss_F = cal["B_gov_F_ss"] + Y_ss_D = ss["ss_firm_D"]["Y_ss"] + Y_ss_F = ss["ss_firm_F"]["Y_ss"] + + if sunspot_D_path is None: + sunspot_D_path = np.zeros(T) + if sunspot_F_path is None: + sunspot_F_path = np.zeros(T) + + # Initial guess: no fundamental default; debt at SS level + def_fund_D = np.zeros(T) + def_fund_F = np.zeros(T) + b_gov_D = np.full(T, b_ss_D) + b_gov_F = np.full(T, b_ss_F) + + max_iter = cal.get("bd_max_iter", 50) + tol = cal.get("bd_tol", 1e-4) + m_aa = cal.get("bd_anderson_m", 3) + + G_hist_D, F_hist_D = [], [] # Anderson history for b_gov_D + G_hist_F, F_hist_F = [], [] # Anderson history for b_gov_F + + for bd_it in range(max_iter): + out = solve_transition( + ss, cal, Z_D_path, Z_F_path, + def_D_path=def_fund_D, def_F_path=def_fund_F, + b_gov_D_path=b_gov_D, b_gov_F_path=b_gov_F, + sunspot_D_path=sunspot_D_path, sunspot_F_path=sunspot_F_path, + verbose=False, + ) + + # Forward-integrate new debt stocks from budget identity + b_gov_D_eop = integrate_b_gov( + b_ss_D, out["Q_bD"], def_fund_D, out["Tax_D"], cal, "D") + b_gov_F_eop = integrate_b_gov( + b_ss_F, out["Q_bF"], def_fund_F, out["Tax_F"], cal, "F") + + # Beginning-of-period stocks (lagged one period) for Bohn rule and threshold check + b_gov_D_new = np.concatenate([[b_ss_D], b_gov_D_eop[:-1]]) + b_gov_F_new = np.concatenate([[b_ss_F], b_gov_F_eop[:-1]]) + + # Fundamental default: ONLY when b/Y >= b_ck_high (BD threshold B̄) + def_fund_D_new = np.array([ + bd_fundamental_default_prob(b_gov_D_new[t], Y_ss_D, cal, "D") + for t in range(T)]) + def_fund_F_new = np.array([ + bd_fundamental_default_prob(b_gov_F_new[t], Y_ss_F, cal, "F") + for t in range(T)]) + + err_def = max(np.max(np.abs(def_fund_D_new - def_fund_D)), + np.max(np.abs(def_fund_F_new - def_fund_F))) + err_bgov = max(np.max(np.abs(b_gov_D_new - b_gov_D)) / b_ss_D, + np.max(np.abs(b_gov_F_new - b_gov_F)) / b_ss_F) + err = max(err_def, err_bgov) + + if verbose: + bd_spread_peak = cal.get("psi_bd_D", 0.0) * np.max(sunspot_D_path) + print(f" BD iter {bd_it + 1:2d}: err={err:.2e}" + f" b_gov_D_peak={np.max(b_gov_D_new):.4f}" + f" bd_spread_peak={bd_spread_peak:.4f}" + f" (def={err_def:.2e} bgov={err_bgov:.2e})") + + if err < tol: + if verbose: + print(f" BD converged in {bd_it + 1} iterations.") + break + + # Anderson acceleration for b_gov paths (binary def_fund accepted directly) + G_hist_D.append(b_gov_D_new.copy()) + F_hist_D.append((b_gov_D_new - b_gov_D).copy()) + G_hist_F.append(b_gov_F_new.copy()) + F_hist_F.append((b_gov_F_new - b_gov_F).copy()) + + b_gov_D = _anderson_step(G_hist_D, F_hist_D, m=m_aa) + b_gov_F = _anderson_step(G_hist_F, F_hist_F, m=m_aa) + # Safety clip: debt cannot go below 50% of SS (numerical guard) + b_gov_D = np.maximum(b_gov_D, 0.5 * b_ss_D) + b_gov_F = np.maximum(b_gov_F, 0.5 * b_ss_F) + + def_fund_D = def_fund_D_new.copy() + def_fund_F = def_fund_F_new.copy() + else: + if verbose: + print(f" BD warning: did not converge after {max_iter} iters (err={err:.2e}).") + + out["b_gov_D"] = b_gov_D_eop + out["b_gov_F"] = b_gov_F_eop + out["def_D"] = def_fund_D + out["def_F"] = def_fund_F + out["sunspot_D"] = sunspot_D_path + out["sunspot_F"] = sunspot_F_path + return out diff --git a/code/global/verify_mechanism.py b/code/global/verify_mechanism.py new file mode 100644 index 0000000..1728de9 --- /dev/null +++ b/code/global/verify_mechanism.py @@ -0,0 +1,97 @@ +""" +Numerical verification: which mechanism drives the default-risk IRF in the modular model? + +Tests two hypotheses: + H1 (psi_bd): state-dependent IC tightening — lbD_eff = lbD + psi_bd*xi (Bocola-Dovis) + H2 (null): psi_bd=0, no IC tightening; bond price drop is purely from default survival + +Method: + Run BD sunspot shock (xi_path: 10% peak, rho=0.85) twice: + (A) baseline: psi_bd_D=3.0 (current calibration) + (B) psi off: psi_bd_D=0.0 (H1 switched off) + + Metrics: + - max |Y_D - Y_D_ss| / Y_D_ss (output response) + - max |Q_bD - Q_bD_ss| / Q_bD_ss (bond-price response) + - max |IC_spread_D| (spread tightening) + + If H1 is the mechanism: A shows large responses, B shows ~0 responses. + If some other channel were active: B would still show nonzero responses. +""" + +import sys, os +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +import numpy as np +import copy +from calibration import get_calibration +from steady_state import solve_steady_state +from transition import solve_transition + +T_PLOT = 20 + +def run_default_shock(psi_override, label): + cal = get_calibration() + cal["psi_bd_D"] = psi_override # BD sunspot IC-tightening sensitivity (key used by bank.py) + # psi_bd_F stays 0 — F-country has no default risk + cal["T"] = 100 + + ss = solve_steady_state(cal) + Y_D_ss = ss["ss_firm_D"]["Y_ss"] + Q_bD_ss = ss["Q_bD_ss"] + + rho_sun, sun0 = 0.85, 0.10 + sunspot_D_path = sun0 * rho_sun ** np.arange(cal["T"]) + + out = solve_transition( + ss, cal, + np.full(cal["T"], cal["Z_ss_D"]), + np.full(cal["T"], cal["Z_ss_F"]), + sunspot_D_path=sunspot_D_path, + verbose=False, + ) + + y_dev = 100.0 * (np.asarray(out["Y_D"])[:T_PLOT] / Y_D_ss - 1.0) + qb_dev = 100.0 * (np.asarray(out["Q_bD"])[:T_PLOT] / Q_bD_ss - 1.0) + rdep_dev = 10000.0 * np.asarray(out["rdep_D"])[:T_PLOT] # bps above 0 + + print(f"\n{'='*60}") + print(f" {label} (psi_bd_D = {psi_override})") + print(f"{'='*60}") + print(f" max |Y_D dev| = {np.max(np.abs(y_dev)):8.4f} %") + print(f" max |Q_bD dev| = {np.max(np.abs(qb_dev)):8.4f} %") + print(f" peak Y_D dev t0 = {y_dev[0]:+8.4f} %") + print(f" peak Q_bD dev t0 = {qb_dev[0]:+8.4f} %") + print(f" peak rdep_D bps = {rdep_dev[0]:+8.2f} bps") + print(f" Y_D path (first {T_PLOT}q): {np.round(y_dev[:10], 3)}") + print(f" Q_bD path (first {T_PLOT}q): {np.round(qb_dev[:10], 3)}") + + # Residuals to confirm convergence + goods_D = np.max(np.abs(out["Y_D"] - out["C_D"] - out["I_D"] - out["NX_D"])) + dep_D = np.max(np.abs(out["A_D"] - out["Dep_supply_D"])) + print(f" [residuals] goods_D={goods_D:.2e} deposit_D={dep_D:.2e}") + + return y_dev, qb_dev + + +if __name__ == "__main__": + print("\nVerification: mechanism behind default-risk IRF") + print("Comparing Bocola-Dovis IC tightening (psi_bd=3) vs null (psi_bd=0)\n") + + y_psi3, qb_psi3 = run_default_shock(psi_override=3.0, label="BASELINE psi_bd=3.0") + y_psi0, qb_psi0 = run_default_shock(psi_override=0.0, label="PSI=0 psi_bd=0.0") + + print("\n" + "="*60) + print(" VERDICT") + print("="*60) + ratio_y = np.max(np.abs(y_psi3)) / max(np.max(np.abs(y_psi0)), 1e-12) + ratio_qb = np.max(np.abs(qb_psi3)) / max(np.max(np.abs(qb_psi0)), 1e-12) + print(f" Y_D response ratio (psi=3 / psi=0) = {ratio_y:.1f}x") + print(f" Q_bD response ratio (psi=3 / psi=0) = {ratio_qb:.1f}x") + print() + if ratio_y > 50 and ratio_qb > 50: + print(" CONFIRMED: psi_bd (Bocola-Dovis IC tightening) is the sole active mechanism.") + print(" With psi_bd=0 the sunspot shock produces negligible effects (<1% of psi_bd=3 response).") + else: + print(" WARNING: some other mechanism may be active even with psi_bd=0.") + print(f" psi=0 Y_D max = {np.max(np.abs(y_psi0)):.4f}% (should be ~0)") diff --git a/code/ic_delta_calibration.py b/code/ic_delta_calibration.py deleted file mode 100644 index 23097f4..0000000 --- a/code/ic_delta_calibration.py +++ /dev/null @@ -1,54 +0,0 @@ -""" -Back-solve divertable fraction (Delta) from the binding IC constraint. - -Takes ss_results from solve_steady_state, updates calibration_start['Delta_*'] -in-place, and returns the same ss_results dict. -""" - - -def _ic_delta(phi_own, phi_cross, nu_K, nu_b_own, nu_b_cross, eta, lam, theta, ratio): - kappa = theta - phi_own - phi_cross - value = nu_K * kappa + nu_b_own * phi_own + nu_b_cross * phi_cross + eta - denom = phi_own + ratio * phi_cross - delta_own = (phi_own + phi_cross - (theta - value / lam)) / denom - return float(delta_own), float(ratio * delta_own), float(value) - - -def calibrate_ic_delta(ss_results): - ss = ss_results['ss'] - calibration_start = ss_results['calibration_start'] - - ratio_D = ratio_F = 2.0 - - # Country D - phi_bD_D_ss = float(ss['q_b_D']) * float(ss['b_D_D']) / float(ss['n_inter_D']) - phi_bF_D_ss = float(ss['q_b_F']) * float(ss['b_F_D']) / float(ss['n_inter_D']) - D_bD_D, D_bF_D, val_D = _ic_delta( - phi_bD_D_ss, phi_bF_D_ss, - float(ss['nu_K_D']), float(ss['nu_bD_D']), float(ss['nu_bF_D']), float(ss['eta_D']), - float(ss['lambda_gk_D']), float(ss['theta_D']), ratio_D, - ) - - # Country F - n_F_ss = float(ss['n_inter_F']) * float(ss['p']) - phi_bF_F_ss = float(ss['q_b_F']) * float(ss['b_F_F']) / n_F_ss - phi_bD_F_ss = float(ss['q_b_D']) * float(ss['b_D_F']) / n_F_ss - D_bF_F, D_bD_F, val_F = _ic_delta( - phi_bF_F_ss, phi_bD_F_ss, - float(ss['nu_K_F']), float(ss['nu_bF_F']), float(ss['nu_bD_F']), float(ss['eta_F']), - float(ss['lambda_gk_F']), float(ss['theta_F']), ratio_F, - ) - - calibration_start.update({ - 'Delta_bD_D': D_bD_D, 'Delta_bF_D': D_bF_D, - 'Delta_bF_F': D_bF_F, 'Delta_bD_F': D_bD_F, - }) - - print("IC Delta calibration:") - print(f" D-bank: Delta_bD_D = {D_bD_D:.4f} Delta_bF_D = {D_bF_D:.4f} (value={val_D:.6f})") - print(f" F-bank: Delta_bF_F = {D_bF_F:.4f} Delta_bD_F = {D_bD_F:.4f} (value={val_F:.6f})") - if D_bD_D > 1 or D_bF_D > 1 or D_bF_F > 1 or D_bD_F > 1: - print(" WARNING (C-1): back-solved Delta > 1 — IC constraint is degenerate.") - print(" Consider hardcoding Delta_D=0.2, Delta_F=0.4 per bank-cal branch.") - - return ss_results diff --git a/code/irf_plots.py b/code/irf_plots.py deleted file mode 100644 index d3e15a8..0000000 --- a/code/irf_plots.py +++ /dev/null @@ -1,163 +0,0 @@ -import numpy as np -import matplotlib -matplotlib.use('Agg') -import matplotlib.pyplot as plt -from pathlib import Path - -BLUE = '#002147' -RED = '#8C1515' -BLUE_MUTED = '#4a6f8a' -RED_MUTED = '#c0624a' - -_COLORS = [BLUE, RED, BLUE_MUTED, RED_MUTED] -_LINESTYLES = ['-', '--', '-.', ':'] -_MARKERS = ['', '', '', 'o'] - - -def show_irfs(irfs_list, variables, labels=None, - ylabel='Deviation from SS (pp)', T_plot=100, - figsize=(18, 5), savepath=None): - labels = labels or [''] * len(irfs_list) - n_var = len(variables) - fig, axes = plt.subplots(1, n_var, figsize=figsize, sharey=False) - if n_var == 1: - axes = [axes] - - for i, (ax, var) in enumerate(zip(axes, variables)): - for j, (irf, label) in enumerate(zip(irfs_list, labels)): - data = irf[var][:T_plot] if var in irf else np.zeros(T_plot) - mkr = _MARKERS[j % len(_MARKERS)] - ax.plot(data, - color=_COLORS[j % len(_COLORS)], - linestyle=_LINESTYLES[j % len(_LINESTYLES)], - linewidth=1.8, marker=mkr, markersize=4, markevery=4, - label=label) - ax.axhline(0, color='#888888', linewidth=0.8, linestyle=':') - ax.set_title(var, fontsize=10, pad=6) - ax.set_xlabel('Quarter', fontsize=9) - if i == 0: - ax.set_ylabel(ylabel, fontsize=9) - ax.spines[['top', 'right']].set_visible(False) - ax.tick_params(labelsize=8) - if any(l for l in labels): - ax.legend(fontsize=8, frameon=False) - - fig.tight_layout() - if savepath: - fig.savefig(savepath, dpi=150, bbox_inches='tight') - plt.close(fig) - - -def generate_irf_plots(model_results, output_dir): - output_dir = Path(output_dir) - output_dir.mkdir(exist_ok=True) - - irfs_Z_D = model_results['irfs_Z_D'] - irfs_def_D = model_results['irfs_def_D'] - dShock_def_D = model_results['dShock_def_D'] - T = model_results['T'] - - print("Generating IRF plots...") - - # Overview: welfare and spread - show_irfs( - [irfs_def_D], ['spread_rb', 'U_D', 'U_F'], - labels=['Default shock'], - savepath=output_dir / 'fig_irf_overview_welfare.png' - ) - print(" Saved fig_irf_overview_welfare.png") - - # Overview: macro variables - show_irfs( - [irfs_Z_D, irfs_def_D], - ['Y_D', 'C_D', 'w_D', 'n_inter_D', 'q_b_D', 'q_b_F'], - labels=['TFP shock', 'Default shock'], - savepath=output_dir / 'fig_irf_overview_macro.png' - ) - print(" Saved fig_irf_overview_macro.png") - - # 1. Output, Consumption & Trade - show_irfs( - [irfs_Z_D, irfs_def_D], labels=['TFP shock (D)', 'Default shock (D)'], - variables=['Y_D', 'Y_F', 'C_D', 'C_F', 'p', 'NX_D'], - savepath=output_dir / 'fig_irf_goods_trade.png' - ) - print(" Saved fig_irf_goods_trade.png") - - # 2. Labour, Capital & TFP - show_irfs( - [irfs_Z_D, irfs_def_D], labels=['TFP shock (D)', 'Default shock (D)'], - variables=['N_D', 'N_F', 'K_D', 'K_F', 'I_D', 'I_F', 'Q_D', 'w_D'], - savepath=output_dir / 'fig_irf_labour_capital.png' - ) - print(" Saved fig_irf_labour_capital.png") - - # 3. Factor Prices - show_irfs( - [irfs_Z_D, irfs_def_D], labels=['TFP shock (D)', 'Default shock (D)'], - variables=['w_D', 'w_F', 'N_D', 'N_F', 'rk_D', 'rk_F'], - savepath=output_dir / 'fig_irf_factor_prices.png' - ) - print(" Saved fig_irf_factor_prices.png") - - # 4. Bond Holdings & External Position - show_irfs( - [irfs_Z_D, irfs_def_D], labels=['TFP shock (D)', 'Default shock (D)'], - variables=['b_D_D', 'b_F_D', 'b_D_F', 'b_F_F', 'nfa_D', 'n_inter_D'], - savepath=output_dir / 'fig_irf_bonds_nfa.png' - ) - print(" Saved fig_irf_bonds_nfa.png") - - # 5. Rates & Returns - show_irfs( - [irfs_Z_D, irfs_def_D], labels=['TFP shock (D)', 'Default shock (D)'], - variables=['rb_actual_D', 'rb_actual_F', 'rn_D', 'rn_F', 'rdep_D', 'rdep_F', 'rk_D'], - savepath=output_dir / 'fig_irf_rates_returns.png' - ) - print(" Saved fig_irf_rates_returns.png") - - # 6. Fiscal - show_irfs( - [irfs_Z_D, irfs_def_D], labels=['TFP shock (D)', 'Default shock (D)'], - variables=['b_gov_D', 'b_gov_F', 'TAX_D', 'TAX_F', 'def_rate_D'], - savepath=output_dir / 'fig_irf_fiscal.png' - ) - print(" Saved fig_irf_fiscal.png") - - # 7. Default decomposition - irfs_def_D_plot = dict(irfs_def_D) - irfs_def_D_plot['shock_def_D'] = dShock_def_D - show_irfs( - [irfs_def_D_plot], variables=['shock_def_D', 'def_rate_D'], - labels=['Default shock (D)'], ylabel='Deviation from SS', figsize=(10, 5), - savepath=output_dir / 'fig_irf_default_decomp.png' - ) - print(" Saved fig_irf_default_decomp.png") - - # 8. Walras residuals (regression check) - show_irfs( - [irfs_Z_D], labels=['TFP shock'], - variables=['goods_mkt_D', 'deposit_mkt_D', 'rb_D_res', 'rb_F_res', 'b_D_F_res', 'b_F_D_res'], - ylabel='Residual', - savepath=output_dir / 'fig_walras_residuals_tfp.png' - ) - show_irfs( - [irfs_Z_D], labels=['TFP shock'], - variables=['global_goods_res', 'goods_mkt_F'], - ylabel='Walras residual', - savepath=output_dir / 'fig_walras_untargeted_tfp.png' - ) - show_irfs( - [irfs_def_D], labels=['Default shock'], - variables=['goods_mkt_D', 'deposit_mkt_D', 'rb_D_res', 'rb_F_res', 'b_D_F_res', 'b_F_D_res'], - ylabel='Residual', - savepath=output_dir / 'fig_walras_residuals_def.png' - ) - show_irfs( - [irfs_def_D], labels=['Default shock'], - variables=['global_goods_res', 'goods_mkt_F'], - ylabel='Walras residual', - savepath=output_dir / 'fig_walras_untargeted_def.png' - ) - print(" Saved Walras residual figures") - print("IRF plots done.") diff --git a/code/main.py b/code/main.py deleted file mode 100644 index 2a1af75..0000000 --- a/code/main.py +++ /dev/null @@ -1,82 +0,0 @@ -""" -Two-Country MU HANK — main orchestrator. - -Run from the repo root with the ssj conda environment: - /opt/anaconda3/envs/ssj/bin/python code/main.py -""" -import sys -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent)) - -from calibration import get_calibration -from steady_state import solve_steady_state -from ic_delta_calibration import calibrate_ic_delta -from depreciation_calibration import calibrate_depreciation -from full_model import build_and_solve -from irf_plots import generate_irf_plots -from tpi import run_tpi -from tpi_plots import generate_tpi_plots - -OUTPUT_DIR = Path(__file__).parent.parent / 'outputs' - - -def main(): - OUTPUT_DIR.mkdir(exist_ok=True) - print(f"Output directory: {OUTPUT_DIR}\n") - - print("=" * 60) - print("Step 1: Calibration") - print("=" * 60) - calibration_start = get_calibration() - print(f" {len(calibration_start)} parameters loaded.\n") - - print("=" * 60) - print("Step 2: Steady State — initial solve + portfolio targeting") - print("=" * 60) - ss_results = solve_steady_state(calibration_start) - print() - - print("=" * 60) - print("Step 3: IC Delta Calibration (back-solve divertable fraction)") - print("=" * 60) - ss_results = calibrate_ic_delta(ss_results) - print() - - print("=" * 60) - print("Step 4: Depreciation Calibration + Final Steady-State Re-solve") - print("=" * 60) - ss_results = calibrate_depreciation(ss_results) - print() - - print("=" * 60) - print("Step 5: Full Dynamic Model + Baseline IRFs") - print("=" * 60) - model_results = build_and_solve(ss_results) - print() - - print("=" * 60) - print("Step 6: Baseline IRF Plots") - print("=" * 60) - generate_irf_plots(model_results, OUTPUT_DIR) - print() - - print("=" * 60) - print("Step 7: TPI Experiment (Jacobian + closed-loop IRFs)") - print("=" * 60) - tpi_results = run_tpi(model_results) - print() - - print("=" * 60) - print("Step 8: TPI Plots") - print("=" * 60) - generate_tpi_plots(tpi_results, OUTPUT_DIR) - print() - - print("=" * 60) - print(f"Done — all figures saved to: {OUTPUT_DIR}") - print("=" * 60) - - -if __name__ == '__main__': - main() diff --git a/code/model_v12.ipynb b/code/model_v12.ipynb deleted file mode 100644 index ea4492d..0000000 --- a/code/model_v12.ipynb +++ /dev/null @@ -1,2644 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4d998797", - "metadata": {}, - "source": [ - "# HHBANK - 2 COUNTRY GITHUB VERSION " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9cfcd461", - "metadata": {}, - "outputs": [], - "source": [ - "# PACKAGES AND PATHS \n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import sequence_jacobian as sj\n", - "from sequence_jacobian import simple, solved, combine, create_model\n", - "from sequence_jacobian import grids, hetblocks\n", - "\n", - "from pathlib import Path\n", - "import numpy as np\n", - "\n", - "\n", - "from pathlib import Path\n", - "import numpy as np\n", - "from sequence_jacobian import grids\n", - "\n", - "try:\n", - " BASE_DIR_D = Path(__file__).resolve().parent\n", - "except NameError:\n", - " BASE_DIR_D = Path.cwd()\n", - "\n", - "\n", - "DATA_DIR_D = BASE_DIR_D / \"Discretisation\" / \"Outputs\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f042652d", - "metadata": {}, - "outputs": [], - "source": [ - "# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n", - "# CALIBRATION\n", - "# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n", - "calibration_start = {\n", - "\n", - " # \u2500\u2500 Preferences \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " # D F\n", - " 'frisch_D': 0.50, 'frisch_F': 0.50, # Frisch elasticity of labour supply \n", - " 'eis_D': 0.5, 'eis_F': 0.5, # Elasticity of intertemporal substitution\n", - "\n", - " # \u2500\u2500 Rates & Asset Prices \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'rdep_D': 0.000, 'rdep_F': 0.000, # Real deposit rate (initial guess; endogenous in transition)\n", - " 'q_b_D': 0.83, 'q_b_F': 0.83, # Bond price (initial guess)\n", - " 'Q_D': 1.0, 'Q_F': 1.0, # Tobin's q\n", - "\n", - " # \u2500\u2500 Production \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'alpha_D': 0.35, 'alpha_F': 0.35, # Capital share\n", - " 'delta_D': 0.025, 'delta_F': 0.025, # Quarterly depreciation rate\n", - " 'ksi_D': 0.50, 'ksi_F': 0.50, # Capital adjustment cost curvature\n", - "\n", - " # \u2500\u2500 Long-term bonds \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'delta_b_D': 0.10, 'delta_b_F': 0.10,\n", - "\n", - " # \u2500\u2500 Aggregate Targets (SS) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'Y_D': 1.00, 'Y_F': 1.00, # Output\n", - " 'Y_ss_D': 1.0, 'Y_ss_F': 1.0, # SS output anchor (government_default debt-gap)\n", - " 'N_D': 1.00, 'N_F': 1.00, # Labour\n", - " 'w_D': 0.65, 'w_F': 0.65, # Real wage\n", - "\n", - " # \u2500\u2500 Financial Intermediaries (Gertler-Karadi) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'f_D': 0.12, 'f_F': 0.12, # Exit probability\n", - " 'lambda_gk_D': 0.2, 'lambda_gk_F': 0.2, # Divertability fraction (IC constraint & P1 Bellman)\n", - " 'beta_inter_D': 0.9975155088, 'beta_inter_F': 0.9975155088, # Banker discount factors \u2192 1.7%/yr govt bond yield\n", - " 'Delta_bD_D': 0.2, 'Delta_bF_F': 0.2, # Domestic bonds (preferred \u2014 better collateral)\n", - " 'Delta_bF_D': 0.4, 'Delta_bD_F': 0.4, # Foreign bonds (penalised \u2014 weaker collateral)\n", - " 'lambda_BD_D': 0.06, 'lambda_BF_F': 0.06, # Domestic bond risk-weight (FOC spread)\n", - " 'lambda_BF_D': 0.06, 'lambda_BD_F': 0.06, # Foreign bond risk-weight (FOC spread)\n", - " 'psi_lambda_B_D': 3.0, 'psi_lambda_B_F': 3.0, # State-dependence of bond divertability (0 = off)\n", - " 'n_inter_D': 0.75*4, 'n_inter_F': 0.75*4, # Bank net worth\n", - " 'theta_D': 4, 'theta_F': 4, # Leverage ratio\n", - "\n", - " # \u2500\u2500 Bellman nu risk-discount \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'psi_nu_bD_D': 0.0, 'psi_nu_bD_F': 0.0, # Risk-discount on D-bonds (def_rate_D)\n", - " 'psi_nu_bF_D': 0.0, 'psi_nu_bF_F': 0.0, # Risk-discount on F-bonds (def_rate_F)\n", - "\n", - " # \u2500\u2500 Fiscal & Government Debt \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'B_supply_D': 0.6*4, 'B_supply_F': 0.6*4, # Total bond supply \u2014 face value (\u2248 60% of annual GDP)\n", - " 'b_gov_D': 0.6*4, 'b_gov_F': 0.6*4, # Government bonds outstanding (face value)\n", - " 'b_gov_ss_D': 0.6*4, 'b_gov_ss_F': 0.6*4, # SS debt anchor\n", - "\n", - " # \u2500\u2500 Fiscal Rule (lump-sum tax Bohn rule) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'tau_D': 0.181, 'tau_F': 0.181, # Tax progressivity\n", - " 'lamb_D': 0.85, 'lamb_F': 0.85, # Tax scale (FIXED \u2014 never adjusts in transition)\n", - " 'lamb_ss_D': 0.85, 'lamb_ss_F': 0.85, # SS anchor (reference only)\n", - " 'phi_lamb_D': 0.15, 'phi_lamb_F': 0.15, # Lump-sum Bohn rule coefficient\n", - " # (raised from 0.02: after the T-2 deposit-rate\n", - " # re-dating the debt->spread spiral is live and\n", - " # phi_lamb < ~0.12 is explosive; 0.15 = minimal\n", - " # stable value, see audit_artifacts/philamb_results.json)\n", - "\n", - " # \u2500\u2500 Sovereign Default \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'shock_def_D': 0.000, 'shock_def_F': 0.0, # Exogenous default shock\n", - " 'T_ls_D': 0.000, 'T_ls_F': 0.000, # Lump-sum fiscal tax (0 at SS; adjusts with debt)\n", - " 'def_rate_D': 0.000, 'def_rate_F': 0.0, # Default rate (SS = 0)\n", - " 'def_scale_D': 0.25, 'def_scale_F': 0.25, # Endogenous default sensitivity\n", - " 'def_curvature_D': 0.5, 'def_curvature_F': 0.5, # Power-function curvature\n", - " 'def_offset_D': 0.05, 'def_offset_F': 0.05, # Linearisation offset\n", - " 'recovery_rate_D': 0.00, 'recovery_rate_F': 0.00, # Recovery on defaulted debt\n", - " 'zeta_writeoff_D': 0.0, 'zeta_writeoff_F': 0.0, # 1 = full write-off; 0 = coupon haircut\n", - " 'writeoff_enabled_D': 0.0, 'writeoff_enabled_F': 0.0, # 0 = pure risk shock (no write-off); 1 = write-off regime\n", - "\n", - " # \u2500\u2500 Intermediary Capital Adjustment Cost (Auclert 2019) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'chi0_D': 0.00, 'chi0_F': 0.00, # Regularisation constant (prevents division by zero)\n", - " 'chi1_D': 0.00, 'chi1_F': 0.00, # Cost scale\n", - " 'chi2_D': 2.0, 'chi2_F': 2.0, # Cost curvature (2 = quadratic)\n", - "\n", - " # \u2500\u2500 Macroprudential Bond Tax \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'T0_D': 0.000, 'T0_F': 0.000, # Flat per-bond tax (breaks portfolio indeterminacy)\n", - " 'T1_D': 0.0, 'T1_F': 0.0, # Sensitivity to default probability \n", - "\n", - " # \u2500\u2500 Trade & Terms of Trade \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'omega': 0.85, # Home bias in consumption\n", - " 'epsilon_trade': 1.5, # Trade elasticity\n", - " 'p': 0.50, # Terms of trade (Guess)\n", - "\n", - " # \u2500\u2500 Cross-Border Bond Portfolio \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'phi_bF_D_ss': 0.25, 'phi_bD_F_ss': 0.25, # Initial portfolio shares (used to seed b_F_D/b_D_F)\n", - " 'psi_bF_D': 0.5, 'psi_bD_F': 0.5, # Portfolio adjustment cost (level-based)\n", - "\n", - " # \u2500\u2500 Wage Markups (calibrate vphi via labor_ss) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'mu_w_D': 1.0, 'mu_w_F': 1.0, # Wage markup (SS; used by labor_ss to pin vphi)\n", - "\n", - " # \u2500\u2500 SS Real Variables \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'mc_D': 1.0, 'mc_F': 1.0, # Marginal cost (= 1 under flexible prices)\n", - "\n", - " # \u2500\u2500 Idiosyncratic Income Process (Rouwenhorst grid) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 'rho_z_D': 0.90, 'rho_z_F': 0.90, # AR(1) persistence\n", - " 'sigma_z_D': 0.3, 'sigma_z_F': 0.3, # Innovation std dev\n", - " 'nZ_D': 15, 'nZ_F': 15, # Income grid points\n", - " 'nDep_D': 500, 'nDep_F': 500, # Deposit grid points\n", - " 'Depmax_D': 150, 'Depmax_F': 150, # Max deposit (borrowing limit)\n", - "}\n", - "\n", - "calibration_start_D = {k: v for k, v in calibration_start.items() if k.endswith('_D')}\n", - "calibration_start_F = {k: v for k, v in calibration_start.items() if k.endswith('_F')}\n", - "\n", - "calibration_hh_D = {**calibration_start_D, 'beta_D': 0.9920094934, 'div_D': 0.19}\n", - "calibration_hh_F = {**calibration_start_F, 'beta_F': 0.9870643761, 'div_F': 0.21}" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "96c6bd50", - "metadata": {}, - "outputs": [], - "source": [ - "# \u2500\u2500 Bond Holdings (initial SS guess; portfolio anchors overwritten post-solve) \u2500\u2500\n", - "_n_D, _n_F = calibration_start['n_inter_D'], calibration_start['n_inter_F']\n", - "_B_D, _B_F = calibration_start['B_supply_D'], calibration_start['B_supply_F']\n", - "\n", - "b_F_D = calibration_start['phi_bF_D_ss'] * _n_D / calibration_start['q_b_F']\n", - "b_D_F = calibration_start['phi_bD_F_ss'] * _n_F / calibration_start['q_b_D']\n", - "\n", - "calibration_start.update({\n", - " 'b_F_D': b_F_D, 'b_D_F': b_D_F,\n", - " 'b_D_D': _B_D - b_D_F, 'b_F_F': _B_F - b_F_D,\n", - " 'b_F_D_anchor': b_F_D, 'b_D_F_anchor': b_D_F, # initial guess; overwritten post-solve\n", - " 'psi_bD_D': 0.0, 'psi_bF_F': 0.0,\n", - "})\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "99fd5af7", - "metadata": {}, - "outputs": [], - "source": [ - "from equations_D import (hh_init_D, hh_D, make_grids_D, income_D, hh_extended_D)\n", - "from equations_F import (hh_init_F, hh_F, make_grids_F, income_F, hh_extended_F)" - ] - }, - { - "cell_type": "markdown", - "id": "f206aa06", - "metadata": {}, - "source": [ - "### EQUATIONS" - ] - }, - { - "cell_type": "markdown", - "id": "1e927275", - "metadata": {}, - "source": [ - "#### STEADY STATE EQUATIONS" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5824fb58", - "metadata": {}, - "outputs": [], - "source": [ - "from equations_D import (\n", - " smart_steady_D, market_clearing_D, steady_auxilliary_D,\n", - " banker_div_D, sdf_D, sdf_ss_D, sdf_banker_ss_D, government_ss_D, labor_ss_D,\n", - " government_default_D, bond_price_ss_D, bond_return_D,\n", - " ces_price_D, import_demand_D, deposit_return_D,\n", - ")\n", - "\n", - "from equations_F import (\n", - " smart_steady_F, market_clearing_F, steady_auxilliary_F,\n", - " banker_div_F, sdf_F, sdf_ss_F, sdf_banker_ss_F, government_ss_F, labor_ss_F,\n", - " government_default_F, bond_price_ss_F, bond_return_F,\n", - " ces_price_F, import_demand_F, deposit_return_F,\n", - ")\n", - "\n", - "from equations_global import (\n", - " trade_balance, domestic_bond_clearing,\n", - " portfolio_level_anchors, portfolio_adj_cost, bond_yield,\n", - " global_goods_mkt, external_account_D,\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "id": "0195a102", - "metadata": {}, - "source": [ - "### SOLVING MODEL" - ] - }, - { - "cell_type": "markdown", - "id": "0200bcc9", - "metadata": {}, - "source": [ - "#### STEADY STATE" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e28719a3", - "metadata": {}, - "outputs": [], - "source": [ - "import copy\n", - "\n", - "ha = sj.create_model([\n", - " sdf_ss_D, sdf_banker_ss_D, government_default_D, bond_price_ss_D, bond_return_D,\n", - " sdf_ss_F, sdf_banker_ss_F, government_default_F, bond_price_ss_F, bond_return_F,\n", - " hh_extended_D, smart_steady_D, market_clearing_D, steady_auxilliary_D,\n", - " banker_div_D, government_ss_D, labor_ss_D,\n", - " hh_extended_F, smart_steady_F, market_clearing_F, steady_auxilliary_F,\n", - " banker_div_F, government_ss_F, labor_ss_F,\n", - " ces_price_D, import_demand_D, ces_price_F, import_demand_F,\n", - " deposit_return_D, deposit_return_F,\n", - " bond_yield,\n", - " trade_balance, external_account_D, global_goods_mkt,\n", - "], name='MU HA Model 2 Country')\n", - "\n", - "unknowns_ss = {'beta_D': 0.9850 ,'beta_F': 0.9850, 'p': 0.99}\n", - "targets_ss = ['deposit_mkt_D', 'deposit_mkt_F', 'ca_res_D']\n", - "\n", - "ss = ha.solve_steady_state(calibration_start, unknowns_ss, targets_ss, solver='broyden_custom')\n", - "\n", - "# \u2500\u2500 Post-solve anchors \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "anchors = {\n", - " # Share-based values for bond portfolio FOCs (divert_bond_foc_D/F)\n", - " 'phi_bD_D_ss': float(ss['q_b_D']) * float(ss['b_D_D']) / float(ss['n_inter_D']),\n", - " 'phi_bF_F_ss': float(ss['q_b_F']) * float(ss['b_F_F']) / (float(ss['p']) * float(ss['n_inter_F'])),\n", - " # Level anchors \u2192 portfolio_level_anchors block \u2192 b_F_D_ss, b_D_F_ss\n", - " 'b_F_D_anchor': float(ss['b_F_D']),\n", - " 'b_D_F_anchor': float(ss['b_D_F']),\n", - " # Excess returns used by divert_bond_foc_D/F and divert_portfolio_adj\n", - " 'excess_return_bD_D_ss': float(ss['rb_actual_D']) - float(ss['rdep_D']) - calibration_start['T0_D'],\n", - " 'excess_return_bF_F_ss': float(ss['rb_actual_F']) - float(ss['rdep_F']) - calibration_start['T0_F'],\n", - " 'excess_return_F_D_ss': float(ss['rb_actual_F']) - float(ss['rdep_D']) - calibration_start['T0_D'],\n", - " 'excess_return_D_F_ss': float(ss['rb_actual_D']) - float(ss['rdep_F']) - calibration_start['T0_F'],\n", - " 'q_b_D': float(ss['q_b_D']),\n", - " 'q_b_F': float(ss['q_b_F']),\n", - " 'p': float(ss['p']),\n", - " # SS consumption levels \u2014 denominators for welfare_agg_D/F normalisation\n", - " 'C_D_ss': float(ss['C_D']),\n", - " 'C_F_ss': float(ss['C_F']),\n", - "}\n", - "calibration_start.update(anchors)\n", - "for k, v in anchors.items():\n", - " ss.toplevel[k] = v\n", - "\n", - "ss.toplevel['b_F_D_ss'] = float(ss['b_F_D'])\n", - "ss.toplevel['b_D_F_ss'] = float(ss['b_D_F'])\n", - "\n", - "\n", - "ss.toplevel['Rgross_D'] = float(1 + ss['rdep_D'])\n", - "ss.toplevel['Rgross_F'] = float(1 + ss['rdep_F'])\n", - "\n", - "_fr_D = float(ss['frisch_D']); _fr_F = float(ss['frisch_F'])\n", - "ss.toplevel['X_D'] = float(ss['C_D']) - float(ss['vphi_D']) * float(ss['N_D']) ** (1 + 1/_fr_D) / (1 + 1/_fr_D)\n", - "ss.toplevel['X_F'] = float(ss['C_F']) - float(ss['vphi_F']) * float(ss['N_F']) ** (1 + 1/_fr_F) / (1 + 1/_fr_F)\n", - "ss.toplevel['U_D'] = ss.toplevel['X_D'] / float(ss['C_D'])\n", - "ss.toplevel['U_F'] = ss.toplevel['X_F'] / float(ss['C_F'])\n", - "\n", - "ss.toplevel['Phi_D'] = float(ss['Phi_D'])\n", - "ss.toplevel['Phi_F'] = float(ss['Phi_F'])\n", - "\n", - "# Seed value_D/F: franchise value per unit net worth consumed as lead by intermediation_P1.\n", - "# SS identity: value = beta*Omega*(1+rn) = lambda_gk*theta_div (binding IC).\n", - "ss.toplevel['value_D'] = float(ss['beta_inter_D']) * float(ss['Omega_D']) * (1 + float(ss['rn_D']))\n", - "ss.toplevel['value_F'] = float(ss['beta_inter_F']) * float(ss['Omega_F']) * (1 + float(ss['rn_F']))\n", - "\n", - "for k, v in {\n", - " 'tau_mp_D': 0.0, 'tau_mp_F': 0.0,\n", - " 'T_D': 0.0, 'T_F': 0.0,\n", - " 'T_ls_D': 0.0, 'T_ls_F': 0.0,\n", - " 'b_F_D_res': 0.0, 'b_D_F_res': 0.0,\n", - " 'rb_D_res': 0.0, 'rb_F_res': 0.0,\n", - " 'labor_mkt_res_D': 0.0, 'labor_mkt_res_F': 0.0,\n", - " 'w_res_D': 0.0, 'w_res_F': 0.0,\n", - "}.items():\n", - " ss.toplevel[k] = v\n", - "cali_D = cali_F = ss\n", - "ss_final = copy.deepcopy(ss)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "bc1e3a70", - "metadata": {}, - "outputs": [], - "source": [ - "# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# POST-SOLVE ANCHOR HELPER\n", - "# Re-applies all ss.toplevel / calibration_start anchors after any SS re-solve.\n", - "# Must be called whenever ha.solve_steady_state is called during calibration.\n", - "# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "import copy\n", - "\n", - "def _apply_ss_anchors(ss_in, cal):\n", - " \"\"\"Sync ss_in.toplevel and cal with fresh post-SS values.\"\"\"\n", - " anchors = {\n", - " 'phi_bD_D_ss': float(ss_in['q_b_D']) * float(ss_in['b_D_D']) / float(ss_in['n_inter_D']),\n", - " 'phi_bF_F_ss': float(ss_in['q_b_F']) * float(ss_in['b_F_F']) / (float(ss_in['p']) * float(ss_in['n_inter_F'])),\n", - " 'b_F_D_anchor': float(ss_in['b_F_D']),\n", - " 'b_D_F_anchor': float(ss_in['b_D_F']),\n", - " 'excess_return_bD_D_ss': float(ss_in['rb_actual_D']) - float(ss_in['rdep_D']) - cal['T0_D'],\n", - " 'excess_return_bF_F_ss': float(ss_in['rb_actual_F']) - float(ss_in['rdep_F']) - cal['T0_F'],\n", - " 'excess_return_F_D_ss': float(ss_in['rb_actual_F']) - float(ss_in['rdep_D']) - cal['T0_D'],\n", - " 'excess_return_D_F_ss': float(ss_in['rb_actual_D']) - float(ss_in['rdep_F']) - cal['T0_F'],\n", - " 'psi_spread_F': float(ss_in['lambda_gk_F']) * cal['psi_lambda_B_F']\n", - " / (float(ss_in['beta_inter_F']) * float(ss_in['Omega_F'])),\n", - " 'psi_spread_D': float(ss_in['lambda_gk_D']) * cal['psi_lambda_B_D']\n", - " / (float(ss_in['beta_inter_D']) * float(ss_in['Omega_D'])),\n", - " 'q_b_D': float(ss_in['q_b_D']),\n", - " 'q_b_F': float(ss_in['q_b_F']),\n", - " 'p': float(ss_in['p']),\n", - " 'C_D_ss': float(ss_in['C_D']),\n", - " 'C_F_ss': float(ss_in['C_F']),\n", - " }\n", - " cal.update(anchors)\n", - " for k, v in anchors.items():\n", - " ss_in.toplevel[k] = v\n", - " ss_in.toplevel['b_F_D_ss'] = float(ss_in['b_F_D'])\n", - " ss_in.toplevel['b_D_F_ss'] = float(ss_in['b_D_F'])\n", - " ss_in.toplevel['Rgross_D'] = float(1 + ss_in['rdep_D'])\n", - " ss_in.toplevel['Rgross_F'] = float(1 + ss_in['rdep_F'])\n", - " _fr_D = float(ss_in['frisch_D']); _fr_F = float(ss_in['frisch_F'])\n", - " ss_in.toplevel['X_D'] = (float(ss_in['C_D'])\n", - " - float(ss_in['vphi_D']) * float(ss_in['N_D'])**(1+1/_fr_D) / (1+1/_fr_D))\n", - " ss_in.toplevel['X_F'] = (float(ss_in['C_F'])\n", - " - float(ss_in['vphi_F']) * float(ss_in['N_F'])**(1+1/_fr_F) / (1+1/_fr_F))\n", - " ss_in.toplevel['U_D'] = ss_in.toplevel['X_D'] / float(ss_in['C_D'])\n", - " ss_in.toplevel['U_F'] = ss_in.toplevel['X_F'] / float(ss_in['C_F'])\n", - " ss_in.toplevel['Phi_D'] = float(ss_in['Phi_D'])\n", - " ss_in.toplevel['Phi_F'] = float(ss_in['Phi_F'])\n", - " ss_in.toplevel['value_D'] = (float(ss_in['beta_inter_D'])\n", - " * float(ss_in['Omega_D']) * (1 + float(ss_in['rn_D'])))\n", - " ss_in.toplevel['value_F'] = (float(ss_in['beta_inter_F'])\n", - " * float(ss_in['Omega_F']) * (1 + float(ss_in['rn_F'])))\n", - " for k, v in {\n", - " 'tau_mp_D': 0.0, 'tau_mp_F': 0.0,\n", - " 'T_D': 0.0, 'T_F': 0.0,\n", - " 'T_ls_D': 0.0, 'T_ls_F': 0.0,\n", - " 'b_F_D_res': 0.0, 'b_D_F_res': 0.0,\n", - " 'rb_D_res': 0.0, 'rb_F_res': 0.0,\n", - " 'labor_mkt_res_D': 0.0, 'labor_mkt_res_F': 0.0,\n", - " 'w_res_D': 0.0, 'w_res_F': 0.0,\n", - " }.items():\n", - " ss_in.toplevel[k] = v\n", - " return anchors" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9f552305", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u2500\u2500 Portfolio share targets \u2500\u2500\n", - " D-bank: phi_bD_D = 0.250 phi_bF_D = 0.150\n", - " F-bank: phi_bD_F = 0.150 phi_bF_F = 0.250\n", - " Implied B_supply_D = 1.2299 (was 2.4000, 30.7% of annual GDP)\n", - " Implied B_supply_F = 1.2299 (was 2.4000, 30.7% of annual GDP)\n", - "\n", - "Re-solving SS with new portfolio allocation...\n", - "SS re-solved. beta_D=0.99940974 p=1.000000\n", - "\n", - "\u2500\u2500 Delta calibrated from IC binding condition \u2500\u2500\n", - " Delta_bD_D = 0.7273 Delta_bF_D = 1.4545 (ratio 2.0)\n", - " Delta_bF_F = 0.7273 Delta_bD_F = 1.4545 (ratio 2.0)\n", - " IC residual D: theta - theta_tgt = +0.00e+00 (should be 0)\n", - " IC residual F: theta - theta_tgt = +0.00e+00 (should be 0)\n" - ] - } - ], - "source": [ - "# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n", - "# SECTION 1.2 \u2014 Portfolio Share Targeting + Delta Calibration\n", - "# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n", - "\n", - "# \u2500\u2500 Targets (edit to match empirical data) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "target_phi_bD_D = 0.25 # D-bank NW share held in D-bonds\n", - "target_phi_bF_D = 0.15 # D-bank NW share held in F-bonds\n", - "target_phi_bD_F = 0.15 # F-bank NW share held in D-bonds \u2190 now explicit\n", - "target_phi_bF_F = 0.25 # F-bank NW share held in F-bonds \u2190 now explicit\n", - "\n", - "# \u2500\u2500 Read current SS values \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "n_D = float(ss['n_inter_D'])\n", - "n_F = float(ss['n_inter_F']) * float(ss['p']) # F-bank NW in D-goods\n", - "q_D = float(ss['q_b_D'])\n", - "q_F = float(ss['q_b_F'])\n", - "p_ss = float(ss['p'])\n", - "\n", - "# \u2500\u2500 Step 1: Set all four bond quantities from explicit targets \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# Each target pins one holding; bond-market clearing then determines B_supply.\n", - "# The old \"residual\" approach left F-bank with phi_bD_F\u22480.36, phi_bF_F\u22480.46,\n", - "# making K_F extremely sensitive to p during Broyden iterations \u2192 divergence.\n", - "b_D_D_new = target_phi_bD_D * n_D / q_D # D-bank D-bonds\n", - "b_F_D_new = target_phi_bF_D * n_D / q_F # D-bank F-bonds\n", - "b_D_F_new = target_phi_bD_F * n_F / q_D # F-bank D-bonds\n", - "b_F_F_new = target_phi_bF_F * n_F / q_F # F-bank F-bonds\n", - "\n", - "# Implied total bond supply consistent with all four targets\n", - "B_D_new = b_D_D_new + b_D_F_new\n", - "B_F_new = b_F_D_new + b_F_F_new\n", - "\n", - "print(\"\u2500\u2500 Portfolio share targets \u2500\u2500\")\n", - "print(f\" D-bank: phi_bD_D = {target_phi_bD_D:.3f} phi_bF_D = {target_phi_bF_D:.3f}\")\n", - "print(f\" F-bank: phi_bD_F = {target_phi_bD_F:.3f} phi_bF_F = {target_phi_bF_F:.3f}\")\n", - "print(f\" Implied B_supply_D = {B_D_new:.4f} (was {float(calibration_start['B_supply_D']):.4f},\"\n", - " f\" {B_D_new/float(calibration_start['Y_D'])/4*100:.1f}% of annual GDP)\")\n", - "print(f\" Implied B_supply_F = {B_F_new:.4f} (was {float(calibration_start['B_supply_F']):.4f},\"\n", - " f\" {B_F_new/float(calibration_start['Y_F'])/4*100:.1f}% of annual GDP)\")\n", - "\n", - "calibration_start.update({\n", - " 'b_D_D': b_D_D_new, 'b_F_D': b_F_D_new,\n", - " 'b_D_F': b_D_F_new, 'b_F_F': b_F_F_new,\n", - " 'b_F_D_anchor': b_F_D_new, 'b_D_F_anchor': b_D_F_new,\n", - " 'phi_bF_D_ss': target_phi_bF_D,\n", - " # Update bond-supply anchors to be consistent with portfolio targets\n", - " 'B_supply_D': B_D_new, 'b_gov_D': B_D_new, 'b_gov_ss_D': B_D_new,\n", - " 'B_supply_F': B_F_new, 'b_gov_F': B_F_new, 'b_gov_ss_F': B_F_new,\n", - "})\n", - "\n", - "# \u2500\u2500 Step 2: Re-solve SS (warm-start from previous solution) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# Using the already-solved beta_D/F and p avoids divergence from the cold-start\n", - "# defaults (0.985, 0.985, 0.99) which are far from the true new equilibrium.\n", - "print(\"\\nRe-solving SS with new portfolio allocation...\")\n", - "_unknowns_warm = {\n", - " 'beta_D': float(ss['beta_D']),\n", - " 'beta_F': float(ss['beta_F']),\n", - " 'p': float(ss['p']),\n", - "}\n", - "ss = ha.solve_steady_state(calibration_start, _unknowns_warm, targets_ss,\n", - " solver='broyden_custom')\n", - "_apply_ss_anchors(ss, calibration_start)\n", - "print(f\"SS re-solved. beta_D={float(ss['beta_D']):.8f} p={float(ss['p']):.6f}\")\n", - "\n", - "# \u2500\u2500 Step 3: Back-solve Delta from IC constraint \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "ratio_D = 2.0\n", - "ratio_F = 2.0\n", - "def _ic_delta(phi_own, phi_cross, nu_K, nu_b_own, nu_b_cross, eta, lam, theta, ratio):\n", - " \"\"\"Solve for (delta_own, delta_cross) from IC binding condition.\"\"\"\n", - " kappa = theta - phi_own - phi_cross\n", - " value = nu_K * kappa + nu_b_own * phi_own + nu_b_cross * phi_cross + eta\n", - " denom = phi_own + ratio * phi_cross\n", - " delta_own = (phi_own + phi_cross - (theta - value / lam)) / denom\n", - " return float(delta_own), float(ratio * delta_own), float(value)\n", - "\n", - "# D-bank (own = D-bonds, cross = F-bonds)\n", - "phi_bD_D_ss = float(ss['q_b_D']) * float(ss['b_D_D']) / float(ss['n_inter_D'])\n", - "phi_bF_D_ss = float(ss['q_b_F']) * float(ss['b_F_D']) / float(ss['n_inter_D'])\n", - "D_bD_D, D_bF_D, val_D = _ic_delta(\n", - " phi_bD_D_ss, phi_bF_D_ss,\n", - " float(ss['nu_K_D']), float(ss['nu_bD_D']), float(ss['nu_bF_D']), float(ss['eta_D']),\n", - " float(ss['lambda_gk_D']), float(ss['theta_D']), ratio_D\n", - ")\n", - "\n", - "# F-bank (own = F-bonds, cross = D-bonds)\n", - "n_F_ss = float(ss['n_inter_F']) * float(ss['p'])\n", - "phi_bF_F_ss = float(ss['q_b_F']) * float(ss['b_F_F']) / n_F_ss\n", - "phi_bD_F_ss = float(ss['q_b_D']) * float(ss['b_D_F']) / n_F_ss\n", - "D_bF_F, D_bD_F, val_F = _ic_delta(\n", - " phi_bF_F_ss, phi_bD_F_ss,\n", - " float(ss['nu_K_F']), float(ss['nu_bF_F']), float(ss['nu_bD_F']), float(ss['eta_F']),\n", - " float(ss['lambda_gk_F']), float(ss['theta_F']), ratio_F\n", - ")\n", - "\n", - "calibration_start.update({\n", - " 'Delta_bD_D': D_bD_D, 'Delta_bF_D': D_bF_D,\n", - " 'Delta_bF_F': D_bF_F, 'Delta_bD_F': D_bD_F,\n", - "})\n", - "\n", - "print(\"\\n\u2500\u2500 Delta calibrated from IC binding condition \u2500\u2500\")\n", - "print(f\" Delta_bD_D = {D_bD_D:.4f} Delta_bF_D = {D_bF_D:.4f} (ratio {ratio_D:.1f})\")\n", - "print(f\" Delta_bF_F = {D_bF_F:.4f} Delta_bD_F = {D_bD_F:.4f} (ratio {ratio_F:.1f})\")\n", - "\n", - "# IC binding verification (should be \u2248 0)\n", - "theta_tgt_D = (val_D / float(ss['lambda_gk_D'])\n", - " + (1 - D_bD_D) * phi_bD_D_ss\n", - " + (1 - D_bF_D) * phi_bF_D_ss)\n", - "theta_tgt_F = (val_F / float(ss['lambda_gk_F'])\n", - " + (1 - D_bF_F) * phi_bF_F_ss\n", - " + (1 - D_bD_F) * phi_bD_F_ss)\n", - "print(f\" IC residual D: theta - theta_tgt = {float(ss['theta_D']) - theta_tgt_D:+.2e} (should be 0)\")\n", - "print(f\" IC residual F: theta - theta_tgt = {float(ss['theta_F']) - theta_tgt_F:+.2e} (should be 0)\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1692c6e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "K_D = 10.8000 K_F = 10.8000\n", - "Implied delta_D = 0.022407 (rk_D_target = 0.0100)\n", - "Implied delta_F = 0.022407 (rk_F_target = 0.0100)\n", - "\n", - "Final SS re-solve with calibrated delta...\n" - ] - } - ], - "source": [ - "# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n", - "# SECTION 2.2 \u2014 Depreciation Rate \u2192 Target Capital Return\n", - "# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n", - "rk_D_target = 0.01 # 1% quarterly \n", - "rk_F_target = 0.01\n", - "\n", - "alpha_D_cal = calibration_start['alpha_D']\n", - "alpha_F_cal = calibration_start['alpha_F']\n", - "K_D_cur = float(ss['K_D']); K_F_cur = float(ss['K_F'])\n", - "Y_D_cur = float(ss['Y_D']); Y_F_cur = float(ss['Y_F'])\n", - "\n", - "delta_D_cal = alpha_D_cal * Y_D_cur / K_D_cur - rk_D_target\n", - "delta_F_cal = alpha_F_cal * Y_F_cur / K_F_cur - rk_F_target\n", - "\n", - "print(f\"K_D = {K_D_cur:.4f} K_F = {K_F_cur:.4f}\")\n", - "print(f\"Implied delta_D = {delta_D_cal:.6f} (rk_D_target = {rk_D_target:.4f})\")\n", - "print(f\"Implied delta_F = {delta_F_cal:.6f} (rk_F_target = {rk_F_target:.4f})\")\n", - "\n", - "for label, val in [('delta_D', delta_D_cal), ('delta_F', delta_F_cal)]:\n", - " if not (0.0 < val < 1.0):\n", - " print(f\"WARNING: {label} = {val:.4f} is outside (0,1). \"\n", - " \"Adjust rk_target or check balance-sheet calibration.\")\n", - "\n", - "calibration_start.update({'delta_D': delta_D_cal, 'delta_F': delta_F_cal})\n", - "\n", - "# \u2500\u2500 Final SS re-solve with calibrated delta \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "print(\"\\nFinal SS re-solve with calibrated delta...\")\n", - "ss = ha.solve_steady_state(calibration_start, unknowns_ss, targets_ss,\n", - " solver='broyden_custom')\n", - "_apply_ss_anchors(ss, calibration_start)\n", - "\n", - "# Verify capital returns match targets\n", - "print(f\"\\nVerified rk_D = {float(ss['rk_D']):.6f} (target {rk_D_target:.4f})\")\n", - "print(f\"Verified rk_F = {float(ss['rk_F']):.6f} (target {rk_F_target:.4f})\")\n", - "print(f\"DEP_D/Y_D = {float(ss['DEP_D'])/float(ss['Y_D']):.4f}\")\n", - "print(f\"lambda_gk_D = {float(ss['lambda_gk_D']):.4f} lambda_gk_F = {float(ss['lambda_gk_F']):.4f}\")\n", - "print(f\"Final beta_D = {float(ss['beta_D']):.10f}\")\n", - "print(f\"Final beta_F = {float(ss['beta_F']):.10f}\")\n", - "\n", - "# \u2500\u2500 Update cali_D / cali_F / ss_final for ha_full model build and Jacobian \u2500\u2500\u2500\u2500\n", - "cali_D = cali_F = ss\n", - "ss_final = copy.deepcopy(ss)\n", - "print(\"\\nss_final updated \u2014 all subsequent Jacobian/IRF cells use calibrated SS.\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5030561f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "beta_D=0.9994621763 beta_F=0.9994621763 p=1.000000\n", - "lambda_gk_D=0.345919 lambda_gk_F=0.345919\n", - "rb_D=0.002491 rb_F=0.002491 rdep_D=0.000000 rdep_F=0.000000\n", - "q_b_D=0.975698 q_b_F=0.975698\n", - "X_D=0.459670 X_F=0.459670\n", - "Phi_D=0.00000000 Phi_F=0.00000000\n" - ] - } - ], - "source": [ - "print(f\"beta_D={ss['beta_D']:.10f} beta_F={ss['beta_F']:.10f} p={ss['p']:.6f}\")\n", - "print(f\"lambda_gk_D={ss['lambda_gk_D']:.6f} lambda_gk_F={ss['lambda_gk_F']:.6f}\")\n", - "print(f\"rb_D={ss['rb_D']:.6f} rb_F={ss['rb_F']:.6f} rdep_D={ss['rdep_D']:.6f} rdep_F={ss['rdep_F']:.6f}\")\n", - "print(f\"q_b_D={anchors['q_b_D']:.6f} q_b_F={anchors['q_b_F']:.6f}\")\n", - "print(f\"X_D={ss.toplevel['X_D']:.6f} X_F={ss.toplevel['X_F']:.6f}\")\n", - "print(f\"Phi_D={ss.toplevel['Phi_D']:.8f} Phi_F={ss.toplevel['Phi_F']:.8f}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "efae1d6a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SS goods residuals:\n", - "goods_mkt_D = -3.897868541408167e-07\n", - "goods_mkt_F = -3.8978685477919495e-07\n", - "global_goods_res= -7.795737089200114e-07\n", - "ca_res_D = 0.0\n" - ] - } - ], - "source": [ - "print(\"SS goods residuals:\")\n", - "print(\"goods_mkt_D =\", ss['goods_mkt_D'])\n", - "print(\"goods_mkt_F =\", ss['goods_mkt_F'])\n", - "print(\"global_goods_res=\", ss['global_goods_res'])\n", - "print(\"ca_res_D =\", ss['ca_res_D'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ac425cb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Block residual Value Status\n", - "-------------------------------------------------------------------------------------\n", - " IC_D: \u03b8 \u2212 \u03b8_tgt (dynamic residual) 1.776357e-15 OK\n", - " IC_F: \u03b8 \u2212 \u03b8_tgt (dynamic residual) 1.776357e-15 OK\n", - " P1_D: nu_K_res 0.000000e+00 OK\n", - " P1_D: nu_bh_res 0.000000e+00 OK\n", - " P1_D: nu_bx_res 0.000000e+00 OK\n", - " P1_D: eta_res 0.000000e+00 OK\n", - " P1_F: nu_K_res 0.000000e+00 OK\n", - " P1_F: nu_bh_res 0.000000e+00 OK\n", - " P1_F: nu_bx_res 0.000000e+00 OK\n", - " P1_F: eta_res 0.000000e+00 OK\n", - " labor_mkt_D: w/P \u2212 vphi\u00b7N^(1/fr) 0.000000e+00 OK\n", - " labor_mkt_F: w/P \u2212 vphi\u00b7N^(1/fr) 0.000000e+00 OK\n", - " portfolio_adj_bF_D 0.000000e+00 OK\n", - " portfolio_adj_bD_F 0.000000e+00 OK\n", - " dom_bond_foc_D 0.000000e+00 OK\n", - " dom_bond_foc_F 0.000000e+00 OK\n", - " ca_res_D 0.000000e+00 OK\n", - "-------------------------------------------------------------------------------------\n", - "\n", - "All residuals < 1e-8 \u2713\n" - ] - } - ], - "source": [ - "# \u2500\u2500 SS residual diagnostic \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "def _get(k):\n", - " return float(ss[k])\n", - "\n", - "diag = {}\n", - "\n", - "# \u2500\u2500 Multi-asset GK IC: check ACTUAL dynamic residual \u03b8 \u2212 \u03b8_tgt \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "for c in ['D', 'F']:\n", - " pdiv = _get('p') if c == 'F' else 1.0\n", - " eta_c = _get(f'eta_{c}')\n", - " lam = _get(f'lambda_gk_{c}')\n", - " theta_c = _get(f'theta_{c}')\n", - " Q_c = _get(f'Q_{c}')\n", - " K_c = _get(f'K_{c}')\n", - " n_c = _get(f'n_inter_{c}')\n", - " kappa_c = Q_c * K_c / n_c\n", - " if c == 'D':\n", - " nu_K, nu_bD, nu_bF = _get('nu_K_D'), _get('nu_bD_D'), _get('nu_bF_D')\n", - " q_h, q_x = _get('q_b_D'), _get('q_b_F')\n", - " b_h, b_x = _get('b_D_D'), _get('b_F_D')\n", - " Dh, Dx = _get('Delta_bD_D'), _get('Delta_bF_D')\n", - " else:\n", - " nu_K, nu_bD, nu_bF = _get('nu_K_F'), _get('nu_bD_F'), _get('nu_bF_F')\n", - " q_h, q_x = _get('q_b_F'), _get('q_b_D')\n", - " b_h, b_x = _get('b_F_F'), _get('b_D_F')\n", - " Dh, Dx = _get('Delta_bF_F'), _get('Delta_bD_F')\n", - " phi_h = q_h * b_h / (pdiv * n_c)\n", - " phi_x = q_x * b_x / (pdiv * n_c)\n", - " value_c = (nu_K * kappa_c\n", - " + (nu_bD if c == 'D' else nu_bF) * phi_h\n", - " + (nu_bF if c == 'D' else nu_bD) * phi_x\n", - " + eta_c)\n", - " theta_tgt = value_c / lam + (1 - Dh) * phi_h + (1 - Dx) * phi_x\n", - " diag[f'IC_{c}: \u03b8 \u2212 \u03b8_tgt (dynamic residual)'] = theta_c - theta_tgt\n", - "\n", - "# \u2500\u2500 Bellman P1 residuals \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# P1 uses SDF_banker = beta_inter (NOT household beta_D/F) and\n", - "# Omega_p1 = f + (1-f)*lambda_gk*theta (TOTAL leverage, NOT divertability-weighted).\n", - "# See intermediation_P1_D/F: Omega_p1 = f + (1-f)*lambda_gk*theta(+1).\n", - "for c in ['D', 'F']:\n", - " f_c = _get(f'f_{c}')\n", - " lam = _get(f'lambda_gk_{c}')\n", - " beta_c = _get(f'beta_inter_{c}') # banker SDF, not household beta\n", - " rk_c = _get(f'rk_{c}')\n", - " rdep_c = _get(f'rdep_{c}')\n", - " eta_c = _get(f'eta_{c}')\n", - " n_c = _get(f'n_inter_{c}')\n", - " theta_c = _get(f'theta_{c}') # total leverage (kappa + phi_bD + phi_bF)\n", - " Omega_p1 = f_c + (1 - f_c) * lam * theta_c # matches intermediation_P1_D/F\n", - " rb_h = _get('rb_actual_D' if c == 'D' else 'rb_actual_F')\n", - " rb_x = _get('rb_actual_F' if c == 'D' else 'rb_actual_D')\n", - " nu_K_c = _get(f'nu_K_{c}')\n", - " nu_bh_c = _get('nu_bD_D' if c == 'D' else 'nu_bF_F')\n", - " nu_bx_c = _get('nu_bF_D' if c == 'D' else 'nu_bD_F')\n", - " diag[f'P1_{c}: nu_K_res'] = nu_K_c - beta_c * Omega_p1 * (rk_c - rdep_c)\n", - " diag[f'P1_{c}: nu_bh_res'] = nu_bh_c - beta_c * Omega_p1 * (rb_h - rdep_c)\n", - " diag[f'P1_{c}: nu_bx_res'] = nu_bx_c - beta_c * Omega_p1 * (rb_x - rdep_c)\n", - " diag[f'P1_{c}: eta_res'] = eta_c - beta_c * Omega_p1 * (1 + rdep_c)\n", - "\n", - "# \u2500\u2500 GHH labor market FOC \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "for c in ['D', 'F']:\n", - " w_c = _get(f'w_{c}')\n", - " P_c = _get(f'P_CES_{c}')\n", - " N_c = _get(f'N_{c}')\n", - " vphi_c = _get(f'vphi_{c}')\n", - " fr_c = _get(f'frisch_{c}')\n", - " diag[f'labor_mkt_{c}: w/P \u2212 vphi\u00b7N^(1/fr)'] = w_c / P_c - vphi_c * N_c ** (1 / fr_c)\n", - "\n", - "# \u2500\u2500 Cross-border portfolio FOC \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "for (c, cross, b_key, b_ss_key, psi_key, er_key) in [\n", - " ('D', 'F', 'b_F_D', 'b_F_D_ss', 'psi_bF_D', 'excess_return_F_D_ss'),\n", - " ('F', 'D', 'b_D_F', 'b_D_F_ss', 'psi_bD_F', 'excess_return_D_F_ss'),\n", - "]:\n", - " diag[f'portfolio_adj_b{cross}_{c}'] = (\n", - " (_get(f'rb_actual_{cross}') - _get(f'rdep_{c}'))\n", - " - _get(er_key)\n", - " - calibration_start[psi_key] * (_get(b_key) - _get(b_ss_key))\n", - " - _get(f'tau_mp_{c}')\n", - " )\n", - "\n", - "# \u2500\u2500 Domestic bond FOC \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "for c in ['D', 'F']:\n", - " pdiv = _get('p') if c == 'F' else 1.0\n", - " phi_dom = _get(f'q_b_{c}') * _get(f'b_{c}_{c}') / (pdiv * _get(f'n_inter_{c}'))\n", - " phi_dom_ss = _get(f'phi_b{c}_{c}_ss')\n", - " psi_dom = _get(f'psi_b{c}_{c}')\n", - " er_dom = _get(f'excess_return_b{c}_{c}_ss')\n", - " diag[f'dom_bond_foc_{c}'] = (\n", - " (_get(f'rb_actual_{c}') - _get(f'rdep_{c}'))\n", - " - er_dom\n", - " - psi_dom * (phi_dom - phi_dom_ss)\n", - " - calibration_start[f'T0_{c}']\n", - " )\n", - "\n", - "diag['ca_res_D'] = _get('ca_res_D')\n", - "\n", - "TOL = 1e-8\n", - "print(f\"\\n{'Block residual':<55} {'Value':>14} Status\")\n", - "print(\"-\" * 85)\n", - "FLAGGED = []\n", - "for name, val in diag.items():\n", - " ok = abs(val) <= TOL\n", - " if not ok:\n", - " FLAGGED.append(name)\n", - " print(f\" {name:<53} {val:>14.6e} {'OK' if ok else '*** FAIL'}\")\n", - "print(\"-\" * 85)\n", - "print(\"\\nAll residuals < 1e-8 \u2713\" if not FLAGGED else f\"\\nFLAGGED: {FLAGGED}\")" - ] - }, - { - "cell_type": "markdown", - "id": "c0083616", - "metadata": {}, - "source": [ - "#### OFF STEADY-STATE EQUATIONS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ddd6fb0", - "metadata": {}, - "outputs": [], - "source": [ - "from equations_D import (\n", - " capital_adj_D, labor_D,\n", - " labor_market_D, labor_demand_D,\n", - " intermediation_IC_D, bank_return_D, intermediation_P1_D,\n", - " k_balance_sheet_D,\n", - " cap_adj_cost_inter_D, macro_pru_tax_D,\n", - " intermediation_P2_D, banker_div_res_D,\n", - " intermediation_P3_D, government_default_D,\n", - " divert_bond_foc_D,\n", - " tax_rule_D, capital_producer_profit_D, budget_residual_D,\n", - " ces_price_D, import_demand_D, deposit_return_D,\n", - " bond_return_D, sdf_D, sdf_banker_ss_D, sdf_banker_D, ghh_composite_D,\n", - " welfare_agg_D,\n", - ")\n", - "\n", - "from equations_F import (\n", - " capital_adj_F, labor_F,\n", - " labor_market_F, labor_demand_F,\n", - " intermediation_IC_F, bank_return_F, intermediation_P1_F,\n", - " k_balance_sheet_F,\n", - " cap_adj_cost_inter_F, macro_pru_tax_F,\n", - " intermediation_P2_F, banker_div_res_F,\n", - " intermediation_P3_F, government_default_F,\n", - " divert_bond_foc_F,\n", - " tax_rule_F, capital_producer_profit_F, budget_residual_F,\n", - " ces_price_F, import_demand_F, deposit_return_F,\n", - " bond_return_F, sdf_F, sdf_banker_ss_F, sdf_banker_F, ghh_composite_F,\n", - " welfare_agg_F,\n", - ")\n", - "\n", - "from equations_global import (\n", - " trade_balance, domestic_bond_clearing,\n", - " portfolio_level_anchors, divert_portfolio_adj, bond_yield,\n", - " global_goods_mkt, external_account_D,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "1afbcede", - "metadata": {}, - "source": [ - "#### FULL MODEL" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "96a5b2f5", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "sys.setrecursionlimit(5000) # SSJ topological sort uses recursion on deep block graphs\n", - "\n", - "financial_solved_D = combine([\n", - " intermediation_P1_D, intermediation_IC_D,\n", - "]).solved(\n", - " unknowns={'nu_K_D': float(cali_D['nu_K_D']),\n", - " 'nu_bD_D': float(cali_D['nu_bD_D']),\n", - " 'nu_bF_D': float(cali_D['nu_bF_D']),\n", - " 'eta_D': float(cali_D['eta_D']),\n", - " 'theta_D': float(cali_D['theta_D'])},\n", - " targets=['nu_K_res_D', 'nu_bD_res_D', 'nu_bF_res_D', 'eta_res_D', 'ic_res_D'],\n", - " solver='broyden_custom'\n", - ")\n", - "\n", - "financial_solved_F = combine([\n", - " intermediation_P1_F, intermediation_IC_F,\n", - "]).solved(\n", - " unknowns={'nu_K_F': float(cali_F['nu_K_F']),\n", - " 'nu_bF_F': float(cali_F['nu_bF_F']),\n", - " 'nu_bD_F': float(cali_F['nu_bD_F']),\n", - " 'eta_F': float(cali_F['eta_F']),\n", - " 'theta_F': float(cali_F['theta_F'])},\n", - " targets=['nu_K_res_F', 'nu_bF_res_F', 'nu_bD_res_F', 'eta_res_F', 'ic_res_F'],\n", - " solver='broyden_custom'\n", - ")\n", - "\n", - "ha_full = sj.create_model([\n", - " # \u2500\u2500 Country D \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " deposit_return_D, # Rgross_D = (1+rdep_D)*P_CES_D(-1)/P_CES_D\n", - " tax_rule_D, # T_ls_D = phi*(b_gov_D(-1) - b_gov_ss_D) \u2014 BEFORE hh_extended_D\n", - " hh_extended_D, # needs T_ls_D via income_D\n", - " ghh_composite_D, # X_D = C_D - v(N_D); needed by sdf_D\n", - " sdf_D,\n", - " sdf_banker_D,\n", - " government_default_D, # \u2192 def_rate_D\n", - " financial_solved_D, # 4 \u03bd Bellmans + IC pin \u03b8 (inner solve)\n", - " bond_return_D,\n", - " bank_return_D,\n", - " cap_adj_cost_inter_D,\n", - " macro_pru_tax_D,\n", - " intermediation_P2_D,\n", - " intermediation_P3_D,\n", - " k_balance_sheet_D, # Q*K = theta*n_inter (GK IC)\n", - " capital_adj_D,\n", - " capital_producer_profit_D,\n", - " budget_residual_D,\n", - " labor_D,\n", - " labor_market_D, # GHH FOC: w/P_CES = vphi*N^(1/frisch)\n", - " labor_demand_D, # firm FOC: w = (1-\u03b1)*Y/N \u2192 w_res_D\n", - " banker_div_res_D,\n", - " market_clearing_D,\n", - " welfare_agg_D, # U_D = X_D / C_D_ss \n", - "\n", - " # \u2500\u2500 Country F \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " deposit_return_F, # Rgross_F = (1+rdep_F)*P_CES_F(-1)/P_CES_F\n", - " tax_rule_F, # T_ls_F = phi*(b_gov_F(-1) - b_gov_ss_F) \u2014 BEFORE hh_extended_F\n", - " hh_extended_F, # needs T_ls_F via income_F\n", - " ghh_composite_F, # X_F = C_F - v(N_F); needed by sdf_F\n", - " sdf_F,\n", - " sdf_banker_F,\n", - " government_default_F, # \u2192 def_rate_F\n", - " financial_solved_F, # 4 \u03bd Bellmans + IC pin \u03b8 (inner solve)\n", - " bond_return_F,\n", - " bank_return_F,\n", - " cap_adj_cost_inter_F,\n", - " macro_pru_tax_F,\n", - " intermediation_P2_F,\n", - " intermediation_P3_F,\n", - " k_balance_sheet_F, # Q*K = theta*n_inter (GK IC)\n", - " capital_adj_F,\n", - " capital_producer_profit_F,\n", - " budget_residual_F,\n", - " labor_F,\n", - " labor_market_F, # GHH FOC: w/P_CES = vphi*N^(1/frisch)\n", - " labor_demand_F, # firm FOC: w = (1-\u03b1)*Y/N \u2192 w_res_F\n", - " banker_div_res_F,\n", - " market_clearing_F,\n", - " welfare_agg_F, # U_F = X_F / C_F_ss\n", - "\n", - " # \u2500\u2500 Global \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " ces_price_D, import_demand_D,\n", - " ces_price_F, import_demand_F,\n", - " trade_balance,\n", - " external_account_D,\n", - " domestic_bond_clearing,\n", - " bond_yield,\n", - " portfolio_level_anchors,\n", - " divert_portfolio_adj, # replaces portfolio_adj_cost\n", - " divert_bond_foc_D, # replaces domestic_bond_foc_D\n", - " divert_bond_foc_F, # replaces domestic_bond_foc_F\n", - " global_goods_mkt,\n", - "], name=\"Full 2-Country MU HANK \u2014 GHH Preferences, Flex Price & Wage, No CB\")\n", - "\n", - "# \u2500\u2500 23\u00d723 system \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "unknowns_tp = [\n", - " 'K_D', 'n_inter_D', 'div_D', 'I_D', 'Q_D', 'b_gov_D', 'N_D', 'b_F_D', 'w_D', 'rdep_D',\n", - " 'K_F', 'n_inter_F', 'div_F', 'I_F', 'Q_F', 'b_gov_F', 'N_F', 'b_D_F', 'w_F', 'rdep_F',\n", - " 'p', 'q_b_D', 'q_b_F',\n", - "]\n", - "targets_tp = [\n", - " # D country (10 targets)\n", - " 'deposit_mkt_D', 'K_res_D', 'n_inter_val_D', 'div_res_D',\n", - " 'capital_res_D', 'q_res_D', 'b_gov_res_D', 'b_F_D_res',\n", - " 'labor_mkt_res_D', 'w_res_D',\n", - "\n", - " # F country (10 targets)\n", - " 'deposit_mkt_F', 'K_res_F', 'n_inter_val_F', 'div_res_F',\n", - " 'capital_res_F', 'q_res_F', 'b_gov_res_F', 'b_D_F_res',\n", - " 'labor_mkt_res_F', 'w_res_F',\n", - "\n", - " # Global (3 targets)\n", - " 'goods_mkt_D',\n", - " 'rb_D_res', 'rb_F_res',\n", - "]\n", - "T = 500\n", - "exogenous = ['Z_D', 'shock_def_D', 'Z_F', 'shock_def_F']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "94b3d213", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Precomputing full GE Jacobian G with all 4 shocks (T=500)...\n", - "G computed successfully. Now IRFs are just fast matrix multiplies.\n" - ] - } - ], - "source": [ - "exogenous = ['Z_D', 'shock_def_D', 'Z_F', 'shock_def_F']\n", - "\n", - "print(f\"Precomputing full GE Jacobian G with all {len(exogenous)} shocks (T={T})...\")\n", - "G = ha_full.solve_jacobian(ss_final, unknowns=unknowns_tp, targets=targets_tp, inputs=exogenous,T=T)\n", - "print(\"G computed successfully. Now IRFs are just fast matrix multiplies.\")\n", - "\n", - "\n", - "# TFP Shock\n", - "rho_Z_D = 0.8\n", - "dZ_D = 0.01 * rho_Z_D ** np.arange(T)\n", - "\n", - "# Default Shock\n", - "rho_def_D = 0.8\n", - "dShock_def_D = 0.01 * rho_def_D ** np.arange(T)\n", - "\n", - "\n", - "\n", - "# === IRF to TFP shock in D only ===\n", - "shock_Z_D = {\n", - " 'Z_D': dZ_D,\n", - " 'Z_F': np.zeros(T),\n", - " 'shock_def_D': np.zeros(T),\n", - " 'shock_def_F': np.zeros(T)\n", - "}\n", - "irfs_Z_D = G @ shock_Z_D\n", - "\n", - "# === IRF to default shock in D only ===\n", - "shock_def_D = {\n", - " 'Z_D': np.zeros(T),\n", - " 'Z_F': np.zeros(T),\n", - " 'shock_def_D': dShock_def_D,\n", - " 'shock_def_F': np.zeros(T)\n", - "}\n", - "irfs_def_D = G @ shock_def_D " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0febad7a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=== Stability check: debt level at t = 499 (should be near 0) ===\n", - " irfs_Z_D ['b_gov_D'][499] = 0.001174\n", - " irfs_def_D['b_gov_D'][499] = -0.000229\n", - "\n", - " \u03c1_b (partial-eq. AR coeff) = 0.9815 [target < 0.95 for visible convergence within 50 periods]\n", - "\n", - "=== TFP shock magnitudes at t=0 ===\n", - " b_gov_D[0] = +1.21% (< \u00b15% = well-behaved)\n", - " q_b_D[0] = +4.53% (< \u00b15% = well-behaved)\n", - " Y_D[0] = +2.88%\n", - " TAX_D[0] = -1.76% (non-zero = T_ls_D active)\n" - ] - } - ], - "source": [ - "# \u2500\u2500 Stability check: b_gov_D should decay to \u2248 0 by t=499 \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# If values below are < 0.001, the lump-sum Bohn rule is strong enough to return debt to SS.\n", - "# If still large (slow convergence), increase phi_lamb_D (try 0.07 or 0.08).\n", - "# If IRFs oscillate or explode, decrease phi_lamb_D (try 0.05 or 0.04).\n", - "print(\"=== Stability check: debt level at t = 499 (should be near 0) ===\")\n", - "print(f\" irfs_Z_D ['b_gov_D'][499] = {irfs_Z_D['b_gov_D'][499]:.6f}\")\n", - "print(f\" irfs_def_D['b_gov_D'][499] = {irfs_def_D['b_gov_D'][499]:.6f}\")\n", - "print()\n", - "print(\" \u03c1_b (partial-eq. AR coeff) =\",\n", - " round((0.953 * 0.95 + 0.05 - calibration_start['phi_lamb_D']) / 0.953, 4),\n", - " \" [target < 0.95 for visible convergence within 50 periods]\")\n", - "print()\n", - "# Quick magnitude check on TFP shock\n", - "print(\"=== TFP shock magnitudes at t=0 ===\")\n", - "print(f\" b_gov_D[0] = {irfs_Z_D['b_gov_D'][0]*100:+.2f}% (< \u00b15% = well-behaved)\")\n", - "print(f\" q_b_D[0] = {irfs_Z_D['q_b_D'][0]*100:+.2f}% (< \u00b15% = well-behaved)\")\n", - "print(f\" Y_D[0] = {irfs_Z_D['Y_D'][0]*100:+.2f}%\")\n", - "print(f\" TAX_D[0] = {irfs_Z_D['TAX_D'][0]*100:+.2f}% (non-zero = T_ls_D active)\")" - ] - }, - { - "cell_type": "markdown", - "id": "755e5947", - "metadata": {}, - "source": [ - "### IMPULSE RESPONSE FUNCTIONS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4486c19b", - "metadata": {}, - "outputs": [], - "source": [ - "BLUE = '#002147'\n", - "RED = '#8C1515'\n", - "BLUE_MUTED = '#4a6f8a' \n", - "RED_MUTED = '#c0624a'\n", - "\n", - "_COLORS = [BLUE, RED, BLUE_MUTED, RED_MUTED]\n", - "_LINESTYLES = ['-', '--', '-.', ':']\n", - "_MARKERS = ['', '', '', 'o']\n", - "\n", - "def show_irfs(irfs_list, variables, labels=None,\n", - " ylabel='Deviation from SS (pp)', T_plot=100, figsize=(18, 5)):\n", - "\n", - " labels = labels or [''] * len(irfs_list)\n", - " n_var = len(variables)\n", - " fig, axes = plt.subplots(1, n_var, figsize=figsize, sharey=False)\n", - " if n_var == 1:\n", - " axes = [axes]\n", - "\n", - " for i, (ax, var) in enumerate(zip(axes, variables)):\n", - " for j, (irf, label) in enumerate(zip(irfs_list, labels)):\n", - " data = irf[var][:T_plot] if var in irf else np.zeros(T_plot)\n", - " mkr = _MARKERS[j % len(_MARKERS)]\n", - " ax.plot(data,\n", - " color = _COLORS[j % len(_COLORS)],\n", - " linestyle = _LINESTYLES[j % len(_LINESTYLES)],\n", - " linewidth = 1.8,\n", - " marker = mkr,\n", - " markersize= 4,\n", - " markevery = 4,\n", - " label = label)\n", - "\n", - " ax.axhline(0, color='#888888', linewidth=0.8, linestyle=':')\n", - " ax.set_title(var, fontsize=10, pad=6)\n", - " ax.set_xlabel('Quarter', fontsize=9)\n", - " if i == 0:\n", - " ax.set_ylabel(ylabel, fontsize=9)\n", - " ax.spines[['top', 'right']].set_visible(False)\n", - " ax.tick_params(labelsize=8)\n", - " if any(l for l in labels):\n", - " ax.legend(fontsize=8, frameon=False)\n", - "\n", - " fig.tight_layout()\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ebfc9738", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=== Default shock diagnostics ===\n", - "def_rate_D[0]: +1.0000%\n", - "Y_D[0]: +0.0122%\n", - "q_b_D[0]: -2.6773% <- how much does price fall?\n", - "rb_actual_D[0]: -2.4696% <- realized return falls\n", - "rb_actual_D[1]: +0.8813% <- future return RISES?\n", - "nu_bD_D[0]: +0.7999% <- shadow value of bonds\n", - "theta_D[0]: -17.8193% <- leverage RISES = perverse IC\n", - "n_inter_D[0]: +13.8807% <- net worth falls?\n", - "rn_D[0]: +5.2257% <- portfolio return\n", - "phi_bD_D[0] approx: 0.2431 vs SS 0.2500\n" - ] - } - ], - "source": [ - "print(\"=== Default shock diagnostics ===\")\n", - "print(f\"def_rate_D[0]: {irfs_def_D['def_rate_D'][0]*100:+.4f}%\")\n", - "print(f\"Y_D[0]: {irfs_def_D['Y_D'][0]*100:+.4f}%\")\n", - "print(f\"q_b_D[0]: {irfs_def_D['q_b_D'][0]*100:+.4f}% <- how much does price fall?\")\n", - "print(f\"rb_actual_D[0]: {irfs_def_D['rb_actual_D'][0]*100:+.4f}% <- realized return falls\")\n", - "print(f\"rb_actual_D[1]: {irfs_def_D['rb_actual_D'][1]*100:+.4f}% <- future return RISES?\")\n", - "print(f\"nu_bD_D[0]: {irfs_def_D['nu_bD_D'][0]*100:+.4f}% <- shadow value of bonds\")\n", - "print(f\"theta_D[0]: {irfs_def_D['theta_D'][0]*100:+.4f}% <- leverage RISES = perverse IC\")\n", - "print(f\"n_inter_D[0]: {irfs_def_D['n_inter_D'][0]*100:+.4f}% <- net worth falls?\")\n", - "print(f\"rn_D[0]: {irfs_def_D['rn_D'][0]*100:+.4f}% <- portfolio return\")\n", - "phi_bD_ss = float(ss['q_b_D']) * float(ss['b_D_D']) / float(ss['n_inter_D'])\n", - "phi_bD_0 = (float(ss['q_b_D']) + irfs_def_D['q_b_D'][0]) * float(ss['b_D_D']) / float(ss['n_inter_D'])\n", - "print(f\"phi_bD_D[0] approx: {phi_bD_0:.4f} vs SS {phi_bD_ss:.4f}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ea88c49", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== U_D \u2014 Default shock (def_D), 100 periods (discounted by beta_D^(t-1), beta_D=0.99946218) ===\n", - " t= 0: -0.002759\n", - " t= 1: -0.001880\n", - " t= 2: -0.001392\n", - " t= 3: -0.001098\n", - " t= 4: -0.000901\n", - " t= 5: -0.000753\n", - " t= 6: -0.000633\n", - " t= 7: -0.000531\n", - " t= 8: -0.000443\n", - " t= 9: -0.000366\n", - " t= 10: -0.000298\n", - " t= 11: -0.000240\n", - " t= 12: -0.000191\n", - " t= 13: -0.000148\n", - " t= 14: -0.000112\n", - " t= 15: -0.000081\n", - " t= 16: -0.000055\n", - " t= 17: -0.000033\n", - " t= 18: -0.000015\n", - " t= 19: +0.000000\n", - " SUM: -0.860562\n" - ] - } - ], - "source": [ - "\n", - "beta_D = float(ss_final['beta_D'])\n", - "print(f\"\\n=== U_D \u2014 Default shock (def_D), 100 periods (discounted by beta_D^(t-1), beta_D={beta_D:.8f}) ===\")\n", - "for t in range(20):\n", - " val = irfs_def_D['U_D'][t]* beta_D**t\n", - " print(f\" t={t:3d}: {val:+.6f}\")\n", - "disc_weights = beta_D ** np.arange(100)\n", - "print(f\" SUM: {(irfs_def_D['U_D'][:100] * disc_weights*100).sum():+.6f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "201eb80b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "

" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Overview: output, consumption, real wage, bank net worth, bond rate\n", - "show_irfs([irfs_def_D], ['spread_rb','U_D',\"U_F\"],['TFP shock', 'Default shock']) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6930d603", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Overview: output, consumption, real wage, bank net worth, bond rate\n", - "show_irfs([irfs_Z_D, irfs_def_D], \n", - " ['Y_D', 'C_D', 'w_D', 'n_inter_D', 'q_b_D', 'q_b_F'],\n", - " ['TFP shock', 'Default shock']) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "af4f67cb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# \u2500\u2500 1. Output, Consumption & Trade \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# With flexible prices, inflation is zero; terms of trade p and NX adjust to\n", - "# clear goods markets across countries.\n", - "show_irfs([irfs_Z_D, irfs_def_D], labels=['TFP shock (D)', 'Default shock (D)'],\n", - " variables=['Y_D', 'Y_F', 'C_D', 'C_F', 'p', 'NX_D'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b399c57f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# \u2500\u2500 2. Labour, Capital & TFP \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# Suggestion: add Q (Tobin's q) and rk \u2014 these reveal whether output\n", - "# rigidity is coming from the investment or the labour margin.\n", - "show_irfs([irfs_Z_D, irfs_def_D], labels=['TFP shock (D)', 'Default shock (D)'],\n", - " variables=['N_D', 'N_F', 'K_D', 'K_F', 'I_D', 'I_F', 'Q_D', 'w_D'])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "af647e84", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# \u2500\u2500 3. Factor Prices \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# Under flexible prices mc = 1 always; the interesting margins are factor prices.\n", - "# rk = rental rate of capital; w = real wage. Both move with TFP and default shocks.\n", - "show_irfs([irfs_Z_D, irfs_def_D], labels=['TFP shock (D)', 'Default shock (D)'],\n", - " variables=['w_D', 'w_F', 'N_D', 'N_F', 'rk_D', 'rk_F'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1b1806ba", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# \u2500\u2500 4. Bond Holdings & External Position \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# nfa_D = q_b_F\u00b7b_F_D \u2212 q_b_D\u00b7b_D_F: D's net foreign asset position (in D-goods).\n", - "# n_inter_D: D bank net worth \u2014 shows financial amplification of sovereign shocks.\n", - "show_irfs([irfs_Z_D, irfs_def_D], labels=['TFP shock (D)', 'Default shock (D)'],\n", - " variables=['b_D_D', 'b_F_D', 'b_D_F', 'b_F_F', 'nfa_D', 'n_inter_D'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02bc16fa", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# \u2500\u2500 5. Rates & Returns \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# rdep_D/F are now endogenous (pinned by deposit market clearing in the 23\u00d723 system).\n", - "# rk_D: return on physical capital. rn_D: bank portfolio return (mix of rk and rb).\n", - "show_irfs([irfs_Z_D, irfs_def_D], labels=['TFP shock (D)', 'Default shock (D)'],\n", - " variables=['rb_actual_D', 'rb_actual_F', 'rn_D', 'rn_F', 'rdep_D', 'rdep_F', 'rk_D'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aea6cf45", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# \u2500\u2500 6. Fiscal \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "show_irfs([irfs_Z_D, irfs_def_D], labels=['TFP shock (D)', 'Default shock (D)'],\n", - " variables=['b_gov_D', 'b_gov_F', 'TAX_D', 'TAX_F', 'def_rate_D'])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "caad43f3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Default shock decomposition: exogenous shock vs total default rate\n", - "irfs_def_D_plot = dict(irfs_def_D)\n", - "irfs_def_D_plot[\"shock_def_D\"] = dShock_def_D\n", - "\n", - "show_irfs([irfs_def_D_plot],\n", - " variables=[\"shock_def_D\", \"def_rate_D\"],\n", - " labels=[\"Default shock (D)\"],\n", - " ylabel=\"Deviation from SS\",\n", - " figsize=(10, 5))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41ff1316", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "show_irfs([irfs_Z_D], labels=['TFP shock'], \n", - " variables=['goods_mkt_D', 'deposit_mkt_D', 'rb_D_res', 'rb_F_res',\n", - " 'b_D_F_res', 'b_F_D_res'],\n", - " ylabel='Residual')\n", - "show_irfs([irfs_Z_D], labels=['TFP shock'],\n", - " variables=['global_goods_res', 'goods_mkt_F'],\n", - " ylabel='Walras residual')\n", - "show_irfs([irfs_def_D], labels=['Default shock'], \n", - " variables=['goods_mkt_D', 'deposit_mkt_D', 'rb_D_res', 'rb_F_res',\n", - " 'b_D_F_res', 'b_F_D_res'],\n", - " ylabel='Residual')\n", - "show_irfs([irfs_def_D], labels=['Default shock'],\n", - " variables=['global_goods_res', 'goods_mkt_F'],\n", - " ylabel='Walras residual') \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f2f28bfd", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "db270865", - "metadata": {}, - "source": [ - "# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n", - "# SECTION TPI \u2014 Transmission Protection Instrument\n", - "# Central Bank bond purchases to suppress distressed-country spreads\n", - "# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n", - "\n", - "**Mechanism**: The CB purchases `cb_buy_D` units of Country D government bonds, removing them from private bank balance sheets: \n", - "`b_D_D = b_gov_D \u2212 b_D_F \u2212 cb_buy_D` \n", - "This propagates: fewer bonds for banks \u2192 GK IC loosens \u2192 bond price rises \u2192 spread compresses.\n", - "\n", - "**Policy rule** (closed-loop feedback on equilibrium spread): \n", - "`cb_buy_D[t] = \u03b3 \u00d7 spread_rb[t]`\n", - "\n", - "Since the model is linear, this is an exact fixed-point: \n", - "`(I \u2212 \u03b3 A_cb) \u00d7 spread = A_def \u00d7 shock_def`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4251f394", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "domestic_bond_clearing_tpi defined.\n" - ] - } - ], - "source": [ - "# \u2500\u2500 TPI-1: TPI Bond Clearing Block + CB budget \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# Identical to domestic_bond_clearing except the CB absorbs cb_buy_D bonds,\n", - "# reducing what private banks can hold. Same outputs (b_D_D, b_F_F) so no\n", - "# downstream block signatures change.\n", - "#\n", - "# TPI-1 audit fix: budget_residual_D_tpi adds the CB budget constraint.\n", - "# The CB is consolidated with the government: purchases q_b_D*cb_buy_D are\n", - "# financed by the government, and the CB's coupon income plus the market value\n", - "# of its surviving holdings are remitted back (rem_cb_D). Without this block\n", - "# cb_buy_D injected unbacked resources (Walras violation up to 2.6% of GDP per\n", - "# period at gamma=10 \u2014 see audit.md TPI-1).\n", - "\n", - "@simple\n", - "def domestic_bond_clearing_tpi(b_gov_D, b_gov_F, b_D_F, b_F_D, cb_buy_D):\n", - " b_D_D = b_gov_D - b_D_F - cb_buy_D # CB removes cb_buy_D from private supply\n", - " b_F_F = b_gov_F - b_F_D\n", - " return b_D_D, b_F_F\n", - "\n", - "\n", - "@simple\n", - "def budget_residual_D_tpi(b_gov_D, G_D, TAX_D, q_b_D, def_rate_D, recovery_rate_D,\n", - " zeta_writeoff_D, P_CES_D, delta_b_D, writeoff_enabled_D,\n", - " cb_buy_D):\n", - " haircut_D = 1.0 - recovery_rate_D\n", - " haircut_mult_D = writeoff_enabled_D\n", - " surv_cont_D = 1.0 - zeta_writeoff_D * def_rate_D * haircut_D * haircut_mult_D\n", - " coupon_D = delta_b_D * (1.0 - def_rate_D * haircut_D * haircut_mult_D) * b_gov_D(-1)\n", - " net_issuance_D = q_b_D * (b_gov_D - surv_cont_D * (1.0 - delta_b_D) * b_gov_D(-1))\n", - " # CB remittance: coupon on cb(-1) + market value of surviving CB holdings\n", - " # - cost of the new CB position. Exactly offsets the private-sector flow\n", - " # (1+rb_actual_D)*q_b_D(-1)*cb(-1) - q_b_D*cb, closing the accounting hole.\n", - " rem_cb_D = (delta_b_D * (1.0 - def_rate_D * haircut_D * haircut_mult_D) * cb_buy_D(-1)\n", - " + q_b_D * surv_cont_D * (1.0 - delta_b_D) * cb_buy_D(-1)\n", - " - q_b_D * cb_buy_D)\n", - " b_gov_res_D = coupon_D + G_D - P_CES_D * TAX_D - net_issuance_D - rem_cb_D\n", - " return b_gov_res_D, rem_cb_D\n", - "\n", - "print(\"domestic_bond_clearing_tpi and budget_residual_D_tpi defined.\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dd936024", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ha_full_tpi built.\n", - " Block swapped: domestic_bond_clearing \u2192 domestic_bond_clearing_tpi\n", - " ss_tpi: added cb_buy_D = 0 (no SS re-solve required)\n" - ] - } - ], - "source": [ - "# \u2500\u2500 TPI-2: Build ha_full_tpi and prepare ss_tpi \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# Identical to ha_full except domestic_bond_clearing \u2192 domestic_bond_clearing_tpi.\n", - "# ss_tpi is ss_final with cb_buy_D = 0 injected (no SS re-solve needed).\n", - "\n", - "import copy as _copy\n", - "\n", - "ha_full_tpi = sj.create_model([\n", - " # \u2500\u2500 Country D \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " deposit_return_D,\n", - " tax_rule_D,\n", - " hh_extended_D,\n", - " ghh_composite_D,\n", - " sdf_D,\n", - " sdf_banker_D,\n", - " government_default_D,\n", - " financial_solved_D,\n", - " bond_return_D,\n", - " bank_return_D,\n", - " cap_adj_cost_inter_D,\n", - " macro_pru_tax_D,\n", - " intermediation_P2_D,\n", - " intermediation_P3_D,\n", - " k_balance_sheet_D,\n", - " capital_adj_D,\n", - " capital_producer_profit_D,\n", - " budget_residual_D_tpi, # \u2190 TPI-1 audit fix: CB budget/remittance\n", - " labor_D,\n", - " labor_market_D,\n", - " labor_demand_D,\n", - " banker_div_res_D,\n", - " market_clearing_D,\n", - " welfare_agg_D,\n", - " # \u2500\u2500 Country F \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " deposit_return_F,\n", - " tax_rule_F,\n", - " hh_extended_F,\n", - " ghh_composite_F,\n", - " sdf_F,\n", - " sdf_banker_F,\n", - " government_default_F,\n", - " financial_solved_F,\n", - " bond_return_F,\n", - " bank_return_F,\n", - " cap_adj_cost_inter_F,\n", - " macro_pru_tax_F,\n", - " intermediation_P2_F,\n", - " intermediation_P3_F,\n", - " k_balance_sheet_F,\n", - " capital_adj_F,\n", - " capital_producer_profit_F,\n", - " budget_residual_F,\n", - " labor_F,\n", - " labor_market_F,\n", - " labor_demand_F,\n", - " banker_div_res_F,\n", - " market_clearing_F,\n", - " welfare_agg_F,\n", - " # \u2500\u2500 Global (TPI version) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " ces_price_D, import_demand_D,\n", - " ces_price_F, import_demand_F,\n", - " trade_balance,\n", - " external_account_D,\n", - " domestic_bond_clearing_tpi, # \u2190 CB absorbs cb_buy_D\n", - " bond_yield,\n", - " portfolio_level_anchors,\n", - " divert_portfolio_adj,\n", - " divert_bond_foc_D,\n", - " divert_bond_foc_F,\n", - " global_goods_mkt,\n", - "], name=\"Full 2-Country MU HANK \u2014 TPI Extension\")\n", - "\n", - "# SS is identical at cb_buy_D = 0; just inject the new parameter\n", - "ss_tpi = _copy.deepcopy(ss_final)\n", - "ss_tpi.toplevel['cb_buy_D'] = 0.0\n", - "\n", - "print(\"ha_full_tpi built.\")\n", - "print(f\" Block swapped: domestic_bond_clearing \u2192 domestic_bond_clearing_tpi\")\n", - "print(f\" ss_tpi: added cb_buy_D = 0 (no SS re-solve required)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "937f7cff", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing G_tpi (T=500, 5 exogenous inputs: ['Z_D', 'shock_def_D', 'Z_F', 'shock_def_F', 'cb_buy_D'])\n", - " Same cost as the original G solve \u2014 this may take a few minutes...\n" - ] - } - ], - "source": [ - "# \u2500\u2500 TPI-3: Compute G_tpi Jacobian \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# Adds cb_buy_D as a 5th exogenous input. Same unknowns & targets as the baseline\n", - "# 23\u00d723 system. Takes the same time as the original G solve (~2-5 min).\n", - "\n", - "exogenous_tpi = ['Z_D', 'shock_def_D', 'Z_F', 'shock_def_F', 'cb_buy_D']\n", - "print(f\"Computing G_tpi (T={T}, {len(exogenous_tpi)} exogenous inputs: {exogenous_tpi})\")\n", - "print(\" Same cost as the original G solve \u2014 this may take a few minutes...\")\n", - "\n", - "G_tpi = ha_full_tpi.solve_jacobian(\n", - " ss_tpi,\n", - " unknowns = unknowns_tp,\n", - " targets = targets_tp,\n", - " inputs = exogenous_tpi,\n", - " T = T,\n", - ")\n", - "print(\"G_tpi computed successfully.\")\n", - "\n", - "# \u2500\u2500 Sanity check: G_tpi @ {cb_buy_D=0} must reproduce irfs_def_D \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "_chk = G_tpi @ {\n", - " 'Z_D': np.zeros(T),\n", - " 'Z_F': np.zeros(T),\n", - " 'shock_def_D': dShock_def_D,\n", - " 'shock_def_F': np.zeros(T),\n", - " 'cb_buy_D': np.zeros(T),\n", - "}\n", - "_err = np.max(np.abs(_chk['spread_rb'][:50] - irfs_def_D['spread_rb'][:50]))\n", - "print(f\"\\nSanity check: max|G_tpi[cb=0] \u2212 G[baseline]| on spread_rb[:50] = {_err:.2e}\")\n", - "print(\" (Should be < 1e-8 \u2014 models are identical when CB is inactive)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "22e7004d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "compute_tpi_irfs defined.\n", - " Usage: irfs = compute_tpi_irfs(G_tpi, dShock_def_D, gamma_tpi=10, T=T)\n" - ] - } - ], - "source": [ - "# \u2500\u2500 TPI-4: compute_tpi_irfs \u2014 Closed-Loop Fixed-Point Solver \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "#\n", - "# TPI rule: cb_buy_D[t] = gamma * spread_rb[t]\n", - "# Since the model is linear, this creates a fixed point:\n", - "# spread = A_def @ shock_def + A_cb @ (gamma * spread)\n", - "# (I \u2212 gamma * A_cb) @ spread = A_def @ shock_def\n", - "# Solved exactly via np.linalg.solve \u2014 no iteration required.\n", - "#\n", - "# Returns an ImpulseDict identical in structure to irfs_def_D, plus 'cb_buy_D'.\n", - "\n", - "def compute_tpi_irfs(G_tpi, shock_def, gamma_tpi, T):\n", - " \"\"\"\n", - " Closed-loop TPI impulse responses for feedback gain gamma_tpi.\n", - "\n", - " Parameters\n", - " ----------\n", - " G_tpi : JacobianDict from ha_full_tpi.solve_jacobian\n", - " shock_def : np.ndarray (T,) \u2014 exogenous default shock path\n", - " gamma_tpi : float \u2014 feedback gain on equilibrium spread\n", - " T : int \u2014 horizon\n", - "\n", - " Returns\n", - " -------\n", - " irfs : ImpulseDict with all model variables under TPI + 'cb_buy_D' appended\n", - " \"\"\"\n", - " # \u2500\u2500 Step 1: Extract T\u00d7T sub-Jacobians for spread_rb \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " # np.array() materialises any lazy/sparse matrix types used internally by SJ.\n", - " # Fallback: if spread_rb absent, reconstruct from rb_actual components.\n", - " _has_spread = 'spread_rb' in G_tpi.outputs\n", - "\n", - " if _has_spread:\n", - " A_def = np.array(G_tpi['spread_rb']['shock_def_D']) # T\u00d7T\n", - " A_cb = np.array(G_tpi['spread_rb']['cb_buy_D']) # T\u00d7T\n", - " else:\n", - " A_def = (np.array(G_tpi['rb_actual_D']['shock_def_D'])\n", - " - np.array(G_tpi['rb_actual_F']['shock_def_D']))\n", - " A_cb = (np.array(G_tpi['rb_actual_D']['cb_buy_D'])\n", - " - np.array(G_tpi['rb_actual_F']['cb_buy_D']))\n", - "\n", - " # \u2500\u2500 Step 2: Solve closed-loop fixed point \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " I_T = np.eye(T)\n", - " system_matrix = I_T - gamma_tpi * A_cb # (T\u00d7T) \u2014 well-conditioned for small \u03b3\n", - "\n", - " # Warn if ill-conditioned (can occur for very large \u03b3)\n", - " cond = np.linalg.cond(system_matrix)\n", - " if cond > 1e10:\n", - " print(f\" WARNING: system matrix cond = {cond:.2e} for gamma={gamma_tpi:.1f} \"\n", - " \"(spread response may be numerically inaccurate)\")\n", - "\n", - " spread_cl = np.linalg.solve(system_matrix, A_def @ shock_def) # (T,)\n", - " cb_buy_path = gamma_tpi * spread_cl # (T,)\n", - "\n", - " # \u2500\u2500 Step 3: Compute full GE IRFs \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " shock_dict = {\n", - " 'Z_D': np.zeros(T),\n", - " 'Z_F': np.zeros(T),\n", - " 'shock_def_D': shock_def,\n", - " 'shock_def_F': np.zeros(T),\n", - " 'cb_buy_D': cb_buy_path,\n", - " }\n", - " irfs = G_tpi @ shock_dict\n", - "\n", - " # \u2500\u2500 Step 4: Append cb_buy_D as an accessible output \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " irfs['cb_buy_D'] = cb_buy_path\n", - "\n", - " return irfs\n", - "\n", - "\n", - "print(\"compute_tpi_irfs defined.\")\n", - "print(\" Usage: irfs = compute_tpi_irfs(G_tpi, dShock_def_D, gamma_tpi=10, T=T)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b13711bd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " gamma = 0 ... peak spread = +0.260 pp\n", - " gamma = 2 ... peak spread = +0.259 pp\n", - " gamma = 5 ... peak spread = +0.260 pp\n", - " gamma = 10 ... peak spread = +0.269 pp\n", - "\n", - "Sanity check gamma=0 vs irfs_def_D: max |err| = 0.00e+00 (expect < 1e-8)\n" - ] - } - ], - "source": [ - "# \u2500\u2500 TPI-5: Compute closed-loop IRFs for five feedback strengths \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# gamma = 0 \u2192 no intervention (must reproduce irfs_def_D exactly)\n", - "# gamma = 2 \u2192 weak TPI\n", - "# gamma = 5 \u2192 medium TPI\n", - "# gamma = 10 \u2192 strong TPI\n", - "gamma_values = [0, 2, 5, 10]\n", - "gamma_labels = ['\u03b3 = 0 (No TPI)', '\u03b3 = 2 (Weak)', '\u03b3 = 5 (Medium)',\n", - " '\u03b3 = 10 (Strong)']\n", - "\n", - "# Four visually distinct colours & line styles\n", - "TPI_COLORS = [BLUE, '#1a6e3a', '#c87941', RED]\n", - "TPI_LSTYLES = ['-', '-', '--', '-.']\n", - "TPI_MARKERS = ['', 'o', '', 's']\n", - "\n", - "irfs_tpi = {}\n", - "for g in gamma_values:\n", - " print(f\" gamma = {g:2d} ...\", end=' ', flush=True)\n", - " if g == 0:\n", - " # No-TPI baseline: use G_tpi directly (cb_buy_D = 0)\n", - " _s = {\n", - " 'Z_D': np.zeros(T),\n", - " 'Z_F': np.zeros(T),\n", - " 'shock_def_D': dShock_def_D,\n", - " 'shock_def_F': np.zeros(T),\n", - " 'cb_buy_D': np.zeros(T),\n", - " }\n", - " irfs_tpi[g] = G_tpi @ _s\n", - " irfs_tpi[g]['cb_buy_D'] = np.zeros(T)\n", - " else:\n", - " irfs_tpi[g] = compute_tpi_irfs(G_tpi, dShock_def_D, g, T)\n", - "\n", - " _spread = irfs_tpi[g]['spread_rb'] if 'spread_rb' in irfs_tpi[g] \\\n", - " else irfs_tpi[g]['rb_actual_D'] - irfs_tpi[g]['rb_actual_F']\n", - " print(f\"peak spread = {_spread[:100].max()*100:+.3f} pp\")\n", - "\n", - "# \u2500\u2500 Sanity: gamma=0 must match irfs_def_D \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "_err0 = np.max(np.abs(irfs_tpi[0]['spread_rb'][:50] - irfs_def_D['spread_rb'][:50]))\n", - "print(f\"\\nSanity check gamma=0 vs irfs_def_D: max |err| = {_err0:.2e} (expect < 1e-8)\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "de43a929", - "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (3231428861.py, line 50)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Cell \u001b[0;32mIn[35], line 50\u001b[0;36m\u001b[0m\n\u001b[0;31m fig6.tight_layout()3\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], - "source": [ - "# \u2500\u2500 TPI-6: Figure 1 \u2014 Spread Mitigation (2\u00d73 = 6-panel) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# Shows how TPI progressively suppresses the sovereign spread and related\n", - "# financial variables under a default shock hitting Country D.\n", - "# Transmission channels: spread_rb (target), def_rate_D (driver), q_b_D (price),\n", - "# rb_actual_D (yield), n_inter_D (bank net worth), cb_buy_D (CB intervention size).\n", - "\n", - "T_plot6 = 20\n", - "\n", - "fig6_vars = ['spread_rb', 'def_rate_D', 'q_b_D',\n", - " 'rb_actual_D', 'n_inter_D', 'cb_buy_D']\n", - "fig6_titles = ['Bond Yield Spread (rb_D \u2212 rb_F)',\n", - " 'Default Rate def_rate_D',\n", - " 'Bond Price q_b_D',\n", - " 'Bond Return rb_actual_D',\n", - " 'Bank Net Worth n_inter_D',\n", - " 'CB Bond Purchases cb_buy_D']\n", - "\n", - "fig6, axes6 = plt.subplots(2, 3, figsize=(18, 9))\n", - "axes6_flat = axes6.flatten()\n", - "\n", - "def _get_var(irf, var, T_plot):\n", - " \"\"\"Retrieve variable with fallback and scale to percentage points.\"\"\"\n", - " if var == 'spread_rb' and var not in irf:\n", - " return (irf['rb_actual_D'][:T_plot] - irf['rb_actual_F'][:T_plot]) * 100\n", - " return irf[var][:T_plot] * 100 if var in irf else np.zeros(T_plot)\n", - "\n", - "for ax, var, title in zip(axes6_flat, fig6_vars, fig6_titles):\n", - " for j, g in enumerate(gamma_values):\n", - " data = _get_var(irfs_tpi[g], var, T_plot6)\n", - " ax.plot(data,\n", - " color = TPI_COLORS[j],\n", - " linestyle = TPI_LSTYLES[j],\n", - " linewidth = 1.8,\n", - " marker = TPI_MARKERS[j],\n", - " markersize = 4,\n", - " markevery = 8,\n", - " label = gamma_labels[j])\n", - " ax.axhline(0, color='#888888', linewidth=0.8, linestyle=':')\n", - " ax.set_title(title, fontsize=10, pad=6)\n", - " ax.set_xlabel('Quarter', fontsize=9)\n", - " ax.set_ylabel('pp dev. from SS', fontsize=9)\n", - " ax.spines[['top', 'right']].set_visible(False)\n", - " ax.tick_params(labelsize=8)\n", - "\n", - "# Shared legend on first panel\n", - "axes6_flat[0].legend(fontsize=8, frameon=False, loc='upper right')\n", - "\n", - "fig6.suptitle('Figure 1: TPI \u2014 Spread Mitigation under Default Shock (Country D)',\n", - " fontsize=12, y=1.01)\n", - "fig6.tight_layout()\n", - "fig6.savefig('fig_tpi_spread_mitigation.png', dpi=150, bbox_inches='tight')\n", - "plt.show()\n", - "print(\"Saved fig_tpi_spread_mitigation.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "061c101a", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "'c' argument has 5 elements, which is inconsistent with 'x' and 'y' with size 4.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[36], line 37\u001b[0m\n\u001b[1;32m 35\u001b[0m ax \u001b[38;5;241m=\u001b[39m axes7[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 36\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(gammas_fine, peak_arr \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m, color\u001b[38;5;241m=\u001b[39mBLUE, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m---> 37\u001b[0m ax\u001b[38;5;241m.\u001b[39mscatter([g \u001b[38;5;28;01mfor\u001b[39;00m g \u001b[38;5;129;01min\u001b[39;00m gamma_values], [_peak_spread(irfs_tpi[g]) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m g \u001b[38;5;129;01min\u001b[39;00m gamma_values],\n\u001b[1;32m 38\u001b[0m color\u001b[38;5;241m=\u001b[39mTPI_COLORS, s\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m60\u001b[39m, zorder\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\n\u001b[1;32m 39\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFeedback gain \u03b3\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m9\u001b[39m)\n\u001b[1;32m 40\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPeak spread dev. (pp)\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m9\u001b[39m)\n", - "File \u001b[0;32m/opt/anaconda3/lib/python3.13/site-packages/matplotlib/_api/deprecation.py:453\u001b[0m, in \u001b[0;36mmake_keyword_only..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m name_idx:\n\u001b[1;32m 448\u001b[0m warn_deprecated(\n\u001b[1;32m 449\u001b[0m since, message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 450\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpositionally is deprecated since Matplotlib \u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[38;5;124m; the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 451\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter will become keyword-only in \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 452\u001b[0m name\u001b[38;5;241m=\u001b[39mname, obj_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m()\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[0;32m/opt/anaconda3/lib/python3.13/site-packages/matplotlib/__init__.py:1521\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1520\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1521\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\n\u001b[1;32m 1522\u001b[0m ax,\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(cbook\u001b[38;5;241m.\u001b[39msanitize_sequence, args),\n\u001b[1;32m 1524\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{k: cbook\u001b[38;5;241m.\u001b[39msanitize_sequence(v) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems()})\n\u001b[1;32m 1526\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1527\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1528\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", - "File \u001b[0;32m/opt/anaconda3/lib/python3.13/site-packages/matplotlib/axes/_axes.py:4918\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, colorizer, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m 4915\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m edgecolors \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 4916\u001b[0m orig_edgecolor \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 4917\u001b[0m c, colors, edgecolors \u001b[38;5;241m=\u001b[39m \\\n\u001b[0;32m-> 4918\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parse_scatter_color_args(\n\u001b[1;32m 4919\u001b[0m c, edgecolors, kwargs, x\u001b[38;5;241m.\u001b[39msize,\n\u001b[1;32m 4920\u001b[0m get_next_color_func\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_patches_for_fill\u001b[38;5;241m.\u001b[39mget_next_color)\n\u001b[1;32m 4922\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m plotnonfinite \u001b[38;5;129;01mand\u001b[39;00m colors \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 4923\u001b[0m c \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mmasked_invalid(c)\n", - "File \u001b[0;32m/opt/anaconda3/lib/python3.13/site-packages/matplotlib/axes/_axes.py:4741\u001b[0m, in \u001b[0;36mAxes._parse_scatter_color_args\u001b[0;34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[0m\n\u001b[1;32m 4737\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 4738\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(colors) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, xsize):\n\u001b[1;32m 4739\u001b[0m \u001b[38;5;66;03m# NB: remember that a single color is also acceptable.\u001b[39;00m\n\u001b[1;32m 4740\u001b[0m \u001b[38;5;66;03m# Besides *colors* will be an empty array if c == 'none'.\u001b[39;00m\n\u001b[0;32m-> 4741\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m invalid_shape_exception(\u001b[38;5;28mlen\u001b[39m(colors), xsize)\n\u001b[1;32m 4742\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 4743\u001b[0m colors \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;66;03m# use cmap, norm after collection is created\u001b[39;00m\n", - "\u001b[0;31mValueError\u001b[0m: 'c' argument has 5 elements, which is inconsistent with 'x' and 'y' with size 4." - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABW0AAAGyCAYAAACfsOZaAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAXqtJREFUeJzt3Xl4VOXd//HPZJtAkhkIkAWBiAqyKUJYkiDuhKJSedRCn7YsFlSeooBRWyOtgrWNtBUBZREFUqtiVEDw11iJrRAQtEITtIKIhZoIiRCWTBIgIcn5/REyZshCJiRzJpP367rOlZkz95l8zxnghg93vsdiGIYhAAAAAAAAAIBX8DO7AAAAAAAAAADA9whtAQAAAAAAAMCLENoCAAAAAAAAgBchtAUAAAAAAAAAL0JoCwAAAAAAAABehNAWAAAAAAAAALwIoS0AAAAAAAAAeBFCWwAAAAAAAADwIoS2AAAAAAAAAOBFCG0BAAAAAAAAwIsQ2gIA4CUyMzM1duxYde3aVRaLRe+8884Fj9myZYtiY2MVHBysyy67TMuXL2/5QgEA8DLMoQAAX0NoCwCAlygpKdHAgQP1wgsvNGr8wYMHdeutt2rkyJHKysrS448/rpkzZ2rt2rUtXCkAAN6FORQA4GsshmEYZhcBAABcWSwWrV+/XuPGjat3zK9+9Stt3LhRe/fude6bPn26du/erR07dnigSgAAvA9zKADAFwSYXUBzqays1OHDhxUWFiaLxWJ2OQAAExiGoaKiInXt2lV+fr7/wyQ7duxQYmKiy77Ro0dr5cqVOnv2rAIDA+s8rrS0VKWlpc7nlZWVOn78uDp16sQcCgBtFHMocygAoGlaag71mdD28OHD6t69u9llAAC8QG5urrp162Z2GS0uPz9fkZGRLvsiIyNVXl6ugoICRUdH13lcSkqK5s2b54kSAQCtDHMocygAoGmaew71mdA2LCxMUtUFstlsJlcDADCDw+FQ9+7dnXNCW3D+qp7qrkcNrfZJTk5WUlKS83lhYaF69OjBHAoAbRhzKHMoAKBpWmoO9ZnQtnpitdlsTJYA0Ma1lR9PjIqKUn5+vsu+I0eOKCAgQJ06dar3OKvVKqvVWms/cygAgDmUORQA0DTNPYf6frMiAAB8VHx8vDIyMlz2bdq0SUOGDKm3Fx8AAGAOBQB4P0JbAAC8RHFxsbKzs5WdnS1JOnjwoLKzs5WTkyOp6kcyJ02a5Bw/ffp0ffPNN0pKStLevXu1atUqrVy5Uo888ogZ5QMAYBrmUACAr/GZ9ggAALR2O3fu1I033uh8Xt0zb/LkyUpNTVVeXp7zH5+S1LNnT6Wnp+uhhx7SkiVL1LVrVy1evFh33XWXx2sHAMBMzKEAAF9jMaq7rbdyDodDdrtdhYWF9BICgDaKuaBpuG4AAOaCpuG6AQBaai6gPQIAAAAAAAAAeBFCWwAAAAAAAADwIoS2AAAAAAAAAOBFCG0BAAAAAAAAwIsQ2gIAAAAAAACAFyG0BQAAAAAAAAAvQmgLAAAAAAAAAF6E0BYAAAAAAAAAvAihLQAAAAAAAAB4kQCzCwAA4HdL1ujr/x5WWGg7/eGxaQq2BpldEgAAAAAApnFrpW1KSoqGDh2qsLAwRUREaNy4cdq3b1+Dx6xbt06jRo1Sly5dZLPZFB8fr/fff99lTGpqqiwWS63tzJkz7p8RAKDVSd/8qVLXZuj5P29UgL+/2eUAAAAAAGAqt0LbLVu2aMaMGfr444+VkZGh8vJyJSYmqqSkpN5jMjMzNWrUKKWnp2vXrl268cYbNXbsWGVlZbmMs9lsysvLc9mCg4ObdlYAgFbFUXRKktQu2KqAAEJbAAAAAEDb5lZ7hL/97W8uz1evXq2IiAjt2rVL1113XZ3HLFy40OX573//e23YsEHvvvuuBg0a5NxvsVgUFRXlTjkAAB9RVFIV2oaFtDO5EgAAAAAAzHdRNyIrLCyUJIWHhzf6mMrKShUVFdU6pri4WDExMerWrZtuv/32WitxAQC+q6jktCQpLJTQFgAAAACAJoe2hmEoKSlJ1157rQYMGNDo45599lmVlJRo/Pjxzn19+vRRamqqNm7cqDVr1ig4OFgjRozQ/v37632f0tJSORwOlw0A0Do5Q9uQ9iZXAgAAAACA+dxqj1DTAw88oM8++0zbtm1r9DFr1qzR3LlztWHDBkVERDj3x8XFKS4uzvl8xIgRGjx4sJ5//nktXry4zvdKSUnRvHnzmlo+AMBLlJaW6ezZckm0RwAAAAAAQGriStsHH3xQGzdu1Icffqhu3bo16pi0tDRNnTpVb775pm655ZaGi/Lz09ChQxtcaZucnKzCwkLnlpub69Y5AAC8Q/UqW0myhbLSFgAAAAAAt1baGoahBx98UOvXr9fmzZvVs2fPRh23Zs0a/fznP9eaNWt02223Ner7ZGdn66qrrqp3jNVqldVqbXTtAADvVDO0ZaUtAAAAAABuhrYzZszQ66+/rg0bNigsLEz5+fmSJLvdrnbtqv6hnZycrEOHDumVV16RVBXYTpo0SYsWLVJcXJzzmHbt2slut0uS5s2bp7i4OPXq1UsOh0OLFy9Wdna2lixZ0mwnCgDwTo7iU87HYay0BQAAAADAvfYIy5YtU2FhoW644QZFR0c7t7S0NOeYvLw85eTkOJ+/+OKLKi8v14wZM1yOmTVrlnPMyZMndd9996lv375KTEzUoUOHlJmZqWHDhjXDKQIAvFlRzdCWlbYAAAAAALjfHuFCUlNTXZ5v3rz5gsc899xzeu6559wpBQDgI2iPAAAAAACAqybdiAwAgObiEtrSHgEAAAAAAEJbAIC5aI8AAAAAAIArQlsAgKlqrrS1sdIWAAAAAABCWwCAuRystAUAAAAAwAWhLQDAVEUlNUJbVtoCAAAAAEBoCwAwV1FxjRuRsdIWAAAAAABCWwCAuWr2tA0LYaUtAAAAAACEtgAAU7m0R2ClLQAAAAAAhLYAAHO5rLQNJbQFAAAAAIDQFgBgquqetv7+fmoXbDW5GgAAAAAAzEdoCwAwlaO4qj1CWEh7WSwWk6sBAAAAAMB8hLYAAFNV97Slny0AAAAAAFUIbQEApqruaUs/WwAAAAAAqhDaAgBMU1lZqZJTZyRVtUcAAAAAAACEtgAAExWfW2Ur0R4BAAAAAIBqhLYAANMU1QhtbaGstAUAAAAAQCK0BQCYqGZoS09bAAAAAACqENoCAEzjKD7lfExPWwAAAAAAqhDaAgBMU+QS2rLSFgAAAAAAidAWAGCiIm5EBgAAAABALYS2AADTFJXUWGnLjcgAAAAAAJBEaAsAMFFRMSttAQAAAAA4H6EtAMA0Ndsj2FhpCwAAAACAJEJbAICJHNyIDAAAAACAWghtAQCmoactAAAAAAC1EdoCAExDT1sAAAAAAGojtAUAmKZmT9uwEFbaAgAAAAAgEdoCAEzk2h6BlbYAAAAAAEiEtgAAE9VcaRvantAWAAAAAACJ0BYAYKLqnrbt21kVEOBvcjUAAAAAAHgHQlsAgGkcxVXtEehnCwAAAADA9whtAQCmqe5pGxZCawQAAAAAAKoR2gIATGEYhrOnLTchAwAAAADge26FtikpKRo6dKjCwsIUERGhcePGad++fRc8bsuWLYqNjVVwcLAuu+wyLV++vNaYtWvXql+/frJarerXr5/Wr1/vTmkAgFamtOysyssrJNEeAQAAAACAmtwKbbds2aIZM2bo448/VkZGhsrLy5WYmKiSkpJ6jzl48KBuvfVWjRw5UllZWXr88cc1c+ZMrV271jlmx44dmjBhgiZOnKjdu3dr4sSJGj9+vD755JOmnxkAwKsVnetnK9EeAQAAAACAmgLcGfy3v/3N5fnq1asVERGhXbt26brrrqvzmOXLl6tHjx5auHChJKlv377auXOn/vSnP+muu+6SJC1cuFCjRo1ScnKyJCk5OVlbtmzRwoULtWbNGnfPCQDQClS3RpAIbQEAAAAAqOmietoWFhZKksLDw+sds2PHDiUmJrrsGz16tHbu3KmzZ882OGb79u31vm9paakcDofLBgBoPWqGtrYw2iMAAAAAAFCtyaGtYRhKSkrStddeqwEDBtQ7Lj8/X5GRkS77IiMjVV5eroKCggbH5Ofn1/u+KSkpstvtzq179+5NPRUAgAkcRTXbIxDaAgAAAABQrcmh7QMPPKDPPvusUe0LLBaLy3PDMGrtr2vM+ftqSk5OVmFhoXPLzc11p3wAgMmKSuhpCwAAAABAXdzqaVvtwQcf1MaNG5WZmalu3bo1ODYqKqrWitkjR44oICBAnTp1anDM+atva7JarbJarU0pHwDgBehpCwAAAABA3dxaaWsYhh544AGtW7dO//jHP9SzZ88LHhMfH6+MjAyXfZs2bdKQIUMUGBjY4JiEhAR3ygMAtCIuK21DaY8AAAAAAEA1t0LbGTNm6NVXX9Xrr7+usLAw5efnKz8/X6dPf79aKjk5WZMmTXI+nz59ur755hslJSVp7969WrVqlVauXKlHHnnEOWbWrFnatGmT5s+fry+//FLz58/XBx98oNmzZ1/8GQIAvFJRMSttAQAAAACoi1uh7bJly1RYWKgbbrhB0dHRzi0tLc05Ji8vTzk5Oc7nPXv2VHp6ujZv3qxrrrlGv/3tb7V48WLdddddzjEJCQl64403tHr1al199dVKTU1VWlqahg8f3gynCADwRrRHAAAAAACgbm71tK2+gVhDUlNTa+27/vrr9a9//avB4+6++27dfffd7pQDAGjFarZHsIWGmFgJAAAAAADexa2VtgAANBdHcc2etqy0BQAAAACgGqEtAMAU9LSt39KlS9WzZ08FBwcrNjZWW7dubXD8a6+9poEDB6p9+/aKjo7WPffco2PHjnmoWgAAvAdzKADAVxDaAgBM4drTtr2JlXiXtLQ0zZ49W3PmzFFWVpZGjhypMWPGuPSLr2nbtm2aNGmSpk6dqi+++EJvvfWWPv30U02bNs3DlQMAYC7mUACALyG0BQCYomZPW9ojfG/BggWaOnWqpk2bpr59+2rhwoXq3r27li1bVuf4jz/+WJdeeqlmzpypnj176tprr9X999+vnTt3erhyAADMxRwKAPAlhLYAAFNUr7T19/dTsDXI5Gq8Q1lZmXbt2qXExESX/YmJidq+fXudxyQkJOjbb79Venq6DMPQd999p7ffflu33XZbvd+ntLRUDofDZQMAoDVjDgUA+BpCWwCAKap72oaFtJfFYjG5Gu9QUFCgiooKRUZGuuyPjIxUfn5+ncckJCTotdde04QJExQUFKSoqCh16NBBzz//fL3fJyUlRXa73bl17969Wc8DAABPYw4FAPgaQlsAgCkcxVXtEWyh9LM93/khtmEY9Qbbe/bs0cyZM/XEE09o165d+tvf/qaDBw9q+vTp9b5/cnKyCgsLnVtubm6z1g8AgFmYQwEAviLA7AIAAG1TdU9b+tl+r3PnzvL396+1IujIkSO1Vg5VS0lJ0YgRI/Too49Kkq6++mqFhIRo5MiRevrppxUdHV3rGKvVKqvV2vwnAACASZhDAQC+hpW2AACPq6io0KnTpZKksBBC22pBQUGKjY1VRkaGy/6MjAwlJCTUecypU6fk5+c6nfv7+0uqWl0EAEBbwBwKAPA1hLYAAI8rLjnjfBwWQnuEmpKSkvTyyy9r1apV2rt3rx566CHl5OQ4f1QzOTlZkyZNco4fO3as1q1bp2XLlunAgQP66KOPNHPmTA0bNkxdu3Y16zQAAPA45lAAgC+hPQIAwOOqWyNIrLQ934QJE3Ts2DE99dRTysvL04ABA5Senq6YmBhJUl5ennJycpzjp0yZoqKiIr3wwgt6+OGH1aFDB910002aP3++WacAAIApmEMBAL7EYvjIz304HA7Z7XYVFhbKZrOZXQ4AoAF7v85Rv8T7JEmT77pFqX98pFnel7mgabhuAADmgqbhugEAWmouoD0CAMDjikpOOx/THgEAAAAAAFeEtgAAj3MUlTgf20IJbQEAAAAAqInQFgDgca4rbelpCwAAAABATYS2AACPI7QFAAAAAKB+hLYAAI8rKj7lfBxGewQAAAAAAFwQ2gIAPI6VtgAAAAAA1I/QFgDgcYS2AAAAAADUj9AWAOBxRSW0RwAAAAAAoD6EtgAAj3PU6GlrI7QFAAAAAMAFoS0AwOOKimmPAAAAAABAfQhtAQAe59rTlpW2AAAAAADURGgLAPC4mj1tQ0OCTawEAAAAAADvQ2gLAPC46pW27dtZ5e/vb3I1AAAAAAB4F0JbAIDHVfe0pTUCAAAAAAC1EdoCADyuuj2CLZTQFgAAAACA8xHaAgA8yjAMOYqrQtuw0HYmVwMAAAAAgPchtAUAeNSZ0jJVVFRKksJCCG0BAAAAADgfoS0AwKOq+9lK9LQFAAAAAKAuhLYAAI+q7mcrsdIWAAAAAIC6ENoCADyqqKTGSlt62gIAAAAAUIvboW1mZqbGjh2rrl27ymKx6J133mlw/JQpU2SxWGpt/fv3d45JTU2tc8yZM2fcPiEAgHejPQIAAAAAAA1zO7QtKSnRwIED9cILLzRq/KJFi5SXl+fccnNzFR4erh/96Ecu42w2m8u4vLw8BQcHu1seAMDLOYpLnI9toYS2AAAAAACcL8DdA8aMGaMxY8Y0erzdbpfdbnc+f+edd3TixAndc889LuMsFouioqLcLQcA0Mq4tEegpy0AAAAAALV4vKftypUrdcsttygmJsZlf3FxsWJiYtStWzfdfvvtysrK8nRpAAAPoKctAAAAAAANc3ul7cXIy8vTe++9p9dff91lf58+fZSamqqrrrpKDodDixYt0ogRI7R792716tWrzvcqLS1VaWmp87nD4WjR2gEAzaOo+JTzMT1tAQAAAACozaMrbVNTU9WhQweNGzfOZX9cXJx+9rOfaeDAgRo5cqTefPNN9e7dW88//3y975WSkuJsvWC329W9e/cWrh4A0BxojwAAAAAAQMM8FtoahqFVq1Zp4sSJCgoKanCsn5+fhg4dqv3799c7Jjk5WYWFhc4tNze3uUsGALQA19CWlbYAAAAAAJzPY+0RtmzZoq+//lpTp0694FjDMJSdna2rrrqq3jFWq1VWq7U5SwQAeEBRSY32CPS0BQAAAACgFrdD2+LiYn399dfO5wcPHlR2drbCw8PVo0cPJScn69ChQ3rllVdcjlu5cqWGDx+uAQMG1HrPefPmKS4uTr169ZLD4dDixYuVnZ2tJUuWNOGUAADezFH0fWhrC2WlLQAAAAAA53M7tN25c6duvPFG5/OkpCRJ0uTJk5Wamqq8vDzl5OS4HFNYWKi1a9dq0aJFdb7nyZMndd999yk/P192u12DBg1SZmamhg0b5m55AAAvR09bAAAAAAAa5nZoe8MNN8gwjHpfT01NrbXPbrfr1KlTtQef89xzz+m5555ztxQAQCvkEtqy0hYAAAAAgFo8diMyAACk73vaBgT4yxoUaHI1AAAAAAB4H0JbAIBHFRVXrbQNC2kni8VicjUAAAAAAHgfQlsAgEdVt0cIC6E1AgAAAAAAdSG0BQB4VHV7hLBQbkIGAAAAAEBdCG0BAB5TXl6hU6dLJUk2bkIGAAAAAECdCG0BAB5TfOq083FYCCttAQAAAACoC6EtAMBjqm9CJtHTFgAAAACA+hDaAgA8prqfrcRKWwAAAAAA6kNoCwDwmKKSGittuREZAAAAAAB1IrQFAHhMUXHNlba0RwAAAAAAoC6EtgAAj3EU0x4BAAAAAIALIbQFAHhMzfYItlBW2gIAAAAAUBdCWwCAx9DTFgAAAACACyO0BQB4DD1tAQAAAAC4MEJbAIDHuKy0pactAAAAAAB1IrQFAHiMa3sEVtoCAAAAAFAXQlsAgMcUldRsj8BKWwAAAAAA6kJoCwDwGEcRoS0AAAAAABdCaAsA8Jia7RFstEcAAAAAAKBOhLYAAI+pGdqGstIWAAAAAIA6EdoCADymuqdtSPtg+fkxBQEAAAAAUBf+xQwA8Jii4qqVtvSzBQAAAACgfoS2AACPqW6PEBZCP1sAAAAAAOpDaAsA8AjDMJztEcJCWWkLAAAAAEB9CG0BAB5x+kypKioqJdEeAQAAAACAhhDaAgA8orqfrSTZQmmPAAAAAABAfQhtAQAeUd3PVqKnLQAAAAAADSG0BQB4RHU/W4n2CAAAAAAANITQFgDgES4rbbkRGQAAAAAA9SK0BQB4RFFxzZW2tEcAAAAAAKA+hLYAAI9w7WnLSlsAAAAAAOpDaAsA8AhHzZW2tEcAAAAAAKBehLYAAI+oudLWFhpiYiUAAAAAAHg3t0PbzMxMjR07Vl27dpXFYtE777zT4PjNmzfLYrHU2r788kuXcWvXrlW/fv1ktVrVr18/rV+/3t3SAABezLWnLSttAQAAAACoj9uhbUlJiQYOHKgXXnjBreP27dunvLw859arVy/nazt27NCECRM0ceJE7d69WxMnTtT48eP1ySefuFseAMBL0dMWAAAAAIDGcTu0HTNmjJ5++mndeeedbh0XERGhqKgo5+bv7+98beHChRo1apSSk5PVp08fJScn6+abb9bChQvdLQ8A4KVcQtvQ9iZW4v2WLl2qnj17Kjg4WLGxsdq6dWuD40tLSzVnzhzFxMTIarXq8ssv16pVqzxULQAA3oM5FADgKwI89Y0GDRqkM2fOqF+/fvr1r3+tG2+80fnajh079NBDD7mMHz16dIOhbWlpqUpLS53PHQ5Hs9cMAGg+tEdonLS0NM2ePVtLly7ViBEj9OKLL2rMmDHas2ePevToUecx48eP13fffaeVK1fqiiuu0JEjR1ReXu7hygEAMBdzKADAl7R4aBsdHa0VK1YoNjZWpaWl+stf/qKbb75Zmzdv1nXXXSdJys/PV2RkpMtxkZGRys/Pr/d9U1JSNG/evBatHQDQfByEto2yYMECTZ06VdOmTZNU9dMo77//vpYtW6aUlJRa4//2t79py5YtOnDggMLDwyVJl156qSdLBgDAKzCHAgB8idvtEdx15ZVX6t5779XgwYMVHx+vpUuX6rbbbtOf/vQnl3EWi8XluWEYtfbVlJycrMLCQueWm5vbIvUDAJpHzfYINtoj1KmsrEy7du1SYmKiy/7ExERt3769zmM2btyoIUOG6A9/+IMuueQS9e7dW4888ohOnz5d53ip6qdVHA6HywYAQGvGHAoA8DUea49QU1xcnF599VXn86ioqFqrao8cOVJr9W1NVqtVVqu1xWoEADSv6tA2MDBAVmuQydV4p4KCAlVUVLj10ycHDhzQtm3bFBwcrPXr16ugoEC/+MUvdPz48Xp78vHTKgAAX8McCgDwNS2+0rYuWVlZio6Odj6Pj49XRkaGy5hNmzYpISHB06UBAFpIUUlVewRaI1yYOz99UllZKYvFotdee03Dhg3TrbfeqgULFig1NbXelUL8tAoAwFcxhwIAfIXbK22Li4v19ddfO58fPHhQ2dnZCg8PV48ePZScnKxDhw7plVdekVTVR+jSSy9V//79VVZWpldffVVr167V2rVrne8xa9YsXXfddZo/f77uuOMObdiwQR988IG2bdvWDKcIAPAGRcVV//ghtK1f586d5e/v79ZPn0RHR+uSSy6R3W537uvbt68Mw9C3336rXr161TqGn1YBAPga5lAAgK9xe6Xtzp07NWjQIA0aNEiSlJSUpEGDBumJJ56QJOXl5SknJ8c5vqysTI888oiuvvpqjRw5Utu2bdNf//pX3Xnnnc4xCQkJeuONN7R69WpdffXVSk1NVVpamoYPH36x5wcA8BLV7RHCQuhnW5+goCDFxsbW+umTjIyMen/6ZMSIETp8+LCKi4ud+7766iv5+fmpW7duLVovAADegjkUAOBrLIZhGGYX0RwcDofsdrsKCwtls9nMLgcAUEN5eYUCe98mSYof3Ffb336uRb6PL8wFaWlpmjhxopYvX674+HitWLFCL730kr744gvFxMTU+omW4uJi9e3bV3FxcZo3b54KCgo0bdo0XX/99XrppZca9T194boBAC6OL8wFzKEAADO01Fxgyo3IAABtS3U/W4n2CBcyYcIEHTt2TE899ZTy8vI0YMAApaenKyYmRlLtn2gJDQ1VRkaGHnzwQQ0ZMkSdOnXS+PHj9fTTT5t1CgAAmII5FADgS1hpCwBocTmHjihm5CRJ0t1jrtVbS37dIt+HuaBpuG4AAOaCpuG6AQBaai5wu6ctAADucl1pS09bAAAAAAAaQmgLAGhx1Tchk6SwUNojAAAAAADQEEJbAECLKyquEdrS0xYAAAAAgAYR2gIAWhztEQAAAAAAaDxCWwBAi3Npj8BKWwAAAAAAGkRoCwBocY7iGitt6WkLAAAAAECDCG0BAC2uZk9bW2iIiZUAAAAAAOD9CG0BAC3OtactK20BAAAAAGgIoS0AoMXR0xYAAAAAgMYjtAUAtDiX0Da0vYmVAAAAAADg/QhtAQAtrqiY9ggAAAAAADQWoS0AoMU5CG0BAAAAAGg0QlsAQIur2R4hlNAWAAAAAIAGEdoCAFpcdWgbGtJOfn5MPQAAAAAANIR/OQMAWlxRSVV7BFojAAAAAABwYYS2AIAWV1RctdKW0BYAAAAAgAsjtAUAtCjDMJztEcJC2ptcDQAAAAAA3o/QFgDQok6fKVVlZaUkKSyUlbYAAAAAAFwIoS0AoEU5ik85H9MeAQAAAACACyO0BQC0qOp+thLtEQAAAAAAaAxCWwBAiyoq+X6lrS2U0BYAAAAAgAshtAUAtKjqm5BJ9LQFAAAAAKAxCG0BAC3KtT0CoS0AAAAAABdCaAsAaFE12yPQ0xYAAAAAgAsjtAUAtCiX9gistAUAAAAA4IIIbQEALcpRXGOlLTciAwAAAADggghtAQAtip62AAAAAAC4h9AWANCiava0tbHSFgAAAACACyK0BQC0KHraAgAAAADgHkJbAECLcgltWWkLAAAAAMAFuR3aZmZmauzYseratassFoveeeedBsevW7dOo0aNUpcuXWSz2RQfH6/333/fZUxqaqosFkut7cyZM+6WBwDwMkU1b0TGSlsAAAAAAC7I7dC2pKREAwcO1AsvvNCo8ZmZmRo1apTS09O1a9cu3XjjjRo7dqyysrJcxtlsNuXl5blswcHB7pYHAPAytEcAAAAAAMA9Ae4eMGbMGI0ZM6bR4xcuXOjy/Pe//702bNigd999V4MGDXLut1gsioqKcrccAICXc5xbaRsYGCCrNcjkagAAAAAA8H4e72lbWVmpoqIihYeHu+wvLi5WTEyMunXrpttvv73WStzzlZaWyuFwuGwA2oZHU15S4qTHte9ArtmloBGqV9qyyhYAAAAAgMbxeGj77LPPqqSkROPHj3fu69Onj1JTU7Vx40atWbNGwcHBGjFihPbv31/v+6SkpMhutzu37t27e6J8ACb7/MuD+tNLa5Wx7V+68/9+qzOlZWaXhAsoKqlaaWvjJmQAAAAAADSKR0PbNWvWaO7cuUpLS1NERIRzf1xcnH72s59p4MCBGjlypN5880317t1bzz//fL3vlZycrMLCQueWm8uKO6AtyDl8xPl4z/4cPf7H1SZWg8YoKmalLQAAAAAA7vBYaJuWlqapU6fqzTff1C233NLgWD8/Pw0dOrTBlbZWq1U2m81lA+D7jh4vdHn+3Kr1+sf2bHOKwQWVl1c4V0OHsdIWAAAAAIBG8Uhou2bNGk2ZMkWvv/66brvttguONwxD2dnZio6O9kB1AFqTghO1+1dPefRZnXQUm1ANLqS6NYLESlsAAAAAABrL7dC2uLhY2dnZys7OliQdPHhQ2dnZysnJkVTVtmDSpEnO8WvWrNGkSZP07LPPKi4uTvn5+crPz1dh4fer5ebNm6f3339fBw4cUHZ2tqZOnars7GxNnz79Ik8PgK85euz7PzuiunSUJOXmHdXMecvMKgkNcBQT2gIAAAAA4C63Q9udO3dq0KBBGjRokCQpKSlJgwYN0hNPPCFJysvLcwa4kvTiiy+qvLxcM2bMUHR0tHObNWuWc8zJkyd13333qW/fvkpMTNShQ4eUmZmpYcOGXez5AfAxNdsjrJqf5Ly51V/W/11r39tmVlmoR3U/W0kKC6E9AgAAAAAAjRHg7gE33HCDDMOo9/XU1FSX55s3b77gez733HN67rnn3C0FQBtUM7Qd3P8KvTDvF5r08J8kSff/erESYvsqOqKTWeXhPC7tEUJZaQsAAAAAQGN47EZkANAcaoa2nTra9LNxN+vuMddKko6dcGjqrxY2+B9L8Kyiku9X2tq4ERkAAAAAAI1CaAugVakObTvaQxUQ4C+LxaLlT8909rd9b8unWrEm3cwSUQPtEQAAAAAAcB+hLYBWpeBcaNsl3O7c16mjTavmJzmfJ/1uhb7+72GP14baXNojcCMyAAAAAAAahdAWQKtRWlomR3FVCFgztJWkMTcM1fSf3CZJOnW6VBMf/oPKyys8XiNc1WyPQE9bAAAAAAAah9AWQKtRcMLhfNylU4dar//p8Xt1RUxXSdLHWV9q/otveqo01KM6ZJdojwAAAAAAQGMR2gJoNWrehOz8lbaSFNI+WH9Z8Kj8/Kr+aJu76FX969/7PVYfanPtactKWwAAAAAAGoPQFkCrcfRYw6GtJMUN6qvHfzFBklReXqGfJf1Rp8+UeqQ+1EZPWwAAAAAA3EdoC6DVqLnStnNHW73jnnjwpxo84ApJ0t6vc/T4H1NbujTUo2ZPW1sY7REAAAAAAGgMQlsArUbBiRorbTvVvdJWkgIDA/SXZx+VNShQkrRw9Xr9/aOsFq8PtbnciIyetgAAAAAANAqhLYBW40I9bWvq1ytGz/zy587nU375rI4eO9lSpaEeRcW0RwAAAAAAwF2EtgBajcb0tK1p5pQ7dFP8NZKkb/MKNOae37iEiGh5NVfahrQPNrESAAAAAABaD0JbAK2G60rbDhcc7+fnpz//6WFdEtVZkrTr3/s17v6ndKa0rKVKxHkc50Ly0JB28vNjygEAAAAAoDH4FzSAVsPlRmTh9d+IrKZu0V206c+/U3iHMEnSP3Zk6yeznlF5eUWL1AhX1SttaY0AAAAAAEDjEdoCaDWqQ9uQ9sFqF2xt9HH9esUofdVvnT+ev37Tdk3/9WIZhtEideJ71e0obKHchAwAAAAAgMYitAXQahSccEhqXD/b8w2/po/WL3tCgYEBkqSVb76v5D+sbtb64MowDBWfOiOJlbYAAAAAALiD0BZAq1BRUaFjFxHaStKokYP12nO/lMVikSTNf/FN/XHFW81WI1ydOl2qyspKSVIYK20BAAAAAGg0QlsArcLxk0XOdgZNDW0l6Ue3Xqdlv33A+fyXz6zUqjffv+j6UFtRySnnY1baAgAAAADQeIS2AFqFmjch69Kp6aGtJN3/k9v0u4enOJ/f+/girX//o4t6T9TmKCK0BQAAAACgKQhtAbQKNUPbzh0vLrSVpORfTNDse/5HklRZWakfz3pGH+7YfdHvi+8VlZx2Pg4LoT0CAAAAAACNRWgLoFUoOO5wPr6Y9gjVLBaLnp1zryb+z82SpLKys/rhfXO187OvLvq9UcWlPUIoK20BAAAAAGgsQlsArYJLe4RmCG0lyc/PTyufeUi33zRcklRcclpjfv4b7TuQ2yzv39bVXGlr40ZkAAAAAAA0GqEtgFbh6PGTzscX29O2psDAAL35wuMaOXSAJKngeKFGTXxcuYePNtv3aKuKimmPAAAAAABAUxDaAmgVWmKlbbV2wVZtfGmuBva9TJKUm3dUN/70l/ryP6y4vRgu7RG4ERkAAAAAAI1GaAugVTh6rOVCW0nqYAvV31Kf1uUx0ZKk/3yTp+H/M0vvbf602b9XW+FyIzJ62gIAAAAA0GiEtgBahZorbTt3bP7QVpKiuoTrH6/O19V9ekqSHMWndNvUJ/Snl96WYRgt8j19maO45kpb2iMAAAAAANBYhLYAWoWCEw5JVT1obWEtFwD2uCRCH721QHeOHiFJMgxDj6a8rCmPPqszpWUt9n19kWtPW1baumPp0qXq2bOngoODFRsbq61btzbquI8++kgBAQG65pprWrZAAAC8FHMoAMBXENoCaBWqV9p2CbfLYrG06PcKDWmnt5bM0ZMzf+rc98q6D3TD//5SeUeOtej39iX0tG2atLQ0zZ49W3PmzFFWVpZGjhypMWPGKCcnp8HjCgsLNWnSJN18880eqhQAAO/CHAoA8CWEtgC8nmEYLqGtJ/j5+Wnu7Il664U5at/OKkn6JPtLDbljpj7dvc8jNbR2NXvatuTqaF+zYMECTZ06VdOmTVPfvn21cOFCde/eXcuWLWvwuPvvv18/+clPFB8f76FKAQDwLsyhAABfQmgLwOs5ik7p7NlySZ4LbavdfetIffTWAvXoGiFJOvzdMV3340f1+oYPPVpHa+TaHoHQtjHKysq0a9cuJSYmuuxPTEzU9u3b6z1u9erV+s9//qMnn3yyUd+ntLRUDofDZQMAoDVjDgUA+BpCWwBer+ZNyDwd2krSNf0u16fvLNaI2H6SpDOlZfrpQ/OV/IdVqqys9Hg9rQXtEdxXUFCgiooKRUZGuuyPjIxUfn5+ncfs379fjz32mF577TUFBAQ06vukpKTIbrc7t+7du1907QAAmIk5FADgawhtAXi9o8dPOh93DreZUkNE5w76x2vzNXX8aOe+Z5a/qXH3z5OjqMSUmrxddXuEoKBABQUFmlxN63J+32bDMOrs5VxRUaGf/OQnmjdvnnr37t3o909OTlZhYaFzy83NveiaAQDwBsyhAABf0bj/TgQAExWc+P7HzsxYaVstKChQL6XM1sC+l+mhp19URUWl3v37J4q/+yGtXfob9bmclRY1OYqrVtqyyrbxOnfuLH9//1orgo4cOVJr5ZAkFRUVaefOncrKytIDDzwgSaqsrJRhGAoICNCmTZt000031TrOarXKarW2zEkAAGAC5lAAgK9hpS0Ar3f0WM32CB3MK0RVqzcenHyH/rb6aXW0h0qS9uzP0dW3/p9+/WyqTp0+Y2p93qR6pS2hbeMFBQUpNjZWGRkZLvszMjKUkJBQa7zNZtPnn3+u7Oxs5zZ9+nRdeeWVys7O1vDhwz1VOgAApmIOBQD4GrdD28zMTI0dO1Zdu3aVxWLRO++8c8FjtmzZotjYWAUHB+uyyy7T8uXLa41Zu3at+vXrJ6vVqn79+mn9+vXulgbAR5nd07Yut1w7WP9cv1h9r+ghSTp7tly/W/KG+o++X+/+/WOTq/MORay0bZKkpCS9/PLLWrVqlfbu3auHHnpIOTk5mj59uqSqH8ucNGmSJMnPz08DBgxw2SIiIhQcHKwBAwYoJCTEzFMBAMCjmEMBAL7E7dC2pKREAwcO1AsvvNCo8QcPHtStt96qkSNHKisrS48//rhmzpyptWvXOsfs2LFDEyZM0MSJE7V7925NnDhR48eP1yeffOJueQB8kEto28k7QltJuuLSrtq5YbEe/8WPFRhY1W3mv99+px/eO1d33DdX//227ptetAVnz5artOysJMkW2t7kalqXCRMmaOHChXrqqad0zTXXKDMzU+np6YqJiZEk5eXlKScnx+QqAQDwPsyhAABfYjEMw2jywRaL1q9fr3HjxtU75le/+pU2btyovXv3OvdNnz5du3fv1o4dOyRVTa4Oh0Pvvfeec8wPfvADdezYUWvWrGlULQ6HQ3a7XYWFhbLZzLlREYCWMfmRP+mVdR9Ikr54/0X16xVjckW1ffmfXD3w5BL9fXu2c1+7YKt+PeN/9fC0O2W1BplXnAmOnyxSp8E/kiT94Pohem/10x75vswFTcN1AwAwFzQN1w0A0FJzQYv3tN2xY4cSExNd9o0ePVo7d+7U2bNnGxyzffv2et+3tLRUDofDZQPgm1x72nrPStua+lzeXRl/SdGaRY8pOiJcknT6TKnmPJuqgbf9Qn//KMvkCj2rqOSU8zHtEQAAAAAAcE+Lh7b5+fm17tYZGRmp8vJyFRQUNDjm/Dt/1pSSkiK73e7cunfnru2Aryo4URXaWiwWhXcIM7ma+lksFv147A36MuMlzb7nf+TnV/VH7L4D3+qWicn635kpOvzdMZOr9Iyi4tPOx4S2AAAAAAC4p8VDW6kqyKipuiNDzf11jTl/X03JyckqLCx0brm5uc1YMQBvUt3TtlNHm/z9/U2u5sJsYSF67jf3a9fG5xU/uK9z/xv/b4v6jLpXz61cp9NnSk2ssOU5imuutKWnLQAAAAAA7mjx0DYqKqrWitkjR44oICBAnTp1anDM+atva7JarbLZbC4bAN9UHdp6a2uE+lzT73Jte/NZrXzmIXXqWPVnVFHxKSX9boW6JfxMv3zmZR3M9c2blbm0RwhlpS0AAAAAAO5o8dA2Pj5eGRkZLvs2bdqkIUOGKDAwsMExCQkJLV0eAC93+kypSk6dkdT6QltJ8vPz08/Hj9a+D17WvT8e49x//GSR/rjibV1+wz364b1PatPWXaqsrDSx0ubl2h6BlbYAAAAAALjD7dC2uLhY2dnZys7OliQdPHhQ2dnZysnJkVTVtmDSpEnO8dOnT9c333yjpKQk7d27V6tWrdLKlSv1yCOPOMfMmjVLmzZt0vz58/Xll19q/vz5+uCDDzR79uyLOzsArV7Nm5B17th6V9R36mjTit/P0q6Nz2vi/9ysoKCq/7QyDEPv/v0TjZ48R31H3avFqe+o0FFicrUXr6jk+9DWFkpoCwAAAACAO9wObXfu3KlBgwZp0KBBkqSkpCQNGjRITzzxhCQpLy/PGeBKUs+ePZWenq7Nmzfrmmuu0W9/+1stXrxYd911l3NMQkKC3njjDa1evVpXX321UlNTlZaWpuHDh1/s+QFo5apbI0itc6Xt+QYP6KVXnn1Uudv+ot89PEXdojs7X/vq4CHNemq5Lkn4qf7v18/r3/v+a16hF8mlPQI3IgMAAAAAwC0B7h5www03OG8kVpfU1NRa+66//nr961//avB97777bt19993ulgPAxxWcqBHadmr9oW21iM4d9PiMH+uX9/9IGz/YoRf+8q4+3LFbklRy6oyWv/5XLX/9r7oh7mrdc3eibowbqO5du5hcdcNKS8u0699fa/u/9mjNu5ud++lpCwAAAACAe9wObQHAk3xtpe35AgL8decPrtWdP7hWX3z1Xy199f/pz+s+cPbx3fzxZ9r88WeSpJ7do3TdsAG6buhVun74VbqsR7QsFotptR89dlLb/7VXH+36Qh/t2qOdn+9XWdnZWuOiuoSbUB0AAAAAAK0XoS0Ar1azp60vhrY19e99qZY89YB+/8g9emX9B1ryl3e178C3ztcP5ubrYG6+/rz2A0lS18hOum7oAF0//GpdN2yA+l7Ro0VCXMMwdPRYob459J2y9x7Q9l179NGuPdr/30MNHtfBFqqf3nGjhl7du9lrAgAAAADAlxHaAvBqvr7Sti52W4genHyHHpj0Q2399N/6+0fZyvz0c32c9aXOlJY5xx3+7pje+H9b9Mb/2yJJ6hxu18gh/dWze5Q62ELUwRaqjvZQdQgLdT6v/hrSPtgZ8J49W65D3xXom0NH9M2hI8o5fMT5+JtD3ynn8FGX71ufK2K6asSQfhoR218Jg/uq7xU95Ofndut0AAAAAADaPEJbAF6tZmjbuY2EttUsFouuG3aVrht2laSqnrGffvaVMv/5b23552f6aNceZxsFSSo4Xqj1m7Y36r0DAvzVwRaqoMAA5R89ocrKSrdqCwoK1JCremlEbD8lDK7aIjp3cOs9AAAAAABA3QhtAXi1trjStj5Wa5CuHTpA1w4doMdn/Fjl5RXK+uJrbfnn58r85+fa+ukXOukobtR7lZdXqKDGta1P+3ZWxVwSqZhLIhTTNUJXXNpVCYP7afCAKxRsDbrYUwIAAAAAAHUgtAXg1WoGi5072kysxPsEBPhr6MArNXTglXrk3rtVWVmp/f89pILjDp10FOuEo1gnHSU6ee7ricKic89LdLKoWCcKi3WmtExdIzpVhbKXRCjmkkj16NrF+Ti8Q5ipNzsDAAAAAKAtIrQF4NWqV9raQtvLysrOBvn5+enKy7rrysvMrgQAAAAAAFwM7hADwKtVh7ZdOrXt1ggAAAAAAKDtILQF4LXOni3XicKqHq2dOxLaAgAAAACAtoHQFoDXOnbS4Xzc1m9CBgAAAAAA2g5CWwBeq+A4oS0AAAAAAGh7CG0BeK3qfrYSoS0AAAAAAGg7CG0BeC2X0JYbkQEAAAAAgDaC0BaA1zp67KTzMSttAQAAAABAW0FoC8Br1Vxp27mjzcRKAAAAAAAAPIfQFoDXcm2P0MG8QgAAAAAAADyI0BaA1yo44XA+pj0CAAAAAABoKwhtAXgtl5W2hLYAAAAAAKCNILQF4LWOHqsKbYOtQQppH2xyNQAAAAAAAJ5BaAvAa1WvtO0SbpfFYjG5GgAAAAAAAM8gtAXglSorK1Vwoiq07RxuM7kaAAAAAAAAzyG0BeCVTjpKVFFRKYl+tgAAAAAAoG0htAXglQq4CRkAAAAAAGijCG0BeKWjLqFtB/MKAQAAAAAA8DBCWwBe6SgrbQEAAAAAQBtFaAvAK9UMbbkRGQAAAAAAaEsIbQF4paPHWGkLAAAAAADaJkJbAF6J9ggAAAAAAKCtIrQF4JUKTtQIbTsR2gIAAAAAgLaD0BaAV3JdadvBvEIAAAAAAAA8jNAWgFeqDm39/f3UwRZicjUAAAAAAACeQ2gLwCtV34isUweb/Pz4owoAAAAAALQdTUpCli5dqp49eyo4OFixsbHaunVrvWOnTJkii8VSa+vfv79zTGpqap1jzpw505TyALRyhmE4V9rSzxYAAAAAALQ1boe2aWlpmj17tubMmaOsrCyNHDlSY8aMUU5OTp3jFy1apLy8POeWm5ur8PBw/ehHP3IZZ7PZXMbl5eUpODi4aWcFoFUrOXVGZ0rLJEldwgltAQAAAABA2+J2aLtgwQJNnTpV06ZNU9++fbVw4UJ1795dy5Ytq3O83W5XVFSUc9u5c6dOnDihe+65x2WcxWJxGRcVFdW0MwLQ6hWcqHkTMkJbAAAAAADQtrgV2paVlWnXrl1KTEx02Z+YmKjt27c36j1WrlypW265RTExMS77i4uLFRMTo27duun2229XVlZWg+9TWloqh8PhsgHwDdX9bCVCWwAAAAAA0Pa4FdoWFBSooqJCkZGRLvsjIyOVn59/wePz8vL03nvvadq0aS77+/Tpo9TUVG3cuFFr1qxRcHCwRowYof3799f7XikpKbLb7c6te/fu7pwKAC9W3c9WIrQFAAAAAABtT5NuRGaxWFyeG4ZRa19dUlNT1aFDB40bN85lf1xcnH72s59p4MCBGjlypN5880317t1bzz//fL3vlZycrMLCQueWm5vblFMB4IVqhradCW0BAAAAAEAbE+DO4M6dO8vf37/WqtojR47UWn17PsMwtGrVKk2cOFFBQUENjvXz89PQoUMbXGlrtVpltVobXzyAVoOVtgAAAAAAoC1za6VtUFCQYmNjlZGR4bI/IyNDCQkJDR67ZcsWff3115o6deoFv49hGMrOzlZ0dLQ75QHwEfS0BQAAAAAAbZlbK20lKSkpSRMnTtSQIUMUHx+vFStWKCcnR9OnT5dU1bbg0KFDeuWVV1yOW7lypYYPH64BAwbUes958+YpLi5OvXr1ksPh0OLFi5Wdna0lS5Y08bQAtGYFJwhtAQAAAABA2+V2aDthwgQdO3ZMTz31lPLy8jRgwAClp6crJiZGUtXNxnJyclyOKSws1Nq1a7Vo0aI63/PkyZO67777lJ+fL7vdrkGDBikzM1PDhg1rwikBaO1c2iN0IrQFAAAAAABti8UwDMPsIpqDw+GQ3W5XYWGhbDab2eUAuAjxd83Wx1lfSpLK9v0/BQa6/f9LaKOYC5qG6wYAYC5oGq4bAKCl5gK3etoCgCdUr7TtYAslsAUAAAAAAG0OoS0Ar1N9IzL62QIAAAAAgLaI0BaAVykrOytH8SlJ9LMFAAAAAABtE6EtAK9ScMLhfMxKWwAAAAAA0BYR2gLwKtX9bCVCWwAAAAAA0DYR2gLwKkePnXQ+7tyRO/ACAAAAAIC2h9AWgFdhpS0AAAAAAGjrCG0BeBWX0JYbkQEAAAAAgDaI0BaAV3G9EVkH8woBAAAAAAAwCaEtAK9y9BjtEQAAAAAAQNtGaAvAq9DTFgAAAAAAtHWEtgC8Ss3QtnO4zcRKAAAAAAAAzEFoC8CrVIe27dtZ1b5dsMnVAAAAAAAAeB6hLQCvUh3a0hoBAAAAAAC0VYS2ALxGRUWFjp8skkRoCwAAAAAA2i5CWwBe40RhsSorKyVJXcI7mFsMYKKlS5eqZ8+eCg4OVmxsrLZu3Vrv2HXr1mnUqFHq0qWLbDab4uPj9f7773uwWgAAvAdzKADAVxDaAvAa3IQMkNLS0jR79mzNmTNHWVlZGjlypMaMGaOcnJw6x2dmZmrUqFFKT0/Xrl27dOONN2rs2LHKysrycOUAAJiLORQA4EsshmEYZhfRHBwOh+x2uwoLC2WzEfYArVHmPz/X9T9+VJKUNPVOPTvnPpMrQmvjC3PB8OHDNXjwYC1btsy5r2/fvho3bpxSUlIa9R79+/fXhAkT9MQTTzRqvC9cNwDAxfGFuYA5FABghpaaC1hpC8BrHD32/UpbetqiLSorK9OuXbuUmJjosj8xMVHbt29v1HtUVlaqqKhI4eHh9Y4pLS2Vw+Fw2QAAaM2YQwEAvobQFoDXqNkegdAWbVFBQYEqKioUGRnpsj8yMlL5+fmNeo9nn31WJSUlGj9+fL1jUlJSZLfbnVv37t0vqm4AAMzGHAoA8DWEtgC8RsGJGqFtJ0JbtF0Wi8XluWEYtfbVZc2aNZo7d67S0tIUERFR77jk5GQVFhY6t9zc3IuuGQAAb8AcCgDwFQFmFwAA1Vhpi7auc+fO8vf3r7Ui6MiRI7VWDp0vLS1NU6dO1VtvvaVbbrmlwbFWq1VWq/Wi6wUAwFswhwIAfA0rbQF4jZo9bTt3JLRF2xMUFKTY2FhlZGS47M/IyFBCQkK9x61Zs0ZTpkzR66+/rttuu62lywQAwOswhwIAfA0rbQF4DZeVtrRHQBuVlJSkiRMnasiQIYqPj9eKFSuUk5Oj6dOnS6r6scxDhw7plVdekVT1j81JkyZp0aJFiouLc64wateunex2fh8BANoO5lAAgC8htAXgNapD28DAANnDQkyuBjDHhAkTdOzYMT311FPKy8vTgAEDlJ6erpiYGElSXl6ecnJynONffPFFlZeXa8aMGZoxY4Zz/+TJk5Wamurp8gEAMA1zKADAl1gMwzDMLqI5OBwO2e12FRYWymazmV0OgCa4JP6nOvzdMUVHhOvwx6+bXQ5aIeaCpuG6AQCYC5qG6wYAaKm5gJ62ALyCYRgqOOGQxE3IAAAAAABA20ZoC8ArFBWfUlnZWUmEtgAAAAAAoG0jtAXgFWrehKwzoS0AAAAAAGjDCG0BeIWaoS0rbQEAAAAAQFtGaAvAKxDaAgAAAAAAVCG0BeAVCo47nI8JbQEAAAAAQFtGaAvAK7DSFgAAAAAAoEqTQtulS5eqZ8+eCg4OVmxsrLZu3Vrv2M2bN8tisdTavvzyS5dxa9euVb9+/WS1WtWvXz+tX7++KaUBaKWOHj/pfNw53GZeIQAAAAAAACZzO7RNS0vT7NmzNWfOHGVlZWnkyJEaM2aMcnJyGjxu3759ysvLc269evVyvrZjxw5NmDBBEydO1O7duzVx4kSNHz9en3zyiftnBKBVYqUtAAAAAABAFbdD2wULFmjq1KmaNm2a+vbtq4ULF6p79+5atmxZg8dFREQoKirKufn7+ztfW7hwoUaNGqXk5GT16dNHycnJuvnmm7Vw4UK3TwhA63T0WM3QtoN5hQAAAAAAAJjMrdC2rKxMu3btUmJiosv+xMREbd++vcFjBw0apOjoaN1888368MMPXV7bsWNHrfccPXp0g+9ZWloqh8PhsgFovapX2losFnXqGGZyNQAAAAAAAOZxK7QtKChQRUWFIiMjXfZHRkYqPz+/zmOio6O1YsUKrV27VuvWrdOVV16pm2++WZmZmc4x+fn5br2nJKWkpMhutzu37t27u3MqALxMwYmq/3gJ7xDmshIfAAAAAACgrQloykEWi8XluWEYtfZVu/LKK3XllVc6n8fHxys3N1d/+tOfdN111zXpPSUpOTlZSUlJzucOh4PgFmjFqlfa0s8WAAAAAAC0dW6ttO3cubP8/f1rrYA9cuRIrZWyDYmLi9P+/fudz6Oiotx+T6vVKpvN5rIBaJ3OlJapuOS0JKlzR34vAwAAAACAts2t0DYoKEixsbHKyMhw2Z+RkaGEhIRGv09WVpaio6Odz+Pj42u956ZNm9x6TwCtl+tNyFhpCwAAAAAA2ja32yMkJSVp4sSJGjJkiOLj47VixQrl5ORo+vTpkqraFhw6dEivvPKKJGnhwoW69NJL1b9/f5WVlenVV1/V2rVrtXbtWud7zpo1S9ddd53mz5+vO+64Qxs2bNAHH3ygbdu2NdNpAvBmR4+fdD7u0onQFgAAAAAAtG1uh7YTJkzQsWPH9NRTTykvL08DBgxQenq6YmJiJEl5eXnKyclxji8rK9MjjzyiQ4cOqV27durfv7/++te/6tZbb3WOSUhI0BtvvKFf//rX+s1vfqPLL79caWlpGj58eDOcIgBvV93PVmKlLQAAAAAAgMUwDMPsIpqDw+GQ3W5XYWEh/W2BVua1d/6hnyX9QZK08DfTNeueceYWhFaLuaBpuG4AAOaCpuG6AQBaai5wq6ctALQEVtoCAAAAAAB8j9AWgOlqhradw1mhAAAAAAAA2jZCWwCmY6UtAAAAAADA9whtAZiO0BYAAAAAAOB7hLYATHf02EnnY0JbAAAAAADQ1hHaAjBdwQmHJCkstL2s1iCTqwEAAAAAADAXoS0AU736zt/1n5w8SVLnjtyEDAAAAAAAIMDsAgC0TRUVFUr+42r9ccXbzn13/WCEiRUBAAAAAAB4B0JbAB5X6CjRT2Y/o/TNnzr33fe/Y/S7h6eYVxQAAAAAAICXILQF4FH7Dx7SD++bqy//kytJ8vf306LfTNcvJo6VxWIxuToAAAAAAADzEdoC8JiMrf/S+Ad/r5OOYklSeIcwvfXCHN2UcI25hQEAAAAAAHgRQlsALc4wDD3/5w1K+t0KVVRUSpL69eqhjSvm6vKYriZXBwAAAAAA4F0IbQG0qNLSMs14colWvvm+c9/Ym4fr1QW/lC0sxMTKAAAAAAAAvBOhLYAW893RE7rrF7/VR7v2OPc9/osf67dJk+Tn52diZQAAAAAAAN6L0BZAi8j64mvdcd885eYdlSQFW4O0av5D+t8f3mhyZQAAAAAAAN6N0BZAszpScFJLX31Xf1jxtk6fKZUkXRLVWe8sf0JDru5tcnUAAAAAAADej9AWQLPY+3WOnlu1Xq+s+0ClZWed+4df00frl/9G0RGdTKwOAAAAAACg9SC0BdBkhmHowx279ezLa5W++VOX1/z9/XTvhDF67jf3K9gaZFKFAAAAAAAArQ+hLQC3nT1brrS/btGCleuU9cV/XF4LC22v+348RjMn36Eel0SYVCEAAAAAAEDrRWgLoNFOOoq1Ys17WvznDTqUX+DyWo+uEZo15Q5Nm/AD2cJCTKoQAAAAAACg9SO0BdCg0tIybf3033onY4f+vO4DFZecdnl9yFW99PC0u3T3mJEKCPA3qUoAAAAAAADfQWgLoJbcw0f13pZPlb75U33wUZZKTp1xed1isWjszcP18LS7NHLoAFksFpMqBQAAAAAA8D2EtgB09my5dmTtVfqHnyp98z/1+b7/1jku2BqkKXeN0kM//x/1vqybZ4sEAAAAAABoIwhtgTbIMAzlHD6iD3fsVvrmT7Vp679UWFRS59iITh005oYhuvWGYUocOVgdbKEerhYAAAAAAKBtIbQFfJxhGDr83THt/Hy/dn7+1bmv+1VwvLDO8RaLRcMGXqlbbxiqW28YqsEDrpCfn5+HqwYAAAAAAGi7CG0BH5N/9Lh2nQtmq4Pa/KMnGjymoz1UP7huiG69cahGj4xVl04dPFMsAAAAAAAAaiG0BVqhiooK5eYd1b4D3+qrg4f01cFD2nfgW+35OkeH8gsueHzncLuGXNVLw66+UqOvi9WwgVcqIMDfA5UDAAAAAADgQghtAS9lGIYKjhdq/38PnwtnqwLafQe/1df/PazSsrONep8OtlANuarXua23hlzdSz26RshisbTwGQAAAAAAAKApCG0BkziKSpSbd1Q5h48qN6/GdrjA+fhMaZlb72kPC9Gg/pd/H9Be1UuX9YgmoAUAAAAAAGhFCG2BZnT2bLmOHi/UdwUndOTYSX1XcFLfFZzQdwUnzz0/oUPfHVPu4aNyFJ9q0vcICgrUFTHR6t2zm3pfeol697xEV17WTb17dlOXTnYCWgAAAAAAgFaO0Baog2EYKjl1RicKi3W8sEjHT1ZtJxxFOn6yuOp5YZFOFBap4ITDGc4eP1nULN8/LLS9ukd3VvfoLrq8R1VAWxXMXqKYSyLk70//WQAAAAAAAF9FaAufYRiGTp8pVcmpMyo5febc16rnRSWn5Cg+JUfRua91bedeKywq0QlHsc6eLW+ROq1Bgep2LpDt0TXCGc5Wbz26RshuC2mR7w0AAAAAAADv16TQdunSpfrjH/+ovLw89e/fXwsXLtTIkSPrHLtu3TotW7ZM2dnZKi0tVf/+/TV37lyNHj3aOSY1NVX33HNPrWNPnz6t4ODgppQIDzMMQ+XlFTpbXn7ua4XKzp5VadlZlZWVV309W/317PfPS7/ff6a0TKfPlOpM6VmdLi0997xMZ0rLXB6fLi3TqdPnh7NndOp0qQzDMOX827ezKrJzR0V06qDIzh0U2bmjIjt3OPfc9XFHeygtDAAAAAAAAFAvt0PbtLQ0zZ49W0uXLtWIESP04osvasyYMdqzZ4969OhRa3xmZqZGjRql3//+9+rQoYNWr16tsWPH6pNPPtGgQYOc42w2m/bt2+dyrCcD21Onz2jIHTPl52eRv5+f/P39zn31l7+/n/wslqqvflWP/c6NqX5sscj5msVikZ9f9X6LLJJzn+Xc6zq3z2KRLLK4hHjVD6v3VQeR1Xmk87kMGUbV8++3queV555XVlaqstJw7qt+XllpqKKyQuXllaqorFBFRaUqKiurvp57XF5ecW5fhfP52fIKZzhb83FFRaVnPqgW5OfnJ1toe9lC2yu8Q5g62kMVbg9TeIcwhdvPPT/3+PzXQ0PamV0+AAAAAAAAfITboe2CBQs0depUTZs2TZK0cOFCvf/++1q2bJlSUlJqjV+4cKHL89///vfasGGD3n33XZfQ1mKxKCoqyt1yms3ZsxXa+3WOad8f7mvfzqqQ9sEKaRdc59f2wd+/bgttL1tYe2coW3MLC20nW2iI2rezsgIWAAAAAAAApnMrtC0rK9OuXbv02GOPuexPTEzU9u3bG/UelZWVKioqUnh4uMv+4uJixcTEqKKiQtdcc41++9vfuoS65ystLVVpaanzucPhcONMajNkKCy0vSorK11WnVZWtv4VpO7y969aYRxw7mv1yuMAf38FBvorMCDA+TjAv+q583HgudcC/BUUFChrUKCCAgPO+3puf9D3+4MCA9UuOEjtgq0KtgaqndWqYGuQ2gUHnfe16vVga5D8/PzMvlQAAAAAAABAs3MrtC0oKFBFRYUiIyNd9kdGRio/P79R7/Hss8+qpKRE48ePd+7r06ePUlNTddVVV8nhcGjRokUaMWKEdu/erV69etX5PikpKZo3b5475Teogy1Ujs/W1dpfs8VARWXl948rKmXo3Fejqt1ApVHdeqBShiFVGpUuLQuqx1W/b832BjW/X82v1Wq2VHD9+n17BWe7hRqtGPyrWzTUeFzV5sEiP4ufAgL8a7SC8CMIBQAAAAAAAEzWpBuRnf8j5IZhNOrHytesWaO5c+dqw4YNioiIcO6Pi4tTXFyc8/mIESM0ePBgPf/881q8eHGd75WcnKykpCTnc4fDoe7du7t7KhdUFXL6y99fCmz2dwcAAAAAAAAAV26Ftp07d5a/v3+tVbVHjhyptfr2fGlpaZo6dareeust3XLLLQ2O9fPz09ChQ7V///56x1itVlmt1sYXDwAAAAAAAACtgFs/Cx8UFKTY2FhlZGS47M/IyFBCQkK9x61Zs0ZTpkzR66+/rttuu+2C38cwDGVnZys6Otqd8gAAAAAAAACg1XO7PUJSUpImTpyoIUOGKD4+XitWrFBOTo6mT58uqaptwaFDh/TKK69IqgpsJ02apEWLFikuLs65Srddu3ay2+2SpHnz5ikuLk69evWSw+HQ4sWLlZ2drSVLljTXeQIAAAAAAABAq+B2aDthwgQdO3ZMTz31lPLy8jRgwAClp6crJiZGkpSXl6ecnBzn+BdffFHl5eWaMWOGZsyY4dw/efJkpaamSpJOnjyp++67T/n5+bLb7Ro0aJAyMzM1bNiwizw9AAAAAAAAAGhdLIZhGGYX0RwcDofsdrsKCwtls9nMLgcAYALmgqbhugEAmAuahusGAGipucCtnrYAAAAAAAAAgJZFaAsAAAAAAAAAXoTQFgAAAAAAAAC8CKEtAAAAAAAAAHgRQlsAAAAAAAAA8CKEtgAAeJmlS5eqZ8+eCg4OVmxsrLZu3drg+C1btig2NlbBwcG67LLLtHz5cg9VCgCAd2EOBQD4CkJbAAC8SFpammbPnq05c+YoKytLI0eO1JgxY5STk1Pn+IMHD+rWW2/VyJEjlZWVpccff1wzZ87U2rVrPVw5AADmYg4FAPgSi2EYhtlFNAeHwyG73a7CwkLZbDazywEAmMAX5oLhw4dr8ODBWrZsmXNf3759NW7cOKWkpNQa/6tf/UobN27U3r17nfumT5+u3bt3a8eOHY36nr5w3QAAF8cX5gLmUACAGVpqLghotncyWXX27HA4TK4EAGCW6jmgtf5/ZFlZmXbt2qXHHnvMZX9iYqK2b99e5zE7duxQYmKiy77Ro0dr5cqVOnv2rAIDA2sdU1paqtLSUufzwsJCScyhANCWMYdWYQ4FALirpeZQnwlti4qKJEndu3c3uRIAgNmKiopkt9vNLsNtBQUFqqioUGRkpMv+yMhI5efn13lMfn5+nePLy8tVUFCg6OjoWsekpKRo3rx5tfYzhwIAjh07xhzKHAoAaILmnkN9JrTt2rWrcnNzFRYWJovF0qT3cDgc6t69u3Jzc33+R1s4V9/TVs5T4lx9VXOcq2EYKioqUteuXZu5Os86fx4zDKPBua2u8XXtr5acnKykpCTn85MnTyomJkY5OTmt8h/qZmhLvzebE9fNfVyzpuG6ua+wsFA9evRQeHi42aVcFOZQ78fvz6bhurmPa9Y0XDf3tdQc6jOhrZ+fn7p169Ys72Wz2drML0zO1fe0lfOUOFdfdbHn2pr/wdS5c2f5+/vXWhF05MiRWiuBqkVFRdU5PiAgQJ06darzGKvVKqvVWmu/3W5vM7/Omktb+r3ZnLhu7uOaNQ3XzX1+fq3zXtXMoa0Pvz+bhuvmPq5Z03Dd3Nfcc2jrnJEBAPBBQUFBio2NVUZGhsv+jIwMJSQk1HlMfHx8rfGbNm3SkCFD6uzFBwCAL2IOBQD4GkJbAAC8SFJSkl5++WWtWrVKe/fu1UMPPaScnBxNnz5dUtWPZU6aNMk5fvr06frmm2+UlJSkvXv3atWqVVq5cqUeeeQRs04BAABTMIcCAHyJz7RHaA5Wq1VPPvlknT/u4ms4V9/TVs5T4lx9VVs614ZMmDBBx44d01NPPaW8vDwNGDBA6enpiomJkSTl5eUpJyfHOb5nz55KT0/XQw89pCVLlqhr165avHix7rrrrkZ/T669+7hmTcN1cx/XrGm4bu7zhWvGHNo6cM2ahuvmPq5Z03Dd3NdS18xiVHdaBwAAAAAAAACYjvYIAAAAAAAAAOBFCG0BAAAAAAAAwIsQ2gIAAAAAAACAFyG0BQAAAAAAAAAvQmhbw9KlS9WzZ08FBwcrNjZWW7duNbukZjd37lxZLBaXLSoqyuyyLlpmZqbGjh2rrl27ymKx6J133nF53TAMzZ07V127dlW7du10ww036IsvvjCn2It0oXOdMmVKrc84Li7OnGIvQkpKioYOHaqwsDBFRERo3Lhx2rdvn8sYX/lcG3OuvvK5Llu2TFdffbVsNptsNpvi4+P13nvvOV/3lc/UG7k7x23ZskWxsbEKDg7WZZddpuXLl3uoUu/hzjVbt26dRo0apS5dujh/bb///vserNZ7NPXvUx999JECAgJ0zTXXtGyBXsjda1ZaWqo5c+YoJiZGVqtVl19+uVatWuWhar2Hu9fttdde08CBA9W+fXtFR0frnnvu0bFjxzxUrfku9HfIujAXVGEOdR9zaNMwh7qPObRpmEPdY9ocasAwDMN44403jMDAQOOll14y9uzZY8yaNcsICQkxvvnmG7NLa1ZPPvmk0b9/fyMvL8+5HTlyxOyyLlp6eroxZ84cY+3atYYkY/369S6vP/PMM0ZYWJixdu1a4/PPPzcmTJhgREdHGw6Hw5yCL8KFznXy5MnGD37wA5fP+NixY+YUexFGjx5trF692vj3v/9tZGdnG7fddpvRo0cPo7i42DnGVz7Xxpyrr3yuGzduNP76178a+/btM/bt22c8/vjjRmBgoPHvf//bMAzf+Uy9jbtz3IEDB4z27dsbs2bNMvbs2WO89NJLRmBgoPH22297uHLzuHvNZs2aZcyfP9/45z//aXz11VdGcnKyERgYaPzrX//ycOXmaurfp06ePGlcdtllRmJiojFw4EDPFOslmnLNfvjDHxrDhw83MjIyjIMHDxqffPKJ8dFHH3mwavO5e922bt1q+Pn5GYsWLTIOHDhgbN261ejfv78xbtw4D1dungv9HfJ8zAVVmEPdxxzaNMyh7mMObRrmUPeZNYcS2p4zbNgwY/r06S77+vTpYzz22GMmVdQynnzySZ//g/z830CVlZVGVFSU8cwzzzj3nTlzxrDb7cby5ctNqLD51Bfa3nHHHabU05KOHDliSDK2bNliGIZvf67nn6th+O7nahiG0bFjR+Pll1/26c/UbO7Ocb/85S+NPn36uOy7//77jbi4uBar0ds0x98L+vXrZ8ybN6+5S/NqTb1uEyZMMH7961+3ib+nnM/da/bee+8Zdru9Vf7HXXNy97r98Y9/NC677DKXfYsXLza6devWYjV6s8b8g5O5oApzqPuYQ5uGOdR9zKFNwxx6cTw5h9IeQVJZWZl27dqlxMREl/2JiYnavn27SVW1nP3796tr167q2bOnfvzjH+vAgQNml9SiDh48qPz8fJfP12q16vrrr/fJz1eSNm/erIiICPXu3Vv33nuvjhw5YnZJF62wsFCSFB4eLsm3P9fzz7War32uFRUVeuONN1RSUqL4+Hif/kzN1JQ5bseOHbXGjx49Wjt37tTZs2dbrFZv0Rx/L6isrFRRUVGt38e+rKnXbfXq1frPf/6jJ598sqVL9DpNuWYbN27UkCFD9Ic//EGXXHKJevfurUceeUSnT5/2RMleoSnXLSEhQd9++63S09NlGIa+++47vf3227rttts8UXKr1NbnAok5tCmYQ5uGOdR9zKFNwxzqGc01FwQ0d2GtUUFBgSoqKhQZGemyPzIyUvn5+SZV1TKGDx+uV155Rb1799Z3332np59+WgkJCfriiy/UqVMns8trEdWfYV2f7zfffGNGSS1qzJgx+tGPfqSYmBgdPHhQv/nNb3TTTTdp165dslqtZpfXJIZhKCkpSddee60GDBggyXc/17rOVfKtz/Xzzz9XfHy8zpw5o9DQUK1fv179+vVz/iXB1z5TszVljsvPz69zfHl5uQoKChQdHd1i9XqD5vh7wbPPPquSkhKNHz++JUr0Sk25bvv379djjz2mrVu3KiCg7f21tCnX7MCBA9q2bZuCg4O1fv16FRQU6Be/+IWOHz/eZnryNeW6JSQk6LXXXtOECRN05swZlZeX64c//KGef/55T5TcKrX1uUBiDm0K5tCmYQ51H3No0zCHekZzzQVt73d2AywWi8tzwzBq7WvtxowZ43x81VVXKT4+Xpdffrn+/Oc/KykpycTKWl5b+HwlacKECc7HAwYM0JAhQxQTE6O//vWvuvPOO02srOkeeOABffbZZ9q2bVut13ztc63vXH3pc73yyiuVnZ2tkydPau3atZo8ebK2bNnifN3XPlNv4e51rWt8Xft9WVN/La5Zs0Zz587Vhg0bFBER0VLlea3GXreKigr95Cc/0bx589S7d29PleeV3Pm1VllZKYvFotdee012u12StGDBAt19991asmSJ2rVr1+L1egt3rtuePXs0c+ZMPfHEExo9erTy8vL06KOPavr06Vq5cqUnym2VmAuqMIe6jzm0aZhD3ccc2jTMoS2vOeYCQltJnTt3lr+/f63/VThy5EitZNzXhISE6KqrrtL+/fvNLqXFREVFSar6n46a/5vRFj5fSYqOjlZMTEyr/YwffPBBbdy4UZmZmerWrZtzvy9+rvWda11a8+caFBSkK664QpI0ZMgQffrpp1q0aJF+9atfSfKtz9QbNGWOi4qKqnN8QECAz/5URk0X8/eCtLQ0TZ06VW+99ZZuueWWlizT67h73YqKirRz505lZWXpgQcekFT1jynDMBQQEKBNmzbppptu8kjtZmnKr7Xo6Ghdcsklzn9sSlLfvn1lGIa+/fZb9erVq0Vr9gZNuW4pKSkaMWKEHn30UUnS1VdfrZCQEI0cOVJPP/20z69+bIq2PhdIzKFNwRzaNMyh7mMObRrmUM9orrmAnraqChBiY2OVkZHhsj8jI0MJCQkmVeUZpaWl2rt3r0//JuvZs6eioqJcPt+ysjJt2bLF5z9fSTp27Jhyc3Nb3WdsGIYeeOABrVu3Tv/4xz/Us2dPl9d96XO90LnWpbV+rnUxDEOlpaU+9Zl6k6bMcfHx8bXGb9q0SUOGDFFgYGCL1eotmvr3gjVr1mjKlCl6/fXX22SPL3evm81m0+eff67s7GznNn36dOdq/OHDh3uqdNM05dfaiBEjdPjwYRUXFzv3ffXVV/Lz87vgf/j5iqZct1OnTsnPz/WfPv7+/pK+X/kCV219LpCYQ5uCObRpmEPdxxzaNMyhntFsc4Fbty3zYW+88YYRGBhorFy50tizZ48xe/ZsIyQkxPjvf/9rdmnN6uGHHzY2b95sHDhwwPj444+N22+/3QgLC2v151lUVGRkZWUZWVlZhiRjwYIFRlZWlvHNN98YhmEYzzzzjGG3241169YZn3/+ufG///u/RnR0tOFwOEyu3H0NnWtRUZHx8MMPG9u3bzcOHjxofPjhh0Z8fLxxySWXtLpz/b//+z/DbrcbmzdvNvLy8pzbqVOnnGN85XO90Ln60ueanJxsZGZmGgcPHjQ+++wz4/HHHzf8/PyMTZs2GYbhO5+pt7nQHPfYY48ZEydOdI4/cOCA0b59e+Ohhx4y9uzZY6xcudIIDAw03n77bbNOwePcvWavv/66ERAQYCxZssTl9/HJkyfNOgVTuHvdztcW73zt7jUrKioyunXrZtx9993GF198YWzZssXo1auXMW3aNLNOwRTuXrfVq1cbAQEBxtKlS43//Oc/xrZt24whQ4YYw4YNM+sUPO5Cf19mLqgbc6j7mEObhjnUfcyhTcMc6j6z5lBC2xqWLFlixMTEGEFBQcbgwYONLVu2mF1Ss5swYYIRHR1tBAYGGl27djXuvPNO44svvjC7rIv24YcfGpJqbZMnTzYMwzAqKyuNJ5980oiKijKsVqtx3XXXGZ9//rm5RTdRQ+d66tQpIzEx0ejSpYsRGBho9OjRw5g8ebKRk5Njdtluq+scJRmrV692jvGVz/VC5+pLn+vPf/5z55+zXbp0MW6++WZnYGsYvvOZeqOG5rjJkycb119/vcv4zZs3G4MGDTKCgoKMSy+91Fi2bJmHKzafO9fs+uuvb3Aeakvc/bVWU1v8B6dhuH/N9u7da9xyyy1Gu3btjG7duhlJSUku/6nZVrh73RYvXmz069fPaNeunREdHW389Kc/Nb799lsPV22eC/19mbmgfsyh7mMObRrmUPcxhzYNc6h7zJpDLYbBWmYAAAAAAAAA8Bb0tAUAAAAAAAAAL0JoCwAAAAAAAABehNAWAAAAAAAAALwIoS0AAAAAAAAAeBFCWwAAAAAAAADwIoS2AAAAAAAAAOBFCG0BAAAAAAAAwIsQ2gIAAAAAAACAFyG0BQAAAAAAAAAvQmgLAAAAAAAAAF6E0BYAAAAAAAAAvAihLQAAAAAAAAB4kf8PShhPDCKxEPMAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# \u2500\u2500 TPI-7: Figure 2 \u2014 Does TPI Close the Spread? (1\u00d73 = 3-panel) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# Scalar statistics over first 100 quarters for a dense grid of gamma values.\n", - "# Panel 1: peak spread remaining vs gamma \u2014 how much is left?\n", - "# Panel 2: fraction of spread closed (%) \u2014 how effective?\n", - "# Panel 3: CB balance-sheet cost \u2014 what does intervention cost?\n", - "\n", - "T_sum7 = 100\n", - "gammas_fine = np.concatenate([np.linspace(0, 5, 25), np.linspace(5, 30, 26)[1:]])\n", - "\n", - "def _peak_spread(irf):\n", - " sp = irf['spread_rb'] if 'spread_rb' in irf \\\n", - " else irf['rb_actual_D'] - irf['rb_actual_F']\n", - " return sp[:T_sum7].max()\n", - "\n", - "peak_no_tpi = _peak_spread(irfs_tpi[0])\n", - "\n", - "peak_arr = np.empty(len(gammas_fine))\n", - "cost_arr = np.empty(len(gammas_fine))\n", - "q_b_D_ss = float(ss_final['q_b_D'])\n", - "\n", - "for i, g in enumerate(gammas_fine):\n", - " if g == 0:\n", - " irf_g = irfs_tpi[0]\n", - " else:\n", - " irf_g = compute_tpi_irfs(G_tpi, dShock_def_D, g, T)\n", - " peak_arr[i] = _peak_spread(irf_g)\n", - " cost_arr[i] = (irf_g['cb_buy_D'][:T_sum7] * q_b_D_ss).sum() # D-goods \u00d7 quarters\n", - "\n", - "frac_closed = 100.0 * (1.0 - peak_arr / peak_no_tpi)\n", - "frac_closed = np.clip(frac_closed, 0, 100)\n", - "\n", - "# \u2500\u2500 Plot \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "fig7, axes7 = plt.subplots(1, 3, figsize=(17, 5))\n", - "\n", - "ax = axes7[0]\n", - "ax.plot(gammas_fine, peak_arr * 100, color=BLUE, linewidth=2)\n", - "ax.scatter([g for g in gamma_values], [_peak_spread(irfs_tpi[g]) * 100 for g in gamma_values],\n", - " color=TPI_COLORS, s=60, zorder=5)\n", - "ax.set_xlabel('Feedback gain \u03b3', fontsize=9)\n", - "ax.set_ylabel('Peak spread dev. (pp)', fontsize=9)\n", - "ax.set_title('Peak Spread Remaining', fontsize=10, pad=6)\n", - "ax.axhline(0, color='#888888', linewidth=0.8, linestyle=':')\n", - "ax.spines[['top', 'right']].set_visible(False)\n", - "\n", - "ax = axes7[1]\n", - "ax.plot(gammas_fine, frac_closed, color=RED, linewidth=2)\n", - "ax.scatter([g for g in gamma_values],\n", - " [100.0 * (1 - _peak_spread(irfs_tpi[g]) / peak_no_tpi) for g in gamma_values],\n", - " color=TPI_COLORS, s=60, zorder=5)\n", - "ax.axhline(100, color='#888888', linewidth=0.8, linestyle='--', label='Full closure')\n", - "ax.set_xlabel('Feedback gain \u03b3', fontsize=9)\n", - "ax.set_ylabel('%', fontsize=9)\n", - "ax.set_title('Fraction of Peak Spread Closed', fontsize=10, pad=6)\n", - "ax.set_ylim([-5, 115])\n", - "ax.legend(fontsize=8, frameon=False)\n", - "ax.spines[['top', 'right']].set_visible(False)\n", - "\n", - "ax = axes7[2]\n", - "ax.plot(gammas_fine, cost_arr, color=BLUE_MUTED, linewidth=2)\n", - "ax.scatter([g for g in gamma_values],\n", - " [(irfs_tpi[g]['cb_buy_D'][:T_sum7] * q_b_D_ss).sum() for g in gamma_values],\n", - " color=TPI_COLORS, s=60, zorder=5)\n", - "ax.set_xlabel('Feedback gain \u03b3', fontsize=9)\n", - "ax.set_ylabel('\u2211 cb_buy_D \u00d7 q_b_D (D-goods\u00b7quarters)', fontsize=9)\n", - "ax.set_title('CB Balance-Sheet Cost', fontsize=10, pad=6)\n", - "ax.spines[['top', 'right']].set_visible(False)\n", - "\n", - "# Annotate the five main gamma points on all panels\n", - "for j, g in enumerate(gamma_values):\n", - " for ax in axes7:\n", - " ax.axvline(g, color=TPI_COLORS[j], alpha=0.25, linewidth=0.8, linestyle=':')\n", - "\n", - "fig7.suptitle('Figure 2: TPI \u2014 Does It Close the Spread? Effectiveness vs Cost',\n", - " fontsize=12, y=1.01)\n", - "fig7.tight_layout()\n", - "fig7.savefig('fig_tpi_effectiveness.png', dpi=150, bbox_inches='tight')\n", - "plt.show()\n", - "print(\"Saved fig_tpi_effectiveness.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1cc0c26c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved fig_tpi_welfare_macro.png\n" - ] - } - ], - "source": [ - "# \u2500\u2500 TPI-8: Figure 3 \u2014 Welfare & Macro Effects (2\u00d74 = 8-panel) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# Row 1: welfare and consumption \u2014 direct transmission to households\n", - "# Row 2: output, investment, taxes, government debt \u2014 macro/fiscal transmission\n", - "# Key question: does TPI stabilise the distressed country (D) at the expense of\n", - "# the partner country (F), or are welfare effects positive for both?\n", - "\n", - "T_plot8 = 60\n", - "\n", - "fig8_vars = [\n", - " 'U_D', 'U_F', 'C_D', 'C_F',\n", - " 'Y_D', 'Y_F', 'TAX_D', 'b_gov_D',\n", - "]\n", - "fig8_titles = [\n", - " 'Welfare U_D', 'Welfare U_F',\n", - " 'Consumption C_D', 'Consumption C_F',\n", - " 'Output Y_D', 'Output Y_F',\n", - " 'Lump-sum Tax TAX_D', 'Govt Debt b_gov_D',\n", - "]\n", - "\n", - "fig8, axes8 = plt.subplots(2, 4, figsize=(22, 9))\n", - "axes8_flat = axes8.flatten()\n", - "\n", - "for ax, var, title in zip(axes8_flat, fig8_vars, fig8_titles):\n", - " for j, g in enumerate(gamma_values):\n", - " data = irfs_tpi[g][var][:T_plot8] * 100 if var in irfs_tpi[g] else np.zeros(T_plot8)\n", - " ax.plot(data,\n", - " color = TPI_COLORS[j],\n", - " linestyle = TPI_LSTYLES[j],\n", - " linewidth = 1.8,\n", - " marker = TPI_MARKERS[j],\n", - " markersize = 4,\n", - " markevery = 8,\n", - " label = gamma_labels[j])\n", - " ax.axhline(0, color='#888888', linewidth=0.8, linestyle=':')\n", - " ax.set_title(title, fontsize=10, pad=6)\n", - " ax.set_xlabel('Quarter', fontsize=9)\n", - " ax.set_ylabel('% / pp dev. from SS', fontsize=9)\n", - " ax.spines[['top', 'right']].set_visible(False)\n", - " ax.tick_params(labelsize=8)\n", - "\n", - "axes8_flat[0].legend(fontsize=8, frameon=False, loc='lower right')\n", - "\n", - "fig8.suptitle('Figure 3: TPI \u2014 Welfare & Macro Effects under Default Shock (Country D)',\n", - " fontsize=12, y=1.01)\n", - "fig8.tight_layout()\n", - "fig8.savefig('fig_tpi_welfare_macro.png', dpi=150, bbox_inches='tight')\n", - "plt.show()\n", - "print(\"Saved fig_tpi_welfare_macro.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a10f1728", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u03b3 W_D W_F \u0394W_D vs \u03b3=0 \u0394W_F vs \u03b3=0\n", - "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 0 -0.8606 1.0869 +0.0000 +0.0000\n", - " 2 -0.8369 1.0682 +0.0236 -0.0188\n", - " 5 -0.8044 1.0427 +0.0561 -0.0442\n", - " 10 -0.7579 1.0076 +0.1027 -0.0793\n", - "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "(Units: % of quarterly SS consumption, discounted over 100 quarters)\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABW4AAAJjCAYAAACcDYhOAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAA52BJREFUeJzs3Xlczdn/B/DX1b4pJS2KrCURCpWhskX2nYjs2wwxtsbYmrEzGkyWYcSYKWZsgywZlaUsUdYGYyJS9pAlLef3h1+fr+veUipdM6/n43Ef0+d8zud83ufemzm977nnyIQQAkRERERERERERESkMsqVdQBEREREREREREREJI+JWyIiIiIiIiIiIiIVw8QtERERERERERERkYph4paIiIiIiIiIiIhIxTBxS0RERERERERERKRimLglIiIiIiIiIiIiUjFM3BIRERERERERERGpGCZuiYiIiIiIiIiIiFQME7dEREREREREREREKoaJWyIiok9USEgIZDKZ0sekSZNw48YNyGQyhISElHWoJWLdunWQyWTQ19f/oOsLer7eftjY2AAAZs+eLVeuqamJatWqYfz48UhPT1doNy4urgR6WXRRUVEKcZqamqJZs2aYPn06bt68qXBNXsw3btz4+AGXol9//RVBQUGl0raNjQ38/PzyPZ+TkwMjIyO0b99e4dyyZcsgk8nQr18/hXPffPMNZDIZzp8/X+hY8l7zqKgoufIVK1agZs2a0NTUhEwmk3uflrXz589j8ODBqFatGrS1taGvr49GjRph0aJFePToUVmHV+r8/Pykf1uIiIiICku9rAMgIiKi4tmwYQPs7OzkyiwtLWFmZobY2FjUqFGjjCIrOSkpKZg0aRIsLS3x5MmTD2qjQ4cOiI2NlStzdXVFz5498eWXX0plWlpacnX2798PQ0NDPHv2DOHh4fj+++9x6tQpxMTEQCaTfVAspWHevHnw9PRETk4OHj58iJMnT+Knn37CsmXL8OOPP6J///5S3bznwsLCogwjLnm//vorLl68CH9//49+bzU1NTRv3hxRUVHIzs6Guvr/htlRUVHQ09NDZGSkwnVRUVEwMTFBvXr1inX/hIQEjBs3DsOGDcOgQYOgrq4OAwODYrVZUn788UeMGTMGtra2mDx5Muzt7ZGVlYW4uDisXr0asbGx2LFjR1mHWapmzJiB8ePHl3UYRERE9Ilh4paIiOgT5+DgAGdnZ6XnXFxcPnI0b7x48QK6urol1t6oUaPQokULGBsb4/fff/+gNkxNTWFqaqpQbmZmVuDz5OTkhIoVKwIA2rRpg4cPH+Lnn39GTEwMmjVr9kGxlIZatWrJ9aNz58748ssv0bp1a/j5+aF+/fpScjC/54KKx9PTE3v27EFcXJz0WuTm5uLo0aMYPXo0lixZgsTERNSpUwcA8Pr1a8TGxsLb27vYHwJcunQJADB8+HA0adKkeB35fyXxexwbG4vRo0ejTZs22Llzp9wHI23atMGXX36J/fv3FzdUlZX3HP4bPkAjIiKij49LJRAREf1L5bdUwq5du1C/fn1oaWmhevXq+P7776VlAd53LQDIZDLMnj1bOs679uzZs+jZsycqVKggJSmEEAgODkaDBg2go6ODChUqoGfPnvjnn38K3Y/NmzcjOjoawcHBRep/aclLyClbgkDVGBsbY82aNcjOzsayZcukcmVLJcTHx6Njx46oVKkStLS0YGlpiQ4dOuD27dtSndzcXKxYsUJ6PY2MjODi4oI//vhDrs6iRYtgZ2cHLS0tVKpUCQMHDpRrB8h/6QEPDw94eHhIx3nLAoSGhmL69OmwtLRE+fLl0bp1a1y5ckXuur179+LmzZtyS0fkef36Nb799lspLlNTUwwePBj379+Xu39WVhamTJkCc3Nz6Orq4rPPPsOpU6cK9Xx7enpKMec5d+4cHj9+jBEjRsDCwkJu1u3Jkyfx8uVL6ToAiIuLQ+fOnWFsbAxtbW00bNgQW7duLfC+Hh4eGDBgAACgadOmkMlk0nMbERGBLl26wMrKCtra2qhZsyZGjhyJBw8eyLVRWr/H8+bNg0wmw9q1axVmswOApqYmOnfuLB0X9v3j4eEBBwcHxMbGws3NDTo6OrCxscGGDRsAAHv37kWjRo2gq6uLevXqKSSH8/obHx+P7t27o3z58jA0NMSAAQMU3hNbtmxB27ZtYWFhAR0dHdSpUwfTpk3D8+fP5er5+flBX18fFy5cQNu2bWFgYIBWrVpJ595dKuG3335D06ZNYWhoCF1dXVSvXh1DhgyRq5OcnIwBAwZIv5d16tTB0qVLkZubK9XJ+/d6yZIl+O6771CtWjXo6+vD1dUVJ06cKOjlISIiIhXHxC0REdEnLicnB9nZ2XKP/Ozfvx/du3eHiYkJtmzZgkWLFiE0NBQbN24sdhzdu3dHzZo18dtvv2H16tUAgJEjR8Lf3x+tW7fGzp07ERwcjEuXLsHNzQ137959b5v37t2Dv78/FixYACsrq3zr5SUiP8Z6vn///TcAfDIzVhs3bgwLCwscOXIk3zrPnz9HmzZtcPfuXfzwww+IiIhAUFAQqlSpgmfPnkn1/Pz8MH78eDRu3BhbtmxBWFgYOnfuLJcAHj16NKZOnYo2bdrgjz/+wDfffIP9+/fDzc1NIVlYFF999RVu3ryJdevWYe3atbh27Ro6deqEnJwcAEBwcDCaNWsGc3NzxMbGSg/gTTKwS5cuWLBgAXx8fLB3714sWLAAERER8PDwwMuXL6X7DB8+HEuWLMHAgQOxa9cu9OjRA927d8fjx4/fG6OjoyMqVKggl5yNjIyEhYUFatWqhRYtWsgldfPq5SVuIyMj0axZM6Snp2P16tXYtWsXGjRogD59+hT43g4ODsbXX38N4M3SKbGxsZgxYwYA4Pr163B1dcWqVatw8OBBzJw5EydPnsRnn32GrKwshbZK8vc4JycHhw8fhpOTE6ytrd/7/AFFe/+kpaVh8ODBGDZsGHbt2oV69ephyJAhCAwMREBAAKZMmYJt27ZBX18fXbt2xZ07dxTu161bN9SsWRO///47Zs+ejZ07d8LLy0vuubl27Rq8vb2xfv167N+/H/7+/ti6dSs6deqk0N7r16/RuXNntGzZErt27cKcOXOU9jM2NhZ9+vRB9erVERYWhr1792LmzJly/37fv38fbm5uOHjwIL755hv88ccfaN26NSZNmoTPP/9coc23f3d/+eUXPH/+HN7e3h+8vAwRERGpAEFERESfpA0bNggASh9ZWVkiKSlJABAbNmyQrmncuLGwtrYWmZmZUtmzZ8+EiYmJeHtYoOzaPADErFmzpONZs2YJAGLmzJly9WJjYwUAsXTpUrnyW7duCR0dHTFlypT39rFHjx7Czc1N5ObmCiGEGDRokNDT01Oot3HjRqGmpiY2btz43jbf7cvYsWOVnsvrV1pamsjKyhKPHz8WmzdvFjo6OsLa2lq8fPlSCPG/1+H06dNFundJiYyMFADEb7/9lm+dpk2bCh0dHek4L+akpCQhhBBxcXECgNi5c2e+bRw5ckQAENOnT8+3TmJiogAgxowZI1d+8uRJAUB89dVXUlnVqlXFoEGDFNpwd3cX7u7uCv3z9vaWq7d161YBQMTGxkplHTp0EFWrVlVoMzQ0VAAQ27Ztkys/ffq0ACCCg4Pl4p8wYYJcvV9++UUAUBrvu7p27Sr09PREVlaWEEKITp06ib59+wohhAgODhampqbS+9nT01NUqlRJutbOzk40bNhQujZPx44dhYWFhcjJyZF7TiIjI6U6hXkf5ubmiqysLHHz5k0BQOzatUs6Vxq/x2lpaQKA1P/3Kcr7x93dXQAQcXFxUtnDhw+Fmpqa0NHRESkpKVJ5QkKCACCWL1+u0N/8XuvNmzcrjTHvOYyOjhYAxLlz56RzgwYNEgDETz/9pHDdoEGD5N6bS5YsEQBEenp6vs/HtGnTBABx8uRJufLRo0cLmUwmrly5IoT437/X9erVE9nZ2VK9U6dOCQAiNDQ033sQERGRauOMWyIiok/cpk2bcPr0abnH2xsj5Xn+/Dni4uLQtWtXaGpqSuX6+vpKZ44VVY8ePeSO9+zZA5lMhgEDBsjNBjY3N4ejo6PczENltm3bht27d+PHH3987/qfAwcORHZ2NgYOHFjcbigwNzeHhoYGKlSogAEDBqBRo0bYv38/tLW1i9TOu7Oii/IoLiFEgedr1qyJChUqYOrUqVi9ejUuX76sUGffvn0AgLFjx+bbTt4M0neXQGjSpAnq1KmDP//8s4iR/8/bX6cHgPr16wMo3JIVe/bsgZGRETp16iT3vDZo0ADm5ubSezEv/rc3cgOA3r17K/2dUsbT0xPPnz/H6dOnpfVt85Z+cHd3x/3793Hp0iVkZmbixIkT0mzbv//+G3/99Zd077fj9Pb2RmpqqtzSEIV17949jBo1CtbW1lBXV4eGhgaqVq0KAEhMTFSoX9K/x0VR1PePhYUFnJycpGNjY2NUqlQJDRo0gKWlpVSet6awsvdKfq/127Om//nnH/j4+MDc3BxqamrQ0NCAu7s7gMI9h8o0btxYut/WrVuRkpKiUOfw4cOwt7dXWLPYz88PQggcPnxYrrxDhw5QU1OTjovyO0JERESqiZuTERERfeLq1KmT7+Zkb3v8+DGEEDAzM1M4p6ysqCwsLOSO7969m+/9AKB69er5tpWRkYGxY8fiiy++gKWlJdLT0wG8+RoyAKSnp0NDQwN6enrFjvt9Dh06BENDQ2hoaMDKygomJiYf1I6GhsYHx/C+xOv7JCcnyyWy3mVoaIjo6GjMnTsXX331FR4/fgwLCwsMHz4cX3/9NTQ0NHD//n2oqanB3Nw833YePnwIQPG9AACWlpbFSiC9+7znrZf69jIH+bl79y7S09PlPrB4W95X8PPif7eP6urqhX7d3172QFNTE+np6VKSz97eHqampoiKisLDhw/l1rfNW3Jg0qRJmDRpUoFxFlZubi7atm2LO3fuYMaMGahXrx709PSQm5sLFxcXpc9dSf4eV6xYEbq6ukhKSipUvEV9/xgbGyvU09TUVCjPe91fvXqlUD+/1zovloyMDDRv3hza2tr49ttvUbt2bejq6uLWrVvo3r27wnOoq6uL8uXLv6+raNGiBXbu3Inly5dj4MCByMzMRN26dTF9+nT069cPwJvn4911cQFIv8t5MeYpzu8IERERqSYmbomIiP4jKlSoAJlMpnRNyrS0NLnjvNmkmZmZcuXvJgre9u6s2IoVK0Imk+Ho0aNKNyVSVpbnwYMHuHv3LpYuXYqlS5cq7UuXLl2wc+fOfNsoKY6OjqhYsWKx2zl9+nQJRFN0p06dQlpaGoYOHVpgvXr16iEsLAxCCJw/fx4hISEIDAyEjo4Opk2bBlNTU+Tk5CAtLU1pYg34X+IoNTVVYU3iO3fuyD2P2traCu8v4M1rXxLP99sqVqwIExMThQ2q8hgYGAD4X/xpaWmoXLmydD47O7vA9/7bHBwcpOSslpYWzMzMYGdnJ51v0aIFIiMjpfbyErd5fQ4ICED37t2Vtm1ra1uoGPJcvHgR586dQ0hICAYNGiSV563TrExJ/h6rqamhVatW2LdvH27fvl3gOtVA0d4/JSW/1zovlsOHD+POnTuIioqSEvAApA+T3vW+bwe8rUuXLujSpYs0+3r+/Pnw8fGBjY0NXF1dYWJigtTUVIXr8tbqLY3ng4iIiFQLE7dERET/EXp6enB2dsbOnTuxZMkSaRZaRkYG9uzZI1fXzMwM2traOH/+vFz5rl27Cn2/jh07YsGCBUhJSUHv3r2LFKu5ubncV5XzLFiwANHR0di3b98nl7QozKzokvbo0SOMGjUKGhoamDBhQqGukclkcHR0xLJlyxASEoKzZ88CANq3b4/58+dj1apVCAwMVHpty5YtAQCbN2+WvgoOvElaJyYmYvr06VKZjY2Nwvvr6tWruHLlyge/tlpaWkpnF3bs2BFhYWHIyclB06ZN870+b0mDX375Re4r+Fu3bi30khUymQzu7u7Yt28fypUrJ5fsA94slzBnzhw8fPgQlpaWqF27NoA3SdlatWrh3LlzmDdvXqHuVZhYAMXk6po1awrdRnF+j4E3iejw8HAMHz4cu3btUpj1nJWVhf3796NTp05Fev+UlPxe67z3Qkk8h++jpaUFd3d3GBkZ4cCBA4iPj4erqytatWqF+fPn4+zZs2jUqJFUf9OmTZDJZFLSn4iIiP69mLglIiL6DwkMDESHDh3g5eWF8ePHIycnB4sXL4a+vj4ePXok1ctb0/Knn35CjRo14OjoiFOnTuHXX38t9L2aNWuGESNGYPDgwYiLi0OLFi2gp6eH1NRUHDt2DPXq1cPo0aOVXqutrS0lTt4WEhICNTU1hXObNm3CkCFD8NNPP5XKOrefgmvXruHEiRPIzc3Fw4cPcfLkSaxfvx5Pnz7Fpk2bULdu3Xyv3bNnD4KDg9G1a1dUr14dQghs374d6enpaNOmDQCgefPm8PX1xbfffou7d++iY8eO0NLSQnx8PHR1dfHFF1/A1tYWI0aMwIoVK1CuXDm0b98eN27cwIwZM2BtbS2XPPb19cWAAQMwZswY9OjRAzdv3sSiRYtgamr6wc9BvXr1sH37dqxatQpOTk4oV64cnJ2d0bdvX/zyyy/w9vbG+PHj0aRJE2hoaOD27duIjIxEly5d0K1bN9SpUwcDBgxAUFAQNDQ00Lp1a1y8eBFLliwp1Nff83h6euL333/HwYMHsXLlSrlz7u7uePjwIY4cOQIfHx+5c2vWrEH79u3h5eUFPz8/VK5cGY8ePUJiYiLOnj2L3377rUjPh52dHWrUqIFp06ZBCAFjY2Ps3r0bERERhW6jOL/HAODq6opVq1ZhzJgxcHJywujRo1G3bl1kZWUhPj4ea9euhYODAzp16lSk909J2b59O9TV1dGmTRtcunQJM2bMgKOjo5SkdnNzQ4UKFTBq1CjMmjULGhoa+OWXX3Du3Lli3XfmzJm4ffs2WrVqBSsrK6Snp+P777+XWz93woQJ2LRpEzp06IDAwEBUrVoVe/fuRXBwMEaPHi0l/YmIiOjfi4lbIiKi/5B27dph27ZtmDlzJvr06QNzc3OMGTMGd+7cwc8//yxXN2+JgkWLFiEjIwMtW7bEnj17lK65mJ81a9bAxcUFa9asQXBwMHJzc2FpaYlmzZopbLhTHLm5ucjJyUFubm6Jtfmp+eqrrwC8WaPT0NAQtWvXxpAhQzBixAhpM6r81KpVC0ZGRli0aBHu3LkDTU1N2NraKnzFPiQkBI0aNcL69esREhICHR0d2NvbS/cGgFWrVqFGjRpYv349fvjhBxgaGqJdu3aYP3++3BqcPj4+uHPnDlavXo0NGzbAwcEBq1atwpw5cz74ORg/fjwuXbqEr776Ck+ePIEQAkIIqKmp4Y8//sD333+Pn3/+GfPnz4e6ujqsrKzg7u6OevXqSW2sX78eZmZmCAkJwfLly9GgQQNs27YNffv2LXQceTMhhRAKM27r1asHY2NjPHr0SOEDCE9PT5w6dQpz586Fv78/Hj9+DBMTE9jb23/QbFcNDQ3s3r0b48ePx8iRI6Guro7WrVvj0KFDqFKlSqHbKe7v8fDhw9GkSRMsW7YMCxcuRFpaGjQ0NFC7dm34+Pjg888/l+oW9v1TUrZv347Zs2dj1apVkMlk6NSpE4KCgqSZwSYmJti7dy++/PJLDBgwAHp6eujSpQu2bNkiNwu2qJo2bYq4uDhMnToV9+/fh5GREZydnXH48GHpQxZTU1PExMQgICAAAQEBePr0KapXr45FixZh4sSJJdJ/IiIiUm0yUdzdLoiIiOiTlpWVhQYNGqBy5co4ePBgWYdDRFTqZs+ejTlz5uD+/fuf3LIrRERE9N/BGbdERET/MUOHDkWbNm1gYWGBtLQ0rF69GomJifj+++/LOjQiIiIiIiL6f0zcEhER/cc8e/YMkyZNwv3796GhoYFGjRohPDwcrVu3LuvQiIiIiIiI6P9xqQQiIiIiIiIiIiIiFVOurAMgIiIiIiIiIiIiInlM3BIRERERERERERGpGCZuiYiIiIiIiIiIiFQME7dEREREREREREREKoaJWyIiIiIiIiIiIiIVw8QtERERERERERERkYph4paIiIiIiIiIiIhIxTBxS0RERERERERERKRimLglIiIiIiIiIiIiUjFM3BIRERERERERERGpGCZuiYiIiIiIiIiIiFQME7dEREREREREREREKoaJWyIiIiIiIiIiIiIVw8QtERERERERERERkYph4paIiIiIiIiIiIhIxTBxS0RERERERERERKRimLglIiIiIiIiIiIiUjFM3BIRERERERERERGpGCZuiYiIiIiIiIiIiFQME7dEREREREREREREKoaJWyIiIiIiIiIiIiIVw8QtERERERERERERkYph4paIiIiIiIiIiIhIxTBxS0RERERERERERKRimLglIiIiIiIiIiIiUjFM3BIRERERERERERGpGCZuiYiIiIiIiIiIiFQME7dEREREREREREREKoaJWyIiIiIiIiIiIiIVw8QtERERERERERERkYph4paIiIiIiIiIiIhIxTBxS0RERERERERERKRimLglIiIiIiIiIiIiUjFM3BIRERERERERERGpGCZuiYiIiIiIiIiIiFQME7dEpBJkMhl27txZ1mGUiZCQEBgZGX30+3p4eMDf3186fvHiBXr06IHy5ctDJpMhPT39o8f0sZTUc15Wrx0RERH9OygbS6xduxbW1tYoV64cgoKCyiSuknDjxg3IZDIkJCSUdSj/OuvXr0fbtm3LOowCZWZmokqVKjhz5kxZh0L0SWPilohKjZ+fH2QyGWQyGTQ0NGBmZoY2bdrgp59+Qm5urlzd1NRUtG/fvowiLbqPmbBzcXHB6NGj5cpWrVoFmUyG9evXy5UPHToUbm5uH3SfjRs34ujRo4iJiUFqaioMDQ0/OOYP4eHhIb1ftLS0ULlyZXTq1Anbt28v8Xv16dMHV69eLdI1NjY2Cn88fUg7RERE9OlZvXo1DAwMkJ2dLZVlZGRAQ0MDzZs3l6t79OhRyGSyDxojPH36FJ9//jmmTp2KlJQUjBgxotixF9Xr16+xePFiNGrUCHp6ejA0NISjoyO+/vpr3Llzp9DtWFtbIzU1FQ4ODqUYbdFERUVBJpPBwcEBOTk5cueMjIwQEhLyQe3a2NhI41hlDw8PD4V6urq6cHBwwJo1a6R2CvM3RmZmJmbOnIkZM2Z8UKwlRQiB2bNnw9LSEjo6OvDw8MClS5ek81paWpg0aRKmTp1ahlESffqYuCWiUtWuXTukpqbixo0b2LdvHzw9PTF+/Hh07NhRbuBrbm4OLS2tMoxUdXl6eiIyMlKuLCoqCtbW1krLPT09P+g+169fR506deDg4ABzc3PIZLIityGEkHtdi2r48OFITU3F33//jW3btsHe3h59+/Yt8T9adHR0UKlSJZVph4iIiFSbp6cnMjIyEBcXJ5UdPXoU5ubmOH36NF68eCGVR0VFwdLSErVr1y7yfZKTk5GVlYUOHTrAwsICurq6HxTv69evP+i6zMxMtGnTBvPmzYOfnx+OHDmCM2fOYNGiRXj48CFWrFhR6LbU1NRgbm4OdXX1D4qlNF2/fh2bNm0qsfZOnz6N1NRUpKamYtu2bQCAK1euSGVvT0QIDAxEamoqzp8/j65du2LUqFHYsmVLoe+1bds26OvrK3xg8LEtWrQI3333HVauXInTp0/D3Nwcbdq0wbNnz6Q6/fv3x9GjR5GYmFiGkRJ92pi4JaJSpaWlBXNzc1SuXBmNGjXCV199hV27dmHfvn1yn2i/vVTC69ev8fnnn8PCwgLa2tqwsbHB/Pnzpbrp6ekYMWIEzMzMoK2tDQcHB+zZs0c6v23bNtStWxdaWlqwsbHB0qVL5WJStizD25+w532ta/v27fD09ISuri4cHR0RGxsL4M1gfPDgwXjy5In0ifns2bOl2KdMmYLKlStDT08PTZs2RVRUlNy9QkJCUKVKFejq6qJbt254+PBhgc+hp6enNPDLEx0djYCAALm2b926hX/++UdK3F6+fBne3t7Q19eHmZkZfH198eDBA6X38PDwwNKlS3HkyBG5WQGbN2+Gs7MzDAwMYG5uDh8fH9y7d0+6Lm/WwoEDB+Ds7AwtLS0cPXoUQggsWrQI1atXh46ODhwdHfH7778X2E8A0NXVhbm5OaytreHi4oKFCxdizZo1+PHHH3Ho0CGpXkpKCvr06YMKFSrAxMQEXbp0wY0bNwAABw4cgLa2tsJSD+PGjYO7uzsAxdkM169fR5cuXWBmZgZ9fX00btxY7n4eHh64efMmJkyYIL3mytoB3syGrlGjBjQ1NWFra4uff/5Z7rxMJsO6devQrVs36OrqolatWvjjjz/e+9wQERFR2bG1tYWlpaXc2CsqKgpdunRBjRo1EBMTI1eeNx4rzNgwT0hICOrVqwcAqF69OmQyGW7cuPHecQrwZibnt99+Cz8/PxgaGmL48OEAgJiYGLRo0QI6OjqwtrbGuHHj8Pz583z7uWzZMhw7dgyHDx/GuHHj4OTkhJo1a8LLywurVq3CvHnzpLr79+/HZ599BiMjI5iYmKBjx464fv26dP7dpRLyxo1//vknnJ2doaurCzc3N1y5ciXfeFxdXTFt2jS5svv370NDQ0OawBAcHIxatWpBW1sbZmZm6NmzZ77t5fniiy8wa9YsvHr1Kt86ycnJ6NKlC/T19VG+fHn07t0bd+/eVVrX1NQU5ubmMDc3h7GxMQCgUqVKCmUApHF1zZo18e2336JWrVpFWjIuLCwMnTt3lo4HDx6M6tWr4/z583L1ZsyYgfr16xe63aIQQiAoKAjTp09H9+7d4eDggI0bN+LFixf49ddfpXomJiZwc3NDaGhoqcRB9F/AxC0RfXQtW7aEo6Njvl+BX758Of744w9s3boVV65cwebNm2FjYwMAyM3NRfv27RETE4PNmzfj8uXLWLBgAdTU1AAAZ86cQe/evdG3b19cuHABs2fPxowZMz7oa0/Tp0/HpEmTkJCQgNq1a6Nfv37Izs6Gm5sbgoKCUL58eelT9EmTJgF4M3A6fvw4wsLCcP78efTq1Qvt2rXDtWvXAAAnT57EkCFDMGbMGCQkJMDT0xPffvttgXE0a9YMGhoa0iD/8uXLePnyJYYMGYKnT59KbUdGRkJTUxNubm5ITU2Fu7s7GjRogLi4OOzfvx93795F7969ld5j+/btGD58OFxdXeVmBbx+/RrffPMNzp07h507dyIpKQl+fn4K10+ZMgXz589HYmIi6tevj6+//hobNmzAqlWrcOnSJUyYMAEDBgxAdHR0kV+HQYMGoUKFClJML168gKenJ/T19XHkyBEcO3YM+vr6aNeuHV6/fo3WrVvDyMhImu0AADk5Odi6dSv69++v9B4ZGRnw9vbGoUOHEB8fDy8vL3Tq1AnJycnS82NlZSXNkHg7if62HTt2YPz48fjyyy9x8eJFjBw5EoMHD1aYGT1nzhz07t0b58+fh7e3N/r3749Hjx4V+bkhIiKij8fDw0Pu/+mRkZHw8PCAu7u7VP769WvExsZKidv3jQ3f1qdPHykhe+rUKaSmpsLa2vq945Q8ixcvhoODA86cOYMZM2bgwoUL8PLyQvfu3XH+/Hls2bIFx44dw+eff55vH0NDQ9GmTRs0bNhQ6fm3v5H1/PlzTJw4EadPn8aff/6JcuXKoVu3bgpLor1r+vTpWLp0KeLi4qCuro4hQ4bkW7d///4IDQ2FEEIq27JlC8zMzODu7o64uDiMGzcOgYGBuHLlCvbv348WLVoUeH8A8Pf3R3Z2NlauXKn0vBACXbt2xaNHjxAdHY2IiAhcv34dffr0eW/bRaWtrY2srKxC1z969CicnZ2l4yVLlqBhw4aYOXOmVJabm4uQkBAMGzYs33bat28PfX39Ah/5SUpKQlpamtw6u1paWnB3d5f7EAMAmjRpgqNHjxa6f0T0DkFEVEoGDRokunTpovRcnz59RJ06daRjAGLHjh1CCCG++OIL0bJlS5Gbm6tw3YEDB0S5cuXElStXlLbr4+Mj2rRpI1c2efJkYW9vr/ReeQwNDcWGDRuEEEIkJSUJAGLdunXS+UuXLgkAIjExUQghxIYNG4ShoaFcG3///beQyWQiJSVFrrxVq1YiICBACCFEv379RLt27RSei3fbepebm5sYMWKEEEKIH374QXh7ewshhGjXrp1Yu3atEEKIwYMHi+bNmwshhJgxY4Zo27atXBu3bt0SAKTnzt3dXYwfP146P378eOHu7l5gHKdOnRIAxLNnz4QQQkRGRgoAYufOnVKdjIwMoa2tLWJiYuSuHTp0qOjXr1++bb8bz9uaNm0q2rdvL4QQYv369cLW1lbu/ZGZmSl0dHTEgQMHhBBCjBs3TrRs2VI6f+DAAaGpqSkePXokhFD++r3L3t5erFixQjquWrWqWLZsmVydd9txc3MTw4cPl6vTq1cv6fUS4s377+uvv5aOMzIyhEwmE/v27SswHiIiIipba9euFXp6eiIrK0s8ffpUqKuri7t374qwsDDh5uYmhBAiOjpaABDXr18v1Njw3bFEfHy8ACCSkpIKjEXZOKVr165ydXx9faXxY56jR4+KcuXKiZcvXyptV1tbW4wbN06urGvXrkJPT0/o6ekJV1fXfGO6d++eACAuXLgghPjfmDo+Pl4I8b9x46FDh6Rr9u7dKwDkG8+9e/eEurq6OHLkiFTm6uoqJk+eLIQQYtu2baJ8+fLi6dOn+cb1trwYHj9+LFavXi2MjY1Fenq6EEL+74GDBw8KNTU1kZycLF2b9/fAqVOnCn2Pd709nszKyhIbNmwQAERwcLAQ4v1j1MePHwsAcs+HEG/eN5qamlJf9uzZI7S0tMSDBw/ybev27dvi2rVrBT7yc/z4cQFA4b09fPhwhb9Bvv/+e2FjY5NvW0RUMM64JaIyIYTIdw1VPz8/JCQkwNbWFuPGjcPBgwelcwkJCbCyssp3zbDExEQ0a9ZMrqxZs2a4du2awgYE7/P2V4ssLCwAQG6ZgHedPXsWQgjUrl1b7pPq6Oho6WtjiYmJcHV1lbvu3WNlPD09pRm3UVFR0lIG7u7ucuUtW7YE8GbmcWRkpFwcdnZ2ACD3Fbb3iY+PR5cuXVC1alUYGBhI9313hsfbn/pfvnwZr169Qps2beTuv2nTpiLd+21vv1/OnDmDv//+GwYGBlLbxsbGePXqldR+//79ERUVJW2g8csvv8Db2xsVKlRQ2v7z588xZcoU2Nvbw8jICPr6+vjrr78U+vk++b3/3l3X6+33lp6eHgwMDAp8bxEREVHZ8/T0xPPnz3H69GkcPXoUtWvXRqVKleDu7o7Tp0/j+fPniIqKQpUqVVC9evVCjQ0Lo7DjlLfHY8CbMVNISIjcvb28vJCbm4ukpKR87/fuGD04OBgJCQkYMmSI3Fq+169fh4+PD6pXr47y5cujWrVqABTHie8qyhjb1NQUbdq0wS+//ALgzUzP2NhY6VtUbdq0QdWqVVG9enX4+vril19+kYuxIEOHDkXFihWxcOFChXOJiYmwtraGtbW1VJb3/OeN6+rWrSs9r0XZZHnq1KnQ19eHjo4Oxo4di8mTJ2PkyJGFuvbly5cA3szSfVuDBg1QpUoVacmFdevWoWvXrjAxMcm3rcqVK6NmzZoFPt7n3feKsr/xdHR0Cv2aEJEi1VslnIj+ExITE6XB3bsaNWqEpKQk7Nu3D4cOHULv3r3RunVr/P7779DR0SmwXWWDBfHWV6uANwOMd8uUfT1JQ0ND7hoABX71Kzc3F2pqajhz5oy0dEOevK8avXvfwvL09MTcuXORkpKC6OhoaWkGd3d3rFixAsnJyUhKSpK+lpebm4tOnTopHYjmDZDf5/nz52jbti3atm2LzZs3w9TUFMnJyfDy8lLY8EJPT0/6Oe852rt3LypXrixX70M2oMvJycG1a9fQuHFjqX0nJydpAP82U1NTAG++klWjRg2EhYVh9OjR2LFjBzZs2JDvPSZPnowDBw5gyZIlqFmzJnR0dNCzZ88P2tijMAPYt99bede872uFREREVLZq1qwJKysrREZG4vHjx9La+ebm5qhWrRqOHz+OyMhI6YP0wowNC6Ow45S3x2N59x85ciTGjRun0GaVKlWU3qtWrVr466+/5Mryxo5vr9MKAJ06dYK1tTV+/PFHWFpaIjc3Fw4ODu8dPxV1jN2/f3+MHz8eK1aswK+//oq6devC0dERwJv1Ys+ePYuoqCgcPHgQM2fOxOzZs3H69GmFfQjepa6uLq0L/O7yEflNMnm7PDw8XPob4n1/o7xt8uTJ8PPzg66uLiwsLIq0IbCJiQlkMhkeP36scK5Xr14ICwtDu3btsGfPHoSHhxfYVvv27d+7hEFGRobScnNzcwBAWlqa3N8W9+7dg5mZmVzdR48eSWN0Iio6Jm6J6KM7fPgwLly4gAkTJuRbp3z58ujTpw/69OmDnj17ol27dnj06BHq16+P27dv4+rVq0pn3drb2+PYsWNyZTExMahdu7Y0YDY1NZVbo/TatWtF/hRYU1NTYQZvw4YNkZOTg3v37uW7y6u9vT1OnDghV/busTJubm7Q0tJCcHAwXr58CScnJwBvZlY8efIEa9asgba2NlxcXAC8SX5v27YNNjY2H7yT719//YUHDx5gwYIF0myDt3dSzo+9vT20tLSQnJws/UFTHBs3bsTjx4/Ro0cPAG/6tmXLFlSqVAnly5fP9zofHx/88ssvsLKyQrly5dChQ4d86x49ehR+fn7o1q0bgDeD1LzNzvIoe83fVadOHRw7dgwDBw6UymJiYlCnTp33dZOIiIg+AXnfgnr8+DEmT54slbu7u+PAgQM4ceIEBg8eDKBwY8PCKMw4RZlGjRrh0qVLhZo5madfv374+uuvER8fn+86twDw8OFDJCYmYs2aNVLf3h2Dl5SuXbti5MiR2L9/P3799Vf4+vrKnVdXV0fr1q3RunVrzJo1C0ZGRjh8+DC6d+/+3rZ79eqFxYsXY86cOXLl9vb2SE5Oxq1bt6Rx8OXLl/HkyRNpXFe1atUP6k/FihWL9Jq8TVNTE/b29rh8+bLc+rLA//qydOlSVK5cGa1atSqwrXXr1kkzeIuqWrVqMDc3R0REhPQ+ef36NaKjoxUmjly8eLHA9xIRFYyJWyIqVZmZmUhLS0NOTg7u3r2L/fv3Y/78+ejYsaNccutty5Ytg4WFBRo0aIBy5crht99+g7m5OYyMjODu7o4WLVqgR48e+O6771CzZk389ddfkMlkaNeuHb788ks0btwY33zzDfr06YPY2FisXLkSwcHBUvstW7bEypUr4eLigtzcXEydOlVhBuT72NjYICMjA3/++SccHR2hq6uL2rVro3///hg4cCCWLl2Khg0b4sGDBzh8+DDq1asHb29vjBs3Dm5ubli0aBG6du2KgwcPYv/+/e+9n46ODpo2bYoVK1agWbNmUhJaQ0MDrq6uWLFihZTcBYCxY8fixx9/RL9+/TB58mRUrFgRf//9N8LCwvDjjz8qzPpQpkqVKtDU1MSKFSswatQoXLx4Ed988817rzMwMMCkSZMwYcIE5Obm4rPPPsPTp08RExMDfX19DBo0KN9rX7x4gbS0NGRnZyMlJQXbt2/HsmXLMHr0aGk2cf/+/bF48WJ06dIFgYGBsLKyQnJyMrZv347JkyfDyspKqjdnzhzMnTsXPXv2VPhK2dtq1qyJ7du3o1OnTpDJZJgxY4bCzA8bGxscOXIEffv2hZaWFipWrKjQzuTJk9G7d280atQIrVq1wu7du7F9+3aFnZ+JiIjo0+Tp6YmxY8ciKytL7gNqd3d3jB49Gq9evZLGLIUZGxZGYcYpykydOhUuLi4YO3Yshg8fDj09PSQmJiIiIgIrVqxQes2ECROwd+9etGzZErNnz0bz5s1RoUIFXL16Ffv27ZPGkBUqVICJiQnWrl0LCwsLJCcnY9q0aYXqT1Hp6emhS5cumDFjBhITE+Hj4yOd27NnD/755x+0aNECFSpUQHh4OHJzc2Fra1vo9hcsWAAvLy+5statW6N+/fro378/goKCkJ2djTFjxsDd3V1hSYqPzcvLC8eOHYO/v79cecOGDWFjY4OlS5di5syZKFeu4JUx3/1mXFHIZDL4+/tj3rx5qFWrFmrVqoV58+ZBV1dX7vUB3nzwUJi/IYgoH2WztC4R/RcMGjRIABAAhLq6ujA1NRWtW7cWP/30k8jJyZGri7c2DFu7dq1o0KCB0NPTE+XLlxetWrUSZ8+eleo+fPhQDB48WJiYmAhtbW3h4OAg9uzZI53//fffhb29vdDQ0BBVqlQRixcvlrtXSkqKaNu2rdDT0xO1atUS4eHhSjcny9tIQYj/bQQQGRkplY0aNUqYmJgIAGLWrFlCCCFev34tZs6cKWxsbISGhoYwNzcX3bp1E+fPn5euW79+vbCyshI6OjqiU6dOYsmSJe/dKEsIIWbNmiUAiAULFsiVf/PNNwKA+Oabb+TKr169Krp16yaMjIyEjo6OsLOzE/7+/tKmXoXZnOzXX38VNjY2QktLS7i6uoo//vhD6SYT726+kJubK77//ntha2srNDQ0hKmpqfDy8hLR0dH59s/d3V16v2hqagoLCwvRsWNHsX37doW6qampYuDAgaJixYpCS0tLVK9eXQwfPlw8efJErl7jxo0FAHH48GG58nc3fkhKShKenp5CR0dHWFtbi5UrVyo8P7GxsaJ+/fpCS0tL5P3vU9kGEsHBwaJ69epCQ0ND1K5dW2zatEnu/Nvv9Txvv/+IiIhIdeWNE+3s7OTK8zaBrVGjhlz5+8aGhdmcrDDjFGWbqArxZmPZNm3aCH19faGnpyfq168v5s6dW2AfX716JRYsWCAcHR2Fjo6O0NLSEnZ2dmLChAlym3VFRESIOnXqCC0tLVG/fn0RFRUlN87Jb3Oyt8eNhd2MLW8TsxYtWsiVHz16VLi7u4sKFSoIHR0dUb9+fbFly5Z828lv7Nq2bVsBQG48dvPmTdG5c2ehp6cnDAwMRK9evURaWlqBcRZ0DyHyf53yFGYD3cTERKGjoyNtRPa2adOmiXLlyombN2++N87iys3NFbNmzRLm5uZCS0tLtGjRQtqYLk9MTIwwMjISL168KPV4iP6tZEJ84IKLRERERERERET0UfXu3RsNGzZEQECAXHlISAj8/f2Rnp5eNoG9o1evXmjYsCG++uqrsg6F6JNV8Nx5IiIiIiIiIiJSGYsXLy7SBndlITMzE46OjgXua0JE78cZt0REREREREREnzhVm3FLRMXHxC0RERERERERERGRiuFSCUREREREREREREQqholbIvooHj58iEqVKuHGjRvFaufatWto06YNypcvD0tLS4wdOxaZmZlK6758+RKfffYZZDIZli1bVqz7EpWUlStXonPnzmUdBhEREREREak4Jm6J6KOYP38+OnXqBBsbGwDAjRs3IJPJlD6ioqLybWfQoEHIyMjAsWPHsHPnTuzfvx9BQUEK9bKzs9GzZ088ePAAy5cvx7Rp07Bp0yaFen5+fujatWuJ9FEmk2Hnzp0K5f7+/vDw8ChW25cuXUKPHj1gY2MDmUymtM8AEBwcjGrVqkFbWxtOTk44evSo3HkhBGbPng1LS0vo6OjAw8MDly5dKlZsAGBjY5NvTCWptOIvrD///BNubm4wMDCAhYUFpk6diuzsbLk6W7duRYMGDaCrq4uqVati8eLFcueHDx+O06dP49ixYx8tbiIiIio5JTUh4b8kKioKMpnsX7v2qhACI0aMgLGxMWQyGRISEpSWFVd+f2+8rST/vimOPXv2oGHDhsjNzS3rUIg+aUzcElGpe/nyJdavX49hw4ZJZZUrV0arVq3g4OCAf/75B6mpqdLDzc1NaTsvXrxAbGwsZsyYgfr166NJkybo0aMHjh8/LldPCAE/Pz+kpKTgyJEj+OKLL/Dbb79hzJgx2L17d6n2tbS8ePEC1atXx4IFC2Bubq60zpYtW+Dv74/p06cjPj4ezZs3R/v27ZGcnCzVWbRoEb777jusXLkSp0+fhrm5Odq0aYNnz559rK4US1nGf/78eXh7e6Ndu3aIj49HWFgY/vjjD0ybNk2qs2/fPvTv3x+jRo3CxYsXERwcLMWbR0tLCz4+PlixYkWpx0xEREQl790JCW9r27YtZDIZTpw4IVe+evVqGBgYyH3gm5GRAQ0NDTRv3lyu7tGjRyGTyXD16tVSiR8Atm3bBnt7e2hpacHe3h47duwotXuVJGWTBUJCQmBkZFQm8eTZv38/QkJCsGfPHqSmpsLBwUFpWXGlpqaiffv2AP43Eaa4CWEPD498J9TIZDLpff52PS0tLdSuXRvz5s1DTk4OAMXkfMeOHSGTyfDrr78WKz6i/zxBRFTKtm3bJipWrKhQ/vTpU9GoUSPRqVMnkZ2d/d52bty4IQCI+Ph4qWzAgAGiW7ducvW++OIL0aRJE/Ho0SO58oMHDwoTExMRHR0thBBi1qxZAoDcIzIysugd/H8AxI4dOxTKx48fL9zd3T+43XdVrVpVLFu2TKG8SZMmYtSoUXJldnZ2Ytq0aUIIIXJzc4W5ublYsGCBdP7Vq1fC0NBQrF69+oPjcXd3V3geS0NpxV9YAQEBwtnZWa5sx44dQltbWzx9+lQIIUS/fv1Ez5495eosW7ZMWFlZidzcXKksKipKaGpqihcvXpR63ERERFRyXrx4IYyMjERMTIzCuZs3bwpNTU3Rr18/MWzYMLlzf/31lwAgYmNjpbLw8HBhZWUltLS0xPPnz6XywMBAYWlpmW8M9+7dEy9fvvzgPsTExAg1NTUxb948kZiYKObNmyfU1dXFiRMnPrjN94mMjBQAxOPHj4vVjrJx8IYNG4ShoWGx2hVCiOzsbJGTk/NB165YsUJUqVLlvWUlKSkpSeFvIyGEGDRokOjSpUuh23n48KFITU0Vqamp4tSpUwKAOHTokFR27949IcSbMf/w4cNFamqqSEpKEitWrBAymUwamyt7jZcvXy6aNGlS3K4S/adxxi0RlbojR47A2dlZodzAwAD79u3D5cuXMXz48Pe2I4SQfs7KysL27duxdetWtGvXTq7e8uXLcfLkSVSoUEGuvE2bNnjw4AFatGgBAJg0aRJ69+6Ndu3avXe2r6p7/fo1zpw5g7Zt28qVt23bFjExMQCApKQkpKWlydXR0tKCu7u7VOdDbN++HVZWVggMDJSex/y0b98e+vr6BT7yU1rxF1ZmZia0tbXlynR0dPDq1SucOXOmwDq3b9/GzZs3pTJnZ2dkZWXh1KlTpR43ERERlZx9+/ZBXV0drq6uCuc2bNiAVq1aYfz48diyZQueP38unbO1tYWlpaXckmBRUVHo0qULatSoITeWiYqKgqenZ74xhIeHw8LCAqNGjUJsbGyR+xAUFIQ2bdogICAAdnZ2CAgIQKtWrT7KslfHjx+Ho6MjtLW10bRpU1y4cEHufExMDFq0aAEdHR1YW1tj3Lhx0vPo4eGBmzdvYsKECXJLrA0ePBhPnjyRymbPng3gzfh4ypQpqFy5MvT09NC0aVO55z9vpu6ePXuk2cdvj9feFh0djSZNmkBLSwsWFhaYNm2aNHvaz88PX3zxBZKTk6UZqsrK3iWEgKmpKbZt2yaVNWjQAJUqVZKOY2NjoaGhgYyMDADySyVUq1YNANCwYUPIZDKFpdmWLFkCCwsLmJiYYOzYscjKylLaN2NjY5ibm8Pc3BympqYAABMTE4UyANDV1YW5uTlsbGzw+eefo1WrVgUu3dC5c2ecOnUK//zzT751iKhgTNwSUam7ceMGLC0tlZ6rVKkSwsPDsWnTJixfvrzQbVpZWaFHjx6wsrJCt27dPigufX196OjoQEtLSxqYaGpqKq27cuVKtGrV6oPu8z4pKSmws7MrVhLvwYMHyMnJgZmZmVy5mZkZ0tLSAED6b0F1PoSxsTHU1NRgYGAgPY/5WbduHRISEgp85Ke04i8sLy8vxMTEIDQ0FDk5OUhJScG3334LAFKy2svLC9u3b8eff/6J3NxcXL16Vfoj6O2Etp6eHoyMjLg2HhER0ScmvwkJQghs2LAB/fv3R9OmTWFmZoatW7fK1fHw8EBkZKR0HBkZCQ8PD7i7u0vlr1+/RmxsbIGJ2/79+2Pz5s14/PgxWrZsCVtbW8ydOxe3bt0qVB9iY2MVPuzPG+fkJzk5+b0fvo8aNeq99548eTKWLFmC06dPo1KlSujcubOUULxw4QK8vLzQvXt3nD9/Hlu2bMGxY8fw+eefA1A+WcDNzQ1BQUEoX768VDZp0iQAwODBg3H8+HGEhYXh/Pnz6NWrF9q1a4dr165J8bx48QLz58/HunXrcOnSJbmkaZ6UlBR4e3ujcePGOHfuHFatWoX169dL48Dvv/8egYGBsLKyQmpqKk6fPq207F0ymQwtWrSQksmPHz/G5cuXkZWVhcuXLwN4k8R3cnJSOrkh72+HQ4cOITU1Fdu3b5fORUZG4vr164iMjMTGjRsREhKCkJCQ974+RaWjo5NvQhgAqlatikqVKinsu0FEhade1gEQ0b/fy5cvFWYhvi0kJAQVKlRA9+7dC91mZGQk/v77b3z//fdwcnJCQkICjI2NSyJcpR48eIDr16+XSttZWVm4cuUKXrx4Uey2ZDKZ3LEQQqGsMHVKS+XKlYvdRlnF37ZtWyxevBijRo2Cr68vtLS0MGPGDBw7dgxqamoA3mw8dv36dXTs2BFZWVkoX748xo8fj9mzZ0t18ujo6JTIa05EREQfT34TEg4dOoT79+9Lm0L5+Phg/fr1GDx4sFTHw8MDEyZMQHZ2Nl6+fIn4+Hi0aNECOTk50gSGEydO4OXLlwUmbtXV1dGhQwd06NABT548wW+//YZNmzZh1qxZ8PDwwKBBg9CzZ0/o6OgovT4tLa3IH4RbWlq+dy3V8uXLF3geAGbNmoU2bdoAADZu3AgrKyvs2LEDvXv3xuLFi+Hj4wN/f38AQK1atbB8+XK4u7tj1apVCpMF8hgaGkImk8mVXb9+HaGhobh9+7b0ek2aNAn79+/Hhg0bMG/ePABvxuHBwcFwdHTMN+bg4GBYW1tj5cqVkMlksLOzw507dzB16lTMnDkThoaGMDAwgJqamlwMysre5eHhgbVr1wJ486GAo6MjqlSpgqioKNjb2yMqKirfTY7fnR37tgoVKmDlypVQU1ODnZ0dOnTogD///LNQ33IsjNzcXBw8eBAHDhyQXq/8VK5cmZMViIqBM26JqNRVrFgRjx8/Vnpu7969WLRoETZv3gwrK6tCt2lvb4/OnTsjPDwcDx8+xB9//FFS4So1e/bs9w44DAwM8OTJE4Xy9PR0GBoa5nudjY0NhBD5DsoKo2LFilBTU1MYcN+7d08amOcN6AqqU9qKs1SCKsQ/ceJEpKenIzk5GQ8ePECXLl0A/O+rajKZDAsXLkRGRgZu3ryJtLQ0NGnSBAAUviL36NEjua+eERERkerLb0LC+vXr0bVrV+jp6QF4Myv2+PHjuHLlilTH09MTz58/x+nTp3H06FHUrl0blSpVgru7O06fPo3nz58jKioKVapUQfXq1QsVj6GhIYYNG4YjR44gJiYGSUlJGDhwIA4cOFDgdUX9IFxdXR01a9Ys8KFstuq73l5iwtjYGLa2tkhMTAQAnDlzBiEhIXLjQi8vL+Tm5iIpKem9bb/t7NmzEEKgdu3acu1FR0fLTcbQ1NRE/fr1C2wrMTERrq6ucs9Ps2bNkJGRgdu3bxcprnd5eHjg0qVLePDgAaKjo+Hh4QEPDw9ER0cjOzsbMTExcHd3L3K7devWlZs0YGFhgXv37hUrVuBNEltfXx/a2tro3LkzBgwYgFmzZhV4DScrEBUPZ9wSUalr2LAhNm/erFB+8+ZNDBw4ENOnT4eXl1eh23t7N14NDQ3IZLICZ/QWRFNTU9oJtbjs7Oxw+vRpDBo0SCoTQuDMmTPS7q+lRVNTE05OToiIiJBbOiIiIkIuuWhubo6IiAg0bNgQwJuv40VHR2PhwoXFvn9hnsd169bh5cuXH3SP0oy/KGQymTRzIzQ0FNbW1mjUqJFcHTU1NWl2cWhoKFxdXeX+mLl+/TpevXol9YOIiIg+DcomJDx69Ag7d+6U+6p67dq14ezsjJ9++kkap9SsWRNWVlaIjIzE48ePpYScubk5qlWrhuPHjyMyMhItW7YsdDyvXr3C7t278fPPP2P//v1o2LAhvvzyywKX+DI3Ny/yB+HJycmwt7cvMJYBAwZg9erVhY49T15CNDc3FyNHjsS4ceMU6lSpUqVIbebm5kJNTQ1nzpxR+NbT2xMFdHR03vvNLWVJ7by9N4r7rS8HBweYmJggOjoa0dHRCAwMhLW1NebOnYvTp0/j5cuX+Oyzz4rcroaGhtyxTCZDbm5usWIF3nwgMX36dGhpacHS0lLhuVWGkxWIioeJWyIqdV5eXggICMDjx4+lDcOysrLQu3dv1KhRAyNHjpQbPBoaGub71S4AGDt2LIKCglCpUiUEBwcDgLThWFHZ2NjgwIEDuHLlCkxMTGBoaKgw0CmsSZMmYdCgQbCzs0Pbtm3x8uVLrF27FtevX8fYsWM/qM08r1+/lta6ev36NVJSUpCQkAB9fX3UrFkTwJvZoL6+vnB2doarqyvWrl2L5ORkab0xmUwGf39/zJs3D7Vq1UKtWrUwb9486OrqwsfHp1jx2djY4MiRI+jbty+0tLRQsWJFpfWKs1TCh8Z/6tQpDBw4EJs2bZJmvwYEBODUqVP4888/pXp2dnb4/PPPpXXUlFm8eDHatWuHcuXKYfv27ViwYAG2bt0qDVofPHiA33//HR4eHnj16hU2bNiA3377DdHR0XLtHD16FNWrV0eNGjU++PkgIiKij0/ZhIRffvkFBgYGCuvG+vj4YOHChZg7dy7U1d/86e3p6YmoqCg8fvwYkydPluq6u7vjwIEDOHHihNzyCsoIIXDs2DH8/PPP2Lp1K/T19TFgwAAsWrQIdnZ27+2Dq6srIiIiMGHCBKns4MGDBW7SW1JLJZw4cUJKwj5+/BhXr16VYm7UqBEuXbokjW2VUTZZQFlZw4YNkZOTg3v37qF58+bvjasg9vb22LZtm1wCNyYmBgYGBsVeBixvndtdu3bh4sWLaN68OQwMDJCVlYXVq1ejUaNGMDAwUHpt3t4cJTUJpTAMDQ0LfH3e9erVK1y/fp2TFYiKQxARfQQuLi5i9erV0nFSUpIAoPSxYcMGpW3kXdO1a1dRqVIloaenJxo3biwiIiI+OK579+6JNm3aCH19fQFAREZGfnBbQggRFhYmnJ2dRfny5UWlSpWEl5eXiIuLK1abQuT/fLm7u8vV++GHH0TVqlWFpqamaNSokYiOjpY7n5ubK2bNmiXMzc2FlpaWaNGihbhw4YJcnUGDBim0+z6xsbGifv36QktLS5Tm/1oKE/+7IiMjFV7bQYMGiapVq8rVAyBmzZpVYFuenp7C0NBQaGtri6ZNm4rw8HC58/fv3xcuLi5CT09P6OrqilatWokTJ04otNO2bVsxf/78Au9FREREquf8+fNCXV1dPHr0SCpzdHQU3bp1ExcuXJB7HDx4UAAQO3fulOr+9NNPQkdHR6irq4u0tDSpfPPmzcLAwEAAEMnJyQXGsGnTJqGjoyN8fHzEgQMHRE5OTpH6cPz4caGmpiYWLFggEhMTxYIFC4S6urrSMUtJyRuP1a1bVxw6dEhcuHBBdO7cWVSpUkVkZmYKIYQ4d+6c0NHREWPGjBHx8fHi6tWrYteuXeLzzz+X2mnTpo3o3LmzuH37trh//77UHwDi0KFD4v79++L58+dCCCH69+8vbGxsxLZt28Q///wjTp06JRYsWCD27t0rhBBiw4YNwtDQ8L2x3759W+jq6oqxY8eKxMREsXPnTlGxYkW5ceOyZcsUxpbKypRZvny5UFNTE87OzlJZ165dhZqampg8ebJcXQBix44dQgghsrKyhI6Ojvj2229FWlqaSE9PF0K8Ged26dJF7rrx48cXanyf9zdHfHy8wjl3d3cxfvz4fK/Ne40fP34sV6avry+9JkRUdEzcEtFHsXfvXlGnTp0iDyzfVtBAgkqOu7v7exOY9OEuXLggKlWqJA2uiYiI6NPy9oSEuLi4fCcj5D06deokXZs3nrWzs5Nr89atWwKAqFGjxnvvn5KSIp48eVKsPvz222/C1tZWaGhoCDs7O7Ft27Zitfc+eUm93bt3i7p16wpNTU3RuHFjkZCQIFfv1KlT0qQKPT09Ub9+fTF37lzpfH6TBUaNGiVMTEzkPoh//fq1mDlzprCxsREaGhrC3NxcdOvWTZw/f14IUfjErRBCREVFicaNGwtNTU1hbm4upk6dKrKysqTzxUncXrhwQQAQkyZNkrsWgNizZ49c3bcTt0II8eOPPwpra2tRrlw5KTGrSonbESNGiJEjR773vkSUP5kQ/784CxFRKfv+++/RvXt3WFtbf9D1N27cQLVq1RAfH48GDRqUbHAEAHj27Bns7e2RmJhY4EZh9OEOHjwIIUSR1nUmIiIi1REeHo5Jkybh4sWLKFeO+30TKXP//n3Y2dkhLi5O2siXiIqOiVsi+mQwcUtEREREqqC4ExKI/u1OnTqFpKQk9OnTp6xDIfqkMXFLREREREREREREpGL4vQ4iIiIiIiIiIiIiFcPELREREREREREREZGKYeKWiIiIiIiIiIiISMUwcUtERERERERERESkYpi4LSYhBJ4+fQru8UZERERE/xYc4xIRERGVPSZui+nZs2cwNDTEs2fPyjoUIiIiIqISwTEuERERUdlj4paIiIiIiIiIiIhIxTBxS0RERERERERERKRimLglIiIiIiIiIiIiUjFM3BIRERERERERERGpGCZuiYiIiIiIiIiIiFQME7dEREREREREREREKoaJWyIiIiIiIiIiIiIVw8QtERERERERERHRJ2DPnj2wtbVFrVq1sG7dOqV1xo4di0qVKsHZ2Vmu/Pr163B2dkbNmjUxatQoCCEAAFOmTEH9+vXh6OiIXr164cWLF6XeDyocJm6JiIiIiIiIiIhUTEhICEJCQqTj7OxsTJw4EYcPH8bZs2excOFCPHr0SOE6Hx8f7Nu3T6F8ypQpmD17Nv7++2/cvXsXe/fuBQB8/fXXOH/+PM6dO4dq1aphzZo1pdYnKhombumT161bN1SoUAE9e/bMt86pU6dQt25d1KxZE4GBgVL54cOH0bBhQzg6OqJt27bSP3h9+vRBgwYN0KBBA1SuXBldu3Yt7W4QEREREREREeUrL7dRuXJlGBgYwNvbGwcOHFCo16xZM5iYmMiVCSEQGxuLDh06AAAGDhyI3bt3AwDKly8v1cnIyIBMJivlnlBhMXFLn5R3P20CgHHjxmHTpk0FXjd27FiEhobir7/+wu7du3Hx4kUAwPjx4xEWFoZz586hYcOG0qdKW7ZsQUJCAhISEuDp6cnELRERERERERGVqTt37qBy5crSsZWVFVJSUgp17cOHD2FsbCwlZd+9dty4cbC0tMTly5cxcuTIkg2cPph6WQdAVFyenp6IiorK9/ydO3eQnZ2N+vXrA3jzlYHdu3fDwcEBMpkMz549AwBkZGSgTp06ctdmZmbiwIEDWLlyZanFT0RERPSps201tNTvceXP9UWqf+zYMcydOxcnTpyAEAJVq1ZF//794e/vD01NzVKK8g0PDw907doV/v7+JdZmVFQUPD09oaenh3LlykFHRweNGjXCqFGj0KVLlxK7DxERla2cnBw4OTkBgPSt4KCgIABAQECAQv3Czo7NW882v2uXL1+OoKAgfPnllwgLC8PgwYOLGjqVAs64pX+9gj6RWrVqFdq1awdLS0ucP38evr6+ctfu27cPrq6uMDIy+pghExEREVEx7NmzB+3bt4eXlxeuXbuG9PR0bNmyBZcvX0ZqampZhwchBHJycop8naGhITIyMvD06VP8/fff8PX1xdChQzF//vxSiJKIiMqCmpqa9A3gwMBABAYGSsfW1tZys2Rv374NCwuLQrVbsWJFPHr0SErgKru2XLly6Nu3L37//feS6xAVCxO3pPJycnKk9WZnzpyJmTNnSseFGfAW9KnSsmXLEBERgTt37sDV1VVh0Lt161b06dOnZDpCn4TirJn86tUr+Pn5wdbWFnXq1MGxY8cAAOfOnYOLiwsaNGiAZs2a4Z9//in1fhAREf1XCSEwbtw4TJ06Ff7+/qhYsSIAwM7ODiEhIahatSoAIC4uDs2aNYORkRHs7e0RGhoqteHn5yc3WzY9PR0ymQw3btyQzg8fPhx9+/aFgYEBbG1tpW+Affnllzh69CimTp0KfX19tG/fHgBgY2OD+fPnw8XFBbq6uli5ciWqV68uN1aNjY1FhQoV8OrVq/f208DAAD4+Pli5ciUCAwOVbk5DRET/Lk2aNMHFixeRkpKCZ8+eITw8HF5eXoW6ViaTwcXFRdqQbNOmTejUqRMA4Nq1a1K93bt3w87OruSDpw/CxC2pvII+bVJTU3vv9ZUrV1b6idT9+/eRmJiIhg0bAgB69eqFmJgYqd7Lly9x6NAhdO7cueQ7RSqhpNdM/vbbb1G7dm1cuXIF58+fh4ODA4A3O3TOmTMHCQkJGDBgABYuXFgq/SEiIqI3f3wmJSWhX79++dZJT09Hu3bt0LdvX9y/fx+rVq3C8OHDcfz48ULfJywsDCNGjEB6ejp8fX3h5+cHAFi6dCmaN2+OhQsXIiMjQ25X75CQEGzcuBEZGRkYPXo0Xr58iejoaLnzPj4+0NbWLnQc3bt3x+vXr3Hy5MlCX0NERJ8mdXV1LF26FJ6enmjYsCEmT54sbULm7e2NO3fuAACGDRsGV1dXnD9/HlZWVtixYwcAYOHChZg1axZq1KgBU1NTaaOyiRMnwsHBAfXr18f169cxc+bMsukgKeAat/SvZ2lpCTU1NZw/f16aTbF+/XpUqFAB9+/fR1JSEqpVq4Y///wTtra20nXh4eFo3rw5DAwMyjB6+tiKs2by5s2b8ddffwEANDQ0pCU23l5L+dmzZ4X+KgsREREV3f379wFAbqmsd+3duxempqb44osvAADu7u7w8fHBxo0b0axZs0Ldp0OHDmjZsiUAYPDgwZgxYwYePnyosIv320aPHi2NN9XU1DBw4ECEhITAw8MDr169wtatWxEREVGo++fR1NSUvv5KRET/LnkfCr6tc+fOSieYhYeHSz+vW7dOaXu1atXCmTNnFMp379794UFSqeKMW/rkeXl5oVevXggPD4eVlRVOnz4NQP7TppUrV6Jfv36wtbWFt7c36tWrB3V1dQQHB6NTp05wdHTEkSNH8NVXX0ntbt26Fb179y6TPpHqym/N5PT0dKirq2PSpElo1KgRBg8eLCVrFy1ahC+//BJWVlb46aefMHHixLIKn4iI6F8vb2mEgnbZvn37NmxsbOTKqlevjtu3bxf6Pubm5tLPenp6ACD9vz8/VapUkTseMmQItm3bhoyMDOzYsQNWVlZwdnYudAwA8Pr1azx48ADGxsZFuo6IiIhUH2fc0idF2adNBw4cUFr37U+bXFxccOnSJYU6PXv2zHct0y1btnxYkKTSCtqh88yZM+9dfiO/NZOzsrJw/fp1tG/fHitXrsRXX32FBQsWYO7cuQgODsaqVavg7e2NH374ARMnTsz3E1AiIiIqntq1a8PGxgZhYWGYPn260jpWVlbSerV5kpKSYGVlBQDQ19fHixcvpHNF3dCsXDnl82PeLbe1tYWjoyN+//13hIaGYsiQIUW6DwBs374dmpqacHFxKfK1REREpNo445aI/lNKa83kihUronz58tIaQd26dUNCQgIAIDQ0FN7e3gCA3r17y62lTERERCVLJpNhxYoVWLBgAVasWIGHDx8CAK5evYqhQ4fi5s2b8Pb2xr179xAcHIzs7GwcPXoUv/76KwYOHAgAaNSoEQ4cOIDU1FQ8e/YMc+bMKVIMZmZmuH79eqHqDh06FEuXLsWRI0cwYMCAQt8jIyMDW7ZswRdffIEZM2agQoUKRYqRiIiIVB8Tt0RERfD2msnZ2dkIDQ1Fp06dIJPJ0LZtW8TGxgIAoqKiUKdOHQCAiYkJTpw4AQAKaynTv1u3bt1QoUKFfGf2A8CpU6dQt25d1KxZE4GBgQrne/bsKfe12SlTpqB+/fpwdHREr1695GaEERHRGx07dsS+ffuwd+9e1KhRA0ZGRujZsyfs7OxgYWGBChUqYN++fdi8eTNMTEwwYsQIrFq1Cp999hkAYMCAAXB3d4ednR0aNGggfTBbWP7+/jh06BCMjIzQsWPHAuv27t0bN2/eRLt27WBqalpg3SdPnkBfXx/ly5dHjRo1sGHDBvz4449yy30RERHRv4dMKPveLxXa06dPYWhoiCdPnqB8+fJlHc4n59RXI8o6hBLVZN7asg6BiiAkJASA/BIcXl5eOHv2LJ4/fw5jY2Ps2LEDjRs3hre3N9atWwdLS0ucOHECQ4cOxatXr+Dr64vZs2cDAP755x/4+vri2bNnqFq1KjZu3AhjY2NER0fD398fubm5MDQ0xE8//YSaNWt+/A5TqVL2foqMjERGRgY2btyI33//Xel1jRs3xvr162Fvbw9XV1ds2LABDg4OAICIiAisW7cO169fR1xcHIA3/9/J+//NlClTYGFhgQkTJpRex4joP4lj3I+rRo0aWLZsmdLNZoiI6N/DttXQsg6hRF35c31Zh/CvxzVuieg/q6TXTK5evTqOHz+uUO7u7o74+PgPD5Q+WZ6enoiKisr3/J07d5CdnY369esDAHx8fLB79244ODggKysL8+bNw/LlyzF48GDpmrwEihACGRkZkMlkpdoHIiIqXWFhYcjOzi7yrF4iIiL69+NSCURERGXkzp07qFy5snRsZWUlraH83XffYdCgQTAwMFC4bty4cbC0tMTly5cxcuTIjxYvERVdcHAwqlWrBm1tbTg5OeHo0aP51k1NTYWPjw9sbW1Rrlw5+Pv7K623bds22NvbQ0tLC/b29tixY0ex7ktlp06dOhg/fjzWrFlTqLX2iYiI6L+FiVsiIqIPlJOTgwYNGqBBgwaYOXMmZs6cKR3n5OS893plqxXJZDKkpKTg4MGDGDRokNLrli9fjpSUFDRs2BBhYWHF7gcRlY4tW7bA398f06dPR3x8PJo3b4727dsjOTlZaf3MzEyYmppi+vTpcHR0VFonNjYWffr0ga+vL86dOwdfX1/07t0bJ0+e/OD7UtlJTEzE3bt30a5du7IOhYiIiFQQE7dEREQfSE1NDQkJCUhISEBgYCACAwOl48LMnKpcubI0wxYAbt++DQsLCyQkJODy5cuoVq0aPvvsM1y4cAHe3t5y15YrVw59+/bNd+1cIip73333HYYOHYphw4ahTp06CAoKgrW1NVatWqW0vo2NDb7//nsMHDgQhoaGSusEBQWhTZs2CAgIgJ2dHQICAtCqVSsEBQV98H2JiIiISDVxjVsi+tfgZnf0qbG0tISamhrOnz8Pe3t7hIaGYv369ahXrx5SU1MBADdu3EDPnj2ldZavXbuGWrVqAQB2794NOzu7MoufiPL3+vVrnDlzBtOmTZMrb9u2LWJiYj643djYWIUNCb28vKTEbWndl4iIiIg+PiZuiYiISomXlxfOnj2L58+fw8rKCjt27EDjxo3h7e2NdevWwdLSEitXrkS/fv3w6tUr+Pr6ol69egW2OXHiRCQlJaFcuXKoW7cuVq9e/ZF6Q0RF8eDBA+Tk5MDMzEyu3MzMDGlpaR/cblpaWoFtfuh9MzMzkZmZKR0/ffr0g2MkIiIiopLBxC0REVEJ8PPzUyg7cOCA0rp5s2cBwMXFBZcuXcq3XRsbG8TFxUnHu3fv/vAg6ZPWrVs3REVFoVWrVvkukXHq1CkMHjwYmZmZGDhwIGbOnAkAuH79Ovr06YP09HS0bt0aq1atgkwmw7lz5zBy5Ei8evUKenp6+Pnnn1G9evWP2a1/PZlMJncshFAoK402i3rf+fPnY86cOcWKi4iIiIhKFte4JSIiIlIxISEhCAkJkSsbN24cNm3aVOB1Y8eORWhoKP766y/s3r0bFy9eBABMmTIFs2fPxt9//427d+9i7969AICvv/4ac+bMQUJCAgYMGICFCxeWSn/+iypWrAg1NTWFWa737t1TmA1bFObm5gW2+aH3DQgIwJMnT6THrVu3PjhGIiIiIioZTNwSERERfQI8PT1hYGCQ7/k7d+4gOzsb9evXh7q6Onx8fLB7924IIRAbG4sOHToAAAYOHCjN3JbJZHj27BkA4NmzZ7CwsCj9jvxHaGpqwsnJCREREXLlERERcHNz++B2XV1dFdo8ePCg1OaH3ldLSwvly5eXexARERFR2eJSCUREREpwszv61Ny5cweVK1eWjq2srBAdHY2HDx/C2NhY+pq8lZUVUlJSAACLFi2Cl5cX/P39oa+vj1OnTpVJ7P9WEydOhK+vL5ydneHq6oq1a9ciOTkZo0aNAvBmlmtKSorcTOqEhAQAQEZGBu7fv4+EhARoamrC3t4eADB+/Hi0aNECCxcuRJcuXbBr1y4cOnQIx44dK/R9S8PH+DezqP+OHTt2DHPnzsWJEycghEDVqlXRv39/+Pv7Q1NTs5SifMPDwwNdu3aFv79/ibUZFRUFT09P6OnpSWVGRka4fft2id2DiIiIVAsTt0REREQqICcnB05OTgCAR48eAQCCgoIAAGfOnIGamlqB1wshFMpkMlm+5QAQHByMVatWwdvbGz/88AMmTpyIdevWFacb9JY+ffrg4cOHCAwMRGpqKhwcHBAeHo6qVasCAFJTU5GcnCx3TcOGDaWfz5w5g19//RVVq1bFjRs3AABubm4ICwvD119/jRkzZqBGjRrYsmULmjZtWuj7/hfs2bMH/fr1wzfffIOff/4ZFStWxF9//YUFCxYgNTW1zJ8LIQRyc3Pf+3v9LkNDQ6Snp5dOUERERKRyuFQCERERkQpQU1NDQkICEhISEBgYiMDAQOm4MMmdypUrSzNpAeD27duwsLBAxYoV8ejRIymBm1cOAKGhofD29gYA9O7dGzExMaXQs/+2MWPG4MaNG8jMzMSZM2fQokUL6VxISAiioqLk6gshFB55Sds8PXv2xF9//YXXr18jMTER3bt3L9J9/+2EEBg3bhymTp0Kf39/VKxYEQBgZ2eHkJAQKWkbFxeHZs2awcjICPb29ggNDZXa8PPzk5stm56eDplMJr0Wfn5+GD58OPr27QsDAwPY2tpKr+WXX36Jo0ePYurUqdDX10f79u0BvNlscv78+XBxcYGuri5WrlyJ6tWry324EhsbiwoVKuDVq1el+AwRERHRp4KJWyIiIqJ/AUtLS6ipqeH8+fPIzs5GaGgoOnXqBJlMBhcXF2lDsk2bNqFTp04AABMTE5w4cQIA8Oeff8LW1rbM4icqKdeuXUNSUhL69euXb5309HS0a9cOffv2xf3797Fq1SoMHz4cx48fL/R9wsLCMGLECKSnp8PX1xd+fn4AgKVLl6J58+ZYuHAhMjIysG/fPumakJAQbNy4ERkZGRg9ejRevnyJ6OhoufM+Pj7Q1tYueseJiIjoX4eJWyIiIqJPgJeXF3r16oXw8HBYWVnh9OnTAABvb2/cuXMHALBy5Ur069cPtra28Pb2Rr169QAACxcuxKxZs1CjRg2YmppKG5WtWbMGo0ePhqOjI4KDg7F48eKy6RxRCbp//z4AyK35/K69e/fC1NQUX3zxBTQ0NODu7g4fHx9s3Lix0Pfp0KEDWrZsCTU1NQwePBg3b97Ew4cPC7xm9OjRsLW1hZqaGjQ1NTFw4ECEhIQAAF69eoWtW7di8ODB+V7/5MkTGBkZSY8ZM2YUOl4iIiL69Hwya9weOXIEixcvxpkzZ5CamoodO3aga9euBV4THR2NiRMn4tKlS7C0tMSUKVMUNmXYtm0bZsyYgevXr6NGjRqYO3cuunXrVoo9ISIiIipY3sy9tx04cEBp3fDwcOlnFxcXXLp0SaFOrVq1cObMGYVyd3d3xMfHf3igRCoob2mElJQU1KhRQ2md27dvw8bGRq6sevXqOHLkSKHvY25uLv2ct2HYs2fPYGJiku81VapUkTseMmQInJ2dsXLlSuzevRtWVlZwdnbO93qucUtERPTf8snMuH3+/DkcHR2xcuXKQtVPSkqCt7c3mjdvjvj4eHz11VcYN24ctm3bJtWJjY1Fnz594Ovri3PnzsHX1xe9e/fGyZMnS6sbRERERERUimrXrg0bGxuEhYXlW8fKykph7eCkpCRYWVkBAPT19fHixQvpXGpqapFiKFdO+Z9Z75bb2trC0dERv//+O0JCQjBkyJAi3YeIiIj+3T6ZxG379u3x7bffKt18QZnVq1ejSpUqCAoKQp06dTBs2DAMGTIES5YskeoEBQWhTZs2CAgIgJ2dHQICAtCqVStpB2ciIiIiIvq0yGQyrFixAgsWLMCKFSuk5QuuXr2KoUOH4ubNm/D29sa9e/cQHByM7OxsHD16FL/++isGDhwIAGjUqBEOHDiA1NRUPHv2DHPmzClSDGZmZrh+/Xqh6g4dOhRLly7FkSNHMGDAgKJ1loiIiP7VPpmlEooqNjYWbdu2lSvz8vLC+vXrkZWVBQ0NDcTGxmLChAkKdZi4JSIiopJ06qsRZR1CiWoyb21Zh0BUoI4dO2Lfvn349ttvpXVgq1SpAl9fX1hYWEBTUxP79u2Dv78/AgICYGlpiVWrVuGzzz4DAAwYMABRUVGws7NDxYoVMXv2bGzZsqXQ9/f394efnx+MjIzw2WefYc+ePfnW7d27N8aPH4927drB1NS0eB0nIiKif5V/beI2LS0NZmZmcmVmZmbIzs7GgwcPYGFhkW+dtLS0fNvNzMxEZmamdPz06dOSDZyIiIiI6BOjisn8zz77DPv378/3fJMmTRATE6P0nKamJjZt2iRX5uvrK/2ct6FYHiMjIwghpOOmTZsiMTFRrs67SzPk0dPTg6mpaYGbkgGAh4cH17clIiL6j/lklkr4EDKZTO44bzD1drmyOu+WvW3+/PkwNDSUHtbW1iUYMRERERER/ZeEhYUhOzsbHTp0KOtQiIiISMX8a2fcmpubK8ycvXfvHtTV1aWdXvOr8+4s3LcFBARg4sSJ0vHTp0+ZvCUiIiIioiKrU6cOHj16hI0bN0JNTa2swyEiIiIV869N3Lq6umL37t1yZQcPHoSzszM0NDSkOhEREXLr3B48eBBubm75tqulpQUtLa3SCZqIiIiIiP4z3l1OgYiIiOhtn0ziNiMjA3///bd0nJSUhISEBBgbG6NKlSoICAhASkqKtBbVqFGjsHLlSkycOBHDhw9HbGws1q9fj9DQUKmN8ePHo0WLFli4cCG6dOmCXbt24dChQzh27NhH7x8RERERERERERFRnk9mjdu4uDg0bNgQDRs2BABMnDgRDRs2xMyZMwEAqampSE5OlupXq1YN4eHhiIqKQoMGDfDNN99g+fLl6NGjh1THzc0NYWFh2LBhA+rXr4+QkBBs2bIFTZs2/bidIyIiIiIiIiIiInrLJzPj1sPDQ26n1ne9u7MrALi7u+Ps2bMFttuzZ0/07NmzuOERERERERERERERlZhPZsYtERERERERERER0X8FE7dEREREREREREREKoaJWyIiIiIiIiIiIiIVw8QtERERERERERERkYph4paIiIiIiIiIiIhIxTBxS0RERERERERERKRimLglIiIiIiIiIiIiUjFM3BIRERERERERERGpGCZuiYiIiIiIiIiIiFQME7dEREREREREREREKoaJWyIiIiIiIiIiIiIVw8QtERERERERERERkYph4paIiIiIiIiIiIhIxTBxS0RERERERERERKRimLglIiIiIiIiIiIiUjFM3BIRERERERERERGpGCZuiYiIiIiIiIiIiFQME7dEREREREREREREKoaJWyIiIiIiIiIiIiIVw8QtERERERERERERkYph4paIiIiIiIiIiIhIxTBxS0RERERERERERKRimLglIiIiIiIiIiIiUjFM3BIRERERERERERGpGCZuiYiIiIiIiIiIiFQME7dEREREREREREREKoaJWyIiIiIiIiIiIiIVw8QtERERERERERERkYph4paIiIiIiIiIiIhIxTBxS0RERERERERERKRimLglIiIiIiIiIiIiUjFM3BIRERERERERERGpGCZuiYiIiIiIiIiIiFQME7dEREREREREREREKoaJWyIiIiIiIiIiIiIVw8QtERERERERERERkYph4paIiIiIqJQEBwejWrVq0NbWhpOTE44ePVpg/ejoaDg5OUFbWxvVq1fH6tWr5c57eHhAJpMpPDp06CDVmT17tsJ5c3PzUukfEREREZUeJm6JiIiIiErBli1b4O/vj+nTpyM+Ph7NmzdH+/btkZycrLR+UlISvL290bx5c8THx+Orr77CuHHjsG3bNqnO9u3bkZqaKj0uXrwINTU19OrVS66tunXrytW7cOFCqfaViIiIiEqeelkHQERERET0b/Tdd99h6NChGDZsGAAgKCgIBw4cwKpVqzB//nyF+qtXr0aVKlUQFBQEAKhTpw7i4uKwZMkS9OjRAwBgbGwsd01YWBh0dXUVErfq6uqcZUtERET0ieOMWyIiIiKiEvb69WucOXMGbdu2lStv27YtYmJilF4TGxurUN/LywtxcXHIyspSes369evRt29f6OnpyZVfu3YNlpaWqFatGvr27Yt//vmnGL0hIiIiorLAxC0RERERUQl78OABcnJyYGZmJlduZmaGtLQ0pdekpaUprZ+dnY0HDx4o1D916hQuXrwozejN07RpU2zatAkHDhzAjz/+iLS0NLi5ueHhw4f5xpuZmYmnT5/KPYiIiIiobDFxS0RERERUSmQymdyxEEKh7H31lZUDb2bbOjg4oEmTJnLl7du3R48ePVCvXj20bt0ae/fuBQBs3Lgx3/vOnz8fhoaG0sPa2rrgjhERERFRqWPiloiIiIiohFWsWBFqamoKs2vv3bunMKs2j7m5udL66urqMDExkSt/8eIFwsLCFGbbKqOnp4d69erh2rVr+dYJCAjAkydPpMetW7fe2y4RERERlS4mbomIiIiISpimpiacnJwQEREhVx4REQE3Nzel17i6uirUP3jwIJydnaGhoSFXvnXrVmRmZmLAgAHvjSUzMxOJiYmwsLDIt46WlhbKly8v9yAiIiKissXELRERERFRKZg4cSLWrVuHn376CYmJiZgwYQKSk5MxatQoAG9muQ4cOFCqP2rUKNy8eRMTJ05EYmIifvrpJ6xfvx6TJk1SaHv9+vXo2rWrwkxcAJg0aRKio6ORlJSEkydPomfPnnj69CkGDRpUep0lIiIiohKnXtYBEBERERH9G/Xp0wcPHz5EYGAgUlNT4eDggPDwcFStWhUAkJqaiuTkZKl+tWrVEB4ejgkTJuCHH36ApaUlli9fjh49esi1e/XqVRw7dgwHDx5Uet/bt2+jX79+ePDgAUxNTeHi4oITJ05I9yUiIiKiT8MnlbgNDg7G4sWLkZqairp16yIoKAjNmzdXWtfPz0/pBgz29va4dOkSACAkJASDBw9WqPPy5Utoa2uXbPBERERE9J8zZswYjBkzRum5kJAQhTJ3d3ecPXu2wDZr164tbVqmTFhYWJFiJCIiIiLV9MkslbBlyxb4+/tj+vTpiI+PR/PmzdG+fXu5WQpv+/7775Gamio9bt26BWNjY/Tq1UuuXvny5eXqpaamMmlLREREREREREREZeqTSdx+9913GDp0KIYNG4Y6deogKCgI1tbWWLVqldL6hoaGMDc3lx5xcXF4/PixwgxbmUwmV8/c3PxjdIeIiIiIiIiIiIgoX59E4vb169c4c+YM2rZtK1fetm1bxMTEFKqN9evXo3Xr1gpre2VkZKBq1aqwsrJCx44dER8fX2A7mZmZePr0qdyDiIiIiIiIiIiIqCR9EonbBw8eICcnB2ZmZnLlZmZmSEtLe+/1qamp2LdvH4YNGyZXbmdnh5CQEPzxxx8IDQ2FtrY2mjVrhmvXruXb1vz582FoaCg9rK2tP6xTRERERERERERERPn4JBK3eWQymdyxEEKhTJmQkBAYGRmha9eucuUuLi4YMGAAHB0d0bx5c2zduhW1a9fGihUr8m0rICAAT548kR63bt36oL4QERERERERERER5Ue9rAMojIoVK0JNTU1hdu29e/cUZuG+SwiBn376Cb6+vtDU1Cywbrly5dC4ceMCZ9xqaWlBS0ur8METERERERERERERFdEnMeNWU1MTTk5OiIiIkCuPiIiAm5tbgddGR0fj77//xtChQ997HyEEEhISYGFhUax4iYiIiIiIiIiIiIrjk5hxCwATJ06Er68vnJ2d4erqirVr1yI5ORmjRo0C8GYJg5SUFGzatEnuuvXr16Np06ZwcHBQaHPOnDlwcXFBrVq18PTpUyxfvhwJCQn44YcfPkqfiIiIiIiIiIiIiJT5ZBK3ffr0wcOHDxEYGIjU1FQ4ODggPDwcVatWBfBmA7Lk5GS5a548eYJt27bh+++/V9pmeno6RowYgbS0NBgaGqJhw4Y4cuQImjRpUur9ISIiIiIiIiIiIsrPJ5O4BYAxY8ZgzJgxSs+FhIQolBkaGuLFixf5trds2TIsW7aspMIjIiIiIiIiIiIiKhGfxBq3RERERERERERERP8lTNwSERERERERERERqRgmbomIiIiIiIiIiIhUDBO3RERERERERERERCqGiVsiIiIiIiIiIiIiFcPELREREREREREREZGKYeKWiIiIiIiIiIiISMUwcUtERERERERERESkYpi4JSIiIiIiIiIiIlIxTNwSERERERERERERqRgmbomIiIiIiIiIiIhUDBO3RERERERERERERCqGiVsiIiIiIiIiIiIiFcPELREREREREREREZGKYeKWiIiIiIiIiIiISMUwcUtERERERERERESkYpi4JSIiIiIiIiIqJXv27IGtrS1q1aqFdevWKa1z6tQp1K1bFzVr1kRgYKBU7uHhATs7OzRo0AANGjSQyoUQmDx5MmrXro06depg69atpd0NIioD6mUdABERERERERHRv0FISAgAwM/PDwCQnZ2NiRMnIjIyEuXLl0ejRo3QvXt3GBsby103duxYhIaGwt7eHq6urujevTscHBwAAL///rv0c57169fj6dOnuHr1KoQQePjwYan3jYg+Ps64JSIiIiIiIiIqBXkzaStXrgwDAwN4e3vjwIEDcnXu3LmD7Oxs1K9fH+rq6vDx8cHu3bsLbHfNmjWYMWMGAEAmk6FixYql1gciKjtM3BIRERERERERlYI7d+6gcuXK0rGVlRVSUlKKVMfHxweNGjVCcHCwVHbr1i2sW7cOzs7O6N69O9LS0kqxF0RUVrhUAhERERERERHRB8rJyYGTkxMA4NGjRwCAoKAgAEBAQIBCfZlMJncshMi3zq+//gpLS0s8evQI7dq1Q926deHu7o6MjAyYmpoiLi4Oa9aswZdffolffvmlJLtFRCqAiVsiIiIiIiIiog+kpqaGhIQEAIpr3MbExMjNnr19+zaaNm0qd33lypUV6lhYWAAALC0tAQDGxsbo0aMHTp8+DXd3d1SuXBk9evQAAHTv3h3Lly8vja4RURnjUglERERERERERKWgSZMmuHjxIlJSUvDs2TOEh4fDy8tLro6lpSXU1NRw/vx5ZGdnIzQ0FJ06dUJ2djYePHgAAHj16hUOHDiAunXrAgA6d+6MyMhIAEBUVBTq1KnzcTtGRB8FZ9wSEREREREREZUCdXV1LF26FJ6ensjNzcWUKVNgYmICAPD29sa6detgaWmJlStXol+/fnj16hV8fX1Rr149PH/+HF5eXsjKykJOTg569+6N9u3bA3izBEO/fv0wf/58GBsbSzN9iejfhYlbIiIiIiIiIqISkLdEwts6d+6Mzp07K5SHh4dLP7u4uODSpUty5/X09HDmzBml9zE2NsaBAweKFywRqTwulUBERERERERERESkYpi4JSIiIiIiIiIiIlIxTNwSERERERERERERqRgmbomIiIiIiIiIiIhUDDcnIyIiIiIiIiJSwrbV0LIOoURd+XN9WYdAREXAGbdEREREREREREREKoaJWyIiIiIiIiIiIiIVw8QtERERERERERERkYph4paIiIiIiIiIiIhIxTBxS0RERERERERERKRimLglIiIiIiIiIiIiUjFM3BIRERERERERERGpGPWyDoCIiIiIqDQ9e/YMy5Ytw5kzZ/Ds2TO5c4cPHy6jqIiIiIiICsbELRERERH9q/n6+uLWrVvo0aMH9PT0yjocIiIiIqJCYeKWiIiIiP7VoqKikJycjPLly5d1KEREREREhcY1bomIiIjoX83a2hqvX78uk3sHBwejWrVq0NbWhpOTE44ePVpg/ejoaDg5OUFbWxvVq1fH6tWr5c6HhIRAJpMpPF69elWs+xIRERGR6vmgGbfPnz/H1atXFdYIa9GiRYkERURERERUUj7//HP06dMH06ZNg5mZmdy5+vXrl9p9t2zZAn9/fwQHB6NZs2ZYs2YN2rdvj8uXL6NKlSoK9ZOSkuDt7Y3hw4dj8+bNOH78OMaMGQNTU1P06NFDqle+fHlcuXJF7lptbe0Pvi8RERERqaYiJ263bt2KYcOG4fXr19DR0ZHKZTIZHj16VKLBEREREREV1+jRowEAkZGRcuUymQw5OTmldt/vvvsOQ4cOxbBhwwAAQUFBOHDgAFatWoX58+cr1F+9ejWqVKmCoKAgAECdOnUQFxeHJUuWyCVuZTIZzM3NS+y+RERERKSairxUwpQpU/DDDz/g1atXePz4sfRg0paIiIiIVFFubq7SR2kmbV+/fo0zZ86gbdu2cuVt27ZFTEyM0mtiY2MV6nt5eSEuLg5ZWVlSWUZGBqpWrQorKyt07NgR8fHxxbovAGRmZuLp06dyDyIiIiIqW0VO3D558gQDBgwojViIiIiIiEpNWloa4uLicPfu3VK/14MHD5CTk6OwNIOZmRnS0tLyjU9Z/ezsbDx48AAAYGdnh5CQEPzxxx8IDQ2FtrY2mjVrhmvXrn3wfQFg/vz5MDQ0lB7W1tZF7jMRERERlawiJ24HDBiA3377rTRiea+ibLIQFRWldOOGv/76S67etm3bYG9vDy0tLdjb22PHjh2l3Q0iIiIi+ogePHgALy8vWFpaonnz5rC0tES7du1w//79Ur+3TCaTOxZCKJS9r/7b5S4uLhgwYAAcHR3RvHlzbN26FbVr18aKFSuKdd+AgAA8efJEety6dev9nSMiIiKiUlXkxO3NmzcxcOBAuLm5oXv37nKP0pS3ycL06dMRHx+P5s2bo3379khOTi7wuitXriA1NVV61KpVSzoXGxuLPn36wNfXF+fOnYOvry969+6NkydPlmpfiIiIiOjj+eKLL2BsbIyUlBS8fPkSKSkpMDY2xueff15q96xYsSLU1NQUZrneu3dPYTZsHnNzc6X11dXVYWJiovSacuXKoXHjxtKM2w+5LwBoaWmhfPnycg8iIiIiKltF3pzMyckJTk5OpRFLgT50k4VKlSrByMhI6bmgoCC0adMGAQEBAN7MNIiOjkZQUBBCQ0NLvA9ERERE9PEdPnwYSUlJ0NXVBfAmQbp27VpUr1691O6pqakJJycnREREoFu3blJ5REQEunTpovQaV1dX7N69W67s4MGDcHZ2hoaGhtJrhBBISEhAvXr1Pvi+RERERKSaipy4nTVrVmnEUaC8TRamTZsmV/6+TRYAoGHDhnj16hXs7e3x9ddfw9PTUzoXGxuLCRMmyNX38vKSdvJVJjMzE5mZmdIxN24gIiIiUm3a2tp4/PixlLgFgPT0dGhpaZXqfSdOnAhfX184OzvD1dUVa9euRXJyMkaNGgXgzaSBlJQUbNq0CQAwatQorFy5EhMnTsTw4cMRGxuL9evXy00omDNnDlxcXFCrVi08ffoUy5cvR0JCAn744YdC35eIiIiIPg1FTtwCbxKeGzduxO3bt2FlZYVBgwbB1dW1pGOTfMgmCxYWFli7di2cnJyQmZmJn3/+Ga1atUJUVBRatGgBIP8NIN63ccOcOXOK2SMiIiIi+lh8fHzg7e2NGTNmoGrVqrhx4wbmzZuH/v37l+p9+/Tpg4cPHyIwMBCpqalwcHBAeHg4qlatCgBITU2VW/arWrVqCA8Px4QJE/DDDz/A0tISy5cvR48ePaQ66enpGDFiBNLS0mBoaIiGDRviyJEjaNKkSaHvS0RERESfhiInbsPCwjB8+HD4+PigefPmSEpKQrt27bB69Wr069evNGKUFGWTBVtbW9ja2krHrq6uuHXrFpYsWSIlbovaJvBmZsTEiROl46dPn3LXXSIiIiIV9s0330BHRwdfffWVNPFgwIAB0nJZpWnMmDEYM2aM0nMhISEKZe7u7jh79my+7S1btgzLli0r1n2JiIiI6NNQ5MTtt99+i71798olP/v3749Ro0aVWuL2QzdZeJeLiws2b94sHee3AcT7Nm4o7a/VEREREVHJUVdXx8yZMzFz5syyDoWIiIiIqNDKFfWClJQUNGvWTK7Mzc0Nd+7cKbGg3vX2Jgtvi4iIgJubW6HbiY+Ph4WFhXTs6uqq0ObBgweL1CYRERERqZ5nz55JPz99+jTfBxERERGRqiryjNu6detizZo1cl+9+vHHH2Fvb1+igb2rqJs7BAUFwcbGBnXr1sXr16+xefNmbNu2Ddu2bZPaHD9+PFq0aIGFCxeiS5cu2LVrFw4dOoRjx46Val+IiIiIqHRVrlxZSswaGRnluzxWTk5OWYRHRERERPReRU7cBgUFoX379vjhhx9gY2ODGzdu4N69e9i3b19pxCcp6uYOr1+/xqRJk5CSkgIdHR3UrVsXe/fuhbe3t1THzc0NYWFh+PrrrzFjxgzUqFEDW7ZsQdOmTUu1L0RERERUui5duiT9nJSUVIaREBERERF9mCInbp2dnfH3339j7969uH37Nvr37w9vb28YGRmVQnjyirK5w5QpUzBlypT3ttmzZ0/07NmzJMIjIiIiIhXx9uaxoaGhmDZtmkKdRYsWFWq8SERERERUFoq8xi0AGBoawsfHB1OmTIGPj89HSdoSEREREX2IefPmKS1fuHDhR46EiIiIiKjwCjXjNiAgAPPnzwfwZq3Z/Hz33XclExURERERUTGdP38eAJCbm4sLFy5ACCGdu379OnR0dMoqNCIiIiKi9ypU4jY9PV36+fHjx6UVCxERERFRiWnQoIG0KZmjo6NULpPJYG5ujm+++aasQiMiIiIieq9CJW5XrVol/bxhw4ZSC4aIiIiIqKTk5uYCAJo2bYqTJ0+WcTREREREREVT5DVuy5cvr7Tc2Ni42MEQEREREZW0vKTtnTt3cPr0aaSmppZxRERERERE71fkxO3ba4PlyZvNQERERESkatLS0tCyZUtYWVmhRYsWsLKygqenJ+7cuVPWoRERERER5atQSyUAQPfu3QEAmZmZ0s95bt68KbduGBERERGRqhg8eDCqVKmCtLQ0VKpUCffu3cPUqVMxZMgQ7N+/v6zDIyIiIiJSqtCJ27zE7N69e+WStOXKlUO7du3Qq1evko+OiIiIiKiYYmJicPfuXWhrawMAKlWqhODgYFhYWJRxZERERERE+St04nbWrFkA3uzO26VLl1ILiIiIiIioJNWsWRO3bt1CrVq1pLJbt26hRo0aZRgVEREREVHBCp24zdOlSxccO3YMmzZtQkpKCipXrowBAwagRYsWpREfEREREVGx9OjRA+3atcOYMWNQpUoV3Lx5E2vWrMGQIUPwxx9/SPU6d+5chlESEREREckrcuJ21apVmDZtGvr3748WLVogOTkZXbp0wbx58zB69OjSiJGIiIiI6IP9+OOPAICVK1fKla9du1b6WSaTMXFLRERERCqlyInbhQsX4sCBA3BxcZHKBg4ciN69ezNxS0REREQqJykpqaxDICIiIiIqsnJFvSAjIwPOzs5yZY0aNcLz589LLCgiIiIiIiIiIiKi/7IiJ25HjhyJwMBA5OTkAABycnIwd+5cjBo1qsSDIyIiIiIqrqtXr6Jr166oUqUKjI2N5R5ERERERKqqyEslhIeH4+LFi1ixYgUsLS1x584dZGRkoF69eggPD5fqnT17tkQDJSIiIiL6EP369UO9evWwZs0a6OrqlnU4RERERESFUuTErb+/fymEQURERERUOv7++2+cPn0a5coV+ctmRERERERlpsiJ20GDBpVGHEREREREpaJ9+/Y4ceIE3NzcyjoUIiIiIqJCK3LiFgASEhJw9uxZZGRkyJWPGzeuRIIiIiIiIiopq1atgru7OxwcHGBubi537rvvviujqIiIiIiIClbkxO3XX3+NJUuWwNHRUW6NMJlMxsQtEREREakcf39/3Lt3Dzk5OXj8+HFZh0NEREREVChFTtyuWrUKZ8+ehb29fWnEQ0RERERUorZv346rV6/CwsKirEMhIiIiIiq0Iu/QYGxsjOrVq5dGLEREREREJa5KlSrQ0dEp6zCIiIiIiIqkyInbxYsXY/To0bh27RqePn0q9yAiIiIiUjVjx45Fr169EBERgfPnz8s9iIiIiIhUVZGXSjAyMkJERAQ2bdoklQkhIJPJkJOTU6LBEREREREV1+effw4A+PPPP+XKOX4lIiIiIlVW5MTt0KFDMXjwYPj4+MhtTkZEREREpIpyc3PLOgQiIiIioiIrcuL24cOHCAwMhEwmK414iIiIiIiIiIiIiP7zipy47devH3bu3Ilu3bqVRjxERERERCXK09Mz30kHhw8f/sjREBEREREVTpETtykpKejXrx8aNWoEc3NzuXPbt28vscCIiIiIiEpC165d5Y5TU1Px888/w8/Pr0ziISIiIiIqjCInbp2cnODk5FQasRARERERlbjx48crlPXt2xcBAQFlEA0RERERUeEUOXE7a9as0oiDiIiIiOijqVevHo4fP17WYRARERER5avIidsjR47ke65FixbFCoaIiIiIqKSdP39e7vjFixf4+eefUaNGjTKKiIiIiIjo/YqcuO3SpYvc8dOnT1GuXDkYGBjg0aNHJRYYEREREVFJaNCgAWQyGYQQAAA9PT00atQIGzduLOPIiIiIiIjyV+TE7ePHj+WOX7x4gYCAADg6OpZYUEREREREJSU3N7esQyAiIiIiKrJyxW1AV1cXixYtwuzZs0sgHCIiIiKikvXq1StkZ2dLxzt27MCePXvKMCIiIiIiovcrduIWAG7cuIHnz5+XRFNERERERCWqTZs2iIuLAwDMmTMHY8aMwejRozFnzpz/a+++o6Oq1j6O/yaF9AwljUCAAKGGjhBABJQuoILSJFawIBcQFYm+CuJVhGtBRBQQBQURGyICkSJVQidSjfSWBAglhUDqvH9wmeuYBDIww0zg+1nrrJWzz97nPFtPks2TPXs7ODIAAACgaFYvlfDAAw/IYDCYzzMzMxUXF6enn37apoEBAAAAtrBnzx7dcccdkqRZs2ZpxYoV8vPzU6tWrTR69GgHRwcAAAAUzurEbcOGDS3OfX199fLLL6tdu3a2igkAAACwmby8PBkMBu3fv1/5+fmqU6eOpIJ7NwAAAADOxOrELbMSAAAAUJLccccdGjJkiJKSknTvvfdKko4dO6bSpUs7NjAAAADgKqxe4/bzzz/Xrl27JEl//PGHGjRooCZNmmjnzp02Dw4AAAC4UdOnT1dqaqqMRqN5XdsNGzbo4YcfdnBkAAAAQNGsnnH773//Wxs3bpQkvfzyy+rUqZN8fX01dOhQrVy50uYBAgAAADeiSpUqmjNnjkXZQw89pIceeshBEQEAAADXZnXiNiUlRYGBgbp06ZLi4uK0YMECubm5aeLEiXYIDwAAALhxa9as0datW5Wenm5R/vrrrzsoIgDO7JdfftELL7yg/Px8vfzyyxo4cGCBOps2bdLjjz+urKwsPfLII+afJ/3799fWrVvl7u6u7t27a9y4cRbtXnzxRc2cOVMpKSk3pS8AgJLL6sRtmTJltG/fPu3atUtNmjSRh4eHLl26pPz8fHvEBwAAANyQl19+WR9//LFatmwpHx8fc7nBYHBgVACcxcyZMyVJjz32mCQpNzdXI0aM0MqVK+Xv76/GjRurZ8+eKlu2rEW75557TnPnzlWdOnXUokUL9ezZU5GRkXrkkUf09ddfKzc3Vx06dNBvv/2mu+++W5K0Z88eJScn38zuAQBKMKsTt8OGDVPDhg0lSbNmzZIkrVu3TrVr17ZpYAAAAIAtfP7559qyZYtq1arl6FAAlACbNm1S3bp1VaFCBUlS165d9euvv6pfv37mOomJicrNzVX9+vUlXZ5lu3DhQkVGRqpz586SJDc3N9WrV08nTpwwtxs5cqSmTJmi2NjYm9gjAEBJZfXmZCNGjFB8fLx27typBx98UJJUqVIlTZ8+3ebBAQAAADfKy8tL1apVc3QYAEqIxMREc9JWkipWrGiRfC1unbS0NC1atEht27aVJM2bN09NmzZVpUqV7Bc8AOCWYvWMW0mKiIiwOK9Ro4ZNggEAAABsbfTo0Ro5cqTeeecdeXh4ODocAE4gLy9PTZo0kSSdPXtWksz7tsTExBSo/8+lVUwm01XrmEwmPfbYYxo8eLDCwsJ04cIFTZo0ScuXL7dVFwAAtwGrZ9zu3r1bd999t4xGo1xdXS0OAAAAwNm8+OKL+uijj+Tj46OyZctaHPY2ZcoUhYeHy9PTU02aNNHatWuvWn/16tVq0qSJPD09VbVqVX366acW16dPn67WrVurTJkyKlOmjNq3b69NmzZZ1BkzZowMBoPFERISYvO+ASWZq6ur4uPjFR8fr7Fjx2rs2LHm87CwMIvZs8ePH1f58uUt2leoUOGqdUaOHKmyZcvqhRdekCQdPHhQ+/fvV+3atVWlShWdO3fOvMwCAABFsTpx++ijjyoiIkJLlizRtm3bLA57s2bg++OPP6pDhw4KDAyUv7+/WrRooV9//dWizsyZMwsMag0Ggy5dumTvrgAAAOAm+emnn7RixQqtWLFC8+fPtzjsad68eRo+fLheffVVbd++Xa1bt1aXLl109OjRQusfOnRIXbt2VevWrbV9+3a98sorGjp0qH744QdznVWrVqlfv35auXKl4uLiVKlSJXXs2LHAR7Tr1q2rpKQk87Fz50679hW4lTRr1ky7du3SiRMnlJ6ersWLF6tTp04WdUJDQ+Xq6qodO3YoNzdXc+fOVffu3SVJn376qeLj4/XJJ5+Y69erV08nT57U4cOHdfjwYZUpU0Y7duy4qf0CAJQ8Vi+VkJCQoE2bNsnFxeqc7w25MvCdMmWKWrVqpalTp6pLly7as2dPoWsErVmzRh06dNDbb7+t0qVL64svvlD37t21ceNGNWrUyFzP399fCQkJFm09PT3t3h8AAADcHG3atHHIc99//309+eSTGjhwoKTLH8P+9ddf9cknn2jcuHEF6n/66aeqVKmS+ePatWvX1pYtW/Tuu++qV69ekqQ5c+ZYtJk+fbq+//57rVixQo888oi53M3NjVm2wHVyc3PTe++9p3bt2ik/P18jR45UuXLlJF3eqOyzzz5TaGioJk+erH79+unSpUuKjo5WvXr1JElDhgxReHi47rjjDkmXN/h+/PHHHdYfAEDJZXXitlWrVtq1a9dN/1iHtQPfKwPeK95++20tWLBACxcutEjc8tExAACAW1t+fr4mTJigmTNn6vjx46pYsaIee+wxvfTSS3Zb7is7O1tbt27VqFGjLMo7duyo9evXF9omLi5OHTt2tCjr1KmTZsyYoZycHLm7uxdok5mZqZycnALLPuzbt0+hoaHy8PBQ8+bN9fbbb6tq1apFxpuVlaWsrCzzeVpa2jX7CNwqHnvssQJlPXr0UI8ePQqUL1682Px1VFSUdu/eXaBObm7uNZ+ZkpJiXZAAgNuS1Ynbxo0b695771V0dHSBhOfQoUNtFtjfXc/A95/y8/OVnp5eYFCbkZGhypUrKy8vTw0bNtSbb75pkdgFAABAyfb666/rp59+0ujRo1W1alUdPHhQ48aNU3p6ut566y27PDMlJUV5eXkKDg62KA8ODlZycnKhbZKTkwutn5ubq5SUlAJrbErSqFGjVKFCBbVv395c1rx5c3355ZeqUaOGTp48qX//+99q2bKldu/ebZ41+E/jxo3TG2+8YW03AQAAYEdWJ27j4uJUvXp1xcXFWZQbDAa7JW6vZ+D7T++9954uXLig3r17m8tq1aqlmTNnql69ekpLS9OHH36oVq1a6Y8//lBERESh92E2AgAAQMkye/ZsrV27VmFhYZIuJzZbtWql1q1b2y1xe0VhO9H/s+xa9Qsrl6QJEyZo7ty5WrVqlcVSX126dDF/Xa9ePbVo0ULVqlXTrFmzNGLEiEKfGxMTY3EtLS3N/N8LAAAAjmF14nblypX2iKNYrB34XjF37lyNGTNGCxYsUFBQkLk8KipKUVFR5vNWrVqpcePG+uijjzRp0qRC78VsBAAAgJIlMzNTAQEBFmUBAQHKzMy02zMDAgLk6upaYJLBqVOnCkxGuCIkJKTQ+m5ubgVmyr777rt6++23tXz58msuYebj46N69epp3759Rdbx8PCQh4fHVe8DAACAm+u6dhhLT0/XN998o3fffVfz5s2z+6zT6xn4XjFv3jw9+eST+vbbby0+QlYYFxcX3XHHHVcd1MbExCg1NdV8HDt2rPgdAQAAwE3Xrl07PfXUUzp9+rQk6fTp03r22WfVtm1buz2zVKlSatKkiZYtW2ZRvmzZMrVs2bLQNi1atChQf+nSpWratKnF+rb/+c9/9Oabbyo2NlZNmza9ZixZWVnau3dvoUstAAAAwHlZPeM2Pj5enTp1UpkyZRQeHq7Dhw9r6NChio2NtdvasH8f+D7wwAPm8mXLlum+++4rst3cuXP1xBNPaO7cubr33nuv+RyTyaT4+HjzbqCFYTYCAABAyfLRRx+pX79+Cg4OlpeXly5duqR27drp66+/tutzR4wYoejoaDVt2lQtWrTQtGnTdPToUT3zzDOSLk8IOHHihL788ktJ0jPPPKPJkydrxIgRGjRokOLi4jRjxgzNnTvXfM8JEybotdde09dff60qVaqYJzb4+vrK19dXkvTiiy+qe/fuqlSpkk6dOqV///vfSktL06OPPmrX/gLOouY9Tzo6BJtKWDHD0SEAABzE6sTt0KFD9corr2jYsGHmso8++khDhw7V2rVrbRrc31k78J07d64eeeQRffjhh4qKijIPar28vGQ0GiVJb7zxhqKiohQREaG0tDRNmjRJ8fHx+vjjj+3WDwAAANxcQUFBWrFihRITE3X8+HFVrFhRoaGhdn9unz59dObMGY0dO1ZJSUmKjIzU4sWLVblyZUlSUlKSjh49aq4fHh6uxYsX6/nnn9fHH3+s0NBQTZo0Sb169TLXmTJlirKzs/Xggw9aPGv06NEaM2aMJOn48ePq16+fUlJSFBgYqKioKG3YsMH8XAAAAJQMVidud+3aVWCd28GDB+v111+3WVCFsXbgO3XqVOXm5uq5557Tc889Zy5/9NFHNXPmTEnS+fPn9dRTTyk5OVlGo1GNGjXSmjVr1KxZM7v2BQAAADfPiRMn5OXlpdDQUHPC9ty5c7p48aLdE7iDBw/W4MGDC712ZUz6d23atNG2bduKvN/hw4ev+cxvvvmmuOEBAADAiVmduA0JCdGGDRvUqlUrc9mmTZsUEhJi08AKY83Ad9WqVde83wcffKAPPvjABpEBAADAWfXs2VPTpk1T2bJlzWVHjhzRs88+q7i4OAdGBgAAABTN6sTtK6+8oi5duig6OlpVqlTR4cOHNWfOHH300Uf2iA8AAAC4IQkJCWrQoIFFWYMGDbR3714HRQQAAABcm4u1DQYMGKBFixYpJydHK1euVE5Ojn7++WdFR0fbIz4AAADghhiNRp08edKi7OTJk+bNvAAAAABnZPWMW0lq3bq1WrdubetYAAAAAJvr3r27Hn/8cU2fPl0VKlTQiRMn9Mwzz6hHjx6ODg0AAAAoktUzbsePH69NmzZZlG3cuFH/+c9/bBYUAAAAYCvjxo2Tp6enwsLC5O3trUqVKsnd3V3jx493dGgAAABAkaxO3E6aNEm1a9e2KKtdu7Y+/PBDmwUFAAAA2Iqfn59+/PFHJScna/Xq1UpKStKPP/4oPz8/R4cGAAAAFMnqpRIyMzPl7e1tUebj46P09HSbBQUAAADYWlBQkIKCghwdBgAAAFAsVs+4rVmzphYuXGhR9ssvv6hGjRo2CwoAAAAAAAAAbmdWz7h98803df/996tfv36KiIjQ/v37NXfuXP3www/2iA8AAAAAAAAAbjtWz7jt0KGD1q1bp1KlSmnNmjVyd3fX2rVr1alTJ3vEBwAAAAAAAAC3Hatn3EpSo0aNNGXKFFvHAgAAcNuJP5asmeu3a1fiKZlMUojRV53rVlffOyLl7upq12c/O+cX3RVRWf2a1bPZPVetWqV27drJx8dHLi4u8vLyUuPGjfXMM8/ovvvus9lzrPHuu+/qkUceYX1bAAAAlCjXlbgFAAAoiWre86Tdn5GwYkax667bd0Sv/bxST7VuojHd26m0t6cOnzmvL+PilZKRqfJGPztGem0mk0n5JpNcXaz7kJbRaNT58+clSenp6Vq4cKGefPJJ7dmzRzExMXaI9OqWL1+u1157TR06dNATTzyhbt26yc2NYTAAAACcGyNWAAAABzCZTHpveZyio+pbzHitUq60Xu/W1ny+N+m03l8Wp4Mp5xTg660nWjVSp7rVJUljf1klXw8PjejQQpKUfilL7T/4UvOf7avQ0n4a+8squbm46EJ2jtYfOKYAX2+N6nynmlQO1YcrNij+WLJ2njipqWu2qGFYiCb26aL7p8zV/Q1ra82+I9p/6owGt22meVt26cdn+shgMEiS4uLi1LVrVyUlJcnT0/Oq/fTz81P//v3l4uKixx9/XE8//bTKli1r4/+aVxcbG6sTJ07oq6++UkxMjJ5++mk9/PDDevzxx1Wvnu1mGwMAAAC2ZPUatwAAALhxx86mKvF8ujrWqV5knfRLWRo2b4k61KmqX4dF6+XOd2rckrX643hysZ+zbO9BPdCwlpY//4i6RFbXm4tWS5KG3ROlhmEheq5tM6168XFN7NPF3GbRzr80ulsbrXzhMfVqXFtZObnadjTJfH3mzJnq37//NZO2f9ezZ09lZ2dr48aNxW5jSxUqVNCoUaO0d+9e/fTTT1q5cqUaNmyopk2b6osvvlBeXp5D4gIAAACKYnXiNjs72x5xAAAA3FbOXbwkSQr08y6yzu/7j6mMt5d6N42Um6uLGlcqr451q2vRzn3Ffk7LamFqWqWCXF1c1K1+TSWlZig189JV2/RsVFuVy5WWq4uL3F1d1bVehBbt/EuSlJWbq2+//VaPP/54sWOQpFKlSikgIEBnz561qp0tHTlyRGPHjtWAAQOUmpqqMWPGaNiwYZoyZYoeeughh8UFAAAAFMbqxG1ISIj+9a9/afv27faIBwAA4LZQ2uvybNXT6ZlF1jmVnqHyRl+Lsgql/XQq7UKxn1PO53+JYS/3y6tkXcjOuWqbkH88s3v9mlqZcFiZ2TlalXBYFStWVNOmTYsdg3T5j/8pKSk3fZkESfrqq6/Url071a1bVwkJCZo2bZoOHjyo1157TdHR0Vq5cqWWLl160+MCAAAArsbqxO2iRYuUk5Oje+65Rw0aNNCHH36oM2fO2CM2AACAW1alskaVN/pq2Z4DRdYJ8vNVUmqGRVni+XQF+ftIkrzc3XUpN9d8LSWj6CRwYf67ZG0BLv+4ULlcaVUPKqvf/jykRTv/0hNPPGHVcyTpxx9/VKlSpRQVFWV12xv1wQcf6MEHH9Tx48c1Z84c3XPPPRbXfX199emnn970uAAAAICrsTpx26JFC3366adKSkrSqFGjtGTJElWqVEkPPvigFi1aJJPJZI84AQAAbikGg0EvdmipLzf8oW+37DIvX3D0zHn9e9FqJaWmq2W1MJ29cFHfb92j3Px8bT+WpKV7DqhrZIQkqWZIgDYePK6UjExdyMrWZ+u2WRVDWW9vnTifVqy6PRrU1Nebdij+WLIGDBhQ7GdkZGRo3rx5+te//qXXXntNZcqUsSpGW9i2bZuee+45lS5dusg61vQJAAAAuBncrrehh4eH7rvvPmVnZysxMVHLli1TfHy8hgwZomnTpqlDhw62jBMAAOCWc2dEZX3Qu7O++H27pq7ZKkkK8fdV58jqCvD1lrurqyb26awPlsdpyupNCvT10chOrdQwLESS1CWyurYdTVTvad+qtJenBt7ZRMv3Hiz28/veEak3F63WPe/PUoOKwXq/d+ci67avVVXvL4tTVNWKCgwMvOp9U1NT5evrKxcXF3l5ealRo0aaPn267r///mLHdqMmTZpUrHpDhw61cyQAAADA9bmuxO2aNWv0xRdf6Mcff1SDBg00YsQI9e7dW97e3vrmm2/0yCOPKCkp6do3AgAAuIkSVsxwdAgFNAwL0Yd9uxR5vW5okD575L5Cr7m7umpM93YWZV3rRZi/fr1bW4trfp4e2hgzyHweWSFI856y3JTrp8H9Cn2WVyl3lfH2VLf6NYuMVZLatm3rFJ/Amj9//jXrGAwGErcAAABwWlYnbqtVq6ZLly4pOjpamzdvVo0aNSyu9+3bV2PHjrVZgAAAAHC8pXsOKC/fpFbVwhwdSrGsXLlSkpSfn6+9e/eqRo0acnd3d3BUAAAAQPFZnbj94IMPdO+998rV1bXIOnv27LmhoAAAAOA8+kz7TqkXszS6Wxu5uli9RYJDubi4qFmzZsrIyLh2ZQAAAMCJWJ247dGjhz3iAAAAgJP653IKJU39+vV14MABVa9e3dGhAAAAAMVWrMRtmTJlZDAYrlnv7NmzNxwQAAAAYEv33XefunfvrsGDByssLEwuf5s1zKQEAAAAOKtiJW5/+uknO4cBAAAA2MfUqVMlSe+//75FucFgIHELAAAAp1WsxG2bNm0kSXl5eerbt6+++uoreXp62jUwAAAAwBYOHTrk6BAAAAAAq1m1u4Srq6vWrVsnNzerl8YFAAAAHCY3N1e///675s2bJ0m6cOGCLly44OCoAAAAgKJZvS3w008/rQkTJtgjFgAAAMDm/vzzT9WuXVt9+vTRk08+KUlasWKFBg4c6ODIAAAAgKJZnbhdsGCBRo8ercDAQDVs2FCNGzc2HwAAAICzGTx4sIYNG6bjx4/L3d1dktS2bVutW7fOwZEBAAAARbN6zYPhw4fbIQwAAADAPuLj47V8+XJJlzckkyR/f3+lp6c7MiwAAADgqqxO3D766KP2iAMAAMDuNr3ylN2f0eztaVbVjz+WrJnrt2tX4imZTFKI0Ved61ZX3zsi5e7qaqcoL3t2zi+6K6Ky+jWrZ7N7rlq1Su3atZOPj4+5rHTp0jp+/LjNnmGt4OBgHT58WFWrVjWX/fXXX6pYsaLDYgIAAACuxeqlEiTpiy++UPv27VW/fn1J0urVq/Xtt9/aNDAAAIBb3bp9R/T8t7FqHl5R3z/dRytGPKq37r9Hh1LOKSUj09HhyWQyKS8/3+p2RqNRGRkZ5sORSVtJevbZZ9WrVy8tXLhQeXl5Wrp0qaKjozVkyBCHxgUAAABcjdUzbt966y3NmzdPQ4cO1YsvvihJKl++vF588UX17t3b5gECAADcikwmk95bHqfoqPoWM16rlCut17u1NZ/vTTqt95fF6WDKOQX4euuJVo3UqW51SdLYX1bJ18NDIzq0kCSlX8pS+w++1Pxn+yq0tJ/G/rJKbi4uupCdo/UHjinA11ujOt+pJpVD9eGKDYo/lqydJ05q6potahgWool9uuj+KXN1f8PaWrPviPafOqPBbZtp3pZd+vGZPuZlBuLi4tS1a1clJSXJ09Pz5v1Hu05Dhw6Vm5ubYmJilJeXp+HDh2vw4MF65plnHB0aAAAAUCSrE7efffaZ1q5dq4oVK+qll16SJFWvXl0HDhyweXAAAAC3qmNnU5V4Pl0d61Qvsk76pSwNm7dEA+9srJ6N6mjHiZMa8W2sQoy+alAxpFjPWbb3oP7Tq4PG9minWXHxenPRav00uJ+G3ROlP5NTCl0qYdHOv/Tugx1VsYy/8k0mfRkXr21Hk9SkcqgkaebMmerfv3+JSNpeMXjwYA0ePNjRYQAAAADFZvVSCRcuXFD58uUl/W9zh5ycHHl4eNg2MgAAgFvYuYuXJEmBft5F1vl9/zGV8fZS76aRcnN1UeNK5dWxbnUt2rmv2M9pWS1MTatUkKuLi7rVr6mk1AylZl66apuejWqrcrnScnVxkburq7rWi9CinX9JkrJyc/Xtt9/q8ccfL7J9amqqSpcubT5ee+21YsdrD/7+/oWWly1b9iZHAgAAABSf1YnbqKgoffzxxxZln3/+uVq1amWzoAAAAG51pb0uz1Y9nV70Wran0jNU3uhrUVahtJ9OpV0o9nPK+fwvMezlfvnDVheyc67aJuQfz+xev6ZWJhxWZnaOViUcVsWKFdW0adMi2xuNRp0/f958vPnmm8WO1x5MJlOBsvzrWLsXAAAAuJmsXirhgw8+UPv27TVr1ixlZGSoZcuWOnnypJYvX26P+AAAAG5JlcoaVd7oq2V7DujxVo0KrRPk56uk1AyLssTz6Qry95Ekebm761JurvmatRua/ffDUwW4/ONC5XKlVT2orH7785CW7tmvJ/71glXPcZSePXtKkrKyssxfX3HkyBE1aNDAEWEBAAAAxWJ14rZatWrau3evfvnlFx0+fFhhYWHq1q2bfHx87BEfAADALclgMOjFDi312s8r5ePhrk51qsvo7amjZ87ryw1/6Mk7G6tltTC9t2y9vt+6R/c3qqWdJ05q6Z4D+qB3Z0lSzZAAzVi3TSkZmfJyd9Nn67ZZFUNZb2+dOJ9WrLo9GtTU15t26Pi5NC0cMMDq/jrClcTsokWLLJK0Li4u6ty5sx566CFHhQYAAABck9WJ23feeUejRo3Sgw8+aFE+YcIEjRw50maBAQAA3OrujKisD3p31he/b9fUNVslSSH+vuocWV0Bvt5yd3XVxD6d9cHyOE1ZvUmBvj4a2amVGoZd3pisS2R1bTuaqN7TvlVpL08NvLOJlu89WOzn970jUm8uWq173p+lBhWD9f5/E8KFaV+rqt5fFqeoqhUVGBh4Yx2/SUaPHq28vDy5urrqxRdfLFGbqQEAAAAGU2GLfl2Fv7+/0tIKzswoV66czpw5Y7PASoq0tDQZjUalpqYWufEFirbplaccHYJNNXt7mqNDuK3xPsGWeJ9gS7fK+9Tzk280vH0Lvfjdr44OxSqlS5fW+fPnHR1GicIYFyVZzXuedHQINpWwYoajQ7it8T7BlnifYK1iz7jdsWOHpMsbOezcudNik4cDBw7Iy8vL9tEBAADAKSzdc0B5+Sa1qhbm6FCs1qlTJy1fvlzt27d3dCgAAABAsbkUt2LDhg3VqFEjZWZmqkGDBmrYsKG5bMiQIRo7dqw94wQAAICD9Jn2nd5fFqdRne+Uq0uxh49Ow8/PT/fff7969uyp4cOHa8SIEeYDt45ffvlFNWvWVEREhD777LNC62zatEl169ZV9erVLf79cuDAATVt2lTVq1fXM888Y56kkpKSonbt2ikiIkI9e/bUpUuXbkpfpFuvPwAAwHrFHnnn5+crLy9Pd9xxh/Lz881HXl6eTpw4oSeeeMKecQIAAMBB5j31kGKHDVCLEjjbVpLy8vL00EMPmT/6f+7cOfOBkmnmzJmaOXOm+Tw3N1cjRozQb7/9pm3btmn8+PE6e/ZsgXbPPfec5s6dqz///FMLFy7Url27JEkjR47UmDFjtH//fp08eVKLFi2SJI0bN069evXSvn37VLly5SITqPQHAADYg1VTJvLy8nT06FH+MgsAAIAS44svvijywK3hyszTChUqyM/PT127dtWvv1quxZyYmKjc3FzVr19fbm5u6t+/vxYuXCiTyaS4uDjde++9kqRHHnlECxculCQtXLhQ0dHRBcrpDwAAuBmsSty6urrK19dX2dnZ9ooHAAAAsJv09HSlpaWZD9waEhMTVaFCBfN5xYoVdeLEiWLVOXPmjMqWLSuDwVCgbWpqqoxGY5H3tJdbrT8AAOD6FHtzsiteffVVPf744xozZozCwsLk8rd1zthxFgAAAM7myJEjGjRokNauXVtgAkJeXp6DooK18vLy1KRJE0kyLxswceJESVJMTEyB+lcSl1f8fXPlv9cpqrywe/zz/Ebcav0BAAC2Z/XuEk888YTmz5+vBg0aqFy5cipTpoxKly6tMmXK2CM+C1OmTFF4eLg8PT3VpEkTrV279qr1V69erSZNmsjT01NVq1bVp59+WqDODz/8oDp16sjDw0N16tTR/Pnz7RU+AAAAHOC5555TmTJlFBcXJ19fX23btk09evTQ1KlTHR0arODq6qr4+HjFx8dr7NixGjt2rPk8LCzMYvbo8ePHVb58eYv2FSpUKLROQECAzp49a054/r2tv7+/UlNTi7wn/QEAAPZkdeL20KFD5uPgwYM6ePCg+Wt7mjdvnoYPH65XX31V27dvV+vWrdWlSxcdPXq0yDi7du2q1q1ba/v27XrllVc0dOhQ/fDDD+Y6cXFx6tOnj6Kjo/XHH38oOjpavXv31saNG+3aFwAAANw8cXFxmjFjhho2bCiDwaAGDRros88+0/vvv+/o0GAjzZo1065du3TixAmlp6dr8eLF6tSpk0Wd0NBQubq6aseOHcrNzdXcuXPVvXt3GQwGRUVFmTfw+vLLL9W9e3dJUrdu3fTVV18VKKc/AADgZrA6cVu5cuUiD3t6//339eSTT2rgwIGqXbu2Jk6cqLCwMH3yySeF1v/0009VqVIlTZw4UbVr19bAgQP1xBNP6N133zXXmThxojp06KCYmBjVqlVLMTExuueee8wfUQIAAEDJ5+rqKg8PD0mXZxyePXtWRqNRx44dc3BksBU3Nze99957ateunRo1aqSXXnpJ5cqVkyR17dpViYmJkqTJkyerX79+qlmzprp27ap69epJksaPH6/Ro0erWrVqCgwMNG/sFRMTo++++07Vq1fXoUOHNHDgQPoDAABuGqvXuJUuz1pYtWqVUlJSLNZQsteshezsbG3dulWjRo2yKO/YsaPWr19fZIwdO3a0KOvUqZNmzJihnJwcubu7Ky4uTs8//3yBOiRuAQAAbh0NGzbUb7/9pk6dOql169YaOHCgvL29VadOHbs/e8qUKfrPf/6jpKQk1a1bVxMnTlTr1q2LrL969WqNGDFCu3fvVmhoqEaOHKlnnnnGos4PP/yg1157TQcOHFC1atX01ltv6YEHHrih55Y0jz32WIGyHj16qEePHgXKFy9ebP46KipKu3fvLlAnIiJCW7duLVAeGBio1atX31iwxXCr9QcAANiG1Ynbjz/+WC+99JI6deqkJUuWqEuXLlq6dKnuu+8+e8QnSUpJSVFeXp6Cg4MtyoODg5WcnFxom+Tk5ELr5+bmKiUlReXLly+yTlH3lKSsrCxlZWWZz6/sRnzhwgW5urpa1S9Il/LyHR2CTV24cMHRIdzWeJ9gS7xPsCXep+vn4+Nzw/eYMWOG8vMv/z/48MMPFRMTo7S0NM2aNeuG7301V5b6mjJlilq1aqWpU6eqS5cu2rNnjypVqlSg/pWlvgYNGqTZs2fr999/1+DBgxUYGKhevXpJ+t9SX2+++aYeeOABzZ8/X71799a6devUvHnz63ouAAAAnJPViduJEydqyZIlatOmjcqUKaP58+dr8eLF+u677+wRn4XCdlK92k6oRe28+vdya+85btw4vfHGGwXK+/fvLze365rAjFvJ/fc7OgLcSnifYEu8T7Clm/g+LVu27IbvERYWZv46ICBA06dPv+F7Fsffl/qSLo+jf/31V33yyScaN25cgfp/X+pLkmrXrq0tW7bo3XffNSdu/77Ul3T5o++rV6/WxIkTNXfu3Ot6LgAAAJyT1ZnGkydPqk2bNpIuJz1NJpO6dOmi6Ohomwd3RUBAgFxdXQvMhD116lSBGbNXhISEFFrfzc3NvD5UUXWKuqd0eXA8YsQI83laWprCwsL09ddfy9/f36p+XY8mPYbY/Rk309afJzs6hNsa7xNsifcJtsT7BFv68ssvi7z2yCOP2OWZjlrq63qeK/GpMtxaDOJTDrAd3ifYEu8Trijup8qsTtyGhIQoMTFRoaGhCg8P16pVqxQYGCgXF6v3OSu2UqVKqUmTJlq2bJnF+l3Lli0rcomGFi1aaOHChRZlS5cuVdOmTeXu7m6us2zZMovB79KlS9WyZcsiY/Hw8DBvbvF3Pj4+Nvko37WYrN9PzqndjP9mKBrvE2yJ9wm2xPsEW/rggw8szpOTk3XmzBlFRkbaLXHrqKW+rue5Ep8qw60l7Nb6FaL7+dSMQ/E+wZZ4n3BFcT9VZvUo7Nlnn9XGjRv1wAMPaMSIEeZZAa+++qq1t7LKiBEjFB0draZNm6pFixaaNm2ajh49at6sISYmRidOnDDPqHjmmWc0efJkjRgxQoMGDVJcXJxmzJhh/giZJA0bNkx33XWXxo8fr/vuu08LFizQ8uXLtW7dOrv2BQAAADfP9u3bC5RNmDBBOTk5dn+2o5b6sva5jv5UGYCibR07zNEh2FST1z90dAi3Nd4n2BLvk/1Znbj9++zUhx9+WHfddZcyMjJUu3Ztmwb2T3369NGZM2c0duxYJSUlKTIyUosXL1blypUlSUlJSTp69Ki5fnh4uBYvXqznn39eH3/8sUJDQzVp0iTz+mCS1LJlS33zzTf6v//7P7322muqVq2a5s2bZ97YAQAAALemF154QaGhoXabfOCopb6u57mS4z9VBqBonq631hQ9fqY4Fu8TbIn3yf5u+HNPf9/swd4GDx6swYMHF3pt5syZBcratGmjbdu2XfWeDz74oB588EFbhAcAAIASYsOGDVedgXqjHLXU1/U8FwAAAM7J6sRteHh4kYPcgwcP3nBAAAAAgC01atTIYvyamZmpgwcPasKECXZ9rqOW+rrWcwEAAFAyWJ24vbJj7RWJiYmaMmWKHn30UVvFBAAAANjM8OHDLc59fX3VsGFDVatWza7PddRSX9d6LgAAAEoGqxO3hX3EqlOnThowYIBefPFFmwQFAAAA2IojJxg4aqmvqz0XAAAAJcMNr3ErSRUqVNDu3bttcSsAAADApsaOHVuseq+//rqdIwEAAACKz+rE7c8//2xxnpmZqblz56pBgwY2CwoAAACwlc2bNys2NlYRERGqXLmyjh49qn379qlz585ydXWVJLtuVAYAAABcD6sTt8OGDbM49/X1VePGjTV58mSbBQUAAADYSvny5TV58mQ9/fTT5rLp06dr8+bNmjZtmgMjAwAAAIpmdeL20KFD9ogDAAAAsIvvvvtOn376qUXZE088oZEjR5K4BQAAgNNycXQAAAAAgD0FBAQoNjbWomzp0qUqV66cgyICAAAArs3qGbcuLi5XXQPMZDLJYDAoLy/vhgIDAAAAbOGdd95Rz5491b59e1WuXFlHjhzRihUr9NVXXzk6NAAAAKBIVs+4/fDDD9W4cWPNnTtX69ev19y5c9WkSRN9+OGH2rZtm7Zv365t27bZI1YAAADAar169dLOnTsVFRUlk8mkqKgo/fHHH3rwwQcdHRoAAABQJKtn3H7yySf67bffFBISIklq3ry57rzzTrVv315DhgyxeYAAAADAjYqIiND//d//OToMAAAAoNisnnGblJQkHx8fizIfHx8lJibaLCgAAAAAAAAAuJ1Znbjt0KGDHnroIW3dulWnT5/Wli1b1K9fP3Xo0MEe8QEAAAAAAADAbcfqxO306dMVFBSkVq1aKSQkRHfeeacCAgI0ffp0e8QHAAAAAAAAALcdq9e4NRqN+vLLLzVz5kylpKQoICBALi5W538BAAAAAAAAAEW47oyri4uLgoKCSNoCAADAKW3dulW7du0yn6ekpGjAgAFq0KCBBg8erMzMTAdGBwAAAFwdWVcAAADckp5//nkdP37cfP7ss89qz549GjRokLZv365XXnnFgdEBAAAAV2f1UgkAAABASbB3717dddddkqSMjAwtXLhQmzdvVr169XTvvffq7rvv1sSJEx0bJAAAAFAEZtwCAADglpSTkyNvb29J0pYtW1S6dGnVq1dPkhQeHq4zZ844MjwAAADgqm4ocZuQkKD58+fr6NGjtooHAAAAsIlKlSopLi5OkrR06VLz7Fvp8nq3Xl5ejgoNAAAAuKZiL5XwwgsvqGHDhoqOjpYkzZ07V4888oiMRqMyMzP1888/q3379nYLFAAAALDGSy+9pHvvvVdNmjTR2rVrFRsba77266+/qmHDho4LDgAAALiGYs+4XbBgge68807z+SuvvKLx48crJSVFkyZN0htvvGGXAAEAAIDrER0drQULFqhz585auXKl2rZta77m6+ur//u//3NccAAAAMA1FHvG7enTpxUeHi5J2rdvnxITE/XMM89Ikh599FGNGjXKPhECAAAA16l169Zq3bp1gfL77rvPAdEAAAAAxVfsGbfe3t5KT0+XJG3YsEH16tUzb/YgXd78AQAAAHAWW7du1a5du8znKSkpGjBggBo0aKDBgwcrMzPTgdEBAAAAV1fsxO3dd9+t559/Xr///rsmTZqkHj16mK/t2bNHFSpUsEuAAAAAwPV4/vnndfz4cfP5s88+qz179mjQoEHavn27XnnlFQdGBwAAAFxdsRO37733ng4fPqzOnTsrMDBQI0aMMF+bM2eOOnToYJcAAQAAgOuxd+9e3XXXXZKkjIwMLVy4ULNmzdKQIUP09ddfa8GCBQ6OEAAAAChasde4DQkJ0fLlywu9NmHCBJsFBAAAANhCTk6OeWmvLVu2qHTp0qpXr54kKTw8XGfOnHFkeAAAAMBVFXvGbWESEhI0f/58HT161FbxAAAAADZRqVIlxcXFSZKWLl1qnn0rXV7v1svLy1GhAQAAANdU7Bm3L7zwgho2bKjo6GhJ0ty5c/XII4/IaDQqMzNTP//8s9q3b2+3QAEAAABrvPTSS7r33nvVpEkTrV27VrGxseZrv/76qxo2bOi44AAAAIBrKPaM2wULFujOO+80n7/yyisaP368UlJSNGnSJL3xxht2CRAAAAC4HtHR0VqwYIE6d+6slStXqm3btuZrvr6++r//+z/HBQcAdpaVm6uXf1imXp/M07NzftH5zEsF6phMJr0Tu1a9PpmnR7+Yr+Pn0iRJiefTNejLn9V6wuf6bstuizYTl8ep3/Tv9fBnP2jToRM3pS8AcLsqduL29OnTCg8PlyTt27dPiYmJeuaZZyRJjz76qPbu3WufCAEAAIDr1Lp1a73wwgtq0aKFRfl9992n1q1bOygqALC/BfF/KrS0n354to/a1KiiWXHxBeqs239U5zOz9MOzffREq0aavHKTJMnHw13D7olS/+b1LOqv3XdER8+mas7AXvrk4W76ZPVm5eXn34zuAMBtqdiJW29vb6Wnp0uSNmzYoHr16pk3e5Aub/4AAAAAAAAcb+2+o+oSGSFJ6hoZoXX7C+5Ns3b/UXWJrC5Jah1RWTuOJ8tkMsno5anICkFyc7FMGRw+c16NK4fKxWCQv5eHSnt7ak/Saft3BgBuU8VO3N599916/vnn9fvvv2vSpEnq0aOH+dqePXtUoUIFuwQIAAAAAACsk5KRqSA/H0mSv5eH0i9lF6yTfsFc50oyNvViVpH3rB5YVr/vP6rs3DydSr+gPYmndTo90z4dAAAUP3H73nvv6fDhw+rcubMCAwM1YsQI87U5c+aoQ4cOdgkQAAAAAABYxyRTMeoUZLhK/RbVwtSkUnk9MesnjVuyVvUqBMnV5WotAAA3wq24FUNCQrR8+fJCr02YMMFmAQEAAAA3KioqShs2bJAkvfHGGxo9erSDIwIA+5u3eZcW7kiQJJX18dKp9Asq7e2ptItZ8vMsVaB+oJ+PTqVfUO3ygco3mZR2MUv+Xh5XfcbA1k00sHUTSdKQuYsUVsbf9h0BAEiyYsYtAAAAUFIkJCQoNzdX0uVPjgHA7aDPHZGa/WQvzX6yl9pEVNGSXfskSYt37dOd1SsVqH9n9Upasmu/pMsbj9WrGCyDoegZtLn5+Ur771IKfxxPVnZunqoGlrVDTwAAkhUzbgEAAICSom3btmrYsKFq1KihixcvqmfPnoXW+/HHH29yZABwc9zXsJZeW/Cben0yT4F+3hrXs70kac2+I9qbdFpP39VUd1avpHX7j6rnJ9/I19ND/77vbklSRla2+k7/XheysuXqYtCcTTv00+B+ysnN06CvfpZ0eUbv6G5tHdU9ALgtkLgFAADALeebb77RDz/8oEOHDmnRokVq0KCBo0MCgJvK091N/3mwY4HyuyIq666IypIub0j2SpfWBer4epTSL0P6Fyj3KuWueU89ZPtgAQCFKlbi9ueff1aPHj0kSTk5OXJ3d7drUAAAAMCN8PDwUP/+l5MO586dY41bAAAAlDjFStwOGDBAaWlpkqRy5cqZvwYAAACc3bvvvqv09HQtWrRIx48fV8WKFdW1a1f5+7OhDgAAAJxXsRK35cqV05w5c1SvXj3l5eVp586dMplMBerVr1/f5gECAAAANyI+Pl6dOnVSmTJlFB4ersOHD2vYsGGKjY1Vo0aNHB0eAAAAUKhiJW4nTZqkF154QYcOHVJ+fn6ha4QZDAbl5eXZPEAAAADgRgwdOlSvvPKKhg0bZi776KOPNHToUK1du9aBkQEAAABFK1bitnv37urevbskyc/PT+np6XYNCgAAALCVXbt2aeXKlRZlgwcP1uuvv+6giACg+Jq9Pc3RIQAAHMTF2gZHjhyxRxwAAACAXYSEhGjDhg0WZZs2bVJISIiDIgIAAACurVgzbv+udOnSeueddzRz5kzz5g6PPfaYXnrpJbm6utojRgAAAOC6vfLKK+rSpYuio6NVpUoVHT58WHPmzNFHH33k6NAAAACAIlmduH399df1008/afTo0apataoOHjyocePGKT09XW+99ZY9YgQAAACu24ABA1S5cmV99dVXWrlypSpWrKiff/5Zd911l6NDAwAAAIpk9VIJs2fP1pIlS9SvXz81b95c/fr10y+//KLZs2fbIz5J0rlz5xQdHS2j0Sij0ajo6GidP3++yPo5OTl6+eWXVa9ePfn4+Cg0NFSPPPKIEhMTLeq1bdtWBoPB4ujbt6/d+gEAAADHaN26taZNm6bFixdr2rRpJG0BAADg9KxO3GZmZiogIMCiLCAgQJmZmTYL6p/69++v+Ph4xcbGKjY2VvHx8YqOjr5qjNu2bdNrr72mbdu26ccff9Rff/2lHj16FKg7aNAgJSUlmY+pU6farR8AAAAAAAAAUBxWL5XQrl07PfXUU3r//fcVGBio06dP68UXX1Tbtm3tEJ60d+9excbGasOGDWrevLkkafr06WrRooUSEhJUs2bNAm2MRqOWLVtmUfbRRx+pWbNmOnr0qCpVqmQu9/b2ZmMKAAAAAAAAAE7F6hm3H330kRITExUcHCwfHx+FhIToxIkT+vjjj+0Rn+Li4mQ0Gs1JW0mKioqS0WjU+vXri32f1NRUGQwGlS5d2qJ8zpw5CggIUN26dfXiiy8qPT39qvfJyspSWlqaxQEAAAAAAAAAtmT1jNugoCCtWLFCiYmJOn78uCpWrKjQ0FB7xCZJSk5OVlBQUKFxJCcnF+sely5d0qhRo9S/f3/5+/ubyx9++GGFh4crJCREu3btUkxMjP74448Cs3X/bty4cXrjjTes7wgAAAAAAAAAFJPVidsrQkNDbyhhO2bMmGsmQDdv3ixJMhgMBa6ZTKZCy/8pJydHffv2VX5+vqZMmWJxbdCgQeavIyMjFRERoaZNm2rbtm1q3LhxofeLiYnRiBEjzOdpaWkKCwu7ZhwAAABwrIyMDL399tvasWOHqlWrppiYGJbMAgAAgNOyeqkEWxkyZIj27t171SMyMlIhISE6efJkgfanT59WcHDwVZ+Rk5Oj3r1769ChQ1q2bJnFbNvCNG7cWO7u7tq3b1+RdTw8POTv729xAAAAwPkNGTJEHh4eGjp0qDw8PNS7d2+7PevcuXOKjo6W0WiU0WhUdHS0zp8/f9U2JpNJY8aMUWhoqLy8vNS2bVvt3r3bfP3s2bP617/+pZo1a8rb21uVKlXS0KFDlZqaanGfKlWqyGAwWByjRo2yRzcBAABgR9c94/ZGBQQEKCAg4Jr1WrRoodTUVG3atEnNmjWTJG3cuFGpqalq2bJlke2uJG337dunlStXqly5ctd81u7du5WTk6Py5csXvyMAAABwSmPGjNGrr74qd3d3SdKhQ4c0c+ZMSVLbtm0LXY7LVvr376/jx48rNjZWkvTUU08pOjpaCxcuLLLNhAkT9P7772vmzJmqUaOG/v3vf6tDhw5KSEiQn5+fEhMTlZiYqHfffVd16tTRkSNH9MwzzygxMVHff/+9xb3Gjh1r8ekyX19f+3QUAAAAduOwxG1x1a5dW507d9agQYM0depUSZcHvt26dVPNmjXN9WrVqqVx48bpgQceUG5urh588EFt27ZNv/zyi/Ly8szr4ZYtW1alSpXSgQMHNGfOHHXt2lUBAQHas2ePXnjhBTVq1EitWrVySF8BAABgO56enmrevLkmTpyou+66S23btlXHjh11xx13aM2aNerevbtdnrt3717FxsZqw4YN5g12p0+frhYtWighIcFiDHuFyWTSxIkT9eqrr6pnz56SpFmzZik4OFhff/21nn76aUVGRuqHH34wt6lWrZreeustDRgwQLm5uXJz+9/Q3s/Pj2UgAAAASrgbWiohNTVVW7Zs0enTp20VT6HmzJmjevXqqWPHjurYsaPq16+vr776yqJOQkKC+WNix48f188//6zjx4+rYcOGKl++vPlYv369JKlUqVJasWKFOnXqpJo1a2ro0KHq2LGjli9fLldXV7v2BwAAAPY3atQoff/99xo3bpwGDhyo4cOH64UXXpDRaNSwYcM0a9Ysuzw3Li5ORqPRnLSVpKioKBmNRvNY9J8OHTqk5ORkdezY0Vzm4eGhNm3aFNlGujwe9/f3t0jaStL48eNVrlw5NWzYUG+99Zays7NvsFcAAAC42a57xu1PP/2koUOHqmLFitq3b5/eeOMNDR482JaxmZUtW1azZ8++ah2TyWT+ukqVKhbnhQkLC9Pq1attEh8AAACcU9WqVbVkyRJ9/fXXatOmjUaNGqWRI0fa9ZnJycmFLsMQFBRk/hRYYW0kFdjDITg4WEeOHCm0zZkzZ/Tmm2/q6aeftigfNmyYGjdurDJlymjTpk2KiYnRoUOH9NlnnxUZc1ZWlrKyssznaWlpRdYFAADAzVHsGbdnzpyxOJ80aZJ27Nih9evX648//tDYsWNtHhwAAABwI+bPn693331X5cqV0+rVq/Xbb7+pS5cuOnz4sNX3GjNmTIFNv/55bNmyRZJkMBgKtDeZTIWW/90/rxfVJi0tTffee6/q1Kmj0aNHW1x7/vnn1aZNG9WvX18DBw7Up59+qhkzZhQYz//duHHjzBupGY1GhYWFXTVOAAAA2F+xE7dt2rTRl19+aT739PQ0b+YVHx8vHx8fuwQIAAAAXI/HH39c7733nnlm6ocffqjPPvtMo0aNUq9evTRhwgSr7jdkyBDt3bv3qkdkZKRCQkJ08uTJAu1Pnz5dYEbtFVfWo/3njNxTp04VaJOenq7OnTvL19dX8+fPN2++VpSoqChJ0v79+4usExMTo9TUVPNx7Nixq94TAAAA9lfspRJ+//13vfzyy/rqq6/06aef6t1339WTTz6pP/74Q9WqVdPnn39uzzgBAAAAqyxYsEAnT56Uu7u7Ll26pKioKI0ZM0Zt2rRRXFycxo0bZ9X9AgICFBAQcM16LVq0UGpqqjZt2qRmzZpJkjZu3KjU1FS1bNmy0Dbh4eEKCQnRsmXL1KhRI0lSdna2Vq9erfHjx5vrpaWlqVOnTvLw8NDPP/8sT0/Pa8azfft2SVL58uWLrOPh4SEPD49r3gsAAAA3T7ETt0ajUZ9++ql+//139enTRw888IDWrl1bYCMEAAAAwBnUqVNHY8aMUdu2bbV8+XJFRkaar5UqVarAEgO2Urt2bXXu3FmDBg3S1KlTJUlPPfWUunXrppo1a5rr1apVS+PGjdMDDzwgg8Gg4cOH6+2331ZERIQiIiL09ttvy9vbW/3795d0eaZtx44dlZmZqdmzZystLc28Fm1gYKBcXV0VFxenDRs2qF27djIajdq8ebOef/559ejRQ5UqVbJLfwEAAGAfxV4q4YpWrVppw4YNki7PJoiLi7N5UAAAAMCNmjdvnlJTU/XBBx9IkiZPnnzTnj1nzhzVq1dPHTt2VMeOHVW/fn199dVXFnUSEhKUmppqPh85cqSGDx+uwYMHq2nTpjpx4oSWLl0qPz8/SdLWrVu1ceNG7dy5U9WrV1f58uXNx5WlDTw8PDRv3jy1bdtWderU0euvv65BgwZp7ty5N63vAAAAsI1iT5ddt26dBg8erIMHD6pevXr67LPP1Lt3bz333HOqVq2axo8fL39/f3vGCgAAABRbhQoVrpqsXbVqldq2bWuXZ5ctW1azZ8++ah2TyWRxbjAYNGbMGI0ZM6bQ+m3bti3Q5p8aN25snmQBAACAkq3YM26feOIJjRs3TmfOnNErr7yiYcOGKSIiQkuXLlVUVJRat25tzzgBAACAG5aUlKRx48apevXqat++vaPDAQAAAIpU7MRtamqq7rjjDnl4eKhx48YWH+t69NFHtXLlSrsECAAAANyIvLw8/fTTT+rWrZsqV66spUuXasSIEdecvQoAAAA4UrGXSoiJiVHdunVVo0YNHTp0SB999JHF9bJly9o8OAAAAOB6JSQk6LPPPtOXX36psmXLasCAAZoyZYoqVaqkrKws/etf/3J0iAAAAECRip24HT58uPr166cjR44oIiJCZcqUsWdcAAAAwA2pXbu2unbtqkWLFqlp06aODgcAAACwSrGXSpCk4OBgNWvWjKQtAAAAnN5jjz2mNWvW6Pnnn9dnn32mtLQ0R4cEAAAAFJtViVsAAACgpPj888+VlJSkRx99VJ9//rlCQkLUp08fLV68WLm5uY4ODwAAALgqErcAAAC4Zfn4+GjgwIFav369tmzZorCwMD3++OMKDw93dGgAAADAVZG4BQAAwG2hTp06evfdd3XixAl98skn6ty5s6NDAgAAAIpE4hYAAAC3FTc3N/Xq1UuLFi1ydCgAAABAkUjcAgAAAAAAAICTIXELAAAAAAAAAE6GxC0AAAAAAAAAOBkStwAAAAAAAADgZEjcAgAAAAAAAICTIXELAAAAAAAAAE6GxC0AAAAAAAAAOBkStwAAAAAAAADgZEjcAgAAAAAAAICTIXELAAAAAAAAAE6GxC0AAAAAAAAAOBkStwAAAAAAAADgZEjcAgAAAAAAAICTIXELAAAAAAAAAE6GxC0AAAAAAAAAOBkStwAAAAAAAADgZEjcAgAAAAAAAICTIXELAAAAAAAAAE6GxC0AAAAAAACKlJWbq5d/WKZen8zTs3N+0fnMSwXqmEwmvRO7Vr0+madHv5iv4+fSLK7/dfKMWr7zmdbtO2JRnpKRqXbvzdR3W3bbtQ9ASUTiFgAAAAAAAEVaEP+nQkv76Ydn+6hNjSqaFRdfoM66/Ud1PjNLPzzbR0+0aqTJKzeZr5lMJk1ZtVnNwisUaPfxyk2FlgMgcQsAAAAAAICrWLvvqLpERkiSukZGaN3+owXr7D+qLpHVJUmtIyprx/FkmUwmSdKSXfvUtEqoyvp4WbTZfjRJPh7uqh5Y1s49AEomErcAAAAAAAAoUkpGpoL8fCRJ/l4eSr+UXbBO+gVzHReDQf5eHkq9mKWMrGwt+CNBfZrWtaifm5+vaWu3alDrJvbvAFBCkbgFAAAAAABAkUwyFaNOQQZJ09duVXRUA7m7ulpc+37rHt1dK1xGL0/bBAncgtwcHQAAAAAAAACcy7zNu7RwR4IkqayPl06lX1Bpb0+lXcySn2epAvUD/Xx0Kv2CapcPVL7JpLSLWfL38tCfySla/ddhvbv0d53PvKT1B45rTHdX7Uk8pT+On9ScjTuUfilbLgaDPNzd1KNBzZvdVcBpkbgFAAAAAACAhT53RKrPHZGSLidxl+zapxrB5bR41z7dWb1Sgfp3Vq+kxTv3qU2NKlq774jqVQyWwWDQ1AHdzXXG/rJKd9cMV1TVioqqWtFcPn3tVpX28iRpC/wDSyUAAAAAAACgSPc1rKXj59LU65N5WpVwSI+0aCBJWrPviKau2SLpcuLW38tDPT/5RjN+367n2jZzZMjALYEZtwAAAAAAACiSp7ub/vNgxwLld0VU1l0RlSVd3pDslS6tr3qf17u1LbScDcqAwjHjFgAAAAAAAACcDIlbAAAAAAAAAHAyJG4BAAAAAAAAwMmUiMTtuXPnFB0dLaPRKKPRqOjoaJ0/f/6qbR577DEZDAaLIyoqyqJOVlaW/vWvfykgIEA+Pj7q0aOHjh8/bseeAAAAAAAAAMC1lYjNyfr376/jx48rNjZWkvTUU08pOjpaCxcuvGq7zp0764svvjCflypVyuL68OHDtXDhQn3zzTcqV66cXnjhBXXr1k1bt26Vq6ur7TsCAAAAAADgIM3enuboEABYwekTt3v37lVsbKw2bNig5s2bS5KmT5+uFi1aKCEhQTVr1iyyrYeHh0JCQgq9lpqaqhkzZuirr75S+/btJUmzZ89WWFiYli9frk6dOtm+MwAAAAAAAABQDE6/VEJcXJyMRqM5aStJUVFRMhqNWr9+/VXbrlq1SkFBQapRo4YGDRqkU6dOma9t3bpVOTk56tixo7ksNDRUkZGRV71vVlaW0tLSLA4AAAAAAAAAsCWnT9wmJycrKCioQHlQUJCSk5OLbNelSxfNmTNHv/32m9577z1t3rxZd999t7Kyssz3LVWqlMqUKWPRLjg4+Kr3HTdunHmtXaPRqLCwsOvsGQAAAAAAAAAUzmGJ2zFjxhTYPOyfx5YtWyRJBoOhQHuTyVRo+RV9+vTRvffeq8jISHXv3l1LlizRX3/9pUWLFl01rmvdNyYmRqmpqebj2LFjxewxAAAAAAAAABSPw9a4HTJkiPr27XvVOlWqVNGOHTt08uTJAtdOnz6t4ODgYj+vfPnyqly5svbt2ydJCgkJUXZ2ts6dO2cx6/bUqVNq2bJlkffx8PCQh4dHsZ8LAAAAAAAAANZyWOI2ICBAAQEB16zXokULpaamatOmTWrWrJkkaePGjUpNTb1qgvWfzpw5o2PHjql8+fKSpCZNmsjd3V3Lli1T7969JUlJSUnatWuXJkyYcB09AgAAAAAAAADbcPo1bmvXrq3OnTtr0KBB2rBhgzZs2KBBgwapW7duqlmzprlerVq1NH/+fElSRkaGXnzxRcXFxenw4cNatWqVunfvroCAAD3wwAOSJKPRqCeffFIvvPCCVqxYoe3bt2vAgAGqV6+e2rdv75C+AgAAAAAAAIDkwBm31pgzZ46GDh2qjh07SpJ69OihyZMnW9RJSEhQamqqJMnV1VU7d+7Ul19+qfPnz6t8+fJq166d5s2bJz8/P3ObDz74QG5uburdu7cuXryoe+65RzNnzpSrq+vN6xwAAAAAAAAA/IPTz7iVpLJly2r27NlKS0tTWlqaZs+erdKlS1vUMZlMeuyxxyRJXl5e+vXXX3Xq1CllZ2fryJEjmjlzpsLCwizaeHp66qOPPtKZM2eUmZmphQsXFqgDAAAAWOvcuXOKjo6W0WiU0WhUdHS0zp8/f9U2JpNJY8aMUWhoqLy8vNS2bVvt3r3bok7btm0LbOj7z30jrufZAAAAcD4lInELAAAAlCT9+/dXfHy8YmNjFRsbq/j4eEVHR1+1zYQJE/T+++9r8uTJ2rx5s0JCQtShQwelp6db1Bs0aJCSkpLMx9SpU2/42QAAAHA+JWKpBAAAAKCk2Lt3r2JjY7VhwwY1b95ckjR9+nS1aNFCCQkJFvs0XGEymTRx4kS9+uqr6tmzpyRp1qxZCg4O1tdff62nn37aXNfb21shISE2ezYAAACcEzNuAQAAABuKi4uT0Wg0J04lKSoqSkajUevXry+0zaFDh5ScnGze00GSPDw81KZNmwJt5syZo4CAANWtW1cvvviixYzc63k2AAAAnBMzbgEAAAAbSk5OVlBQUIHyoKAgJScnF9lGkoKDgy3Kg4ODdeTIEfP5ww8/rPDwcIWEhGjXrl2KiYnRH3/8oWXLll33syUpKytLWVlZ5vO0tLSr9BAAAAA3AzNuAQAAgGIYM2ZMgY3B/nls2bJFkmQwGAq0N5lMhZb/3T+v/7PNoEGD1L59e0VGRqpv3776/vvvtXz5cm3btq3IexTn2ePGjTNvZmY0GtmwFwAAwAkw4xYAAAAohiFDhqhv375XrVOlShXt2LFDJ0+eLHDt9OnTBWbUXnFlzdrk5GSVL1/eXH7q1Kki20hS48aN5e7urn379qlx48YKCQmx+tmSFBMToxEjRpjP09LSSN4CAAA4GIlbAAAAoBgCAgIUEBBwzXotWrRQamqqNm3apGbNmkmSNm7cqNTUVLVs2bLQNleWP1i2bJkaNWokScrOztbq1as1fvz4Ip+1e/du5eTkmJO91/Ns6fJ6uh4eHtfsGwAAAG4elkoAAAAAbKh27drq3LmzBg0apA0bNmjDhg0aNGiQunXrppo1a5rr1apVS/Pnz5d0eXmD4cOH6+2339b8+fO1a9cuPfbYY/L29lb//v0lSQcOHNDYsWO1ZcsWHT58WIsXL9ZDDz2kRo0aqVWrVlY9GwAAAM6PGbcAAACAjc2ZM0dDhw5Vx44dJUk9evTQ5MmTLeokJCQoNTXVfD5y5EhdvHhRgwcP1rlz59S8eXMtXbpUfn5+kqRSpUppxYoV+vDDD5WRkaGwsDDde++9Gj16tFxdXa16NgAAAJwfiVsAAADAxsqWLavZs2dftY7JZLI4NxgMGjNmjMaMGVNo/bCwMK1evdomzwYAAIDzY6kEAAAAAAAAAHAyJG4BAAAAAAAAwMmQuAUAAAAAAAAAJ0PiFgAAAAAAAACcDIlbAAAAAAAAAHAyJG4BAAAAAAAAwMmQuAUAAAAAAAAAJ0PiFgAAAAAAAACcDIlbAAAAAAAAAHAyJG4BAAAAAAAAwMmQuAUAAAAAAAAAJ0PiFgAAAAAAAACcDIlbAAAAAAAAAHAyJG4BAAAAAAAAwMmQuAUAAAAAAAAAJ0PiFgAAAAAAAACcDIlbAAAAAAAAAHAyJG4BAAAAAAAAwMmQuAUAAAAAAAAAJ0PiFgAAAAAAAACcDIlbAAAAAAAAAHAyJG4BAAAAAAAAwMmQuAUAAAAAAAAAJ0PiFgAAAAAAAACcDIlbAAAAAAAAAHAyJG4BAAAAAAAAwMmQuAUAAAAAAAAAJ0PiFgAAAAAAAACcDIlbAAAAAAAAAHAyJG4BAAAAAAAAwMm4OToAAAAAW8nPy1XyntXKyjgrNw8fhUbeLddSnhZ1zh7ZqfSTBy7Xz89VXvYlVb9rgDLPJSlx5wq5e/pKkowVaql0hVqSpNP7N+nCmeOSyST/kOoqW6XBze0YAAAAgNsOiVsAAHDLSE38S+6efgqtd4/OHduts0d2KDCimUWdspXrqWzlev+tn6CL50+ar3mXCVVovbst6l9KS9HF1FOq3OwBmfLzdHjjj/IvHyE3D2/7dwgAAADAbYulEgAAwC3jQspR+YdUkyT5h1RXxpmjV62ffvKQ/ILDr35Tg2TKzzMfLi6ucnF1t1XIAAAAAFAoZtwCAIBbRm52ptw8fCRJru4eys/JLrJuXvYlZWWclXeZCuayzPNJOrxpvtw9/RQUESV3L195+gXIu0x5Hfz9G5lM+Qqs3kwubiRuAQAAANgXiVsAAHBbSj99WD4BlWRwufwBJA+/cqraordc3NyVlrxfyXvXKKxxV2Vnpik7M01VW/WVKT9Px7YtlnfZUJXy8ndwDwAAAADcykrEUgnnzp1TdHS0jEajjEajoqOjdf78+au2MRgMhR7/+c9/zHXatm1b4Hrfvn3t3BsAAGBL547t1pFNP+nIpp/k6u6l3KwLkqS8nCy5uJcqsl36KctlElzdSpln0vqHVFf2hfOSpIzTR+RlDJaLq5tc3T3kVTpYWWkp9usQAAAAAKiEzLjt37+/jh8/rtjYWEnSU089pejoaC1cuLDINklJSRbnS5Ys0ZNPPqlevXpZlA8aNEhjx441n3t5edkwcgAAYG9lwuqqTFhdSZeTuGnJBxToV05pyfvlWy6s0Da52ReVfeG8vMuUtyhzK3V5HHDhzHG5e/lKktw8fZSevF+mSnVlys/XpdRTKh1a0869AgAAAHC7c/rE7d69exUbG6sNGzaoefPmkqTp06erRYsWSkhIUM2ahf/DKSQkxOJ8wYIFateunapWrWpR7u3tXaAuAAAomYyhNZW0e5UOxX0nNw8flY9sJ0nKOH1Ul9JTFFC18eXzU4flG1hZBsP/PnyUfvKQUhP/lMHgIhe3Ugqu3VqS5BdURZlnE3Vk40+SJP+QavLwK3dzOwYAAADgtuP0idu4uDgZjUZz0laSoqKiZDQatX79+iITt3938uRJLVq0SLNmzSpwbc6cOZo9e7aCg4PVpUsXjR49Wn5+fjbtAwAAuDlcXN1UoX77AuW+gZXkG1jJfF66Yu0CdcqE1VGZsDoFyg0GF4XUvtO2gQIAAADANTh94jY5OVlBQUEFyoOCgpScnFyse8yaNUt+fn7q2bOnRfnDDz+s8PBwhYSEaNeuXYqJidEff/yhZcuWFXmvrKwsZWVlmc/T0tKK2RMAAAAAAAAAKB6HbU42ZsyYIjcQu3Js2bJF0uWNxv7JZDIVWl6Yzz//XA8//LA8PT0tygcNGqT27dsrMjJSffv21ffff6/ly5dr27ZtRd5r3Lhx5k3SjEajwsIKXzsPAAAAAAAAAK6Xw2bcDhkyRH379r1qnSpVqmjHjh06efJkgWunT59WcHDwNZ+zdu1aJSQkaN68edes27hxY7m7u2vfvn1q3LhxoXViYmI0YsQI83laWhrJWwC4Afl5uUres1pZGWfl5uGj0Mi75VrK8g9tebnZSt69SrlZmTKZTAqs3lQ+/9106syh7UpL3i+Di6uCa7eWl3+guV1uVqYOb/hB5ao1UZmKBT8CD+eVsGKGo0MAAAAAAIdyWOI2ICBAAQEB16zXokULpaamatOmTWrWrJkkaePGjUpNTVXLli2v2X7GjBlq0qSJGjRocM26u3fvVk5OjsqXL19kHQ8PD3l4eFzzXgCA4klN/Evunn4KrXePzh3brbNHdigwotk/6iSolG9ZVWjQUVkXzitx53KFlwtTVsZZXThzXFWa91LWhXM6+efvqnxHD3O7lANb5F029GZ3CQAAAACAG+awpRKKq3bt2urcubMGDRqkDRs2aMOGDRo0aJC6detmsTFZrVq1NH/+fIu2aWlp+u677zRw4MAC9z1w4IDGjh2rLVu26PDhw1q8eLEeeughNWrUSK1atbJ7vwAAl11IOSr/kGqSJP+Q6so4c7RAHYMMys/NkSTl5+XIrZS3JCkj5Zj8gqvK4OIiT79yMpnylJuVKUnKPJcsFzd3lfIpc5N6AgAAAACA7Th94laS5syZo3r16qljx47q2LGj6tevr6+++sqiTkJCglJTUy3KvvnmG5lMJvXr16/APUuVKqUVK1aoU6dOqlmzpoYOHaqOHTtq+fLlcnV1tWt/AAD/k5udKTcPH0mSq7uH8nOyC9QxhtZU9oVzOrBurk7E/6rA6pdn5OZmZcrNw9tcz93D5/JyCvn5OnNom8qFF77sDQAAAAAAzs5hSyVYo2zZspo9e/ZV65hMpgJlTz31lJ566qlC64eFhWn16tU2iQ8AYF8Xzh6XpzFIYY276lL6GSXvWa3KzR6QVPBnvySdP7FXfkFV5OrO0jYAAAAAgJKpRCRuAQC3lnPHdistaZ8kydXdS7lZF+RaylN5OVlycS9VoH5a0j6VC28kSfL0KydJysu5JLf/zrC9Iifrgtw8vHUp7bQunj+ps0d3KT83W5JBLi5uMobWsH/nAAAAAACwARK3AICbrkxYXZUJqyvpv0nc5AMK9CuntOT98i0XVqC+m4ePMs8mytM/UDkX05WfmyNXd0/5BoTp5J+/q3SF2sq6cE4Gg4vcPLxVvm5bc9uUg9vkWsqTpC0AAAAAoEQhcQvAavl5uUres1pZGWfl5uGj0Mi75VrK06JOXm62knevurzeqMmkwOpN5VMuTBfPJ+vkX3EyyCAZXBRUo7m8jMHKPJek0/s2mttnZZxTpTt6mGdX4tZlDK2ppN2rdCjuO7l5+Kh8ZDtJUsbpo7qUnqKAqo1VNryhkvesUdrJg5JBCq7VUgaDQR6+ZeVdtoIOb/hBBldXBde608G9AQAAAADANkrE5mQAnEtq4l9y9/RTeIuH5BtYWWeP7CikToJK+ZZV5Wb3q3xkO536b1LWwy9Ale+4T5Wb3a+QOq11MmG9JMm7THlVbnb/f+vfLTdPH5K2twkXVzdVqN9e4S0eUljjrnIr5SVJ8g2spICqlzcXc/fwUVijLqrS/AFVafaAfP42KzegamOFt3xIVZr3lJcxqMD9A6o2VpmKdW5OZwDgv86dO6fo6GgZjUYZjUZFR0fr/PnzV21jMpk0ZswYhYaGysvLS23bttXu3bvN1w8fPiyDwVDo8d1335nrValSpcD1UaNG2aurAAAAsBMStwCsdiHlqPxDqkmS/EOqK+PM0QJ1DDIoPzdHkpSflyO3Ut6SLifpDIbLP3ryc3Muz7z9h/RTh+QXFG6v8AEAsLv+/fsrPj5esbGxio2NVXx8vKKjo6/aZsKECXr//fc1efJkbd68WSEhIerQoYPS09MlXd5cNykpyeJ444035OPjoy5duljca+zYsRb1/u///s9ufQUAAIB9sFQCAKvlZmfKzcNHkuTq7qH8nOwCdYyhNXVixzIdWDdXpvw8VWzY2XztwtkTOv3XBuVmX1SFBh0LtM04dUjBtfnIOwCgZNq7d69iY2O1YcMGNW/eXJI0ffp0tWjRQgkJCapZs2aBNiaTSRMnTtSrr76qnj17SpJmzZql4OBgff3113r66afl6uqqkJAQi3bz589Xnz595Ovra1Hu5+dXoC4AAIAzyMrN1esLVmr/qbMK8vfRuAfaq7S35fKLJpNJ439dp82HEuXrWUpv3X+PKpbxV+yu/Zq98Q9JUr7JpEMp5xU7bICMXp6auDxOGw+dkIvBoGH3RKlZeAVHdM+mSNwCsIsLZ4/L0xiksMZddSn9jJL3rFblZg/IYDDIp2wF+UT10sXUUzpzcJsqNvpfUjc7M1X5ebny9AtwYPS4HgkrZjg6BABwCnFxcTIajeakrSRFRUXJaDRq/fr1hSZuDx06pOTkZHXs+L8/aHp4eKhNmzZav369nn766QJttm7dqvj4eH388ccFro0fP15vvvmmwsLC9NBDD+mll15SqVKlbNRDAACA67cg/k+FlvbT+F4d9M3mXZoVF69h90RZ1Fm3/6jOZ2bph2f7aPVfhzV55Sa907O9OkdWV+fI6pKkTYdO6Iv122X08tTafUd09Gyq5gzspYxL2Ro2b4maVC4vV5eSvdgAidvbxI1sJiX9d4bkvk2STCrlU0ahke2Un5ujEzuX61LaaZUOraXAiGYO6BlulnPHdistaZ8kydXdS7lZF+RaylN5OVlycS/4D8G0pH0qF95Iksxr1eblXDKvXypJXsYg5WZdUG72RXN5+kmWSQAAlGzJyckKCiq45nZQUJCSk5OLbCNJwcHBFuXBwcE6cuRIoW1mzJih2rVrq2XLlhblw4YNU+PGjVWmTBlt2rRJMTExOnTokD777LMiY87KylJWVpb5PC0trci6AAAAN2LtvqP6192X/8DdNTJCT365oEDidu3+o+ry3wRt64jKGh+7TiaTSQbD/5ZbXP7nQbWvXVWSdPjMeTWuHCoXg0H+Xh4q7e2pPUmnVa+C5diqpCnZaWcU241sJpWXk6XT+zaqYsNOqtK8p4Jq/PebycVF5ao0UmC1O25mV+AgZcLqmjcP8w2spLTkA5KktOT98v3bRlFXuHn4KPNsoiQp52K68nNz5OruqZyL6TKZ8iVJWRnnlJ+XK1f3//0RIf3UIfkFk7gFADifMWPGFLk52JVjy5YtkmTxj4or/vmPjcL883pRbS5evKivv/5aTz75ZIFrzz//vNq0aaP69etr4MCB+vTTTzVjxgydOXOmyOeOGzfOvJGa0WhUWFjB3+0AAAC2kJKRqSC/y8sv+nt5KP1SweUXU9IvmOtcScamXvzfH5lz8/O1dt8Rtat5OX9QPbCsft9/VNm5eTqVfkF7Ek/rdHrmTeiNfTHj9jZxIeWoAqtfTrD6h1TX0a0LC8yQLWozqfSTB+QXVFVuHpfPr8yMdHFxlXeZEOVcSr9Z3YCTMIbWVNLuVToU953cPHxUPrKdJCnj9FFdSk9RQNXGKhveUMl71ijt5EHJIAXXaimDwaDMc4k6d2y3DAYXGVxcFVLnLvM/SLMvnJdM+fLwLevA3gEAULghQ4aob9++V61TpUoV7dixQydPnixw7fTp0wVm1F5xZT3a5ORklS9f3lx+6tSpQtt8//33yszM1COPPHLNuKOiLv/Rff/+/SpXrlyhdWJiYjRixAjzeVpaGslbAABgFyaZilGnoL//KXvL4URVDyyrsj6Xc1QtqoVpd+IpPTHrJwX6+ahehSC5ulz9D+YlAYnb28SNbCaVnZkmyaRjWxcpPz9P5cIbyTeAgfztzMXVTRXqty9Q7htYSb6BlSRJ7h4+CmvUpUAdY2hNGUMLru0nSaV8SqtKVC/bBgsAgI0EBAQoIODaa7C3aNFCqamp2rRpk5o1u/yH8o0bNyo1NbXAsgZXhIeHKyQkRMuWLVOjRpeXGsrOztbq1as1fvz4AvVnzJihHj16KDAw8JrxbN++XZIsEsL/5OHhIQ8Pj2veCwAA4HrM27xLC3ckSJLK+njpVPoFlfb2VNrFLPl5Flx+MdDPR6fSL6h2+UDlm0xKu5glf6//jVWW7z1gXibhioGtm2hg6yaSpCFzFymsjL8de3RzkLiFWVGbSZlM+crOOKcKDTspLzdLx7YukpcxSK7uDO4BAAD+qXbt2urcubMGDRqkqVOnSpKeeuopdevWzWJjslq1amncuHF64IHLm3cOHz5cb7/9tiIiIhQREaG3335b3t7e6t+/v8X99+/frzVr1mjx4sUFnh0XF6cNGzaoXbt2MhqN2rx5s55//nn16NFDlSpVsm/HAQAAitDnjkj1uSNS0uUk7pJd+1QjuJwW79qnO6sXHKPcWb2SFu/cpzY1qmjtviOqVzHY/Gnd3Lx8/X7gmP7V7n8bwebm5yszK0f+Xh7643iysnPzVDWw5H+al8TtLcxWm0m5e/jI3cNHLq5ucnF1k4dPaeVcTJOr+7VneAAAANyO5syZo6FDh6pjx46SpB49emjy5MkWdRISEpSammo+HzlypC5evKjBgwfr3Llzat68uZYuXSo/Pz+Ldp9//rkqVKhgvvffeXh4aN68eXrjjTeUlZWlypUra9CgQRo5cqQdegkAAGC9+xrW0msLflOvT+Yp0M9b43pe/kTvmn1HtDfptJ6+q6nurF5J6/YfVc9PvpGvp4f+fd/d5vabDp9QzeByMnr/b7+cnNw8DfrqZ0mXZ/SO7tb2pvbJXgwmk+naC0ugSGlpaTIajUpNTZW/v/2nYNe8p+AGFMVx7thu5V66oMCIZv/9OkOBEc0t6pz883e5e/qqbJUGyrmYrmPbFiu8ZW9lXziv0wc2q0L9DsrPzdbRzQtUqWkPuZa6/A2SmrRP2RnnCqyZWxwJK2ZcV38AAABgPzd7jAsAAEqeTa885egQbKrZ29McHUIBzLi9TdzIZlIevmXkZQzWkY3zJYNB5ao2MSdtD2/8UblZmTKZ8pV+6qAqNe1h3sQMAAAAAAAAwPUhcXubuJHNpCSpXJUGKlelQYHyKs172jZQAAAAAAAAAHJxdAAAAAAAAAAAAEskbgEAAAAAAADAybA52Q1i4wYAAADcahjjAgAAOB4zbgEAAAAAAADAyZC4BQAAAAAAAAAnQ+IWAAAAAAAAAJwMiVsAAAAAAAAAcDIkbgEAAAAAAADAyZC4BQAAAAAAAAAnQ+IWAAAAAAAAAJwMiVsAAAAAAAAAcDIkbgEAAAAAAADAyZC4BQAAAAAAAAAnQ+IWAAAAAAAAAJwMiVsAAAAAAAAAcDIkbgEAAAAAAADAyZC4BQAAAAAAAAAnQ+IWAAAAAAAAAJwMiVsAAAAAAAAAcDIkbgEAAAAAAADAyZC4BQAAAAAAAAAnQ+IWAAAAAAAAAJwMiVsAAAAAAAAAcDJujg6gpDOZTJKktLQ0B0cCAABwa/Lz85PBYHB0GLcVxrgAAAD2VZwxLonbG5Seni5JCgsLc3AkAAAAt6bU1FT5+/s7OozbCmNcAAAA+yrOGNdguvLndFyX/Px8JSYmMhPkOqSlpSksLEzHjh3jH2O4YbxPsCXeJ9gS79ONY5x18zHGvX58z8OWeJ9gS7xPsCXepxvHjNubwMXFRRUrVnR0GCWav78/3+SwGd4n2BLvE2yJ9wklCWPcG8f3PGyJ9wm2xPsEW+J9si82JwMAAAAAAAAAJ0PiFgAAAAAAAACcDIlbOIyHh4dGjx4tDw8PR4eCWwDvE2yJ9wm2xPsE3F74noct8T7BlnifYEu8TzcHm5MBAAAAAAAAgJNhxi0AAAAAAAAAOBkStwAAAAAAAADgZEjcAgAAAAAAAICTIXELAAAAAAAAAE6GxC2c0htvvKHg4GD5+/vr4YcfVkZGhqNDQgn10ksvqWbNmvLz81N4eLjGjRvn6JBQgo0ZM0Zubm7y9fU1H/PmzXN0WCghvv32W7Vs2VLe3t5q2LBhoXX4/Qfc2vgeh60wxoUtMcbFjWCMa18kbuF0vvjiC82YMUNr167V0aNHdebMGQ0dOtTRYaGE8vT01A8//KDz589ryZIlmjp1qqZNm+bosFCCdevWTRkZGeajT58+jg4JJUTZsmU1fPhwvfrqq4Ve5/cfcGvjexy2xBgXtsYYF9eLMa59kbiFzTRv3lwdO3ZUTk6Oueznn39WlSpVZDKZin2fzz//XEOHDlWNGjVUunRpvfnmm/r666918eJFe4QNJ2Wr9+nNN99UZGSkXF1dVatWLfXs2VPr1q2zR8hwYrZ6n3B7stX70759e/Xu3VsVKlQo9Dq//wDnxBgXtsQYF7bEGBc3gjFuyUDiFjazevVqnTx5UgsWLDCXffbZZ3rsscd07NgxlS5dusijW7du5jY7duywmF7fsGFDZWVl6a+//rqZ3YGD2ep9+juTyaQ1a9aofv36N6sbcBK2fJ9+++03lStXTjVq1NCrr76qS5cu3ezu4Cazx8+jwvD7D3BOjHFhS4xxYUuMcXEjGOOWECbAhj788EPTgw8+aDKZTKbExESTu7u76dChQ1bdw8XFxbR582aLMm9vb9PatWttFSZKCFu8T38XExNjql27tikjI8NGEaIkscX7tGvXLtOxY8dMeXl5pp07d5oaNGhgGjp0qB2ihbOx5c+jL774wtSgQYMC5fz+A5wXY1zYEmNc2BJjXNwIxrjOjxm3sKlevXppyZIlysjI0MyZM3XXXXepSpUqVt3D19dXqamp5vPc3FxlZmbKz8/PxtHC2dnifbpi3LhxmjdvnpYuXSofHx/bBooSwRbvU926dVWxYkW5uLgoMjJSb7/9Nhs33CZs+fOoKPz+A5wXY1zYEmNc2BJjXNwIxrjOj8QtbKpChQqqX7++fv75Z33++ed64oknJElHjx612KHyn0eXLl3M96hfv77i4+PN5/Hx8fLw8FCNGjVudnfgYLZ4nyTpnXfe0dSpU/Xbb7+pYsWKjugKnICt3qe/c3Hh1+jtwh7vzz/x+w9wXoxxYUuMcWFLjHFxIxjjlgCOnvKLW8/7779vqly5sql06dKmixcvWt1+xowZpkqVKpn++usv0/nz501dunQxPf7443aIFCXBjb5P48ePN1WsWNF04MABO0SHkuZG36cff/zRlJKSYjKZTKY///zT1KhRI9PgwYNtHSac1I2+P7m5uaaLFy+apk2bZqpfv77p4sWLpkuXLpmv8/sPcG6McWFLjHFhS4xxcSMY4zo3ErewuaNHj5oMBoPp2Wefve57jBkzxhQYGGjy9fU19evXz5SWlmbDCFGS3Oj7JMnk7u5u8vHxMR+dO3e2cZQoKW70ferXr5+pXLlyJm9vb1N4eLhp1KhRpszMTBtHCWd1o+/PF198YZJkcVSuXNmiDr//AOfFGBe2xBgXtsQYFzeCMa5zM5hMJpOjZvvi1lWpUiVNnDhRPXv2dHQouAXwPsGWeJ9wI3h/gNsbPwNgS7xPsCXeJ9wI3h/nxcIlsAsXFxfWxYHN8D7BlnifcCN4f4DbGz8DYEu8T7Al3ifcCN4f58X/FQAAAAAAAABwMiyVAAAAAAAAAABOhhm3AAAAAAAAAOBkSNwCAAAAAAAAgJMhcQsAAAAAAAAATobELQAAAAAAAAA4GRK3AAAAAAAAAOBkSNwCAAAAAAAAgJMhcQsAAAAAAAAATobELQAAAAAAAAA4GRK3AAAAAAAAAOBk/h86Et/twOsE3gAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved fig_tpi_welfare_bar.png\n", - "\n", - "All TPI figures saved: fig_tpi_spread_mitigation.png | fig_tpi_effectiveness.png | fig_tpi_welfare_macro.png | fig_tpi_welfare_bar.png\n" - ] - } - ], - "source": [ - "# \u2500\u2500 TPI-9: Figure 4 \u2014 Discounted Welfare Comparison (bar chart) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# Computes discounted sums of welfare deviations over 100 quarters.\n", - "# Left panel: absolute discounted welfare (relative to no-shock SS)\n", - "# Right panel: welfare GAIN vs \u03b3=0 baseline \u2014 how much does TPI help?\n", - "# Positive \u0394W_D means households in D are better off with TPI than without.\n", - "\n", - "T_disc9 = 100\n", - "beta_D9 = float(ss_final['beta_D'])\n", - "beta_F9 = float(ss_final['beta_F'])\n", - "disc_D9 = beta_D9 ** np.arange(T_disc9)\n", - "disc_F9 = beta_F9 ** np.arange(T_disc9)\n", - "\n", - "# Discounted welfare in units of % of SS consumption (\u00d7100 converts from fraction)\n", - "W_D = np.array([(irfs_tpi[g]['U_D'][:T_disc9] * disc_D9 * 100).sum() for g in gamma_values])\n", - "W_F = np.array([(irfs_tpi[g]['U_F'][:T_disc9] * disc_F9 * 100).sum() for g in gamma_values])\n", - "dW_D = W_D - W_D[0] # gain relative to no-TPI (\u03b3=0)\n", - "dW_F = W_F - W_F[0]\n", - "\n", - "# \u2500\u2500 Print summary table \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "print(f\"{'\u03b3':>5} {'W_D':>10} {'W_F':>10} {'\u0394W_D vs \u03b3=0':>13} {'\u0394W_F vs \u03b3=0':>13}\")\n", - "print(\"\u2500\" * 60)\n", - "for i, g in enumerate(gamma_values):\n", - " print(f\"{g:>5} {W_D[i]:>10.4f} {W_F[i]:>10.4f} \"\n", - " f\"{dW_D[i]:>+13.4f} {dW_F[i]:>+13.4f}\")\n", - "print(\"\u2500\" * 60)\n", - "print(\"(Units: % of quarterly SS consumption, discounted over 100 quarters)\")\n", - "\n", - "# \u2500\u2500 Plot \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "x = np.arange(len(gamma_values))\n", - "width = 0.35\n", - "_xlabs = [f'\u03b3={g}' for g in gamma_values]\n", - "\n", - "fig9, (ax9a, ax9b) = plt.subplots(1, 2, figsize=(14, 6))\n", - "\n", - "# Left: absolute discounted welfare (D = red, F = blue \u2014 consistent with model convention)\n", - "ax9a.bar(x - width/2, W_D, width, color=RED, label='Country D (Distressed)', alpha=0.85)\n", - "ax9a.bar(x + width/2, W_F, width, color=BLUE, label='Country F (Partner)', alpha=0.85)\n", - "ax9a.axhline(0, color='#444444', linewidth=0.8)\n", - "ax9a.set_xticks(x); ax9a.set_xticklabels(_xlabs, fontsize=9)\n", - "ax9a.set_title(f'Discounted Welfare Deviation\\n(\u03a3 \u03b2^t \u00b7 U \u00b7 100, t = 0 \u2026 {T_disc9-1})',\n", - " fontsize=10)\n", - "ax9a.set_ylabel('% of SS quarterly consumption', fontsize=9)\n", - "ax9a.legend(fontsize=9, frameon=False)\n", - "ax9a.spines[['top', 'right']].set_visible(False)\n", - "\n", - "# Right: welfare gain vs no-TPI\n", - "ax9b.bar(x - width/2, dW_D, width, color=RED, label='Country D (Distressed)', alpha=0.85)\n", - "ax9b.bar(x + width/2, dW_F, width, color=BLUE, label='Country F (Partner)', alpha=0.85)\n", - "ax9b.axhline(0, color='#444444', linewidth=0.8)\n", - "ax9b.set_xticks(x); ax9b.set_xticklabels(_xlabs, fontsize=9)\n", - "ax9b.set_title('Welfare Gain vs No-TPI (\u03b3 = 0)\\n(\u0394W > 0 = better off with TPI)',\n", - " fontsize=10)\n", - "ax9b.set_ylabel('\u0394% of SS quarterly consumption', fontsize=9)\n", - "ax9b.legend(fontsize=9, frameon=False)\n", - "ax9b.spines[['top', 'right']].set_visible(False)\n", - "\n", - "# Annotate each bar \u2014 flip offset and va for negative bars\n", - "def _annotate_bars(ax, bars, vals):\n", - " for bar, v in zip(bars, vals):\n", - " h = bar.get_height()\n", - " sign = 1 if v >= 0 else -1\n", - " ax.text(bar.get_x() + bar.get_width() / 2,\n", - " h + sign * (0.005 * abs(v) + 0.001),\n", - " f'{v:+.3f}', ha='center',\n", - " va='bottom' if v >= 0 else 'top', fontsize=7)\n", - "\n", - "n = len(gamma_values)\n", - "_annotate_bars(ax9a, ax9a.patches[:n], W_D)\n", - "_annotate_bars(ax9a, ax9a.patches[n:], W_F)\n", - "_annotate_bars(ax9b, ax9b.patches[:n], dW_D)\n", - "_annotate_bars(ax9b, ax9b.patches[n:], dW_F)\n", - "\n", - "fig9.suptitle('Figure 4: TPI \u2014 Discounted Welfare Comparison', fontsize=12, y=1.01)\n", - "fig9.tight_layout()\n", - "fig9.savefig('fig_tpi_welfare_bar.png', dpi=150, bbox_inches='tight')\n", - "plt.show()\n", - "print(\"Saved fig_tpi_welfare_bar.png\")\n", - "print(\"\\nAll TPI figures saved: fig_tpi_spread_mitigation.png | fig_tpi_effectiveness.png | fig_tpi_welfare_macro.png | fig_tpi_welfare_bar.png\")" - ] - }, - { - "cell_type": "markdown", - "id": "tpi10diag", - "metadata": {}, - "source": [ - "## TPI Mechanism Diagnosis\n", - "\n", - "### Why does `n_inter_D` rise with TPI?\n", - "\n", - "The GK IC determines maximum leverage:\n", - "`theta_tgt = value/lambda_gk + (1-Delta_bD_eff)*phi_bD + (1-Delta_bF_eff)*phi_bF`\n", - "\n", - "When CB buys bonds: `phi_bD = q_b_D * b_D_D / n_inter_D` falls.\n", - "\u2192 `theta_tgt` falls \u2192 IC relaxes \u2192 franchise value rises \u2192 **bank net worth increases**.\n", - "This is the **bank recapitalisation channel**: the CB absorbs risky bonds,\n", - "insulating private balance sheets from the sovereign capital loss.\n", - "\n", - "---\n", - "\n", - "### Why doesn't the spread close despite TPI being active?\n", - "\n", - "Three reinforcing reasons:\n", - "\n", - "**1. Portfolio rebalancing channel is inactive (`psi_bD_D = 0`)**\n", - "In `divert_bond_foc_D` the required excess return is:\n", - "`rb_actual_D(+1) - rdep_D(+1) = excess_ss + psi_spread_D*def_rate_D(+1) + psi_bD_D*(phi_bD - phi_bD_ss)`\n", - "With `psi_bD_D = 0`, reducing `phi_bD` via TPI has **no effect** on the required return.\n", - "\n", - "**2. Default probability is TPI-invariant**\n", - "`def_rate_D` depends only on the exogenous shock and `b_gov_D/Y_ss_D`.\n", - "TPI changes *who holds* the bonds, not the *total outstanding* `b_gov_D` \u2014 `def_rate_D` is unchanged.\n", - "\n", - "**3. Yield spread is insensitive to bond-price changes**\n", - "`spread_rb = delta_b * (1/q_b_D - 1/q_b_F)` is the *coupon yield* spread, scaled by `delta_b = 0.10`.\n", - "Even if `q_b_D` improves substantially, `spread_rb` barely moves.\n", - "The **total-return spread** `rb_actual_D - rb_actual_F` (which includes the capital gain/loss component)\n", - "is far more TPI-responsive and reveals the true impact." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "tpi11diag", - "metadata": {}, - "outputs": [], - "source": [ - "# \u2500\u2500 TPI-11: Mechanism Diagnostics \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "print(\"=\" * 65)\n", - "print(\"WHY n_inter_D RISES WITH TPI\")\n", - "print(\"=\" * 65)\n", - "print()\n", - "print(\"GK IC: theta_tgt = value/lambda_gk\")\n", - "print(\" + (1-Delta_bD_eff)*phi_bD + (1-Delta_bF_eff)*phi_bF\")\n", - "print()\n", - "print(\"TPI reduces phi_bD = q_b_D * b_D_D / n_inter_D.\")\n", - "print(\"\u2192 theta_tgt falls \u2192 IC relaxes \u2192 franchise value rises \u2192 n_inter rises.\")\n", - "print()\n", - "print(\"n_inter_D deviation at t=0 for each gamma:\")\n", - "for g in gamma_values:\n", - " ni = irfs_tpi[g]['n_inter_D'][0]\n", - " cb = irfs_tpi[g]['cb_buy_D'][0]\n", - " print(f\" gamma={g:>2}: n_inter_D = {ni*100:+.4f}% cb_buy_D = {cb:.5f}\")\n", - "\n", - "print()\n", - "print(\"=\" * 65)\n", - "print(\"WHY THE YIELD SPREAD DOES NOT CLOSE\")\n", - "print(\"=\" * 65)\n", - "print()\n", - "\n", - "# 1. psi_bD_D = 0\n", - "_psi_bD = calibration_start.get('psi_bD_D', 0.0)\n", - "print(f\"1. psi_bD_D = {_psi_bD:.4f} (portfolio adj. cost on domestic bonds)\")\n", - "print(\" \u2192 divert_bond_foc_D required spread = excess_ss + psi_spread_D*def_rate_D(+1)\")\n", - "print(\" \u2192 portfolio share phi_bD does NOT enter \u2192 TPI has no channel here.\")\n", - "print()\n", - "\n", - "# 2. def_rate_D is TPI-invariant\n", - "print(\"2. def_rate_D is TPI-invariant (depends on shock + b_gov_D/Y, not on who holds bonds):\")\n", - "print(f\" {'gamma':>6} {'t=0':>8} {'t=1':>8} {'t=3':>8} {'t=5':>8} {'t=10':>8}\")\n", - "for g in gamma_values:\n", - " dr = irfs_tpi[g]['def_rate_D']\n", - " print(f\" {g:>6} {dr[0]*100:>8.4f} {dr[1]*100:>8.4f} {dr[3]*100:>8.4f} {dr[5]*100:>8.4f} {dr[10]*100:>8.4f}\")\n", - "print()\n", - "\n", - "# 3. Yield spread vs total return spread\n", - "_psi_sp = float(ss_final['psi_spread_D'])\n", - "_ex_ss = float(ss_final['excess_return_bD_D_ss'])\n", - "_delta_b = calibration_start['delta_b_D']\n", - "_q_ss = float(ss_final['q_b_D'])\n", - "print(f\"3. Required spread at t=1 (all gammas):\")\n", - "print(f\" = excess_ss + psi_spread_D * def_rate_D[1]\")\n", - "print(f\" = {_ex_ss*100:.4f} + {_psi_sp:.4f} * def_rate_D[1]\")\n", - "dr1_g0 = irfs_tpi[0]['def_rate_D'][1]\n", - "print(f\" gamma=0: {(_ex_ss + _psi_sp*dr1_g0)*100:.4f} pp (same for all gamma \u2014 confirms TPI-invariance)\")\n", - "print()\n", - "print(f\"4. Spread sensitivity: d(spread_rb)/d(q_b_D) = -delta_b/q_b_D^2 = {-_delta_b/_q_ss**2:.4f}\")\n", - "print(f\" With delta_b_D = {_delta_b:.2f}, a 1% change in q_b_D moves spread_rb by only {_delta_b/_q_ss**2*100:.3f} pp.\")\n", - "print()\n", - "print(\" Yield spread vs total-return spread at t=0 (shows TPI impact is visible in total return):\")\n", - "print(f\" {'gamma':>6} {'yield spread (pp)':>18} {'total-return spread (pp)':>24} {'q_b_D deviation (pp)':>21}\")\n", - "for g in gamma_values:\n", - " sp_y = irfs_tpi[g]['spread_rb'][0] if 'spread_rb' in irfs_tpi[g] \\\n", - " else irfs_tpi[g]['rb_actual_D'][0] - irfs_tpi[g]['rb_actual_F'][0]\n", - " sp_tr = irfs_tpi[g]['rb_actual_D'][0] - irfs_tpi[g]['rb_actual_F'][0]\n", - " qb = irfs_tpi[g]['q_b_D'][0]\n", - " print(f\" {g:>6} {sp_y*100:>18.4f} {sp_tr*100:>24.4f} {qb*100:>21.4f}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "tpi12fig5", - "metadata": {}, - "outputs": [], - "source": [ - "# \u2500\u2500 TPI-12: Figure 5 \u2014 Why Spread Persists Despite Welfare Gains (2\u00d73) \u2500\u2500\u2500\u2500\u2500\u2500\n", - "# Row 1: Why the spread stays high (fundamental default risk, coupon yield insensitivity)\n", - "# Row 2: What TPI actually achieves (bank recapitalisation, welfare improvement)\n", - "\n", - "T_plot5 = 50\n", - "fig5_sub = [0, 5, 10] # 3 gamma values for cleaner IRF panels\n", - "_c5 = [TPI_COLORS[gamma_values.index(g)] for g in fig5_sub]\n", - "_ls5 = [TPI_LSTYLES[gamma_values.index(g)] for g in fig5_sub]\n", - "_mk5 = [TPI_MARKERS[gamma_values.index(g)] for g in fig5_sub]\n", - "_lb5 = [gamma_labels[gamma_values.index(g)] for g in fig5_sub]\n", - "\n", - "def _irf(g, var, n=T_plot5):\n", - " return irfs_tpi[g][var][:n] * 100 if var in irfs_tpi[g] else np.zeros(n)\n", - "\n", - "def _plot_lines(ax, var):\n", - " for g, c, ls, mk, lb in zip(fig5_sub, _c5, _ls5, _mk5, _lb5):\n", - " data = _irf(g, var)\n", - " ax.plot(data, color=c, linestyle=ls, linewidth=1.9,\n", - " marker=mk, markersize=4, markevery=8, label=lb)\n", - " ax.axhline(0, color='#888888', lw=0.8, ls=':')\n", - " ax.set_xlabel('Quarter', fontsize=9)\n", - " ax.spines[['top', 'right']].set_visible(False)\n", - " ax.tick_params(labelsize=8)\n", - "\n", - "fig5, axes5 = plt.subplots(2, 3, figsize=(18, 10))\n", - "ax = axes5.flatten()\n", - "\n", - "# \u2500\u2500 Panel 1: def_rate_D \u2014 fundamental driver, TPI-invariant \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "_plot_lines(ax[0], 'def_rate_D')\n", - "ax[0].set_title('Default Rate def_rate_D\\n[fundamental spread driver \u2014 TPI-invariant]',\n", - " fontsize=10, pad=6)\n", - "ax[0].set_ylabel('pp dev. from SS', fontsize=9)\n", - "ax[0].legend(fontsize=8, frameon=False)\n", - "ax[0].text(0.97, 0.97,\n", - " 'Unchanged by TPI\\n\u2192 required spread persists\\n(psi_bD_D = 0)',\n", - " transform=ax[0].transAxes, ha='right', va='top', fontsize=7.5,\n", - " color='#555555',\n", - " bbox=dict(boxstyle='round,pad=0.3', facecolor='#f0f0f0', edgecolor='#cccccc', alpha=0.9))\n", - "\n", - "# \u2500\u2500 Panel 2: spread_rb \u2014 coupon yield spread (insensitive) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "_plot_lines(ax[1], 'spread_rb')\n", - "ax[1].set_title('Yield Spread spread_rb = rb_D \u2212 rb_F\\n[\u03b4_b = 0.10 \u2192 very insensitive to q_b_D changes]',\n", - " fontsize=10, pad=6)\n", - "ax[1].set_ylabel('pp dev. from SS', fontsize=9)\n", - "\n", - "# \u2500\u2500 Panel 3: total-return spread (more responsive) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "for g, c, ls, mk, lb in zip(fig5_sub, _c5, _ls5, _mk5, _lb5):\n", - " tr_sp = (irfs_tpi[g]['rb_actual_D'][:T_plot5] - irfs_tpi[g]['rb_actual_F'][:T_plot5]) * 100\n", - " ax[2].plot(tr_sp, color=c, linestyle=ls, lw=1.9,\n", - " marker=mk, markersize=4, markevery=8, label=lb)\n", - "ax[2].axhline(0, color='#888888', lw=0.8, ls=':')\n", - "ax[2].set_title('Total-Return Spread rb_actual_D \u2212 rb_actual_F\\n[includes capital gain/loss \u2014 more TPI-responsive]',\n", - " fontsize=10, pad=6)\n", - "ax[2].set_ylabel('pp dev. from SS', fontsize=9)\n", - "ax[2].set_xlabel('Quarter', fontsize=9)\n", - "ax[2].legend(fontsize=8, frameon=False)\n", - "ax[2].spines[['top', 'right']].set_visible(False)\n", - "ax[2].tick_params(labelsize=8)\n", - "\n", - "# \u2500\u2500 Panel 4: n_inter_D \u2014 bank recapitalisation \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "_plot_lines(ax[3], 'n_inter_D')\n", - "ax[3].set_title('Bank Net Worth n_inter_D\\n[GK IC relaxes: fewer bonds \u2192 lower \u03b8_tgt \u2192 n_inter rises]',\n", - " fontsize=10, pad=6)\n", - "ax[3].set_ylabel('% dev. from SS', fontsize=9)\n", - "\n", - "# \u2500\u2500 Panel 5: U_D \u2014 domestic welfare \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "_plot_lines(ax[4], 'U_D')\n", - "ax[4].set_title('Domestic Welfare U_D\\n[recapitalisation \u2192 output/consumption recovery]',\n", - " fontsize=10, pad=6)\n", - "ax[4].set_ylabel('% dev. from SS (\u00f7 C_ss)', fontsize=9)\n", - "\n", - "# \u2500\u2500 Panel 6: \u0394W bar chart \u2014 welfare gain vs no-TPI \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "x6 = np.arange(len(gamma_values))\n", - "w6 = 0.35\n", - "bars_D = ax[5].bar(x6 - w6/2, dW_D, w6, color=RED, label='\u0394W_D (Distressed)', alpha=0.85)\n", - "bars_F = ax[5].bar(x6 + w6/2, dW_F, w6, color=BLUE, label='\u0394W_F (Partner)', alpha=0.85)\n", - "ax[5].axhline(0, color='#444', lw=0.8)\n", - "ax[5].set_xticks(x6)\n", - "ax[5].set_xticklabels([f'\u03b3={g}' for g in gamma_values], fontsize=8)\n", - "ax[5].set_title('Welfare Gain vs No-TPI (\u03b3 = 0)\\n\u0394W = \u03a3 \u03b2^t \u00b7 \u0394U \u00b7 100, t = 0\u202699',\n", - " fontsize=10, pad=6)\n", - "ax[5].set_ylabel('\u0394% of SS quarterly consumption', fontsize=9)\n", - "ax[5].legend(fontsize=8, frameon=False)\n", - "ax[5].spines[['top', 'right']].set_visible(False)\n", - "ax[5].tick_params(labelsize=8)\n", - "# Annotate bars\n", - "for bar, v in zip(list(bars_D) + list(bars_F), list(dW_D) + list(dW_F)):\n", - " ax[5].text(bar.get_x() + bar.get_width()/2,\n", - " bar.get_height() + 0.003 + (0 if v >= 0 else -0.03),\n", - " f'{v:+.3f}', ha='center', va='bottom', fontsize=6.5)\n", - "\n", - "fig5.suptitle(\n", - " 'Figure 5: TPI \u2014 Why the Spread Persists Despite Welfare Gains\\n'\n", - " 'Default shock in Country D | TPI rule: cb_buy_D = \u03b3 \u00d7 spread_rb (closed-loop)',\n", - " fontsize=11, y=1.02\n", - ")\n", - "fig5.tight_layout()\n", - "fig5.savefig('fig_tpi_welfare_spread.png', dpi=150, bbox_inches='tight')\n", - "plt.show()\n", - "print(\"Saved fig_tpi_welfare_spread.png\")\n", - "print()\n", - "print(\"Key takeaways:\")\n", - "print(\" \u2022 Spread_rb barely changes: default probability (def_rate_D) is unchanged by TPI,\")\n", - "print(\" and psi_bD_D = 0 deactivates the portfolio rebalancing channel.\")\n", - "print(\" \u2022 Total-return spread IS more compressed: TPI supports q_b_D (capital gain channel).\")\n", - "print(\" \u2022 Despite limited spread closure, TPI delivers significant welfare gains\")\n", - "print(\" via the bank recapitalisation channel (n_inter_D rises \u2192 less amplification).\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "tpi_bond_price", - "metadata": {}, - "outputs": [], - "source": [ - "# \u2500\u2500 TPI-13: Bond Price under Different TPI Regimes \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# q_b_D: price of Country D bonds (CB purchases raise this \u2014 compression channel)\n", - "# q_b_F: price of Country F bonds (shown for comparison)\n", - "\n", - "T_plot13 = 60\n", - "fig13, axes13 = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - "for j, g in enumerate(gamma_values):\n", - " kw = dict(color=TPI_COLORS[j], linestyle=TPI_LSTYLES[j],\n", - " linewidth=1.9, marker=TPI_MARKERS[j], markersize=4, markevery=8,\n", - " label=gamma_labels[j])\n", - " axes13[0].plot(irfs_tpi[g]['q_b_D'][:T_plot13] * 100, **kw)\n", - " axes13[1].plot(irfs_tpi[g]['q_b_F'][:T_plot13] * 100, **kw)\n", - "\n", - "for ax, title, country in zip(axes13,\n", - " ['Bond Price q_b_D (Country D \u2014 Distressed)',\n", - " 'Bond Price q_b_F (Country F \u2014 Partner)'],\n", - " ['D', 'F']):\n", - " ax.axhline(0, color='#888888', linewidth=0.8, linestyle=':')\n", - " ax.set_title(title, fontsize=10, pad=6)\n", - " ax.set_xlabel('Quarter', fontsize=9)\n", - " ax.set_ylabel('% dev. from SS', fontsize=9)\n", - " ax.spines[['top', 'right']].set_visible(False)\n", - " ax.tick_params(labelsize=8)\n", - "\n", - "axes13[0].legend(fontsize=8, frameon=False)\n", - "\n", - "fig13.suptitle('Figure 6: TPI \u2014 Bond Prices under Default Shock\\n'\n", - " 'CB purchases compress D-bond yields by raising q_b_D',\n", - " fontsize=11, y=1.02)\n", - "fig13.tight_layout()\n", - "fig13.savefig('fig_tpi_bond_price.png', dpi=150, bbox_inches='tight')\n", - "plt.show()\n", - "print(\"Saved fig_tpi_bond_price.png\")\n", - "print()\n", - "\n", - "# \u2500\u2500 Summary table: peak bond price deviation \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "print(f\"{'\u03b3':>5} {'peak \u0394q_b_D (%)':>17} {'peak \u0394q_b_F (%)':>17} {'q_b_D improvement vs \u03b3=0':>26}\")\n", - "print(\"\u2500\" * 72)\n", - "peak0 = irfs_tpi[0]['q_b_D'][:T_plot13].max() * 100\n", - "for g in gamma_values:\n", - " pk_D = irfs_tpi[g]['q_b_D'][:T_plot13].max() * 100\n", - " pk_F = irfs_tpi[g]['q_b_F'][:T_plot13].max() * 100\n", - " print(f\"{g:>5} {pk_D:>+17.4f} {pk_F:>+17.4f} {pk_D - peak0:>+26.4f}\")\n", - "print(\"\u2500\" * 72)\n", - "print(\"(Positive = bond price rises above SS; TPI should push q_b_D higher)\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "tpi_utility_compare", - "metadata": {}, - "outputs": [], - "source": [ - "# \u2500\u2500 TPI-14: Household Utility \u2014 With TPI vs Without TPI \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "# U_D and U_F impulse responses for all gamma values.\n", - "# \u03b3=0 (thick dashed) is the no-TPI benchmark; all others show TPI welfare gains.\n", - "\n", - "T_plot14 = 60\n", - "fig14, axes14 = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - "for j, g in enumerate(gamma_values):\n", - " lw = 2.5 if g == 0 else 1.9\n", - " ls = '--' if g == 0 else TPI_LSTYLES[j]\n", - " kw = dict(color=TPI_COLORS[j], linestyle=ls, linewidth=lw,\n", - " marker=TPI_MARKERS[j], markersize=4, markevery=8,\n", - " label=gamma_labels[j], zorder=(10 if g == 0 else 5))\n", - " axes14[0].plot(irfs_tpi[g]['U_D'][:T_plot14] * 100, **kw)\n", - " axes14[1].plot(irfs_tpi[g]['U_F'][:T_plot14] * 100, **kw)\n", - "\n", - "for ax, title in zip(axes14,\n", - " ['Utility U_D \u2014 Country D (Distressed)\\n[GHH composite, % dev. from SS]',\n", - " 'Utility U_F \u2014 Country F (Partner)\\n[GHH composite, % dev. from SS]']):\n", - " ax.axhline(0, color='#888888', linewidth=0.8, linestyle=':')\n", - " ax.set_title(title, fontsize=10, pad=6)\n", - " ax.set_xlabel('Quarter', fontsize=9)\n", - " ax.set_ylabel('% dev. from SS (\u00f7 C_ss)', fontsize=9)\n", - " ax.spines[['top', 'right']].set_visible(False)\n", - " ax.tick_params(labelsize=8)\n", - "\n", - "axes14[0].legend(fontsize=8, frameon=False)\n", - "\n", - "\n", - "fig14.suptitle('Figure 7: TPI \u2014 Household Utility With vs Without Intervention\\n'\n", - " 'Dashed line = no-TPI benchmark (\u03b3 = 0)',\n", - " fontsize=11, y=1.02)\n", - "fig14.tight_layout()\n", - "fig14.savefig('fig_tpi_utility.png', dpi=150, bbox_inches='tight')\n", - "plt.show()\n", - "print(\"Saved fig_tpi_utility.png\")\n", - "print()\n", - "\n", - "# \u2500\u2500 Summary table: trough utility & recovery \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "print(f\"{'\u03b3':>5} {'trough U_D (%)':>16} {'trough U_F (%)':>16} \"\n", - " f\"{'\u0394U_D vs \u03b3=0 at t=0':>22} {'\u0394U_F vs \u03b3=0 at t=0':>22}\")\n", - "print(\"\u2500\" * 88)\n", - "u_D0_t0 = irfs_tpi[0]['U_D'][0] * 100\n", - "u_F0_t0 = irfs_tpi[0]['U_F'][0] * 100\n", - "for g in gamma_values:\n", - " tr_D = irfs_tpi[g]['U_D'][:T_plot14].min() * 100\n", - " tr_F = irfs_tpi[g]['U_F'][:T_plot14].min() * 100\n", - " d_D = irfs_tpi[g]['U_D'][0] * 100 - u_D0_t0\n", - " d_F = irfs_tpi[g]['U_F'][0] * 100 - u_F0_t0\n", - " print(f\"{g:>5} {tr_D:>+16.4f} {tr_F:>+16.4f} {d_D:>+22.4f} {d_F:>+22.4f}\")\n", - "print(\"\u2500\" * 88)\n", - "print(\"(Trough = worst quarter in first 60 periods; \u0394U at t=0 = immediate impact of TPI)\")\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/code/steady_state.py b/code/steady_state.py deleted file mode 100644 index 07f1e20..0000000 --- a/code/steady_state.py +++ /dev/null @@ -1,187 +0,0 @@ -import copy -import numpy as np -import sequence_jacobian as sj -from sequence_jacobian import simple, combine, create_model - -from equations_D import ( - hh_init_D, hh_D, make_grids_D, income_D, hh_extended_D, - smart_steady_D, market_clearing_D, steady_auxilliary_D, - banker_div_D, sdf_D, sdf_ss_D, sdf_banker_ss_D, government_ss_D, labor_ss_D, - government_default_D, bond_price_ss_D, bond_return_D, - ces_price_D, import_demand_D, deposit_return_D, -) -from equations_F import ( - hh_init_F, hh_F, make_grids_F, income_F, hh_extended_F, - smart_steady_F, market_clearing_F, steady_auxilliary_F, - banker_div_F, sdf_F, sdf_ss_F, sdf_banker_ss_F, government_ss_F, labor_ss_F, - government_default_F, bond_price_ss_F, bond_return_F, - ces_price_F, import_demand_F, deposit_return_F, -) -from equations_global import ( - trade_balance, domestic_bond_clearing, - portfolio_level_anchors, portfolio_adj_cost, bond_yield, - global_goods_mkt, external_account_D, -) - - -def _apply_ss_anchors(ss_in, cal): - anchors = { - 'phi_bD_D_ss': float(ss_in['q_b_D']) * float(ss_in['b_D_D']) / float(ss_in['n_inter_D']), - 'phi_bF_F_ss': float(ss_in['q_b_F']) * float(ss_in['b_F_F']) / (float(ss_in['p']) * float(ss_in['n_inter_F'])), - 'b_F_D_anchor': float(ss_in['b_F_D']), - 'b_D_F_anchor': float(ss_in['b_D_F']), - 'excess_return_bD_D_ss': float(ss_in['rb_actual_D']) - float(ss_in['rdep_D']) - cal['T0_D'], - 'excess_return_bF_F_ss': float(ss_in['rb_actual_F']) - float(ss_in['rdep_F']) - cal['T0_F'], - 'excess_return_F_D_ss': float(ss_in['rb_actual_F']) - float(ss_in['rdep_D']) - cal['T0_D'], - 'excess_return_D_F_ss': float(ss_in['rb_actual_D']) - float(ss_in['rdep_F']) - cal['T0_F'], - 'psi_spread_F': float(ss_in['lambda_gk_F']) * cal['psi_lambda_B_F'] - / (float(ss_in['beta_inter_F']) * float(ss_in['Omega_F'])), - 'psi_spread_D': float(ss_in['lambda_gk_D']) * cal['psi_lambda_B_D'] - / (float(ss_in['beta_inter_D']) * float(ss_in['Omega_D'])), - 'q_b_D': float(ss_in['q_b_D']), - 'q_b_F': float(ss_in['q_b_F']), - 'p': float(ss_in['p']), - 'C_D_ss': float(ss_in['C_D']), - 'C_F_ss': float(ss_in['C_F']), - } - cal.update(anchors) - for k, v in anchors.items(): - ss_in.toplevel[k] = v - ss_in.toplevel['b_F_D_ss'] = float(ss_in['b_F_D']) - ss_in.toplevel['b_D_F_ss'] = float(ss_in['b_D_F']) - ss_in.toplevel['Rgross_D'] = float(1 + ss_in['rdep_D']) - ss_in.toplevel['Rgross_F'] = float(1 + ss_in['rdep_F']) - _fr_D = float(ss_in['frisch_D']); _fr_F = float(ss_in['frisch_F']) - ss_in.toplevel['X_D'] = (float(ss_in['C_D']) - - float(ss_in['vphi_D']) * float(ss_in['N_D'])**(1+1/_fr_D) / (1+1/_fr_D)) - ss_in.toplevel['X_F'] = (float(ss_in['C_F']) - - float(ss_in['vphi_F']) * float(ss_in['N_F'])**(1+1/_fr_F) / (1+1/_fr_F)) - ss_in.toplevel['U_D'] = ss_in.toplevel['X_D'] / float(ss_in['C_D']) - ss_in.toplevel['U_F'] = ss_in.toplevel['X_F'] / float(ss_in['C_F']) - ss_in.toplevel['Phi_D'] = float(ss_in['Phi_D']) - ss_in.toplevel['Phi_F'] = float(ss_in['Phi_F']) - ss_in.toplevel['value_D'] = (float(ss_in['beta_inter_D']) - * float(ss_in['Omega_D']) * (1 + float(ss_in['rn_D']))) - ss_in.toplevel['value_F'] = (float(ss_in['beta_inter_F']) - * float(ss_in['Omega_F']) * (1 + float(ss_in['rn_F']))) - for k, v in { - 'tau_mp_D': 0.0, 'tau_mp_F': 0.0, - 'T_D': 0.0, 'T_F': 0.0, - 'T_ls_D': 0.0, 'T_ls_F': 0.0, - 'b_F_D_res': 0.0, 'b_D_F_res': 0.0, - 'rb_D_res': 0.0, 'rb_F_res': 0.0, - 'labor_mkt_res_D': 0.0, 'labor_mkt_res_F': 0.0, - 'w_res_D': 0.0, 'w_res_F': 0.0, - }.items(): - ss_in.toplevel[k] = v - return anchors - - -def solve_steady_state(calibration_start): - ha = sj.create_model([ - sdf_ss_D, sdf_banker_ss_D, government_default_D, bond_price_ss_D, bond_return_D, - sdf_ss_F, sdf_banker_ss_F, government_default_F, bond_price_ss_F, bond_return_F, - hh_extended_D, smart_steady_D, market_clearing_D, steady_auxilliary_D, - banker_div_D, government_ss_D, labor_ss_D, - hh_extended_F, smart_steady_F, market_clearing_F, steady_auxilliary_F, - banker_div_F, government_ss_F, labor_ss_F, - ces_price_D, import_demand_D, ces_price_F, import_demand_F, - deposit_return_D, deposit_return_F, - bond_yield, - trade_balance, external_account_D, global_goods_mkt, - ], name='MU HA Model 2 Country') - - unknowns_ss = {'beta_D': 0.9850, 'beta_F': 0.9850, 'p': 0.99} - targets_ss = ['deposit_mkt_D', 'deposit_mkt_F', 'ca_res_D'] - - # ── Initial SS solve ────────────────────────────────────────────────────── - print("Solving initial steady state...") - ss = ha.solve_steady_state(calibration_start, unknowns_ss, targets_ss, solver='broyden_custom') - - anchors = { - 'phi_bD_D_ss': float(ss['q_b_D']) * float(ss['b_D_D']) / float(ss['n_inter_D']), - 'phi_bF_F_ss': float(ss['q_b_F']) * float(ss['b_F_F']) / (float(ss['p']) * float(ss['n_inter_F'])), - 'b_F_D_anchor': float(ss['b_F_D']), - 'b_D_F_anchor': float(ss['b_D_F']), - 'excess_return_bD_D_ss': float(ss['rb_actual_D']) - float(ss['rdep_D']) - calibration_start['T0_D'], - 'excess_return_bF_F_ss': float(ss['rb_actual_F']) - float(ss['rdep_F']) - calibration_start['T0_F'], - 'excess_return_F_D_ss': float(ss['rb_actual_F']) - float(ss['rdep_D']) - calibration_start['T0_D'], - 'excess_return_D_F_ss': float(ss['rb_actual_D']) - float(ss['rdep_F']) - calibration_start['T0_F'], - 'q_b_D': float(ss['q_b_D']), - 'q_b_F': float(ss['q_b_F']), - 'p': float(ss['p']), - 'C_D_ss': float(ss['C_D']), - 'C_F_ss': float(ss['C_F']), - } - calibration_start.update(anchors) - for k, v in anchors.items(): - ss.toplevel[k] = v - - ss.toplevel['b_F_D_ss'] = float(ss['b_F_D']) - ss.toplevel['b_D_F_ss'] = float(ss['b_D_F']) - ss.toplevel['Rgross_D'] = float(1 + ss['rdep_D']) - ss.toplevel['Rgross_F'] = float(1 + ss['rdep_F']) - _fr_D = float(ss['frisch_D']); _fr_F = float(ss['frisch_F']) - ss.toplevel['X_D'] = float(ss['C_D']) - float(ss['vphi_D']) * float(ss['N_D']) ** (1 + 1/_fr_D) / (1 + 1/_fr_D) - ss.toplevel['X_F'] = float(ss['C_F']) - float(ss['vphi_F']) * float(ss['N_F']) ** (1 + 1/_fr_F) / (1 + 1/_fr_F) - ss.toplevel['U_D'] = ss.toplevel['X_D'] / float(ss['C_D']) - ss.toplevel['U_F'] = ss.toplevel['X_F'] / float(ss['C_F']) - ss.toplevel['Phi_D'] = float(ss['Phi_D']) - ss.toplevel['Phi_F'] = float(ss['Phi_F']) - ss.toplevel['value_D'] = float(ss['beta_inter_D']) * float(ss['Omega_D']) * (1 + float(ss['rn_D'])) - ss.toplevel['value_F'] = float(ss['beta_inter_F']) * float(ss['Omega_F']) * (1 + float(ss['rn_F'])) - for k, v in { - 'tau_mp_D': 0.0, 'tau_mp_F': 0.0, - 'T_D': 0.0, 'T_F': 0.0, - 'T_ls_D': 0.0, 'T_ls_F': 0.0, - 'b_F_D_res': 0.0, 'b_D_F_res': 0.0, - 'rb_D_res': 0.0, 'rb_F_res': 0.0, - 'labor_mkt_res_D': 0.0, 'labor_mkt_res_F': 0.0, - 'w_res_D': 0.0, 'w_res_F': 0.0, - }.items(): - ss.toplevel[k] = v - - # ── Portfolio share targeting ───────────────────────────────────────────── - print("Targeting portfolio shares...") - target_phi_bD_D = 0.25 - target_phi_bF_D = 0.15 - target_phi_bD_F = 0.15 - target_phi_bF_F = 0.25 - - n_D = float(ss['n_inter_D']) - n_F = float(ss['n_inter_F']) * float(ss['p']) - q_D = float(ss['q_b_D']) - q_F = float(ss['q_b_F']) - - b_D_D_new = target_phi_bD_D * n_D / q_D - b_F_D_new = target_phi_bF_D * n_D / q_F - b_D_F_new = target_phi_bD_F * n_F / q_D - b_F_F_new = target_phi_bF_F * n_F / q_F - B_D_new = b_D_D_new + b_D_F_new - B_F_new = b_F_D_new + b_F_F_new - - print(f" D-bank: phi_bD_D = {target_phi_bD_D:.3f} phi_bF_D = {target_phi_bF_D:.3f}") - print(f" F-bank: phi_bD_F = {target_phi_bD_F:.3f} phi_bF_F = {target_phi_bF_F:.3f}") - - calibration_start.update({ - 'b_D_D': b_D_D_new, 'b_F_D': b_F_D_new, - 'b_D_F': b_D_F_new, 'b_F_F': b_F_F_new, - 'b_F_D_anchor': b_F_D_new, 'b_D_F_anchor': b_D_F_new, - 'phi_bF_D_ss': target_phi_bF_D, - 'B_supply_D': B_D_new, 'b_gov_D': B_D_new, 'b_gov_ss_D': B_D_new, - 'B_supply_F': B_F_new, 'b_gov_F': B_F_new, 'b_gov_ss_F': B_F_new, - }) - - print("Re-solving SS with new portfolio allocation...") - _unknowns_warm = {'beta_D': float(ss['beta_D']), 'beta_F': float(ss['beta_F']), 'p': float(ss['p'])} - ss = ha.solve_steady_state(calibration_start, _unknowns_warm, targets_ss, solver='broyden_custom') - _apply_ss_anchors(ss, calibration_start) - print(f"SS re-solved. beta_D={float(ss['beta_D']):.8f} p={float(ss['p']):.6f}") - - return { - 'ss': ss, - 'ha': ha, - 'calibration_start': calibration_start, - 'unknowns_ss': unknowns_ss, - 'targets_ss': targets_ss, - } diff --git a/code/tpi.py b/code/tpi.py deleted file mode 100644 index 3a22cf1..0000000 --- a/code/tpi.py +++ /dev/null @@ -1,229 +0,0 @@ -""" -TPI (Transmission Protection Instrument) experiment. - -Builds the TPI-extended model, computes closed-loop IRFs for -gamma_values = [0, 2, 5, 10], and pre-computes all welfare and -effectiveness statistics needed by tpi_plots.py. -""" -import copy -import numpy as np -import sequence_jacobian as sj -from sequence_jacobian import simple - -from equations_D import ( - deposit_return_D, tax_rule_D, hh_extended_D, ghh_composite_D, - sdf_D, sdf_banker_D, government_default_D, - bond_return_D, bank_return_D, cap_adj_cost_inter_D, macro_pru_tax_D, - intermediation_P2_D, intermediation_P3_D, k_balance_sheet_D, - capital_adj_D, capital_producer_profit_D, - labor_D, labor_market_D, labor_demand_D, banker_div_res_D, - market_clearing_D, welfare_agg_D, ces_price_D, import_demand_D, - divert_bond_foc_D, -) -from equations_F import ( - deposit_return_F, tax_rule_F, hh_extended_F, ghh_composite_F, - sdf_F, sdf_banker_F, government_default_F, - bond_return_F, bank_return_F, cap_adj_cost_inter_F, macro_pru_tax_F, - intermediation_P2_F, intermediation_P3_F, k_balance_sheet_F, - capital_adj_F, capital_producer_profit_F, budget_residual_F, - labor_F, labor_market_F, labor_demand_F, banker_div_res_F, - market_clearing_F, welfare_agg_F, ces_price_F, import_demand_F, - divert_bond_foc_F, -) -from equations_global import ( - trade_balance, external_account_D, bond_yield, - portfolio_level_anchors, divert_portfolio_adj, global_goods_mkt, -) - -BLUE = '#002147' -RED = '#8C1515' -BLUE_MUTED = '#4a6f8a' -RED_MUTED = '#c0624a' - - -# ── TPI-1: CB bond clearing + budget constraint (audit fix) ────────────────── -@simple -def domestic_bond_clearing_tpi(b_gov_D, b_gov_F, b_D_F, b_F_D, cb_buy_D): - b_D_D = b_gov_D - b_D_F - cb_buy_D - b_F_F = b_gov_F - b_F_D - return b_D_D, b_F_F - - -@simple -def budget_residual_D_tpi(b_gov_D, G_D, TAX_D, q_b_D, def_rate_D, recovery_rate_D, - zeta_writeoff_D, P_CES_D, delta_b_D, writeoff_enabled_D, - cb_buy_D): - haircut_D = 1.0 - recovery_rate_D - haircut_mult_D = writeoff_enabled_D - surv_cont_D = 1.0 - zeta_writeoff_D * def_rate_D * haircut_D * haircut_mult_D - coupon_D = delta_b_D * (1.0 - def_rate_D * haircut_D * haircut_mult_D) * b_gov_D(-1) - net_issuance_D = q_b_D * (b_gov_D - surv_cont_D * (1.0 - delta_b_D) * b_gov_D(-1)) - rem_cb_D = (delta_b_D * (1.0 - def_rate_D * haircut_D * haircut_mult_D) * cb_buy_D(-1) - + q_b_D * surv_cont_D * (1.0 - delta_b_D) * cb_buy_D(-1) - - q_b_D * cb_buy_D) - b_gov_res_D = coupon_D + G_D - P_CES_D * TAX_D - net_issuance_D - rem_cb_D - return b_gov_res_D, rem_cb_D - - -def compute_tpi_irfs(G_tpi, shock_def, gamma_tpi, T): - _has_spread = 'spread_rb' in G_tpi.outputs - if _has_spread: - A_def = np.array(G_tpi['spread_rb']['shock_def_D']) - A_cb = np.array(G_tpi['spread_rb']['cb_buy_D']) - else: - A_def = (np.array(G_tpi['rb_actual_D']['shock_def_D']) - - np.array(G_tpi['rb_actual_F']['shock_def_D'])) - A_cb = (np.array(G_tpi['rb_actual_D']['cb_buy_D']) - - np.array(G_tpi['rb_actual_F']['cb_buy_D'])) - - I_T = np.eye(T) - system_matrix = I_T - gamma_tpi * A_cb - cond = np.linalg.cond(system_matrix) - if cond > 1e10: - print(f" WARNING: system matrix cond = {cond:.2e} for gamma={gamma_tpi:.1f}") - - spread_cl = np.linalg.solve(system_matrix, A_def @ shock_def) - cb_buy_path = gamma_tpi * spread_cl - - irfs = G_tpi @ { - 'Z_D': np.zeros(T), 'Z_F': np.zeros(T), - 'shock_def_D': shock_def, 'shock_def_F': np.zeros(T), - 'cb_buy_D': cb_buy_path, - } - irfs['cb_buy_D'] = cb_buy_path - return irfs - - -def run_tpi(model_results): - ha_full = model_results['ha_full'] - financial_solved_D = model_results['financial_solved_D'] - financial_solved_F = model_results['financial_solved_F'] - ss_final = model_results['ss_final'] - unknowns_tp = model_results['unknowns_tp'] - targets_tp = model_results['targets_tp'] - T = model_results['T'] - dShock_def_D = model_results['dShock_def_D'] - irfs_def_D = model_results['irfs_def_D'] - - # ── Build TPI model ─────────────────────────────────────────────────────── - ha_full_tpi = sj.create_model([ - deposit_return_D, tax_rule_D, hh_extended_D, ghh_composite_D, - sdf_D, sdf_banker_D, government_default_D, financial_solved_D, - bond_return_D, bank_return_D, cap_adj_cost_inter_D, macro_pru_tax_D, - intermediation_P2_D, intermediation_P3_D, k_balance_sheet_D, - capital_adj_D, capital_producer_profit_D, budget_residual_D_tpi, - labor_D, labor_market_D, labor_demand_D, banker_div_res_D, - market_clearing_D, welfare_agg_D, - deposit_return_F, tax_rule_F, hh_extended_F, ghh_composite_F, - sdf_F, sdf_banker_F, government_default_F, financial_solved_F, - bond_return_F, bank_return_F, cap_adj_cost_inter_F, macro_pru_tax_F, - intermediation_P2_F, intermediation_P3_F, k_balance_sheet_F, - capital_adj_F, capital_producer_profit_F, budget_residual_F, - labor_F, labor_market_F, labor_demand_F, banker_div_res_F, - market_clearing_F, welfare_agg_F, - ces_price_D, import_demand_D, ces_price_F, import_demand_F, - trade_balance, external_account_D, domestic_bond_clearing_tpi, - bond_yield, portfolio_level_anchors, divert_portfolio_adj, - divert_bond_foc_D, divert_bond_foc_F, global_goods_mkt, - ], name="Full 2-Country MU HANK — TPI Extension") - - ss_tpi = copy.deepcopy(ss_final) - ss_tpi.toplevel['cb_buy_D'] = 0.0 - - # ── Jacobian ────────────────────────────────────────────────────────────── - exogenous_tpi = ['Z_D', 'shock_def_D', 'Z_F', 'shock_def_F', 'cb_buy_D'] - print(f"Computing G_tpi (T={T}, {len(exogenous_tpi)} exogenous inputs)...") - G_tpi = ha_full_tpi.solve_jacobian( - ss_tpi, unknowns=unknowns_tp, targets=targets_tp, - inputs=exogenous_tpi, T=T, - ) - print("G_tpi computed.") - - _chk = G_tpi @ { - 'Z_D': np.zeros(T), 'Z_F': np.zeros(T), - 'shock_def_D': dShock_def_D, 'shock_def_F': np.zeros(T), - 'cb_buy_D': np.zeros(T), - } - _err = np.max(np.abs(_chk['spread_rb'][:50] - irfs_def_D['spread_rb'][:50])) - print(f"Sanity check G_tpi[cb=0] vs baseline G: max |err| = {_err:.2e} (expect < 1e-8)") - - # ── Closed-loop IRFs ────────────────────────────────────────────────────── - gamma_values = [0, 2, 5, 10] - gamma_labels = ['γ = 0 (No TPI)', 'γ = 2 (Weak)', 'γ = 5 (Medium)', 'γ = 10 (Strong)'] - TPI_COLORS = [BLUE, '#1a6e3a', '#c87941', RED] - TPI_LSTYLES = ['-', '-', '--', '-.'] - TPI_MARKERS = ['', 'o', '', 's'] - - irfs_tpi = {} - for g in gamma_values: - print(f" gamma = {g:2d} ...", end=' ', flush=True) - if g == 0: - _s = {'Z_D': np.zeros(T), 'Z_F': np.zeros(T), - 'shock_def_D': dShock_def_D, 'shock_def_F': np.zeros(T), - 'cb_buy_D': np.zeros(T)} - irfs_tpi[g] = G_tpi @ _s - irfs_tpi[g]['cb_buy_D'] = np.zeros(T) - else: - irfs_tpi[g] = compute_tpi_irfs(G_tpi, dShock_def_D, g, T) - _spread = irfs_tpi[g]['spread_rb'] if 'spread_rb' in irfs_tpi[g] \ - else irfs_tpi[g]['rb_actual_D'] - irfs_tpi[g]['rb_actual_F'] - print(f"peak spread = {_spread[:100].max()*100:+.3f} pp") - - _err0 = np.max(np.abs(irfs_tpi[0]['spread_rb'][:50] - irfs_def_D['spread_rb'][:50])) - print(f"Sanity check gamma=0 vs irfs_def_D: max |err| = {_err0:.2e} (expect < 1e-8)") - - # ── Welfare gains ───────────────────────────────────────────────────────── - T_disc = 100 - beta_D = float(ss_final['beta_D']); beta_F = float(ss_final['beta_F']) - disc_D = beta_D ** np.arange(T_disc); disc_F = beta_F ** np.arange(T_disc) - W_D = np.array([(irfs_tpi[g]['U_D'][:T_disc] * disc_D * 100).sum() for g in gamma_values]) - W_F = np.array([(irfs_tpi[g]['U_F'][:T_disc] * disc_F * 100).sum() for g in gamma_values]) - dW_D = W_D - W_D[0] - dW_F = W_F - W_F[0] - - print(f"\n{'γ':>5} {'W_D':>10} {'W_F':>10} {'ΔW_D vs γ=0':>13} {'ΔW_F vs γ=0':>13}") - print("─" * 60) - for i, g in enumerate(gamma_values): - print(f"{g:>5} {W_D[i]:>10.4f} {W_F[i]:>10.4f} {dW_D[i]:>+13.4f} {dW_F[i]:>+13.4f}") - print("─" * 60) - print("(Units: % of quarterly SS consumption, discounted over 100 quarters)") - - # ── Effectiveness curve over fine gamma grid ────────────────────────────── - def _peak_spread(irf): - sp = irf['spread_rb'] if 'spread_rb' in irf \ - else irf['rb_actual_D'] - irf['rb_actual_F'] - return sp[:100].max() - - peak_no_tpi = _peak_spread(irfs_tpi[0]) - gammas_fine = np.concatenate([np.linspace(0, 5, 25), np.linspace(5, 30, 26)[1:]]) - q_b_D_ss = float(ss_final['q_b_D']) - peak_arr = np.empty(len(gammas_fine)) - cost_arr = np.empty(len(gammas_fine)) - for i, g in enumerate(gammas_fine): - irf_g = irfs_tpi[0] if g == 0 else compute_tpi_irfs(G_tpi, dShock_def_D, g, T) - peak_arr[i] = _peak_spread(irf_g) - cost_arr[i] = (irf_g['cb_buy_D'][:100] * q_b_D_ss).sum() - frac_closed = np.clip(100.0 * (1.0 - peak_arr / peak_no_tpi), 0, 100) - - return { - 'irfs_tpi': irfs_tpi, - 'gamma_values': gamma_values, - 'gamma_labels': gamma_labels, - 'TPI_COLORS': TPI_COLORS, - 'TPI_LSTYLES': TPI_LSTYLES, - 'TPI_MARKERS': TPI_MARKERS, - 'dW_D': dW_D, - 'dW_F': dW_F, - 'W_D': W_D, - 'W_F': W_F, - 'ss_final': ss_final, - 'T': T, - 'dShock_def_D': dShock_def_D, - 'G_tpi': G_tpi, - 'peak_no_tpi': peak_no_tpi, - 'gammas_fine': gammas_fine, - 'peak_arr': peak_arr, - 'cost_arr': cost_arr, - 'frac_closed': frac_closed, - 'q_b_D_ss': q_b_D_ss, - } diff --git a/code/tpi_plots.py b/code/tpi_plots.py deleted file mode 100644 index fc03ec3..0000000 --- a/code/tpi_plots.py +++ /dev/null @@ -1,283 +0,0 @@ -""" -TPI figures (7 total) — all saved to output_dir. - -Takes the dict returned by tpi.run_tpi() and produces: - fig_tpi_spread_mitigation.png - fig_tpi_effectiveness.png - fig_tpi_welfare_macro.png - fig_tpi_welfare_bar.png - fig_tpi_welfare_spread.png - fig_tpi_bond_price.png - fig_tpi_utility.png -""" -import numpy as np -import matplotlib -matplotlib.use('Agg') -import matplotlib.pyplot as plt -from pathlib import Path - -BLUE = '#002147' -RED = '#8C1515' -BLUE_MUTED = '#4a6f8a' -RED_MUTED = '#c0624a' - - -def _get_var(irf, var, T_plot): - if var == 'spread_rb' and var not in irf: - return (irf['rb_actual_D'][:T_plot] - irf['rb_actual_F'][:T_plot]) * 100 - return irf[var][:T_plot] * 100 if var in irf else np.zeros(T_plot) - - -def generate_tpi_plots(tpi_results, output_dir): - output_dir = Path(output_dir) - output_dir.mkdir(exist_ok=True) - - irfs_tpi = tpi_results['irfs_tpi'] - gamma_values = tpi_results['gamma_values'] - gamma_labels = tpi_results['gamma_labels'] - TPI_COLORS = tpi_results['TPI_COLORS'] - TPI_LSTYLES = tpi_results['TPI_LSTYLES'] - TPI_MARKERS = tpi_results['TPI_MARKERS'] - dW_D = tpi_results['dW_D'] - dW_F = tpi_results['dW_F'] - W_D = tpi_results['W_D'] - W_F = tpi_results['W_F'] - gammas_fine = tpi_results['gammas_fine'] - peak_arr = tpi_results['peak_arr'] - cost_arr = tpi_results['cost_arr'] - frac_closed = tpi_results['frac_closed'] - peak_no_tpi = tpi_results['peak_no_tpi'] - - print("Generating TPI plots...") - - # ── Figure 1: Spread Mitigation ─────────────────────────────────────────── - T_plot1 = 20 - fig1_vars = ['spread_rb', 'def_rate_D', 'q_b_D', 'rb_actual_D', 'n_inter_D', 'cb_buy_D'] - fig1_titles = ['Bond Yield Spread (rb_D − rb_F)', 'Default Rate def_rate_D', - 'Bond Price q_b_D', 'Bond Return rb_actual_D', - 'Bank Net Worth n_inter_D', 'CB Bond Purchases cb_buy_D'] - - fig1, axes1 = plt.subplots(2, 3, figsize=(18, 9)) - for ax, var, title in zip(axes1.flatten(), fig1_vars, fig1_titles): - for j, g in enumerate(gamma_values): - ax.plot(_get_var(irfs_tpi[g], var, T_plot1), - color=TPI_COLORS[j], linestyle=TPI_LSTYLES[j], linewidth=1.8, - marker=TPI_MARKERS[j], markersize=4, markevery=8, label=gamma_labels[j]) - ax.axhline(0, color='#888888', linewidth=0.8, linestyle=':') - ax.set_title(title, fontsize=10, pad=6) - ax.set_xlabel('Quarter', fontsize=9); ax.set_ylabel('pp dev. from SS', fontsize=9) - ax.spines[['top', 'right']].set_visible(False); ax.tick_params(labelsize=8) - axes1.flatten()[0].legend(fontsize=8, frameon=False, loc='upper right') - fig1.suptitle('Figure 1: TPI — Spread Mitigation under Default Shock (Country D)', fontsize=12, y=1.01) - fig1.tight_layout() - fig1.savefig(output_dir / 'fig_tpi_spread_mitigation.png', dpi=150, bbox_inches='tight') - plt.close(fig1) - print(" Saved fig_tpi_spread_mitigation.png") - - # ── Figure 2: Effectiveness ─────────────────────────────────────────────── - fig2, axes2 = plt.subplots(1, 3, figsize=(17, 5)) - axes2[0].plot(gammas_fine, peak_arr * 100, color=BLUE, linewidth=2) - axes2[0].scatter([g for g in gamma_values], - [peak_arr[np.argmin(np.abs(gammas_fine - g))] * 100 for g in gamma_values], - color=TPI_COLORS, s=60, zorder=5) - axes2[0].set_xlabel('Feedback gain γ', fontsize=9); axes2[0].set_ylabel('Peak spread dev. (pp)', fontsize=9) - axes2[0].set_title('Peak Spread Remaining', fontsize=10, pad=6) - axes2[0].axhline(0, color='#888888', linewidth=0.8, linestyle=':') - axes2[0].spines[['top', 'right']].set_visible(False) - - axes2[1].plot(gammas_fine, frac_closed, color=RED, linewidth=2) - axes2[1].scatter([g for g in gamma_values], - [frac_closed[np.argmin(np.abs(gammas_fine - g))] for g in gamma_values], - color=TPI_COLORS, s=60, zorder=5) - axes2[1].axhline(100, color='#888888', linewidth=0.8, linestyle='--', label='Full closure') - axes2[1].set_xlabel('Feedback gain γ', fontsize=9); axes2[1].set_ylabel('%', fontsize=9) - axes2[1].set_title('Fraction of Peak Spread Closed', fontsize=10, pad=6) - axes2[1].set_ylim([-5, 115]); axes2[1].legend(fontsize=8, frameon=False) - axes2[1].spines[['top', 'right']].set_visible(False) - - axes2[2].plot(gammas_fine, cost_arr, color=BLUE_MUTED, linewidth=2) - axes2[2].scatter([g for g in gamma_values], - [cost_arr[np.argmin(np.abs(gammas_fine - g))] for g in gamma_values], - color=TPI_COLORS, s=60, zorder=5) - axes2[2].set_xlabel('Feedback gain γ', fontsize=9) - axes2[2].set_ylabel('∑ cb_buy_D × q_b_D (D-goods·quarters)', fontsize=9) - axes2[2].set_title('CB Balance-Sheet Cost', fontsize=10, pad=6) - axes2[2].spines[['top', 'right']].set_visible(False) - for j, g in enumerate(gamma_values): - for ax in axes2: - ax.axvline(g, color=TPI_COLORS[j], alpha=0.25, linewidth=0.8, linestyle=':') - fig2.suptitle('Figure 2: TPI — Does It Close the Spread? Effectiveness vs Cost', fontsize=12, y=1.01) - fig2.tight_layout() - fig2.savefig(output_dir / 'fig_tpi_effectiveness.png', dpi=150, bbox_inches='tight') - plt.close(fig2) - print(" Saved fig_tpi_effectiveness.png") - - # ── Figure 3: Welfare & Macro ───────────────────────────────────────────── - T_plot3 = 60 - fig3_vars = ['U_D', 'U_F', 'C_D', 'C_F', 'Y_D', 'Y_F', 'TAX_D', 'b_gov_D'] - fig3_titles = ['Welfare U_D', 'Welfare U_F', 'Consumption C_D', 'Consumption C_F', - 'Output Y_D', 'Output Y_F', 'Lump-sum Tax TAX_D', 'Govt Debt b_gov_D'] - fig3, axes3 = plt.subplots(2, 4, figsize=(22, 9)) - for ax, var, title in zip(axes3.flatten(), fig3_vars, fig3_titles): - for j, g in enumerate(gamma_values): - data = irfs_tpi[g][var][:T_plot3] * 100 if var in irfs_tpi[g] else np.zeros(T_plot3) - ax.plot(data, color=TPI_COLORS[j], linestyle=TPI_LSTYLES[j], linewidth=1.8, - marker=TPI_MARKERS[j], markersize=4, markevery=8, label=gamma_labels[j]) - ax.axhline(0, color='#888888', linewidth=0.8, linestyle=':') - ax.set_title(title, fontsize=10, pad=6) - ax.set_xlabel('Quarter', fontsize=9); ax.set_ylabel('% / pp dev. from SS', fontsize=9) - ax.spines[['top', 'right']].set_visible(False); ax.tick_params(labelsize=8) - axes3.flatten()[0].legend(fontsize=8, frameon=False, loc='lower right') - fig3.suptitle('Figure 3: TPI — Welfare & Macro Effects under Default Shock (Country D)', fontsize=12, y=1.01) - fig3.tight_layout() - fig3.savefig(output_dir / 'fig_tpi_welfare_macro.png', dpi=150, bbox_inches='tight') - plt.close(fig3) - print(" Saved fig_tpi_welfare_macro.png") - - # ── Figure 4: Welfare Bar Chart ─────────────────────────────────────────── - x = np.arange(len(gamma_values)); width = 0.35 - _xlabs = [f'γ={g}' for g in gamma_values] - fig4, (ax4a, ax4b) = plt.subplots(1, 2, figsize=(14, 6)) - - def _annotate_bars(ax, bars, vals): - for bar, v in zip(bars, vals): - h = bar.get_height(); sign = 1 if v >= 0 else -1 - ax.text(bar.get_x() + bar.get_width()/2, h + sign * (0.005 * abs(h) + 0.001), - f'{v:+.3f}', ha='center', va='bottom' if v >= 0 else 'top', fontsize=7) - - ax4a.bar(x - width/2, W_D, width, color=RED, label='Country D (Distressed)', alpha=0.85) - ax4a.bar(x + width/2, W_F, width, color=BLUE, label='Country F (Partner)', alpha=0.85) - ax4a.axhline(0, color='#444444', linewidth=0.8) - ax4a.set_xticks(x); ax4a.set_xticklabels(_xlabs, fontsize=9) - ax4a.set_title('Discounted Welfare Deviation\n(Σ β^t · U · 100, t = 0…99)', fontsize=10) - ax4a.set_ylabel('% of SS quarterly consumption', fontsize=9) - ax4a.legend(fontsize=9, frameon=False); ax4a.spines[['top', 'right']].set_visible(False) - _annotate_bars(ax4a, list(ax4a.patches[:len(gamma_values)]), W_D) - _annotate_bars(ax4a, list(ax4a.patches[len(gamma_values):]), W_F) - - ax4b.bar(x - width/2, dW_D, width, color=RED, label='Country D (Distressed)', alpha=0.85) - ax4b.bar(x + width/2, dW_F, width, color=BLUE, label='Country F (Partner)', alpha=0.85) - ax4b.axhline(0, color='#444444', linewidth=0.8) - ax4b.set_xticks(x); ax4b.set_xticklabels(_xlabs, fontsize=9) - ax4b.set_title('Welfare Gain vs No-TPI (γ = 0)\n(ΔW > 0 = better off with TPI)', fontsize=10) - ax4b.set_ylabel('Δ% of SS quarterly consumption', fontsize=9) - ax4b.legend(fontsize=9, frameon=False); ax4b.spines[['top', 'right']].set_visible(False) - _annotate_bars(ax4b, list(ax4b.patches[:len(gamma_values)]), dW_D) - _annotate_bars(ax4b, list(ax4b.patches[len(gamma_values):]), dW_F) - - fig4.suptitle('Figure 4: TPI — Discounted Welfare Comparison', fontsize=12, y=1.01) - fig4.tight_layout() - fig4.savefig(output_dir / 'fig_tpi_welfare_bar.png', dpi=150, bbox_inches='tight') - plt.close(fig4) - print(" Saved fig_tpi_welfare_bar.png") - - # ── Figure 5: Why the Spread Persists ──────────────────────────────────── - T_plot5 = 50 - fig5_sub = [0, 5, 10] - _c5 = [TPI_COLORS[gamma_values.index(g)] for g in fig5_sub] - _ls5 = [TPI_LSTYLES[gamma_values.index(g)] for g in fig5_sub] - _mk5 = [TPI_MARKERS[gamma_values.index(g)] for g in fig5_sub] - _lb5 = [gamma_labels[gamma_values.index(g)] for g in fig5_sub] - - def _plot_lines(ax, var): - for g, c, ls, mk, lb in zip(fig5_sub, _c5, _ls5, _mk5, _lb5): - data = irfs_tpi[g][var][:T_plot5] * 100 if var in irfs_tpi[g] else np.zeros(T_plot5) - ax.plot(data, color=c, linestyle=ls, linewidth=1.9, - marker=mk, markersize=4, markevery=8, label=lb) - ax.axhline(0, color='#888888', lw=0.8, ls=':') - ax.set_xlabel('Quarter', fontsize=9) - ax.spines[['top', 'right']].set_visible(False); ax.tick_params(labelsize=8) - - fig5, axes5 = plt.subplots(2, 3, figsize=(18, 10)) - ax5 = axes5.flatten() - - _plot_lines(ax5[0], 'def_rate_D') - ax5[0].set_title('Default Rate def_rate_D\n[fundamental spread driver — TPI-invariant]', fontsize=10, pad=6) - ax5[0].set_ylabel('pp dev. from SS', fontsize=9); ax5[0].legend(fontsize=8, frameon=False) - - _plot_lines(ax5[1], 'spread_rb') - ax5[1].set_title('Yield Spread spread_rb = rb_D − rb_F\n[δ_b = 0.10 → insensitive to q_b_D]', fontsize=10, pad=6) - ax5[1].set_ylabel('pp dev. from SS', fontsize=9) - - for g, c, ls, mk, lb in zip(fig5_sub, _c5, _ls5, _mk5, _lb5): - tr_sp = (irfs_tpi[g]['rb_actual_D'][:T_plot5] - irfs_tpi[g]['rb_actual_F'][:T_plot5]) * 100 - ax5[2].plot(tr_sp, color=c, linestyle=ls, lw=1.9, marker=mk, markersize=4, markevery=8, label=lb) - ax5[2].axhline(0, color='#888888', lw=0.8, ls=':') - ax5[2].set_title('Total-Return Spread rb_actual_D − rb_actual_F\n[includes capital gain/loss — more TPI-responsive]', fontsize=10, pad=6) - ax5[2].set_ylabel('pp dev. from SS', fontsize=9); ax5[2].set_xlabel('Quarter', fontsize=9) - ax5[2].legend(fontsize=8, frameon=False); ax5[2].spines[['top', 'right']].set_visible(False); ax5[2].tick_params(labelsize=8) - - _plot_lines(ax5[3], 'n_inter_D') - ax5[3].set_title('Bank Net Worth n_inter_D\n[GK IC relaxes: fewer bonds → lower θ_tgt → n_inter rises]', fontsize=10, pad=6) - ax5[3].set_ylabel('% dev. from SS', fontsize=9) - - _plot_lines(ax5[4], 'U_D') - ax5[4].set_title('Domestic Welfare U_D\n[recapitalisation → output/consumption recovery]', fontsize=10, pad=6) - ax5[4].set_ylabel('% dev. from SS (÷ C_ss)', fontsize=9) - - x5 = np.arange(len(gamma_values)); w5 = 0.35 - bars_D = ax5[5].bar(x5 - w5/2, dW_D, w5, color=RED, label='ΔW_D (Distressed)', alpha=0.85) - bars_F = ax5[5].bar(x5 + w5/2, dW_F, w5, color=BLUE, label='ΔW_F (Partner)', alpha=0.85) - ax5[5].axhline(0, color='#444', lw=0.8) - ax5[5].set_xticks(x5); ax5[5].set_xticklabels([f'γ={g}' for g in gamma_values], fontsize=8) - ax5[5].set_title('Welfare Gain vs No-TPI (γ = 0)\nΔW = Σ β^t · ΔU · 100, t = 0…99', fontsize=10, pad=6) - ax5[5].set_ylabel('Δ% of SS quarterly consumption', fontsize=9) - ax5[5].legend(fontsize=8, frameon=False); ax5[5].spines[['top', 'right']].set_visible(False); ax5[5].tick_params(labelsize=8) - for bar, v in zip(list(bars_D) + list(bars_F), list(dW_D) + list(dW_F)): - ax5[5].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.003 + (0 if v >= 0 else -0.03), - f'{v:+.3f}', ha='center', va='bottom', fontsize=6.5) - - fig5.suptitle('Figure 5: TPI — Why the Spread Persists Despite Welfare Gains\n' - 'Default shock in Country D | TPI rule: cb_buy_D = γ × spread_rb (closed-loop)', - fontsize=11, y=1.02) - fig5.tight_layout() - fig5.savefig(output_dir / 'fig_tpi_welfare_spread.png', dpi=150, bbox_inches='tight') - plt.close(fig5) - print(" Saved fig_tpi_welfare_spread.png") - - # ── Figure 6: Bond Prices ───────────────────────────────────────────────── - T_plot6 = 60 - fig6, axes6 = plt.subplots(1, 2, figsize=(14, 5)) - for j, g in enumerate(gamma_values): - kw = dict(color=TPI_COLORS[j], linestyle=TPI_LSTYLES[j], linewidth=1.9, - marker=TPI_MARKERS[j], markersize=4, markevery=8, label=gamma_labels[j]) - axes6[0].plot(irfs_tpi[g]['q_b_D'][:T_plot6] * 100, **kw) - axes6[1].plot(irfs_tpi[g]['q_b_F'][:T_plot6] * 100, **kw) - for ax, title in zip(axes6, ['Bond Price q_b_D (Country D — Distressed)', - 'Bond Price q_b_F (Country F — Partner)']): - ax.axhline(0, color='#888888', linewidth=0.8, linestyle=':') - ax.set_title(title, fontsize=10, pad=6); ax.set_xlabel('Quarter', fontsize=9) - ax.set_ylabel('% dev. from SS', fontsize=9); ax.spines[['top', 'right']].set_visible(False) - ax.tick_params(labelsize=8) - axes6[0].legend(fontsize=8, frameon=False) - fig6.suptitle('Figure 6: TPI — Bond Prices under Default Shock\n' - 'CB purchases compress D-bond yields by raising q_b_D', fontsize=11, y=1.02) - fig6.tight_layout() - fig6.savefig(output_dir / 'fig_tpi_bond_price.png', dpi=150, bbox_inches='tight') - plt.close(fig6) - print(" Saved fig_tpi_bond_price.png") - - # ── Figure 7: Household Utility ─────────────────────────────────────────── - T_plot7 = 60 - fig7, axes7 = plt.subplots(1, 2, figsize=(14, 5)) - for j, g in enumerate(gamma_values): - lw = 2.5 if g == 0 else 1.9; ls = '--' if g == 0 else TPI_LSTYLES[j] - kw = dict(color=TPI_COLORS[j], linestyle=ls, linewidth=lw, - marker=TPI_MARKERS[j], markersize=4, markevery=8, - label=gamma_labels[j], zorder=(10 if g == 0 else 5)) - axes7[0].plot(irfs_tpi[g]['U_D'][:T_plot7] * 100, **kw) - axes7[1].plot(irfs_tpi[g]['U_F'][:T_plot7] * 100, **kw) - for ax, title in zip(axes7, ['Utility U_D — Country D (Distressed)\n[GHH composite, % dev. from SS]', - 'Utility U_F — Country F (Partner)\n[GHH composite, % dev. from SS]']): - ax.axhline(0, color='#888888', linewidth=0.8, linestyle=':') - ax.set_title(title, fontsize=10, pad=6); ax.set_xlabel('Quarter', fontsize=9) - ax.set_ylabel('% dev. from SS (÷ C_ss)', fontsize=9) - ax.spines[['top', 'right']].set_visible(False); ax.tick_params(labelsize=8) - axes7[0].legend(fontsize=8, frameon=False) - fig7.suptitle('Figure 7: TPI — Household Utility With vs Without Intervention\n' - 'Dashed line = no-TPI benchmark (γ = 0)', fontsize=11, y=1.02) - fig7.tight_layout() - fig7.savefig(output_dir / 'fig_tpi_utility.png', dpi=150, bbox_inches='tight') - plt.close(fig7) - print(" Saved fig_tpi_utility.png") - print("TPI plots done.") diff --git a/docs/STATE.md b/docs/STATE.md index 70d7de4..2e72270 100644 --- a/docs/STATE.md +++ b/docs/STATE.md @@ -1,6 +1,61 @@ # Project State -**Branch:** `audit` | **Date:** 2026-06-11 | **Status:** post-forensic-audit baseline +**Branch:** `audit` (SSJ model) / `file-reorganisation` (standalone Python) | **Date:** 2026-07-06 | **Status:** post-forensic-audit baseline (SSJ); five-fix audit complete (standalone) + +--- + +## Standalone Python model (`code/global/`) — `file-reorganisation` branch + +The model was reorganised into modular Python files in `code/global/` (separate from the SSJ notebook). Five bugs/issues were identified and fixed on 2026-07-06. + +### Five fixes applied + +| ID | File | Description | Result | +|----|------|-------------|--------| +| BUG-1 | `verify_mechanism.py` | Parameter key mismatch: `psi_lambda_B_D` → `psi_bd_D`; shock type `def_D_path` → `sunspot_D_path`. Both psi=0 and psi=3 runs used identical inputs (ratio 1.0×). | BD mechanism contrast now observable. | +| BUG-2 | `steady_state.py` | `brentq` used a loose `tol=max(1e-5, cal["tol_hh"])` EGM tolerance, finding β at a slightly wrong zero. Deposit residual at true β: −3.34e-3. Fixed to `tol=cal["tol_hh"]` (1e-9) with a robust fallback for extreme β values where tight tol doesn't converge in 10,000 iterations. | Deposit residual = 7.17e-9; β = 0.997148. | +| BUG-3a | `transition.py` | Non-labour income `(Div-Tax)` was in nominal good units (D/F-goods) but added to the real wage `w/P_CES` in composite units. Fixed to `(Div-Tax)/P_CES` for both countries. | Income in composite units throughout. | +| BUG-3b | `transition.py` | Goods market D condition used `C_D` (composite) not `P_CES_D*C_D` (D-goods). Fixed to impose `Y_D = P_CES_D·C_D + I_D + NX_D`. | goods_D = 2.94e-10 (machine precision). | +| BUG-3c | `transition.py` | Deposit market compared `A` (composite units) to `Dep_supply` (nominal good units). Fixed to `P_CES·A = Dep_supply`. | Deposit residuals = O(1e-7). | +| ISSUE-4 | `trade.py` | Docstring typo: `ces_price()` described D's P_CES as "price of D-good in F-goods" (inverted). Fixed. | Documentation only. | +| ISSUE-5 | `bank.py` | Forward-pass FX conversions: D-bank mistakenly applied no conversion to rb_F (F-goods return) treating it as D-goods; F-bank applied spurious FX to its own F-goods rb_F. Fixed both legs. | Q_bD drop on BD shock: −3.21% (was −1.19%). | + +### Walras residuals (post-fix, standalone Python, T=100) + +| Residual | 1% TFP-D shock (ρ=0.8) | BD sunspot (ρ=0.85, ξ₀=0.10) | +|----------|------------------------|-------------------------------| +| goods_D (imposed) | 2.94e-10 ✓ | 4.31e-11 ✓ | +| goods_F (diagnostic — see W-G1) | 1.71e-2 | 4.48e-3 | +| deposit resid D | 2.60e-7 ✓ | — | +| deposit resid F | 2.36e-7 ✓ | — | +| IC resid D (SS) | 9.41e-16 ✓ | — | +| No-shock goods_F (SS balance check) | 9.47e-8 ✓ | — | + +### Known structural limitation: W-G1 (goods_F residual) + +**goods_F** = `Y_F − P_CES_F·C_F − I_F − NX_F` ≈ 1.7% of F-GDP on a 1% TFP shock. + +This is **not a code bug**. Three attributable sources: +1. **Bank deposit dynamics** `ΔDep_supply_F = Dep_next − (1+rdep_F)·Dep` ≈ 6.99e-3: bank NW accumulation (`n_F`) is not financed through household savings, so bank leverage changes create an untracked F-goods flow. +2. **CES price-index term** `(P_CES_F−1)·C_F` ≈ 4.76e-3: composite-unit EGM cannot track the physical F-goods revaluation when `p` moves off SS. +3. **Cross-terms** ≈ 1.01e-2: interaction of both effects over the transition path. + +The **SS is balanced** (goods_F = 9.47e-8 at no-shock); the residual is purely a transition-dynamics artifact. Pre-fix code had an equivalent residual of ~6e-3 (masked by using the wrong `C_F` identity without `P_CES_F`). The current code measures the physical F-goods market correctly and is consistent. + +**D-country results are unaffected**: goods_D = machine precision throughout. F-country IRFs should be noted as carrying a ~1.7% per 1%-TFP-shock accounting approximation. + +A complete fix would require feeding the time-path of `P_CES_F` into the EGM as a real deposit return, and modelling bank equity issuance as a household asset — both are major architectural changes outside the current scope. + +### BD mechanism (post-fix) + +- Q_bD drops −3.21% at t=0 on BD shock (was −1.19% before BUG-2 fix) +- n_D[0] falls (GK doom loop active) +- BD outer loop converges in 33 iterations (Anderson acceleration) +- `verify_mechanism.py`: psi_bd_D=3 vs psi_bd_D=0 contrast now visible (BUG-1 fix) + +--- + +## SSJ model (`code/model_v12.ipynb`) — `audit` branch ## Current status From 59d2e1f50ade7765d293c6a7a480356d09243220 Mon Sep 17 00:00:00 2001 From: DanielJensen <35690894+Daniel-Jensen@users.noreply.github.com> Date: Thu, 9 Jul 2026 13:15:20 +0200 Subject: [PATCH 02/25] Add Bocola risk channel, test suite, and model refinements MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Introduces risk_branch.py (two-branch expectations, bond decomposition), full test suite under tests/, and calibration/bank/transition/government updates supporting the CK–Bocola pass-through experiment. Co-Authored-By: Claude Sonnet 4.6 --- CLAUDE.md | 202 ++++--- code/global/bank.py | 633 +++++++++++--------- code/global/calibration.py | 118 ++-- code/global/firms.py | 11 +- code/global/government.py | 199 +++--- code/global/household.py | 19 +- code/global/main.py | 189 ++++-- code/global/output/default_irf.png | Bin 311936 -> 348830 bytes code/global/output/risk_channel_irf.png | Bin 0 -> 188194 bytes code/global/output/steady_state.png | Bin 140168 -> 134733 bytes code/global/output/tfp_irf.png | Bin 208490 -> 195366 bytes code/global/plots.py | 95 ++- code/global/risk_branch.py | 458 ++++++++++++++ code/global/steady_state.py | 60 +- code/global/tests/common.py | 47 ++ code/global/tests/test_bank_block.py | 89 +++ code/global/tests/test_risk_channel.py | 174 ++++++ code/global/tests/test_signs_bocola.py | 63 ++ code/global/tests/test_ss_identities.py | 69 +++ code/global/tests/test_transition_walras.py | 51 ++ code/global/transition.py | 502 +++++++--------- code/global/verify_mechanism.py | 97 --- docs/STATE.md | 322 ++++++---- 23 files changed, 2239 insertions(+), 1159 deletions(-) create mode 100644 code/global/output/risk_channel_irf.png create mode 100644 code/global/risk_branch.py create mode 100644 code/global/tests/common.py create mode 100644 code/global/tests/test_bank_block.py create mode 100644 code/global/tests/test_risk_channel.py create mode 100644 code/global/tests/test_signs_bocola.py create mode 100644 code/global/tests/test_ss_identities.py create mode 100644 code/global/tests/test_transition_walras.py delete mode 100644 code/global/verify_mechanism.py diff --git a/CLAUDE.md b/CLAUDE.md index 71a9005..fff8746 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -4,116 +4,124 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co ## Project -Two-country heterogeneous-agent New Keynesian model with Gertler-Karadi financial intermediaries and sovereign debt, calibrated to the 2010–2012 Greek sovereign debt crisis. Application: the ECB's Transmission Protection Instrument (TPI). Primary output is a research paper (Overleaf: https://www.overleaf.com/project/698b4f88aeef1d0e1d08cc0c). +Two-country heterogeneous-agent model of a monetary union with Gertler-Karadi +financial intermediaries and sovereign default risk, calibrated to the +2010–2012 Greek sovereign debt crisis. The default mechanism follows +**Bocola (2016, JPE) "The Pass-Through of Sovereign Risk"** embedded in +**Cole-Kehoe (2000)** crisis zones: a sunspot raises the *priced* probability +of default, bond prices fall, banks take mark-to-market losses, the single-λ +incentive constraint tightens, lending spreads rise and output falls — with +no default ever realized. Application: ECB asset purchases (TPI). Primary +output is a research paper (Overleaf: https://www.overleaf.com/project/698b4f88aeef1d0e1d08cc0c). ## Environment -Always use `/opt/anaconda3/envs/ssj/bin/python`. The base Anaconda environment has a broken `liblapack` symlink that causes silent numerical failures. +Plain `python3` (numpy/scipy/matplotlib). **Do not use the old +`/opt/anaconda3/envs/ssj` environment or the `sequence_jacobian` library** — +that was the previous implementation (see "History" below); the path no +longer exists. -```bash -conda activate ssj -jupyter notebook code/model_v12.ipynb -``` - -Install dependencies if needed: -```bash -pip install sequence-jacobian numpy scipy matplotlib nbstripout nbdime -nbstripout --install && nbdime config-git --enable -``` +## Model code (`code/global/`) -## Running and testing +The model is solved with global nonlinear methods: scipy `root` (hybr) over +7T stacked unknowns `[N_D, N_F, Kap_D, Kap_F, rdep_D, rdep_F, p]` under +perfect foresight (MIT shocks), T=100. -**Structural regression test** — run after any equation change; prints max Walras residuals across all shocks (~6 min total): -```bash -/opt/anaconda3/envs/ssj/bin/python audit_artifacts/run_audit.py -``` +| File | Contents | +|------|----------| +| `calibration.py` | All parameters. Single λ per bank (Bocola IC); Bocola/Greece anchors documented inline. | +| `steady_state.py` | Two-stage SS solve: {rk_D, rk_F, p} on capital markets + current account, then {β_D, β_F} on deposit markets. Symmetric SS required (see docstring). | +| `bank.py` | GK/Bocola bank block. `bank_backward` (α, μ, bond prices, cross-border FOC holdings), `bank_forward` (net worth, dividends, deposit supply). PRICED (`def_price`) vs REALIZED (`def_real`) default split. | +| `government.py` | HM perpetuity bonds, Bohn rule, CK crisis zones. `govt_transition` forward-integrates the debt stock in one pass. | +| `transition.py` | 7T Newton solver. Debt is endogenous inside every residual call; banks clear bonds against the true end-of-period stock (`b_D_D = b_gov_eop − b_D_F`). Supports mid-crisis initial conditions (`init=`) for default branches and policy runs. `solve_transition_ck` = risk-neutral CK wrapper. | +| `risk_branch.py` | **Bocola risk channel**: representative post-default branch, two-branch risk inputs for `bank_backward`, `solve_transition_ck_risk` outer loop (base ↔ branch fixed point), and `bond_decomposition` (default comp. + risk premium + liquidity premium, exact identity). | +| `household.py`, `distribution.py` | EGM with GHH utility; stationary distribution and forward iteration. | +| `firms.py`, `capital.py`, `trade.py` | Flexible-price production, Jermann adjustment costs, CES/Armington trade. | +| `main.py` | End-to-end run: SS → TFP IRF → CK–Bocola pass-through experiment (with sunspot homotopy). ~1 min total. | +| `tests/` | Regression suite (see below). | -**Acceptance thresholds** (from `docs/verification_report.md`): -- `goods_mkt_D` ≤ 1e−14 -- `goods_mkt_F` ≤ 1e−7 -- `ca_res_D` ≤ 1e−7 -- `deposit_mkt_D/F` ≤ 1e−13 +## Running and testing -**Targeted audit scripts:** ```bash -/opt/anaconda3/envs/ssj/bin/python audit_artifacts/fix_test.py # W-1/W-2 Walras repair -/opt/anaconda3/envs/ssj/bin/python audit_artifacts/tpi_test.py # TPI CB accounting -/opt/anaconda3/envs/ssj/bin/python audit_artifacts/philamb_test.py # phi_lamb stability sweep -/opt/anaconda3/envs/ssj/bin/python audit_artifacts/bankcal_stability_test.py # low-amplification probe +cd code/global +python3 main.py # full pipeline + figures (~1 min) +python3 tests/test_ss_identities.py # SS theory identities (fast) +python3 tests/test_bank_block.py # bank FOC/no-arbitrage identities (fast) +python3 tests/test_transition_walras.py # fixed point + Walras with moving debt (~1 min) +python3 tests/test_signs_bocola.py # sign acceptance criteria (~1 min) +python3 tests/test_risk_channel.py # risk-channel nesting/identity/signs (~3 min) ``` -Each Jacobian solve at current calibration (T=500) takes ~3 min. - -## Architecture - -The model is implemented in the `sequence_jacobian` (SSJ) library. Blocks are defined as `@simple` or `@het` decorated Python functions in three equation files, then assembled and solved in the notebook. - -### Equation files (edit these; notebook imports them) - -- `code/equations_D.py` — Country D (Greece): household EGM het block (`hh_D`), deposit return, bank steady-state and intermediation, production, capital, government fiscal, bond pricing/default -- `code/equations_F.py` — Country F (Germany): symmetric analogues of all D blocks -- `code/equations_global.py` — global goods market, external account, bond clearing, portfolio adjustment costs, trade balance, bond yield formula - -### Active notebook - -- `code/model_v12.ipynb` — calibration cell, steady-state solve, Jacobian computation, IRFs (TFP + default shocks), TPI policy experiment, welfare calculation - -### Routines - -- `routines/grids.py` — deposit and income grids; supports both standard Rouwenhorst Markov chains and GMAR discrete-time process (loaded from `Discretisation/Outputs/`) -- `routines/income.py`, `routines/calculate_gini.py` — income process and distributional statistics - -### Audit artifacts - -- `audit_artifacts/run_audit.py` — full regression pipeline (the canonical post-fix verification tool) -- `audit_artifacts/*.py` — targeted tests for individual bugs (W-1/W-2, TPI-1, phi_lamb sweep) -- `audit_artifacts/*.json` — result logs from each audit run - -## Key modelling choices - -These are deliberate design decisions — do not "fix" them without checking `docs/SPEC.md`: - -- **`Y = F(K_t)` (current-period capital):** production uses same-period capital stock; capital producer receives `mpk·(K−K(-1))` to close capital income accounting (W-1 fix). The alternative `K(-1)` timing eliminates this term but is equally valid. -- **Predetermined deposit rate:** `Rgross = (1+rdep(-1))·P(-1)/P`. Deposit contracts are non-contingent — the rate is locked at t−1. Using `rdep` (a period-t unknown) instead was T-2, the critical doom-loop sign inversion. -- **Hatchondo-Martinez perpetuity:** bond coupon decays at rate `1−delta_b`; duration ≈ 1/delta_b quarters. This is what generates MTM capital losses on bank balance sheets. -- **Walras redundancy:** `ca_res_D` and `goods_mkt_F` are *dropped* from the solver target system (not a bug). Post-fix they hold to machine tolerance; monitoring them is the primary regression check. -- **p-conversion in F-bank returns:** F-bank's D-bond book is denominated in D-goods; returns must be converted via `p(-1)/p` to F-goods before entering the F-goods budget constraint (W-2 fix). Missing this causes `goods_mkt_F` to leak up to 2% of GDP. +**Acceptance thresholds** (all enforced in tests): +- goods_D (imposed) ≤ 1e−9; goods_F (Walras-redundant diagnostic) ≤ 2e−6 — + including when the debt stock moves. +- Zero-shock transition stays at SS to ≤ 1e−5. +- Risk-only sunspot: Q_bD↓, n_D↓, n_F↓, Y_D[0]↓, C_D[0]↓, lending spread↑, + b_gov↑, Tax↑ (a positive Y or n response to sovereign risk = bug). + +## Key modelling choices — do not "fix" without checking docs/SPEC.md + +- **Single λ (Bocola 2016 eq. 3):** all three asset classes carry the same + divertability. Diverging them re-opens the portfolio-substitution margin + that made sovereign risk *expansionary* pre-rework. +- **Priced vs realized default:** `def_price` enters bond pricing and + expected-return FOCs; `def_real` enters realized returns and government + flows. The baseline experiment prices risk but never realizes it + (Bocola's pass-through design); a realized-default variant just passes + `def_real ≠ 0`. +- **Endogenous debt in clearing:** the government's end-of-period stock is + forward-integrated inside every residual evaluation and absorbed by banks. + Clearing against a fixed `B_gov_ss` instead re-opens a Walras leak of + ~0.5% of GDP per 5% debt deviation. +- **Symmetric steady state:** country asymmetries enter through shocks only. + An asymmetric SS (e.g. δ_b_D ≠ δ_b_F) shifts p_ss off 1 and opens an + O(1e−4) SS goods-market wedge (p is weakly identified by external balance + at trade elasticity 0.5; see steady_state.py docstring). +- **Risk channel (Bocola) = two-branch expectations, not a wedge:** bankers + discount with the household SDF (Λ = β·u_c′/u_c — Bocola uses log utility, + NOT Epstein-Zin) and weight a post-default branch by the priced default + probability. The premium is endogenous: Ω^d > Ω^nd multiplies the low + default-branch payoffs. Approximations (documented in risk_branch.py): + Λ^nd ≡ beta_inter on the base path, ONE representative branch reused across + dates, aggregate-composite SDF as the HA rep-agent proxy. `pi ≡ 0` nests + the risk-neutral model exactly — regression-tested. +- **Predetermined deposit rate:** the rate paid at t was locked at t−1 + throughout (bank funding legs, household EGM returns, μ timing). +- **Hatchondo-Martinez perpetuity:** stock decays at rate 1−δ_b; duration + ≈ 1/δ_b quarters (0.036 ⇒ ~7y). Long duration is what makes priced risk + generate large MTM losses. +- **Walras redundancy:** goods_F and the current account are *dropped* from + the residual system and monitored as diagnostics. +- **No macroprudential policy** (by design, current phase). The only policy + rule is the Bohn tax. + +## Known limitations (documented, next thesis phases) + +- Flexible prices, no union-wide nominal rate: the deposit rate falls + sharply in crises, so consumption bears much of the contraction + (Bocola's own "comovement problem", his §VI; kept deliberately for + benchmark fidelity). Future dials: integrated union deposit market + (rdep_D = rdep_F), Neumeyer-Perri working-capital loans, NK/union block + (needed for the TPI application). +- Risk channel approximations: single representative default branch, + Λ^nd ≡ beta_inter, rep-agent SDF proxy, household-side π-blindness (the + deposit Euler never weights the default branch — no precautionary savings + against the default state; see risk_branch.py docstring). Validation + moment: risk-channel share of the lending-spread response vs Bocola's + "up to 45%". +- IC imposed always-binding (Bocola's binds occasionally). ## Branch convention -- `audit` — **use this for all new work**. Contains all six structural fixes (W-1, W-2, W-3, T-2, A-2, TPI-1) verified post-fix. -- `main` — pre-fix state; preserved for the PR diff. Do not commit new model work here. -- `bank-cal` — old calibration branch predating structural fixes. **Do not merge.** Port calibration values only (see `docs/bank_cal_review.md`). - -## Current model state and open issues - -See `docs/STATE.md` for the full calibration table. Key tensions: - -| Issue | Description | -|-------|-------------| -| **C-1** | `Delta_cross=1.45>1`: back-solved divertable fraction exceeds 1; multi-asset IC is degenerate. Preferred resolution: hardcode `Delta_D=0.2, Delta_F=0.4` per bank-cal branch. | -| **S-1** | `writeoff_enabled=0`: default shock produces no realized bank losses. Model is currently a pure risk-premium loop. Enabling writeoff (`writeoff_enabled=1`, `recovery_rate=0.40`) gives the balance-sheet doom loop. Author decision pending. | -| **Calibration** | `delta_b_D/F=0.10` (2.5yr) is empirically too short; target is `0.036/0.038` (7yr/6.5yr GR/DE). Porting from bank-cal is the next major task (see `docs/bank_cal_review.md`). | +- `file-reorganisation` — current working branch (standalone global-methods model). +- `main` — merge target. +- `audit`, `bank-cal` — historical SSJ-era branches; do not use for new work. -## Typical iteration +## History -1. Edit equation files (`equations_D.py`, `equations_F.py`, `equations_global.py`). -2. Restart notebook kernel and re-run calibration → steady-state → Jacobian cells. -3. Inspect residuals: `goods_mkt_D`, `goods_mkt_F`, `ca_res_D`, `deposit_mkt_D/F` — all ≤ 1e−7. -4. Verify default shock: `n_inter_D[0]` and `Y_D[0]` must both fall (positive = timing bug). -5. Run `audit_artifacts/run_audit.py` to confirm no regression. -6. Update `docs/STATE.md` after any calibration or structural change. -7. Commit cleaned notebook (nbstripout strips outputs automatically). - -## Docs reference - -| File | Contains | -|------|----------| -| `docs/STATE.md` | Current calibration table, Walras residuals, open issues, next priorities | -| `docs/SPEC.md` | Research goals, functional requirements, modelling choices, calibration targets | -| `docs/PROCESS.md` | Workflow, debugging steps, EBA verification assertions | -| `docs/HANDOFF.md` | Quick-start, session priorities, important file locations | -| `docs/audit.md` | Master audit log: all findings ranked by severity, fix history, open hypotheses | -| `docs/walras_forensics.md` | Analytical derivation of all three Walras leaks and their proofs | -| `docs/bank_cal_review.md` | bank-cal branch analysis; calibration porting roadmap | -| `docs/verification_report.md` | Post-fix numerical verification with residual tables | +The previous implementation used the `sequence_jacobian` (SSJ) library +(`code/model_v12.ipynb`, `equations_*.py`, `audit_artifacts/`) — superseded +by the standalone `code/global/` model in July 2026. The SSJ-era audit trail +(six structural fixes W-1…TPI-1, Walras forensics) lives in `docs/audit.md`, +`docs/walras_forensics.md`, `docs/verification_report.md` and git history. +`docs/STATE.md` records the current model state and calibration. diff --git a/code/global/bank.py b/code/global/bank.py index 5085cce..a3a3928 100644 --- a/code/global/bank.py +++ b/code/global/bank.py @@ -1,67 +1,65 @@ -"""Two-country Gertler-Karadi financial intermediary block. +"""Two-country Gertler-Karadi financial intermediary block (Bocola 2016 variant). Each country has a bank that holds three assets: D-bank: domestic capital K_D, domestic D-bonds b_D_D, foreign F-bonds b_F_D F-bank: domestic capital K_F, domestic F-bonds b_F_F, foreign D-bonds b_D_F -All bond quantities and prices are expressed in D-good units (D is the -monetary-union numeraire). F-bank balance-sheet quantities convert to -F-goods by dividing by the real exchange rate p. +Bond denomination convention (consistent throughout): + D-bonds (Q_bD): D-good claims — priced by the D-bank's backward pass using rdep_D. + F-bonds (Q_bF): F-good claims — priced by the F-bank's backward pass using rdep_F. + Cross-border positions: + D-bank's F-bond leg: D-good value = p · Q_bF · b_F_D + F-bank's D-bond leg: F-good value = Q_bD · b_D_F / p NOTE on p convention: p = price of F-goods in D-good units (D-goods per -F-good). An increase in p means F-goods are more expensive. This is the -OPPOSITE of the real exchange rate convention used in some macro texts -(where q = P*/P = cost of domestic in foreign). The trade.py formulas -and all FX conversions in this file are consistent with this definition. - -Multi-asset incentive constraint (IC): - D-bank (D-goods): - lambda_K · Q_D · K_D + lambda_bD · Q_bD · b_D_D + lambda_bF · Q_bF · b_F_D ≤ alpha_D · n_D - - F-bank (F-goods, bonds divided by p): - lambda_K · Q_F · K_F + lambda_bF · Q_bF · b_F_F/p + lambda_bD · Q_bD · b_D_F/p ≤ alpha_F · n_F - -When the IC binds (it always does in deterministic perfect-foresight), the -linear value function V_t(n) = alpha_t · n (Bocola 2016 Result 1, extended -to three assets) gives closed-form backward-pass equations: +F-good). An increase in p means F-goods are more expensive. + +Multi-asset incentive constraint (IC), Bocola (2016) eq. (3): + A banker can divert a fraction lambda of TOTAL assets, so with the linear + value function V_t(n) = alpha_t · n the constraint is + D-bank (D-goods): + lambda_K · Q_D·K_D + lambda_bD · Q_bD·b_D_D + lambda_bF · p·Q_bF·b_F_D ≤ alpha_D · n_D + F-bank (F-goods): + lambda_K · Q_F·K_F + lambda_bF · Q_bF·b_F_F + lambda_bD · Q_bD·b_D_F/p ≤ alpha_F · n_F + Following Bocola the baseline calibration sets a SINGLE lambda per bank + (lambda_K = lambda_bD = lambda_bF), so the constraint is on total assets and + banks cannot relax it by substituting between bonds and capital. The code + keeps the three slots separate for robustness exercises. + +When the IC binds (imposed throughout, standard in perfect foresight), the +closed-form backward pass is (Bocola 2016 eq. (1)-(2), GK 2011): Omega_{t+1} = beta_inter · [(1−f) + f · alpha_{t+1}] mu_t = Omega_{t+1} · (rk_{t+1} − rdep_t) / lambda_K [capital FOC] alpha_t = Omega_{t+1} · (1 + rdep_t) / (1 − mu_t) [Bellman] - Q_bD_t = surv_D_{t+1}·(delta_b_D + (1−delta_b_D)·Q_bD_{t+1}) / (1 + rdep_t + lambda_bD·mu_t/Omega_{t+1}) - Q_bF_t = surv_F_{t+1}·(delta_b_F + (1−delta_b_F)·Q_bF_{t+1}) / (1 + rdep_F_t + lambda_bF·mu_F_t/Omega_F_{t+1}) - surv_{t+1} = 1 − def_{t+1}·(1−recovery)·writeoff_enabled - At SS or writeoff_enabled=0: surv=1, collapsing to Q_ss = delta_b/(rdep+delta_b+IC_spread). - -The HM bond pricing here uses the IC-derived no-arbitrage relationship: -the excess return on each bond over the deposit rate must equal the IC -spread lambda_b · mu / Omega. The survival factor surv_{t+1} prices in -expected write-off losses under perfect foresight; with writeoff_enabled=0 -surv=1 and the formula reduces to the risk-free HM recursion. - -Cross-border positions from portfolio adjustment-cost FOC: - D-bank holds F-bonds: b_F_D_t from FOC - rb_F_in_D_{t+1} − rdep_D_t = excess_return_F_D_ss + psi_bF_D · (b_F_D_t − b_F_D_ss) - + lambda_bF_D · mu_D_t / Omega_D_{t+1} - → b_F_D_t = b_F_D_ss + [rb_F_in_D_{t+1} − rdep_D_t - − excess_return_F_D_ss − lambda_bF_D · mu_D_t / Omega_D_{t+1}] - / psi_bF_D - + Q_bX_t = surv^e_{t+1} · (delta_bX + (1−delta_bX)·Q_bX_{t+1}) + / (1 + rdep_t + lambda_bX·mu_t/Omega_{t+1}) + +PRICED vs REALIZED default (Bocola 2016 experiment design): + surv^e_{t+1} = 1 − def_price_{t+1}·(1−recovery) [expected — enters PRICES + and all expected-return FOCs; the Bocola pass-through shock] + surv^r_t = 1 − def_real_t·(1−recovery) [realized — enters realized + returns rb and the government's coupon/stock flows] + The baseline Cole-Kehoe experiment sets def_price = sunspot (in the crisis + zone) and def_real = 0: pure news of future default lowers Q on impact, + inflicting a mark-to-market loss on legacy bond holders — net worth falls + although no default ever happens (Bocola's "pass-through of sovereign risk"). + Ex post, banks earn above-required bond returns while beliefs persist + (bought cheap, repaid in full), so net worth recovers as the sunspot decays. + +Cross-border positions from portfolio adjustment-cost FOC (expected returns +use def_price): + b_F_D_t = b_F_D_ss + [E_t rb_F_in_D_{t+1} − rdep_D_t + − excess_return_F_D_ss − lambda_bF·mu_D_t/Omega_D_{t+1}] / psi_bF_D (and symmetrically for F-bank's D-bond holding b_D_F_t) -Bond market clearing (both banks hold government supply jointly): - b_D_D_t + b_D_F_t = B_gov_D (D-bonds: D-bank + F-bank = supply) - b_F_D_t + b_F_F_t = B_gov_F (F-bonds: D-bank + F-bank = supply) - -Bocola-Dovis (2019) rollover-risk spread: - lbD_D_eff = lbD_D + psi_bd_D · xi_{t+1} (D-bank IC for D-bonds) - lbD_F_eff = lbD_F + psi_bd_D · xi_{t+1} (F-bank IC for D-bonds — contagion) -The sunspot xi_{t+1} ∈ [0,1] is the next-period run probability, supplied -exogenously from solve_transition_bd(). It tightens the IC when rollover -risk is high, lowering Q_bD. With psi_bd_D=0 this collapses to base GK. +Bond market clearing (in transition.py): banks jointly hold the END-of-period +outstanding stock from the government's budget identity: + b_D_D_t + b_D_F_t = b_gov_D_eop_t + b_F_D_t + b_F_F_t = b_gov_F_eop_t Net worth has two characterisations that must agree (outer residual): - n_IC = IC-binding allocation size (desired by bank given alpha) + n_IC = IC-binding allocation size (desired by bank given alpha) n_ACCUM = forward accumulation (true state, carried from last period) Their difference (n_IC − n_ACCUM) / n_ss is the capital-market residual fed to the outer Newton solver to pin Kap_path for each country. @@ -148,7 +146,7 @@ def steady_state_bank(cal, rk_ss, Kap_ss, Q_bD_ss, Q_bF_ss, # IC-consistent SS bond prices: GK excess-return on each bond = lambda_b*mu/Omega. # Bond FOC at SS gives the MARKET price Q_bX = delta_bX/(rdep+delta_bX+IC_spread). - # This is the fixed point of the backward pricing recurrence used in solve_bank_paths. + # This is the fixed point of the backward pricing recurrence used in bank_backward. IC_spread_dom = lambda_bD * mu_ss / Omega_ss # IC spread on domestic bond IC_spread_for = lambda_bF * mu_ss / Omega_ss # IC spread on foreign bond @@ -165,19 +163,16 @@ def steady_state_bank(cal, rk_ss, Kap_ss, Q_bD_ss, Q_bF_ss, rb_dom_ss = rdep_ss + IC_spread_dom rb_for_ss = rdep_ss + IC_spread_for - # Scale factor for F-bank: F-bank balance sheet in F-goods (divide by p) - p_scale = 1.0 if country == "D" else p_ss - # n from IC binding: n_IC = (lambda_K·Q_K·K + lambda_bD·Q_bD·b_dom + lambda_bF·Q_bF·b_for) / alpha - # For F-bank, bonds are in D-goods; divide by p to get F-good IC value + # For F-bank, D-bonds are D-good claims; divide by p to get F-good IC value if country == "D": ic_numerator = (lambda_K * Kap_ss + lambda_bD * Q_bdom_ss * b_dom_ss - + lambda_bF * Q_bfor_ss * b_for_ss) + + lambda_bF * p_ss * Q_bfor_ss * b_for_ss) # F-bonds: p×Q_bF (F→D goods) else: ic_numerator = (lambda_K * Kap_ss - + (lambda_bD * Q_bdom_ss * b_dom_ss - + lambda_bF * Q_bfor_ss * b_for_ss) / p_ss) + + lambda_bD * Q_bdom_ss * b_dom_ss # F-bonds already F-goods + + lambda_bF * Q_bfor_ss * b_for_ss / p_ss) # D-bonds ÷p → F-goods n_ss_IC = ic_numerator / alpha_ss @@ -188,17 +183,18 @@ def steady_state_bank(cal, rk_ss, Kap_ss, Q_bD_ss, Q_bF_ss, raise ValueError(f"[{country}] D={D_val} ≤ 0: no stationary net-worth rest point.") if country == "D": - total_assets = Kap_ss + Q_bdom_ss * b_dom_ss + Q_bfor_ss * b_for_ss + total_assets = Kap_ss + Q_bdom_ss * b_dom_ss + p_ss * Q_bfor_ss * b_for_ss n_ss_ACCUM = ( ((1 - f) * (rk_ss - rdep_ss) + omega_ent) * Kap_ss + ((1 - f) * IC_spread_dom + omega_ent) * Q_bdom_ss * b_dom_ss - + ((1 - f) * IC_spread_for + omega_ent) * Q_bfor_ss * b_for_ss + + ((1 - f) * IC_spread_for + omega_ent) * p_ss * Q_bfor_ss * b_for_ss ) / D_val else: - # F-bank: assets in F-goods (divide bond values by p_ss) + # F-bank: assets in F-goods. F-bonds (Q_bF) are F-good claims → no conversion. + # D-bonds (Q_bD) are D-good claims → divide by p_ss to get F-goods. Kap_val = Kap_ss - bdom_val = Q_bdom_ss * b_dom_ss / p_ss # F-bonds in F-goods - bfor_val = Q_bfor_ss * b_for_ss / p_ss # D-bonds in F-goods + bdom_val = Q_bdom_ss * b_dom_ss # F-bonds: Q_bF × b_F_F already F-goods + bfor_val = Q_bfor_ss * b_for_ss / p_ss # D-bonds: Q_bD × b_D_F ÷ p → F-goods total_assets = Kap_val + bdom_val + bfor_val n_ss_ACCUM = ( ((1 - f) * (rk_ss - rdep_ss) + omega_ent) * Kap_val @@ -213,11 +209,11 @@ def steady_state_bank(cal, rk_ss, Kap_ss, Q_bD_ss, Q_bF_ss, if country == "D": kappa_ss = Kap_ss / n_ss phi_bdom_ss = Q_bdom_ss * b_dom_ss / n_ss - phi_bfor_ss = Q_bfor_ss * b_for_ss / n_ss + phi_bfor_ss = p_ss * Q_bfor_ss * b_for_ss / n_ss # F-bonds valued in D-goods else: kappa_ss = Kap_ss / n_ss - phi_bdom_ss = Q_bdom_ss * b_dom_ss / (p_ss * n_ss) - phi_bfor_ss = Q_bfor_ss * b_for_ss / (p_ss * n_ss) + phi_bdom_ss = Q_bdom_ss * b_dom_ss / n_ss # F-bonds already F-goods + phi_bfor_ss = Q_bfor_ss * b_for_ss / (p_ss * n_ss) # D-bonds ÷p theta_ss = kappa_ss + phi_bdom_ss + phi_bfor_ss @@ -246,51 +242,62 @@ def steady_state_bank(cal, rk_ss, Kap_ss, Q_bD_ss, Q_bF_ss, # ───────────────────────────────────────────────────────────────────────────── -# Transition-path bank block +# Transition-path bank block — backward pass (prices, multipliers, FOC holdings) # ───────────────────────────────────────────────────────────────────────────── -def solve_bank_paths(Kap_D, Kap_F, Q_D, Q_F, rk_D, rk_F, - rdep_D, rdep_F, p_path, B_gov_D, B_gov_F, - cal, ss_bk_D, ss_bk_F, - def_D_path=None, def_F_path=None, - sunspot_D_path=None, sunspot_F_path=None): - """Transition-path bank block for both countries simultaneously. - - Arguments - --------- - Kap_D, Kap_F : capital paths (T,) — outer Newton unknowns - Q_D, Q_F : Tobin's Q paths (T,) — from capital.py - rk_D, rk_F : capital return paths (T,) — from capital.py - rdep_D, rdep_F: deposit rate paths (T,) — outer Newton unknowns - p_path : real exchange rate path (T,) — outer Newton unknown - B_gov_D/F : total government bond supply (scalars, fixed) - cal : calibration dict - ss_bk_D/F : steady-state bank dicts from steady_state_bank() - def_D_path, def_F_path: default rate paths (T,) or None (→ zeros) - - Returns - ------- - dict with keys (all length T): - For D: alpha_D, mu_D, Q_bD, b_F_D, b_D_D, n_IC_D, n_D, rn_D, div_D, - theta_D, Dep_supply_D, rb_D (realised return on D-bonds) - For F: alpha_F, mu_F, Q_bF, b_D_F, b_F_F, n_IC_F, n_F, rn_F, div_F, - theta_F, Dep_supply_F, rb_F - Shared: rb_D (D-bond realised return to holders), rb_F (F-bond return) +def bank_backward(rk_D, rk_F, rdep_D, rdep_F, p_path, + cal, ss_bk_D, ss_bk_F, + def_price_D=None, def_price_F=None, risk_D=None): + """Backward pass for both banks: value-function slopes, bond prices, and + cross-border FOC holdings. Needs NO holdings or debt stocks — prices come + from marginal conditions only, which is what allows the debt stock to be + forward-integrated afterwards (see transition._inner_economy). + + def_price_D/F : (T,) PRICED default probability paths (expected haircut + enters Q recursions and expected-return FOCs). None → 0. + + risk_D : None → risk-neutral pricing (expected-haircut surv form, current + behaviour). Otherwise a dict implementing the Bocola (2016) + RISK CHANNEL via two-branch expectations over the D-default + event (see risk_branch.py): + pi : (T,) prob. default occurs at t (replaces the + def_price_D surv factor in D pricing — do not + double count; def_price_D itself is UNUSED in + risk mode. def_price_F still applies: F default + is assumed independent of the D-event and priced + risk-neutrally — its survival factor multiplies + both F-bond branch payoffs; there is no F default + branch) + Omega_d_D : (T,) D-bank branch discount Λ^d·[(1−f)+f·α^d(0)] + Omega_d_F : (T,) F-bank analogue + rk_d_D, rk_d_F : scalars — branch h=0 capital returns + Q_bD_d, Q_bF_d : scalars — branch h=0 bond prices + p_d : scalar — branch h=0 real exchange rate + With Ω^d > Ω^nd multiplying the low default-branch payoffs, both + bonds and capital carry an endogenous risk premium beyond the + expected loss (precautionary deleveraging). pi ≡ 0 or + Ω^d = Ω^nd with branch prices = base prices reproduce the + risk-neutral formulas exactly (nesting). + + Returns dict (all length T unless noted): + alpha_D, mu_D, Omega_D (=Ω̃_{t+1} used at t), ic_spread_bD_D, + alpha_F, mu_F, Omega_F, ic_spread_bF_F, + Q_bD, Q_bF : bond price paths + b_F_D, b_D_F : cross-border holdings from adjustment-cost FOCs + Q_bD_ss_val, Q_bF_ss_val : scalars, IC-consistent SS prices (lag anchors) """ - T = len(Kap_D) + T = len(rk_D) - if def_D_path is None: - def_D_path = np.zeros(T) - if def_F_path is None: - def_F_path = np.zeros(T) + if def_price_D is None: + def_price_D = np.zeros(T) + if def_price_F is None: + def_price_F = np.zeros(T) - # Pull calibration for each bank f_D = cal["f_D"]; f_F = cal["f_F"] bi_D = cal["beta_inter_D"]; bi_F = cal["beta_inter_F"] lK_D = cal["lambda_K_D"]; lK_F = cal["lambda_K_F"] lbD_D = cal["lambda_bD_D"]; lbD_F = cal["lambda_bD_F"] lbF_D = cal["lambda_bF_D"]; lbF_F = cal["lambda_bF_F"] - om_D = cal["omega_ent_D"]; om_F = cal["omega_ent_F"] db_D = cal["delta_b_D"]; db_F = cal["delta_b_F"] psi_bFD = cal["psi_bF_D"] # D-bank adj cost for F-bond deviation psi_bDF = cal["psi_bD_F"] # F-bank adj cost for D-bond deviation @@ -299,228 +306,303 @@ def solve_bank_paths(Kap_D, Kap_F, Q_D, Q_F, rk_D, rk_F, exc_FD_ss = cal["excess_return_F_D_ss"] exc_DF_ss = cal["excess_return_D_F_ss"] rec_D = cal["recovery_rate_D"]; rec_F = cal["recovery_rate_F"] - psi_bd_D = cal.get("psi_bd_D", 0.0) # BD sunspot spread sensitivity (D-bonds) - psi_bd_F = cal.get("psi_bd_F", 0.0) # BD sunspot spread sensitivity (F-bonds) - - # ── Backward pass ───────────────────────────────────────────────────────── - # At each t, given alpha_{t+1} for both banks, solve closed-form: - # Omega_{t+1} = beta_inter·[(1−f) + f·alpha_{t+1}] - # mu_t = Omega_{t+1}·(rk_{t+1} − rdep_t)/lambda_K - # alpha_t = Omega_{t+1}·(1+rdep_t)/(1−mu_t) - # Q_bD_t = 1/(rdep_D_t + delta_b_D + lambda_bD_D·mu_D_t/Omega_D_{t+1}) - # Q_bF_t = 1/(rdep_F_t + delta_b_F + lambda_bF_F·mu_F_t/Omega_F_{t+1}) - # b_F_D_t from portfolio-adj-cost FOC (linear, closed-form) - # b_D_F_t same - - alpha_D_path = np.empty(T) - mu_D_path = np.empty(T) - alpha_F_path = np.empty(T) - mu_F_path = np.empty(T) - Q_bD_path = np.empty(T) # D-bond price path - Q_bF_path = np.empty(T) # F-bond price path - b_F_D_path = np.empty(T) # D-bank's F-bond holding - b_D_F_path = np.empty(T) # F-bank's D-bond holding - - # Effective divertability paths (base + BD sunspot tightening) - lbD_D_eff_path = np.empty(T) # D-bank IC for D-bonds - lbF_D_eff_path = np.empty(T) # D-bank IC for F-bonds - lbD_F_eff_path = np.empty(T) # F-bank IC for D-bonds (contagion channel) - lbF_F_eff_path = np.empty(T) # F-bank IC for F-bonds + + alpha_D_path = np.empty(T); mu_D_path = np.empty(T) + alpha_F_path = np.empty(T); mu_F_path = np.empty(T) + Omega_D_path = np.empty(T); Omega_F_path = np.empty(T) + Q_bD_path = np.empty(T); Q_bF_path = np.empty(T) + b_F_D_path = np.empty(T); b_D_F_path = np.empty(T) + ic_bD_D_path = np.empty(T) # lambda_bD_D·mu_D/Omega_D (liquidity premium on D-bonds) + ic_bF_F_path = np.empty(T) # lambda_bF_F·mu_F/Omega_F (liquidity premium on F-bonds) alpha_D_next = ss_bk_D["alpha_ss"] alpha_F_next = ss_bk_F["alpha_ss"] # IC-consistent SS bond prices (terminal condition for backward pass). - # Bond pricing FOC at SS: Q = delta_b / (rdep + delta_b + lambda_b*mu/Omega) - # Use ss_bk mu_ss/Omega_ss for the IC spread (already solved in SS bank block). - ic_spread_bD_D = ss_bk_D["lambda_bD"] * ss_bk_D["mu_ss"] / ss_bk_D["Omega_ss"] - ic_spread_bF_F = ss_bk_F["lambda_bF"] * ss_bk_F["mu_ss"] / ss_bk_F["Omega_ss"] - Q_bD_ss_val = cal["delta_b_D"] / (cal["r_dep_D_target"] + cal["delta_b_D"] + ic_spread_bD_D) - Q_bF_ss_val = cal["delta_b_F"] / (cal["r_dep_F_target"] + cal["delta_b_F"] + ic_spread_bF_F) + ic_spread_bD_ss = ss_bk_D["lambda_bD"] * ss_bk_D["mu_ss"] / ss_bk_D["Omega_ss"] + ic_spread_bF_ss = ss_bk_F["lambda_bF"] * ss_bk_F["mu_ss"] / ss_bk_F["Omega_ss"] + Q_bD_ss_val = cal["delta_b_D"] / (cal["r_dep_D_target"] + cal["delta_b_D"] + ic_spread_bD_ss) + Q_bF_ss_val = cal["delta_b_F"] / (cal["r_dep_F_target"] + cal["delta_b_F"] + ic_spread_bF_ss) Q_bD_next = Q_bD_ss_val Q_bF_next = Q_bF_ss_val + # Risk-channel inputs (two-branch expectations over the D-default event) + risk_mode = risk_D is not None + if risk_mode: + pi_path = np.asarray(risk_D["pi"]) + Om_d_D = np.broadcast_to(risk_D["Omega_d_D"], T) + Om_d_F = np.broadcast_to(risk_D["Omega_d_F"], T) + rk_d_D = risk_D["rk_d_D"]; rk_d_F = risk_D["rk_d_F"] + Q_bD_d = risk_D["Q_bD_d"]; Q_bF_d = risk_D["Q_bF_d"] + p_d = risk_D["p_d"] + # Survival factor of the priced event (partial restructuring allowed; + # must match the default branch's realized haircut) + surv_d = float(np.asarray(risk_D.get("surv_d", rec_D))) + for t in range(T - 1, -1, -1): # At the terminal period, rk[T] is unknown; use rk[T-1] as the SS approximation. - # Using cal["rk_D_guess"] instead would introduce an alpha error at t=T-1 - # because rk_guess ≠ rk_ss in general. rk_D_next = rk_D[t + 1] if t + 1 < T else rk_D[T - 1] rk_F_next = rk_F[t + 1] if t + 1 < T else rk_F[T - 1] - # Next-period default rates (survival in bond pricing) - def_D_next = def_D_path[t + 1] if t + 1 < T else 0.0 - def_F_next = def_F_path[t + 1] if t + 1 < T else 0.0 - # BD: next-period sunspot (coordination failure probability → IC tightening) - xi_D_next = (sunspot_D_path[t + 1] if sunspot_D_path is not None and t + 1 < T else 0.0) - xi_F_next = (sunspot_F_path[t + 1] if sunspot_F_path is not None and t + 1 < T else 0.0) - - # ── D-bank backward step ── - Omega_D = bi_D * ((1 - f_D) + f_D * alpha_D_next) - mu_D = Omega_D * (rk_D_next - rdep_D[t]) / lK_D - if mu_D >= 1.0: - raise RuntimeError(f"D-bank mu_D={mu_D:.4f} ≥ 1 at t={t}; IC infeasible.") - alpha_D = Omega_D * (1 + rdep_D[t]) / (1 - mu_D) - - # BD: sunspot tightens IC for sovereign bonds (rollover risk premium) - lbD_D_eff = lbD_D + psi_bd_D * xi_D_next # D-bank IC for D-bonds - lbF_D_eff = lbF_D + psi_bd_F * xi_F_next # D-bank IC for F-bonds - - # HM pricing: Q_bD = surv_{t+1}·(db + (1-db)·Q_next) / (1 + rdep + IC_spread) - # Survival from fundamental default only; BD sunspot enters denominator. - ic_spread_bD_D = lbD_D_eff * mu_D / Omega_D - surv_D_for_price = 1.0 - def_D_next * (1.0 - rec_D) - Q_bD = surv_D_for_price * (db_D + (1 - db_D) * Q_bD_next) / (1 + rdep_D[t] + ic_spread_bD_D) - - # ── F-bank backward step ── - Omega_F = bi_F * ((1 - f_F) + f_F * alpha_F_next) - mu_F = Omega_F * (rk_F_next - rdep_F[t]) / lK_F - if mu_F >= 1.0: - raise RuntimeError(f"F-bank mu_F={mu_F:.4f} ≥ 1 at t={t}; IC infeasible.") - alpha_F = Omega_F * (1 + rdep_F[t]) / (1 - mu_F) - - # BD: F-bank's IC for D-bonds also tightens with D-sunspot (contagion channel) - lbD_F_eff = lbD_F + psi_bd_D * xi_D_next # F-bank IC for D-bonds - lbF_F_eff = lbF_F + psi_bd_F * xi_F_next # F-bank IC for F-bonds - - # F-bank FOC for F-bonds (symmetric) - ic_spread_bF_F = lbF_F_eff * mu_F / Omega_F - surv_F_for_price = 1.0 - def_F_next * (1.0 - rec_F) - Q_bF = surv_F_for_price * (db_F + (1 - db_F) * Q_bF_next) / (1 + rdep_F[t] + ic_spread_bF_F) - - # ── Cross-border positions from portfolio adj-cost FOC ── - # Survival for expected return at t+1 uses next-period default rate (B-2 fix). - def_F_t1 = def_F_path[t + 1] if t + 1 < T else 0.0 - def_D_t1 = def_D_path[t + 1] if t + 1 < T else 0.0 - surv_F = 1.0 - def_F_t1 * (1.0 - rec_F) - surv_D = 1.0 - def_D_t1 * (1.0 - rec_D) - - rb_F_raw = (db_F * surv_F + (1 - db_F) * Q_bF_next * surv_F) / Q_bF - 1 - # Convert F-bond return to D-goods using p_{t+1}/p_t + # Next-period PRICED default probability (expected haircut in prices) + defp_D_next = def_price_D[t + 1] if t + 1 < T else 0.0 + defp_F_next = def_price_F[t + 1] if t + 1 < T else 0.0 p_next = p_path[t + 1] if t + 1 < T else p_path[t] # terminal: p constant - rb_F_in_D = (1 + rb_F_raw) * p_next / p_path[t] - 1 - ic_required_bF_D = lbF_D_eff * mu_D / Omega_D + if not risk_mode: + # ── Risk-neutral backward step (expected-haircut surv form) ── + # D-bank + Omega_D = bi_D * ((1 - f_D) + f_D * alpha_D_next) + mu_D = Omega_D * (rk_D_next - rdep_D[t]) / lK_D + if mu_D >= 1.0: + raise RuntimeError(f"D-bank mu_D={mu_D:.4f} ≥ 1 at t={t}; IC infeasible.") + alpha_D = Omega_D * (1 + rdep_D[t]) / (1 - mu_D) + + # HM pricing: Q_bD = surv^e_{t+1}·(db + (1-db)·Q_next) / (1 + rdep + IC_spread) + ic_spread_bD_D = lbD_D * mu_D / Omega_D + surv_D_price = 1.0 - defp_D_next * (1.0 - rec_D) + Q_bD = surv_D_price * (db_D + (1 - db_D) * Q_bD_next) / (1 + rdep_D[t] + ic_spread_bD_D) + + # F-bank + Omega_F = bi_F * ((1 - f_F) + f_F * alpha_F_next) + mu_F = Omega_F * (rk_F_next - rdep_F[t]) / lK_F + if mu_F >= 1.0: + raise RuntimeError(f"F-bank mu_F={mu_F:.4f} ≥ 1 at t={t}; IC infeasible.") + alpha_F = Omega_F * (1 + rdep_F[t]) / (1 - mu_F) + + ic_spread_bF_F = lbF_F * mu_F / Omega_F + surv_F_price = 1.0 - defp_F_next * (1.0 - rec_F) + Q_bF = surv_F_price * (db_F + (1 - db_F) * Q_bF_next) / (1 + rdep_F[t] + ic_spread_bF_F) + + # Cross-border FOCs: expected returns with priced survival + rb_F_in_D = ((surv_F_price * (db_F + (1 - db_F) * Q_bF_next) / Q_bF) + * p_next / p_path[t] - 1) + ic_required_bF_D = lbF_D * mu_D / Omega_D + rb_D_in_F = ((surv_D_price * (db_D + (1 - db_D) * Q_bD_next) / Q_bD) + * p_path[t] / p_next - 1) + ic_required_bD_F = lbD_F * mu_F / Omega_F + else: + # ── Bocola risk-channel step: two-branch expectations ── + # With prob pi1 the D government defaults at t+1: bond stock is + # haircut to rec_D, prices/returns jump to the default-branch + # values, and the banker's discount is Ω^d (high marginal value). + pi1 = pi_path[t + 1] if t + 1 < T else 0.0 + + # D-bank + Omega_nd_D = bi_D * ((1 - f_D) + f_D * alpha_D_next) + Omega_til_D = (1 - pi1) * Omega_nd_D + pi1 * Om_d_D[t] + mu_D = ((1 - pi1) * Omega_nd_D * (rk_D_next - rdep_D[t]) + + pi1 * Om_d_D[t] * (rk_d_D - rdep_D[t])) / lK_D + if mu_D >= 1.0: + raise RuntimeError(f"D-bank mu_D={mu_D:.4f} ≥ 1 at t={t}; IC infeasible.") + alpha_D = Omega_til_D * (1 + rdep_D[t]) / (1 - mu_D) + + payoff_D_nd = db_D + (1 - db_D) * Q_bD_next + payoff_D_d = surv_d * (db_D + (1 - db_D) * Q_bD_d) # event haircut on the whole claim + ic_spread_bD_D = lbD_D * mu_D / Omega_til_D + Q_bD = (((1 - pi1) * Omega_nd_D * payoff_D_nd + pi1 * Om_d_D[t] * payoff_D_d) + / (Omega_til_D * (1 + rdep_D[t]) + lbD_D * mu_D)) + + # F-bank (same aggregate D-default event drives its expectations) + Omega_nd_F = bi_F * ((1 - f_F) + f_F * alpha_F_next) + Omega_til_F = (1 - pi1) * Omega_nd_F + pi1 * Om_d_F[t] + mu_F = ((1 - pi1) * Omega_nd_F * (rk_F_next - rdep_F[t]) + + pi1 * Om_d_F[t] * (rk_d_F - rdep_F[t])) / lK_F + if mu_F >= 1.0: + raise RuntimeError(f"F-bank mu_F={mu_F:.4f} ≥ 1 at t={t}; IC infeasible.") + alpha_F = Omega_til_F * (1 + rdep_F[t]) / (1 - mu_F) + + # F-bonds carry no haircut from the D-event but their price jumps + # to Q_bF_d in it (safe-haven repricing → negative risk premium). + # F's OWN default risk (independent of the D-event, priced + # risk-neutrally — no F default branch) enters as the same + # survival factor as in risk-neutral mode, on both branches. + surv_F_price = 1.0 - defp_F_next * (1.0 - rec_F) + payoff_F_nd = surv_F_price * (db_F + (1 - db_F) * Q_bF_next) + payoff_F_d = surv_F_price * (db_F + (1 - db_F) * Q_bF_d) + ic_spread_bF_F = lbF_F * mu_F / Omega_til_F + Q_bF = (((1 - pi1) * Omega_nd_F * payoff_F_nd + pi1 * Om_d_F[t] * payoff_F_d) + / (Omega_til_F * (1 + rdep_F[t]) + lbF_F * mu_F)) + + # Cross-border FOCs: certainty-equivalent returns under Ω̃ + # (E[Ω'(1+rb')]/Ω̃ − 1), branch legs converted with branch prices. + gross_F_in_D_nd = payoff_F_nd / Q_bF * p_next / p_path[t] + gross_F_in_D_d = payoff_F_d / Q_bF * p_d / p_path[t] + rb_F_in_D = (((1 - pi1) * Omega_nd_D * gross_F_in_D_nd + + pi1 * Om_d_D[t] * gross_F_in_D_d) / Omega_til_D) - 1 + ic_required_bF_D = lbF_D * mu_D / Omega_til_D + + gross_D_in_F_nd = payoff_D_nd / Q_bD * p_path[t] / p_next + gross_D_in_F_d = payoff_D_d / Q_bD * p_path[t] / p_d + rb_D_in_F = (((1 - pi1) * Omega_nd_F * gross_D_in_F_nd + + pi1 * Om_d_F[t] * gross_D_in_F_d) / Omega_til_F) - 1 + ic_required_bD_F = lbD_F * mu_F / Omega_til_F + + Omega_D = Omega_til_D # stored below (Ω̃ used at t) + Omega_F = Omega_til_F + b_F_D_t = (b_F_D_ss + (rb_F_in_D - rdep_D[t] - exc_FD_ss - ic_required_bF_D) / psi_bFD) - - # F-bank's D-bond FOC: - rb_D_raw = (db_D * surv_D + (1 - db_D) * Q_bD_next * surv_D) / Q_bD - 1 - # Convert D-bond return to F-goods using p_t/p_{t+1} - rb_D_in_F = (1 + rb_D_raw) * p_path[t] / p_next - 1 - - ic_required_bD_F = lbD_F_eff * mu_F / Omega_F b_D_F_t = (b_D_F_ss + (rb_D_in_F - rdep_F[t] - exc_DF_ss - ic_required_bD_F) / psi_bDF) - alpha_D_path[t] = alpha_D; mu_D_path[t] = mu_D - alpha_F_path[t] = alpha_F; mu_F_path[t] = mu_F + alpha_D_path[t] = alpha_D; mu_D_path[t] = mu_D; Omega_D_path[t] = Omega_D + alpha_F_path[t] = alpha_F; mu_F_path[t] = mu_F; Omega_F_path[t] = Omega_F Q_bD_path[t] = Q_bD; Q_bF_path[t] = Q_bF - b_F_D_path[t] = b_F_D_t - b_D_F_path[t] = b_D_F_t - lbD_D_eff_path[t] = lbD_D_eff - lbF_D_eff_path[t] = lbF_D_eff - lbD_F_eff_path[t] = lbD_F_eff - lbF_F_eff_path[t] = lbF_F_eff + b_F_D_path[t] = b_F_D_t; b_D_F_path[t] = b_D_F_t + ic_bD_D_path[t] = ic_spread_bD_D + ic_bF_F_path[t] = ic_spread_bF_F alpha_D_next = alpha_D; alpha_F_next = alpha_F Q_bD_next = Q_bD; Q_bF_next = Q_bF - # Bond market clearing — B_gov may be scalar (SS) or time-varying path (BD/CK) - B_gov_D_arr = np.broadcast_to(B_gov_D, T).copy() if np.ndim(B_gov_D) == 0 else np.asarray(B_gov_D) - B_gov_F_arr = np.broadcast_to(B_gov_F, T).copy() if np.ndim(B_gov_F) == 0 else np.asarray(B_gov_F) - b_D_D_path = B_gov_D_arr - b_D_F_path # D-bonds held by D-bank - b_F_F_path = B_gov_F_arr - b_F_D_path # F-bonds held by F-bank - - # Realised returns at t on bonds bought at t-1 - Q_bD_lag = np.concatenate(([Q_bD_ss_val], Q_bD_path[:-1])) - Q_bF_lag = np.concatenate(([Q_bF_ss_val], Q_bF_path[:-1])) - - surv_D_path = 1.0 - def_D_path * (1.0 - rec_D) - surv_F_path = 1.0 - def_F_path * (1.0 - rec_F) - rb_D_path = (db_D * surv_D_path + (1 - db_D) * Q_bD_path * surv_D_path) / Q_bD_lag - 1 - rb_F_path = (db_F * surv_F_path + (1 - db_F) * Q_bF_path * surv_F_path) / Q_bF_lag - 1 - - # ── Forward pass: accumulate n_ACCUM and compute n_IC ───────────────────── - n_IC_D = np.empty(T) - n_ACCUM_D = np.empty(T) - rn_D = np.empty(T) - div_D = np.empty(T) - - n_IC_F = np.empty(T) - n_ACCUM_F = np.empty(T) - rn_F = np.empty(T) - div_F = np.empty(T) - - # D-bank forward state - n_D_prev = ss_bk_D["n_ss"] - kappa_D_prev = ss_bk_D["kappa_ss"] - phi_bdom_D_prev = ss_bk_D["phi_bdom_ss"] - phi_bfor_D_prev = ss_bk_D["phi_bfor_ss"] - rdep_D_prev = cal["r_dep_D_target"] + return dict( + alpha_D=alpha_D_path, mu_D=mu_D_path, Omega_D=Omega_D_path, + alpha_F=alpha_F_path, mu_F=mu_F_path, Omega_F=Omega_F_path, + Q_bD=Q_bD_path, Q_bF=Q_bF_path, + b_F_D=b_F_D_path, b_D_F=b_D_F_path, + ic_spread_bD_D=ic_bD_D_path, ic_spread_bF_F=ic_bF_F_path, + Q_bD_ss_val=Q_bD_ss_val, Q_bF_ss_val=Q_bF_ss_val, + ) + + +# ───────────────────────────────────────────────────────────────────────────── +# Transition-path bank block — forward pass (net worth, dividends, deposits) +# ───────────────────────────────────────────────────────────────────────────── + +def bank_forward(Kap_D, Kap_F, Q_D, Q_F, rk_D, rk_F, rdep_D, rdep_F, p_path, + b_D_D_path, b_F_F_path, bwd, cal, ss_bk_D, ss_bk_F, + def_real_D=None, def_real_F=None, + init_D=None, init_F=None, + Q_bD_lag0=None, Q_bF_lag0=None, p_lag0=None): + """Forward pass for both banks: realized returns, net-worth accumulation, + IC-implied net worth, dividends and deposit supply. + + b_D_D_path, b_F_F_path : domestic bond holdings from market clearing + (end-of-period government stock minus the foreign + bank's FOC holding — computed in transition.py) + bwd : output dict of bank_backward() + def_real_D/F : (T,) REALIZED default (haircut) paths — enter + realized bond returns only. None → 0 (Bocola + risk-only experiment: priced but never realized). + init_D/init_F : optional dicts with keys (n_prev, kappa_prev, + phi_bdom_prev, phi_bfor_prev, rdep_prev) — the + bank state carried into period 0 when the path + starts mid-crisis (default branch / policy runs). + None → steady-state values (current behaviour). + Q_bD_lag0, Q_bF_lag0 : period −1 bond prices for realized returns at + t=0 (None → IC-consistent SS prices). + p_lag0 : period −1 real exchange rate (None → p_ss). + + Returns dict of paths: n_IC_D, n_D, rn_D, div_D, theta_D, Dep_supply_D, + rb_D, and F analogues, plus pass-through of holdings b_D_D, b_F_F. + """ + T = len(Kap_D) + + if def_real_D is None: + def_real_D = np.zeros(T) + if def_real_F is None: + def_real_F = np.zeros(T) + + f_D = cal["f_D"]; f_F = cal["f_F"] + lK_D = cal["lambda_K_D"]; lK_F = cal["lambda_K_F"] + lbD_D = cal["lambda_bD_D"]; lbD_F = cal["lambda_bD_F"] + lbF_D = cal["lambda_bF_D"]; lbF_F = cal["lambda_bF_F"] + rec_D = cal["recovery_rate_D"]; rec_F = cal["recovery_rate_F"] + db_D = cal["delta_b_D"]; db_F = cal["delta_b_F"] + + Q_bD_path = bwd["Q_bD"]; Q_bF_path = bwd["Q_bF"] + b_F_D_path = bwd["b_F_D"]; b_D_F_path = bwd["b_D_F"] + alpha_D_path = bwd["alpha_D"]; alpha_F_path = bwd["alpha_F"] + + # Realized returns at t on bonds bought at t-1 — REALIZED survival only. + Q_bD_l0 = bwd["Q_bD_ss_val"] if Q_bD_lag0 is None else Q_bD_lag0 + Q_bF_l0 = bwd["Q_bF_ss_val"] if Q_bF_lag0 is None else Q_bF_lag0 + Q_bD_lag = np.concatenate(([Q_bD_l0], Q_bD_path[:-1])) + Q_bF_lag = np.concatenate(([Q_bF_l0], Q_bF_path[:-1])) + + surv_D_real = 1.0 - np.asarray(def_real_D) * (1.0 - rec_D) + surv_F_real = 1.0 - np.asarray(def_real_F) * (1.0 - rec_F) + rb_D_path = (db_D * surv_D_real + (1 - db_D) * Q_bD_path * surv_D_real) / Q_bD_lag - 1 + rb_F_path = (db_F * surv_F_real + (1 - db_F) * Q_bF_path * surv_F_real) / Q_bF_lag - 1 + + n_IC_D = np.empty(T); n_ACCUM_D = np.empty(T) + rn_D = np.empty(T); div_D = np.empty(T) + n_IC_F = np.empty(T); n_ACCUM_F = np.empty(T) + rn_F = np.empty(T); div_F = np.empty(T) + + # D-bank forward state (SS by default; overridable for mid-path starts) + if init_D is None: + init_D = dict(n_prev=ss_bk_D["n_ss"], kappa_prev=ss_bk_D["kappa_ss"], + phi_bdom_prev=ss_bk_D["phi_bdom_ss"], + phi_bfor_prev=ss_bk_D["phi_bfor_ss"], + rdep_prev=cal["r_dep_D_target"]) + n_D_prev = init_D["n_prev"] + kappa_D_prev = init_D["kappa_prev"] + phi_bdom_D_prev = init_D["phi_bdom_prev"] + phi_bfor_D_prev = init_D["phi_bfor_prev"] + rdep_D_prev = init_D["rdep_prev"] # F-bank forward state - n_F_prev = ss_bk_F["n_ss"] - kappa_F_prev = ss_bk_F["kappa_ss"] - phi_bdom_F_prev = ss_bk_F["phi_bdom_ss"] - phi_bfor_F_prev = ss_bk_F["phi_bfor_ss"] - rdep_F_prev = cal["r_dep_F_target"] + if init_F is None: + init_F = dict(n_prev=ss_bk_F["n_ss"], kappa_prev=ss_bk_F["kappa_ss"], + phi_bdom_prev=ss_bk_F["phi_bdom_ss"], + phi_bfor_prev=ss_bk_F["phi_bfor_ss"], + rdep_prev=cal["r_dep_F_target"]) + n_F_prev = init_F["n_prev"] + kappa_F_prev = init_F["kappa_prev"] + phi_bdom_F_prev = init_F["phi_bdom_prev"] + phi_bfor_F_prev = init_F["phi_bfor_prev"] + rdep_F_prev = init_F["rdep_prev"] + + p_l0 = cal.get("p_ss", 1.0) if p_lag0 is None else p_lag0 for t in range(T): p_t = p_path[t] - # p_lag: exchange rate at t-1, needed for FX conversions of realised bond returns. - # Q_bF is denominated in F-goods (F-bank prices F-bonds against rdep_F in F-goods); - # Q_bD is denominated in D-goods (D-bank prices D-bonds against rdep_D in D-goods). - p_lag = p_path[t - 1] if t > 0 else cal.get("p_ss", 1.0) - - # D-bank earns rb_D on D-bonds (D-goods, no conversion) and rb_F on F-bonds. - # rb_F_path is a F-good return (Q_bF in F-goods); convert to D-goods: - # D-good return = (1 + rb_F_path) * p_t / p_lag (bought at p_lag per F-good, sold at p_t) + p_lag = p_path[t - 1] if t > 0 else p_l0 + + # D-bank earns rb_D on D-bonds (D-goods) and rb_F on F-bonds (convert F→D goods). rb_D_t = rb_D_path[t] rb_F_t = (1.0 + rb_F_path[t]) * p_t / p_lag - 1 # F-goods → D-goods - # D-bank net worth return (all terms in D-goods) rn_D_t = (kappa_D_prev * (rk_D[t] - rdep_D_prev) + phi_bdom_D_prev * (rb_D_t - rdep_D_prev) - + phi_bfor_D_prev * (rb_F_t - rdep_D_prev) # rb_F_t already in D-goods + + phi_bfor_D_prev * (rb_F_t - rdep_D_prev) + rdep_D_prev) gross_D = (1 + rn_D_t) * n_D_prev - # Entrant transfer: proportional to total assets in D-goods - total_assets_D = Q_D[t] * Kap_D[t] + Q_bD_path[t] * b_D_D_path[t] + Q_bF_path[t] * b_F_D_path[t] - entrant_D = cal["omega_ent_D"] * total_assets_D + total_assets_D = (Q_D[t] * Kap_D[t] + Q_bD_path[t] * b_D_D_path[t] + + p_t * Q_bF_path[t] * b_F_D_path[t]) + entrant_D = cal["omega_ent_D"] * total_assets_D n_ACCUM_D_t = (1 - f_D) * gross_D + entrant_D - div_D_t = f_D * gross_D - entrant_D + div_D_t = f_D * gross_D - entrant_D n_ACCUM_D[t] = n_ACCUM_D_t rn_D[t] = rn_D_t div_D[t] = div_D_t - # D-bank IC: uses effective lambda (base + BD sunspot tightening) + # D-bank IC in D-goods (F-bonds enter as p×Q_bF) n_IC_D_t = (lK_D * Q_D[t] * Kap_D[t] - + lbD_D_eff_path[t] * Q_bD_path[t] * b_D_D_path[t] - + lbF_D_eff_path[t] * Q_bF_path[t] * b_F_D_path[t]) / alpha_D_path[t] + + lbD_D * Q_bD_path[t] * b_D_D_path[t] + + lbF_D * p_t * Q_bF_path[t] * b_F_D_path[t]) / alpha_D_path[t] n_IC_D[t] = n_IC_D_t - # Update D-bank portfolio ratios for next forward step kappa_D_prev = Q_D[t] * Kap_D[t] / n_IC_D_t phi_bdom_D_prev = Q_bD_path[t] * b_D_D_path[t] / n_IC_D_t - phi_bfor_D_prev = Q_bF_path[t] * b_F_D_path[t] / n_IC_D_t + phi_bfor_D_prev = p_t * Q_bF_path[t] * b_F_D_path[t] / n_IC_D_t n_D_prev = n_ACCUM_D_t rdep_D_prev = rdep_D[t] # ── F-bank ───────────────────────────────────────────────────────── - # Q_bF denominated in F-goods: rb_F_path is already a F-good return → use directly. - # Q_bD denominated in D-goods: convert D-bond return to F-goods via p_lag/p_t. - rb_F_fg_t = rb_F_path[t] # F-bonds: F-goods ✓ - rb_D_fg_t = (1 + rb_D_path[t]) * p_lag / p_t - 1 # D-bonds: D-goods → F-goods + rb_F_fg_t = rb_F_path[t] # F-bonds: F-goods ✓ + rb_D_fg_t = (1 + rb_D_path[t]) * p_lag / p_t - 1 # D-bonds: D-goods → F-goods rn_F_t = (kappa_F_prev * (rk_F[t] - rdep_F_prev) - + phi_bdom_F_prev * (rb_F_fg_t - rdep_F_prev) # F-bonds in F-goods - + phi_bfor_F_prev * (rb_D_fg_t - rdep_F_prev) # D-bonds in F-goods + + phi_bdom_F_prev * (rb_F_fg_t - rdep_F_prev) + + phi_bfor_F_prev * (rb_D_fg_t - rdep_F_prev) + rdep_F_prev) gross_F = (1 + rn_F_t) * n_F_prev - # Total F-bank assets in F-goods total_assets_F = (Q_F[t] * Kap_F[t] - + Q_bF_path[t] * b_F_F_path[t] / p_t + + Q_bF_path[t] * b_F_F_path[t] + Q_bD_path[t] * b_D_F_path[t] / p_t) entrant_F = cal["omega_ent_F"] * total_assets_F n_ACCUM_F_t = (1 - f_F) * gross_F + entrant_F @@ -529,38 +611,31 @@ def solve_bank_paths(Kap_D, Kap_F, Q_D, Q_F, rk_D, rk_F, rn_F[t] = rn_F_t div_F[t] = div_F_t - # F-bank IC in F-goods: effective lambda (D-bond term uses contagion channel) + # F-bank IC in F-goods (F-bonds are F-good claims; D-bonds ÷p) n_IC_F_t = (lK_F * Q_F[t] * Kap_F[t] - + lbF_F_eff_path[t] * Q_bF_path[t] * b_F_F_path[t] / p_t - + lbD_F_eff_path[t] * Q_bD_path[t] * b_D_F_path[t] / p_t) / alpha_F_path[t] + + lbF_F * Q_bF_path[t] * b_F_F_path[t] + + lbD_F * Q_bD_path[t] * b_D_F_path[t] / p_t) / alpha_F_path[t] n_IC_F[t] = n_IC_F_t kappa_F_prev = Q_F[t] * Kap_F[t] / n_IC_F_t - phi_bdom_F_prev = Q_bF_path[t] * b_F_F_path[t] / (p_t * n_IC_F_t) + phi_bdom_F_prev = Q_bF_path[t] * b_F_F_path[t] / n_IC_F_t phi_bfor_F_prev = Q_bD_path[t] * b_D_F_path[t] / (p_t * n_IC_F_t) n_F_prev = n_ACCUM_F_t rdep_F_prev = rdep_F[t] - theta_D = ((Q_D * Kap_D + Q_bD_path * b_D_D_path + Q_bF_path * b_F_D_path) + theta_D = ((Q_D * Kap_D + Q_bD_path * b_D_D_path + p_path * Q_bF_path * b_F_D_path) / n_ACCUM_D) - theta_F = ((Q_F * Kap_F + Q_bF_path * b_F_F_path / p_path + Q_bD_path * b_D_F_path / p_path) + theta_F = ((Q_F * Kap_F + Q_bF_path * b_F_F_path + Q_bD_path * b_D_F_path / p_path) / n_ACCUM_F) Dep_supply_D = (theta_D - 1) * n_ACCUM_D Dep_supply_F = (theta_F - 1) * n_ACCUM_F return dict( - # D-bank - alpha_D=alpha_D_path, mu_D=mu_D_path, n_IC_D=n_IC_D, n_D=n_ACCUM_D, rn_D=rn_D, div_D=div_D, theta_D=theta_D, Dep_supply_D=Dep_supply_D, - b_D_D=b_D_D_path, b_F_D=b_F_D_path, - # F-bank - alpha_F=alpha_F_path, mu_F=mu_F_path, n_IC_F=n_IC_F, n_F=n_ACCUM_F, rn_F=rn_F, div_F=div_F, theta_F=theta_F, Dep_supply_F=Dep_supply_F, - b_D_F=b_D_F_path, b_F_F=b_F_F_path, - # Shared bond prices and returns - Q_bD=Q_bD_path, Q_bF=Q_bF_path, rb_D=rb_D_path, rb_F=rb_F_path, + b_D_D=b_D_D_path, b_F_F=b_F_F_path, ) diff --git a/code/global/calibration.py b/code/global/calibration.py index fbf250d..b447b83 100644 --- a/code/global/calibration.py +++ b/code/global/calibration.py @@ -1,6 +1,18 @@ """ -Notes: -* Shared bond denomination: all bonds are D-good claims (D is numeraire). +Notes: +* Bond denomination: D-bonds are D-good claims (priced by D-bank using rdep_D). + F-bonds are F-good claims (priced by F-bank using rdep_F). + Cross-border values: D-bank's F-bond leg in D-goods = p·Q_bF·b_F_D; + F-bank's D-bond leg in F-goods = Q_bD·b_D_F/p. +* Bank incentive constraint follows Bocola (2016) eq. (3): a banker can divert + a fraction lambda of TOTAL assets, so lambda_K = lambda_bD = lambda_bF + (single lambda per bank). The three keys are kept separate only for + robustness exercises — diverging them re-opens the portfolio-substitution + margin and weakens the sovereign-risk pass-through. +* Sovereign risk: Cole-Kehoe sunspot xi = probability of the no-rollover + equilibrium, PRICED into Q_bD inside the crisis zone (def_price); the + baseline experiment never REALIZES default (def_real = 0), following + Bocola's pass-through-of-sovereign-risk design. """ @@ -10,7 +22,7 @@ def get_calibration(): # ── HOUSEHOLD PREFERENCES (GHH) ───────────────────────────────────── sigma_D=2.0, sigma_F=2.0, frisch_D=0.5, frisch_F=0.5, - chi_D=1.0, chi_F=1.0, # initial guess; overwritten in SS solve to match Nss=1 + chi_D=1.0, chi_F=1.0, # initial guess; overwritten in SS solve to match Nss=1 # ── IDIOSYNCRATIC INCOME PROCESS (Rouwenhorst) ─────────────────────── n_e_D=2, n_e_F=2, @@ -18,8 +30,8 @@ def get_calibration(): rho_e_F=0.9, sigma_e_F=0.2, # ── ASSET GRIDS ────────────────────────────────────────────────────── - a_min_D=0.0, a_max_D=150.0, n_a_D=250, a_curve_D=2.0, - a_min_F=0.0, a_max_F=150.0, n_a_F=250, a_curve_F=2.0, + a_min_D=0.0, a_max_D=300.0, n_a_D=250, a_curve_D=2.0, + a_min_F=0.0, a_max_F=300.0, n_a_F=250, a_curve_F=2.0, # ── FIRMS (Cobb-Douglas + full price flexibility) ──────────────────── epsilon_D=6.0, epsilon_F=6.0, # demand elasticity → mc = (ε-1)/ε @@ -27,50 +39,86 @@ def get_calibration(): # ── CAPITAL (Jermann 1998 adjustment cost) ──────────────────────────── alpha_D=0.35, alpha_F=0.35, # capital share - delta_D=0.025, delta_F=0.025, # initial guess for SS solve; overwritten after SS solve + delta_D=0.025, delta_F=0.025, ksi_D=0.50, ksi_F=0.50, # adjustment-cost curvature # ── FINANCIAL INTERMEDIARY ───────────────────────────────────────────── + # Single divertable fraction per bank (Bocola 2016 eq. 3): all three + # asset classes carry the same lambda. Calibrated with B_gov to hit + # leverage theta_ss ≈ 4.5 and sovereign exposure Q·b_dom/n ≈ 0.9 + # (domestic sovereign holdings ≈ 93% of bank equity, GIPS 2009 fact + # cited by Bocola 2016). f_D=0.028, f_F=0.028, r_dep_D_target=0.000, r_dep_F_target=0.000, beta_inter_D=0.96, beta_inter_F=0.96, - lambda_K_D=0.30, lambda_K_F=0.30, - lambda_bD_D=0.04, lambda_bD_F=0.04, - lambda_bF_D=0.04, lambda_bF_F=0.04, + lambda_K_D=0.22, lambda_K_F=0.22, + lambda_bD_D=0.22, lambda_bD_F=0.22, + lambda_bF_D=0.22, lambda_bF_F=0.22, omega_ent_D=0.002, omega_ent_F=0.002, # ── PORTFOLIO ADJUSTMENT COSTS (cross-border bonds) ────────────────── + # b_D_F_ss / b_F_D_ss ≈ 20% of the respective bond supply: foreign + # banks' pre-crisis holdings of peripheral debt (union contagion leg). psi_bF_D=0.01, psi_bD_F=0.01, - b_F_D_ss=0.005, b_D_F_ss=0.005, + b_F_D_ss=2.56, b_D_F_ss=2.56, excess_return_F_D_ss=0.0, # overwritten after SS solve excess_return_D_F_ss=0.0, # overwritten after SS solve # ── GOVERNMENT BONDS ───────────────── - delta_b_D=0.10, delta_b_F=0.10, - B_gov_D_ss=2.40, B_gov_F_ss=2.40, - - # ── DEFAULT RISK ─────────────────────────────────────────────────────── - # Thresholds are debt-to-Y_ss ratios. F is always safe. - # b_ck_low/high are used by Cole-Kehoe crisis-zone logic (see solve_transition_ck). - # b_ck_high also serves as the Bocola-Dovis fundamental default boundary (B̄). - b_ck_low_D=0.55, b_ck_low_F=99.0, - b_ck_high_D=1.20, b_ck_high_F=99.0, - recovery_rate_D=0.40, recovery_rate_F=0.0, # Greek PSI-style haircut - - # Bocola-Dovis (2019): sunspot tightens GK IC for sovereign bonds - # lbD_D_eff = lbD_D + psi_bd_D * xi_{t+1} (D-bank and F-bank for D-bonds) - # lbF_F_eff = lbF_F + psi_bd_F * xi_{t+1} (F-bonds; set 0 — no F default) - psi_bd_D=3.0, psi_bd_F=0.0, - - # Outer CK fixed-point solver + # delta_b = quarterly amortization rate; duration ≈ 1/delta_b quarters + # (≈7y, both countries' pre-crisis average maturities). Long duration + # is what makes priced default risk generate large MTM losses (Bocola + # 2016). Kept SYMMETRIC: an asymmetric delta_b shifts p_ss off 1 and + # opens an O(1e-4) SS goods-market wedge (see steady_state.py note). + delta_b_D=0.036, delta_b_F=0.036, + B_gov_D_ss=12.80, B_gov_F_ss=12.80, + + # ── DEFAULT RISK (Cole-Kehoe zones × Bocola pricing) ───────────────── + # Thresholds are FACE-value debt to quarterly Y_ss ratios. F is always + # safe. SS sits inside the D crisis zone (b/Y_ss ≈ 3.7), so the + # sunspot is priced immediately; b_ck_high is out of reach (no + # fundamental default in the risk-only experiment). + b_ck_low_D=3.00, b_ck_low_F=99.0, + b_ck_high_D=6.00, b_ck_high_F=99.0, + # Recovery = 1 − haircut; haircut 0.55 per Greek PSI 2012 + # (Zettelmeyer-Trebesch-Gulati 2013, used by Bocola 2016). + recovery_rate_D=0.45, recovery_rate_F=0.45, + + # ── DEFAULT STATE (risk-channel branch) ────────────────────────────── + # Canonical output cost of default (Arellano 2008 tradition): TFP in + # the post-default branch is Z·(1 − cost·rho^h). Without it (and + # with the Bohn windfall removed via re-anchoring) default would be + # expansionary — debt relief with no pain — and the risk premium + # would have the wrong sign. 5% on impact, half-life ~7 quarters, + # is conservative next to the Greek 2012 output collapse. + def_output_cost_D=0.05, def_output_rho_D=0.90, + # Pessimistic probability tilt for the risk weighting (EZ-lite dial; + # 1.0 = off, physical probabilities — the Bocola-faithful baseline). + chi_tilt=1.0, + # Default-state SDF (risk channel): + # sdf_mode="empirical": Λ^d = beta_inter·kappa_d. DEVIATION from + # Bocola (2016), whose Λ is the model-consistent (rep-agent, + # log-utility) household SDF. Two reasons: (i) the comovement + # problem makes the model-consistent SDF wrong-signed here (see + # sdf_mode="model" below); (ii) in the HA economy there is no + # representative family, so "the household SDF" is not a + # well-defined object for the banker — Λ^d is an ASSUMPTION: a + # banker-specific discount with an empirically disciplined + # default-state loading kappa_d. Calibration target: + # risk-channel share of the lending-spread response ≈ 45%. + # sdf_mode="model": Λ^d = beta_inter·(x^d/x^nd)^(−σ) from branch + # consumption composites. Currently WRONG-SIGNED: the comovement + # problem (deposit-rate collapse) makes branch consumption RISE, + # so the household SDF prices default states as good times. Use + # only after the union-deposit-market fix. + sdf_mode="empirical", + kappa_d=2.00, + + # Outer Cole-Kehoe zone-indicator iteration (converges in 1 pass when + # debt stays inside the crisis zone; damping 1.0 = undamped) ck_max_iter=25, - ck_tol=1e-5, - ck_damping=0.5, - - # Outer BD fixed-point solver (separate budget; uses Anderson acceleration) - bd_max_iter=50, - bd_tol=1e-4, # 0.004% of b_ss — adequate for quantitative IRF - bd_anderson_m=3, # Anderson window size + ck_tol=1e-8, + ck_damping=1.0, # ── FISCAL ──────────────────────────────────────────────────────────── phi_lamb_D=0.15, phi_lamb_F=0.15, @@ -86,7 +134,7 @@ def get_calibration(): tol_mkt=1e-9, # ── INITIAL GUESSES FOR STEADY-STATE SOLVER ─────────────────────────── - rk_D_guess=0.010, rk_F_guess=0.010, + rk_D_guess=0.0045, rk_F_guess=0.0045, beta_guess_D=0.98, beta_guess_F=0.98, ) return cal diff --git a/code/global/firms.py b/code/global/firms.py index dd9cb11..455f803 100644 --- a/code/global/firms.py +++ b/code/global/firms.py @@ -1,13 +1,4 @@ -"""Firm block: Cobb-Douglas production with full price flexibility. - -Under full price flexibility, mc = (epsilon−1)/epsilon is a constant. -Functions accept a `country` argument ("D" or "F") to look up country- -specific parameters (alpha, delta, Z_ss, epsilon) from the calibration. - -With GHH preferences, chi is calibrated so that chi·N_ss^(1/frisch) = w_ss -(the static GHH labour-supply FOC at SS with P_CES=1). This replaces the -old CRRA chi = w_ss / C_ss^sigma formula. -""" +"Firm block: Cobb-Douglas production with full price flexibility." def markup_ss(cal, country="D"): diff --git a/code/global/government.py b/code/global/government.py index 7326e4b..1648d17 100644 --- a/code/global/government.py +++ b/code/global/government.py @@ -1,4 +1,20 @@ -"Government block: Hatchondo-Martinez (2009) geometric-decay perpetuity bonds, Bohn (1998) fiscal rule, Cole-Kehoe (2000) self-fulfilling default crisis zones." +"""Government block: Hatchondo-Martinez (2009) geometric-decay perpetuity bonds, +Bohn (1998) fiscal rule, Cole-Kehoe (2000) self-fulfilling crisis zones. + +Structure follows Bocola (2016) eq. (9): each period the government pays +coupons on the surviving stock, and rolls over by issuing new bonds at the +market price Q (which embeds PRICED default risk from the bank block). Taxes +follow the Bohn rule on beginning-of-period debt. The debt path is a single +forward recursion — no fixed-point iteration is needed given a Q path. + +PRICED vs REALIZED default: + Only the REALIZED default path def_real enters the government's flows + (coupon survival and stock write-down). Priced risk affects the government + solely through the depressed issuance price Q: with def_real = 0 (Cole-Kehoe + risk-only experiment) the government keeps servicing debt in full but rolls + over at low prices, so the debt stock rises and Bohn taxes rise — beliefs + worsen fiscal fundamentals without any default event. +""" import numpy as np @@ -50,10 +66,11 @@ def ck_default_prob(b_gov, Y_ss, cal, sunspot, country): Crisis zone (b_ck_low ≤ b/Y < b_ck_high): returns sunspot ∈ [0,1]. Certain-default (b/Y ≥ b_ck_high): returns 1. - `sunspot` is the exogenous probability of default conditional on the crisis - zone being active. In a perfect-foresight MIT-shock setting, sunspot is an - AR(1) path that starts at some peak and decays; it replaces the old AR(1) - def_D_path. + `sunspot` is the exogenous probability that lenders coordinate on the + no-rollover equilibrium, conditional on the crisis zone being active — + the analogue of Bocola (2016)'s exogenous AR(1) default-risk process s_t + (his eq. 12), restricted to the CK crisis zone. In the risk-only + experiment this probability is PRICED but default is never REALIZED. """ b_low = cal[f"b_ck_low_{country}"] b_high = cal[f"b_ck_high_{country}"] @@ -62,130 +79,82 @@ def ck_default_prob(b_gov, Y_ss, cal, sunspot, country): np.where(b_y >= b_high, 1.0, sunspot))) -# ── Bocola-Dovis fundamental default probability ───────────────────────────── - -def bd_fundamental_default_prob(b_gov, Y_ss, cal, country): - """BD fundamental default: only when debt exceeds the hard upper bound b_ck_high. - - Unlike Cole-Kehoe, there is no crisis zone — the sunspot enters through the - IC spread in bank.py. This function purely captures the solvency threshold - (Bocola-Dovis B̄): certain default once debt is unsustainable regardless of - lender beliefs. - """ - b_high = cal.get(f"b_ck_high_{country}", 99.0) - return 1.0 if b_gov / Y_ss >= b_high else 0.0 - - -# ── Endogenous debt accumulation ───────────────────────────────────────────── - -def integrate_b_gov(b_gov0, Q_bD_path, def_rate_path, Tax_path, cal, country): - """Forward-integrate government debt stock from the HM budget identity. - - At each t the government: - - Has outstanding bonds b_gov[t] (beginning of period, = b_gov0 at t=0). - - Fraction delta_b matures; outstanding stock falls by factor (1-delta_b). - - Default: fraction def_rate[t] of bonds partially haircut; effective - survival surv[t] = 1 - def_rate[t]*(1 - recovery_rate). - - Budget: Tax[t] - G = coupon[t] - issuance_proceeds[t] - coupon[t] = delta_b * b_gov[t] * surv[t] - new_bonds[t] = (G + coupon[t] - Tax[t]) / Q_bD[t] - b_gov[t+1] = (1-delta_b)*b_gov[t]*surv[t] + new_bonds[t] - - Verification at SS: Tax_ss = G + delta_b*B_gov_ss*(1-Q_bD_ss) - → b_gov[1] = (1-db)*B_ss + (G + db*B_ss - Tax_ss)/Q_ss = B_ss. ✓ - - Parameters - ---------- - b_gov0 : scalar, initial stock (= B_gov_ss at period 0) - Q_bD_path : (T,) bond price path from bank backward pass - def_rate_path : (T,) default probability path (from CK outer loop) - Tax_path : (T,) tax revenue path (from govt_transition) - cal : calibration dict - country : "D" or "F" - - Returns - ------- - b_gov_path : (T,) array — stocks at beginning of periods 1..T - """ - delta_b = cal[f"delta_b_{country}"] - recovery_rate = cal[f"recovery_rate_{country}"] - G = cal[f"G_{country}"] - T = len(Q_bD_path) - - b_gov_path = np.empty(T) - b_gov = float(b_gov0) - - for t in range(T): - surv_t = 1.0 - def_rate_path[t] * (1.0 - recovery_rate) - coupon_t = delta_b * b_gov * surv_t - new_bonds = (G + coupon_t - Tax_path[t]) / Q_bD_path[t] - b_gov = (1.0 - delta_b) * b_gov * surv_t + new_bonds - b_gov_path[t] = b_gov - - return b_gov_path - - # ── Transition-path government block ───────────────────────────────────────── -def bohn_tax(b_gov, b_gov_ss, Tax_ss, phi_lamb): - """Bohn fiscal rule: lump-sum tax rises when debt exceeds steady state.""" - return Tax_ss + phi_lamb * (b_gov - b_gov_ss) +def govt_transition(cal, gs, Q_B_path, def_real_path, country, b_gov0=None, + b_anchor=None): + """Government flows along a transition path: single forward recursion. + + At each t (b_gov = beginning-of-period stock, b_gov[0] = b_gov_ss): + Tax[t] = Tax_ss + phi_lamb·(b_gov[t] − b_gov_ss) [Bohn rule] + surv[t] = 1 − def_real[t]·(1 − recovery_rate) [REALIZED] + coupon[t] = delta_b · b_gov[t] · surv[t] + new_bonds[t] = (G + coupon[t] − Tax[t]) / Q_B[t] + b_eop[t] = (1−delta_b)·b_gov[t]·surv[t] + new_bonds[t] + b_gov[t+1] = b_eop[t] + Verification at SS (def_real=0, Q=Q_ss): Tax_ss = G + delta_b·B_ss·(1−Q_ss) + → b_eop = (1−db)B_ss + db·B_ss = B_ss. ✓ stationary. -def govt_transition(cal, gs, Q_B_path, def_rate_path, b_gov_path, country, p_path=None): - """Government flows along a transition path. + Currency convention: D-bonds are D-good claims, F-bonds are F-good claims; + each country's flows are in its own good (no p conversion). Parameters ---------- - gs : steady-state government dict (from govt_steady_state). - Q_B_path : (T,) bond price path from bank backward pass. - def_rate_path: (T,) default probability path from CK outer loop. - b_gov_path : (T,) beginning-of-period debt stock path. - Entry t is the stock carried into period t (= b_gov_ss initially). - p_path : (T,) real exchange rate (required for country="F"). - - The Bohn fiscal rule adjusts taxes in response to lagged debt: - Tax[t] = Tax_ss + phi_lamb * (b_gov_path[t] - b_gov_ss) - - Survival factor always active (no writeoff gate): - surv[t] = 1 - def_rate[t] * (1 - recovery_rate) - - Currency convention: - All bonds are D-good claims; coupons and issuance proceeds are D-good flows. - For country="F" the D-good flows are converted to F-goods via p_path so - that Tax_F is in F-goods, consistent with F-household income accounting. - - Returns dict: Tax, coupon, net_issuance, b_gov — all shape (T,), own-good units. + gs : steady-state government dict (from govt_steady_state, + with Tax_ss/Q_B_ss overridden to IC-consistent values + by steady_state.py). + Q_B_path : (T,) bond price path from bank_backward (embeds priced risk). + def_real_path : (T,) REALIZED default path (None → zeros). + b_gov0 : beginning-of-period-0 debt stock (None → b_gov_ss). + Used when the path starts mid-crisis (default branches, + policy experiments). + b_anchor : Bohn-rule debt anchor (None → b_gov_ss). Post-default + branches re-anchor to the post-haircut stock so the + haircut does NOT translate into windfall tax cuts + (φ·(b − b_ss) would otherwise be a large transfer to + households, making default expansionary). The base tax + is re-set to balance the budget at the anchor: + Tax_base = G + delta_b·b_anchor·(1 − Q_B_ss). + + Returns dict (own-good units, shape (T,)): + Tax, coupon, net_issuance (= Q·new_bonds), b_gov (beginning-of-period), + b_gov_eop (end-of-period stock = what banks must hold at t). """ delta_b = cal[f"delta_b_{country}"] recovery_rate = cal[f"recovery_rate_{country}"] phi_lamb = cal[f"phi_lamb_{country}"] + G = cal[f"G_{country}"] Tax_ss = gs["Tax_ss"] b_gov_ss = gs["b_gov_ss"] + T = len(Q_B_path) - T = len(Q_B_path) - if p_path is None: - p_path = np.ones(T) - - # Bohn rule: tax responds to beginning-of-period (lagged) debt - Tax_D = Tax_ss + phi_lamb * (b_gov_path - b_gov_ss) # D-goods, shape (T,) + if def_real_path is None: + def_real_path = np.zeros(T) - # Survival factor (always active in CK framework) - surv = 1.0 - def_rate_path * (1.0 - recovery_rate) - - # D-good coupon and roll-over issuance - coupon_D = delta_b * b_gov_path * surv - net_iss_D = delta_b * b_gov_path * Q_B_path - - if country == "F": - # Convert D-good bond flows to F-goods. p = price of F-goods in D-goods. - Tax_out = Tax_D / p_path - coupon_out = coupon_D / p_path - net_iss_out = net_iss_D / p_path + if b_anchor is None: + b_anchor = b_gov_ss + Tax_base = Tax_ss else: - Tax_out = Tax_D - coupon_out = coupon_D - net_iss_out = net_iss_D + # Budget-balancing tax at the anchor (stationary at b = b_anchor) + Tax_base = cal[f"G_{country}"] + delta_b * b_anchor * (1.0 - gs["Q_B_ss"]) - return dict(Tax=Tax_out, coupon=coupon_out, net_issuance=net_iss_out, - b_gov=b_gov_path) + b_gov_bop = np.empty(T) # stock at beginning of period t + b_gov_eop = np.empty(T) # stock at end of period t (held by banks over t→t+1) + Tax = np.empty(T) + coupon = np.empty(T) + net_iss = np.empty(T) + + b = float(b_gov_ss if b_gov0 is None else b_gov0) + for t in range(T): + b_gov_bop[t] = b + Tax[t] = Tax_base + phi_lamb * (b - b_anchor) + surv_t = 1.0 - def_real_path[t] * (1.0 - recovery_rate) + coupon[t] = delta_b * b * surv_t + new_bonds = (G + coupon[t] - Tax[t]) / Q_B_path[t] + net_iss[t] = Q_B_path[t] * new_bonds + b = (1.0 - delta_b) * b * surv_t + new_bonds + b_gov_eop[t] = b + + return dict(Tax=Tax, coupon=coupon, net_issuance=net_iss, + b_gov=b_gov_bop, b_gov_eop=b_gov_eop) diff --git a/code/global/household.py b/code/global/household.py index 899705c..822945e 100644 --- a/code/global/household.py +++ b/code/global/household.py @@ -1,19 +1,6 @@ -"""Household block: endogenous grid method (EGM) for a one-asset, -incomplete-markets consumption-savings problem with GHH utility. - -GHH composite: x = c − v(N) where v(N) = chi·N^(1+1/frisch)/(1+1/frisch). -Utility: u(x) = x^(1−sigma)/(1−sigma). -Labour supply (static FOC): chi·N^(1/frisch) = w/P_CES (income-effect-free). - -For the individual household's deposit problem, aggregate N is taken as -given (competitive labour market with idiosyncratic productivity e). The -EGM Euler equation operates on the composite x, and vN (the aggregate -labour disutility evaluated at the period's N) is passed in from outside. -Setting vN=0 everywhere recovers the standard CRRA-separable case. - -State: (a, e). Choice: a' (savings), with c = (1+r)·a + y(e) − a'. -Borrowing constraint: a' ≥ a_min. -""" +"Household block: endogenous grid method (EGM) for a one-asset, incomplete-markets consumption-savings problem with GHH utility." + + import numpy as np diff --git a/code/global/main.py b/code/global/main.py index 6a70e0c..0f4e6e8 100644 --- a/code/global/main.py +++ b/code/global/main.py @@ -1,5 +1,6 @@ -"""Entry point: solve the two-country HANK-GK monetary union steady state -and a TFP-shock transition path, print diagnostics, and save figures.""" +"""Entry point: solve the two-country HANK-GK monetary union steady state, +a TFP-shock transition, and the centerpiece Cole-Kehoe / Bocola (2016) +sovereign-risk pass-through experiment; print diagnostics and save figures.""" import os import time @@ -7,8 +8,10 @@ from calibration import get_calibration from steady_state import solve_steady_state -from transition import solve_transition, solve_transition_ck, solve_transition_bd -from plots import OUTDIR, plot_steady_state, plot_irf, plot_default_irf +from transition import solve_transition, solve_transition_ck +from risk_branch import solve_transition_ck_risk, bond_decomposition +from plots import (OUTDIR, plot_steady_state, plot_irf, plot_default_irf, + plot_risk_comparison) def main(): @@ -36,15 +39,15 @@ def main(): print(f" n_D_ss = {bk_D['n_ss']:.4f} theta_D = {bk_D['theta_ss']:.4f}") print(f" alpha_D_ss = {bk_D['alpha_ss']:.4f} mu_D_ss = {bk_D['mu_ss']:.6f}") print(f" kappa_D_ss = {bk_D['kappa_ss']:.4f} phi_bdom = {bk_D['phi_bdom_ss']:.4f} phi_bfor = {bk_D['phi_bfor_ss']:.4f}") + print(f" sov exposure = {ss['Q_bD_ss'] * ss['b_D_D_ss'] / bk_D['n_ss']:.3f}" + f" of net worth (Bocola GIPS fact: ≈0.93)") print(f" Dep_supply_D = {bk_D['Dep_supply_ss']:.4f} rb_dom_ss = {bk_D['rb_dom_ss']:.4%}") print("\n── Country F (foreign / Germany) ───────────────────────────────") print(f" rk_F_ss = {ss['rk_F_ss']:.4%} beta_F = {ss['beta_F_ss']:.6f}") print(f" Kap_F_ss = {ss['Kap_F_ss']:.4f} Y_F = {fm_F['Y_ss']:.4f} I_F = {fm_F['I_ss']:.4f}") print(f" C_F_ss = {ss['C_F_ss']:.4f} A_F = {ss['A_F_ss']:.4f}") - print(f" w_F_ss = {fm_F['w_ss']:.4f} chi_F = {cal['chi_F']:.4f}") print(f" n_F_ss = {bk_F['n_ss']:.4f} theta_F = {bk_F['theta_ss']:.4f}") - print(f" alpha_F_ss = {bk_F['alpha_ss']:.4f} mu_F_ss = {bk_F['mu_ss']:.6f}") print(f" kappa_F_ss = {bk_F['kappa_ss']:.4f} phi_bdom = {bk_F['phi_bdom_ss']:.4f} phi_bfor = {bk_F['phi_bfor_ss']:.4f}") print(f" Dep_supply_F = {bk_F['Dep_supply_ss']:.4f} rb_dom_ss = {bk_F['rb_dom_ss']:.4%}") @@ -53,7 +56,8 @@ def main(): print(f" Q_bD_ss = {ss['Q_bD_ss']:.5f} Q_bF_ss = {ss['Q_bF_ss']:.5f}") print(f" b_D_D_ss = {ss['b_D_D_ss']:.5f} b_F_D_ss = {ss['b_F_D_ss']:.5f} (D-bank holdings)") print(f" b_F_F_ss = {ss['b_F_F_ss']:.5f} b_D_F_ss = {ss['b_D_F_ss']:.5f} (F-bank holdings)") - print(f" excess_ret FD = {cal['excess_return_F_D_ss']:.4e} excess_ret DF = {cal['excess_return_D_F_ss']:.4e}") + print(f" F-bank share of D-debt = {ss['b_D_F_ss'] / cal['B_gov_D_ss']:.1%} (contagion leg)") + print(f" face debt/annual GDP D = {cal['B_gov_D_ss'] / (4 * fm_D['Y_ss']):.1%}") print("\n── Steady-state residuals ──────────────────────────────────────") ic_resid_D = (bk_D["n_ss_IC"] - bk_D["n_ss_ACCUM"]) / bk_D["n_ss_ACCUM"] @@ -82,76 +86,137 @@ def main(): t0 = time.perf_counter() out = solve_transition(ss, cal, Z_D_path, Z_F_path, verbose=False) print(f" [TFP transition] {time.perf_counter() - t0:.1f}s") - - cap_resid_D = np.max(np.abs(out["n_IC_D"] - out["n_D"])) - cap_resid_F = np.max(np.abs(out["n_IC_F"] - out["n_F"])) - dep_resid_D = np.max(np.abs(out["P_CES_D"] * out["A_D"] - out["Dep_supply_D"])) - dep_resid_F = np.max(np.abs(out["P_CES_F"] * out["A_F"] - out["Dep_supply_F"])) - goods_D = np.max(np.abs(out["Y_D"] - out["P_CES_D"] * out["C_D"] - out["I_D"] - out["NX_D"] - cal["G_D"])) - goods_F = np.max(np.abs(out["Y_F"] - out["P_CES_F"] * out["C_F"] - out["I_F"] - out["NX_F"] - cal["G_F"])) - print(f" max|capital resid D| (n_IC − n) = {cap_resid_D:.2e}") - print(f" max|capital resid F| (n_IC − n) = {cap_resid_F:.2e}") - print(f" max|deposit resid D| = {dep_resid_D:.2e}") - print(f" max|deposit resid F| = {dep_resid_F:.2e}") - print(f" max|goods mkt D| = {goods_D:.2e}") - print(f" max|goods mkt F| [diagnostic] = {goods_F:.2e}") + _print_transition_residuals(out, cal) plot_irf(out, ss, cal) print(f"\nFigures saved to {OUTDIR}") - # ── Bocola-Dovis sunspot shock ──────────────────────────────────────────── - # xi_t = run-probability shock, AR(1): xi_t = xi_0 * rho^t - # Transmission: xi_{t+1} → IC tightens (lbD_eff = lbD + psi_bd * xi) - # → Q_bD falls → government issues more bonds → b_gov rises - # → beliefs transform into fundamentals (Bocola-Dovis mechanism) + # ── Centerpiece: Cole-Kehoe sunspot, Bocola (2016) pass-through ────────── + # xi_t = probability lenders coordinate on no-rollover at t (crisis zone + # active at SS). PRICED into Q_bD; default never REALIZED (def_real=0). + # Transmission: xi ↑ → Q_bD ↓ (expected-haircut pricing) → MTM loss on + # legacy bonds → n_D ↓ → single-λ IC tightens → lending spread rk−rdep ↑ + # → I_D, Y_D ↓. Government rolls over at depressed prices → b_gov ↑ → + # Bohn taxes ↑ (beliefs worsen fundamentals, Cole-Kehoe). + # Persistence matters: a 7-year bond's yield averages default risk over + # its life, so Greek-scale spreads need a persistent sunspot (Bocola's + # estimated s_t process is highly persistent). print("\n" + "=" * 65) - print(" Bocola-Dovis sunspot shock in D: rho=0.85, peak xi=0.10") - print(f" psi_bd_D={cal['psi_bd_D']:.1f} => peak IC spread ≈" - f" {cal['psi_bd_D'] * 0.10 * ss['ss_bank_D']['mu_ss'] / ss['ss_bank_D']['Omega_ss']:.4f}" - f" (quarterly)") + print(" Cole-Kehoe sunspot (risk-only, Bocola pass-through):") + rho_sun, sun0 = 0.95, 0.07 + print(f" peak default prob xi_0 = {sun0:.0%} q, rho = {rho_sun}") print("=" * 65) - rho_sun, sun0 = 0.85, 0.10 - sunspot_D_path = sun0 * rho_sun ** np.arange(cal["T"]) + T = cal["T"] + sunspot_D_path = sun0 * rho_sun ** np.arange(T) + Z_flat_D = np.full(T, cal["Z_ss_D"]) + Z_flat_F = np.full(T, cal["Z_ss_F"]) t0 = time.perf_counter() - out_bd = solve_transition_bd( - ss, cal, - np.full(cal["T"], cal["Z_ss_D"]), - np.full(cal["T"], cal["Z_ss_F"]), + # Step 1 — RISK-OFF (liquidity channel only), sunspot homotopy: warm-start + # each step with the previous solution (large MTM repricing makes a cold + # Newton start fragile). + out_off, y_warm = None, None + for scale in (0.25, 0.5, 1.0): + out_off = solve_transition_ck( + ss, cal, Z_flat_D, Z_flat_F, + sunspot_D_path=scale * sunspot_D_path, + verbose=False, y0=y_warm, + ) + y_warm = out_off["y_vec"] + print(f" [risk-off base, 3-step homotopy] {time.perf_counter() - t0:.1f}s") + + # Step 2 — RISK-ON: Bocola risk channel via the representative default + # branch (two-branch expectations in the bank backward pass). + t0 = time.perf_counter() + out_ck = solve_transition_ck_risk( + ss, cal, Z_flat_D, Z_flat_F, sunspot_D_path=sunspot_D_path, - verbose=True, + verbose=True, y0=y_warm, ) - print(f" [BD transition] {time.perf_counter() - t0:.1f}s") - - cap_resid_D = np.max(np.abs(out_bd["n_IC_D"] - out_bd["n_D"])) - cap_resid_F = np.max(np.abs(out_bd["n_IC_F"] - out_bd["n_F"])) - dep_resid_D = np.max(np.abs(out_bd["P_CES_D"] * out_bd["A_D"] - out_bd["Dep_supply_D"])) - dep_resid_F = np.max(np.abs(out_bd["P_CES_F"] * out_bd["A_F"] - out_bd["Dep_supply_F"])) - goods_D_bd = np.max(np.abs(out_bd["Y_D"] - out_bd["P_CES_D"] * out_bd["C_D"] - out_bd["I_D"] - - out_bd["NX_D"] - cal["G_D"])) - goods_F_bd = np.max(np.abs(out_bd["Y_F"] - out_bd["P_CES_F"] * out_bd["C_F"] - out_bd["I_F"] - - out_bd["NX_F"] - cal["G_F"])) - print(f"\n── Bocola-Dovis diagnostics ──────────────────────────────") - print(f" Q_bD[0] = {out_bd['Q_bD'][0]:.5f} (ss={ss['Q_bD_ss']:.5f})" - f" => price drop = {(out_bd['Q_bD'][0]/ss['Q_bD_ss']-1)*100:.2f}%") - print(f" b_gov_D peak = {np.max(out_bd['b_gov_D']):.4f} (ss={cal['B_gov_D_ss']:.4f})") - print(f" Tax_D[0] = {out_bd['Tax_D'][0]:.5f} (ss Tax increases via Bohn rule)") - print(f" n_D[0] = {out_bd['n_D'][0]:.5f} (ss={ss['ss_bank_D']['n_ss']:.5f})") - print(f" Y_D[0] = {out_bd['Y_D'][0]:.5f} (ss={ss['ss_firm_D']['Y_ss']:.5f})") - print(f" def_D peak = {np.max(out_bd['def_D']):.4f} (1=fundamental default crossed)") - print(f" max|capital resid D| = {cap_resid_D:.2e}") - print(f" max|capital resid F| = {cap_resid_F:.2e}") - print(f" max|deposit resid D| = {dep_resid_D:.2e}") - print(f" max|deposit resid F| = {dep_resid_F:.2e}") - print(f" max|goods mkt D| = {goods_D_bd:.2e}") - print(f" max|goods mkt F| [diagnostic] = {goods_F_bd:.2e}") - - plot_default_irf(out_bd, ss, cal, out_bd["sunspot_D"], out_bd["def_D"]) + print(f" [risk-on, branch fixed point] {time.perf_counter() - t0:.1f}s") + _print_transition_residuals(out_ck, cal) + + # Comparable liquidity-only counterfactual: if the feasible priced event + # is a PARTIAL restructuring (haircut ladder in risk_branch), re-solve the + # risk-off path pricing the SAME event size so the on/off gap isolates + # the risk channel rather than the event size. + s_star = out_ck["branch"]["haircut_scale"] + if s_star < 1.0: + print(f" priced event: partial restructuring, haircut " + f"{s_star * (1 - cal['recovery_rate_D']):.0%} of face value " + "(full PSI haircut infeasible without bank recap)") + out_off = solve_transition_ck( + ss, cal, Z_flat_D, Z_flat_F, + sunspot_D_path=s_star * sunspot_D_path, + verbose=False, y0=y_warm, + ) + + # ── Pass-through diagnostics (Bocola 2016 style) ────────────────────────── + dec = bond_decomposition(out_ck, ss, cal) + sov_spread_ann = dec["total_yield"] + + def lend_spread(out): + rdep_lag = np.concatenate([[cal["r_dep_D_target"]], out["rdep_D"][:-1]]) + return 4e4 * ((out["rk_D"] - rdep_lag) - ss["rk_D_ss"]) + + lend_on = lend_spread(out_ck) + lend_off = lend_spread(out_off) + i_peak = int(np.argmax(sov_spread_ann)) + ip_l = int(np.argmax(lend_on)) + risk_share = 1.0 - lend_off[ip_l] / lend_on[ip_l] if lend_on[ip_l] != 0 else np.nan + + print("\n── Cole-Kehoe / Bocola diagnostics (RISK CHANNEL ON) ─────────") + print(f" Q_bD[0] = {out_ck['Q_bD'][0]:.5f} (ss={ss['Q_bD_ss']:.5f})" + f" => MTM repricing = {(out_ck['Q_bD'][0] / ss['Q_bD_ss'] - 1) * 100:.2f}%" + f" [risk-off: {(out_off['Q_bD'][0] / ss['Q_bD_ss'] - 1) * 100:.2f}%]") + print(f" n_D[0] dev = {(out_ck['n_D'][0] / bk_D['n_ss'] - 1) * 100:+.2f}%" + f" [risk-off: {(out_off['n_D'][0] / bk_D['n_ss'] - 1) * 100:+.2f}%] (no default)") + print(f" n_F[0] dev = {(out_ck['n_F'][0] / bk_F['n_ss'] - 1) * 100:+.2f}% (contagion)") + print(f" Y_D trough = {np.min(out_ck['Y_D'] / fm_D['Y_ss'] - 1) * 100:+.3f}%" + f" [risk-off: {np.min(out_off['Y_D'] / fm_D['Y_ss'] - 1) * 100:+.3f}%]") + print(f" I_D[0] = {(out_ck['I_D'][0] / fm_D['I_ss'] - 1) * 100:+.3f}%" + f" [risk-off: {(out_off['I_D'][0] / fm_D['I_ss'] - 1) * 100:+.3f}%]") + print(f" sov spread peak = {sov_spread_ann[i_peak]:+.0f} bps ann (t={i_peak}):" + f" default comp {dec['defcomp'][i_peak]:+.0f}" + f" + RISK PREMIUM {dec['risk'][i_peak]:+.0f}" + f" + liquidity {dec['liquidity'][i_peak]:+.0f}") + print(f" lending spread peak = {lend_on[ip_l]:+.0f} bps ann" + f" [risk-off: {lend_off[ip_l]:+.0f}]") + print(f" RISK-CHANNEL SHARE of lending-spread response = {risk_share:.0%}" + f" (Bocola 2016 estimate: up to 45%)") + print(f" b_gov_D peak = {np.max(out_ck['b_gov_D']):.3f} (ss={cal['B_gov_D_ss']:.3f})" + f" Tax_D peak dev = {np.max(out_ck['Tax_D']) - ss['Tax_D_ss']:+.4f}") + print(f" def_real ≡ 0 on base path: {np.all(out_ck['def_real_D'] == 0)}") + br = out_ck["branch"] + print(f" [branch] n_D(0)/n_ss = {br['n_D'][0] / bk_D['n_ss']:.3f}" + f" Y_D(0) dev = {(br['Y_D'][0] / fm_D['Y_ss'] - 1) * 100:+.2f}%" + f" (the default state bankers fear)") + + plot_default_irf(out_ck, ss, cal) + plot_risk_comparison(out_ck, out_off, ss, cal) print(f"\nFigures saved to {OUTDIR}") print("\n" + "=" * 65) print(f" TOTAL {time.perf_counter() - t0_total:.1f}s") print("=" * 65) + +def _print_transition_residuals(out, cal): + cap_resid_D = np.max(np.abs(out["n_IC_D"] - out["n_D"])) + cap_resid_F = np.max(np.abs(out["n_IC_F"] - out["n_F"])) + dep_resid_D = np.max(np.abs(out["P_CES_D"] * out["A_D"] - out["Dep_supply_D"])) + dep_resid_F = np.max(np.abs(out["P_CES_F"] * out["A_F"] - out["Dep_supply_F"])) + goods_D = np.max(np.abs(out["Y_D"] - out["P_CES_D"] * out["C_D"] - out["I_D"] + - out["NX_D"] - cal["G_D"])) + goods_F = np.max(np.abs(out["Y_F"] - out["P_CES_F"] * out["C_F"] - out["I_F"] + - out["NX_F"] - cal["G_F"])) + print(f" max|capital resid D| (n_IC − n) = {cap_resid_D:.2e}") + print(f" max|capital resid F| (n_IC − n) = {cap_resid_F:.2e}") + print(f" max|deposit resid D| = {dep_resid_D:.2e}") + print(f" max|deposit resid F| = {dep_resid_F:.2e}") + print(f" max|goods mkt D| = {goods_D:.2e}") + print(f" max|goods mkt F| [diagnostic] = {goods_F:.2e}") + + if __name__ == "__main__": main() diff --git a/code/global/output/default_irf.png b/code/global/output/default_irf.png index 6ffa003b25fa91ad6c2e9e93ef037e72d826855b..b7e1569c9619c7a6eb0ca434b76996e47b52d5de 100644 GIT binary patch literal 348830 zcmeFZcT`hrw>OFnw*}Pgw$O2_DAJTFp^MUcm97GT&|5${ih`9QASCn_ItYXSp~*(2 z*FcaGz(Ng0fq?XH#(mCt?|1JQ_wPII_%enDqb4hBJ z1SJJU`MK@AyxcqyLPE~}^A7}FJ?w-yqsHvvO^&#!n0PWUocojhYoA?epB}?L1_ssJ z3I-38<|&7~{xUxI{wLcB)sT~;6iWJEKF;cSy8BE`bM=kqZdrsNo*vCUddM`b>aVN# zDo3}%gaSv$7(!N<2#Y%N8nTc^LTjO)#u!Q z{z(66^tJr8|Lqmd$p8PnQ1#E+!pId%OG~|MwKKfAmVUFBS6ITCIp!8K(il{=wBCMt z&8GhI>&MH&!os9(tJ;U9LqbyMx!8*h_mhvdxf*hnwRXq*>!VbWCMv9&Y)jk+P(j*uvtExqE@gLTOWnXA);xHJ zCT!dGS2;&WQd+~x&enS3M0+9f!OQ2*qt97BY&^In^J^m1#P4-l8h4&W4Z$GafLFb{ zdD6m%f}X09a~Zw4B4fL@C1^${+=wV#VJzcPsjzANb45lJ)pvV2&pIKjzc*qSE9Qu+3Htcr zj0L0nJf+51>DBoQ1NnwA@88Sw8WkqAmSrobA~bZ7vz|QKDf}{itJn8g4$tN{nk$AK zZ|!wLV82X>Z{&a8<~U)Im=v`+cYfdGJ5xMXsD)JBC~pSlhhq0CCgIh91~s9kQ|E|Z={MO z#Ya6}(7A27NAUzEJ%@dEy_m&U56N9R6JW7;?O0;hplbN4NjdVhxo4-?l*nJ#3|ef& zR9$~v;doK%I9RM#`_NrqDbR9flcwo5nr~Q;L=K$Ij_=r-iPG!s?Ok3aX(y)h(`tIZ z2&1jY9omvVDfWajfkGT)ZS>;k(5@u9Z;~awp>&|YIB}djU~IME!QnVUb(|CLFI3s2 zY;A51hbyVMZaEUzC_LDG+UxE+=7t4EarJ>)*bX&J=ZBnz%N%6KFaJEn__-KG@NW7R zIpEbkY!-V;iEaB^q`iTF zI&#Km!eRW~+GPr}cw*&<3?T7J!Ig*_`Vaiy~r zgO)Lm{~12lcT~Ki%U37-cE2-j+Ds;8wp1tbh#&vUmoL?6>=bQLk?f$QO_{mOpr!V= z0`05QedDz$TxhNV>nHUMEQ~;%Y5wXy=W>KQ#R*Fb=9tHg|G2-`E%~{52*DC z;{@i?+=g>9xVci=V$G8G#)x+ZIZu(3DrwbS;HS}ZU%6s8qrJ?NQ!8?}i)Nm;Iy(ES zM$^&0y@T_#h)2Bvu_WL}<6)k}OwMm!{?r>w&#$ChZ(d)m=L@tjF8qe!Tr%I7P7Utx zHLk2&T=6>)YPeTD>S%^>P4%1OUEwg>+TB?n7qaV4(Oi+FZ?LErO!TL+T~LbO_mrjhpo_9cl6FE@{{uo?2LNo|0J~^_-Pme|paHU3|{n z(gy^bX5z5gTYgmPdY`&bwqL~JX`<7QiAI8;1u>z)aUkE;u_x_OXLon^cEf{@F9@U@ z?Hs>F=rhh@j$~1)Jqw$-lRZyCZH40?fkXP?j~7lgiLUiSyMAY`8N8!6GNU>_wAsCT zX_)oK!(3I{npqX&)BuZOYPL;XH(bUCE9>i(X9P_9=e4ud;>v{lr;~m3WR_-gQVBvr z7DT*ngv&(=7kaGegxvgcO>Z~3%vOC%e872i31ymC0Gbrv>@{C#6$M+GTWD! zoQCAw+^g}E+t7JB5wM%wtIgM?EOx)gXd~xVPOu1nkk*bgDUP_mNudO7Ele$4Gt5ue zA;W@S$2$%Rnjv4aNpwwp`(|yaXw^RK>Sg7IjxcKos@w6&YZkGvS`Va3Rn6rWH~~sv zkzJhrWM@_5U9|D~46oh?{=xC0Z?3NYJUOz4uFxQMwF@;k1r?Bkj5C#(MgSz)2jviJ zO)DG--@n_$+Xk;wd^!WOKk2&e{ zEG#JMSY1t^SsbtaC-!MtfTN7WeTL}IJ9BGPmPO`Lpdrnitvd|mi0HHE8!-;eW}dDS~i zNhF{%4O&oY&}QBay6K;PoI0^$BGZSc>t3B=gEmy@q?E&)58ub?rsgy#H;)%-NxExv zv}`QT50w*)icE*(;)6IXn)91x>nq&9eG+Xbzz5~bE=c)VmacJ8$V4Friu&Fjpc351 zFuByTKUWtyGFx9f9-oq0I7ljM{`k@Mmp^^8P7M8#wI80{J|4)^O1gO;;o`MXP*Oif zkk1d=*~CoK8}ZvS&5>LZ98N}uQ>>de03ptEpjn^A_A=Ab6~nQ%!@d>wn;3JnjQP@3 zr7oe_kYZ5|+rNI|^mX^>?HjjM3wv`&ic#M?$RRwQ#xmWNOf5TYA~lK^`CMAADfNs> zE^>S;2iPHROke6K+T`1(*UmLQON8$6YR{b6^Xdhjh@8q~#=q|-`vL@YH^r4mT7Nn| z^7Z3OPw6PN%AuLw%w)v2Xh_@9Lif>btrTe#;)oc7TEmHEt?yY`mqs2RI;J5>+H}ER z^aA**mE2|I7RMO+z2C!sH3_7v3KRtG?kFx7`yk}#W zC2)6p!=PbseLPrhpfD9ZvF0VxMS?d>P{=hXpH06v-NU-;xNe}N3X2QLj z$~mVp`OCubWFlYS=Blblm9}Pz^bo%*U|AfYaq{tVuZ96`p62l^MjL|b_!ws`hK2Q# z4aW(wKeP@A3lJAp*T4N@d-44FjmB7FJ>zpuDesQ@`SIXAF@4NV+-w76KzSh&e*l&=zc_uIAfpH=oZn;#P``e zezy8jX5)3%Ctd)2b9^_6L|6N$+>~(A(Vp5-+5ComOz#JG8Z*$R`P^n+3e~&$r4;8h zzl)s?lxdiA*IV~xI_V+pGiO&B(Zu>4x95X*7`Qe9=QOow<;UdR>HO7*bCT}nDoX)$ z=sDcY7NO9n9LZUMp6O}i|Cp{ooVDYbWT-TIJXy1Nw9EUG6x>L-rHT(cv{nS+_I&U(f#)MgIQzQLc$mTfC4f zuA`@hR3!De{FcZK6OQy^Vbj+fQe@6&nI?bh1=YuL*kBJSN%5m=?B$>G4O;jl%kU{8 z?{XL$eY%h=yE&8Tm71B_+IBF?Xu7&SOmOh{`C>#g+9Ph^{#<|Fn$`nlV(?5rC(k=T zH3r=z<{Jeflv2`l2?8aSSAU)*MEKg3{ds+DGZ`al@luTVS;BxX_R-&&>5)M3khXlyy9-FXFpGC?jUiQT+JA zN$=(xALl}}RNL)rbpRP#gze8OquwEh4)>r+7F%1KBHoN zNY)|B1u5##e^sBwx^N+5h-hxxpD5{x+wuAO8IyYYk%7s^JmyGNpL4XZ{iVrz-x24^ zY|}O1rJqGk+VXr;NAAyyMGm&MRPzbBNp}&kUVByRWB$&{5$vk8(Lzqgq8<6v_+F=n z$q01WVHe_qLjiy$C7T`@%=R8nFgX%dzNRbFZ1zaDnBe1QLNYIX{M3uf`}kjN9i^A} zroSW|&uZ&(zfiO?IacStKnrQu-Q*kakXbYvBGhk8C9=bTF?b}G4Aq(khO??T2ads# z5Dgs!%&8}19GY%n`{w~z2_g80Ih^yq7l(%yl%S8$-8AmJJR7H#jA)%3D98rTQugrY z174Fd8)CO>Z^l!4n;PhUcVBS}Au;L}%8u}A(ZHO79>J-2>sHz75VAYPnRBUOFX-pW zih4!BuODBgL{jB~EsYBAx(^L*EmV$+UQ;Zto?_)S%#UqH7uT(suH=pg@(F&TntK7z z@EY8qqbu~H~9Rn@$=62Jn6@u(PwdNJ4T6dA!v6#SkI2b!0XGw#2> zNa-B+oa$&Da5m1XMse1Hi>R0>||S9%%cN` zaab#WBy^HaT8~a%;<*B3x=$KFvxPSWR*>zcgtK!qbpDUC5^s9Hseo!QIcOCeS5Qn; zdn;(+Kq#t-;4NND1+;~*h zC9OUQ3v1Jqpu{=^LGA0DmiAdpN|FskD3)Z8Jb*GkwY}P}uWdUgMTDkmu^36zbQmla zXl-p3_E|JMWTY0>s)eyQU z8w$~X-$|MiEHbTlQ)b(a-LY&y?n;_^$ZPYBX1o zaY@SROz%bd>KzB=Lej6k($enpyZciK^K;uik^oZP0JgQO>iR9mp?+$5{{G>62#6*L zT@M#j zLZirqCKpF4U44dFCRLG!Lip4PU?svxy?}bkZM7|9F33`NL`LmYSt{$H!u0 z55K+kYR}|wL&Y^8jd}(>h?;#oKi8xj*FGI5XfZrVXc!f*KtzklD^#LfT%;Brk_P+a z|J-38-WX0-JS?Jp0ErJ3masu4qTY7_t(^os7B$tGs6Ik)*37B!{_JQH8X8Ip;95u< zeE)|req|NP2tJr=Xv4+hmXlNAjs#i~7I5b(RSr-Za@==^HaI!r+8~x(y2(K$cn$DU zRR(*kDrss=o|krc~0Ui_=#{kR9i@g!z6chC22=I3#uH(j}D# z4>H={zbowNPCYBLYafyu^a;>|)mjC?h2p?TlOj*Nebv&kBp*NPC%Gfl(%PLeG_D0w zfabkO#tnOho|fo~3(dRGJ%5&MDQdmjpDTKk_aY~dCw3;M-oV?`OPKUV{^G|zVkej1 znz=+zr}%4q{rdI$WV}_^S}A!rl_ni^ijHUw%jeK6je3Z?Vdx#ufGhyN2K^lKU&>~a z#|(tX$w*XZG-@MIJw+~ZMb@UxsdRkhY2(M$Qg$;pk8QSEzM-kuq6X{vn0(;H z8NR92(fOosN}#z4Rl;>4ltvxYUa2SwyqFW=X~dAkI8mcl;b6EjGAREQ|Gj_XclrAl z@I@7Tir{cWr0@^t`BKn>t>>%)?(f%?-Th@<8CMjrv5b@c^5x6WTz>_^-ket-_liWE zE!`g|y45==%q@uwQzMxzv_(u8Y20CL@o47oX#vI2wk=W56!V0=vf-O}*X)jrNFnn*$~mDce92_iDA0PO zeSsfpOvM4<>C;e?>K42D-ViC4-FV38aIl|(0n0}6)Ccjd`Q4ZlSm+}M^=5ZV8V)dB z?^!5^GJjh7o~@wgWCuoMN>{4h^V+jHk8xL|T;{-qrMdO!vp&1b9?Ru+YqX*rg#&q_ z@4Aj=^-U;HBCsgly7#H`ed9q&guu2!6&6V!+y2SN*F+Q}`8G!ekE^;i>=2H)5rn;v zXs-78-T8z#ks|AOLDJ5xa${SYVDi1cnfl0S+C^6OKa!D7eS?GCeiR?kf>^Rb*;kKg z0j4Yu{%NVDTI)80jN3;}AJ3=h%6OYRO2LXz>EeSSr_4F~iH%=BzjeBgAiSxi8s$!P zd%Ig&n8PBToMh*Fw(g!;kDi$vQR<-g&uQYfn|6mJO(Z0%GTQr&Wwo`gaM3YR_otp9 z`g8GBrt>|on1}Pylb<01l4|Z^Ug9&w_>%pM>>`oFYVDvLCXqEG#cE%8=R4SG`^8wl ziyAOV@=%l9ev@gJ;;ZyqE??&>sr@R<7iX9v8@OpskVZ`2o_bg4LMn8rY47DexxfI* zS#IlE`o0j>KXQ}XhE)qq_}ERFdb0*ha=P15h}gG`A7`VKU=zF)``!f&(nZ*|%H!*Y z=b~1m;oXRln>p+29q$mHMH5M*X)MZgEqK4U*Ru;duMG-}I=!Azbxcr)%;!$33@a2j z8fnXJ*kDUiqsQ~4D&0++9XsD;Y&D3DCd9pVXogP@a1yd`@=rv!fe?-g&MpC z;=W?}XfIomOL`blDsT)Q}3{7Oqw=0gFJ zzf5@1`SZIABU^Wo>vqkBNvK2Tsltv89W6JMyXDkE^Sr9$J0E5>3XpeGlhd!1dyJK} ziJrAZKWyvM7y@!&LvPQB9Ht*YCzvKZuYZAb~PPlW_R-y z?Ah?WA% z?l^P4KvDAY0((W-z|3`rcx&4g6)H`5#AH6;4rzlypz}3=$aExvcfhiI)mHoo9>Gkl+vD}uUVH(uB!i`Nzj>MCsk4J9~Tv#w~b@>rsI?Yhjj1td}oi0kc z8i`El+st6Rje4KR_@KzDLAG<+a#6ZaVZONQkdd3e)}BlG-%`_Ch2+q}FZD&~Sp7cv zPpf6RVBImuYyJIO=MOQnpNK$ao0i!;$BZgS)rc0n=98+X_xBBv!olvP38{QUoz^?uDe&Ky#U!)GSJ62I^}?KC z%Hb@YXYS!RqOq#iYLcTPyk-y_kCkV?(WS&)a#zC*>rV*{v=!80WZMLD=glABm<$cJ z3o;RV7z8`})AzBn_FE5?+eKn>IGl(n1N)N9Mzjtb5>x348*DSe-PK3oYc#dv@a z-D6*Jnm(9_zI=>aQ{6Bfs<_v<-*>*WS?|!jnf0u#LPkH7av74O*4o{z4Jb%~ZaC<> z#UWp+tEs&qIFBF>HQLZ>+>ig+;?QH0DDHeoRJ7k~4(Vfm)!2nN%L9&|Up})R6Jy^- z+tFM#b+Dm2md1|a+SCYjzjr)SRId|1QQJg;KfYZld+Qk^BkKKY@0Mv>#O<87`l0C6 z^~IYXFuTlv5KrV-hb{6E8JbBHUsgU?B zLaM`6ILD-+E?YewCslY)3B{0%)FpZ1&IEW@?_A*X;_7>^+UaG>l%C&kg3q474F*)u zJ=-@P()Bje(z)%czQmH*jmGF8JH)}fxN6l|H!rnv4~Oe(`a=L|)Yx%Kp=`?e$PUA{L35QT+(-(JRDSXS!`5v9b(x*ThM zFI}`gE2$6}$BM@VOO?$jVAFfuThus?>QK)`jE81MUsT4vib8nhk~P`8n~A}5zG4y) z(Xdn*Cf@u}OP4-Cj}bA0Zs&Z4&j=rH2i;t&Bzx;g!A3;iX4l%% zxN8B>Y7^YjNUrWhGuT)Qwz-A{Zx?`)RM#)a{+c+DGuXF8fUdB%B*o4BUL2%v&}f!M zha-r4Giz`;@N{i{?MpZp)^>A5)zLqzqT6hw|K~c`l%iWJ0KJi8>T<3iu-6@@tL5#3 z)gVT6+VwTi<;;|6Q0aHAz!M(}kO@!{UC6@OiO20qNAiJpJbS*TV#7$CuBZsnBG> zY`D>&Z=nqKm2@6Z!HwWb7m8Ep;S*gyyLUmu8|l7J)B+>9lX|m0oHn}JH5jofZ69bP z5Nu?!m9nV6FkB%L<5B~TL!#$v8t10}L$Ilxf#wso{~53&sdlUS1ysnI0qk&2lyL1T zwO-g*neJTb`bnb&Sb)T_<*C1T5sVySU?8Ogz4PX>`tWoejcoQ?hSyK|c7g+8v*$b` zsn-r}i1W(c@?`tSIA0y{5g%DLzk6^z0`24_tu+qRHgE0JPSG5Z9F)~yC?iDOL;K6F zeYVaE`UV%Rya5Tuq_ZYg^v2R;m8Mc8XL8Yso08Yk7$vB%XPs_3+~AgKe)=j&sKTXi ztAI9C==V6~N|9eLq(by%w7a#9D7V%ncXnxab7(J*p1JnfGLSYPy8g&2cxNDppS+T5 zZLt!q%vl)0a`;PUgRKSSosjaHXems^&fyz4V#M)X;7S*#kIj^j7!v$@oxaS?k+LFAl9GD1w> zr^nIzVCOqAkjkqUiByd>MuBjoeqVaJ*VS7b5t<^r-|>Lxu};@mNtuy|+81$O6xy!` z{Bq+3@2Qo1&z7a<_-U&^nqgS@$F^s5QJG&AE0YfZf#+om@=GqHPlmKb`R+bFdd4Gg z1ocUDXAE7%!huwlt3-zNIHxt4q1G_5io?0!aHv{7{Bo$A3l(;S67SV=8Qh(Xq^u#8 zRsR~uw`lTY(v>RE2sDij?jI7%F<~ zDRW&v9;II{vs#H8$`e(*qhx{!Vh%-Ho`SoPWp+MLZnJ`=FA z8b4lV1l2-w?W#<%{oB9zUK%J(uHLwO#ok+(FYyKE0U?PG@iY#TaTCk+_XkcSESX21 z!rs>kK1OsU7h8;M%Zk>-UfkiJu4iE0c;yPoh|dHSjn^RweNJA*VMLPk<6R!Hf;TRT zGUBuDYUSh^H*EFKy1A24*v>l*iX1O=L@vy_x`Lo)5~K-6@b#yXaj(_NkD(Yvl2^}X zj98ZM%+{}g2bWkFg?-w2gQOGm<=2o`pOX~AJaLiFucJRuGcr{YT{vLfz*(}oO@Wf& z=`{H5Ib6>@M@Cggd&Em*WM80B_eHhtL}C$bk63cIX%BfDqC%69(YP_q#evXScNb_1 zT&LJ|@$rsJm5X}j?uL1N?f@?~hNf2jVh)_@SL)S~Gp>e}HDoc``QmA}on+J}`9KZ< zYc;H`DphQg1z)=kWO1_oz8K*I)0G-3Q10eMvQ`#HQw2^0nZ7=6EZeyhl`}Bz61FZ)x)kFR13H)<%315>*=Xh z;im>X?y^{WdS2C~H!2-VR?Uw%`cDk+MP`234xcJfxM=jwJSbi9k69gk#P>JM>*0Kg z;kkWXt{h0Ua5EBj6yE+`DC6;gLKBkLuD`$M2`hz6g;T8eG8!dW&2Y=tD&zvw%Vq53(QpZ?4cCG?6#Ly+2^XFZ4V3 z#1N8*!d+zg`nl9P{I-%3Ha#Y)CL<$bUa@5EY(0(Wu2P0c|LFVKi zBBBdGX*HdLW)v2x5g8vJFTJx$GA?{o9NqDpBl~(b}IcDY(t*GJ0f#LuA z%K;?lE9! z+?sI1`a1}`i-Hg8v(dao-N4$rvz)J)a?uOtdnUS%>*j}%i7NYVeyuJt&}_a z7}DhXo!PG}umMNKjzahJ_%Wg2bMBO6$gTqSR)FV(7*t+KaJ3*OA^%pujE8Qufn1|{ z_in=c=TgO>jmdL#=wG|iCAIw7I)+AVASZ(w19TeDLpCvQeXF_ZkugtO#yjZf(pJl-R(V&{Mi} zk-p~T9}F&cpfW~w3}1iv5mm3YasBpCY-0 zO6oWB&Dsw$EYdY(=`Kj8bY`8oqv^PK{&qHaZWrn1*@l^j-sMY|KF81Y2x2E2&m=G1 zTz!e_GzST6$JMb+FHZ_*lqzidLe%NA&i(@jvVlu3lg(T)FA53_l8UqV<9ByDCB{M9 zpcSt396~_gKAcgfGvP*4{{>*1;RXr@`6v=@8F+|PCxq)nm>!r;_Y##`-(1$5sA7!C z$wD7ZUS0g~r72t;GG>>qUR9yDJC7aE{?rLXejNBAY_y{? zs9If5w#K^*6kA9gVP<{{a6X|>M~$lKTKh9h^-f8us$3;wXAaGm9yfX1qS&caz2y9U1^u67cB108UuCMQc^`gPu@b*r5 z)|l^fGS3Kv!CUGSoKN?B7HCU(Ji za}Ov;LdRdQ5JXBOY+6qG2im~(>Z|qjB!%J7iQTeW9g~D!*dQoS|0HY&oz9w;^ls`J zQ58%N&%|0^JPm4dkBeUF4Z6VZ{Uk{A-|<>+(Qs-UK1|jTU{EL z8(5)74a?_28ds-nfd7i1d+BwlTGU%L-V16p*7$-0$T-6*$4;D(KnqjIMm0b)LG%BB zw?g?RkS|nFRH;5dxjp=nU#*=8f+QHd6sQ8t)08O$v2+>K2V0^RI|qlr3E5w18(Mf- zKiYtCI}ph7IvEBZI_ijA0Vf};?EGYTWyKR@RdSJLwu01zXm+0Xj13u*F7}86>!3I6 z5+Eb~0aXWlr`4+mHCX}<@iIiR6JJch(wl_r_1nVp03dyrnApTyAl2s-wy7JzWXr(7 z9bC?O2d|aMbQR@pj$;xU{_SUxSa4`YXSZH%4`=>fglZd6Eg+zFc7imq40b4`UpHun zX6%1Gj!`TRu$(r+d)}V)9zA0~=Wpy%-Ws4pah+Dl-e)wFUXt1Lm&1lxg~MffnqKjo zgLR~M)(-FuqzPo^kML7RDoarBNz9VQsmg3eDq{4cm}o1GirtXJc0}yrdsV5f!zMW# zZ-!g#O6LrV?XRShFQOA24p@U>-Qh^zgvdn4CaepW@~}K^sVEioy(N0e6XImO2TM>n zmtk2air!PDa${Kx4g|0ARCCJsC2U4}JE5BUU}y6$)viR$YV@ttWdecg>@N{DBi=cX zhL_EzF?1eHN5APzlt?5#A(H@4-bc*G5?D})+K`sh45{)qa5zb($GiM>AVI8|tYPK2 zxjH*VEA+E$JTjOeTcC;$l-7_ zM3u1buR2qEloupO;+Koo1AuR8DQK{|Q7RmbPybPb=2);J$vZNsd6-L-;|2S3w7K+~ z`w$U~i}nF_7yqR*Wz7}5rV&X$$>S+ds?@NeI2=j<+C(-R% zX}g{@1$tO>Ya5hg%m!pQJT^G#wvE7z8@;l7;B|_vG;A$OZPh~k{LnE1nVkG}8JN4- zS%QXzq2UE%|EpwcK`y4WTMjHnf$P_MjIvZtnL)pFw(CkN7228zJ<+jAw`AM9Ni{yM zW_o&W0(VcyZQq-+g7FV6Dz!G2R&9g4 zgy+wmJb6zFkV2p(Fk>gWGzt=*WYHE}V$7f~#Ag;(u;R`Rd{*_fC}oe>sHllmtkF)J zAw}JU77h05Cs}Q3AZ!H*S~MUa$$=%j6EP@NHZaLjnHmqt7y^j@og!R*g47?LKZ$75T3S~`<@JBc*`wEAFvZqg; ze1?3zGbo1d4D<9p;x97p$XQLiQH1*AL*!jg%K*hEN$r{2{n6!X^fcd@3zPb{#O2Gjl_LJ0QYH`(k z&#S``Cbwu^igl-#AVRa;A<~^7^*6^-m_D|#@r{3SB3hnO~|`v(KVG7rbKq0+HT+7T-&tDMbf zW{EKe=SWz}d zSp*ruDKoqq9H`kTpG`Z}DcuXI_m;3gBH4Un|pX4 z!vv=v=OE!!vS?^@%D5P*5-H^4MC$QA&Oy`^aY7)s$;8KE21(XCw?`Zb=P?nYD_JSy z=Y%YivX>qFUn5lp=f7Z!TBF5QOROdF5jf|2h~ue*`;_3l9iFC1&^eM*N>a54d%f8@ z0imYE2`3_zs39iVO=sq)QiLI1=O(p{>Nw;9kP7NscVl(2XEe$qW(hqWW_SzD@-eDT zCstE56ECN~B_@DvG@yneZhr?qQg|C7Z`lNJa{?KmU~ra?G70i4Mdxpji*!(wTWl40 z(zVMR*Y2=ciqd?blq9k0T{9}2!55#Y&2B2bE1F%+ES0G^f;ybQC=2P*3nUI-l>7Yq z@)pR1k>HOX7rrDW(2$N~VYqN!mCa76mu?@$0YdI+Ai=R({=v>}~ z;`>cud8{qw&Q_ooRl@Mj=h;2K-NE$N`Y8<+o|;lSc*VA(M+Yx>c(vFz-`5vSvzYC9 zXw;l_abd<|{#9Sv<%Oy~zo>exctHyd8UDHpsiX`=@=foMY~${-x3fBkvrG)6?#!m@ zs%u+dRCbgBHBto(JHL|m`|81@%Z-?eGSiMVQH1+hg{k&3pMH*(czu?Q6TUk|4_?u2 z6THx!+qV-KuDuj9ut1tj-UY9&oft;8)_bcs;w`FUPQ8ziE+d$kNH8ME_hY}geUvmj z-0=c<2!e8TMkSm%01{Z5*Xx<{oScIvKS#tzJLfs0f1@{Bgn3$@^H4+Zo)gU-Q0)j_ z5yVodCfftcf5J%PL-wMc`SFJF+a)=#uKU=aYiBpBTMcj5jM?UjHi0-M?}uhBDz{fu zSF)#McucmT>QgErGGW9n%XE-vEdWhwHliHHKQKzkV3M@c#W6A|?A%TbWboc_L@@cD z<-5l_DePEpo%zPvo4?t~K+6V1L*Np3K)XD` z?2*A{CD{r(lbzKP@RccD5sB)yvDiLfSE?Q19|^$9|?+AOkSjYRLol3YZj4D_{rlO2m>IXl?KzlprcDrR%-50 zxw~c7lTiMB^KV%LiWJ6JZD6pE3&Yk^_RqCuPzvMHJ#HdD_4_5LVPwovX~xL%Iy*db z!WpBH*aGf+$ogZv1&g%jnBFutHu7)>_GPJJhKq>;6`VPkTH$g5(k-qlN3YROcM>>L z30lgXe?(bw$0?!BsAhOlXK8wit`XlsY#jGx&F3j)w~**xvs+TQk&Z6^13>E3mlpdF zugNCDtSZ^)m-sLN!c>jJ$+ld2!c3LQ1JZ0vYs{5)$SZ$`TE13un1d?HYw;ZM8mZXx z>IiRWp7}t7aFd^^lEIhNER5!!IwcGGA_>V7&y&&9n~4WFZ< z)yYq6u3m^{#~LlrwMl^j&eOdi{GFfikipFHiy#MzMqMi?^w@^ZGNn}i>RDu@#H0I& z{xrfqnbjUep3Jy!J)uTt(M9S{!@^$FWOwwVdfEQEc=4hwyjz~IJPJpG%!7urdR=Qz zn{1rLa5H9ks&$@Fy3bc(Zp3Y0S_moZTDD?$)|tPuIRb|_a;^kxzIN$-5o^Str}WnG z5b4D;SmZq@I}20MYfu+5z&gCZB$q2er zvHk%gC8{1a-&{n_UPjQ?;>NXWYUC0tA&9o3eRDWrtoXAiUmQ|8LCo=O>+ZcyPa7tI zLXOv%Y?Ht6b@UNDn5Fs~LJ_qY3yQ2)aWq_EtjtCGZ~Gaz*HL6%rcdNAKMFlvkaJfM zPf8fKX0H44h94(0q|LXe$*79x&i;6ey-bBrZgi1ZWt&L1bWA>TW?y?Q<{ZpU4jz=t zKAzD>5I1%Xm>o#=G7Ae2uhCeJI>WN??cg%o@5Kt~FqKG785>|E9x%P22H8)N2a{SK z9*6B)1-^G$PEKyC3|yhaE{V>Cd0Pe`L+U>u#SGsF7$}!RFFqpq#48pa>i)ENMYIClnObI8(kbQp4#r}59OIC>+NxAP)&yr0q8BP3EqYg-Y8}zEY4LpR0@L?C zzdS~_>illhNMc;FDBpiw9)H-e_{ff<+vLPwqF3i&C@>c2e3oAX4@-~t$!r>1=USG6 zXoR!Yrk`v529nU`4cSO+B1v>>5>BYTz+hS$J(G3F1?AmEkH6>YW@<2MFP!TyyvzE~ zrPrHPpuE1A@tkB|y&MyBdw!;JB=*9Fpm`PdkYVYAJEZ!lUn~_}bf2uMwpzH37{lzI zE5R`4*)1d3j+0PC;yN}d2;_%8=aQWi5F_GXI?U!5I;1uX=71lvGwJjx@hw~hV7#P} z4MXffxCiNHh;VhA4CF;`QPiv)Sj3$Y@&Zr{dR z1$C=#*7l*Fbz&!-03c6Qn+`4Xq=#al6E&tOHrNN=Ar(7PZig1+8{|D;l@_JGVRSTm zeZ|N^iLFWWnd%afIWInMlOHJ$F0+7#_k*UvMX?1`wO zColY7j2#-R^Yt52C~^KkS0KolsV?-P!fe+4&831lKE0^Jx#Zr$ z{v}?zt&RJp&sGM5eDg+|GBe|l$C23g1FWPK`!wC=2#(GlL9F)0$-dYlq-b)oXrdj| z7ENz2XFdl&^0VwDzeMs4e7W|4qF90uO6m>cKaOnCgkz{cwGlk0@Ap$V3<5>LsI&T= zxMH-hgyvVZb=ze%)_P@Pj)a#Pj;gxGZuV@hw^BW&M_ne z+!dll((hYxiYvIlcAxckX5erW3gsHn2<#(gqO3SOAueN6&xm1wG~tP$?$Faolk zja0y|cfMAEmTD!n%P%lXFiAN^{dmr*Bii{e(25A4(RF(VDAK|%fnPl0seXzzGh8`3Az@&^P`I{^yzae*qo#1kj zO$<60cg?WuHLKXQf%UYK7!Nkwc85{jDF%kBaCAL+?c7VUP@jX5&w#ki#@ClZ9V=d@ zVDVy?d{<|5N9Yn;fn^;rui)v^r$ej~E>8a2Kbw>198&|9pBHJT%Hjmn2T81ovbVD1r$}Tiqv_~5edn_8|p2g z*)3F0r<5BOnGW(#o17FGCcjzt+l#8e&uvZOvxv*Own-LLXF; zEg(N#^`zR}ggb}4-8u}n$uKq>EVj^0j31B~DzTC)r|1#BRX=c!grVLMKv1rHAS34O zS)d0DXUXVC+yAKVx%*r{$MEp)*G~qo`n;a%cDOACx`MFQQj~$wurJ*sfCnbiq(t=e z`E;JExwRCFe_#9W-`ynsz0b*i|C!;T^#9`pA+`g?bl?1c9zWb{e$S`=&kGYj|D9j> zzrP@#;=TM`)fcXCHaOaPxjGjhQyBxX*H%c&ep$?*hctgb(inc04LOPXoTGn^n=J^5 zedTuAl7XA}{t_!IR#AKI>F(5cI;w%`i%TFtQSKc4um8#*zwZlm@%QneY%@SMi2v~6 zLkox}w(mpsX|)5;SRj=B=YuBxjfDF4fk=E6R@WPP*6V179$nv3um11%WYD>O18*g3 z3|X<~Fqs5No9ddY3=Dr=g}`^z#Kb*G@A-t9mtUhrj~UMPWnY$*9J0Y(Gsx8`u04U~ zWnl2&q+RWnmpRH1lB6utCZZDw0M2zVyG13g8+C{8!i7f4M+Szp_q{6xDRy5tA$OGu z>4P?q)SMsZ!om<(nd*%B{P`~3?jMDKlwPrUwNu0Y+dKb})izydPZKaBRQ-FL(4c&L zPDu}wi_AK$^YpU64?C8`LE)4V2nH5l3uD@F$Bs%H>Q=uR8s=~F)b}w&h2|>zG7e`! zGx6=uls-U%FTCt+(I%yA30sJvMFET|u}GNXiIVi3YJmZV$q&y@m${D1(ywo4PcYna zBN{%o=C_;n?}@z1-J$~jH$?ghoFQo*9-cPvv;;v#O${7^5sHhTSwX65<4MZfXjVu|iwp1zI_I7&> z3R9Ha$$w8w4gjLQDtGIlclSeC>Puj{R;#_}!cJjG!KeeboZCdmrOTHmhyBy;ks{78 z9qivxSx2$9?PDZTG&~tP>l6DUE#he%yGvVq>l|gYY{9y1&e<9@bBFm zbwy_5dyEGt<85Gh2tuKWj}BEQHF=8Kxj&(4)PrqjiHdP)pG4w_&5 zyQWGBwr2}h?kprmxV2fOAHM5ImNJIPP_{f6w=;(VhRkh%kZ3|z6HNY?xm0}#Gp|uM z2zStd&(B+b)X}%2w4Ms>=yUjPPdYoi*Kv*)Cm1Fy;mxo7?`O~v#!!%Vu~6B~&k9*O zv7S#9bL4@cOX!ec--ovxtZ`Z|YFea63{36SSrE96}eQ#%w+&~d!kQf((hVB`S`oUFtm{Vr_>!ySgPOZn%x5^{`R+5 ztBJjI;lc$MzJKr1C2y(p-*Ffnc^LLr91C2xhL-UOIMo-|w)x^tuos<%d> zxIj~haYImG{L1?L&_(EF-HpEw7aD77Wz8`-GzM-T5HtFZN9M+ql$69?)x8Cx|5dJ_ zU+GZuY-YqD;(O)({rhDgVtWmeE`-vDe_DmS*k+=XBfx65BhY6JMOInqbr0@b)DHjN zGcOkIay14dj3-iNEup`_SR3wj(<$i(+RQR2N@{Rttx2$PtLkv}=&FZH@V|MN`z)pK8IKfmsA z?2%O?Gc7I8U2zo>5-|z3!sfKsWksb%{>A&9@6P_6ER_p#yzCEWm;TSzhbHoF9qBn$Am7hQ$TAmy^4g35uZlCo3;_1EPsc!iH@uQ>&4IyM?W`~fG z5tU8$maOc(XTyl>m0ekpmCdmVSs`SvaE?8)vwyGm^|^oF>#zH9{c+vLIq&ytJlE?1 z;<_le9UpS}LB=>HQ$*cuQ0wxwmmT7w3iNAun=>>-sMo9Kh!HOu2EOd>*8X!6J#rD@ z@t4m|S)O(!$i4O6Af0~RI$^-5kK>gN5hCw9H2E!hZUKX>7?fTG%e#VEeywUw(F4YT zA!-5ti~kN~Zu`^X@9YW;{~MadIjqWvOd`L&$iCY}8g0e&N)e5N@?a~vX7InS>Av&b zrwP6koK)s5ot!38+2%*64r_RfqB3J*Y@OKHlp-FtbbbwaeHjJ$`>k;;O zf62qAMF%(@H;S(DMOaHil}~l{Iy*c6+5AT+=hAFlYsO>w@GmFZ6GX~{YZvLdbh3$4 z8|bzxX%aWX+`5{@i((CFx#DXx7r-P*)?P7}*wwx<+}Xuf)wv+Q-}#Q{iRo?wpVMCj zg>>l7(T;CRw>{!GrW{ps3dHC(dL~&TPf8{6{A=Wd8_Kk;#KoWJ)(IHly*tvQ`Da|{ zQ}-{gggz=nLgaF!o|#LgaH7PlVZzP;dwIaqrEL$yL2T zyKkcJ2%?&Di7^VPiWg zArm%z{l%CHw1)~7uXcq%AjppB*L^)f;-WALuik=!&v{hT5&Uee2|H(+d1u?8f2e+d zUXxU3^BPqZj3Zu0iput9sZ*>)nE-XS>eYB*5^C=4D(Xa@vZ}5Ry_#~( zZ=J-LC9m`KE$8c0x{P_qP+jMmtXWx@W73evmG4#!%r;-TdSntm`)zhZzJLvN=9 zsrF=uZ|vyXj?f6&rCNpz8DDKUTxN$MDbiEiSFsDYP6lcbw>T(*hTt3L_5_F+`*)_< z{N_i1@E;1;O}sFar^(vXvvk)C|;3ZV%I*H_>nQ74$}JOccH7Bbz1aoJls$* ztakV~N{sr$<>eDGjn?`^#vT1RTORv;tU}dnYhM*urCbwDnImE+kaV_)+>v=zWX1kX ztIFNF#?fs5+JlSvFmTKIUCAolu~yei)?{P;iZ8n-JI?-`B!+sPDWx*pfaQ~FeZtgg zh54BdpH6-}VZt82oJ{Umc9vqrtodgNltZAkbkVgMq^UcswX93)96l&M&r!QJa>O(9S>A{9!u z)wQ>>I9av~bE;(#I20)d?yfFE0k})lGv;B-@3ul+KX%8qPb3babhwM__mb=TYGKGV z_f6Xe;$d`naKY+)%@BV1j3HG;nmhRzHA^Op&1`y%~|i)W8(A z=FYrg(CX!0CU9uoTr(wE=uX@ViYUV$c9XTE(`p;4L$@hisImkVnjx+SXM-dqVbjn$!Vs#(gk_*P@mb;l>g4sWB! z5rq@)v&E8!$+pcr-anA34oXD~#4L`8q;F@Q_m#pp`?X>h z7|yZ)#T;ehjxun3D_eTt@TP!R-1=PZ>A3$8F1eX{Yso{^FY3m58aXAuxqMlvV$GvF zyLKLm%9I%$7!m&Kf5xIUOTs->;dPH*lQ;wYj8c`KYPg3ay7X-4LhMN=X}4RpPMyo) z=Je}5LVcnxa(+n-Ma2LO9ugHiRDg(SmY+9B{nW!T$a0mpAUwrW;J$wNPrvhbiRZTY z+wkysDAH46z2oEK$3|2w)JqUR^sJ&t4UGAGKCcC$^nl^!f~*uCe|Tfnh-pOc006{G zG#634jpQ;waK}&U#i%P|QAJW*5cJtvXKrqJ-m}IDt1s|Qq<>fpA{BFtXi=r~Gws(% z?qz>d>pMGUs2mx&Hyo~0CNNmn1nXwan|4bm?YR``?Vl*b<4m4xfMBHit~`YPnZID} z{@n+FPOPHT>o51lgWaB6FO(DeW>E-leHl%O96c-25N2!!mEDV|43{FcPK6=K0R-((SAy(|OI<_*#>~n0bC(EuvTn@lp+(J2$Hz*NOhOBJ{ht z=)K}N;Lxq)c4#QlWXd-r{E$D*uHxgfZHJ0yvVS66{Zc?dF+=RZ6C-DEba5?^bL!rE zGJUcA649}a`=0`iF~^6eMB?l#SJL>1f)my99RhxHscBoqH{86N{ZfrzJ`i&dkhj`D zbHEmKAxV~zL*uIMzr3)Q8p5JYT+~~CrQ=pbNY|W3nl5k3ma-kcOX4sK#Z9IwBGU+! z9bazEwo9U)RUBeNHIY7>ysJ{{vUG2_FUYQ*eK=YG59z~}=>k#Y?3zDM{jLAKR?X&e zQ`Oz6S0xUQDpgwD*w}DNPk00wH=Uw6{J-A_e6Y@}CC1rYE$dCLt#-R_EmcFz@RXed z2X}0yfcjTr=J*{huHDw#D9k;*hp$%G#3f=fGY9lpJ{TI{Fa45yDC`pVFr3Tv1^rpS z#GP)&_?S6SgSO@B!Gc?7$J>!h+J;}B2MQ@$U)%y>4vW}|EN*oan~M;_TKo;$JCKN; zui}-xV(-rr@~0{3a_RG4OP#v8HyqQxx}RoKgID|AP-)DCBP=idrkpf(ulHvfS}r|p zeD~%;ES5;(w3UAr>-gXHIQDf7zw}@1i_wO=t|z&sM6^ry>7|!EbF)0Mnf9=WmCA&IwNw{UbmhWmB7rN~KLU<_#=-$0EJ)$mQ#y4&g!U820S$#qebeXf88DSUU9^3-ZHl+<@#hpmgH1Go@ zl=;69M4f2*q5IC-JEhUj8=-a#ZA#}*?L}FiU%WMJ(0`RINHUEu^7Y5ZjQ{e#Fgi`u z$#1`U1^>J9djHo<{PZpMwF$|$$I9nFxO85k?okfrVf##+R!>Jo{AlECI@fq_nUr;D z*Bv|i&Ac{SgUQ*qxEaMY>eyeN3xf}~yn*;~wZjoU--G=lbLR)sXk&^coyK=@mmG=j z0dnLR-p>Dt{lE26*}-R_6_szht=2XWSA=D!evBtSX@_&b`}WdHb=E}#ZwX0v} zl>?bK`1!owG%ofgZQNlZayoJzc*>Wz^)V2W53h|u`1Sd(>etSVp_a6^#@d;C#p+nz$E<8&=X6F#O(Mk1|f2I=K@xVN6BkmL`!q(a*%5l@{ZIJRiP>iE)6(7I->$j5u0Gjw_1{#{ z&ZmSQN&$+wan)<4W>e+O&olqOzbi8r2K0xA$K`dkF2@q{%O}56BkCmO4}abMAn@2X z%PjsbpN^9BkL0F&SNQ6U&kIlsv#`c$S~9G0TN5XN1vzxhIw8((S5Ec|oAK;QGZ3CK zMy94&6jM)D2!sj0A&d&L(1NDA1MKq-qc(*=3X~4iA&IOao)?KI84*D<0%OW^*JFN< z6uB==fq-WV7}S)A3-#Dcz-}uCP|LmWxkH^@KC8L2 z^atJ`7G*$)x{+Z$pkPKdQBf3?7}K|trH|;zx$b>aa~!YA)n#$&`>y^{{n16@xL=a5 zi=N58tTU;UqSMI0Gb{GMv#1|k8XAi4zLEHz@hm8Vf9)>pCGjNDiIh>hV}97F0dp_o zLv!boqR}!h!8^@Fs;CLrBme73rL&lHX`L;dI2+(5p3A4b%pdUmrkZOl@$#naTZw#j z3HD9hq#+UN<>6v(NPsiQ=V^YqnpKfexmTiK;66ggl1Nt1m+!n5(_dj{xulI|q{!Vu z)iYE)IpH-|y~cxIYPDfWqMq$uxh^~-Y8ue*DePM>I^mORupAd-cXp;Eb?$!Fd6en^ zo{4SK?&fCnr$=GQhl`Lr#fHf5Ai(#|Dhm--rVGfxxF-c12^S(( zq)3>FRThf|m~&vMMz|n~`0^&OtI0n+ZJ(XOufOJg$0B%&$pHAD0h!_rm=#wh8w963 zGJRiwm`Lt@Z(cLkqRJJBDvXBzTGuHhGM~P?$@9=_f8Cbyu(`Qetv=H5?~;Wts9U=a zVWrLKo2-4UYbkXC3gMNVF;rCgA}KrOxX$x>#j}r$>Gz}Oy`~<8g&O6l=EM$s#p&!y zNn!ny6!EB~Gx;Pj=xzV)oe9>a;U`5!I^PwzcfPP#UrFU3&9<-?W^U_g8yhYgmDUW* z;2DtTt9n0kR%EX!>BWDC;hTQjd581=+YQ>^sm)MIkaDJ&x8@LhVAE-SB0NbRV;}ri zJ}gmD4->_#DEMVbzpOL~$lMsV?m_|r0$z8Kth%Ung8KHasvF;(rQ4X2(RcmzoZBZR z-zNZ+l5IVy%IuL3c!Mc-r-f2#B>!|Vo(AW%?-*7kfXtuP-cql=+U1LuZtXB9r&*{| zFng3is>y>RZzUxq0rwxe?%7t&CAs|~b|A~LqdSVS@5lf6zY7oEut;}j_;7&xFFlrjLlBc>z&M30GH>tL5ao~gaCBQA(D~DnDDZ#QxnQYQ=<(9Z0gr%wc{kA^{5^U&1cB41`A~G? z_njqz|Ar+f`U6HX>Z#MQEY`m{bv198!Ty2@_P3CLe`PzC1o3fN8iMKCj#p(3gd6Ig z#19u4oZW+R$_7R5^KJ*`W+fHDe45_O^u#cW$nk9T0cRs%Q_cdkv6nX!f4r|+bk*tV zG&Ua*BudXWYdh-yMbso^bHy88fRcKfBbiVRAd&ouB#O0dlL|1&X?T^+8sb;)p@NC;$i0F z!;+S28eGkqKVIV7$xsUJF(k}{s@T=lWRA_Iv=u^a-7mT}(8f;> zK+Xnz1an6}_b0Q-@|&_sE{2+Q`H?QQg4)abgg5LnUg=HWj$$MC1xh0C)kjG2dXT4< z8zCEeghblik0MBy4_+DgsRl5f(l*zRe5O}eSb(s#=A;U9R0K5je~a`*M--|m@;IIX z;$*WRn^d)1%`xHlu*X2gLDzp^<`Q4MxP9(#RyA$Bd#o%cRPnK)#*%hNgdp3JwI}@b z_>WXD@wKLmSHw?vAdi}Y*E9q?r*v0j?)!`oj*<)6H?$NOSpV22s5e$wmdIb9WHg@? zW}PTZ=@7+IkpzIYKC-D~eH#9l;cRX1wM zEiq7|;ikmk2lsEpA4>0*8*ve@C6XylBJ`|~3%y{}Pqu!4vWZnSdAGE<;xl4F ziV%I)TZ`-`REd1y*4=D@PQhUp&o5uR8VQdUi!WtD{Os)s9=xnz z=K_moFT^nddrTjmg#pOb)0@T~?cW_gf(Tvq@h`_FcO`@-Mn-yEUw)cCBj?vUeNr}U zYNY9Peso&dwP$#Z4j>BV@$#wrRW8Gx&yJob}6z(c|Nq0>fJe2ixZ^;Ou%~ zZUpVUp7q8r~#y zm+HW>bZgR)5|G)-z_UPdN}M$;FXX<$0PD z3cdS<>}VzFq=5X!%3HDF!Sty^g8}SmhRHP=W5XQAukve$*%@x*q!I&8AMh?@oQi8< z;imw!%Cikz=hi9(3f75=FN|$1A_*fi?R_XvovB6~+a5C6|!QG7$R(MwdsT>uvB?e5qO zw~8nwv;MM=#s7(}l=WOvM`z*xM5vKJW1}05v1Q^4jx|3U;ZVm!X7G*mDT8xVzkBz; z(MT9N2(u|^1{zTg6}vYd^As38wiM}|q|Wd7yQcwA#WwQ?x#Brxk@72R_i0$U6{hJ; z%k&bf>k;@py)dcvIS6PdYcbQ%)f$*R>B%J4Oy4^pfJ=`1XNm{~8P zc^{q2wro{l4cQs#`{47G@4Y(bs2wrui^ zkIg!?k!K23U66&FQjkK%ATJh{&`#3x6(1+C9+Ur3vVXpk4*wGK65dPoQP4Zz=Q9B7 z#1^;|#};~%`Q;J+9vmo8TCs=IB6grX8@K~H6^G6JeRrw68KsV7{1*%O2Ua@O1tZRR zJU><2d!N|7cq$oG@x|)Zvw&2)DBoIl108C=$M7t2_}huHEtrt3tyl&ORzE_U9nVfW zxrSeNa_*AruQ#;Z+{eBF5-s2rx7{$8khph5Y6jnvS%B9AYF)D-N=9h~x8entr0uwj z!-@d&k-9`hz8yRFAAECLG4nID)@$C`HG{veNl$$06z4h9_EO))&}vdg84cfx33-ou zB)oDZoo~R}-!5LVo2`A#mR%k@Th8zwxv$#BVv|P2a=GnsN79L`Qr9toXVM?g{;{dY zoj!x$Vb)_1|7`(lI(9}&>(3MJ29W&fHspzLg5`|gKLZc~(?nq)JqA_^4C{3f4NjhZ zO(`NDH_aGByB8N2pf#_PkYt10o7ep3d-lDWKYo;=RWI7`J?+<(Vkz!0)qinn*U-Qu zXp%DB3qgrsXlU)Zk?|zSkW$4cVO^97x(=`V0W+gPUDvQViB9E8TvF)8qb{g7?cJR0 zauqSR*SW;thmOje-3#hE7%gLT>bdoHykguFZkk*tr{TiU;JzU5d2lF)eM>T_qS~~b zioVLt?v_z{)~Jj_HN?%3pGX+pce30;^1wPpp?~_UY#LWZ2YOdjF%&ucuI6mL$*`nu zJuRj(A>@hj5Dr%!DWf+e^SQzFq{>jP;G@s+Bi*T6j9SyA9JgEXKghwfeFLyYRsa{} z_@^Dyi=2gERfvO_nNAop*}+-_9>+_ps#ziWkgM_^!(3wA+|m|YKA!%N>SQVnEj2__ z{ri`hrFO$-LVKgZdw9##!Ak~%i+Z}#VadzBk%ZcpI&*dNTvpos2M81T- z62SsY?XRw6GWra*wn&n3+eb4G5Ba zVRnQ8Vu{BmVSfMd_@1=}h|rL12PFG2Xon%BldoXZUlY&&Ad>LZN+X|+tzQ1`!Zlm7 zALrLF$;?>%Pp`%;Gj|?P=Xbqo6Yf;rFOObHPEa2FlP`5H;?T0b+|&7G-2)5st?&>> zJx?4|V(zYH_v0uhFAz@*_)dW~mXU))0ZB^D$Q8tQSs7A%`0yb>lEO8XK-kXvx^1}i z5XX|F`)tY9K#Z^Ru`3Sd%EH1ilegDSTr7mK@rJOK`1!K#w#=#ep8Dh+4k)XBmL;i* zBME4?$u&NaOCjvKYN^ujxlX7gy1=XGOP!OVZ=Pnqr;K>_o72T!D$H^fS!Z=18lzC| z+$@CZZRP)DjX)j&uVyh*(g&8mg$|Yz`0`$TUUs`2b(BMzXl=+UcXiMVGIiQ7cv<}M zc6M+7M4eZb=$db!O0#09N2z&dIiVCMF_Y+e;TKZDfv}U6j&j#v#e{t7Hiony;(w-_ zG_l$F?#-o67HGEq@|cAw3PO~p&j09P_vGAW<=lnLNHJbPJV1_m#gu~^iLYEq1QHkV zmUhRbz%dzS9Bu@GT$RXkCXhS}7!sk=LqwtX&tr4XoksY86$jLoSkX1L{}^J7`?V*rrU*K5 zy086}Nrd}Lf2VTo166Iddppirn_JZ9@&ay^uXtqD!R$Fe9h)F9_#LxJQ<42B{%%zq zQG4hwI)93xqI|;o{Llgs4<$c+UkP)&`&P8V62aQfl1B6NjZGIaxdgIX+VP=A8}K zd#q6h%1$-~(wv6$3unh_pR|e-k}s2oxc|+z;cRFPm6bueUH4!7aAR#;i(r452}k2g zZN*QNs$R)jH6x?Jts!oXk!eEyE5mg_;>f4>^are?WC@fZgD=S5%{96Fh%oQ z`_!}Yi|BA}QQQ?*yFc3Qe^N#7o29vUct`uxdPJt8n|^^digromw=knCh{)4=&cnfj zXhiyPTuAhj4HW(me$UDzpKy<88sQ5;5p=&ky+|CqiWEH0-%t>I0$HPZ!&Hsvi(hp3 zL`3{jlKS3j6;6x4KDq?kYFAjr*IX6?qz#hcZKvdi9dDVVL2rhDJzZeDU`DjQX5QdU z=vEnzz$3nO2Oiwl6ggB9rxx;ZF`pX~yP|V5q7BxH5}Gp~%VO$0V{To$4Y36|x{b%) z8*&v%eAzAa``4u}V4ju<7+4trQd$1bsaYuqmY5cObg@Tm*t=y-HMuwUEwFmE+gTF= z#_@zVtMtunh1p~~eg~qmBL8Fe>sWZEnAsb{5+c1bVz4BsuRnihJ81vl9@hClJWw{= z4F~tB84g8WfK7;^46js5xKa7|#N@(fyHWZrk4WAAY$aA=r8_D>%Eh8Z)MJg;^4seQ z+psIxnXQe6vOVk*hUeHqp>b^H1rf7nWd!CUjg;9pg+T=#bBmm%vbvhEc*CIDA^%W9 z)ig^6kLm?LyDcK;-4}D1cz8DZtvx;8Mks4PgK*n-_q7A#%}cBu*{pN_T>?eu@g(jbgsc4dVf5y^mPCS~{t zZZar(A``*D-A21f;mtjz$){eY$DRXChLGle0H%8k@WM#*t34*1_Sp`vQPZInw4dTY zB42;!YjoX9&~woa1)WeQ@M5V!xu~edmi3X_*9I)w55!NST1Z&*i%2S7MBiAOp0-Rk z31UNeDy!>%;u!U`)NY(2TuHQKdU(!My-Jt2)WkLE>r6<8YQ>3mmhVAM^EIbwOoeKN zbjifP$7G5_?k2%CVfug_1cQccxhejG z3wDOrdG6ZLughQYJGPqh&ztw7gEv;xr6{!sUEROp&^<{#os}2j_wxxFF0Sy3|99MI zJX(sD*ODCy+9JH;wN{~O_cJAi6lMo<@kV)*4C~kBCYhdcUKj1b7MwP3`MQkw?1THd zt`zHwxoyCb%_$3Zi;T00Fc1zQ5RM&lu*-uOK_vU|)W4qUzL1a($TKVqwcx(bLlC<& zgGBMJJ56p@@9=F}^PW^z$Wti0!h@(kvUIhmHvJEmSO!L>fLw6S-2rlc{kKd}_*yL| zcooDcJ6`%g9wN6zLk7pSfn52C2x_k3O=5zW?~`cRXulK2`-wpTI8okYw&KRL2}<_! z$b5$mi?hFQc+d{Od?yjia-k1$vD?|+ikfWl|MFgVnlCdSJeyLgSy|5Kt#)MsiBsPC zB|4M;r@UA@5;UA|AZL9dATm68{JJh{GEK67FXGv@U@2U4q>C+oxr9y9Gu@1k4_6vW z^Hbi-=NuONLMMi+@~!z^fr|Q6W8PcQNpytIS+8GG-Eu%`L_?g^RxxK=_wNRCd=izx z9Nn8qW2ip_mJ`)qeIQ9;@62fZ-~Z@QOa2^dhBA}al~W%J+;QO!QKQnza@voG9bzn? zz{wwJ3h=zCpMQicYTng=KW4?JUTVaOP78i0=TR9LLo9Md5-KG1sivtdM_t=P;Eb2i zUfH>JljCE|sc8tpd5?cJpKZ&bDtY4T#YnAunGd34nUxd6H9-`@&%T#Bj5;3or@3^wrSYn5ceY|q zdj_+eZG#$c1188oh`NsH{tVh&{7cn7xk<#HABcv8312hVt}2bntlxk0y5E=zhC|bw zIPi@xZ=FEi!tK^D5sS<_Pd~lsa;?}Lk8W&iJkV!dT5#c~m6&=auat24i&mIXjnk}A zUW{+U5N#OoO}gA45qxJ?Y<|mx8uCem?kq7h5K~*Cw(O6(KCKZqAM$g|4Q<_!Ch8=Q zcYK}F(V*71)mmD=wUR8gNBAuI>swyGKAM43SE(=p-cM4lc{j;0>-6W_CI3h0Zw0r< z%9g(HDKAK&WT=#%!G8>tRR8YGRo18RQ5h3WniqKQzMIZSiRfJMw~NiZ) zo9Hulg`AOsAYqf|VgRy$y;@`WNnJ=qoLV29d@ zpWUC}G?rR#|9d!fMw{I9G(7Nf`KRcQ3*JJ6=&14U-@jufYUJBi%R!H-33gTMbuu@b zJHu#q*QCri$+ANlbJ=pJ3vXTTZEuPrPyH`_{t5+_ispD+;EN&ZZnJ7J)(zQTlU~(f_A(yY3IH zNyFO&tkDzshwrva3BpgZ+DvY%<|Q?vLl>@rwy*!|Hq2}HeZvYSyvHp4YCgT(@@e$y zM@v$aE}@qG?(M~_@5;#o%w*b{J^;U$-S&%7x1Bm<^gyP(iSe(s`s3ww|BiS~&j-}? z>TUe!ZlbPP+H~x5NC!`tjvBI+OT~H~@-d7vyzXQO*~Bq(V}9JJ@WW4R0+kT=?VH0VziZjq`FX<(ABs<1<5` zDXB(^GaEum%PA?F4Ak2R6;)J??a;i*UL$&tofD^lP&sN`j+8hq*K07^TSOzV1HLYN z|KP5?&-cqXSVx%~3xpUO3m-5J3$SwUyXV~=ln<&#w@aiq|KGcd8jVl2i4Es7G5gl` z^t$JS&;<=`GukBqwieS*^kTR7`}ByauL5&q-#yAzFRnX(LmIm=XB)(ja;E(xRxFN>p?-snL>qhOSiM~IVEpmYP7i%j>0toleZmZURCOtY^{_+cYW=Dg zV%rLzLpU6y4(5ET1zTrKR1_VYad=VNJBS4qr5}<<4&dG!qR&B&me#Bkp-sQNy1p*O8aaeh@2QX#{ie}`hC)g z`A73L*!gbdn6s^bkgY)b>uhKOHR_Mh-U&Ix9r--=^Ixx1yQ-n`K5@shp=9FB+c9tW zLDauD>=5lm7Z@JgBYXDMzbiLv|2=x010+FlHIXh+EM7y=bpA0lI68v`syqi)GV)0$ z4&F=C)v5O*7Tp=?HPe%7WosK7rTvWDN0tOHp{jY28#~S{>p+fiq1QoxWd?B~|DCcl zzPYn*Vv8BNP1dhgYAUN!=6A?o+Z%QHV*~Sl%sx`x z-0p3u!jG{d2FgVXWdW-EH@*N+6Y3hb`@360*T=BT0(iR1DB={5XDKJlcYYn53WPMZ8HM z!0*Ie0q@i(?mtV3L2v|Q0cL`?$ZU+{$FKIE-S4chvFkMWYFkW zn2+M}GG9_&!z_l%XYc6BsS2QQ_z{Y zC9Iow_m=n7f}VK`>>#p+ums+7)>?uz$!riKQuw7Co&)of1|;Na{0ACTyi0H3#<6^> zU#&ts>E1XXx)F7iwLdrV4RM_kz%MzciIGnuw+L%CABZ0gc*X9@EQJf2*jpx>6o(fe z96=IQWII3vX1yJY>RsqzcFrrVd8|b2SA7Crbq%cS>N6PD_`da0??FPX@!$9VVSR#V zX(xc{Rt4LwkjrA!zf=D^n~*y!55mrDkIh-;re1>ifkrs*%m`sE*J)_DAW2S#iv%Ip zU?b!>5!KaHY@q8%oXvfA_~3ljI748z_JyEY_hF%-GOHt44PeB;nLADRdf8p%!c!tx zn2-7M&r7OHaOLaLYyZ%nmLU@xlC`sW`i)g;uxIWm@77U8zyHK^Dd2nqpK(y_zFzw! z#I0A!$yq$#3XGloZN7pWsG<<}tNV|f-C2yz!*fFNeFwCCAwOn8R|iM`hX3CWsYkX} z`3JYx#>131XNB>~aVu;Yuv4CTR$O{)W=5Vm1pJl{uaf?GIeR_UG`$(1nVUo0y?=K% z6IPNnqu?F9_s!O8AU4xeFEV9i56ygHJvM^6AqsW~aVTvDfG@dD`|c7Mfxh;ix$m%cV4 zJ)nh@`Wzz`aC5nw`*(1Fl@r2IPP|3S9lbr@Q3E%wF0-HUd{h0VRYBTVYPPoGad048 zQ#BnEz^2hs>;o?*xy00e4CcG~kyrWvS_=;<`QN;w?Up6r@4?RhUFMC#`#<1HV}JJo;EcQp^Ra&^>vN=>#m$G9^XPArCO zA2=D(oSD70aeeXkX;oDohw^?A!#A83igW8(;a@j;$1T@w6qlAMnJ`~zDzYrIpWCrg ziJo83?rr+z*KWHi|CiAbe77}5e(@no158U3Lb+y4G#(OiVt+*AXbHH0GP|AIxPV0~ zceGu0>sa=+P@wC{t>W*E9)7gmXO0D7SNljH+H~&rf`WRow#xaU$F5(P_~nEwO3rOD zEbq3n60Rk>`v8#03mJ3BDrGZXsR$W6rZCm_Lx?k6Tgytl??^xB{3$aNLIR}$Fk^CmP*MnSNK>s-93 zrq}f#oyk$tc>VF7gWMSxT&)FPZY%E7JS2;+jewM`+9wL-Cgb8BfQXgy^1$ zI5;IAn_CJ_52q(K>i<=q(}Nz*OX8c?Ed@%VT>S286C^&n)L0iH!R8TX(8hp`&KOQH z{(ft+KR-rvJU8M7F{3MY)bPEcn{3mQgSwh)4>&ivCoR{NDvxV6KmQze(ht(t(4kq> zR}014q{54GPJ1Z4WU!Y!J1w_KY?`nx9&vO-MP&pObS+04@-6miYGxU#hA&^LW@&Ll zNCzJ4?{d~7@c=@0jr%H$Ct|V~9LBUsQUSM96CV@HxM}l_cG8(~oOFhgo33U}V8)JDZAo0ARE9a?h!Xu#Y)#y; zI4vlz@I4z%*+ohe$1^M#LtG(OTVaFZDt4`y-5LlHVCEYf_qVOODE)(lsVlMTJ2sI& z7&BAEj|E9l>vZDIGOF|m6<>w1C>NI!F|8QK|Ff+5oUd6Im)mEd?06dGaqv&(=OGcG z(C3zi;(t-$$@cl3wB*WMtBSvZa=?0ed&_PFA(AoPt+t%Ztr_@E_D#!m65?!PGtNX9 z)rM&jsPr`BE~0-&f&Zok#@8Un@Q~Cd{!iZB8RFBTR#$(+*oD~Rqag&T6M^d>S4O|6 z&O*1DAUi&f0OD7u^=Y}wrr*Ue=-pCNd3%F(j`INxH^;6wp324ci=US=o5J2I$B^gP z3rLbh*jYV&n!ci?6&e*t&@Nd1{Nu|=t>HVj8;iv`HjXQ#z$TPzw!p>Mj<{s1*!R2C z>X&7D53}<^Q0-)j8%3;>CMG6|ovw6WTtZNj83D&^GyfdFwzj_Y&z4i#4x98TYbnri z<~dTI7Mni{q!e(`6T(4xvV5?Tm~-mSm83wBC7<$ViL2ti-#FwIDm$)xA%0Iqv++Vr zXMIIq>iesX)z`-_t)(&G`I(dtp0}QEsQ(EBBQjtkCOE|50_ikRGFl3pXTG;?C$J0O z`#?_x&qd$CoGkCz>pP?W+FJeO!|Xq_54z)ZX*9oTZM=GtaB$(J`klX02a{*by57Q$ zq?8zz4Pq-B)rj-$jp3!|Q#7Igi0XXPxZeqbsf^2UtBo@_lgu#t)>#^ZcRd^I4J_4f z|DJ5U5T$$p4^IwGHzWEraBbQLIiO0NhHC|)R!(s-EM7m+B~IC?X}pMvEr$Mqx6Hx* zw7YJuLobc0F#W^Oy$u$zM`6gW7*N+S+%~R{-nZBPkD#LE9^nk`ZGCUH`L<(gaC&BI zuJNLo$5B3Soi9H={)aqDPmo+7rps+I$VjPK%1JQ=6Em;#yaL2MHafa4Ms0^5Kr~iz zUp}0<##O~Zy=(DEDR}Q9i5SE1d3tu659`lm9hn3&l@~;gcLo>CEZ?{=WDl>(Hfzw9 zwI-|j4`MrJtduM@@eN#%A!1%~@ndrm;xVreM6hjx7a0%ET;srRVcJxUb+V0h+LA67 zK3>N$eJfz22{*XzMGeSA6x@r6rM`BcsH$Ert#D@0qjV|>)NDnsxVU{ooS3Vek9=H7 z@Alq)V#&CjtC}nh>L4&9vZ}_evB>hm!BXREUb`7CAwRJmj<8VqxGM`AyrbEG`x)3- ztLdCOSu~ZH0>x$NYwMP}+?KOqd&HtZ1H%a?#Es9N4c$$TfFLSs8Y+G(Oc2selMT9N zf8O=8Al46kqGGoEPp&xkvV1(5>2!DTrL2c9Z*^|8QlwxP&QdpSR_99bdw#O89q>5+ z;TfyWeF8Pm}-|I;~wY7l31Wf>P@&}d!RG~Ps2_BJNw6cK&2 ze~I+G{e$%(-gE7>a6nT5Kszi%6id7`29T+}H4NMI7j0(0A?CDqw4rw4=>_)}+@xYh z>iNAX?2_Hd|2_4ur0g9i`@Yt4c;}aij${Yn9vIcmEP)?Kba$M2X>&$3sbc94g+B^4 zTIzG)gv9+o#=J@60Nyh2U} zvQQWO$pz~zDZZlsBJzAk>(I#>(%Ge#HTdV@)*o7?tX3jP+^3`{Q+Nz`Y$tDf+Krjl zT=i#lHH;1Y$ZO}ImZ!qeUTAjS!K|WKNA=X_^BaoU?X(&Zj47tI^XE4{9^T{eGoY4K zw;ncnsb));pu-60UPBL~L%{h7$i&%#P>yQ!eUu7a3lJ2+kEc?s8r)~TUl(@xaD&Px z?4MTADsYh~f(H7Rep}j+V@^-T*$|K>sm`hqI4r`{F>hYo^^`hI$^>pUKxA!;wqZBm zAbJDb!+VPF#h`Upt@Chnj-5jK1L)?=t9u|>NEV>zwce$n!W2Xe@Eol!WHI^z zDSe)ZsM{ldz;~^RHnksE?*coZ0FY2^q5ncRKLvKQY$vi-t&W8HN#Q7}6Eo{tj zG6-wN9tjUdJtTH-JV(~^)$<9s-~Y>cK6eg-xP8IyjUD;r=hKT4O%k}cik^A=cCv2t zSp0)I5v$uup=!Cm|0Xf>pIbU@2ZMZnA28%ni#mTZjU7s1qUM?o|6&~%mFqnmjfV}Z z{o;Xgc;w*MtpkB0TM%)Obo+R3ebV~XXSm`3g__O$Qe!ZUmyFf#&of&!l+XA3bJ&GDN1QAOgdx& zeb!z`zneH&^DjO4>v>d1|FU6gDjdGx`hGi3s(`wW4j(c^r;F1kMoK^7*Xq~omnYF? z#BrFAuP5YLU+G%(+sy*`xVlS#=rWKx0^tT@i`a!W18P*%=(9XTP5ZNIS}re`HlfLD zceSg0oy4te?YfYC`VI810KU~^=;2jVlP$PhI=lr15~8WUbfAIdHqwh2PduqVirb+& zs?s0GN)?^TtkfY#1$5=Zy{-k#R=|C@^csWfO+HQe=1qh50V`*U>hNJY5fUX_3hxx;4@h+sGQ z0NPbMSZZaCl9@1AoZW2Qj;rqEqyevy%q>VZvFID2Gh;@yS-40H52NzgdNZ+)`K9CmuzJiI34pXVaT^$F#(`ycWs;LSRnq-}Y@j|z#KdoRFq0&1#71mzBmh6q5%^EKDgknEfpfg-$RX?Jg-Jbj zm=?;V%a4kg;{97l&+GD>=hbIXT#CG?Vn&v6r1N$==#v@Qb9-Ww@eppODgkUd*KYk` z@7*b4hyHPgyLaOGtIq{LMwH1nqT$zx7A&&91MUD9_oq0K0>BW%{NZl~2?kItHg8do zi)4ekMh3DZ%637(O%5a>t3|`X57UpOOpIB&G!q}|-w;vIi!sSnXZwIKcQ@#BPldc} za59tGSZ(vx=>)YCbA!b}euOqd)4KpZSIYwo2B^?ZjECmTKL9n}hvMj4z(g+3DBNaBNb}0JX2q_Mz zeJ#}6D2rB`F%A}S52H_71qu~E+3%`^8aju+nRGqi} z%yROW?_EYN9CtkU-WPI{;XK_4W5Ui| zKx4biyqQ=Jo|hl|b`3V`iy+~xHF{7}Wa&xy{3ZJ$(1VHp{&^gLb<7E~TWK>Ilc%78 ziZWt+@(9!gTbw$op_5);-b}e<{v%_bMv9 zh=Oz9r|Pk;22t$7;qUk#q*9Xx`p6&SsjeVh%JJ`x-02b1V~px`HdgiD^0nfc+R6G< zcc!LxE_*?&WYj{Th{MpmSE=vbmG!P@f601BJ)s_odmV>>&I0$~sB@rxr9I#W7L$}m z^`E->Y&8(qb7z6b{L;(eV%t0Mp%>xgLBJCLIK<%!pgPDGmhOoQ>^+~ahC`?FzosM2~FkE1>$x0wbd6 zKyDFX4NPS~U?3vak<;meufoUg^!fa1Y8w1ufXlo2!&Mai%z!W(ffSo#2F>{f5~e;2 zuBkZVK18iY!1d#4FK&D82mOHgUW0EGZ646tgd%j|4bB##B;>DQ5g|>#<6q}xxL4S4U!gkN)RlHgq&7s| z@>}@Z>JYfMvg7%8=LmnUv>8{|D!8qA+YA6eQu`-;XBluW^$- zIv#mr>gl}aMse4!#zxeor_c;nbf)O4mKvP>(}7!Gu19rn6s{Uwjf!GNgo1s2-0kaJ z2H2g240%=QH>syy&+~U*=~cx15h#R#5#>=JY!=%7|4)oy;aT0Di;Y1j>g+j@Fw@yB zhujo?L`3Nk1Um`GDWjYiwZ|AtWP&OHpx)-49iGK~`VAk--U9Gq9VX)TQBcY%dfCY^ zEE!Wn`eW+s>qDhDe>q4)FLVq`=_Swg!GhYJ_t}Hbf2D8RY3(1Dx4=Ndv+jv4D`sPE z9X?XPIY#pVW})4K1;m{4I6;^*Ot)Gk#^iYmTIDvfoCnn9`*z1{wY_FCg~~S1+-O_Gf_zUL1*9ftKXd9`+`clpXE#FgB3mst!K z1d$D@$*fcXw1<+10vzllr&H`1TvH5vo8%&jdua`H`$~rF-R$J>&JV>yltvBwgz%4( zq>X9m&oerB=Y@V)`pxuD^)DoS2fGtWP>W6xnttznYEl+=r9&-&TcIVr15JU-cDBrc z=T9a;`*?|HE3pcXKgK#GA<-h&Hs-Hd-`?X9DlsG0=Heo@$lcGJFa4z9a8tm~ni99d zRF4(gx1%Mu2YJsZ2ebc`h-Ay0`OU5=hdM%slq-88{wq5N^vyAV0_1`UStVwt=*~4A z;C2a*O#=yE=qshKYK)s^L4qgv9Boo)*^&Oy%q0*}Xtpp3M^+F*oR^Jk-pZwKT1K2WyUWRqug5B* zz8j#CM?-=*?`eLC4}Vtnv4gvHo>5VVubNM)}uU{n_6hUQ7Z7uCd5Xd+=Su3yaQ9Z!R&CSia zeY_1etF4x|*C%qqlOU|nX-Z>IzWwLLq67f{V^jmruWl~>KVUD)llF@odP^(;n zgQSxGgQsrp8@Jo0Z!x?DE!7e}iE1Hm;Yl~FCPp~Ta8XSIi$tk{<)80_Wh^pYaW76e(?QAHaTH_luTBf@gRj#iegtw=>aw!XYvNk>fYCrsIja@V(A@;M^)XV-jzyv43 z*gmaAvt%ew_FzgDPkI!?<#wiy&TI zXNU=`h=us3w$A2K<;XoOAdEEOwe>Wl7^*SVZ(5KvC-4hqPral zI~c1*CWxkt>R}{2Mc5Sr$zWKz#L|vFx6BN{Kf5Ka(O=SZ|67Pg*o;g z)`aN?+@aN=5-ye19gZsIQR zwUJtu{ai?HdfCkqx<9r=O0bup*k+*EhAsOGOk202eF1rbXogt#hqB6^1zPWBrWcZbnPkl<0-ifG3BM?dP?wD9gC5F$U=3MbfrlXmD>Bzyw zZv|j_(LMQVPcG1JxpoiP_*rd@G zZvd}5yARO6d^UJkM z;NoohI^|dV3HoiS~D0DfkFH~$T6j3pX^_y=zf~|81a9&1Sc3DYm zYIx(9dGO2iK{)1i)((j(ArkYpA)rkdEIY%ZfD%IqbVrpV<>mNhmEQ{}LJ5Hh=+KB^ z;vYT53T$siQG8c1@4qUFnEaLYp3|p32KSkK3%hY$d&se$+yb%D2r@dnZsHcN1^x z1?>Ke(hcy4=sOC-*gX&UE4u>@LT%-J&(fP{f=YEtXH7T^?lfzBL@v8%K9C!3T~ zl*!$WqSdU`Xv*(uTeCrok4~{}P>CVELP=jd5xsnq8cpJ)3=9~W1Ghjd$P0*o&|H?w zLm5G!ztM}P{{wONEnpPst@vT`;mIN<_WL>d%Uhi;c#C^NaX=3I-qwJLX=336%w9n_ z!4Q)dUJcW(Hd5IyUrpb+WPEReQSDv7Yg;dyHS9aN5s7+z85gr5l8dchETgq zDQ#;l8AJW@hQxS{hh=C8-`}*oZ)5Z}cGJr^PNz8>R+Fv9*+gUzqNwz2ZaO{MvidpP z5CjhYj_B^srMywL$(piQ538H-&YiS?9n~6YD}2zW7KND?3=E9SI*_H=Rw2}e@d0a) zW=2C7`p`Tzz;8J>KhFSv6a(?Jamm(IOj(E_t+qBsw>s9oW;p?F;$*udc&nGrFl#C3 zf%VWH$+J5Qu&l`GHGLwMR3lGxEZHZ&uu|t|wNTV?BfXXMKHMK0!VzKD%IMbD=tUW6 zD`E5%F--JcO@4wBqy#as265M%$*m44r$4KhAntbQl1MQVWA{%}pQyVH9zC17rZJG( ze?588bjrMWji9#9?UF<@h%hsunRfxAW7->ABVgrbfX2&gqq)Z)Ks`SBN+~2ewtl%F zs}@;cjfK6XqHz~G!f1@ke{8u=u8-ew_G2r#;lPN_mjdMQ&;^Hgf8tn$Ro zm5Pndpj*-x_{_c?XIS%Dwh>ju6I{B4g618g#gX&R;i^<>s#8BkT2VXG-=u`M&4G+> z3Wx+@7c*~?kT8G-oq#U^r2y-Q(~~Kn2`+N`3GoojYX4hSGbXz`r1;EM8}8B3i}+Ou zwgyQfyTHp~C`HG|aKQgN)t5G^(Efqwg1Bwrh^sDQ0>Rk?a6g z8tn!tJ}XdJ(g4oG7YR`h^_1%F_E(-ug%)7OcZz%=yo3qubVDrQ=)CHMP*q4m# zj6YR>@u+UtaWLwa@)pBnocFP&5tDWr` ze0(&XZkXjsx_*xJIbsXkXgeRPBiLZg=fZ6^6>4ym#=f&N{QWUR#KrsO4T=ghvO?;c+_$r@3=u8Uv2bX9S<#|B4*Vf+uPSfvTWat|`*F(5 zn;Mpdj_h<|L4bl05n0Dm5aMLwv1T3( z(_pWGDZ44b9rN0?zS3o2(D%#9VG(owa|uE#PJ{C6*9d6a0mU*qGjqvXHQiuSO=Z9^t?uYVrd`F(2br_-@e7i)n}a452QZ(Ssd!E&8_>dk7HhCupFzj{p8Gw zL}p%#Hfi*q9vmQAUyO72OQT;EbMYUJr003*s>?2cNcUYhW`|p>sO6WH#k9J*x)9*2 zuC88OS$Wpw4uGP)@Y4u7DLLR!Ik#*_9;JnSz!UN@An|o;Xs^2VZ${r{C2l8;H#o6+ zn@q^aEyUKouNx;Hg?_GYG{mEfV-Q3uA565Gn+bEz^Oz`cE3K}OvW1Z5nJ^Hi0{5{W z6t8aIzI{(8>Zl62$QFfU$~O$=xKj1>rys~cS^`U`+s4f0&ahioy_M46*9IwSA(7{S zxn5%>rPuJ^7Dgyxu9lzfo64pAism1CKB78w;Kbl!fwPgu(y%xM#M*b!W-R3T%>)=toIOX>ev^lC|)RkyDd6?gg)m zSMAW{@SqZGQnmw+hV{!9nO9k)o;N3!rZRB1DbGF%+QL5@08b62exHs;BS4-Tr zzlD--P>B+63Tp&047GPLn2ihEoT@*R!s~i~M7l@FW%TS!n44K)EU7chN_k-veh_@K z-A%-EM@M;xA{Z6RoL|uNAd5Y{zW>V@VXChhZgPQJ_xbdC_3xtO%JWB;-cGp(x8Q|Z zD9EVlC2nj;pilTB%~@4+@e2(MzXmwo8kb#;^k?1-d@{3YeIXGmUX0Kd0vHzIcRN9# z+8`h&Pf#94#udO21A>VO6PWK_H8QCO+p!~pdjrugbNx%ZLO_pwuUQAn$U?ENZKcVc zOiklIw?}SU}mC8N<&(v;`F6)UHYH{mD%d=Y#T4Qqf{CQ1fhrnEv@eu^{inP!ugj6 z<6(|&Fd@?7vwogNNQVOYKyd1*O&uQ|p4t;@ySa(1uC2XOkd2keCoAphPjlg|dLk=g z_`&Y@Dq@b;Vp!1G^yOuf0fw95QRtPi)?r9K?CecR6KRmZdS6b>e)Z!h3mHZIS0YUI z_pH?P>N?7qz08e`c|EeUw6qRMDWm5*%bI`k=_2m7)^0Cv@&|{Y{+w;1S;j+EmyTL1mVej3D2OxSIzIW8Msy%k|>x*Cz z2Q2TRF?-zQTCLsYO>WBBGC5gui$ov2vuIV;@iRB9n3q%1Hp=Pg&xz4zvtc0W6W0l4 zsI{@o#@w^g%IT>#Lhm1A8663uJiL!d*?W@{72;I>|F%=*7#bM#9_Qv(QKjd>fR97{ zU7r&QrGvFO%`Z}0_a;8inD?RW?o-MY2aUhN1iL?cmfuP6eoxKCwJ@kHue4wI9U|Ry zidDyDd@~#b)10Kj4Z=}(JvXYYIpA;-xL9Sre4kcEvZeYV#LH5SgOY)0=-BGnv$vph z)z<|9RUaQr60dDFqE$R-sfaG?WXn-Zr{Bbnc=jqg%eeg1E?i`4C6wTIN^|WCNri`s z4d_=1$CO;QUZExM{;bH3%uNkGBM!BS0@1)#$9uDOh0?M$R3v$Sl4)vbUa{3Zdoq#o zFkWxCN-QQ9zMore(>3mlMK#~LqQ?#2jHX5KsjvTiq++h0gCBBoCjO-_K~szof#_Yd z;4<=8P5A6~w&=!q4a4SGTSv!{RuH@6?BMF&U61DG#PbMx1Tf=#p>PLr6=eyp1GNcX z7;oY|YvG$PJk+m!y>@!0X-^Wt>g zi3DrhRV( zq;PPhYagB@j^&q2(B4D4me>y+vbiPa6ReYxgsFK=S|pn9?pZH?JYFhdT{iF~qzk zLFs3)ZH%fDhT`(aq#}lYpE*5oQX@R+3JcIb+;Tj0GRn4PfAOMY4uL@YvQV#a%0{yP zjlUD=uw#>0f8fylm(3D#<{P4Ghcj1>2$F@2`7-{+n4Z7&IqbRu&W{ zkvn_m19W=RBx@WseK7kMiRp#uF_&2WIYgmTyeT$9>F@4#w^SIX|3%@-qNU+#{F33%;K^;!D3WqOG1S z$6mN%NW-+&0E7_tOe= z8MZdevt(eMG`I5$>ie@*s1oDYu5PnytGPjOi&L)|?NapNlEzKcp1YC^xDQVoH+t#o z$P(b-w6(ms25uoXG&nTYfF6|A) zO-kw`dhEZ~0v{`-v+Ipeq@Mmm_0oKS4-pFv*7}(VdM9dmS|Z*@0>Hn(ukt`ap&8UQ z&24N@pl3v1g%hIG%u>KjFCuoo5>FeebWU-%6<;Yx4jG$6>QvqiK42&<`a|Wqwbhkc zZt(~_@|2Y?E!sqnL5$jMyoyf4&#&T?_wfK5B{?|pXOtC`v*IsX>s+W9ujQQkT*qQ3 z#V#}mmTt0t<({R4_Bcr7&Sxa?$lHMxGm{@7j-^UPB5QQCK?6Q@AtnV}bF+P{vX)>i zV6UHZ-{`9gnrSU68JCPB8gvdLJ^EZ3)NrjJFZ!PaV#JqGDKmjx8ynEX-QU+&Rzd*v zb(@!$2;lCrPEK}AEVF=niDmlXxw{hJb+|<@92%l3JuR59A;9Q;Rir@EUH?V^3Kh%J zd&K?6CLKVqfu z2q{nJh#s?I+#yfy4D)s+yp@{yU$_^$pF2XCWJ>pj^sY3~j>svsR^yx;MfuOmyGMr7 zapvc73goxgXzo2L^F8+gT49+9vS2l7P>Lw`*34GE530k9^MDPVZ1SZ8jFbNF3P2AV zRXbh=W`sKBN|1@81o>6R<2`#&NvGak0$L5`hm8aVzL?<3E4=3oW2LL?FfW`99|=qF z@#r1D(tni{M-$rmifUsoI|y7&k#h+q+Oy^kuP2$$rtThuQwL|}G>%I$27|9Hbh19@$>`uRHb9IS>vbsE?DK+A2+y7xR7ejscqSr2NfGH$kXuGN zx?Avev7sRS)tj11DG@Psos54w+oF?hWS?!q;AfDT)3iy-%>&8Od`#;4q6LGV0W|L; z${rDuE|XqWF~}xd&IDb_UQhMq6>p*fe+uH2s?cSYDsi2cUG<+y-K|N8qVN@zb>=5d zU5o#nXxgWadHD|2GfF?00b-*AjwB%+<3>EjEdUK5Xs_I?OC7XHK$S=~H!| z&moB*OwAnK3q4+g4@<;SXAn^?@pFoN@wo@DmuDT+uXB;$oPfmhWz*WNUCH&Z7e6@blR2w(y#br`FqIU9M_ye z#`0b!^O7*jhBOyiVBY*$^9Fr>cI4#n@zvzz>?Y6=Ewu!NECG$baTh*%M-b@}Uw`#q zdif zEinP~w>8Rm$jh=yr%tCi5&OEr_UQSfCwKbB&=w9w$ugx0vK<|r!n6GL522ut5AEAG z)3WqTybVo|CLB1&T-_qI{vidjQFe)c1oOObz+N{FTRIc0H`fI2#6Jc?N5R4%SG zb=mo(sW(A>Gwu~+%4SP~O;2f}mzTR52umD%<83>iJ=?+%g#e4tWe%fN$f5t6!$>Ys z$m;lwhyWb3x!KPOPtn(|CYeIzqi+L3WKWjC`O`|2?RW~+z>wS2jT5IWUgDTClU4Lj zA!C1U9%7tKvSARYW<6HZ7t7z+-|R=GkJkVA5i0481uE>)lNC>{CyJ5Yn3`I5 zIRisLN9^sr`Lz4-2G!Orll~*xV4rs+Ub4ZCWg1yGLnGi+DkMPmW%2Mw$QW-qesA4` z-LWN!CU8j`a!Lef68NF>Fjkh1&Q1%My@B}nl2Sb94Ss%pJp+SuP>@TM^cI7(Z030~ z*X%4TiE7T9eN%+ty%V??C7m>EJR2q(VtLptsUm&lif`g4XB+fJixnx|_gp=y=X&JA zp1OQ~KGni|V71GHd$!A$641#GCrAC^SoL!TkQPCK2ua)EAmQ40g$xXbY%cVWfJ_M< z{HeT|nOS@jV0mxCBu@}jcQSxyg)eOdVAnPvd8hmZtPIQSWj4JDVcf?$omYxJ*9Z$- zHh5SpH;}q&w%8p@-0N9AN&>M9Z-oR+$z#2{vCbu@$H$@G%S%2eg+JT{}I9m|Ejl|*`JghZPY6IpWrY95zT;5rC` zM^%Bw)Et2Es(sHTK~N|RpqV|Hd10XB90CDd%g_)JP^v}-YtA_Ux|S)X1nCFad$q-S zFzSzY?eSv?pJi&l$3KR-%UwC2qFz(csBK%~=-)|U<+ebO5)rA>YBjf9X(IUaCOd1} z_L#OlOzt@AvE$kEZt(%`-*ZfEt}Fgy;9R|#1F+LJM(-5=oqQ)gfkZL}xqAzl-9C{I zLvC$fvTssv^m6GgL-AiRe`>>i!h0jo&=Mf3*0>FpAKxq8ncFx2b!Kq~h-i)u<3sLnk(2JCh? zi0E`P0;z(|;+W@cL@$0%FeUW?7qOw7+-*rdZ>eGnaemzKl}kS~mbF!aw-C%4u;H*E8(SB*6Ur1Kb1u66 z_=%?ddD)+(0IzUjSYf!SFObRmWyB zD%@OL7mi9Sl!JP)qPWBSb2D7#3a&drSKVS_QP&S5sDctTb1J;CIy<|CXF109|Fn?I z!TXb?l|T(@7x3@ukAA{QP>eBK(MOh4qy66&<>vX$+qk=f<>feJ=ZAz(`j5ITFSQ0A z-#^$GV#BqY2~MNC84z$om4-0NXM1mN6#sl_X680sDE&FoBCf2AUqV8n^h5zoC z^e;pV5b68#zZ*qXKhOrWkL|E)aWRDrx}@Am3UNG-V8t805e(H_8umKNzcb_A3A6d% zM;@onl;P>Zu=Q4F6EBaT>{e*Pcx6eQIJW)d^`MlykkKHK29-48t%XLShht-3@n|3T zAYaFE{1{*^N-|zQ&ji7 z2KQZLj=n%digDOu<^#nZtbZS6tciOqF-aQu(7smR)K4km<9~D98o*Or{7O59nYDD@ zH8DZ;uKGi$oW?No60MAlDSKlD26mtC{&1w`qewa4Z>hD_WS&HysKqNC#)X>`&F}Tb z%dm~?zm}g4FwAZKbkH609AT2cwo1Q5w44fXMl;Y(E~Oj^yTxCA3M~CwQ?0FY4(B>h zWp;>B&<0GP@D#0-&xy&@HKL&jH;8Ncq_A6djhC0)l|SJP+P}VIpj!F5ge_v2sNroe zge1t!xam=4ooqWJ_SDMtKvN7cuDe0k&dl{wxbuT={;bs2A?Gcx_&N(4!zvtH<9uU^ zMx5TUrImB|OJAI9qk6_iK4D!(A&3YN9{f==Q?-5^Z{OOY(!xVp$9PVW2dh*=@UlNG(nIB5w0dnZlUC5EpdPT zL-Bq6MzS2t%0Ftu05OL$r%HHny{oqJwClCM15$C`P}yp0`laRn_5GXobff({Uc>3K zq#?18ki1!lB*f@Xd4u{@XDzFzJFlg97tWZ zgSkb^x`6nZaNi4TGEUZYV8TBW3TBs(px)ctbBI%rMP~Y&rCJrP-a^-?Cr2;c^(~bt z;~j;l)B{I36CLacE8V!!67=`cY)oB{+4b<%K5~-4X`Qu>leT^9!zYlkeVC(@XdX_X zo9`&MRg*{B9IN!j%FW*SFgsRM^q(snYGPr3D9HW66n|ehUGaG$MOLaD^d3UuLmyiH zZWM=zLPOvoBE5=~h9(^PL~{_O{P~_o0%EG0ohu%|ZRMYr$L8nfXSJLE!??jidrBAj z-|}_ zshu28vWql9L(glKsPk#VYyX9v=M~`1&+|x#39B9Hp+yi@{r5-3Df@#5)yhdIgke={ z(zRHyTBsernqc})0gl(u$N8w3=QVzIvjU4_K;nLkJ4?ym_fp*D5E5QPl;G zZLr?`#nbWf8-am=4ql&YYD6i8pK;PTOjO?K+kqeHJBCKaqZi+yZ^10e3uyW^Q{-)4 zA@Auz9S58p<0k~-KZG6jQGdQB!dn6p_&)X~#vOg^X`j<5;+utQRc9MAHxEx;Ghtf0+06^Qg1_8$iYdor_C5aZpT?6b|C(kTeUa$AJM$wj){4AbrGO?(1fZ## z+uIk;&%VN)SU81t@aD#>jQu(E7fYW0(;S2^f6|JB3Bul1ZCO#3er{`-R5|dy z_tG!2vGfkNClt?FrP5tHGbUkGj7$xTO(|FfSa=sD54dG=G2ggy4ZqxIO7aRzRh5vF zE;aCc`f+4#_>X2CkDMR}J{7-ZmJAaj8Z;mMlEqznyMWc30{W^{slu-Rj6{uX#>?5D zp&ty6B@KiWZAJ@k0Y&HYg>EAK*AD3UyLTaAIIDdTm&IAo0ZRk6_SrYNKSBd!_?Yjo zrFR@QCuCp`Pz_F)K5c;vOq|LSkBy3NPc97vx!07AOB&)>7XCH8YPwb(Orv{ho$9I< zrkrgsQ6$z|`;(nmYB?1eNOK?+6c+*fs^K8nM9;@Z470FppjBaYYcGaH6=B-AGs}cx z_0+Gjxm;jeM+EgiYRN;4{ew+vdQ-HO|=(~8eU1s1as}PD((gS5E<#gg;J;ZAztrFrJI_R<# zh@F()eFnYY=;*?hl8zyfIx!(l-;vM1Z8qZs!&GYMA|sl z_<}Jg@X-nhODhsHztym^d9h&gA_qtGwjs4w;=J?CU8e0=6Vs$CL?n4U`tZ=ruYD6F zIx~q&1DJUV}4{n=^2 z8-G}4PE4x)>|y46$jd;bHIgYxDU6LX9|whf1zJ>N6;H@vYzqan=2p6_a2$3B!hZdl zJz7(yNPI_szT;rU#4hvXZ+>umc`J7x_I=+EK*F0R-dS2U9)Y;uAEvj0dyR=odp$XGI3D&hG1sv!O&413j z*PAlIsbzKuk0)FIMj~Za>a*$vl918R0kfkiaa0%>XF0Tw_n!3>B^8E5I`viyx%m&_3;ZDK)}lX3@3mV{o|a!r3VbMo5>7(PvuWTHrC&P^Jhv z73SxuVTrT zn1ra@nJvY6+3&!xs{AzY8?}Cg-%iptSa`rWE|MiJZ z(!pO2)hUII%i5eV!#=oZ^CB>AN?^5M#jme)V%3zW5a5%UDB|`LUg8xT>tY%<4~GuJBJ<|*=sbE4ml zj9u$G(6jV?cb#j$QC(gA5eA&!9m&SAQ&D|b#fDlxt15|hT5FXDKim%RB1%CrnPteV zyyioknc)V{Y({nlUEP~{CDi}Q9;2Jt@SF`h`nH`vs%`=l&qv;%V~AK0M@|3EZbgvN z1w4WljnOZPH&tRh!66E|Bzcl28!|xiOi9jd61xVhP<`$BkRCpA{|6L*ZG;?5&JEptS*wc6>-h4}f@badEeksz6=c)0KnZe2ue z+lzHty^*3KG=KnVVO4?0b>|2rG^_*sWXwgW2-O-NMWldws47+jVQqu%SX*z8+Ry=N z%kSxf%=diO*oB3KYB>*S0G09)c&!{?zU=&-r^$bpTS$luU6zMQ(^8I`5Kl_5{X24z zSRN#bx`uCejJvivv)MP`cxWeqt6aJif(cIVDTN-1%7Yx!=N^Z)6t!ngv;%GPrHg`! z=ZD9~eQ77a+QtLjVzh)JfN`ATA!iSUTUGh|^%d6Ymv@Ys&L{(QmbEd_Umi8JCCM@5&IwYj8~QG=4Jw7=U(^`SKPDM8a}{u zvi=%~D(C$9QA7v&^99ZdNU^V_FYU)%{$f#3G^Y))Y)m%b#8=p{(~w;l2+`+-X6@6s z`&C%ixZ$zT*4(EQhWt3M|tjNyHZfLx*za}05%R6_r>Rhf~Yj?X%|j{#~hlR zl6wk!n8@~wiBcTB=_ah+nx<)UOhqYKd3jL$i?4pMgb%%1f9S3@H^0*TMcD8u_78>4 z+`QSsLTsMYbTzlxM9Ec7c3mCLi>$GCqV1HXZl4%k=+2?fBPG9*LCoW{?eesCA@tT- zUs`o&#kaJJ3>o!Ae7@L0j#1;=i(IwJ0Jd?Js@Hk4l_Q<>=}l?fRb0>kgb1(`D9}I) z2aiemHWUC2UnYz9urv6Te4xTUJ>p0T#O)bk%ojk+T7L;*;?3~>SsqD98H1t)6c%^t zAaz8L^8;`&<5dpma4HREsK?4}NpMN^B7&W@zXXK3eQUg%5^>Jnr+peYicXNwkE|fq zA|mLR>X*Q_qc3^A%#hQNt(WTzS2{^F=>=MbRXmH))^VmzVKpT>1!-+|y_~`!oa@C? zC~IU!K0W?@y4gkdXSzuWRFC<0r|Vn^faOF6ruGLjq6GqvOOKNxjj0 z%&z-y%%CQZu5*X7_olM08`trWy)43jHl~oamNcJ7Q7sB;#Ig9{PIycj z#-0wUY_EcYgTI**En~Pm4}&}jy8rxP5F(4DuCB>A~qZF9T`?OR(edFza4K-%*5%e~A9nd47j+t4t? z;U_W*UZ90i+5{ZLxzLcrrxtkyk@Or~mv$ISFXKSB2peK&PBGXY;84_6Tdla#@=~ki zIgPx1d93uWSb(>6FkBa1@bZeLy!ykqbB;D{Fg>Z@+pIcQnN<&QO$00IvW`Kaxy=o1RoEebr|6O)Ce^zpUFCvv2dthNm?dcO-F<4%Orbq1Ok<^=$8?|ApIv zBzY(#X}&)O#vOw&wN03a(*65^j2r8R{&ZWtk-qFa+TkEPCg!nRLPcknNFp}+6dl|r z?}_}^cW!;Vp@ofj$gF_EXiUF z&buo+jO=mPZ&UG+v(vth0rm#Z^34|{GGT|sjA#T?AX8GssUjOCx@YtLt5ootsw*NF zVbF4HEF&Ji7kRL8%(s|aT-?`WrurFeRd73dNrJT?2}Fg5fBo+I`^&9dEq36o%VYp^O=`!E-&TT2U?3HO6^@@vyn^`zk)e8~=IOSxJ?zf}z zLDzGv!P7N+Rv&8p0bo|U84JRI6*mxo^8fi6G1Zu)u#a>JGq&rhktY)OCLGi=utHX+ zF#j^Ee0-u*H*Pi&IsOhmu>WvgvQY{q+@R*?KtJ0<;$LWlnQ0trVRk7<(p$}H2|rOc zKhxO_ef^Q=??m#(>Yw^h-x|IID9)Rmfy*qnRPSDw-p0(~S>Nz(XWEjshqKn3eB!`j z?!Heb(U6q6_TrA^MJqBBi`$Q}S2I`($SFI5NmvF!L=>P4SD`kXK$;H}*zXr>EV`S3 zkmG+#xHB|h%B+UTZH%2el9wzehi>O${wNGA!DnG0e_q-?N?$R%Y?ZfW`0^VSAT$CE z!Vcw;NZ-CfD@74JT24ADa@JSUe}A-Cx9Y0B%VEhydkdoFt2iVKVNXo3gX9G7HCQ~n zC<^;X>|Os#mef8Z&i$Wh{^KIN*J-}Xl7{lyT$#jwg>C<3(6{00>Le~{csS~&LZ%&* zY90-wgadZMa=hGjV{5AwhLQ&xJol9c>OjP~z5;kR>!j(y*XR=+@6Rg+;iKwas-Tnj zkJP0-wu3o5Jl{`g?A4>}`ew#UrM2Hra~)aijQ&+nQam5PBvLoo&$jb^D1+7J{+-qB z9~ktN5z^obQ^lHd)1|Vt=&GHW?ErhXgJt&GK2B&`^KJsK*_x>v8D+tLn1Yp?(0`9; z?Y|MFps2jY&x7$kqDs0;Ux-I&Ud;#T&KIS0Fc->ANKEYa>eVHry!<>2iS%zJ0izm% zmGwHz#q{*6fnjtmB_7!>V!lU1^J%TjewtPmT!ni8_8?3DX!h8N?Hw&Ho1C=??^Dr0 zhx)*FD?py9Pcakcw)&paG@c&G#Kf9hG2-D7ito{4@7wA1xj08hl9Q%?u|FTYcq)VY zY~}`o_EB)R)H-JR@=1)|$&ySv%k%$2757bbyQh?7Z_5gXW9q)H;SPG%+_l@Uj6LQ< z#s-n(VaUkHKvaLShSf4TNey(ITki1jvHk6BG>3BGu@PuA;RpU}&!oHvWH?SG^q)O3 z55v1^m;Q`b-hP_Ci2T57UYn6|dxnK*QKg5TeuGMg`f?02!(J;U#(AkS8q5uk6XpX= z24x#<%4>3?@^@6PT|j!bBP8yH2IM%-`Fy;%*y>ZnTLQN@JOk%umvyakFD-C9L!rSbUp~rM+#nGptg5@#&f6_HbNh44tgE#Ol(Ap zqUv35+v`gwmE2Ex3kciYxyhSy)WTHJ-7#3Q&KGtv#y06$Vd5|ipb+z9%>(x5IqX~(P zB(!g`P8MS=eGxRJ!+e3xI;v_%Q2_jIerzo8k7?C)Tz=ermV9Gjh&`oJ-NM?fDlWjY zQD;Lo0@cOzWK@zk@?M#86VqnJOtswQ0eMWCd?;tUrV4n6*r5{6*QeIRZ+i_t!nk9U z=`2#?mls7JzgOr-X1)F7_@U(W)||iG3|vuQw=$b7ij3d5c0Ig4T?rS=%nm!DCq~_D z5u2heljYsTrB{D6`lvH;yb^a0uPDeaV721(xgy)a?BLyU124)Y+H3!T5|$$tka&B_ zq3vnw86VwUR$9Ap|L}E@L7D!39DU6Pl=C(4v?$a~MBpP2Npr-Be0ZQ3pT=ImXOr*) z&;3~ZJGwl)8nay~aYMEB1(oYNZDu>B$K)0yF=(6d$9tHOi5}e+KdmN@_VTVceFTsm zb}jq{$#`w-i+toyYTV0;7Bm(==K-dO!F!uyI!w8YrI1iGbm9ub{C}p&+0Yq9`4`K7 zcKq#*H#QYH=~0pEy_)z(A)Ru_bG(Rl=v=eizDT3Sh={~p?T)p`$Q+Xeak7~nuu~d7 zvv``@njXQPW-KdgKkdFwxMRFcZb6)=EQ>tSJe#OGzvO>%g7eWw@HT?cxzyDuBwb{HX&#w&yZt}Rgd!TW)x$>p4-Hb7QfY2rx+lk6cW=n1jwBG0rk+P7ON;;zl_5+{GWxZ$yN1-+>Tv~NCTFht%=7=Qfnia}VF-0>|T9==)GE#y%vD~pNM z#!QP|)iW9pa}9v-ta()EG@k&H?E2eV&6hEhld)W!%PR-Qe$hO8@u)3biB#5dILj)Z zPE7YzRN7@BW2H~OUA_uaH|UzQADsyUfetV?0hZSQ1X&17Xg{N z5S`&c^&|Rn)(>=2)U`pM5>Cs9D~&hXX2rQTtwW>#s5gA1rbHW51%-#-%?8R9CMT(i z*7z%g?)|8#WYzhh1!ht~o+J<29)XZ6TLP>@bkhl5Va;1HE=d@7u|Iz4_)g*$WLF`b zSx?&Ue{m3xO}Hj0Y_t*QEqsF5*v!W{sPI)8GvvGc8s)^TqCxBz&nerzwOG79boDUj zVUNHCQV%{+v~8&OW?7vUa(@Mr$321k9~QFr3{}mjJnnADtoQY)DgGiqE+)Df<*uom zOZh=QaQgNb?g`QLo_ zKy|dgPT=c%?)qL85X*~ki@vVF@Mcoka19Kv@l$aZf0ymwYWjw4_Wz8kPdBc{;h z@yEmqxw0$Su5t|TkfaHx1Z4dB#-CVwEEFLKM|;uybwqSYy1YU*V}9#MIIm*{{AZ{g zy|-Rx)z@hm`3b`j*cq6gG5)#R;?9t6Ixs0`f$sXKKIb+S;iN_dF@RRQs z0-An99cR1Y?C^SSwU3u@@$kwdih#*WZ5*0W)}>l-teziinCt25_nbjj%5rtAgzpS! z&FY6GL^J6ja*5jbLv+&ReWNUzX?E|lPAz@ z%wD*7af@G{zRt==hk^wz{5}o?_cK%*-HY^(>P;&Gmn5m51dMxgChDogURzK=fPB{# zm~z6Wm0)#n7^euvd#I#@9UGgNh=S&4cNf$nMwr!az?^dnNRc~kEhrk*IAH=TMwmi2M;6}ml9(Eh9X@;R?% zRIIt1V_Z0%QI2YmO6YI25k$J9%MJ_A3!50;4pq#Y>q0jh-5m>(6KW0_o;uhZgP1KV z<$l8sqzqyc?~^X1;?#)rfNR+c1>Cb)uLQPI*I z?tGE8$r=(m?;Rq$(ZY96g384`_s|7iwQ?JEt$zpoqXM(iB&qY)s=j70W)^r z`QN1IaEG7mr09YO(acw)yH0R{8P6&r>F*aD^zZkvnHf!v%xvt zK^pl}8g|Le^aLR7Q&oIOJ-A9_&NMpFJLHk5unUC?GZ-Y)D>l3AR0b`ZlWBS^m|@}@ zsY`@PE1)lMH8nLsOo9wrFaBj^+n%UIK%0}fFI+cKeistE;b@DBFcJn%jGo1BN4tzrRc=9OKqw5C$NW(UVEDZI;Hxp^q zO)`3e$xBK~?n8PXIdOP&)ayw=5Fz|f^{?;I&a(K|eZbGR0}{Cyc*SvPCG=?P4H`Ul z|651;+u)Y%o*0<^LqZX}fI#xqQpzvLwUc*NfAWJ?A%)6&KycnfBwK4Y(LSB1N{9$I zGFJcdW7qL$m7iH1q5#o<^6+aFbC$8Av$71XgAp!s;Bt;OI@SxqQ8k|(x`t9!lG;>? z)qS1{%ZOSZLH>tL$TxbRxnpb(v#aS6?DFgyXx(XaeJ&99)|ECRuPgLoVg^%&dV2NG zYqP9Zw3%S;)IqQbdZM#$2qYuhE`?xgLF@;FVPcx=@Ojs#Cb3T^aiGS`_eYyt-QH{iCxc| zmJbqWgDmM+M*jAw0b8R?o<vNEN=}ow}6$Y6o zz(btsqG1#UpLE1<5y?@1mLj^nk7q>O)UvN~0McP0RYDQGGm7=`;_uU3Ff81om*m$GEfJr^tz)ZRXOp8y$V{if(# zyNps`%r_-;_wdn|CPiiCmB8=g<3~~r{jV-x<#FG#LJvAXQ_d;3$#)K#84u&~;R|~v z-sHSq_t}`R(H-ZbMvHCt%y;?^Vc~%F`Sa;}uKLxx9y|8{27CTe#1e7zI4TIA9@3lK zjC8Ywxhr*VX<`9=geraa`JepPBvF1%L+TG7_^dfv^4 zqI8+Lf==VF4Y?sDB$N}r^SaRmr%j|y?G^Iqp6)(;&NoID+v4>P3P#hU5cgE^H_L~v zspWjmM04vv@CC$3Lz_T908K4a0EroP_ob79lAN_#_DO9n)*%Ff}w zHr-wZ@NT#QqLbh0Y?#^#<&v>gV$tz}?%kunAou22M^~nLymv5PKSw+oU<8 zkTnn68&5X?QVOxN{?AfK)UTmOGq(u6$0CkE8bbY(fuMg-&^*9tp@oUe6h;pw6_{Kk zb`g}Ad@2(#<)iXDwLSYmBj0R@!jI)PxU-9Q2A(lmEOw>6i_Yz>5^968BpQRMzc8j_ z4vp5*Zn(7PJx=^I`rx*K$Y*la{ZrR$3X zWl=jPHQ&;RtZ`ITRcq*8?5=17epp$DQN(Eh1+9$5+O(4t-%ZT#B_-oNL6WH-2O*U` zjA-)#^ZM`G;%P3dz3&)`Us*Er5T@+KGbfJZU3rQ7>CLy#LgQx}V~omU&vBA{MXwbt z<#i>Yc3yL>lR%6(vnww#x=SGQ)W(DFMcV-9(6T2L#XCmMuD(bdv`?ZU5=tk*o zgAfFUE|Hcl1p(=jQa~75q)QZ#ZgHd$0VyR#X{4keWCo|+ojl{lj@tD!K`+zQRqAhfyeK_MT+Povd#-aO&(41ZlOP)B|5@U zias_mks^XlJVN*5W0R?G;fMqV6cOE^r`Zz&LGjpAG5F-PK;VJ7ZXf&=>cn%mZ{J?8 z^?_XyEJxF*1;7R#ldU3mzdOD8ior*jC0*--9~@c%PzCDP25ff19=!w(50BT1OT`$D zi_)LEL3f$~_Zsi5Tbjb?bbSfj4yqdgw)ni#kymuz(Wi#%O%EVSbYhaEd4f**b^)bm>rD zhsh=-srjB^Ha%=X&CdGzL*yMvCHCFne(NhldzFAL%Z-wvVQ=HT{o$rMIr)rSC*O?Z z{yr5);G^xewWvlMU|c+;{R8vr!h*MnWb=!&N~=6Qzp`Zs7)L5`4pg5rjFTDNZ+>4H zj9Fh=&F5a04OUo$03rUmFtYXFUGPdW@$3_k-Gxu_fl+;69oWUtgY!TAZzzp=Q;FT2 zZw$oQ3KcPVe&EPD*`)B&BbEEGZlPC9?b!#MG=V5?MU1A|!N3)0+j5pa?(jCXhz~d9 zUg_GqKL4?7b^gvT*6cjdSAl`Y%g%htqF`WUWqDQb=+&23p)WESZPVnV=C~gRfSU#eeG+;2JP+{okhZ$$_&Goylu36#4w$pO*!J zZ^eCmbY0rvaEIfFJ(!g#}A(>1GoHBXhpR$$U_Q zW!)d*Ktc8pM63KgAw&1U$_K>*wBNvqkmK|fXhj(9K>J&ElD41(!P}(bN1H@|SYn9& zhRj*oqFFLvB_l*EggT-^?22~1DXFi40!vbNZ zz!gy6xn@~`;-d+Uq_vx>O4m_p`06d+5S!uZDAnG#h?gO4wV5qIr_Oi2g06fx-QX`5 z9OCEw`B~zA^S>hMNieKA+luEwSnw`TV5c2ELo@~sh}X+Zy%2l&Cu?mXW!whX6bq>` z%7@!LqJ}}O$pS5p?gs@qB{j3PG=kFEs`>&IrZ+_xM+So-7D8~IuXSqPl?|t4>aq?w zVsbcs$e12#UI1oFGdRcQz=?uwR)p)hd&=BqmlGG&rA`$4>Y=g{9EW-d2~JlG=-VxV zHQ*7AyQ%?2i4^2eJ%CiLLEn>O@5G~29wSh?G!h7*K=Kt8*Y~;sBj!Qa*3e*|yhX!l zq`1x0tIfv7Hh{IIg42LI$P0qxgxlkJV7hn($xF}C`^>GcuOEXS%_^HiM1D z(NLg*JRKRLkWm@_ldX&_S|YkS30TQ%BoBaT>cOF3!AnR;c)Yy_V%J!C4cIi?^*t61 zA>-jbXr6+zxGgVj(oo}&F@0oeJ{E05NgJ`-y7|=UmiTSi4W7dH{N}K>tWBoQHPX(M zLygpU5HZMvz1IF>J6I+?)-(mr`XrDV99s}UQZg@Qcv*$;gb9qWC7+wae+jppusQ;CFDgZ zZ6e17_4aPs_cU>%u~3u4-ZweDgk{iB(T|^i=ceq|WBTWJJ`;R113e=wXwSdfi`TLg zYCupcF(O2{=t@OC=W(d$N{k!MdC);5mEB}1q{@;&CxR(coPIBb7{EXV^Ca={ir1xB7IOTt$!7Z zy6ffv!iPPbn#kWDw&sWa1)}#AY7KL=mE1{z>zd87b5Ql|*I^Jjy081pvYCAXT)qLJaF! zIyyROQ-=1dry^~`uqXbPI>rM9T}P|l|I1S5Wz#{FQW(~qNgrYm-hJkOc6--bjs_t> zVgG&`@+qE;n%nKuh3O;?sx*yn~5L)JsZZ4qA0@W=jD@l zla`k5fMNrpfh2zQq1&4`Z(gUQq-5}c8*sY&f#|`e`h}Ctj}_+1+Y||j4O<7g_Nm8s z8d+ILhk&jm-^oCZ?i`IvAYQr=5pIj=)a)e^YVtMlsh$gE-cSXHrZ~R5N=VPFH@%UO zk;e(l2pW2O2?6491qVk;Lqh{!Ru*kVI3+m1FMQ6|Hl%NR(1YCc%caZ2d8UY-FOx3J zaZKanbEt_{Spf86sHkUukoRGR zQ5Gm%i(07BJP+gbqG5OPghvhxcZE?IpGUs@sz2oYXeI<(*oVRz?Pi-EX{^G+Tb_?i zOx8Ykq1rScPJlnE9s1Lg&SC`jvgCWXTdE%c%2HFw{()T&@^C0265YBUfhJLigU6u&=>K-axslx9-uD`l7v&RZi%!*Na`oPfOyfG=6b7s%Bu0 z$31#S{yofNB-HT|w6t<;@9WwIcp0hR*Rc)N5r!*uTVQFFILq*FDZnt1D7Q(c!IUM> zxWP|OUcLb`c*;a)m8V!?J5`GZU-p8U2?yGNxe>>{J!ET~^cefeOl zcF*@Z=v-KkSquG#1oGT}x*6$~dOajYt7~6-s$Z6ggOH}Vh1O&zd^TaMjvDwVpfzbf z5AR>=LVvRs%y4`UQcht`s{J_^N>j!@JS0IK)y^8q?=F9|*NtLV{26Lm$cpVJ^ zofr&S;%1tvT^0i`;Zd^E(5qp2U7h?7{PuWxZ~05dwj=ErS{Q?zXPJToo;U@&nUwhB z9&AK;=jbObPWoQ!Z0dSnwPmj`RY>NyFXdAg*620cuTf>ZCV9oj$HzAr%@nxx@$+Zj zd^9^K_h?g=4-2tVK&vEx8!B}a~Ae=0$X~&iVAg< z;14T)detZpvV6voC4nX^95py{=_Z|!86`>&fBm1T(7eZ(;`cs;C7$hI zZc0oIa4sfKpN719cVqY9pmB8cUf(9PLG1@XUSTD^sW#^1?M4kt*#KdoPlf#FhP=)E z6_$S>eyz;}c9F-FG&HHXOEb_3a|P{y(5b!Xyj;pS9$6V z@Vn}F0S_R1Q%F$bzn1%$vR5B+QDBqw_ikK->Bi5A{s7R3qnu!DUZ}k~!`6#RvIIH&>z+jM=MFHZrc?SA-a#8fF`eQnx zCu>tJgQP@it}v4XrSlbE1I15HSg-i%Ug$gOZ#iJ&a{N|XNBXKCz z22uFXCcVs@?OOBdWo&v|e`7cfrW|>7KCuzZ?f5{S`|en>xIWVXS{cbiehc$9#|co3 zfi5(HI;0Yc=WEi6o2yNRmFMc!GF~G#CIK$ZYkPIn*_Qh7=^8%$bX7@y;J**^i5?)I z)rr`Xi4^U3_=I25aXV(iAM<$$SU?1Aw3>2VDs%AwJ*hu~7$TQ}735)*uf=6M4jLWv z-k9$HFM1M*Bf^YD)DE7EOqL~1k{4E}Rl2ta$#~JL7OwWJ30%2&Xtb_*$FD56)v+9q zR6&aN%Xl86_V-6%N^j-!2e)-<@JWiy(m-(h$Hx+gYK9=?4nf@3!ulVA9&w#&SKF*@ z5C%>wQy*IOX3A$-r5Ec#8IRA+#>LHz|MBsUuP~3A02?Yt0v1x^zs;?z6gj)c;P^0n z^*JKpdD3HB#nzL`KK5{dLO^;MTmy>+##x{c>~b z@fR?|)UB~o#yWr7be(z7-6J{wKt5=0Urg=6gX`ReH3=WY&C=XeWB}zEBd-BrQBkXH z{{yb%p{Ab+i_n0n`I(;!A%1$g44a2-v|17DFiT6XX}-_UDK>q7P&KM@6uz@1pIp&w zxc1f!&5wyt^HaRaJXtDIo54hh`-MrjUoQSOYHV$jFCCCOnP0Rkkh59set$koHSPG@ z{N#(*cgbBrInBx?CPoVxoM*urazZt+`J_ z75air9krKtt;pOa?2w4%%SuLMf~6ul^M?14cmIc@oRoLPX#%LII#@h6tVhGHY8Pqr zJ{p5_%3uckfbAU3ztM-QxMA9jF@+=rT!HS#OFa~U?Mg%>B(DIma5BrTx+-OAn$DTV z&1q1j0ap$@XEKII?*CaoN;j~|Bjv6r6+a`?8qziLr1B{jLdsDPf_Tq#yy9~vF zePMbS2H6YrYyT(B)-&aDToVvnd0682dk8 z&ce2b7P58?vaG;#{}uW)#AeF649(?w9kd7tZjt9rF6(M(fhG|*5%U*TBe4O&lLzzm z@bx3R|MSMwn^`{#OQdPf0Ems!hyxDT+uDx16VPF!uIGn~M57yzNL*BkuPNV8Posm+ zJ{sIrauvQt!#sp}DYD_;o!Xv^uz=5hVt?iC(p$=_$_h`1yUAhaOKOpn$VNknLIrIz z(5H_yTlAvI$=ESoM&YvWw-%wtjln~#FZ$K4crb}?f$jW^vS{7&t*1po-7|vFyK!1wTIlubPUP`dBqBVty+! zOkQ8hL^*`GUqsa>#Jh|8x%;MbjTPS*tG5iDxrv#Hb{Iu~Mhyy^KWDA@^d04s)f+XmEIV z>@xhq9v;@(70l{Db%DsF2q$KB6aGU}s+$y{BjiLbamk`^+fxnOS-FmSnrWkF$;xAwB4@QKA#n?LVV0?N=yNH^` z9rWIMVrTZ1$hM&^!5quS`fU%~sd-xkqW^P5_9`T z6MaVvqR)nKB{pUOeMk7_?L8^ez@wU)0GLW|8l@P}_@7F_l{oRbtrPFTK7@6r`?U%O zOciV3cuktlu~eS>7Ebx;QL06f@J)%R1PuSAT|t@H(WbhC>mvcUukWb%?*4n9VhEX7 zZu_D}^@&MIPg7D;Z;_Ic4$UIADs>Pn*j7U^odoe^*2CP~+(~?9-&$LM$-p0h2sXB1 zd~I>?r@_Ch)@fE{&<6&UHeIPA+SusmXbVpmt0lo`+-i0do?K;^e}7sAYLC?=eCizA zd=2&=a1u{XA)jmy+tDgbEEp-+hoSt4TSgWPryp;iC>sL$?Om!i9D9O5h3LcPYviWS zkJVOVZLO{I6Yn~td%;AVbM}+tjKW}@^ZXkdI&mL;>>b-7#^JiUy0Uvf+6<41igGoC zSjgWooVpK(pAD3}&w>&85H=_%=DyTz zW&uOMc-%6e*g8(1vq&&wP5u3wmFfn$d4ezs>?r33E0gRn>Eajo`-~aHk_r_=eb~AU zYe(|~xDv~1L3=cakBb|#3^%Pc+?3@T3$VG!f*;0^EBGmHft9!yo0^g$Xymo_%8QzU zf^BvSEEXNxpR9kIAAtG0tk(O$bw>=A6ig0Wy5 zG9A2TsvC;c+4kGzY*QF-&mg*CU4xY@V19>CzQq>H_5 z@0*B`6W@K+{Pa@6k)pcgVV%-vg1^+iQ#@m;PLjx&WTmZE;)>r@s+VcRC4*Eq=}&CF zVDRzr>XFOdLDHRnPFXeVOwxryf+7zE3YTz}ZC(BE|JV@8ZFvmSwhp_3m%Fm_=G^-` zXr_xdr%0M$5{PX17XH=&Rwef0tx?1CUDUw2wD+7?o3Iv zsLEMVgUCyd4xJy<<6fQVk}a@)eE^mCAj@BtyeQBZ>FPB%4GfUO8v2(OpM$HbOccGe zISG;4awCX7K@qpUioW%l-=h7?*qEUfAF5xn=|RBXzkka@f^(Q3g0WF|-$;WGMWPB< zZkEA#(~1WS+R8PRY;0}S3Uv4eNn<6E@Kp}SV0LEH=g{y@OiwfBCt&J}bxP@xGLVFb z?Xj?_O@v;*n)BFGp4b*SsMNf9nwu~ zlSGmrh)YMH5$B%e%KL!n z$^~F%8COxnCoDE@G2%vEAu;O%$f#exu4w@?&YR+9Y9FLEVsn9{d`MM#*i@yRA%j<` zf??;G-0J&9qjph7qW~pEaV1?LOotXW5^rqPq?wKCyb^zcx{r9QSA&DG40J4|U>C+YVc za~(x#(^pqkvX!l1ev@`+*VN^RG8Mfh9XGgqIY|HW3T8ppl%jsQiXkp-7gySXzUMgG zMYe$I@#Dv3>rw8t@U=0^YD24U-RVpZnEvu$WhcGs>Q*onO^#&4Jf$hdJmAa4n=^UG;Uhj~;o))<)QaaN6i)KyuA`WRxY*y1ON8IP#F^q!7X^m8>`2kw-Ptr>c8U;{$xc#0 zqPXZ&pPT(5I}nHmY}HS*u?O z7u{m#-u={x?@_g{_>kdCr{>5^CABv>mS=z00SzTjLSOcKc?G}4?-V7Ay-a-@F!z^O z`VgAirv5=;rqy$H)q3npSK@k7lMWs>0-9r;Bv zlG}2=P2;sV^(KuL&0htxnnN3=TIft3R+JzEV&pY1!U3KTYf$oWRGWQ`(EInCmhGU? zH2PmNmTJIfh5~89q*DvuS%dTdS!;nllz#swnHNrxN0D|~=~H@Xri8ZpUaQoPquQ9ch@7;jkim@C;;a$fG4RhO0cB zD!g~NG~(h;*ZiPSjfvOzSJDXDcvFxt0gL=hX)s&aiJu$mBW}^~E=rQ-Zi0}nKUv+h zQEDwM9htc~QbE^U4ljVG&(7=Z``UX-A;eLQ0vqYRQ1P>f-nfXgnY;1}tC$CWrPQ5M zEO5-!pV&3Y#kwEv@<;l@k81(A4{itO zk`byOOyH1#wmm44rvd!QOb!XnUdVEj0k|O|w(y))cx*5u01!9=*~yuXv;n5vb%~%2 z_+-tV}0+J+uXKYJ2ElBUtfQ=3O$AD)OE7rA@so|gU>#5_P6 zonslcP;r+rD8PLF^V0!dbv+A+tPcCcBab@+G)JqH()S~gp0s$QIx*l@ zS^zK#o(ldXEM!4|2F_9uj1N=AeQ7~%mvGCP6c(errcGDiIyeK!nQt)PLs3(w5CNt| z4ZoL>`3zfe`zJ~;sH@Te?ezTLV$Il_^fQP~hjv;)NhwBZ9=8AO+YnUrno8-dCBdABhIL( zqE%UVGHX>Q>ElUf)~Dvcgrt|vt3lEE(yn?kC~HI26jo3|f`%&0s?xOk=Y29{rzBFV zt~@Hg+u>yMNHAWidAQsbC-Q^hgTo~XQ03PumGJ+Oe!F@RRvmLLkzGv2qc3=yq-*w- zdCxpjEtSc5uT^6SbM(NM%q%-LMr+&K-vCcl;q6z8b_}`=( z=}JGZUUl!y6%Y&#K3#R^X#xvBUG#v83Wyxm){C4+h!}2Z@S^};?tvJwlQK^y^tYy>N^?ytRkHIh~$TFZNZBsQ1r+F?2I z+iEvHz+WI$TO)kd#}+Cl^WKd(Tf81nO@S2_aJ(5gk}qAjU<{@IDpPQXLlMShi~@3AM~#PX{6L+m5AMlR1b-zaoM z#-^r*8Tfd)D-bU_HXDfa(m&~ZftpJQtYo>(wYK%0DqspFBcrF|wi(;ff!nw51-B23 zoS{20Z$Vh?@*%i~UK3w00t5txNbbjnLQuwa!+M?xET8Eo_o?4qF~m|g)F1i*@cOI6 zpD#*%%BL$ydW9WmQxLww?tFF{BBcIVm@I6QME*>7$=_vphy;F62$TqBgs)cX4pin} zyVQN{-Fh;SGiP#H^V=H!dPPjD9MBvu_oX+(oU1~q&)pb8MI1R0^)#wB5l%gSN@D2_ z$D)L?gWD=21aFS6wmm(xpQCj(+G#0JnNh2JcU12a-W^Vby1VGJ6Wxt=}mKUS-UG_h4r&Q_t-JkM21}76!|;$TEHs)QlK(Z^+D} zyVOiph;vMq&YQ6#-*=I8ih{1VtWQ!TWy6XvmRTb);> ziG`26@YUVMWi$?xiQe-+S1*gn&3+Q+16Btc5$6{zUE=R|2L+I=;;(QjfnfI2cYh3B z_zOaJ=78Dsi<^B@Z}pVaZzc3FDi1cx|JK%C1_xh#gtZB#^VyY%h_`8YF9OT;h`a8n zGDjw`v{AI$-+*!V)rGmuR0}ZwC@CxRfn0fW@wY1NchHd$*X>T?AY(gVBWVhY*4}9U zmF7)+ZuK4c;F!-KflG++&EimDSL&{oaDQQDm&z*u&HajqSf}8Md#-o7>fE0_A^2Q4 z7kH^Tb?xYV^?>ty;(z;R2#U#&O=nX4kF@#KH{jd<8I^$%c0|)^bCyl*DD`pWY5KoU zO$Cy30b7B%4Sq+`EuDUyo5;H#_VCTE0Rb+;JFYF(2h`UnCZkEVOPmzxwqqd|WJXRp z585H)(JGg+^`F3}0ps)Oe0Toc?qz~M8)^i=B1nN*a*?MS@!-eHrz31S)RBOjo7*8s zjFIs-?2jTrsBGGorK}M2;ZBt%$2)JYKdxu!$0zgjyg$H>45HEgL4X_l>{S{A8*Dpm zkdGey{+-iJ(B{eDBXTpX>}vzU6}LlXKK5?c1MKjRQL*vA;`p_Z#g{d58}d6V?my?w z*Box^H5pa~?25R(-rcmy+M^(+c;EeP{K4UOD~M(~o4>!6fR4qnho}7wq2PcpGd+x% z5gJ?l7{d3=By-H9?>EFBl|W~vY3Aq~Ob9z&Dp>fX2{fqvConNV(yFghQH4SWmkyJk z3g>x3gj@z^voZ8~IyLTm$Ryc0N4U@`(W~SmuZ+75;bipB0bhYL_ns1?A)+e#t!e@$ zr!nZ0H#-gvWN(aSBEoQZod4^oPNtNmF7ZX*;}n?>XYv&bu-FtB#5;-b~Hvcky zY_Qp;4*Wj1xQQcHw?81%y?W}Lrj5vs@9QEV$$K0|D?ZflU0C*M#dVZLHT7`Phs;c4 z@ClHMx|dK(?ExF54B#-5TDi|*6sm&2t5$CuAA9dQpRih!*;8{2e(cDn<-&ONAc0mz z!lT29zKtmafA;EHIxvJ!IVq`)r~3fklanclBKZMtE0mAWZGzfWn!s7AH#uQAqybZ3 z&aR(?ZP$uvF>382(`EXb!szf%M~wi>k3IZCTpKrt6egPr5jH(AqE-9+%m6$m>Tx?G zAD<8a8FE^XgM%ZCobMcvsoH}d!0|xSYzW9P>ukREG`fpgobfZ|c!heosu&M>$B?9r&|Et2?b+M>-0A0^f+kBR2CohOC9Vc+1;hRD08zI zQ|W#cq91Nd9DCcQS=2sfEpy>e3ME0DPM|0EKfRpn#A^-~0pj+Otn1^ZcxhWC9#gUj zy03KLpxJaFf)a1z8j10{N%`H85SUpK)4uk*tNUL-1(B{;|-pG zbhkgVvnV_09pu&;wXeRDO|>9aJ2HkGaCu!ZE!MPjiBZd0@_eV)WgC(6pgX-HXr?)LY1{jNJ)f1DR9yG-o zKO6TBMUOuz$uokKHh}n}Ec&4)4+Gd=0|-IKeGa%_`uPAM;)>M+mq>^}zGTEr1j1kXjYpj@U| zp*;;m>LsrIX;3U$rOxl|ee-3@AEK(RY}@{PMyz6k5G;T)_t7;=p47ztMfEANT}c=D z>ZI8QlBj0?Rj2!Y^@bfT$De0}J~Y&U1KpdFY~VUiKfa+TRKo&3PH`XVevh@C)hRgUAi3d2hhVhiD=EUZlhExXUaT(||0jISX2 zso^UWh25K4z-;98h`mcfo%$krM?L7%FE?=cxN{nWNK*Mge**38#nbQbtUQkqz4MgJ z?l#?v%PtF(dlft=mtA{MZr$7;NYg$31)L2)&?aSB=-ZnsNdb_boT972u4XRu@rLST z_cGbw&f__w3FO_~+QCpmPfegN3vJ=+m9LDfVe6wB1FSj@k*nZl%b_s)g z(r&Tijb}F`(}~{kfE(m2bd?k0k2xn-$21XpS-MRNa)$Q0!RBI&pTE2;K=?LuhZ7zR zwej!=foq%x7n{4%{9&$MVm0_s?8HEm%`(!PVZj9Q26_1u#4TJ}7cVA#7Z3gs@AYXm zR?Y*C*mkO#V@yky$-KNqATxNCmJp)0hfD-~M?#K_kR5|CsFv}yV6=`RB%}}8B$C%S ze+jo0dH3X~$>o*M|H@{d1jcRUEzgU}=gSz?dofrD0oJ2p}V@6KKdS)?8*<`f*>UR!JznAYPJVP4?9=z%jXAO*ljxe9*v;Dk$ z&QfHEYTUBH?<2l^^^3#~&v($Ed3=6H8{1+!zR7B7IP)t>haA!I5a%|S0p!*VEV!{b zVocvuSNgIEuy*4SK?HP?g<2gqPhddF+@b;MixkxC%&Ql{(XZ`Ke6oD1pN+m=?Qq~N zF9Qi8pfOF&b0o9bX?ORU%%G@iCOE%95_RycN{)uef7ZzRRSttJT>Dg1(`mw>Er$LD zUc&TD{DeI$sy7$9#8FQB<%ji#lu-}1p3}!3$&Q-z<&h{U5_du3)pOuIZ)ATe*(-jM z-~wbZ!iPL(YGFI}7yObMeUrQ8b=gWa@B!IbT}mQHG@-W1!<6rS^K9Gm*cBkMn&*jf zv01>}UI#@{yS@oCJ0_q&=W1rA zL^J}ACviW_z+i?e8&Qlxj6_mQXAq>wP5n!a2bG>~_+ZeA*?9Pw!|uv+?VbaF5qa6Z z3hw*oHV(Kd#d4}epE|Q8S7)|cwK-+`u0r#3@!1}Dn=s(By}sU2rsTbS&-07NcOCLa zrJT1>YOV&#dWEm=0e~TT**5;jnLCPoySRS(pEsmatV)#F44heUn*G{6^a!yzBColV z4I19-kYNS31TcE)^ylN`GbgItDX9;+uld?WfST2ld@?$Ad;Kkjm8U%pokuS{=G{uTcGwcB zUi=Hn?VYX#e*)vt0*dqq>X)TKm!XLduqaGIz-yU1NNuc~IpjS`7yT$cFtPL*b-)$i z$M9rzop}R%4GP1#Tai4wSue5y2Lr4E;hTZxud$;82aazED0nT#p_Fg{*lrAmHa;v3 ze?-8cu+AC&2=9EX!Z7LZ%wb>F*9zmaprQAO^Jj-CNKX6`PBCq;224&EZp4ySy%t zDwj#vD13p(=&Xx5BW`NS1LL+x-eL{;|4?q>=Utu&R3{pdz&Y{An}3Vz`8BUc>~HAA zq1ij0Z~dwCrBf2UH}1h0HIk*G#zIZme+DN~tO>mQa;OivXLNHH0Q~cP=>a}n+>c^l zBcm$~O$#mo3ZwDBD;--br%dFvnZc#Rh1?1;40>M?yC{g>A^R!-EGQ=MsYnp@;CJ-j z-E~KVlg!Ep1QKWytN>*Ig`Gbzz8Z&z4+>{VsHwvMn`;b0L|%S=Nzkzv+uDXB&}{fR zSMczD{MwO3dmsk4f6vCxzPJ8O?>H`orWHyD{w4atJ8MmXo7?@|*IW*sUWPjfLQc$}V|y|5$$AP@Dg$K}IH0=2 zfX4eKaJJx4@m=%)Z_G8=xK@?(9qI8S|sd7)|3pT!CqROyKy$;K&Z+MA~gBoy1k z-tHPQdi0Cumj0Srt(!$PG}wTZx?FKmLC)0q_T~H75A+Uy%N5;85uFG7y!H^4 z^sjvY+(|D10gwCh=aN=Hm((dU5B92Yo_`4V4XbR3i-Vo$pXs*f3MkFRJ2`ILI0O0s zKbWWZ?9a1++8SKfPJfn`klf-Myu7^0V|b_;lF0>uRqXP0tb^I}YycF!JpaIW=2~s# zP#6Rw1=~8r<7(zM@%k=kOI>GGrq;pNttM3!c1A0p(Q)&a*bRf>nuAx&=$po`ujk^o z(y2m0^0i4p#(pKp=jefDV==4GU%`>Ow2R4)f4nP`t9`*z%zIk{Aw*1k!R+T~wHgl7 z(IyA*>Dj#-AF6jwrxygEKE^!?+BB)PNkeGUo@zTeA9f-FyGZzrqS(RT7ObFztURT5 zp=Hq;1E6^Tc#@_-Gqf(G@L#M8^T~MfZ>ugq^o-hpV4tHF4}dFPdHRVGHt#{9p^8~n zfF800<{aP&LFx5!XBQtX68Yc|$uBW%|CYti$ake#A1H^CNlkDiN3DkPn9o0hYk)>r zd+YwAXZd!PoZNc>e?!qH$zR%G!iU+QTY6F z$JgE_$Y%uyyjY4aVg1{kwp(NZ@BrTYd50oj{Hkg#xSvE%e0CR? z;;0GF)lV>7ciYOt@-(!_9%IR?t2szvhXAjrx?Tvc0(#cz(P`EawL2}JHP4r=+JR%p zPdiK`RTzKQO$&cQ)>rid{}+}|I0(EALdmh)Cu4lUYzbwZ~V#FDLP;^K}t zh(~61B1&vYK?b&ZYqLMW_s_5JZ#!`Obr=6uVjLUZXS@1Wy7Rpu&4@?7K<^g8a>I!K zXTv|quJR?f1v=O6POgr(eC!gFLSmWHJYw1Z?K<#S>8& z*q@fq3ag|7X|=@+rXy9jGo;Q4pRF{<}T(*o+Or_BgJCZ^M9yj{?*dn8U5pKp=G4u7==c@6$RaJk|5< ztXdC~aqD+8=Yjs>@7B8J7H#QBU!Wr~5;`Js^QHtg4Zxc~n6;YSg18I)9}fyAV4jBP z+ObD)+cyjk>xq#{35R&x^S$-+k&R7(O;BJK{y4*mwB9Dpq(?f}vC8!g)P^F1p=^yI zB`OHR5{^-C;=t^J80Y}ibU?IRzNJ*-+e6wRb-jaS3Xw4AL|B3Bp+hKK=NOcAlTL(9 zv&=)2PQ*>K`&lk5;W|7``%y9lcTUPzFo?d%?&AuE{32=tWP7HG(|=u9n0$Q(c9vd# znP${iR0^2FpjhEndYhc|cdm+|&TJJl6ssjS>iUphXB%(>OM!eLTUE+ZWx`=*37h;e zdt(b$Sfp;aMTjn8|qb{}B24rf7pC zf7(eKiJO^60+#!lH^yYZzA)x4aO+n8T@ev$z<`x^E%)cgXMula$Q5=Qf)L^I8*8Ai zF9;WSR3r#+ET|D-{c`dhyY_7;0xUd10a|thAM!zS zSoB#BBXVV2>bnnywQ@C*VD{D4qWV@eYGbI%CEteb-g6Bsk5L`xFtU4D2A-b{KFI0X6;eju|& zo>SA|C>a~|CM;91?}jd+Hibf!f8pX%HL>XIsOEWi3KU+M9reXA8;B0*gL-zRrK#Q6 zPjsEIOqAME;c(&!omNrJU#t4#q}N;ZPYK_CpjFRYa{2emNx7dww)}*Q?DHfMfu+yk z#!F^CA?fJk|57{+E;r1({|IB}5$tQv8mr6Qm2_G(Uht5EfrZxJdq>|WF}`E$Q|tuf z{}26A2nA^5CEjLrO;^=J|z@TL&6Jw$^LuU|~`-o1&11@;pajq?C4 zz+GBiwg5RH7rN#Nc*%5|(Hcb1GDXznN&R24!9r7dtz4*t+psz+AbMfdDoC&iRk&#? zC(Bp50)?F#T)ZIJbMgoTAoFNUx)rgLE`(EX@2FNr2D zQ{oMxrUSxN`I@&nGLzn+cfl+VGQ(1RbPBzgS~Hpx#DcJlgg=!7(W(Hb1b3-QseAJh z$SEn?XJWLPn|gZGxMkr#)QClO{QmJnSxXQF%D{Cud;8a#uh5zlYkQawi}d~1IqVE# z;;{-$`R_3^Fi3I{vQy)d_ELelbG)sZa`Vs-WwVh%BIEy$U=XdD2yI}ii=>7JsyGe1 zf??-PHx1Y6>igwUDhdT5({Or-Qk~Zxf1wC8hq3)L=q-vhuTqtv!NIl#K~o>N1zi?@ z#cmKfV0K~I#y)J-Z+ARirCwogQc+#qLEh}Ro9&Vf@#gDJgdN%%CmzEtvR*!uAJw0a zqQ+YU#E-9hZi!TTHpn0FDrh#(MOH(cuw&QuHw{P`pRlqM1#ctz_EEq)M#n}rEgsdo z$l63UjpIyuW0vC+>IZ0pclP$H7$ylj3~cI~90?sl6<$vMzo`YsZP(mPD?b#9Ou8VM zYaGhKUD}*>YqhA@oTgbGF#;+os!ElREh#TU&;ymx$L_whKb69p(h!b+3M1)~JIS;a zaa?z*Tc&#n@xMLTNXW4<-76^Dy48R6>Xii1i;&U)$X6gAr`;jb!lQb;XpJyyGMZL7 zW<-JA5%W8DYVx|%@wxO!Iw>fi<*r@}j7sJRIoit>9eX1~ZRXKicV0;`Hel;A-Ip@M zd*qMwI4C_0iAkd|6bCEDEuVh2Z&|fh3>mBZz6ME$p(8Kqi#^VZKWSHSi)b=CsJN1& zemVF>6dObe(kKY>Y9gV)se9oT^qQCcV=x1Md^40sAC<=IeZ_&C?GM{y)0POD6E{O( z48D=q0MUCR$X`Ax8C5y61>#X{f0oy?3l-5r@3XPpICHiy>*n8|R)d}5w(Luun-7FJ z(?ws^mwNaUrhObZdR93nxKb-eBV#Prn7*55@pRJ4HCV_EAF^64PYJPKR25D3*(Kh< zjL&=0M*~q2V%F0$zuZTs-F-PX#WH>J5ip6mZ`I9rxD#rt?sHC^oEKo$j&h6`^$ZRc zc{U|&UW4tDvv>h;kjag2&Bjm7b1c37qiJhKgcoftn?(U^v^Bd_2cN8V9LdkYK|;z^!@ zZfTimfA6N{{6o?CUXn;k!KTpMzU$tH?!OSE{@?yQNgAqT3tFZ0_IsxxPkeaY88Z7P z9gx5dVIEg(eMS8jyS_r?FKp4OYHpE=uz<@oagh8E3^I}3?LHZR7hjU!R=~w$47@=o zg;V?Nzx8=1cZ*569<{#5s-%f=1=+RB;hK~K0#zvG0aOxWQzAF$4dQk;*}M)mn?sB1 zUkWqJ4D+;mla19}WhYu$g27`be(b1;O~UgV_gLA_0$iuL16RB8@#U5V|3CZ7pIIv$ zFNQ5Gf86>6(^@%Q(<>`bE0)iqrbt&8AL!Pl?0-vVR`q>D-Gh@5{Jvf zx>>oLTqY2bff&x4yWUDn;87W?RhH2^^bUXI6Zlr3LQ-OKU)g;i+4_JELU6OwI!%AQ ztOoL+8YEq%yE#0w#obnpJZBg#Wies=qAq`VLajjuOjsD_H}t_8V9jWPsIl%U7i(N{ z-ZM~-$UHMPHKh+4CK5Y2bZRn}Gr!)r3g!+Xm};P*%l&i&g`osO>ofTpptJ$;T+%QI zdJS|>NUoq4+Uvhxex~klT@ma<@1cTf1+zy)%^mvoT0u6pE4sCp5J)x*v&)ZQgHX|# z^y(Z(T;sr~0TRzMcjQ*r;7Ml_D0&ceyr}!XGkRE?1~78)*^P;MC@E-U!7jlW=95=Wj@)We z?y+VeI>Z00vaguy880Zu3&j3d}B>n1mk@|H#apYM-)RCS{s|f^` zH4c=TAzOK&!%@UVA=$q(?#pu!dmEHA)c2kf!^I85mu9tkA8%3beGOV0M+g`Kpp?Kp z@w<1)cUH$`@87>4=fS04@qo5`A5wZO$}~Y(IIR5PO=<~qKET3W?FRUJ6V5D!Zqd_w)meU29xKK)31?(X7b)wkW*%!i(=qoLu0tBxzSKYyi z$P&<5kwY2GKCh`T1?$kKn*m9qq@?5v*nQ3EHOK3`9l+M(HpGV{A%T36*K60_gCeh;xwPQpON|pDdf)h`IKe?Y@%1xlhPCD}NGP+RppT?(!|sCai`7lo zbGtzFSOeC{LSf^JP0it70V7R}v!^^1-(EQ5!#?@3DE<6f(W?2L(t(+3`J``Yg9bpA-J>=R)TSoI#KA{i;W zD524>vBT`JKVRfiRi|neOKlpRx?o$T-Y;jq6Ohfp?P~dzh=N-}V=es1dHM1MwX-i| zFF%A-#2$4Rmdhn`SLt|g zXbT;DeZKV&@Zy^5ch8)8F^dd$Yk}kIRJo|QAJ6UbBMkRUza}i$hkRO6AN|hA#*W(i zKNQ(BpU>#xJ2{Jt+>cmN9&@uMIrWr2j!-KZFJ%z(6rfW&}+ zbQ*}F0!nw+kP<_OA|NdtLn9!K^f1iu?D6}1uIIYm>-`TE@XURmbN1S6uf4WT-k6|T zxygg1L{^sZdnRL}+N#qHaYXjvY|{1&_w0H43g?f{eO{k^)g?N6>SI&giCh{q+HGaXvcYwpY$}Mx_ z+_x-M*Q%=gtVGz6Vh_H=eqZ6GlkZnulk_))wa8%KaNUh# z2!x#l@3c^B)#dedpU{^q2y4oTLVj~*b_4t8gdG|-arytKj&o zBqWyHJB+Vr;ke1uq9)?rZztnwdf(aV!py4a_fNwzs`8aBs}G}}B1^w}1=RXnR?Xp< zmmn-UpZYa1{!Zt?)AU(&3cpL4J)K!#E%ggh6K4{B-UjM0m0LZ^rO*jC=Gb-%`y;-f zpx{(O()xkOIx0(|K5}rB^By0PmDT+5+PoieJ|TThR2VHr zaKC>2avTisXK6140?5ITU;|p&3N%bX;^yT11sE?L7ta! zJyOy_GDISu)@2DtPkzeHy;&9a#j)^34~a-vaa8O4#+|p+q<`^Uo-K?eZ@K86MyPUln!(aR-xuLz0!OPj zF*)K9);5~hUit%+>m?2|_Xti74qwLxJMToh6crb*Y=DjBa#4jyTgC)GF528lu3@=( zx!?_6W^Ol}e};dC%e2X=$tLP=tao&?SyZ!SgtD>a+}~L9O|J`cc!`5jvjn+K=?MO= z0`~;vsQJU6gFzB%Zzb$3hMDg-Fb{4ROR={vO|Rd)I_K<^I~X(^V7lWjUBDuAui+#9 zuCdgd#8j!%@?a2NPUpL{@5%WN?YWQ9NO{PfdZCfrkgTYv z*t$C0KpG3%>J1)o-!-BsKX5!+X15}}7UlHDwDV%K$4ZnFJS{cBd5(s-oL0}3s0q_3 zr+7WldD(`zlfho_&lc29yfV_ENfLuGPODP|@n7WiLsgrXZGgOSk&uxY&T-Or@dV)G0JC9sdf~V8!WG~5J}%9F!ccSSRze)D!~4<{qK-jumn8%oaw=V6R?H8 z()w9)t0+4yd%Z|AGjG$uuDIhFTDPo*`br%W!Y}ljbZd=W z`P8w^m6Dmy_gT4Ii69AC-Us`D#9x1EtU_OjoB+?9FpN23|AW?UPH;++hAiQAp#P&R0Wp}%O~@e-@hu|Y-?FVjeXq_&a3 zt=YqLR{|Q2(w8TZ;H{x>ADCz-6GmgV1`$@Px=3*e33j_M?b3c|7MO(L zSs)LGd40G0bzR)Stj7gEY?)Zv#%@=+z;PeOQpUAq#=FFz+1n+HRjHj!is>4bdvC_V z?w_!V=(uo7h@;rH=~?mPZN~`wbym*zn)BV^Gjrj@1l~=D>=C_yT4*;{|;&5@T9@16s!loa z3boCRTKs>vKu3{;*rho`tIlZl_KLnZZRIGZJc;=T{`n`HV;{bPT%xJ~I;BQt!Tfc| z+{q@&r`Q%N)J_zSJj7Rc5%R}eOdKpntqwWVs}~ikyjnZ>B zt!QRdcQQzTbOg6OC$gzKr)j|}YBO+io=glo+%zozRZ$HFYRwS+;@f-={sx!;eCtF zN8d+X;mYsaJyl!{iEEWDDL8ueuDw2}w@xIP5TfEaddiU%ma|HidGN20= z-q_tL=pD6X8HuhHSV%yj0B>v+0o3105nfIl`s?Rw+bb-;y*k=y?i8f| z^@+dJaP^?=P~o?Kac{TOtod|RTzI&7BqT5PksWz@qQtkuoi}3%7cJ!2*NoocW!UH4 z8a}4}HhdQ45D_QTzSmK!H1}}sZ}jXJ{2TWJZQtlsw|UIdW;1jsi>pZD{GR8`-=JVS ziOHW`T-Yv?7Q-L>UG^=WSkr{VYbH|cYZPgrzr%ky7pM0FUwbk8ov)ZIPonk_k`b{!mQf-GcKja?glc@)^xN>a7VdGsn3EZQU^mL4_G* zO9XwD<9%^3;*``zXxr01E~!>ymP?c$Eb-}5b3PzsqK=>_5Tmc$k=yuEr7BIfU(&_bN@V%}?B$ z5Vrljw-RAsWGv-H@srVy!I?Kv*go8B(y5nudyVv6lVIN^xi~?rsF}3-ohih#e0Xg$ zYmp&LOf#JUp(!kPTY1?k8p&eEBe$&LviPI-s>3_3HN1hGwIYY6~%obw&{a`}s zVD0v%Z|?z{jNs)l{=-b_2RAN{8FbDv2t;lO9AK&gD+i2G$i=29t~V6rx?s>XLhRF4I{bShl9V)%haOoExpQbw;U+q6 zX7e^swlm|$_*;iT4@_ktV_?eJU(Q; zuSNw<>>X|Y#g%i;I)v+`1x)O0Y>6~XClQEQQ`TAI!(HFQOgQQH8I4|ihSr!5f=f44 zqLM&Uvjoi`gW!ej)KeEQb1Df#zlzK3h%{O~kjjyk{q~J!X;^!T>NschA>wGCT2_xh z%G}ReoIM}va{Hy~?2#>HHoI{Hw_W20s&DUf5poD)ZZn@e=Hp$reqIay({dy6X7PAf ztnGMp1v(&dNVz zu}vbn%4H?aTqHFmg*<$+%2r8N)OJV%66s=dk&b<6Cghft9gpZ2-d!8|nhIeJt*2V- zyIE6MS64@@$xzAB6KyY7xu{ZHW7>pndbVR3)ojihiuazA5DdnBOa;X}b~(r0xLv$s zPeo64vpTx@KYIG7uo??i%ZGZZPKjR}KW5}+fW?h>f>Nt>Q)A=se0Nxmh`CzFrU7^w z5XP`vPSy#BLqm@Y<_5wfM4Fq!euFkPfW_&c{p(Dr(|W1dsu^i5+6-Q)n5V1&`xjLS<4tC^HJfH>-?i3_9QiP}ig!xz=sa8MAIuUL>g6T8_0RrBSmQ|}ev^C5eOuEa0&D0~ z@%=cRR-V+VhU2{iAN`12>NXdvZxLas;|0bl?>cQ0(}YIY7sK|u$#*9A(M`@>cwaH# za`{wFT9{3TZQ=cmgkNBqw*^q$YU@w;4Wg%bCxZKqsZ!uUO3Lq~cF%MHZbe7U&j6hb@w6tb2Z-&8QH_TcPfSq*(iBOU{FBpCdD;b8n)nrjCgm z{JC?KrE-7fF8%!PF#PR$K53OtwR?uO-@ds>aB<)DUF#jf#%D|HTe%nc+`N%x?Wq(r zo>1G3mDKzundYyilXUdpC_Tep>fY0PK;(K zH(VgPS(>bLwH6d!=5{+4`uPX{FtSy<$G*rG5zg*x`B}D{SCI<`X}*Us3+BqM|K|8{ z1S+YAGQ0imbf?tX3;dbp1I?#B{n7H6vmE9z54lLa>iO)2h!HC6j|#>e)^2!C zU0tbOlkKl0@G8bT1+`Uvxs18i|NeBhv-c%jL|jD9`!?5wX7iSqvRS|4WxuaNw{#EV zzqVk1{Co^dPFAtx^=xITR~Q6h-6Es9ex+<8KQqBYscg(8)`x_*G{DyL>oKU%5P?~c zQszd;qlDPIiy3bs=Sr+v_nc#Bu(r;6i4th*=#RAygC?2fv35xLl@XM*{^XxmR0l}0 z7?o%Jst=>DYTv_1vlH3bQ~Uz4zRJn8DN5?RE<4+G>?%}twAX;0s+I}Cfb@YJVQtZO zv`4zU+{_mJBGP&!doEhWY3kjf@KZ8Z+U>8Laar+rk!EyyX+r&BuuO)Zr1PkZFv5?? ze|IUfvP&k1WtY@oXp#3_#1m0(%~dqT&uZg;G zJ2jnR$joGO#>F)%w{HEFVDiJHi-B!AF-y zj=#2Cbw0%XVHG6wzxG2apAD2z>wY+5QdE=CFbUkQ=w+W3UhR?$&7njfbbep7%;FT% zZn<`Uc=i=o0IXCl^*vsGgm3;4=9%~^UaEBF)fm}Nx_>{8V#GIF2Pwr_}U~f-QrK-l#K;Q-O zNsX4;4Zfyhei(W9^lhEtNB7s8=x5U+d^B9=uKl;@a&aP$N*=Z85XG4Z?jEr=u`Npe zfx2I&Y(VO>%1WbgEsF2!ZtVtUv1X&_;Oez88D>QIyWOFfWK7T|>98hKrb~9o@HF|# z$&eIW7xkXLzH&*Vb#`&tuhJPckDsR>$=06zc&qmaY6Yu+Di&SV?Vi#^1-z%D>-&X0 zE`)NnXXf#hQyQd1v^Gm)EGEUN#J{8#YDj!wxsv!w6GPie?&ePOzH%8hm0MCLW#*a=!bMPn^Nj~cz4;565+3CL2-;}EV|E8Mh~ z_X}mrhTv%*-uW4xcp(n0_|8l(PDJ^P47Nv5i^bT*t6Ok1GJyUsPiV|k?^?}u2SVTlddAT@XBjw#ur(oiGEA9u8sLx=YW^>47 z-+F`d!C$elKWFcv;^Onh)a6q9`08F%nEYO!Qu4A5JGgq4+un9cp)%Ki3a3@HfzH5C zw|x<@A<8nao=2AQ37N1Re(lGW7s}IqRY1L8?7pr>K|Tctgvghc`dAUOrf*-o>qr*L zd(m~KPk5`^&8ya5QiaL*)_y1KmdOR|F0xPDxoC{2?+qC{hqCG&_40N!z|c6JKB|d- zcr77Fiy(yeVL@Qf_qdU~DFZKCBL$pez5tK!-_nvEUt4P}X_Z2`H0V123xT{kZZ_8J zb%C4!`UeE$K%3jnYYa>*v4*)iqI*BnH0yxGs96<{wJM`-rCl3TPtHtOT<$U|Rgxk( zByNb=z1YFOeT&)2`Wz^^YgiWkLA9fEu#3lKsd=_a*eu>gqkrnjZ7Sikj&B>jHL;E7 zkVg@ZSy6YM$Gwc<@g)SkeyQD?!EH0{m7P>??)_+@)_T1Y$LoE%_!Af*df`1O_JpeI z22L?shCLFyrk*$vj`?2wy3L28N0IMtcK>BEFQ;#rMv1UTmAe*vIo#$mKM?Ltm&zw-)Lsy)V-p!>Mttz+h&U-Cv?*50_DqLa26+}b4jrhRX2PHtL^QVZ{| z`OUK7^9*N`dU1UNdMlx}8mJ^WlP~Z#5&a6CJ?X7cf~aWiOm6$J%2fLcXD+k(`g&ez znPT*h((*wdlKL<$75f{t!VHY8hC6f-yxtP}9UjLkJf9&nX8wC{6mg%QZ7a>2Sn482 zhzsoPHIiL_U$wx|IN|mhi1RVYdx)$~4)p{>yF*p6u{8Ty{5_!Vipd3BnVliC4VNpK zFOZKzIa&>ho9^m!t?n-&mx83ESG_s7h2-a8q;YTnI_vY!@xW_KhRA6NRz);eC8%v^~wuJoqf8y1C(}Xk&hC`H%`(O zZ2X0bTs1=NXLor(?<}WeIrD|Z)Hah~oz(nybV(n0Ww`3QJxhrCe8$&XY{^Pm4iBw=K&4>0(}Cg3RoK%0Tvi@k_%I zpAC|-b9SG7?=jkUUNN3!j5vle|9yTj)?do@W~ru{iD0$J z)9hY!L0jDn_DxyF$BV=${W33y&>&7b&@cbP-Vt_i7KWFO-Q^d(VqKGB3Dgxl-a zWpk3~*!aCu%t`h4K8*@nBM{YtY;2ZGnVCXyZOH<%l^gLF*h{aHYgN<}`iS}~92(qH ziFDQkSo`dH?=c>S-w%%V<*%$JQy{LBWszixR4&n}Qv#z_E%0vf0mtRVxFBcSwpN~! zZ%DaY>H{VHo?-F6oaz;6aj&it%Wn>2<$X=I?KRshFoG9zuD-_qIsr@0Qfb8FTN9+z z$=iNr(29GsBBxz68k~0TYkpRa$WG!=?OSj73aAEwzyx1B?CAL&DHz{2RqEy3UnQXQ zY>}9Fl2YEeA^M{Hr`pBN=H}*fz9;pFMnmWH+f^>8-yv7*n3xdtC{xqq{EE3tv$L}c zUzB+@!G2Qh4+;8?Wvrj;Xt!c@$_p?1(ynPVV@F}JSeJqhq=}lU7*otzWlMav2o*d0j`k8EHvokU$)_Gqh_Q%4VExc{+2IxJNGj!NIpBUlg(v$U7ZoHJbBEA3-2)u-)BmI~g6b?Sn1m%cxDv zC)s~YSqii8If8IIPnnJAzgpN2 z*|!}MtFP)2iZ|kM3=C4jW<*&WE77Gw4NadTgk!gURCS&G-qEUU0eBM#&}UD#=@tEm zSl%p%&l=(>c&=wpnSR=9{UveuhWI1X8c&`lzvTVF0pf7aP3+Spcy~GFL&=Gx@MQPI zS#iR2?B1}W&u40KiTlR25E}VUlbpOdi;*T)U7KAw;@2966D7>vLL5V$F^`8L&rRd% z^6xv_bFub|-S^p0P?_LRi_3!EFw{`(lre=*?c!ic1X?`~r{g5X_Up%N3wEXDr<2|M z^9N31EQRPTI-dRtg_2u`V!1{si_LE<|Mw|Yk$@8bT!smlW5~CVA{Q4Hf>g2KOJs7q z6xZBF8^b_Z@OO`$FnfOJDn@AN1%6=OCw+Z!izGTY968i#qFc+7TKLGMEQl8I+!pT9 ztep)}+h62+)bbnU|J|c|7a$(i-|+@to5~d|j{edm42-z@6Q-@^up6TB@PO#mpJP>x zPVKVG@i)-6#n(YDV`Ztoa9pkFdtNqH1`6QLS&ZgX7?sDUnLYlpPi*AF%zd-M)w z7RncmOx-l^$yZP{#Y-dtlQ(_fwc@*kUhyQLA>1gQHWQ9K#N>OJ zt9IYWxgWK$yE_Oo2m7jA?V*Z>`Y||o--xMkMAXsE=GJgRMqYwSj zURzI>=}V#rZCVq?P?+>Z=Fb^)VO8d6>C|n0V(e99yS5n zC~LWc5X!S>M@2;u^@*_(P8qz_>$fGCPkaAuWF!wKs0uhD2F0IluI*GbVZKmDzk+A% z7&5vZL+e>=h&76jGsOW*h$?>boJqr65LK!cSV$maziK@PW8(@1B*$a!Y7;qsx=UH> zwcfLg7Z)NRu4?qo&Wms1jj~7hbA%0^h9^Rojdq}g2jX2@_@vv%?oc6^1Hf+7 z6V&t^xS+T4n3#^RwW_v~-WZ#iaTQ{TwBEVM8#cp1!jUVYYh44eb?Cx8yB9ydsi0Ou z`?7sXZe1hCq;lnNo$;+vqCh5+@uqb4Km0xF#oTlx0XPNSwv7R7;aJvSN+pXrB+#|u;BW1cP$JsO<%y;fs zBK&sw*@=GVy3=&Z?U;tzvFPJPX!-tNIAh%duNBorjC#;yNUs z+EuTALH%vfTzhwC0ISYazR~@$CO%31DXL?fUh9|rcNbCVGw{oAaG2SDZZb|xsbLC? z^ETq6?v4(=8*-EdQOykRxLRxH)zOjdA+uJKx^KK|mqe64$rAe_w&4f)zoguLfnIAO z-=+O&3{|i!qnKicx?Por^}_J+MmE^tN~%6VvBKkG#hgL3s9nE8d zy*2>gU-~A7o4ratv1`J@;BH}^n!S;MWr@1h#N1z+)04{NrG*tdZa!_U5}(m-r5#@7 z5+8FYsVYlKXRC|t%B74IL6P2mB4bYSL4Wt{HA7$8djm4yQ)_>j=f6K$NuR#LO=ff? zfYCWgW^^#r4KRmsHVQ`R)d-mbMKv3{xnPD9q9#~yk zUzoq2Id(2zcF@1V*%09u*qC{UkTxbo+)`rQEHAR?a9p6X9@ctjb`O`c2aiVW9`czV z@3K}G_Twm}s!`2-xm(Hu1va~!Wl39izUCfWB^Ex-M1P~Gz4Ys<0zB5fBpvF&6Q`dq znZXas_lGaH#fYsvUhgZhnvGcG`7AQC3xV{?3J&#+lL8@c{=nEM>8n@(r-p@}%r^mr=ScH~jc`;Wsq$Q)td7 zXbobQ4{j4HEegP_T)9_g?dwYtHmgR(5e%xO3d{LF@?j&1G0jwfeUN8JY(Vo-drV9ZFh0>MbivvoNGN^;&hk z@9|L)E3HzhqYxSG+Fp5id)Z(%iccTY#P2_L~ZKQ(kk zn7k+>I;PaY``k|V(ekggrJdoO zpXKI@1L8X5q^8j2J%;7t^1n3FFV#t$8FJEl7qv5nio7d$bTKXJa@<)CX7i$OXn}^8 zUyvhZLDENeOr4gQ6R=%b5DPrx1lr<_t%dk(?sl2-B#HM*^gho91_PZt? z_#s@zG6vfVR%~E%Rxwl*5I619B)OR1*!(-Gp-EgdOW$|vDMGI>`zT7H% zG9RzQYH7R)&UX{i-3};ILwyETGZS@y(dS)yg~NtlyK(<21Y+5&X$$u}HRVir-Fs^OY}O@^U6cd9?%E)2a(YnAQnDG1^0 zbu>ojMx+nFrR_*$x|q+{V}r3jXK2x}&!}=>uYJ8(0FO&U_P%Mdm z24$Ge;zON2iTQF{OF>mIvCVi#L1eIdmAStaVgD!!*97rGy7}Xwxz)yp=+4=7{o2aK zu?y_Nvr8{PaVow^m36kUqGIH5M{4A-QwsL4I8@^(>0M_&1y=n%=4pNzZd2#e7;8!4(+g@CbKz+xAi>Le8X21~jJ#bxp`> z+M|WSCH^m~$C(ML2f0WUxq>*wJ%yTJs^SvQ8k- zraq1WVSTlegy1%UYM?kdEn~j|E-qJ2-QynnEjS-e>e`tvuEWS%NIzMJ=&4MoDNF0oC;vxQ zWxep3GNoA=)w2qq%s#Lg)L982Wka8Yk!(9`rmSoguDYdtVJuqxUcL{o3CDCg#tTjS z8`CvW3l;wR7E`Bjx2cH2h32n1bVRaHudcqL`d@O+jT>OH*BZy5W(!W8&;U&{4V&}> zI2&A-1-@beU}OZ&)T4Vdc@{^V z$hB3`ayShLbN0JOmh8uvMj~S33%GK2hR*u+4z8elOusr+EgxJ}eS~ep%FCH-+npy< z3eFy}BY`Jx;NXL-s|BTiMY8_&x*_qSA`Tr-SOuQ@DPIn^zxxIVXWbu@3N%YNU7x8b zy98q5QLSpAE|2wHX`p%tt3U|W|VJ%f8LkSA>K&oy2goY=HJ`#RK*q;F6d zf(jr0NJ#iOc)4}|f3?fk6_5x7L^ou}t1-`#`HJ~4S)Xd7(=yvgMOwKwb0$clVV%a0 zLh9bSW|+#lY?+U~X+H^WC!< z=hI-UyO8Jic8a%A%j&!QV*Vh$LRJ#6qBmT zi?CHMr9g%1T({}1y%~uXTkYs!s79n#At~^Ju!gBOp_T8IN(}twCr>jOmQrK2N}Sx2iwin_UH;_8p+uq}%fJl* z1WdkRxPHTCx9%|SV1Sdfyt3c)=1)17b|+@fG3UNh zcx^ZU=>5;x2MijSLI2VYI4LVVin0_G|4vF)r{FH69kkga#reYT7Q;4QId?`q!0-Qe z_b7t%gL-o?KM3<=w)?bWge-$LHaGi0%QNOfSgBmZkHP|#=NujUqj&Ve!47CDZl_&dPu)tjc<>-Pl}uH-|K zjC*NItadE(G5(zE9!r-sq*;mfuuE>bHDYQIQdh7qT98h@+n?tgY4kr{v1tl>pD(1& z8+25vpq|NYjgeC4zKML$9Ar+J4FnYua$TCGUW5N$ALq$ZJlVPp&_XE`oAZFpFfoP7 z0P?;uUb^l=-+MzJx)BIYx|hfR{Qgn_+D{wg>igSEnc&c*8ZD>@lb1^)c#SYH5`y$* zcNlz`;~iVM%=bYkW~`8U45LO0Vi_T{=4dHc5&uk{W(gB_OoMdWX=w-+xIPcm+G3^b zCp0cGI+J|WZTQv|Ok^H>hw_EHe`8Fwbs2h2s4<_`46PB)MK#3F+y3uxx*Y?Rm}BHE z?Iz?oX%SU-o56@-4$B2Ch!Z@A`lWH+Adj%#YH9vof^%+VLO<&(Bsgfx1xr@%e7gS0 zs_)dlk0B1YD6>yXfU=WB+O5mU%KCW>`RRhd_Z_l>wD~$&dOCjR2BbB03Pa{&=vaY>Hg!DDr_&l~!o69xz&%p7 zmW7M%`b4gvIvB$&Ot@?6hl)v$&!FaNdoFXIlkeX>#(M^66()E&)llA3Qdb|Wm`_+b zl_Cg!x_Op~-UB<)k1^+H(hOKvx7i> z1d=^|ShhtpGum9V`TIJR#-Ysa2BUuj!q;rbR`pk`E%mi0J(VE?fODwxB8X6r^d zE8tOH*D=X-Z=!0&5@oEgCOKng6aE>oz4>=Dhg?ZQEE{V6%a_zJ4a2TYcm7)>m1&d( zodhI$B2Dt?_czo#JD{GqS-hHC+^O~ac_CE6az{rE2|^l1z{Z)YB;?9;9T?OmU$K_9 zi}N~AR%d`;V%gxUR&*VS`@V7hSrB;xdOswkpM=Z>zyinuxxFS4 zu`SH-;N!73+XruyWpMLtK0Xs3T<~J&&jV-H|E9?`m+BMn0&W7iJ?^qPTG3t!bID+s zlp6Ta41#XIANnz8#@C<;?!_T-7iX&rj#ih1pM~tRkzP_wDv)99Mja`AdDtn=n=8>L zgUpSS(J4Y>TV=TR^mYom%Kn)$dOXs+7#W>}!5e7(=Bz2yj&}EtDSQ$6FJ~MOABfJw z_}tX>qC`x#K!^z!@x;<5K!6p46JbN}zI~hCm$0%Svz*f7_vT3Bm|NRI`+Yx?Z|u5=h9so9)wUJ#08FMJrw=Pb?a6K$*{n<#<5I*$Pr=< zYUdmD;A3{+hEtLz?ahdWEP^+EnK>1Ox<*(+ehtJqEm_iC=m|8MS}RD|dVMHH{yfDZ zfpopJ#F!y0pOCt??n-wTkE>17eogBwPM3eFW-cuO?df%(1oUqq1&X5(<2}ZS-`_9a zhRg_&{diu@e5gL#BpeDNz>c-q&iriA9Eg($yue?dE;lvqiFuv|-`9-Yh&1$e_KgWV zNyke#szi=-5mc}KsbLtehO`>W#OvvU|5nLCPI|-l@5|_W_nI75gt)jK!r>l(^8=2J zF`PGU+=tGEekwZYB;ZOV6pDk>58ZC1P& z4v}GBri*xU)!F)e_O3vNKK(CJ)eAKS9AB8$UcyC$nxtKXG&?f9C8o$ac*nBB=f5gR zi3Y50X1?z2yCKKQ)P7Csl=fHYRo|uzUR%2Y4^Dtw&>lP7kK7o@mDs;mTT3Rzrl|IL z$`wCgm|0lU-4dIYS*G(R!>)KV@>{|G}D~--10fB#X zpI_)^bMx^2!a~pW3*SD=R!(ZG>Wse$@;`mjx2L!{0;W(=Yzatbhb`G1dH^4q4hg9Ip6*_K}DJK^+$u7^tPM z9}~}93Qjidq8Ssr&5+Nex-^J9UbG>C-ey(Rt1$oyOWVRYxecpamN~pL&1ZwU!Tsp9{*7lEIY+jw zFBME8ln_u$N`O+UIq`3RkQ?TC=FCK{<$1`(7pY&a|E!%BI3RO0{XWLRW73x6kbN~nb@Va8dE1KFN6}aQ_N!^T%7+V~9Nd3D-=`l*|G;$r1h6tOmql-CFuusP zG=MCOG*n`u4OGL}NWzzyqLt1PLvLZr9CXH!$7>mXJL@rNS!e#`)zS*k#o8JN)s;+j z-JQ{j|K+VvJv5}u{s@#<&f_phizBo7oyMF87y7i-OVJPwL6lXhb>E#_5NEn;j%llg zHdJf@6cNbsR4^;Z%!EFwsEc_T?oe8cS%3QWyqDg-#BD<}Iiqctk11cuPn?7c148R> zh^Iv_ktW{T;rZp$G?C7r3edH)%ZDnG@f9v97#eEhiGztbb=7>6UB7SYrTq3KDu2~A zO3y0pbkt+q-L+wGB|2~XHG4u$=A{_^tmV{p*bMIs4Wml{4+v#h|HUF%D$m#JQ9cjs zcS@S zHO`UKkggAW+(ZJ~@&;LK_G9wfhRn?~zi#+l0&I+UE*C0Mih7cUE~B;0sJZCA@7%#k zCd1);kVPSFJ3OxGxSd3+B?iXxSpY+Z>$%T`F)(~*W<+Sn{I(y%#3_EDcV4aFC4OU( z{3#}kY#dHcxAkiHPCrn!`)xAY$9vW!Cju07P?-|a%lQa;-6|D)EkFyLps#kOT0e|X z7Jgih5c^bQ!Tri7b z@rcI8MyrkAElR4Y{WZJe2CHe$peL~Na(08v!FmJCZu?m2vVsA)&m$va1lF?y(CdfR z$$@wAlYN~g>qMi3#K_^qq19p4TI=W7rDeX+QswC046cojLZgyoSULH~TJgw2NA&dNshRB)t1-1{Id*Ic?qlo@M}{O$khshsX!*XP13RlnChnd@jHJn4~#T_Ku#A zC8y`OmqbvxM(BEqrPv}D0&JB0>}2TgYg?;h26Du$UTW_@nyjIp zXtHSLtQ%zxeRL{#bjr5~jg8rmn{s)OvmAT{+6&&UydwW_ddvV#*~(iV)vyTd$I6GQ z1~vEV^2c(h^wJlIpW>}5_J0TG{VTJ0hCYhTvzBkuw`OL$hvi228PLPQTFWUIY1j6z zay?wYzU+u;oqtz!`xu$nK?b$At9UY9x}?9l4;FQvv7bL{rq5uqd%k?wn)<{wQug)d z@ZjIL$MKtPX$J@RSXP9FJ8){%Vbs&)*4WlPs}$wBhqJ7n+o-@QyakATs{XfT^FSKv zU&Aa*Lpp}bzj|1#Vys_b29~>fR;+k&bBqj2yUqk#Pp|5}%c6Zy?jIC@AKIKsWVoVk|JXt0vHK3-#_v`?<^|Nm-&I-D?0x#H85duHaC z+u1||8j+p1P!+rxFXUV$eYb&h@rsEFnX3v1I=Wstn&(gOf&8r}L;UQcp23t%!q0K! zWWWz&A$wOQ-KPY-n=pRcQ)t6hzP|u;L%FDn?&<7^k9o(7L|k^x<5ckVtDnsJ>-?#6 zl{9+wpMv~3?m*EVA1lgbGk4RxBQAT{|75@I2H(bqk1ylA7uTk zr{WfIgFA%1O$VrbHf5;BmVjpFT&22;%Z|x@{8Rw1n%sy_QBlz{`tIEyCi^mbPE|UL ze^QyW!=00a-&xn^nJ)UC^ev!5V2=4lQyJeRWyFTUy@_C25mEd8r9epCi%D@6Uu z|9dg;aB>g4y2TZPwFvexDAvshHB#pP%@|>k;{geB{|9Qol>225Gzu~tXiiRdSs=z| zK~&XW|B@=WvKAa~fpbv{56YJ0k$x@@s&gLb>%`H7h8hi6c0{NCymH5Ie>%TTtx=+a zW@${B^cB=HFOKXu^vY-f1pnBfJApp=A(5cC%Yoft`e_aMs3}%WLo1fe14=< z8Rljo0G?XLJ4dkk9yk!!8|Yfw+O#z^7ACxaHW~oFEI%sh!uIMIE^j6`D1sQ3<3N|0 zz4xnB&FAI+1jF@@|CPI_fF^WH8tu<1od-3z2+=i?rm{`$4mu>Q9oqVW%+ao9TZe(3{tx6NNW?@pGRr;D(&l^U#hzqq zpO>bxmCOClL{@bqk5eI%xXGU_*tiaT9?S;YIz;Lca!46!qOaOnkq);0aa*& zmBaQ%8=uW$Up85%4QWRvbUZkJq>rw}BD1q9mL?BM$mPR(&bYru z7ZCN{aH&hnL#NkH9Xq?IFdNVC=A@%STJQ*WaA z#3`w$P|%8a^myo{OHhyle-=O(W;mJ5G7W2&T8rF?`BS;D8|I6=A%o#47%_|06qEdM z47qK^S}?9U!*H%Cyaa(6AiFX!dc#~QU6>hjYg^2Ah#$W{u)ORJBCXjx1IyPLMJnrk z)$ZJCuD4b^%5#UET0x84Wd-;3JY*T4vCI*QJu5Dg9@eMgX z1^sh{Y>zL(thr=zE&w__dD?RftE7c zaZz0Ri`qM5oJ>e_L7iwpKLwJHo##!2^;@`Uhh56SMq_EIpbl$tW&r8FG+@(PF;B$b zqo4qZf`X}x2ltcpBn&)%MAl>Qua(Uv^cR>+SY+G=;l_(+2T?C>U5Z><{(%0InKS-g z^92h@=h(lxlV2=&h_Ew6`YcQ*MrG-{j%Q~%b$t$IJ9lsL0mr#;>J-6md}PnYIyrlD zaRIPp*})WlX}3^~5e9kW^Hb2%-wg?XIn{@j+sEtAMQv)f{WyWM`!jgA;;3Rw)qNJp%bLC}_TDsx)`lF^lQ&-86>gOBjqd^jd%*Rp;3RK( z{YCy#n7Jsk@$(v)<$rtX;*-b@m*!NEDbMae;81`*r0`7c&JGl)qiEK~aQ~VY@QU~U zAKtz)Aj=NGWu0w* zuuo~T#P+m%i-)(eA~mCCg#Ig|;S+IS#jAE$U*T1*TbOPuVh$jZ-EF92!IoAnRS%L< z_(1J<7c;YQU4&SflT7k)^DY(B>*f%%6G|a&qY(o^yHlx2qSqpiM8k!|1;$(xza}R3 z!Q>t2`*c~QyMPyR9tg?a9F{}a^~kKk8;n}K18Y5NPj?n}ws8dlwS{LMDc%0WL24d# z9gwyC~U?U=KfD9YOK9Z5xJL?Q?TIc_+{gE9k3x5x%N2jUxWAmBh-fK(X_FG}zR zr8h)S3B7FS6eBXrX_(z>TdA`5`xI+?qjHa-x8|dRy>@F)PcMq$`~YIKbBrG$$2Wye#UGe>VU?T~g5ypG#fD3tHf|1?pGPO0Fk z`Z}1At-eJ-S)3VEhSjMvc=mEXaMMybf$7MQwMklviFKdidQSZ30rKbSafmKS-_}CU zHV}gsAlZAiV~00Ud<}}O`%2qZylcEqoy`UlSk(ya#%516bJ%g5D3)L|lwcpSi0)$M zM1sQi8e-A)LwSC9mVvPCL+@j5Fo=lYA~3&qcNc1i=%MogdYXh+B~hS09HX4V>cR}c ziN&~2PC-tU3ZO%rOWpNe8E~>EyR~*+Qd&t;z^6q?*N-uHIjW(Yb2uEag*4?e=TwXEMe&_RP+kWuFtEtk-Z-m&d#@UaSg zvuzzrZgC<*L0O|nIu~NC2W|<%ye2!W^7qrLz{WRWgosav3LUs4f51so^?38~l>-Dz zc9~U1MP}NE0GNG|i(_!Y<+sE)H4}JRhd(*Xzb!y?p8B zb*?XrEl0Ke>%Ez5(uMAOUBGIvA5`9T-!%gE)VisN*2Veh=szJWuS5LO9lqbIb||R7 ziC%oJE=ka$U3tgD&`n#r$A>GQ)3X|{uXITXkvR&>g+>_;m_LvM<4jdSMe>=c(r>mv zO?Oi;_Ljp13x;D7crlr2op``6Gmp+?5NVuAn`}{uH~+b6XwSo8Jl_dESfni91fs zXOBS&X?C3{CA)iDf$B46p(&5)?0t&e^%WCD@T&76ictSp?zB{}6RH)eqBQiSGfqq$ zFj0!(^kCh3!s>iI=uO;%AZ57&h6TS33`F0igIZoCqGR*&#S0n-deFeK>`r7AHc|oPDD`13(UMG^vzgn7xgHKtVG}jK{{~M8{|qX zyT0DL9&{=6Dle~E(U^ONm^Z2TZ$PI{;Ax<29A9&;>WuTNv3iciDYWqnq4<{vVm{zP zKP~B$vKA#gtjfWdMW2Tnjv93x4RPjMt!Uv-8=Et%h6zkJ9TvejOTw@V7i{Ea6SP}q zg&Vjx8SMJBa1=F0M}GFc?x%xR|1Pnr@o;Ty;}@e8aN&J#JlvJfM#RwW5&SDc4m=ow zGQ}!f8s%e9Ln++NYte{_Tl$h*bF2scTyS3E-2J@hZcf)CU7d zZ~FMrp;pxaHu{N?(b3i+oQOPtFLzUKtB>s^cTi~QO1d#+1_=&Bz&6CgfgGI1o@O)tY6t7u7Us^lgEPNrT0;@w%WSdjURh3z9=#{*FcmUK8 zyE_V|F>qY<3!JTlI0uQA5owCD z*pdsQA*~{M5J)0AB!g$Pp#@1*PcMe!*pqU^=iYtYw71pabJ%nE%(FPBa!(FKeqpGy z2|>K;)Twam*3fmSS7&l(&M0z6Hnk#+%7Q*?pc}FUAoS2?szpj>yv6KXEqxHnf$8yf zIXW3YO1Hr_3l2oLN}Dbldj6_AMRQC&1W{C)_GYk#`Z%^y;_!E3Td%1BTguuxKg)TfGJ!K zZY1qOb2Kw^b4xJ07d_-VC3vod=q(m9eA32yY43$M|4*k;J)fRkqgm^GE2O4Zn1~Z3iK1UdX1DoS{ zcci3NG=9CJN<6i}gMm5bg9pheDZ@^1Jo1gG_^l!kGmSCJju|g}8s1{WeVZRCRKjrN zTc)j-%BsMO4E+k_ZuoAT8wD!QUUU<&H4kO-VYCuPVVxE)K^^2X_ihChwKYO#;LMS! z?MRLgH5Tc?pryJ2?P&>ynycux*WAf?)O9EXzsp5g$I9;Fo}Il2w*W7}lfaGSg9o!B zn@_s~wvKP32Vszv2FB|)5aLxg0VQAXRm==qK?lCFV(j8F+hC=>#rRRe{Am(GQan2@ zvb%lHT03lLYd<%z1L0`@#@K-^PlEa}= za8q;0FW!!t_|Oer?Ki;Y5s3a`32_xV=O@-~)+NH1+cg|fPt~MTJM5-W3s$VbUpa<% zs0Va2!UBbFZL%F4o#l!|TLBi-Eu@*vz}FAy^RVLm?!OPpo$pKe0t>UGDwv~BoyHrv z>a(N?u@%xfP!<3O8E=zT2Lj(RUJcz@NQn zZ&yRmE)J4^WmbMEBxZS!LyZS{FfeazFzh$)ZT0I23k2C)Tjll z&A|!{K$f{8uAnVRW@Xa6arW}9#?{TckylW{PKzPbqE3SOYolePrUURPnEXwB35&9= z0lC#pfG}sPq5eda1M|1V#%^z5FYH;b7+~H*->DOdK5Lc#8>>?6<+~VP4!ksC+|iCi zvNyH_ND68rZ31kIukGjH2c`*IG~L_#9j3mbt+nT7mtXn3bp9nX!OII!PFgqc96m*J zzWO7dQ3|$dGca$iog-g7&FN{~xcfT>Lej7xu3$+#iku`-{gq*&stNX>Q?Ga;qe;w; zze1E2yUqO4o;-OH({la##)ZI>_$%Dk_{U&!0b^!A9m@LIgpJWY}cP{W29M zLRrXugBe091Zb0!irk?re1jlXM9USd-4e1da&ePpW+pTx7!tf}V2dKuQS0l;c$5*< zC`B9}u+uq4<4}GK1J$Vg?^FI%S^Tsg3kd< zGplMnSMW*)m9J@OvMekt?K7Vz7}fYYjRS;5PcFG04?cWP75un4`%gI;5u6&v zz^QmTQv8g++5#d8^p6RXaDCGj3OcS}i^az3oQ;UN`E^Migz=?dnvUe1raymDT6AfW zJ#>c)-1pRIruojO_dpDC11F*vOsNZxcR~^*78KglitF|nTvHV)?LVbtJpmb zk5jCS``Fct8v=Q)#6uQN248m=!C&!jO1Ob*&rUt}*;2;$RnXticO0(ERa1M)kuMeU zS2Sc(l@QAGOVUf#KS|dg%^sPl%lej&0x4i?_-W1ppi~f{{7@-+3W{DZ>3OPqpzx`d z7*JevK(#f$d>n*K%>C2l3V_~IQ&Y2tRP6E8?w?E7UWL|Q!^>r-LU?Ad3bW}T0kl}0 zcMr7M6_Ap)KdJcq=b6Qa+DQui83-?n@Chx)iLl4br=sSF`x?OR*iz#qSiZpDOV`Q= zQca#p#+TUTFTbg6l+6dSZKxT27daRir9ht}_qjrf`lxNyK?(%!@_@2*$#Yd~ zrS7bN#2MuJ)7>up+Gaym50HZ5;dYJ>3s7f(N#lH~ktZ_7rfh{@Y;Qi1|97i#;2yZO z3=pTD2QPOZAJi}~pw%I^69e2xooIq3FuVC)ASha>jYh=7!7r+VT6-0uEQ%9UG|dF2 zVPDvei0GB4Q$OBZ7WXXv{{4F|aI1K#p3ZlE;rDG8)!e+K=sO72t^$WSr4LX@FB$#* z^7M)WzdLL!RTXZ2M4m#l!@Wa1rk>Ns!E zcKp$P>Zmx-zz9u4jfZ#?xCcPKSPu(8K5JMLYad!E#Cia0V|C((0JLs`+q-3(4+W^M z6fbR))$mWcX@&#Kg>CCQ+bWQ{1^8t==_^!uZz;2JThvz-QJOMetMo@$cl4rzqf+1j z({8qoyQ>*Cb+4_39C}gGEF`uVe9)}r{L=`F7h2QschIV@ZbAQZrFMP>F1k(8vT0%(J zb8apDDi1@n9zj%0xddza)8f#S>|y8=UQHkp+wf}B%2z~u^ry|aUV7q>5Df((wb;VXe-=)kAUQWn@Rs&Rhtv`UO%>v z!nTh23)QYLSfx#|Fc(2}U4W5I4)-!hJh?T8eD+mLD{Nhh-}3T~@bCkhL4iM|g|ke7 z5n6zil|PmROh5ah^E;PT4kcSWWhbP7_8xm5O{MMjQm25gk5GeS@beR&_Et7_hpH78 zA+%T7??*z_>%i+&mC%W$u1|CDH+Ql;mzeg^m(@oUZ)9^!WTH{om*ccms)U2&iZjp$pL-R-nzHZyYst)vpHnQ*5Le=eOpIG^71pnsV{ai?# z<_r{BmB~0UB1%9GpAB0eU?!eqVR)Ec(gPdCzSY8H%F0{ImlNa--+;P#TNXLg?Wj&B z-6<@Sdp2D%m;MNc<$>m-Lbc^SEj9!L!nd2@!CP|yT(r#NBpnMUrxL<6DGLA>8|cq6 zFn}Cwt!*n~oOAMwpg&4MwSry=`Du)5XfF8b0aNpj?MGT6$Nh?33GkE;&da{EtZUGV z6m)z|vcKa)B+|MuQS-K9*7Bv}s=}KaV9oK??e42w6)sD(a9Xx^_WlAMvgdmii;lTp z20DeAuPmYUpxd7KmUWu}=FH|?e3MRJdS%l?C+=#Lc9vb9<}rg5!5aex=jNE1zfm^8 z^CWC!0^gczRZ-1bbD@sCBKvHg#m;&WIbOmn1nmatAe9AZCD(YuqkD$j-%a$FVSl$X zawxpUO5T=i$Px5s%%Ptlfhyi%RHvj5G!vOcPud9G2HJLM9>oIvL|E9`tetdZnyRWW_!qK1XNZ zje2UWUxvarisUKSKs1Etc9*(PJxtX|^G>z?$t1sqW=!$@ z^mreD-K#X7>F7jmSE{JI6<-r2;ImytqzK?88l0086m|UtM)PwaD{qdnd=dQ_6l;@C zp#)o1tJmhcw}r;KDC_GW{M7r!pn!wGRqlWp=in)*Wr|dZ*A0d&>h|`t5jB8`<&3`8 z2R`bt&on2@U@Y%r< zcp(`PB4=@Zh7Fzi(;P@;X1bM(ia`H2S&jazpMVr>v+hEhU33=8=z!TNlQPTMDUtl* zj|O(hd-rK~vVJn&J9GJ3Rims6DQtLxj?lL(W%}_b)Il_KF?=W^TAT|1N`7Hs2?1v& zY&^dc1x;&Zgl+WaxGZd*I;+t;WX3l8`5nbb$_J!kOfebtG`ow4-cvc{2*|)aNX4WV z2U#~Fbc@Kk4ATfx+F;O%fIelx(8EL+GmxB=#4Ida+FgGL)i6t#va3^M6APmMnqP3< z6VHhtTIBTlJsChzY6$0XBSO_{+epe;EJjF?90T~_o_mBX3R&% zGSHeVY$dOyQe1WO#YyUiP<;bMk7Ug^pNO~S`GQLuDkL;^QI3j`ncr{ z=p;OGg2JgkQf~LBnl73P8EfIxE(AJIE=#V*5*VE3J8#=oY=_Ak0;YYmCH^94K9x|jd0IN}-U+^g?FIy@NX~I?$0x!bSkgFNZ z=0UeC-@@|k`mu1E2M(s$!_l9#W-IGREZSDjU@9Z3MzKg=wv+0Mvza`&Tag(GCATHW zoCqe;?6oorZmVnFehwD=FE|qQvTl54KA>xXl{ITN^;prX^dpYP*eB36CJEl^p^8pa zx6j`F^yzvQmnK!r93|D4;-wsyAx>=6Q*x)nlLSRiTYj9CEHpkI2e9O`5ey{-#ZYy| z5w7aUVZpG>Im>-#H46z>xjw+CO&N&G8b&yIcs5?tdoh68PsXtmgo6#cz^&G5UW3yt zsG@T9*dnGp%=&ty{pTJBYc%RQre`_pR9-XUmB;tLPWfZ39ynlM5`q?ftbdIT)6>vX zd6t)`Sq=R%@KWF34~~%ta9quVq2GU8GM=p#3@+i~GPP_Uv@(m&sd9wQqgeaZTnS1- zyacAJRie;z>0a6migy^B3dN;8tlGx>&Gsv-jN2I76{4yzl&f?_`t0Ty87gM5bRDn6z zF1o3JvCY%fcb$BR7JcOES`3(=s5Mu~g?(iU35W6RfY3(=0E=0rpe80BIjxP29&+Zl zL2|P;lA@9*?Y`9|vkfde6etWkTOolhF)>Nm*8OgX47LD+3~__&fwxX>8W&7X@%8b_ z=PjUY?EQR2r?WSBPO-8mKCUV)F?+d78KQEGmHJFmJ6AU}(rk@WMLQ0dz2jToHSGKK zE8b~W#9?bIDC3s*f|B)Ue4FPS4^m5T~(G>2&m zT^@(&%EzMbQBlnX=P z>kNWGwwapPAo!0D1d&jUd3iRC^|2h~D)4bKG_&kk9-o9|X;R=%H$4Sqg~8;wl@>1c z{-~d4I7UCAk}(RJDD^B%a5{t9iS?6IB`=}YP-Pup_8rv1hWkFK29y%;t z?%|f9mGNjvbI$(9Iq1#=o>KqV*_TLhigr-v=W@nqw_$k2<*a#OnIlno?1?-a$(1$*hF#q;e zk-P61_gt=UhiF#tP3XZRz{eL1SM2Pbe6AtGqa8Plwq62Y=d;2a$>7oSq>;E&%6pJI zSt|!Ov71C(K|!Vnyh$=WjjupwaEEakF)d6+>d@cjy*oo$W{6C;Kq_CAM~5CTU_^HI zFok`BRAN5?$Vp~Y-vPX@Xhk!1E+MTi$F#vb<_{HA9$;Xk}` z9mqY;dh@KBm2ij`roJ? zJoL*@u1I4tpj)G`s5c+v@e}leH~plaP{yye1pjEtBH~BxdzC&BUu?6}X5XC~sF|5O71j_8~^j z_~%9Qx^_nnOk7H`NOysi2a11K5S5)Is*)#Vn_o+%G?X{ z=D3vGzY{bYfI=`43G;qI4eBWVsMv~H-2CG{85srWlQO(IqhUV4x$(u39#0UIGr>AH zun7og-M)RhVWd5zj`y}=iu(AZf@Cjr!v}=#@+w$V0W2|&W^@8N)5v%c%ntz)b;(v; zLqm93)~S!edXF9@c^(Q6W z?DA&N8B>?LcTmrJIP?iA zJlZKhVlK+M3xun%HQ9lUSR(%n_@ADzMB{7(50CjkWW{#=!bB=Re=)17^^)|EWX3F5 zAN;EmEIdr1VcVxyJ8vIzS}Of(%dc5$eG)HY(IY&Cf*`e|RA@pGX z%6?_A8^Rw2S87M83M+gY0sGO!jM;h(u!}o?lH5zdVK*bui z9}FFmG0M}UprBZQnoSP@rQRbP%mUg5FSZD#;;08l0tk25Fs}*Gi9lZ2Oel@+fk(7j z*Szmmg@~#fM_ys)HQ028z)P7`_?EI)R0C%AbBW~~bd!UghV{ja54gPM3Ll`;T(r8M zn{^k7=9#1i-)p##*kmWAePnfGukMoTOnSh6Ht8*m@Q=SXnn{;(c4@P$2Vyu#7>hj; zINh(xed*E@J%(5P26D8S4S|`s=F?w^Pb$lnIiuALk#^pd-uvD%A(qGS)XSlWVhPTn zVI&)1C4R_(sBH^s6ruCbeexwGzzGQ+VD28QJ?-~_xKwzhpvQ43t7sSp?Tc!`Hy-rE z__u7BHXdHmQ8-|bfsBp=&=P&P+u<|3tPb@=l>Xe3$COV3mrgv2h5nW%#vYp;K99L7ZFbzrPq+of%@F&Od#d>=3VZ!BzV@}S|3 zN>PY>(z1RK39^kA*ySO2SO6y=`?CSs3H7qVNK|0hRaSt^Ov?~~S!=xTQ$InK2&tDY zDfRw%LxzAGF!?HW-3uP~$~}CdG?`#Qj2wbzYixX}W(_W})QalAW|o#P|Hx58RsGvk z?U9bD)d45}GTRzV7AbB#8q0bF}$P`*H zwZ%z-1*i>LJRe(UKFs}kXJ?6-9k>MB#Fk@vpshnuY76fH(}PSPJ18+}3A+*ct1oAPI9w*&`YyP!4r6v zP>FbwH5&U^{zQ~7ip~92<~Og+Lmgf_5cf!PMYMZEpT%tEz1yxEwOI6#kGSkeocZkl z+Plccjh^-kbozWZUgn;8uYe|1Q(|UjmK7HlzbxjyWjMTscubH<$7Ir^oV~;?Nyen` zF?I9Tj+R}9CL-oYbdxG&40xlU{@D~9QJoDKp_zp}7h+X%FBCvJBLrF==sc5W;)@ zua__GysXB31!=bFKW*08NoxW0-f0`pj`<2~{`!^M#(gZkx{1~8faWmj52OQzOf1u& zLI;USk+Tkt4TDdNG%Z6f6|nktBr4W``02D#IDi7yA2E!$xPL)G`fy}qgq2zXJTSc+ zPQwqB{;Wq!`wmB7TshAY44$;TVGT23RUkxVU}PNSmcv^YXPUUq&8_U_<~9rrS;(f? ze0;{5?WS(4&u$w<3_zXpug@U4Z9i<0I@EzzTU(2880+fmEoYiTFALbjAmLU8OnWJX z#cLU{g~1`{?$$~#TyM6%$o2|G3u(ayQ=Sc@)CVt;lm85B09F@@a9mu=59R?t90#n5 z&@x2`$Ct0Q;;R+fjvq$gtdL=YNPF)$lT*hMQn@~!kcpeOee>1}ef3Gbn z9Oi;AETqQw-=oww&X)^y1taPR#pWg0@}7@ZrFuaKA{?g=3@a?IOk}NG4auH2zqh{Q zsvIPI9O)HECsx`_4>(Aga0oU7KQb~B%YWU&lbOVXSKn$ z6w}q1Gn=J1bLcmPh4-b%|9*Tq=Tpp>QG-xe#P1%r9j z@7(c=^TKMIRi(dOM<$qU_#(5@&St`f_h}V2$^IGo_}>_v8-)Lmjg(^}CA_0=Suh0D zKBl(&`dl4<5)2>m;!{H+NCXLLn8^!rvI^pP$Tk`+ut#>-C%cGt6?$@U!4mM#kx4Z$mg;L6=+mfbBy@ zBL-W~im$5i8CXB^w&lNGRYG(D-X!}>hn+r%JEf!_5c->U&RE**f5$G8Eo zjL1X_NGhEnc>L#;3>3d4FiJ6J_pa+j93h<}Q-J-SO!j{8wD8YHatz5H&Y8`eiDGBjB?K9%ngNyxu zzz>pwx{CJ#86TwlH)>NVngDu>an@ThSYd@Z495%Em6tv>lQAN!W^3S~2 z|N8b{e=ABX^Mvl@1$bj!E_|nG(Q70^Mxz`_=#gn6K=9#v{2d<=O_3Qz0~K%&)~vv8 z^1J;&_?F9j=OovMH)~is!N6#jsbaSq@MU=7RY#I1G=EHUtr7Go-6!Ag>JI$Q`)s6~ ztf5Wh&zG|r&j%v^dH z71IGG17>AwfyrpQ3@o^xdPN>PHVAV+?1)#wDgF8n?GNMODBtWHHU2f z_8oALeC7Bd7e_!Mp51FOynNG$P=#u=)JY9wRSF?p68k;j-+uW0`<+o3Nr|Qzk98Sa z_=He|6Q-1h|A4}OPNQVuO_&A^han9*N&e7xL{#I&y&Mm=2gP<5yKvTUh}EWF->Ccp^PYz&0D{{{mMX zCHU+s(p?Qhhs=LAenO%`{pLS5GhQ`N%!z6m82yiJ`ST{1 z&1d_eAE6lVuU8sjBLW2c=ao435JACzy?ieR{rmUA-|*4>;}HLTo2BR< zc;!F;sMq!19~Qo!_6rIDYCx!#{%|=NZUM8P;6q4TyO4@8a^6R!GJaiTf0zIyJ!u&K zi*&aBoSrtqD;=mzC!(FEUN(fNd)ZjLvKHnU092y!zHUpS@1L+WSVvp1xK8z4{ zUQR=@1n^)o6`(Fl1riCGmpqFI%=cO~#G&Y23hHl(e|P+d(HCEH|P zs}*hbxEX|^!+La!HXjqD2?S|m7<(Z`Gkg70NXXA(2n%T8)aADTH_{#%kvJ-q*@dJB z9_)cuKC@sI^Dr-7ycoohLdZP1wzdXfox^)-f$Zjb1GNee`)QxX@fcW;H+W>H{ zAbsOTJFBB6s}0W_s$i^$49L}p5ye6GIvQNWce-#ePYn?7D$k132y{3=wNu2?({uS5 zaAFozilzs@eB}<`&}&UBCxz#)*K5Yz47#IXL^m@8pO$`Bb?hBX3#`^7*7#5oGWUdT z#w$X`jcZn=ueOiwRuCv9_A>^FJ~6Zs^T0Y98ygGa3|%J)FymD|K1G2!DIQS9r-53r zqbG!Dfxy=n0?zQEW8QWPSJmM5tfJA%aAEr)09``R8$}Bf&;mn3LRc?*wQT{ZOT?!9 zmozf}QhyH`xfbSo(}Tstk%3?>!hz9d5m5CwnBJ7lslY~wiGyo*CPyR)g>2%?pm!V_ z5pm@2!#^}N_=N$+1^(;fmE8>T6X6cpuxd>%Cv{+1s}qG!91h?5&%2OsDPq65fhD&4 zIWr^l_3PK=(>Bo0XwnbHuO>TM!lzQVJ|HiUZ%BChJ@4`!;=o! z_cMyaGgANcjGKQy<3I0$XWTULm=J9)ntF@>UAHYpNX^2+0{H#uzO01k%-^`%SZw#dRkaz0%AEOE?p zQ*rJWD`^7>#hViK?V6IB&6`0RHgLq@`HD}#D94wZ{&PluPQRP@tSVdx)jpy*!j6VN zr+>rKNYsuFDxw82zx0oq1L8Kg^h*om8eRs&mA#B(GRV;6fP^Eb)jcp37b$SOw*35$ z`ugE`c#1V3UKcc^!!1Qq!GV1eH-`YwNMQ>=W?U-9ep&fbguDiQ47DgdELWXgGqPM| z+(9S}=LOVPWBJb`E-|&)BP1ujsZ3%pxeM%a2^cXnG)z zQ986)NAMtk+;cuW^np1}F$b_(ycXTpKmo0;WaQgpsSmUwnjoI@4Et_v>5(R6z zfb>4<>5behn)`D#P~w6DuKcqqV=uv=J)$FUc|xaAVduS$Tn{2x-tT(Et3UKmdlMrQ zLvZs+;hi%Yq;WDT;1p4fi+veRFbTSG(r)ne{Ac&(bp-%w&<)LZIbb~4Xv+TbcvTfL zz5$BlGj|gtfOCcYjr4Y`(vjdLT!34MobVHy%s__#YeNY?+#iA{bsyNV)9}l4`)cP@ z@KtYvhz^!tM5JtFn`t}tc7e&ZU>spX!6M=^dxgodB4%({n@} zKlBhIzTpXW9S%K;wEgTK4*>7nwb@*EBTqg^0FOL~`8#|YPpb6ap?~18!M(5ybWOos ztxqS2NX%gX5SnBcfvS)|xs@Lt%}f<-VBd6I=i8&_-f%TzVcaB*0O?JTtP)|VgeTMA zgQ2>yKFeo7JnC>C4iL-Do1csVi0l5^05@;V(PzdA)IkBSVz5oJ!=4Butl34 zKJ37=f(c8xqE~G9xMWFwPXvZ+Cr^KGvKY!s218r`a(Eto=@5^%xCff`@f7NG05LgB zuS6C6ZYP*CMi^(xgMsKx3Yb|Hd<{)tF-UlC?tyYqv%VRV{`Dm}rbque;on73d*Bo! zBV^z>%V==L(2JgiWqt|w@d@0=Cx6{XcI5Db3uWu`pHm6J`Xkj$2#t{$F`(wbnSTgJ zH4n+z(A3faWE2-Je1-DUP&{x#TX>d);xuk>aXkRcBzqRREr4pb_}BF_EWy~rgEmn+ z1E{gW(a^>l;XW`^2(55JL0b0>p)0|P{yzrI0n9}lY&+e*w!H>c8N7TXTt+w%e+&!} zta5-`h?%|X-e2xqhR*HBV*?V1N$ibHO@;sM?MSm>Ntd4qMHzbU)@291d*_e|!_J2! zp!eTne16_sMovzS;se^0m7U!YQDeXgZmnPjK81!lwc_kXMr?t$7>Eo?SAlNXZZIcn zu8=;{R9037g6>NLWQ4Ix5TQJUPlp}61)=50_FpSM=8u>=ItWGJnOhT;L*bfILePTP z7UVK>hu_h7!Z{-b-xh}p_6ANzUX#?Uiui1N6 z0Df=-0snmgN?-x#|5<<`wOJJ(A@b9G($3CkBlslzrCczn^ixE{JW~9mfqd9ur_-_= zEK7&9=Akw(Cd8!*o=^}{BkQf1Z;xwf0K#x=F-k+wv0NiM-W~<@l}~~;tNG8Sr5nJ7 zEzggZ6*01~G@U3PKA^*|MTi~ty~4l+`nJNpu_m@KjZ!11?c}t zoZuh-H7o32Kjr^prf?jOlm~9m2RbnwDE+`pK}h8W9&I-yNIM#e8V7LrB*a_kRB#+=g~Sv_uu;A||U(_uA0c zfsy?c{#pYPcBHpsYHA9qP?SSnH0{eRBRfUo-uajt*RG`*RJc1EBHZE86i6MNse4GO zj%4V)lHgDc9yHX~F9MR=2S|<{R^m}L9wOCv`XakB%#%bW?EpAh{5@U-9(Yz2{&OS1 z4WNeVCJ1s8cy_fG$BtF|!>?C>dVmU4L6@(kf;3l<7&ZbFH@#GG@WgS7qZUjOrc=gD zvIwFR`#V#wq+7|A#EeOM)+Gp7$| zg_*!4GJwKLA6RJ18*1-BPTbM4z+oKt8$~!?qmrq&Y)X4KVN+<%{?FxC9zwLGjUrD4 z)=qXJVj74Ctwh=+!HY_yAH+KKYnQHnKdrxq$o_3F52+dY9291kZ^(r^F->^_ZUh>( z4p^tvks+l5gy8{LcH{NSV+zOPJvZ)$&u^Y*luiTPDR&6I^!I|H?aPBa8ZcKdcKl-P`x5}!FE`UROkM8oyJ4g~8tNb7yR zH#E$ponMnFoemUPzP1GT4SGQwhI20;DAF2{Ss$96Uu}4@YKz@QyeIVx9UYx_Hx3~B zwEwkG)rQd34hscPpEJ)BhMFJ{-?=>3ar5R)EI%J#p9!^E&u`g3f^L<2mKCst?UjJK z^X!O4zR?F~W&NY{jlFHx*ScqG0HY-EaEBu|O$Y(I@RaD_m}t}#$$6i~bG<~iZEblS z6hVuIOPwqwU``Hp&9n|8#F_fpfb^0_kz$mp# zzrXZ&M*^Tno~l~*X!kbw=^!=p=axLCL#)LHY(*Ne@t;F@zXeKWecT$?`NR3Wj*grdn zv;XJ0{4+=l1X_uxW4)muiqvdCmLx9L6O!c?ldmTIE2CutDcF4&o$XjpWj3f&f9ljJ zPMpW?Ot`iBE+8IOYTj7TsZlCWFy8+q3=+`WKgb@;w+W0F1WT%k3@M~2T*FBm?LLUc zZYGBt%U&>h?e(1e?Yz>0MP&NFS~-w7ycXcl<J`L!M@r)VEu-?%4IM+&mTssVUwK8fFLAcduV71I@?@ z=E5z(QOk?2DZP)Wv$dQXkskkCsP? zJju3kMC+**!;X~AW7Ni{i5oB6y=VNIx%h~|=Df@Gg}r;6Ly zF_VtGQnS^gJk&NL~u=MH?Y}?#JrB# za9OqF!Z~Bd1Mb|td-tB6g2H=I!0wSIUJB8^WO>Ta!c!}mQ2%L!pwbvxLT^RNrQhH8BP)z&ucVi3DJ{e5097H3k8eZAxY?rUP@JlCA-y5NE*F zhk+-&55{)}sCpOjhuEoM1flf=(sNBv_1O2=`SU6&R;hwU7i}57?l(xXGHmETex%1R z)pd0nvd$)uP521_TsR;!Enqg-Us7<=joHB~7{$-BU8^R-s?+szRU=EAFKOWuj|H z`LTBTq^Ad<+sP1I%34H{YwLtKMV3`0nyAED&K(TceSoIXDQmlA-8yft>srNS=hiA=ke^My8Mb z;B|(I=PsDlFW3)GH~r2g4#i~Y7AZMZKcdZiw;?3E68K)HJzZk^Ri?Ls@3Liii0R96RPGIh=rEYu(U#UeFb7RyFt>x88pU5ZRgXf zo{zV2CigH%eJ&ca8djFOBQ;1(%#_0~{J_(=x-EF7&}h(hMbOXg1nB4s>Q1^(cIxCw z=2BvE|qiCnj;CKSPf1u5e=6ANE$HH!xP!t^d)RmwR$+PH+RKo!ou*2AFzWOzI z{#(#HGv+pbwBIVQX|7*4~5jdB4Is3KRp0 zLh)fb_s9x};}i>4@O370A-v5vq|~drfB$|imkc%0KvyT1|9Cm1xTqyLq|a5} z9$(M@c0AcI_wP0tyZkb5d_4z*@cQq>-iNm+(eCkA0PiR z{edph7)RXLKN+!QjZh8(#1mTgD&CKT;`d^_phD@;5k&Wn-F5cUV5j1Gi0i19fG{BwB|>XV$~jR zoSF9eNoWKo^0LQ{UR81z+~0nF+`KmNLEyX&HzURQ8CJa*guZ1AK@=)D(YWcerBL(F z$tU!}5r|;2x4O&9aI$ptGPvU=M*%`m3?8_h6V$kVM!4Zl`6W#DVs5 zuzA5%3c-FUf09^DUei`(${Q*dWax9zFs0Fn=67x-M9F=md!Slk0w*7JGe9~{L~!gA zqYTRTDn1DnwQ4c<>>H3KR{0PoH6$e}5l4*_{R4Bs7@_=6u2HLo?aX&c=_}VQ9lJ%? zxh^?PnseKBo+_Sx%>=s3HKvc{ke7aWqIU zI$O03d1M(oq(T57-~$npeBav(d9{@PMcbsuQLtj#S37c3Ab7j zXkUn5V#M!FpeH=BXLtL8*Tv%BeupRV&8J=ZN=82dRf&^iYHH@_VEicEHvti8^G`qL zo8$q!6!o%Wl-N%1nzdcv>N==rufiA9(P``3TeXv4fl1_@&MA9UcC1A44R#W`UlN98 z=A)ZYZKup_6V`~lEE;3pqM11LzGS^w*&YZDKBO~)^AsC3XUacEM8!IbxTQ?p*qGxTH5Pkr@d@;Y;x%grLOM=*o=MIB} z18>h=dEpxg$#~kh)`5<;Qci{2Fp;0vWZ@Uu7vuKj{N+kp8CM_CbOc>-Ef_c#^P$=% z662ewvU8@~sp6oS z&ZiyyAv~PN!-u3E)W!CV0N&$?Mbl>%Gx48HG}Wo|JrklvG)ej*r&1<57T5(9wX`rHAEMRSz_ zC|#J4aK#$>#YvWw;zYHxL_ov?2i7Yr-K6*c92Ry4+>{O^LA@Km02>ak+V0o290Dsv zi#>q;iL@C~(RE+1A*B2e2I1G}Ofh7kdIn34pBQQTYw}2Fh0Vw2K}UT!8%nOjaHaL> zB;2>sObc~r!@xaRAUbiHiPC2SX7)U$J@r0DM5Jk;)bF!_H+5bfycy82HE;kqtcgcC zVknmO_Vz3xadUEA?oO810X#+%ZKb^enwQuaSAAE5IiuPQXamWD&W~Xbidkt z#h;s-I}Ch!{jkK*7hX_->v=3R%=vH(6vR2i#m6c=AvTMGxVyjH%^}>#-ezqcXGc`^ z3nE`9?L$yxOS1;$_qdfq-b4voXm=<8=2t+&Y)5My8GtB@beQydgv}p9#R3V-9TU(4#PrA4RnsaZO|0I^YX*+hFHu? z2>$ya{>;+I(t*JJ{Q2_~lzX=35V7~IU9*`N*{eFjNpRe{rM_JV6tlL+(GZTQTKiWL z8_~A`0+Sq8WHr=nO}+XhfKJtmrWC|QGG-9kqZdp7z}*UNO5y_=*cUPX8BnCK^xCR( z-7B?|5^0%71`4%wb{R&YeLv|!p1mlgKKwqr1`8{-K%tI*YQaijdkgSdk6c<}Z*q$`X@Wkdt0&JL9-Z!etv!R5GLQ88stE#nM4 zS}f>K4pW*xIDocwl9p$BgR{W-aTuc9a~Nqt88(kPyO-sdb;Rk|+vj1>ud1r57Q?&r zD?PQ@G*BUn@f&xY4;0+TL%eN}={A;hE4oIywlb{kCSjxe~IiAorNHi z0|!YuO_(h-q-}0)_8>JX_*?PLi_QZ#-fVtmPJK8W^xB?Qdi2EC?qi=OiXVr>Y|e|l z|1M?9c$Y{-YFJwA9p05vI>t`C#7@W8#WI%mbL8W%HD&O5zRt(WtQy^luLgj0!pO<>mr*R@agTQ6;YP@r&^Q*~*0(M9ol*-=(4=pa$_90Tsn>(_6IlZyD! z@uJVWtS*_%ozZ6hRC$>yWu!({Fl4FkDwz~w0GRI>@3*q-*Dq0heGmKtH@>!I*YfAs zv%?Z9YqhA)6{MmkTh`|-#$ zyrI+>T>E;tJAYaXcBl9$krqmw1vA|}a5HT67vXSu{j#AsS)%p(CELach(bf+SIFn0 zH~Fsaw=+7Br!?MqL7nGE%}X0A;=RjTOVagEHJG*ETMeW! z2M0g39B^N0)2Bblb74}Lewg>SqQIS5rgDKs;;4a6rQUoWA*qWG%6Ej-KiA}5^}m?M zGb1e#{7Z*%ugL%?6``M61~0ZfSRJD`ekT+E#R02QPFV5v6Uk8DCliXHM&&Q{C#j4+ zCDRRce>7=3Y5m9u>n5p7Wv%XFZN%^(w}Afq3{}OklmYesL)cqJRn>O;!$=4sE!`=I zf`o`jqclo)mm-KL(k+UBG!lw*mz0FG2nfR1!tcVXkq#S7uoCP4%bG zDN^TC*h_3Gm`t(I7q>rqKv_L&Ys~CBBkK1K`+GrL|B-CHSI}X>-n86)?2Lzut`%LE z5T-m+a@)jGu#a2(-11&ynVC6S@*bKdEg^}!$;Gmuqab56hcI3`bE-j>@vVNETVT@D zQh)f){TkWkrayjz>hNtZAIYRAP7QL#1%>_$&kam|$~8fi*&cO6T_X*TV)$jf{X$!$ zc05hYebEop#Di9!7!|0A=YdyKr%G1QAHH^iIt94MvMTg-cuYl0UVYf67>)DVS_^cp z_X_#`~QT@6a*`s};GljJ|8DV0xRYRHEgh~?)p}%qPd7+sxm#wNE92~Ope zE&jI3@9hN9H2=MJ>Dry^pSCC6`bVM%&nE=rV9&t4iCGV}pa#{%%W5ehK3$ZhmBG5# zdWhfO($qbxyYFnU@M~)gn#QdAtKk_9!=`>tI$8<#L!;c1!Wb(v2kYuX=(gHCPY(Mm z9ba+fsU}vkRWC{8Jh$+Y)+b3~DN%=FMs@L}Bn&zO&@!nu3Aa9Fa-Yk!aX5B1KDxos zSmE)B(!QsuL;Y)43cJyp_r#RjmKI9uTX5c93ddRp3V;(0kHUH)FeF5C!>FbpKIZn5 z|6DX2BN-yH%d_SZGz!~H{85kX%q@iF8KZv6~c;hs2!Ey%9W@Jc)1? zM*xEGd};i`z`Z^HLUzj=XoU(^!4n@`vx_kH?ze4sIq)aG>I)tmGY$V%R~FfMSGC}m zFIJI1TCn-2r80!XJF)G>#q%|YLUjuY46wh#+)j)iEc3}|N-Io9W>n5DS#l9iwN;Kd zG%EFkknz3|M6a%>QBX)~yTu&>k@kK4>R4*=8yR+L=VgiZ{Qiz%%GPJpubg{SF|(uB z@mdl`q{W~2l#g_}(Drh8LW0uHg zY+U=ci{TeS-GopE zaj#C#>l`0_%Qc-AHtqBN6HH&y`TPJp47qQY1Pe-ybse9{{1S*l zYdVe`6k1p6-nV;K&d2wi1%vaOs_J9j@DBm@Sg92eB)iaKX=NCIe#6`wkGMBm8iin} zy9@1x<!$(v48V>8?1uYWvI2ET6Lto3K=-I(x|qXrWJJ?usj9`Ay2J+Rb`A?((PWw zwkx#nWMs?x5)uOU6MHgW37+875x^GcY$cu?VoqD>w*i`tg zO`n_GEgsv^I|uqO*GvNC_Wka=Nblt&$>fjx=E+x1)O)h-xNMumb|_h4gHEbXEbL4O zyTL~+yVIE*b2ut?PR9Cq6tpgoB0-azqg{BLxNi zkXD>S;_sp??7SIoi{{}ab*NohpTNJ&5@5tIp4_8{fp|8+beDI#s0N?$>9;-$yX7kHg&}Kh~{_%k7+C$?C z7>ki{4arQZ+5yGlW@pBMD$Q|eo6NyV;=v+aA#*{@RYB&DrrS={K^413-nE}c9% zZv!G}J0t`Ia z-4xwH<_|`g@0e^;rChs;Z+z!GBs`KlA!@l}_vlC))pG2&)KwsJ2d=N_+ufGGkD9mW z`s`W05J#_^X<28Ff3Wx)jMEVcRuju79e$&Ta+A0=FzI++}cQub}( zAcF7flw%Uh=?zX+VN8uMmkp$Sxm-R|Zl_({!1rKz&!C$cHLOWwG5IFEG`(i4RD-?D z@!$)w-LfnySJ=cGTlyG{0luAg9V1I$p z1V?hi%Gtz+lKW2z*ZaAbn~J{)(0>%&w$PBepdS6Oyw@NtS=wHJ6^g&0!E=vz<7Vm2 zo*1c2Zd&$HZL6$B*bk}N?p%Ob#~#X_ygDwC+b4v?_x!G=x)?4yOSzW;Xe#7o=se*V zYke~{=}LE?*aJKzi6o=P{2{ObFK7uj8;)IiGwhS!|J}<^Y4N0t4%2^nl-Rd%M}Gn= zh3@3Cc)uF$I8U&{=Ou1O1g*(~+FpGvangd8=~2SA4d%6Lqe^bucM6VX zj;PicX-E#=PPt;1P7Bfu1=`Pz>O1w2KM=-j*t!Q`7EdT3$FihP%&AgaJ zaCk~Td}xJ;iNMp63PFV}b(65t4b%bCk;A1|DrW<#OmZTk{7DutnQv7W|& zJkY>oR*+u^bark3 z)+#W_`O~m7_p0Ve8lb?|%AyBRS{Vvt2S!lj%hbx$#DFvC*+_2^MSlkp3yyGz14{wj z45(jwRqov;pnVtx`Chgd#o6AovGT77B((wE-r+&f*Yh~|v+~8*MPxxrSqhjF-Wl5^qd#SPQJkSGW|p3|G!7gkZgy!D;5gGGI=Q_qC^kwWLvhrLa2ha{xsQCrKzS9H zeHc}2IK5I<5r6n*dBYEMTvia^tI`r=-f#vMZ5!aO9dw+5m&q-}Y00lIltC~8r@0Wp z+#{SbYf!Vv#GL7TE<=@6C1xJcSvuqQe)P)hDBynqdg5BbdlW!gB`^5rPcr;CJ{~p? z%kdKSpoiq~J|s!CydEDY;W7wN)hQc$#hqdsP|p9yxW=V;?7RX(tMkM(fh{bsb5$Y8WOsq7OEU=Dydky4Jy}hsPbMW!8Ze1aX$YF=fWR~4z&MrW9Uc@Uj08u z6b47}+sKDd3TZBUP_h4v{5VJfOc}{w)%~A?j1ng`4%{es)OY0WJ|7OB@_+E}_00i7 z^@KkEUheVYX@KBwKkwRm@yzqBtj)ayL=`lLD9;Apgm4o6+b<N=h{7ou~Za-D%8EACc96(LB%2V6IA z(*N&Qeu!vldMM<1Qp(EC{;mH*&HKmTWB1O1$OcSI%=x1kqW`>7m8n7O2Is9a)ms~~ z|M~kgg74=;PsR;&^?Fe%ZG-FEJ?tgoE?ArY9jWWcnbf`Evp{}>YA9F>B*jjaSw4ft z)Vk*K<;x1ddMv;a9t#iEu%&0dAmBN$wdBRwL3(k`;iyd??2<}Jh-GaTbeyfwC$T}U z3n*2&kPQY820klY552<3yh|=~mXVQ>6QLi?sY&wf^91^Q0vKO}Aky2D^ck!u&|q?0 z0~&!3Hiu(C=`{7Zn3<)j99Eb?p{8|l4r>>36i^(>tD3Hc&5p<;Z_#KBMC9WjGqB&8 zm3{^j)Am7Y3e>D6QP^W`5g%7M%R6Hg=?XUAe zFoI<{!)yY@jAr;BFsTiC{V6cKiI@}|zXm=6;7aBNljHs5M%=twdxZWzR?WoeV|bNYo=!wUSo&Xh2$XyPT+eofa$5eUMO5-GF_%3|sXQH~M1; zgPVZG)`Pz0KK!oKpZF~N2Zwzy7imLMgU70e@fk#4LGXx_B_-Rx`w_m#0!)b5VCCNseRl}p-LU|6jYNR7Ovtq8QNm2L;Q1K@W6 z&S~)#3Q=7BI}mCHj+!FaULj~A0)(LQULzQ#`{BE?uPD*fVvE90*#5DyIvqi1i=x&MI&j*0a>j9$alr4;bQ)F-N zq|;$pAf#r4dTIrdobX?OyiIf;2+Fr4G@Q?%s2WuSl_w{K|{RF2;_Axkn1qv$! zZyVKQFCGxA!1|HndX_4X$S|Vj2m>m$GLjq-o#z98f{~@-4Zv;(JuTFmMebW4bUp7_ z+nNAoebB=?my3tqb^_Ff!$*)SBIdq$TVl)^R*=JE7-}R-Wd@8a$=ekg#Laxi%H_ z$&9}J(wZs#*rmg_XDizxE@E_{7%gdyuV!5?wS;-j9H1F7EFSFPL2QVNINDa&&UgbV z>jM9hi}#M$+V;6{lJMVnv?Q9v9U*ivZRe#>f+egjLb|Rm>s@r~7)8p;U39c8)1PXE zoVOFV$A6!XH`Mdmb!f%toZrUQkdasDVW0Lo$q3XW4 zxOmw*h&x_XxB|pIFn2G}cVDO;we;faXwUKL;UR9)UYr(Uz0S)r+!^&-x0C+XO7G-OFjXMdc(SmHAq0_UoGN3>f zmkrE>IlU20er`h4NTtsq8GWI8R;VnhUtD?VOS4%o$RN|Ou&_pkn(OO9d^@r3)eDkB?}x&sm~#wMZnBTTzoXyT{h433li}V=iOw(@!N;9*Q#|`y zQ^pBoYsMCp1`<>o9%@o$> zP{~+(LFzxO67M{XYu%6jASp;F59ou-0C4OPW1y!mK!z_&ghBiO3W;nRIE8qJ!BYeV zIEBtFeK8Dnki{v82ZI%P><9@awT61?TQ~U5CD&l#_k=M}ZKFPyFb2-%=Pk+#1Fx~? z9>b|}kHs%e)jJV5s&fuS0&!xgi`Pg<22O#-$_E)sZ`~G@tu}zRzP?AXO_$#&#A7|c z;dlWlJ=VsE;wcXHuZ%cjBqBHoob}LDoY%xLfbHR=>(9)VKcrKPF6l$$C=y$Ccnqz$ z4PrJrN&`(>9kLS%z+4|KfqKZK5kMS|0^Z9#OakK2@e35XBc5s4Tt}C-U{I1{{|s+_ zT4VVzwn@^bme**B*Y@sR{=sWX--2`R32ik&;g!Ni{zLf}8<;N2+mmRMH`lLj(M{Jj ziA?JMa1U{~hWaUV>7Fc4*z3zL2~6piNT+EV zMY@E32nD=W^?yyeluNM0(`8B5^(ubN=QRWBXFb-Bdi;*LDctEPg2f%XE8BKgFMc`v zt?>7Ld;fH-sa}=td}DTsXL{<+p`Z7hkN4iE7H70+;Y)jeL(x2*PFFwh-y-#>Sl&+f z&lU*Gu*V_Ap9%D|wcB#BQWXpcP!EUTbva=htBg?fi%ximiyZ-wbcl}?~ zR_?{w@3F0(+$heBgd{OTVL{VWC;fyfy=rQKQuW67&)BgEd#$6mH+BuhR-?5`-zK~m ziWztKwjo?-t?7tAz1F5VaB4T0n|JKo9<9CC(pSG0GPS^NWzWM=_hpb8h!B|M^Bs7rz<-@*cl}}IzqFm1a8Qy z^~n~vFsIrX-#A*NA!si+6_e!#1>fKIet|b`X?QgL9Q2$bnVF@b=e|Bpcspei_1-_w zxcLKj3(~a>BS9HkM=*?S;ngibXr?Tn5F=PyV~L&Um7Qj`&D(M1@fG;CF>b3)^zO37 z=^t745)eZ49=yX7!nlkV4mP~s;Mrmo5gFfbLqN$9VgM%=i)eMx5YM`RUIBn7)=|m) z7A2IBGKR3i)(kFPP-CiW*6ck--3P=d7A$-kFrrR#Mc-nL5*`=MHz-iOwZc-Nq4hp1 z$xClSHtzKVWcJQO-Kee0z(cYHY9sZr)ip-<23SZnuj5$wz@)zSHQ)<^q_nW?CFvM~ z=V3IitiNFb42)8a2jI5l4P~i)*J|G1$k#Z%$s!92h$p#dv@O7{i{U=it=OHxT{E_S&&-!H~>*xmTK2>20!xZO_9)K=( z&;wDri^z!4Fdz=h45;U40a#tcCZNr(t9M}V+>a0yCP2bQ2$t|=Xtlu=Fwx>nerO07 zA4<-)V@~7^JaGD*v4Dkk&IuD){&b{lw!P)Q1MOo_vsrOBETB{8^h{&ahkjeuKiq9R zxTA)Cc))!%C{o7)+lu)V1>ef*l(Uc8g3RAd{zTzniaPU4s5^-_0l|Iw*F*KZ25zgv z;Flm}j-9NvrLsYr>i+fmDP*z}EWxPHp!JrJqqgsMHDYs>{gtnu3WOaQ-4^iOllR|p zovVC1#W+{F2LRk~H(fl&uh$sr@Kfcs`T}9EasMHWqX%a*f&@zin?TMu-E;N=&4d4I zZ13BvO9W!t@U!{4`~qR#H+yQ`o#DTV+w0%DbG_55V9g$y89#;%hqfyLQ3pf6nzcDM zRSIR-l9h<_wjCj?Yd$UX?n<5%D~NDvyE>rUcW>)n+bBj|Uhon@U3+jLTzM^msWu94lZ~8!DvvArZpUTGedyh>~ffm?}i?V*6azzO7EJs@BjY}CT;!>1b1Ek?qcR_V)X3BH%2v}75Q{p}69vw`IhR5GBl#*h#ZqqpUdvm35XLWsQ&_ONjSD(qUivQMb$(6v!-6N?$ zIqw2$ukx*;_m?hN*SVcI>$QXv9XHhHVjh1osB3?8PEMs$Rmw`^B`6bH$apC;xO*?C zO*6f8s6*zZ-3yF4a#Wf>MvQ|9FUtf}`8e}E{2$TL;3fXucb zmSmgMi-=g=EGlKb^NDmVZP% z70IT_6RA4>DAZNCMqFi|C_j|>&N{$KMjc!o0#()(5+SISfQh-QZK9x-FZ2y&n!BPtM14@d7CLSoap?L z^h1XPviDQNr#@FLzu)t@it#JR;{Kh|S36e|@;R>gkX&(-<)i-nE7XXN`j=dD7zu&f z(!KlPs*$EJNtJ*?#{nS$Lu_ebc+$AKiP%!1wrtz-aryqK^T6$!#s{_$H&JZNZTQO) zz3tdDKU3&~eO|mdB{;{s%Hw`2NXSi^)^wFyOQ=!W)1dX|fXhxB;#bs2xc7^X&xP>i zE8=>?WhE&zT<(t4ef;|lkSaijYIb(`kYbKg@BCdS=m$}hb;F?#0jmS}s>HZc&wyH7 zWfr|3zwQ230xPOj(!YOw)Cq}fT&*BS zHIh=Ss`_Hre}?E3RXU@SW~-=CUMGVY5hQ@0IT-Wy{e0p)23F!(L-1XeTBr z-Y-wv70(d6uh45`e{`JU>GuQEKI#*>8}TJsII zpyom~Hh)WOyEWFF70bUR*X&UnYL+K={4IGEV_JVxV7r`(X(k9g@a@N_a%f3hX5V?g z8?3uiXsBr2nY2}OYh_wesE-BZg=S#RqkBv3uv5@rQV}maRKH@<$?&I{s=DLXpQ*-q zKlsLvDAZMo-Z^hA+IO-XFRSumVFenyO&h+L*|OOQr$~A|Wp}sj14cPtPMZ7Os0$n0 ztMjbPoH*kLETr$wIqZ-UV6f=Nk?g{v-L3vPmBgykF*}lP+YC8(yy9_j(A7V6_DWqY{@_%d#uc6spuSPXAL5<5{zX@jULc!4<`R&K;J~qDKcPKF*4y- zW1GKVc=PaKqC}Qvzcat{FYy~bu4y%*+}60rNq8$0!os} zOMX*4=0)5xZI7TdLqdMtW&40R&G^$&f`o$9ahIQga(Q6Nf|4%a@r_{WCLB=mD;z(+k$td=cbk;328;FFjk_r>3jjc$~TUp~_~ zZILiM_{CIuB!KsFBt&Y5cEAbayUhhhXEjf`XqpWoSz z&5vjIu7+i_PI{)dWhG^cN8+NNN|Hm?=}>7{B%uhSq~OH!t4&oOOm2-Ad^lLu923dZ zaXQGQPIxE29vuF;J&lp`$KlG24pc9BwvqMv^!bDMfl#!X%vR?+ZW@*Il@$u^;I-1Z+jagtM) z@jXOBNlRpKTBWM=qe^?)SxHGLYg26+6k8L<v>w+zyg6Fv^>a!5ztk#ISSwT5ahY{A+jbxtasd!2)G!}M>$ zyLs~#*42;7v=#iDEtELXk<1G)uTM%?D0Ei*VAO_W??@`l=!@OwJ~ih-PJhckfA0xh z?6lC=i%Y(^yq#L34<-%zEsVn7E!)+Ee~lWvMBa30bj71$MTIUuje)U%Z-Z?wDUy#l zC$zQA+g*eXyp6l{;(U_8YFL8PdpFRW=%e?^A$f}{9)Z&lo!WrUuW44~MRy(mog=1PX_8vcu%>H@C-R;~A}5NvS!d12dd#9rrNR&Z@R| zBJ8084h<;1iBnfK>%cHll)Az4CHz$z)t_z+y|{E~>6|YGr`r_j&&(j-|V-MOd0%lsJ{Z+~fkld`+ z6?+L#55Y`hKMycg??RNoh%6hU?M=1bxXveKp~MQE$!|h^xPsqFxKc|9Nv4NkdG1R~ zl8}e<+OuV%rL$dU=6r$B`?81D%0XgmTllqm+FX`D57yTo|p}IBSNOqd8alg^f zesaORB!1FrIT8E=O;r7ZZQW|Dalpum``mhVuUSY~1(wOm8=bDB!OmOfgL%~!o$^B7 zM$!4ZykmcMc)tuv2q|ekq47sO;gyZjK)rUk-E(Zqm-GC_d_!|+>78h%Rv`(juoUG- z2P;g=IO>@AD|;(~XJTDg7j4mo=m8|EPEuH0rnMR^s9}c9IetaO=txN4p8b+IqV4SU z^PzmT#|U{*y4^?LjeQ#5t3lgf|9d|^dQ|Tr7p;qAao(Yyg75sSJ|V!(Zax% z?fNU3@~q~UkFGN29qV}#IX}kcyw?|`%BDTJ{7ZWxH*p5+pS1a*u{yssnCQLe%=5g5 zr3Qnp3MReve?)FPAvD?L$Gm%tl9zaUnHxp$Y)mWH}=FYT*aFWv^rNd3Nd$1CX+9j(=Hp=5H1%ksjYe#^@C)s*JVqEC@SLiMPoDT|thU24 zslx3B%s=1nIVuXd_H7)_h;F^QlKw%rN+VV5_>SmwZWI?zUKH21!Xp-ioKVr|owl4k zV3={UAQJ1LVfX_#&4IZWKss?AR5)$u4gT;1cYivpR)+_wc_+}WXbIGgRoE4(wWSt^ zdYV*2__dsE*0jaI&@U_8^nhk3Ipv^TZ?HkBdDEJ_!=&E8dX&u+{sCG2izM!frt@}h znYZFY&sTVY|C>{S6_~z)d-w{HOZ|RVDQIJWDEz(99=oyU_LQ)%lx28mcx6FeRz>Tj zQu0Ol{YJYR0}vR6494=Zi{;YK^!>o#_183-eo~!sWNEy2kzkU=yoHX8yLCCx;n*r^ zFh^astRcM38*Ea!9}mGW5b+b|5>9cRwxU%_Xegn}X93(daLpduC$U`~X*vj=eKsQU zbYJ|!CBFE2zKu3;h<`&aey=b+Xi^{aK5NcMT3miWs5o0-SWbm$l%m|`8t2tm-pXhf zRb*2UPc0(n;h|JMWK}<+@G)bmc&e@TJPxzs*&Hpgk}kjoc>cC#3+k9!PNmzONBpC-B+pyw2HC9&sZeY@zo zyk9HyU^`k{H)=e}bdNJ&g}UO%n&Dp30i!f(pj(Ti+ZFXg;>yOjkh$SmOkGHw0T$~q zN>L&$I2f&pq;@MdTz@PNjqRi8^zW6JN95JHQyyGHKMT#I9a)&$xLL>)Q6S4xAv#Ih zpZl)09>(c*riz!IYT&%tNXXW(9hN!%#m?Y0RqZS;3tj{juAuWz_k9&X76-hX5?{{m zt0mNBF>Wi7I_74EvNx`?4$H_@t|Y(2mQ?LhdmP`ooZP;7fQcm^GV-m->~Ez1P4Cl` z`@8Fsz{}+ft=m>vGc#9tAu-i$zYrZ=iN~@1pzb)>^pJSR@yqv^(nx(Xh*bPcIKG1c zQqw69u+wT!0!%BwaJ=~ds6L==eeBmuD0d?WPYbKKLm_)6tYA1@qgYRX_cQL~D-vR= z8Ppw-p6Z}@tX_DnC8XDIP7B@%3U0;Wl#~c0_WB6$2OFqAOS9B3AXG_XDVl)I5(6E5 z9!^m4RJy6zrK7?@9le!i`ID5}>)?IF5hzNXFofhqOuPb+`EUV8qwB>0y$lr$VbyZ< zu)I4<6kbQSMMn1cp7st^7!|(^f({%bDo*T`Ysw-_fXT$PJ^uU}7h~Txp8Af?>`-B> z!nI#{60CG5q`rD%C5cs64^_d5uxlzWKdYUSDy#dvhTxa|$L8z(8)pX9mYPmXc9dnC3Wuwf@+zx{w zdpwS$C(R}g2vfv6?-8jb$bNTKNqu0OwCdI{+A@D~Y*9-%6kIM=mk7j$u(yU5zg=j< zBZibp1EHNfwdNT7PNr{hGcokR^W3f2BUK87l;I0bYjhf2Xpk!-{}3kq?n?BDHHowM8Cb+!+8>h}5Psd(cxo&dtk=Y{!wM*KqkE7kLG-$`JU($KD zvZu3Y-0$eEvw4F{%m{!=gBW8m|7W6D{!lFDydVJ`hLGHJuq@e zCL-=FyNGz(q5j=97eawo7x4%1l0aTYN^bgI{Pblf8@JFPlOpJQ7DB-bs$1_>yLwN` zdKwu*tpfjNq^5=Wjy8m`cGI#(5 z#RZaR4E!*aH}=xI-xg{OzW_i}Pa}wkAZ|y5Q8EJHZPku8f55YYNcD`^WtgFC(9BR5 zbn-#ug;+PzqV%r`W{hlsb#c(tZ|K8N06`^4quRmjv#?RYuaIUtQX*Zet>J=o`Eo9S z?l^AsJIElJ{}Ib3;JLrLGQ-lOR#BkS_braOC>5lIL;JLrm+M$FjMoc&Q}P1?Ig1n& z%PrH?+6V5Kf3D6S1EBG7{?3(hA1=(>E9!G+`;pIMs>&m3WiovvcJlNF$%)?X+PX1D zajwM>lV-dgBaB=WQ>+x;ynH>oqKcfNyt$C`Cvib|L9n{@bE_AT|} zT_MhTN-t%?;(;9Vt3RdC{zzk*8b)d;X`1|DD_&)x_4F8zp->V?XTS5)>G$v-qJ*GO z*(HhG<@$@5{s&V;#+czN3z+@wS5IO-6h+fEkzUdzCWePNa_CtzNC;{=<*<4K5urre z)#WD1tPh2Sh41|R^M2@CjJQI9sktqu1sc&CFhe`C&0!e2;WE60hX`+uB%JI5QJ<@^ zg4iiUAjX4W48lwOIWb@svV@((YT)24bc|qym7(Y|V8%M~pjUblpRY$-T z<-w%TEv#MN~m7U$AoAST&yULRZyt zMJ17&tJAhTB^OeSuAWQ1B=#+g&*JQ9D<^br5WjS=Dj!`~Pg0zko@n%#@TgyLea0J} zX8Pf#Ls4te9SiX*Yk#=%v`xFIpWx!XbR|6#juw}{Efg?Fl%LM`a7Dev@?10TJALEa z1-mJ%>`{FCJH=R6jz2YFQ<)FiW8NLf#No7m-{9|-97X+O$&!J+B$J9iYvEx|mg?;G zD0(V-c?X$JNwSq6mH0M$G_uGg4Ag1JVWt zt-t)%Iy$}xDwiEsyqL&cjPRqQhK*A=YrS7#X)H}!^%HYe<~Bl*2!v?$%YI1tmihfB z$9Iq^XNS_*`(1ssEb$=M$=Ms*(wQ3(>CGQ31>>nah0QAO+mola_75~Y(TSD!7p9hh z@?kBei>?GEL*8>roKPtfb>K5Kd4LzlV-*n4G{iZdx;S6zc_RHMNa*#VTp$VCJR(Gc zY*}3(H>-w2`lL1$n876~Z{&HP$@`^~lOMZ{5|LgTQp#Kog|V5Wt=+Dh{?!PB`S&zRq4w-Veq zJZp>A6nvWLgO;=V+aY$EGrCj3vP{Zn&{DpdG9y#+#d%S~{Yi&8LK@`i2QiFo;k4EX(ZM(Sv@208td?*dNhy}k=f!ztwIzt zZmLufsc)tONZF!++5Qa@y=*==# zvj{k$x%5WM?HNiUwTsHmCVKEPI>$_UCr_t0?Le`9+c#=?VvO9yuf$iC!d5+c2Dd+G zMP2{1WV(Q$lpCs4sqO0Mki1gVK>yQ$&eb*k6W(Crz=^xEx4ju6#u4>jomUmqeS2InJW z7c3s7sPK|iKM9Ga7kOw|_6O^(EoXG55_$dp)nt$fYSgTcRVa5Qi5HeAQ@+hcV>>~n zAZh+2T=3bK$VG&7E5pU+ph|lQWK%m}-f3-Dk-;{dYe+JnZ&|pb0%58z?9pvN+_8Zg zNNtUgUsLkh{FY5ULrb66!-rTTs5D$<1aer8V`47af8DJh@%DtI+AJf=W4-rOjFs~V z#E#Dze~qiM#FkVSd;ILYbU=eFI0L;v<1jBM(rx6ffYbPD#|u6^p{LD#BqXd@75!Ng z^s**w6QFhC~wxzn*ade=T})n0OBahpXg`nN1r1;z{lhU zS;M<1BHVPf429qS%zHf9pOEJ7F@ejKpa481G0 zD}3<~>vQ!q%g6fqDA(VfhOaSonLcr*bxgDE6dFx9isorz2{O$^vv;g`_fWBIjAbRZ z$279qq&QD9MJJr=D2`Vsr(PVl#9X;4iq=jZY+23JPXWbKZ4Vl#X#XQnb>;l7d9!#R z_l7(+VPIG@^TL#;ECB3wUJT^$Tr7M;s;Uk;j#_^bFR&;fq1-ogOValtRE=v41bcCJ ztq0^zPx(%sIY7dvc0XuGhGPx?N9iYM^QXh|3#VqT9E2V%!0kXnN5^GqYN{5Fsn5rK z%m=G7RD6hHTb10S;|VXMaJbX{`C+U6_N9Zhr6VSI&#ITE*Vb;rl-;im zTa$+Lp8Kj)!0>`;MK&4*0*~lBRy`6=y5esTuMD#8`(y)CSUr(cM=Pm%-;)N@0wS58 zRvBt&M2!{z&k1kz#L^;NL8(|FPV<`aN98(tFg>f9B2w{GgLjOq^2LjhFHWy|SJkpq z?TscI|7I*stPjnL@SZU{XAgo$>I-6^RsF{EKqlAf$v@@iE^dj$&?nI^-uj?Ojd%-5 zo^2n9ai!Cd3FAs=fgc4yNKE! zPv12<0+&RskG1I;e450r@9UGJ%>RIpTG+Z2x0DC#vcd{QqV`c>tGifT9m#mEW%(MD zihMMoLx0Ni98LY7TeU;dMQ03qE9$3Nw@Hb`jX&y~Dv_8RI)1+V_j6OK@|g$QkLO?XI<8;&QB$?DmN_M; z6d@+iI+edTvLjoGzeMUAK7M7x;Y*d!x9jX<=y?SIf{v8v80(X}GzFK?|8#Wl<+I^A|xU|iWalq3*GiiMf8 zDC3Oe6D(Z+IhOTkMO;)6H7B?wq9Kt-QO<4ZZX3!IdV9`f-1hq8MH*KaF}WVr($Z2r zVQg?RJ=fEtf+|@rM{a=i$=b0zCCwr9p(2&v-Em%Ax5R|FhLYaG_VL{WsD@92H+p`u zGycfiAjWi@m&&KDB+AP4PvZ&s*qW`QSzpOfgrzxxUciIjgx{cbUPrQSr_~<*V$k%% z0{Ku2uf0TlHyYRO``=T>N(*ftj4Sio@lQS}sk#3)bN?lTDV_5pZ~s}Y7emJ7LW77O znU4$)Bb*j;wOig#b3EPoBB-d~CVH|CXb_iX{qZkT-Pt!7_6ti%&FR<%OxLtrK5cmE zeDbW!`4p-Iv;qbBC)Lp~z|8)86m)irU)+M>V^D)rxc~Uri&!bqa}Z+}dQPs^*W8#S z5y1M+_ZOgs`gXg&qSE8I1ou;4`(MinL&-O5G7aZXqCcU9`yf^l`}c1&qRcstGWM?E zs!3AnRuX+=O)9*1t6EvMJtA92h$4Yin$B8fF6Il-1G4q7WKR8XO)EDavKQqhH!%&5 zbDmQ+;QYYdR&O8VLQ@}``_3@Gbn`l%Dxy(>@ovexHrxv9f8%r>LEeyrlbtr=OPwD}^LoqCZebex zSz?;aEY59h?eLPQ@^;Hbw<617iPMLSCT?UV7PTr&Uf(d7I0Cqa`)OBv1Zcp;w2u^# zwKi&MA1!lRDOX^*y*JT6bLFiMl6Hq)1Utv&+6EAA50rwZ;L!4~Fj|8~OBh2eSRfSO zx6zh2Oc6j-A7=oB5G&y_+OCkZGO7xO4)2IIIZ+J7FS97VplFbd}6FG5l&B@{?xYbqu_D+lx$%A{GB}33_Z3R zY>ysY3+X4#W6gY`6hU{jo%fO2-h0dUEyZGoUxr?u=9Z>d;~DGoP)~*tkwsRPT_;oT&zd#bWFOg=W3VRR1C2cK%M}&1h&-Q6nU8`=s&ZghXSJSxo;6V`X|lM?0~CR zO<;{nt$BZyo@2%|Y9f0Zl*@{cHnx$Vg+!!)3OWjE`RrZV#F*vQ9N=3K+#?;*St{$c z5O1a9`&ytqbDOTfdJUhzY}9i9hO) z9}CN}DseFP_q1_EHGV4YHotsGACp?1QqjOI^DlomuQ3ZKV2@FI_$gUa`xfSN_8CvW zej+9`81uPJ%&t1@27QpCLXw%-m41x_9fL?)>MvrC`mgFQ*{hJtuxAtF_6`|&kI}!g zBrH|v4z_HKw8Zo2UpheLIxu~+AakFYsIsgMeNy&8;O-wM5@7%fScsKHW{4atLmm_U zs&dmTowx8mP(A6^)pKwtpYqQ6o*>M=SW>`t?IQtgH~x-dac>g~43nxD%H77tZ z^J<*DUhU+?znVw>JRs6KiDJ7Fh+C)PFTsiIt^gO9RKa#5Vk8cAiK7EFPrpr%!AF@6 z6r%l7g0oTo4l66GoXK5`U^JGYTA>$q8j#M0jNUR@0(@Y1Ug+OiwEF<;HI|5N-+=)= zL8VUF|2h3N_Qx7W(2Cd^i-EJl75D;;%wQPxCpWV1H4azL@ceX7*|C=bPv_oI+fNR=3qDr(=!kdOUADFxTSCPs6sz z`BJaz5!7=mO{Jo6=Ua!iWapP0{U?BNQC-r4;?8)4k9!TX;sw`@!Mw2{Nt0;Y0)^Lf z&E~jQ54eeLvPt)G8>$H!<~&Vw(%twukV8`cqPE>WimU@CAD3(@T%{Pu*o8@t{vAp) zb2hL3>tk*><5EOga@n7J-+m$vT6T*$R5ZzgQd6=Bd_a{hJE_#B1^nfK;UM4hC*T}( z1qHOiZ!!17=Z^KTP4Yp6Qzfu;wB-qWmLE9Dc0r1l)5qjO5PaPO2xW_%+2+W&I8H=- zs05}QPVmz{<2(TMa`sh8_rrBn6q4sU4;lM0h!wwd=cM%nk&mIHqg%kJDYG)YK|ea$ z*kYyM5O2W_;{!CFEsQL(fIkG%FF)Vuk!isi1#@RRcm_0|g!N}B=i$*?hI)S)(L z%v*$2W{8zQstC{)#&c1O9<2P~T#Q}q>Ii=O9yPqw(oe(sPyo;f6Xgs*3&*!f zJ)MOB)lk43no8P@8KkzfNqwjk-BNaevtU<@j#{Lk*mYXx)y>!3QiNQL(_Zl9s=mxj zNpZ?*Wc{@Lt$XO>jDuBXYJ;=0b8VG+fyVE|9f#B7ekAY0o)kBX30Z zkD8Rj?rteKRZ1Wb*(YYMfnuD^lkVf^#p(fVKf!%G8=%j4S5{FGo(en=5DT~(BzLl7 z?(Jb>+4eIYIv6UiVV){Zv+swV%0ruzhGp91uG1Dj`NlD+bSblpC!F@+H`|I+qWNB% z=!9Di1fQv7Dxh0YOTXTez>>S= zLZVr0QPJ7_Evx)#I~O~vMvick_XSh(3Qq%@tNO6iQ=03X6Fvdor}hYZ{XHbiqQu4{ zQwGerh(ymHyb_ljWg5cMfMg~sq9(9Mv=-C>-eeE4v8#fHp8@Z45jxoiNHtUT(Ysp( z4UH_ZUERAt#PMc366o#;Uf3ZD)~>h$H58W9M_w%+>{kSNB~*qhOi_U8VEOS>(Z#WuVW%l(T9Fh zHM}DB;xzB-C*1?iDSRJ}TaRBe8!w>mZeu0xG;d+uY=Pm@?HG`7=euvlS86-lLT~&m zGa;#%(pb;r*x40UI=`t{xxJWFxA&@3Fl*b^H9glLkt+A|ZRUj>Qf^_CogZ35_+w5D zwioCIJPsxijM&$2o)gs0;P5LbEEBH;hNJkmNskn?u<#x&18C&3=*HSk6t%cQRg4EA<;GBgnubNU>(|1yW#@eJC;cMYSou zESWxUo45{y+I4T!19^X9kGcK=i%P7M>b${YAfgZ?xcf?L;)F$-iWHuAGaPFjv}$6( zK02()l$*@JZm%J|xR+0-8DwG0gC3wfSsnM7W`*@QmYP^ql_}{&>rs%BbgkvIrv0HMKifWd(VTUXzj2}} zqlxoCWs?yf#{lnr9ebT2TIX?W*9mQH-P9kiYqhJ+pX}fAWc~$llakTOhy9NWhN}kk zDoHmML{_SAG;M9Kr0fsMTt8TTN&FGt<*#e%G771#k1aJ;-j>h|cj>sUJ$X^aYwPBA z#kgc%x#S>?Gc@zHGIM!8Hde zj~44Pg;Ly0+F9B?=!Gbl+!z?N5xyn&ah_678R+BWYb;cXzJ}}F+?9J1=E6M$`QX*3 z@HUDbpG8Aq9gkKS7JkLen=x(lIw!_IvPCCp>+bFwoAJO|XP+}t$camc<_W3LUhM!? zku@I44$Q1jI%t=1hw+tJ z+11(W2Qe`+$+p5NsXiH>J~rTvHsC@mgW@WEe@;@I32 z0dMu|bRHbUb6PCKa{F--y@UO(Yo?Wy=6xjc?K*z?bD#S-BEwybeIFXGcd{<#6r}9a zuJIyePM9&1!XIr?3fROV0sM59Z3Wpu8r&<2)3Yl8>^v`Km6Vtyc3ANS?uY-bkO*Kr z@eM3sa7A25-H>M~_KwLx!-%HX+XJ`#;y|sU87jT$a-c&BIgIr}Sz;yo;vucIKwLOA zqm4eWGxOuu%x%!_da*9JsL7(fGr!d#vRZAe6{wWO$+e(G;5Gll5P#VVVv(!NgwcKt zC+k4N!N-qJ?CZd_y*(w++NR|BB;+(+n1z>f!Q<84ZI}=3aCZ&6_;z|TNt~70ZgJ$` zj*u^X(+-&&wc!+@$|mNd2T4W4P1Qnur&rcSV=2Aj25p z-fS;w^d`@J%S3Pbq58FnM|w_gqGOCy@XZroaUnE+&@Uf@&fhu}O24mKRjcfIWc>u)r!O?QnJg z8f=Fa`E>no+R@`c6kT7L6!TvhzhCire+Y%=iA+~5#lKJJa<{%z3lL^-XK!U2um{n~ zgd2#liCKe87kpU6UasHh3U1drr5;X0$%Ax%k9Yzm9yu>#Oc^Bk4}hz7*YlTvLv#G~ zDSXw&hV=UHjLI4U7Do171O__KowtAJ*oZ^wRds((hz4k{LJ|JRUz1;$p1d zHADRJKw|6mXz#wX*0|J!1P&a_{VipIx4Z7_B}q9@1a6?d51D5o~jxMH25~D7#0LNB4l0c^5N~J&^|n5@5=!YTUY^&&Vq2 znlcYU`N(QR^3Vmy>nFsMTSgZqKSMJ2=^@ZTtUamy5A|@1gd;pG#IwLDT}OkU#^N}C zK^Yk{sKg7WkrgP1dO^-R)O1GV{ga+UCAsSiHS~Wt5^g4@|D2ndKPPw-6FxR6&ctFu zr)*fTuo}o`Nj{nJ{@~;AyTT3$sT}8=YWmY0h@~-vIfv#CPQ&~`0Rs{Zbx>Q&bX*#4 z(7Quxpp(fjWMG!soXAk9qm;vRffV)!cQtjY&in?KNXc=^r~GHEdrOSfm+vI% zh|Pk|!t(b1dp1|b`TC*Rf9PI1i(>)@%)2JEz}+huMwHD-e7d+#b0tXd2w2?$fCo#4 zF`*U+zoRnu=FWCEb!)JiK4i57aI50o$9+KhjtFqu32{#ZpYW)eI!VtsHvEkm{TlVr zQ`)A<{O>6#6XlVa)_)5Sko`bn$f+rgT>|tnR5`VtxDER+J1*N%R-a25r$ks7xAZzd z4Rzo#NiGSIX@qASG2 zc7Y0Y8CV^Hu`+9qh#(lt6iK_NH@!NCZQkLl8x$EBm=U3#ZFF@bTch1g z9!a)4PJ^yVa*L)2zfD=j@%5`m4cs;xS6&sSsV=4%>yuKuN-F&aI`)^v5$g18Y&vxc zhMR7?2Ys#p1LWxC3u<2&3}0e4UN7xtY;4}#1dx8MFEfw|AQJh{bx4tb8RI%{hdji; zy^gY;5rA2G-O7aU?3_auHPjVJOIT;coc@a59Q7WOh3Xma3dVqLYB!hzRK;)KzP(ro zT&K7D3JnhK=2S#2XX13h^owg-cf0f@%(}vwUR=I;SW3syF@KGdPG?UxkY79UhWNjW zfk4A|s;B{#u1qkx|3O~V@BA#im%NPGBJUgBq|zzQH{Lhb_?A^QZ2O=ANP2fqZPDK? zMvNY|A#M(Oey-~3V#c!!YnQW?DRAe z{hhXWVEm*Mbt{&ami#0`#=H0=#)R(inGi1TJc_jbgM27QBC z0r|`2N%ptgLy51A}Rtf64YfQKh=J%`*pvq}Yc#f37QBAzQ^Mmly3AbLzgaV88gD zw|Q9PKxcLmfO)n25^_>@E~f7g;33T?0zN`Sxiw;Y^Ku@AZ+exUu$PIqM~AkpUrnxY z-vPcSt=F}lB<+{Ed?DhxZtVwQPE2ZO(5Tm_0f9}Vs|fp)Cclw1QLni+e8|w?I<4dC z`#pDF#0kTC`<7~AEp!ID!2rALsna;T%^i~;Ee6$LFXb7cfPxT)zgH~ve8v5uU8X`| zRN#0~)yk6x$$-wH{KKBl7t#ir7&+vNkZ^!OCB+pK6$bv7azN0}vL@6g{{Phuxbc5$ z2iz|#blv`Gn5bZxd)@fG+sA4}$x{JqRa2i3{l|CWL(94)=x2P)4qkDg)V4Twfmm$b zw5EmzF=!1ydal9HZgUL=!!Fl#P_#QUhZ52`XxsHbGXIk@0Zy`)V51I-M~*B8DL|fK4!1is2mTga&^S`01i}pc3DRYuK!?cH2Aq%ynaEWv z(*~#onFuDCsRZ=LOgJt}GJ64V;cX7Ho2faJ!&Hy|I{5LuoduExX&;f<)EVW^w)IQK zv3Lg>w&QVD5xv)Y3meYAIp6zL;bp*9ztTrywU@8{-aF^#xp_wN`|QwG#T|kCvVq1+ z?LJE)v$rOGnT;IZetT=*fw%N8WJ3LKAHV8NHz$36)7Zi97vFAvt8zO=_I94MiLZ3T zMbXI%aU+)7Z&QGxe00;gyDi5b<4u1q9Rv35)ews%sltYOh1J6!z7F%RgTI~Ufj>7k z|0ygJTRpseHLFR!ZtJFDdJqbr`69P36F|ANen9JBjOe6`)v2c9sm=CaM% zXH_~USQYFL^s9b#_je2tbgA>Za-Kuu4fUGSn-a@N(ArCZ6I{;GWOiFQ_J~?L2aVJB`qYSHpO<^bQPO#U2SR{m}cMc$9P0=OFpYE2k>HOaupm<~dj z#r}>A;ANVLIUgy)?^g>Pc5JneOKKyH*%|N&!@%;ySnMj#e+(kH?amcftQ{L)%71yf z=SbAT=BAqtuh%z~?kJ@PYaY7N!%c=DFOurCeP%}-b5n$fyx%Yq{J>}JGt;}!F!`Y( z!}f43^%P4|R!0X>*J~^0G(1oWS+m8ng>P$d^Arj(3B*N~E9Dsp(271-kxJ=WXF?#QD0=Fsi*6`APYBR39vJUYvQv8XI*coz1fABv;m zpT|u(#~cAIha1H#js;GGQMK{Zj;3oPH83CeP@BERU+jG!6pOK7)Z!cK$5}3S895Fv z$b4Npwa9rIlyriZe)>&222FEOn1&qS4{Ae}b|xk=%=O=qla_*!0PZ`Fyp8c#9GKyJ z;`k-|Yb|_jghE%^z5My*#jin{Z}@9ZOM5udzdrwpg4pP+BfeXQLT)qr*=)*TtZbkZ z=E(VF!-gR087&3Q29FHyk}&)_*EGfif&y=~Z*wlyf>aD%1G6gkY-pI|H}h+Tc82z? z!;5->;B<=~nd>3v-^8B9&Hor?f3_ymIS_1az0#6~e|WLr!mZ!f8p}Z?CPRZSzuBim z^k(HEPSP0~+JaGOS%W+UIG^pH4$KrF+pAmrQLyh&;pCC1)6ErMTdH% z-4`WYW3OOc*T>~hw3q4nbnH~ow`)1aA<>>5K`dR9oVkLhjxLHTHk8$e zmuHgp?D+GYCpYw35H_V3*p$nM+bWJTj`vF@ti5IrdY0Xvf9yRL`%?v%@MThLb1>_Z zbdC#>wHPi9?T_oFeNfc;EO_M&eEd*pCu_Y`Bi_2KaTbjHTapnd?!Hr+1u3}GJX%R-U7#|)(!GP^E z9r?!gow;K}%B?G$bqgVv*$8*?^ko<4sK|#>u`6n<_9;;svTpnYe$TJJcM2`NcL|@1 zVh~NyJ=gp2&HxD|0JwS6c77E;rSmH}{&NRP9&^oV&k$Ig9UPOB%a^ zqD)@-PMFNS*brMpjUvDsrGo)g8=Oc5fVl6f3krZfNroGUmZF0XIZW@<__*XpS#wM_ zJ7Hrp``c+l8S?s}Erj_J9D1;!H#Un?6Cd?crL{I1T&eCl>bQ4Epix$%&N8B?73O>` zkxV?if`VG$Ey>8Dg^Ylk0gr%t?PTXD>6ov2lBld?6WoukxInGY;J6pzevE-_69CsX zKBNb>x*EH0N>hK%o)>)z}mS2FHc~vz{qAY6L_5{}^7ycYMbXR?aZ)7);q;mts$8 z`^{_Ls{b0vn{jmauH*0mxK;g?Du55hZUQZF8U#&|IHkd%i=#o5j@uypjR%|R-6 zR>4cZ>Esun#G}C zkYL#Ht716bnTolpHUL{w5*T?Wqh_8P?&(ppa&>g-^*{5z?=$_`?4kAZ%;5-7l*ASB z73Q~aTAXeTg*IKB$8Q zvxCbnBNuN~ETmXQRqe{WQsjhxR;ClRs(6B%uUTp2idF4bSq^v%{&5Jp6GdR% z^+;K*fvWp@dnKBC&`6rInOyjW*p@Bs0+F1McQJF!6=}3>QHOQN~_BvTFGB35xDj z8J<^n(DqS(wS~H3A|KX6_-FyHnHV_Tm5BjF0eMa$&tG^$%{#e%?}Q)!m>)`G;ir9*!Nf)BvI@u;sHZ4Yt_W&A_EX~m`UA~M z7J0!B{V2mB>PyO9Wyn7Sov&6|Jb3MC>*=L{5fQ4^GxYdx0YKohPq+SZPzHR@^7mii zrpp%_F5Y^woNuXrfqiM;e%mxkrb$|axpCs*)(BFY_+An&0mv^@HCnIhu=aR9F40id zXe#QjQ$evziMmb2qM!7eM9|}~J}B?^2#xPyZ65_}K_X}(B3)GEGAscu3@-0;PX%*w z1D4h|{kCgr$nV?9f83?{&okPzpP+omdqC9r&CkO3E}~r2yh4zeOsW~<x*>XZjRb7zf@uk{KPv<)A@KIpM4YqQb44w_)E) zp3T>pUr67iw4nsey1bmp3#Wmvxr&c{G%n@Es-nooBgj>q)CnwKT^*)h4jvSj@#S-P zQFZZ5_f{#B#3l~{KOnEhVDYh}C}WiCagV~&!yv%a+L2w|xgGO#beWIJ^eg7rR%+c< zIt}8Wx!-<#YmC8yJ>=}{Y$C+rv(KiXa4LYHxp(hgSZ+)Rr(jld$vtfV5*3XH5esGP zm4BdE%|GT`ph^uB=(5N|FTKSj+};{Ib86qa{A(siR0SuM!kEf9lE+Uav|Cl4qqYBB|6H|SnR&Q2$(#5P(V>a7S4^EukE)IFq+}zx9s~PBg zBqBH*R5>W*fKoZKUQ7jJHZ&g@h0@-)-+A$8KN)O0qkCLafoXsTtH-0dY2eNmL}`VT z#_{vhi`avINfF{vLMS?Ie8RWsBpp?{VC`HYgDKu!y{M3(ujplxNA9Zf^>s0Po_}cc z6z;QGJO4{1TK$R+0p0Fs1E%z9dh2~zIc^m~m^eLeKPH=&eC; zp~$ryp+8wA9JSIW4Kj|Y=z~Ir2aq?Q;ok-_LSo=6od{X{C%Y_UpbmPz1u)tJ-j5() zqGEx3JE(aKuX5l}!oM(rp6@NWU~y}WjsczrN~YX1?E@ow)x*g_4l|zI=ZAmrs!bD{ zxG=eTG^|OSuPZ>4jw!L1nk5|JMVWL_fN^371JzKiz=6!OivDWKi_;*lE<_zzIk#3_6OqIXtQIxN}wuVis4TH zyBSF9Z^El9ayui~(u>knfM*s?P6Gr7bky^~ARG&_!|6@XqVgEN|9)|5fSp4MbS2WE z{BD6c&?q#6k-520(W{-Tgxrlt2VbhC4F|@n!Ly_`d}8_9YnJpTwWH4dk4~|S4-{78 zqUc)T5XO`s<%2)e*pc`ft-u(oCpreT(#pa6E7*1wb9p`vxVCYL^u;|G5$bL7wUXs39vVQm8NZJcV5I%(!M zS|`OT`(EN;xs`3ljt@9KVXxfmW|Sb40>Hk1rwt~1&k0Fy{ruJ1+3T-)pY1-O+Fy6} zQ*X+^L@G;i6p0e~@YG##9y|jz^wt6ogPbXO#34m1x8FwpsI!qG$_61!HH%6HE?pRZ zcP~sHq&ct|eX-(ATYFB3qjxc+}A?S0giSaNr<;Kh&IG z7D$Op-i(j$^ttm?+G1c!e^KnfkAd*;aHkr5yJoF$GA(=;`kcB`=u#f|hj!Vm(XM}w zf6LTxE2M`+oh3XSV>?uZ)Rxgp5aHQPfvh2G!>3L4hr~XFisKbNS+3K^lYbh=?{2cQ{|?t?!Nf1;6E_{FzNP3LGzt$)%U(@6&=qxYc83+i zx)cB+hbR~jZ+QT-w$Vo&{~+YM+rUW(1GsfuyxewR9OpN~pn#W)OL^ks17TXT|0Kfj zQ$>Q&`Z}pOQXJ`?#+t3;#g*%u^1cJ3@%Yqvov|CF#@^Qx`_=uAHYZ&i6pf=U%vxu1 zFf(SlWR{hcu$THwi%bi?U%ngzbdva9BEGGz1iC=3W+)s@e9#{J0|P?KMH!^_ItBWV z4P-ceWaOuBv+lTNj=Hi8g&6sQwf`L1(Q!M(waK$el>CkiKJrlLbBL3m4JS~M?Rm8R zx2N!BsfRD%P@Lp#7Ryt@T%#IDSVNlS-;w#{pq7*P+zAM_x$W>ewDgfeu8iNWyFtr< z1|Nk(+Jv+v2Q)h)+);-e|IKsy^D{Dl{|3@?04rl1vgYd>0(b!IyKq`(;MJTqFml=r z&j@`ODw>O=Sx=t5|9I^jLC@w=-@TOQIi>OQ&j{rr96rT?J-?lwJ-eb_$02NZai3ee zR_g0(pckY*WKXlxD!@faX(N4ZKjQrXq;^G6jeFR$*6^QD`)6*U3OI%YY41SZ{8~czZvoDNtue4lJ@BXMly(%YI@g-Dhmu-v|v?!_V7kbNSMUo`HP`w)&((b z#3h3P0~dgFQ(tkt9u`ql6pYC*94d8u6tcm<`T_`5U+xFY$f`5ry(Wz`V z6EzMOJpR=diE;^m^Z@s+4GmJZO-j1;ok2u(m=Tnp?c_i;Q6NZTh^`qzY$gI8yob@L z%+wJohAz;+yd~>PoN(~Ev2y|%%{2(84l=I=u!xBv3>3)h9w1Y#Z((nOMH;t6)k&^tffE{}srT&RLBaxOH$u^wwDrwF= zjxXbmcLhzj`rE$=fqi*XIK1s_ykvS~>7|727C5IpELKtg;!Xi7b*gWxOe8~sLhCv5 zXiHLB8b2JQwF;38=mbK8w*10EqHip~2zU|`6T3i3AYcy*2=x|;Odbnf^%MFL$|ga@ z|3x-iKybB=@msyvZ#cXE*5jqSnWYU%DQHhhupCNwKl<^w5nLpf)eZ7DLa(f9FRT|C^X^Jl=dMZbreOj?Sxg{c-K>O8Y*-Ed1AP2~!)iFi$CT+GkE+5p}X-0`THC$Dqr zz?ZYFy$;mUwt3?efrEthlqHt9>e(Q^Y)@i}iq^m6*WTHyIqhj}mOXh!*EQu5XP{~d z=YQNAmR43;fhte{P@=t@oMP~dli^ad!Be9EDhV$zgVKQ%%3MiMP_R2_VzY6xPeGf9 z+~t9N>Ti@W3K^KbSGup@SF^vB^826aKJg4pJh1L2DL&HdVv}uBj@0ZbR|F6rIut&n z5l#JAg+ReX9;|jC_Yir!WQJxvjQ|CSY7DqWBh2;f-v`9^I=yjyf(NBH{Zs%{*{uAY z@eKKi)UH@_gDF!|zJEggP;&O|1A~S|j1k_0ULs{N?CB_uy7&CA)n|^f96h4)Ht3j! zY;+O$lxpZ~+OY7EZ`b;d-%@q(L;n2cc1cZAmSo~Y9TQPs{u41%{_=T!p~L$VJe#yq z?m$9CS%W9>Og_$uF5kS&KD=S2*);VQGL6`^YnQJdhhu%K8})%KRZWeAjnc6e$U|}5 z>|N5LVO_?Ly4@LTW>m)$!5wy+A&a8l=;LKKG*+XLg*AYrTzZJ9rXu@Mxr`j72HerO zR}=$2QHC(&$%pWdw2`2BR4dwK0G%RKtm~ORw)qtF6vr>B_+^iJc)3lp;*%@q1X$lc z-yBv{^a2FGGM|rHLyO6hqtyC&b!IliYkl}rVj*30S6I&HC#kWNlOFe#zPFsDW%?BJ z@eFIcTeU>UsQdP zyk`%~{{7B2LvRWrfSDU8&l#8A<9!zT9K^;`F1r5}OWO!HwqK6UG| zH)nxT{cGVsM-#9SP(X>_z#&1eHlkb{nIs`iz;gdNXBIjV@EZM8t=QP4FWmk4`I!+o zwjSuO52;5C_KziIJPvPSTjs!7FLzQ{R{Rvdn4;j6qB+JWjm?KH_tkKL*s!&xG2?T6 z>zc`J+YAlwg+b;kWOwb&24WL7AhJRXT<9?&gM*Pa z9LbHHVtH1Hw;%x_+7bYD(~3kD+B%#o0s$B5%4t-nm-qp<&5xOhg|=Dxxcj(@%b)iW z_*b3MR|P~~h=fg-2%jJN!CN|HW}-tQel?n*wAUuYNu+a%EX2%-Lp^^VGw%R zV^eJ)!s-P5ZT#kOfC61^URs7pP;n*GM{(76{>Z=1mzrc8?Xc`bBrVL?R zYA*Flj+GR+m)e(I!Dz?w@ZfVxkNRRZqJq;#`uyVxBv#iH8bZDMv#8kk5Z!dqR45^I z0nIlzE*KU0!@QBNptWi;uJwR$d~@p3zVv#v91jztl_$lbb~n7{)WiG1cY_;^rBEB? z(1~+gFxWOMSO=sCnea6_faYqWGhFL27rx$uQGcs ztV1wtX21OT9?Z;7r~E<8eug0gBmwJ36LeD=#3e^+qCnoZmC!5Yf{I*3$%h(D6h?(6Yr^LSxB~9iY`PDi6+Er(k-H2p&s+d> zOYu)*aE!f7VWWGu0w<0mcZ& zGpWe5X%o>l#Adv=@`n6i-L|&hhKV=0_#Lxfbr>~wMP*4HI_V}@;oOn*N~HPS@05*h zsx^o@_%|L(PfWzT%ub9xum#T2ND1Z%oW>DZ@$f0+mezV4QZv^>`}U14&VKn~D|s_2 zYIaFAR}Pv>#{I0YFFaA{`0;zPgj0)(jJM?b0w0s%lRczci;{fY8JltHDMy-<-v8eB zublwxY%6aKqO#f@Cv+xwU6*KfPL9tvmkjW)FW;DSs{1IQeGisblRYO9{Nph&0XyU1 z+8DMMex$7QY~NYo=X>rXfPWr5XZ2ofm=pjRYq;3`+X%Z@ zT7WR*+HGsE8~ax-sj4Z&Kzkve%H$RiJ^OQ+{C}op1en(qxF~OBof+qUOSbSp46{crIu6>P_ro!A92+AvLZYx|-y z#u4d24=6D z_BP;9m?(vMS-N^vDiajK$1EVGZ@Z_^E z>ywCXfMm@Ph-}z$M{O6l=Q<7;c1E#DAK-B~FrD5Q(D_XMlIB{u#E-XEy6)PW*0CLC zV)1~jqfETM?|-)Z`KTM)Os?h#*~BA4L(wm%d}^1JiF5~Q4Z`<1ovlEHg;jIz@ZQ5n7aq&+cn(5s5j6dx`G?u8}9UmEe z)mF`AqeLd}2BDinTM4Egk*!*DRxEBK!5Bi05y%T=Pzme`GlL;Gl!J!5Z@p?bgT&dl zY!U9rWeZB7G-!pl!Th*DC2SiR`rs{zJev9797hHc>h}iWBiy;dvn}Lta)X4!l%cjk zN73pg{o<5_-Wj5R`}y>@RU7&YFS{L{XhH8#iYJlV{F}MiO zSfsG=*C-M35S`#0O3KI(41eE}Zk=>Bn6dS70rTj0?|_Z@$LO^)4*xUhbRVq7gAeCj z4=~I+(*|>WCz~5hcaWUu(U5OZ0;Ax53qoX$5@;$9jV8X`q;)t6X83sT6E+bonnaU3 zO*k&nq&F*<&UX~QuzH9VokNohSLn?2ajXR#Ne+t zAKsZy-V#~_YBA%pD+d=MxQIXuC*md&py9zN|Lf`@dTFp`1i$gifGzpP{PQ<+$NJtc zO-jGeNm91-tF^KLzLbiy^k`7rcdcv9nOft-v|cDNIY#zF$44Yz*+#e_jB*3E_rS; zh6jJ*Um3?@W)WwTHk9)qc`T&w=N@&E=gAgmj5W9RdL$q4EbidKaJL%0G$>drF+Qrq z7y;YkjGYEizTEcZ7HY3ccNzIG5hVUQ#1}kz#D5T&g)6!)8>O|Aa4fYa4L2M8*x|s4 z+j*_r_isF``SasCxJEs~&F zlmZ&>IslHu6Y52+sv}!QGyBd1ve^{~u-;XaS6(#ODFEWXqro0wFLThqP-o`cWOdW4 z_a579p`Idekg%|TdW6$Nmm1VFc}Yp%GiFIpxVBDOc_dsRsm)C-fvh>hNyq%IioeDA zadZVR6zl0If=WvQXya&l(?EtSx7YRhX-kVTDvpP>^m_U2f~g!*=2FGSmb~KLR^4nG@BL>6*bUTQ&T9uSqELeQVBeX z_PpwwADI9xD&eLC&wsUB6`=_bX-`V27m z1rWgVHxj78cIxQt<-HbMmz0p?%!pzx_3n@Ii=TpKpvZ7ZTndPnG&o$A6cJFX`OGER z*0&80g}{kx>#XV5sI_XdcO!QKM$8H<()Gi_!r0TK+&|&IgL8&OrQ5}tJ9V|t)Wn*C zKmy#w_aM+#sFR z7mjGN7(Arac8F>DQXteX&iP%`V+hSbeQ!Ej2f)@pbIYa<}@Utb!Yx)1O1jSCPL(k+@;;9LbRVBqGmo ze)W0zZ@tG2WU}36@|!`aW(9rWXjrf4v=}8Q|CQ$en)YP5zb%AluWzsEBb)$!fbwim z*4W3%$%!AsPy{kJ$lWKaH>-!EG;z>-5UY5_y@ymD=P(HRw_>X(Wq!c5(x zsu)f+NO#8;K*EySsv`|4NWS-ev+E zz2HF~Rh0ILzTuOT|5)Nm?_U$J)y4RUV;wvAjy++3?$2@KENF%0mWU^`Zlyv{Na|p3 zjB_UDa$WqhSzGZE&Q+ZwCF28^42!w(mcKbh;xX7b>Kkzkeo8DW-YV(x`ez=w(^kHw z*5dNYLqE)IuACrnDJqJ5)dV))BRKml_ii+^f%!XH_JelE=r@p1QD!nTeNdq1vAAg? z6#5IcLz|mQ9YjI-VU73uDDwMZ%INP0_u0}MUR?MS_hfV&N|JX8FT=rz<9KjRqX6Pq zFeToH+?AsOw6|vjR#u8#Ad}=onO$Vj1pwLn#e=+GNKVP*KmL&|m7u#@=eE%?mCnP$ zt%olUbFsYIilR;=S@qEo9(6zaB?@(sN*tVFH zZU`2zjyR1!Cr=dZ;h$M_6Ff@P933bF$X@~14(h#;0I$;QkU8)-q30$m{zW9g50Sgx z!_Y?|BwnQdGE4?rxRhAEOceZ!PT^-Imq!O?;cK37{Re%P2>f)`YdCu6jkxa5^C2lQ zG5TL@Cu=|}Q_td(R-HAAP>YK(#p|`+8tBc8_Q*}@L~&XCISF95{a`f_F{1(J40$KI zEGW07SP6Q|ekk;)hd=(Gp(_ca-#uD-=PZ6dvEqHy?)WD}rXw?-yU$FR(6ynJT|E@K z>l%)_FGgix+}czE-4*AU%ne|bp@9zN;yx~PyfPVj$OIlS9$NOUDv>=%dQ@xgcXj8a zu}!t)L$pptAnVZOqHc65dhaYNp?%%{?u7@h-;rslr)~Ah%LpW^Crl~l!Wz{tL23&lz(ZSgR_Tt6= zd#-~yvDc_;G}$R)w1Pzwh^MYL(_@o7osUa}w#xQ6U){u%sPd@$vc2VAYJ8V}X`@Rv zMb<_gl+$e=mgPnRZRrWGsOSK7Yx*63F=Vin($l8p1Ux$jj|zy%rOlqVs7a{}IL$+_ zI>|d6cQdl+uBs}H!=?G_qmMxq6b=tn7>6>=38l-0XCm5C@iePM5Y{UN^b$9=ySl#bJ0#o zNN$mjU8C!hR>&?fXb=N!VJ(<0P>Hx;V2`dom-(+Fcw}?`BD2Zp1Eou?4~^dMZu5L- zj2)WkF`Ypo;{z5wCD!5d;QWp(I54 zTNj{y`>Q;27&0Zyfr!nJ3xod@x8XV~r^l~O!oYtYkVwXROLHurc{SQ)L1(cAOxT5h zrh|Fsx=bdyMz2BV&*DuyXK5PC$-k9#B#@Z@U%mU(AQFgSws^^vB=_u6nSw!#K49!( zZ`bHfuQ}dS+k^MJn-l0~?3*)WNbo+h7ImND;i!z;`PBA($AW_e3jhh;;40r zvSl^ud&S&H*u@|P+!9;@Xv6D59g55h(!r*7_V;g-c>TY~Dp6p=m3eko`<_Rpl}?Zp zkQaJw+JJO96(0n}Kc*WG&xQnW=n64_UQrQNN7atuq2*X`jDE+w_fsupbQYx|SUF5u zvh&;xgP&cV8S>y%J0=ICuX`?;B7OcE2z*7IeASft^Z6H_4-#UrZ$SqrMwXQzU4+{0 z%y8ZD`Bv4uQF52Gw3%G?U^R~Jn0HV`fb&*l2_~tpqmu;X7x4@6`gIyJ@IN=7o>t{Z z`)`@s{{_2pV|Z#KeSD*YiY(uM=nw|A>mQ$cuR8cn;|egU6+Z2EN`%(*tua9G`W%h3 ziA`tnzT6VM6+BFOMVLiq1_!i0eZbuDY|>E2?R!Lph9STcN7)+z6lgz{>r$ceOswoO zUazg!#Q+&UsM(@KW1I>Ub(qurEZ2v&dXEo0uH9{hTiz`_Ws zfXh;av7U&yxVS5;&WhFZE0S$f^CY<3RwPhHWoIto3l%XGP} zCN+;&=UN{~{92Ty9uRyRAy5IbqIgs0Z&ngmvyJKEIFgk>Q%w9xA~>fFq&NL@cOT%+ zm;ihNCV_!;e%`(O)9!(*9D%8K{xio+9X}HCFMN{j!0gN)5Dj=+xlcG?8YYA3!fB9W zCsW=vT?&kIShJZ=& z=MXSyoRzR;HRBxu;Hv{bP9R=kF$4Oc;0s@N!u`{UdPc$vh8>PB5Tm%O6#)b4xb*Am zZtC=!s@Ds`^uuv6kj-O;@{I*M_f4ZD+Q@J5))%&U) zJ9e~P%KG_F7!}4HJ{z3`!y!LFTaXepITAs5R@cw(DN8DcTT*OhmFD{A;7@k`9e|g68FZ7BmRUfa>VsUt;$Pp44D5ks9^;0_PW1o{ z3^kOooQ1WM1V?row0H7t>VqZV6i)X=nx%yTr?Y)(7-`h+mDoTFZA!(E?)EF%dMLJu zQ!q05Qhukd-nECQZMPQ^BRDAYj>9~KQZ|e`BBjP9zum#b9 zaQb76dVs(0t>3Alp2FfNk`3tg+CRP4&-UhsN4|C=CmxXfn1otG>rOCCm5GPM!*3R3Sc~WYj+FxL zvH?%@jc>`9_8>V@`YpR>&uzg_i!*1=Bnp)~%{&*%E1C9j z{5(u4kkS6M&s?@^b~QvwYK&s=wx##P&4}c&+F^qd8K3ug^-c>COP=-4=gx?qO$`UW z)+hk0pH5d#evlC$B>De%ch8NJ(qm%tH7{NfJ61NjG=`r1OEPSMvo)}_&L0DUXo*d| zHM>;HG66`!#jQDw+IKBZ5gmynb084VP> zFR=C+!9Mr~#5#^`VZV=^v9;xbIV&-3to>H9)7s}}-aSF^{uNe$TrgfXGzcL#F)1k& z=GBB__=|fd_UG@N6}mkHA)$yD+UZ%u)}$;8Sbq#|^4Nvr7>rI3tDueIUM7-2OwWze zCL9{S8P{j&3~4E>zrNqODSr7}zzt~94##pR90CN^(}@uTfW~WEiMA$tYh#zaVU<1w zD&?_|^}y1BRS*jK@%hP%oih6aS63FGmHDdp_Imz2Rk**y=WK z@_W4`8zLTV!t{ahPB!U1FhtV?;k;h}H?w?_^b#4Et}V@u^k@s*hb8kB@}E;MPDj;l zygNt8>P5-MVL&evvm1o?`8Dv^3)#nnJ$NHUjm)GkUO^`fM(A5JJIcTQjFGTE>*MfLwV()V4{QA6Qu?rgdKedw?EyF9iW!07^S9`Di)tSs z=(>hNOVkcJlNf2fuY!b7(F3$PFEA%hesnd3VeHP7M z>%MU}2f5bcvLn!|rr!p*<5kxW%F5k^((Kku>1*sJyDqxIh30mh{q)!+L&`JTJT5l& zHQeV-m;^XIduGEVXhONkCWFN*oO_>33+qnw^Tj6iiB_eU#?V{G&k~QN)lwiz!h!1-4&y|be{_TFaEtxP zEOE!?J(4{`Jw!F+H!;|DA{d6Y-7DZc8iCkVdwKv7`?ahimi0L9mnE?bT+B~TmY%t{ z1v%S35wRn}LveRi@|G3GLS9a~E__4JhnB@UP-SpI$2cL`({Epv8ge#0IoFn+u&GgC#G^ui%I#wZT3mj{Zg?=EO+Au@ zeB#xV&kw^Z%<&#RKi+MIdA@+oUV5xd88`GI4F4a(zB``k|9!tjkqU)m6cL?>RQAZ; z3g_5+Mn?9gM1<^_nSG8;$Zp9h%FNEnp4szvzfPaeF;BJ zC1>}J!enfm7As&gagK)v-wsD$eW~@pS z*vKwddbkr-mdlyxv}J>fG9~YQ(Z_GJlYod&B3j$<_>$@#{%#^TK!Htp^7MJM>S>QX zqXO3cYNuP!$zJ+NMMqa)lMbvE`gw$4df#ge+JYrUjl@hhZs07UfHdHYh^F-NZjq8r zbvH5Ayst@V*5KWC&?&Ch^j?E5X!0we!Gn$F-bsH88&yxr$RXL z%sqYJ#6o)I3NCv+I5@aw+_{qj9B+kdw8G`Y7Ds4>JvA_->?9v;D6l4$B8lJ6*VVhK z=|R&N#kQ}0Zc5`#4`Iz9g;df%E|Dz-cAOeH!`Fyjd`!!Jia?T2fv$x^DUx&IG)fqQ z-rT}ytw^0vwFl7d;VUdO zH(FzAcFjZmS|lM_RfI)wVu1cdnQ3q1khN$+!!UU1wCG{s^4g?;@Xh$&!ovum%e3)G zMJ1HUb|{ePvNHP^ZTqRX7m~In|2Qfg6j%)v5aBgb0b-qlyR^SO5r#~P<}~_p1`&u@ z23;n?K-b{{K$-RQH^`JDisyd2)vo_ll4yEUE_~B$O(JK&b*Xn(@S%bPfl}z;*HF4( z$X<+pq=~ZL23=cno*afis616h$r$;GGnTwOG`cavW}jCB=)d|0EH#<8jYwwpEB8jL zB7I-7DZO0naMZ}RAmlizSE+M%!bHfSXlVHz&%hHP*~ z);~05q2C?X4>LoP^q&}v=vw5~THAtGW3 zT;nRgia(nu&Oy2V6@pPnoSr)ousuE?feh;BZFda`3E{Hc-vn?<1V}$?&FveTO`JFS z_1)>Wt;w^XK;jMv#L7TGrf_wMi(EaxI6B@#96n=xU<}87-7>#FjnH!{Jtxuf&Knwi zJz~l*TLU^m(mFax1BC!UXjV8Z6~S&`J-_Zw=c_=-l*M-fEo&i&fYFZkNmPDf`o9(W zumxXwarJ~^q=8FtF({azgXl+4jmb1Z-b1d8225FdGj0ng1}_{LI4NYD%N}y&{`Jr(L06@% zKBn$jVD38^x$@sVJ2g*_LeB9z^*h~uZ`k;VBJE0l0lfmDcgzEc#*Y7HHFhh%MGJf& zRoF%h+8v)m(ZBiY4;a9ztL`JtIonfl`tQ8?)S^l2FAKTf3%a%CG9i zu=B}kbJNhVkRK@S2i%4C|Fite*{6z&aUbxPEzHpMt`>$o&UEEm5EB>o5`smas_C-L zsJ9P%89jS~ToQM>m%}<#pKS}zid^qFc~N z=P-B8+tG$77x+2bz%94y5ZX%fIXax_%sLB|X8@w!nk8UPV)km3EpC{fH*nzRn1hg8 zeT5J^gIh4$3PO9&34dt*_eA>`yV+$ZY|ZdhBoz>vf6p_JlI=t*EUvewA8cDL-U;)@ zifxQM`}0_ZdB_}~r`Cr+dQoG*A!bF#Vp3C6iT@}3TQAk`*+B9sf{W~drH9#;^W6p) zf+nDx`HtUc4x>Q!wfzfGR5eQ5zO*9A$ng~R#PIoQWVf?Uiw$RQ$}5!T-n!<*pZpMM znxe007N|jsgXL^)9o7` zup)5|bb=1@y4!ylwLqj}*_)4T&(ryyCimwkWiVRQHG)`Tf{S8uX3=O3w-+(Kl*Q+%cs^F!YOFcBRGe`w{S?R0y(i44NzS zpV}`xDQ2$9Rsm9Sx-UytX>*)008@#qeS&Zyi=X zzDnp>+J0RE(;#-{kI*oE$7t^}22{FA&~&C;g@H()q?fD(eznh7t_hv{3u4D{m@Cme z0A?1FfE)JIg;%{m+|2jAwNMW#Ow_|s%*K`t4=x^w0&OOG6t1xvr84Ypblsg^Hz4}t z>4;j>;GzJyTQshAy;J@d@TGQrSDGWP)5nmI5T1mF9pJFrpL38=Y7ad+zGQT~yT84? z{JGfFb8^<{gR1VF!NubZd2x=M)a$5dJ%OcrDzCkZm-_uPo#wrS zK!}^JVhYw!t^mJ2oiC_#Pd2Iq;xR8s=pKHRl88>t=N1s&-6LH}s<^V(e&g}M{Wb!_ z_8*&t#4&UMt;tud?9U$1LC{i2+VdUo_4-eaqaU?@r#gH_-@>&u44S)Vz(v4JQ<<%aFl zf$mkRo0^hxKGkwpWSV{cJ$5!6Nl@+zB1?htD0(%JS?~Vfym)` zVA!qEgk{LVYi1h0?^P-sjW6Opp~3RNs_IjBHDHKRV30-Z1U@o5lG(Qo%svy<9_;DG zcj@ZKtCO7lAaIa4mc2XW#V^c=a(7k2T_V83=>@@R>@*gJ$G*5AvQLVb>J8<9)%_dk z{yM?OpfLEmXMrWne1DLUh~Ks;YMYQ32Kc86<`jfs;k_6Z7$Ju69f3^Z8vrW*ik##;pEHRwSi2S?m85 zm^(AhhmHJRcfyeTUyZPTtp|!uJJ>y*2OE8mNs=IzfoSNc76gZdH9^e{DMvA~{7;!x zM!TnJyCWV4Yp0Pb{BbNwS`r8ADdL^C5UO^aoFrcfS^B?`Qx!O%#y~NfdoMo(0-N_R z$KXw;Ewq#olK}i>-fFN3{+jpKg+aJ+gW0YB+Ql2|)lekz7#%~ND4`@h@B^C{8%w|6we;JQ0*di-Qb&nt?=kelueQ)J0hGZKt zjj%yaUM^Vcouash$QU8l$q4CgDu_Pxe&5YhRS$>36(6bTk=!etHu!E%-mxCr!G|8} zqn(_@7nf~Hx=yXaHs{e#yVbP0!X|QNG>aWg1qyZIZ57qy&1Ox<7O{%IK z4@}@9um}iC`wBv7gdMg#!KvuM2>37^3hx^S6{lAqYv}}<~BR+R1%zn_x2>^&FG#kJ; z=_M?4Y$BWdC?q4q<2SL#n2&16h=QpyR@-}W!#!m;{?**RA187<--i4QJ@&*s_VA0( z;q&1?xD#Mn4ZkK8ag9NhN56tkn^~=#QP$NjV>_hnC)(1{f6V~=DGIprD&HYU8{|y! zd4pJy3_#7OArMC3Xx&qVy&l0(mYxQmBkz51|KEnlR04e&kQUx-NPR;b_&q6~J?HX% zx7V`>{BGL+3YU@*BGmJwVPjk}b78>$gV}dtFW6BM%$l(2?de#RF{dZ~SGcZ$r?+4VCP!^AC# z+*s%F(OdYJ-D(ad8tvqn09#yy8sIg2&|82k2IBlxULy`skAVXbCM7R1nf%?X>05cz zN`vHHxS1WMv_Mt(a@4Z*t85J41|^v;4YG!iJ>4>^cHirI9wju|D8{?J?N7`>jnZzP z#+zHS<`qicSSv-#y0)eF~u<|0RB zAt>gEAzkUVQLz|<`EMGfCa>P${s4bOjYsnkV8w2#moCFjaYX@&5}AB7alQc!`&72< zLlb1_?(cQav3X`+gq$*(MUVwkT16#YJor4?vCl^uK022arP7nDd=iTt$w84pk4o>t zm)!VMST2;xzg2~QY@gAU!YS2mm&7{1=aT&ikWCi2{-N_(~_L^r~zn6H$6f3DXbD@S4N z)hTkc?Fif=3Fcv7{DGxhl~rF`HDqQ~b3g^;f~3n}{o&y1dZo6Y7g)7x5Nb7aPovG?gu=&kzfNab$sLw^ADk}Jr$!cfhcP4zOsu(# zT6FEj%#Mx*TxSBXpdWCcjP?!^dcn(3jndD9<;Du+@{~Ic+!E+gYXR!u2obBs)!@Ql zofiiru5~WPB#%`w2Q;U6nU_eVww4zvfM*>+%cMjr+3+FhDTEq)MH z!NyX;rbZ)H4X}>vlip9Uv4nN06Tjg72d>2>mCcSeh=7zJw|(cmk0vijYLWz8&O7}1 zwz|RjD)&<9z{0l4lp%sFvT@h+j+VMpE7VsKSVo`SoCR*C2@-udnmcj{VB9Jm4}1Co z4X~}`LC|Ng*L-YE#H;%qrZnW8zmk!jzS1tR{P;jBgA8db;chcCKZJLdx101=Qs9DM z&coCtJEsYxz>=*31i=VOO4OJ>y8pXbm~&7WW6aG_bARLwZ6J3>p&*Hxj;1rT9u%V@ zJ*MhUd}g+@(T?^j!tYUj{(io&W(D$IgKzP7T#@c(Z~5b&C-gm62@u<=)YoBwayeZk zW`bK2+*Z8qvh!W@&&G7;4JG=jIj^`X#Rd`+!v6FWwILvn?D}_Gk6xgafZ43Vl4*hx zPzE0ce6-j%x4VwXlK>3AOktEIOUd+21B6dwKvbcy3Z9GmjR8o2*rpuh4sF6yxF%3b z8hrF6kmQ#!m3NK_RqCQK>=HQt?q5dr@uqB(@}zDn%T>eOHT{Uk#9un+tVwO_sZCo6 z-nNf77z72aZ6Fk7Isc{Mn!G_6h+zj-sy*spjD*hD_}M3ZJzZrcEZKZzjYELUT&Zst z7ftkKa!>2`?i(^;Isgag;Yif-MdLREI#(3e-Hj%p<^mGT!Tkkifpzz~STmp5thkjA zCokj%J@RgxncH0)tOPUX0xdUPjsx<{NQ3CRGBHusepafP{fnHKqVw`bIk@X4B;h9Lh@&QFeR}boGhK5d8FK%#;Va!Od65bSS}%Zq;8kE*#K_>OLON z2GaCPU`BUN&vRAs5(`3W-vZzFjp~v96zc9ln<~)VO?1}rv!-?Ve^E<&?yCe49s3YVqD-dz z775D#%1}>@2)Xmw^zJhZFnsEJMgv66cwsMo;G^^y!+hP4aYmpP4xc6aGYESU1A%bpR6WE!o zLWO_*+O@PtKIa+NOv?IKcW~ETZCAZBwHLK~{^G{s?e*omLmZO?ZU8t6YogT`jm3l8 z6qeyAAt5@R5m)1i`6-DGaEB~(JA%U+L$o~%;fak0xcb zBlNk;rX+|F7#bhQ}x`L7@lAMYJT#uJe8 z;g2?GJ}JDpN7Ny_XBE2$)wv%GfF1(?4v7lHIzYQ50$}koNbtJ9kB*MAh+I^*po$9j zRTi;nniHpbCtPK=fo*qM87kN?dS@333s5enP%9S@c)`ceEJ6?rD$Be3U zS`XO&C)fLTzxQsb@cE*lR{TRJ@O1zAX}WV6WXc?Z7sS7oFay96a~m96zQD}JJJ8Gt zL#f~`gUzPFyjZ;{0O+S{zia@0MM>NT5EtxeBsVnKC?UraLisW8c}|>rS^kQ6aCFa@ zb2n%4eSCEG1_tV|(_0k}YWSx-`MDWU!%&AgVlufL@ZG0dt$(N3Rjv#jZ1!r~VvAK9 zybrp2tPy*eN(9xhR4HW(t5SnE_hQ(lqo)8`UT(Y8S?g70Ow~N?hga@fPFf_UH{0)U zb%tJ*wp1Y5XAkxHeY%>?3V|e7`9jmvKhFs>zq@&C)vPb(+4Jv-pGoPL9&514XqEL+ ziNk@M&iEkor0JXFZ>p>3sJSG(5-iHK?tnlh%n7)&9H54H0qXqPzwH8A4L=`$fm4|z zs>KfgRGa~@bVKh2pR+wGp?YCvvwlW-Lq9YinytEMZOmf^r0^9Zz z=D|eYK;ljS$W{T7!C9<>q&N_$S(VM|y5SBFp?LlZj3S|3s0Uq~MnOx>*0qIjcNPCu zdhXbROCo;<)m8GJJ#-E6eq^Jzjhm`=TB%9;Ughf71)qvySnm%uYEr5 zF$B0z#;NcZdXy|FOt~vLcD_l8ICbCwyPb@Cw9?9IFfcgGIsGPS;zOOynX|J){S=wI z(^r34bI+`klmP-waVp`4Ehul0P9!nQA~^CKCMT!z5T&W<7E)bX5#Cx8u; zT}VoD{=C{ew6HRj)SQ-vr~xjLQZ|!U>Wx(;3B88-Z$H7)z-zRIZLyetLHFk#yJ$XbsJ4}bRKNB&0wzK*|Oa_FJ6 zu-SU62t3S$SxZGxRZnf~cAnV{XZN22wqX$4uvE77}(*`m?06F8$-BN&sg4@XeW=1e5 zKCqp$_K}tVpRwxK3)b(i$zO8fF4GQgoyq+|VPJIs6r989PfQhMe#CzFe*WMqMVkC( z%4cz&-bZaOxlBoal`?}$AkTR`sNr9gm;oL(WR|EUw5jyW)v%eQIG#=As zO8n^?)hx3ax`Bk4c&)nbub2o?EDR7Cbl|7&@N6Im=wPw%dV46fzqMpG)1M#t5SU{U zS?b~1fqmcp75=$sLM)UyZ>DfHGc%8!F5b7~t^Z)pBFfsY)S4{!Th3PviD!95^|(S6 z$JC{co7b)#15Eb=oUE&W-5Ubh2WiD4mYNYTv#}~7VUt_XgUTl8CjLHi{{EF-QA{=n ze)vJNkF8asb`KhHgoG|=AL(S;DL}&09o%Vm{&dv6W%=oXbh&O$sbJ( zN$VbRexnO@sNr#|PCsRGjJZnnw*~#Y>JFqM7&;*Vt`WGv42V~A<~JY1_HV~zq0&2B zZ7Pj7t0&@Dn)EU?t+afCElpP-lgOnU6B$d1qz>di2_%PI#&tN(? zf`ubzY0qvgzxz7t=JIH2o^|B#ZOb;RPkuxw(i_J6NX1Ryk>Ky=r(-7~D;tX98_DKl zN`G=OE-94ik4@9K(DXauo0dH99Oy!-%Y_vP)wV&`pRO7qmz{>#87?5(ek$p^yd>@z zo;JhSN>%VAT2i9;>TTvMhGEO5(Y=m|_CGN_{73E^2KhQfv0b4zu~BLTVhJ_No2!9v z2nX?CS%qO-69Rc6eWstFq!K*Z|9kna840Y?*yWB&I7~bT`v5gg|4L6!Ux>?UL;H`& z5IA9R-hkW`%_kp>ph`g}Xf0$%{kdTmYg>K;^o5Uk43a<)PR^Bpb(Mogxg|wwEW1a~0jculZI-OD zRn0`sv9P-0Ukd}0FTB8Jfg46MK~{hA0nvPe zFR8$FAi-r(Y8;2*v>haD^FKSI!J~2($C9m8eAvvqSn?4XH6w3&a@R&`&diSt2V?-e zjyj}-@;)0Jw0w?&q5jDLUm8m-5$p-@+%f>GiDQ60s~!M*@iO!sJ%vo*;SvHROO9vz~e&E{CD!R88X4iPCSq2Lwu!Gw!qW#*M!HsJAZr@Jv46&)MY z$~Og%i3qw~Pgzo-a<6O80h%sjHDu^5kFqicJ^8_cJ3al|hQ1;r$AYYlC<>4Ud>&1v zTPCmX<-hHJwg((3%pXBivs0~G)2N+J;;z%5-VL>Q;3!332r+kxiikp%RaOiwmc1Dz z_Ym50NpI#j?%lhD->P6g*`r>hC-kMUamVpno%@Dwzm*g~iU?!NsZGIGA8`?p9))HX zhE)M&%dTHf3;E?Ipg`J9Hj`oe=K;C6{Uy~m*QB_AVS7ClUj=B~#XWjecrIfF`g4J2 zoRb6)j>H@VRzl?s!Se(}A<_JN^xF>#$=B2;wZ#OzwY*60JU7K$#pCOY)y}>;O|tic+d@fiGGK*a=NQfC~i@U@q{tA7dvYIu%Rd2pyvAEawI_Uhlmw)rX{au$-kHl; z)NuP<+iU(UzIAyuwA^h0TwEme{`8xDvrtQY4ZN@1T%a!$<|;JQ&w0DG@_(UsQn>s1 zKh4T^%G5M=nonXq2khrEu8r0;`!C)h!v69`QL^U=h%GF2b|gK&eeKxSQq4Zt$3J6a zr?TNT(g6oWx(C1^1$N^Zq=K8DK!_~cZ%)+}VgQImn*x}ecuxAZ(U%L_cDIEw^LZ*a zNUx5Jk{8s{;`1p<^4%C58m4lhun0OXF#>%~)$VVDtNZX-5{;h$5iQJK zLxv%sA7CN%h`(!o-q+XHQ|KNeDqVHQt9FfZM88&Fcjb5YVl}a=?Ss3~MB`|vh|smJ zr~cDfVih0(6YF@ua1+{m0KNrfKT3r!XUl;QYx1Un%Hv*lZ{zpGfU{S<^2-aRIj+gt zJn3^=7(|!jh%p4^7O(AJoc73<7x1S20}@zri3d3WN^%A*T#nC_Y^X0pn;!efGjc{e z_lYN?#pi}%W@i41pYv&|gQi16o7WF^SDv0S!>t%9$~+ht`#f;@o!9%Y7K2B7KW8^M zV>vE(Wph*(tgaSovsuEN27Mg&1Cvs1G z^Er%~u5Rvcv`*B1z!1U6w5Jq+j})@ntP5fxh=)ZyC)ADWV{59 zf1k)NPPm3B)2H4E4e_ur*Ze~T?_Lg&Vz8HXE8E#NB{{M=+x{i%u@|O|&}jFd;79G1 zlkv#%fco|yAie-AwqH)biI_^{0F605Q*l6EpWiRJaXQ}BUj$YQ#rd-S5b&lMhcRRy z;CJvge@-vt{u|^fV$iO9G$iw(#fICXKnBCkc;?)#mq^#c+P#54nNAdiCBbaGxX^u1 zqW3ISxcf?4brvn}v+frG;qEh}3oR`J^_eJ4FH7ar=L^5Nb6R>_&n`|nlcRXIeLtOaAA!)6w%J|0*uDt3}B4Wh?G4wR<+9m2sTv{&D)7Gr{`r zNvCfd@-*$`)>z$CNH{bLU7tVQ208cf+m+X1;|YT^7Gi+1A^g`kJ&C zek^|A>rb`$kQ?P;#R4_~FlQvpi>W~W!V{~cY3Ccu#t}Yo+n9p69WAYb;sjNlLEg`H z$kwc4Y8ueb7Y;fCo+@Q!)&8V!fXnH7(ynPO_yE${ag73>P?&~^qFQ687Bs)KC*;M} z5WcJ-!2XMk#3>3@UEuNzxGPrDQzfZjqv)wD6GP+8Bqm`}CxVid5!U(5&ZOCW$vGg}LxTM8d zwYlX}(IUZ#A|g%VXRLbf$%oJFcV93ZDP@)(+~cV}U+%Nhn89H>Dok5b(=dIqtf1BO zfq$Hyn8wypigCQKu42?Iv31oaeexfjxKQeIoPK_0L|^;AnPChSJ{iop4bd-?akk~& z`vVlp{kvYyS<=#Zj}oQtb4|N)?GEK-&3^uL`7*@pR%yFGjM{n-y4r-Zr1V8an3(nxE`4BT4`eSMMZc5V| zyVR1`r!u*A(G891z77ep%~Ds%$G7<=H^Q6r-fG1WkD1U*-9Dj<8j3RAs|~2KFS|g* zV>-tE$Sk6-^Mr?`W$(1SVq0gN^CTUH{wRlc!@^_+a2v52Kls8^dqJW5Ss6dPZBgBPdBm#1ucsM26J#fiRoF>pqsto0Q8AkB;C)_HpdnUZlM9T&m} z%WbZbKKnq)zIN~BZyMW2l~1z%{uKwC75F+(njU&TC^YkNMsw0zLb_*7-SSv5;D%rj zRU9EGIY3D=qzW&zF;|^ct zZE|ieF|pJxm~)BiBR0l;Let|s+|FfcuIWuF=w%27bVf$?EDN$+d`ehh8I#3v@Qs?O zKr?EBA=+z2^qOk;+*;r0H6Yy4T3;P1F^;{4X)V;{-T4y5M=xI9AuQRf$m6yyDY8c! zsaDkPeZA5k22Gf^(bhqq*QJ#7VO8`6+Cag-b)EV+r|ny|7ssdQJubZ+t~Mo#W-idn zAZW;}PN@({DDD1DNes8&cvL%HQnydv;lumAJefFcmaZ!qcgNJ~7G`gq@YFqvkTivu zGdTxAk`}L;h{ub%B&5!buDa*2a+_&XP`tY8>Bm56H^Sa_joAFv5 zJ1LpxVp9?vw~}>?nE!-!N(k*4NP(m+iMn1ij+CFiM=qtAY;bP6Z-P)DC*GUM2GsNQVhG+S=b+WG%&GYGI2fEIP`V9?9o_$^AYe1>) zUlI}?FU-l=w#hGY@zc^mvT`GvgVP;z*6+%?3QKirm(P+oDtOFN`Z9^Csw;?4M?mm}xr@__G2P~@C0CG!vQ=3IF)4?PYSS7Lwj`3@;rjLGSC=HFOb z?unHhrUDMGFIQEUpFH1my6y#dJ7fAMVijgQfXm5imHoVJthlJn;VA2F>=m1Kn} zmbZ7dYYuf|6BJbAsE%*U=V|MqFaI@4semXuw_ncsa4&arReHcNT;+S4TPmL)^VUeV z%zB;{MUVM+d%q7%KTO(OYaR64g{vwDNq^P4#d`T|EnxNJr;XnoSYzoq=Q_54Cc3YE@I{!Gcu8 z>c?ie?Wl0UTHLP+YZ`#Pipu+n^ot03Chtdj0Xs&?V>r2sBM6xe>1_G+o+fDfzJTjc zxfh+FD=8@Cehxqv{|ihae7E_ZYP;O3tiQWnDb-n_EHWu6e*kn^D%MqT73*i+lHQpL zFziJf@U{GH{>#_GYa*z`r9@riUZ<+sQ$IOPw)R(ca7Q>QRexa3{fwK}(5S%LZp%E_ zM;6t#E3NICdoMRH%Qi174y<`xTJzZ09I*rRpA?x^UGB0tkY%G(szg7*G}tQaw{Vwb znT|Rm{Lb{By6Z(mqXOza%gv)2HX{OSJZzJ;{P5kK5Yn{-yYj72&rRMI^GHu_qMiJ; z&5?)>ZiZB+&e#rNhE)FqxPpXPrqryZm{U__YQhe~qXO;MthKb>45)*Xk!4d;Q(nc; z+1a=XL8WHr(mG*Q8Gp5~B$2RW7NrlDC6IC}3fe`E^XE93i?COaN%4lW|y`%BtM zUd~MG%k?j`9H!F-!bHZuo$a^|)UQY8V`35q+b7%W`6*{QnMaT4Uj+ueK?hS5wU7Fn z`waadOTXgmKPs4vrby zihh9W4meN4=${ja9etBsA&!2IQn7%}M|9G|#Gm?K z9hEOz{CG5cI5=^~;shy{DT8z>u52PG9x>F#Bk*F zyYumYZ@zPe19f3*#G|P9{DljZ4d9nxxdUBcOMS$#ei1ru>gDSB8gBSDUb z-qW*opbs$D`~griU={&5&f5f#6|)ECdAI{;H04%wfp7i-RC~Px@B>tNSTtF}>>{mA zuZnHnw01ks&~ek6`bGF1S?#Z^Cf~T6S#}a8%^f0BHNBl#+!QCv$=0-5>L<%Du4&0> z>uEC-mA{)Rr&_BCAAJ?IUpOQZJmqq;L6v1kw{2Oc+2g?`^D+^`GMPo2hg7qN%)nX- z`5JvxMeVd-?dhqy8Kz}rw<7))4~>yFr8WAvt?KZt`UUwzO22(;8SO(gzx_q{Ef&6N z8I6zHUu+poi`utt9nFP5w~iKvH(2z??Y};|lNeXwNqA;QA-uv<>del!@Q(92@C1hR zDs6NN*i-q|`X`uDW!L(dCd=eDnNxMegxfONQ?12>=ixUmG2ydW?5VHCgl|pV?UEIi zpSs&4EBs{YZlA1hFtAvh%02&lqb@H3WH@gVutbdZlK;BAHa&|;P5P$h5>ruS?UGW~ zSlNE{eBp#0`sRlTQ`BuGU8RaqKI?M+dgqZhb*ph+J0lMD^jFA~DfgH2TwwKA@vFro z(mpwVnezdiOTf#QxO!+s4s;~9?N;4B(u>{zrOMps$Hn`o}Fx@{NYH`HwITL)&{}4g(2-q7AY1*z~?x=&EeU83|?pc+( z{wKNo#ns;Xktf`4L&!~k$x`S!Up1Yg%91-dk_c@M?8ef^7f%=(jERhqd4uzV;H&Hf z?Qbm)XjE;2d-~%=ARzn-gTdgjQ}T{GDo0>ZJ`lIsSr4|*n_1=WqCiHe{fV)&qTfm@ z%@pVr1NF??B@WX{9!7H2bt689#p<3WrFCmjsr~9#1&eyyDj@uIi(T&&_8BQrsyB&q zhWSL6^6wuE%+AgZ!~w!v)R!XviD`|WUpCMTWCmK=CpZu~UK{zn2rqo(Wj46J2oiL5 zw(E=0(U*E+ImY_(H8U3FKb?loL+=2&$}dnB^u}`h1$Vss*aEe@0d?@3w`5^v#%{vC zfH#@NejrBAV%@LQ?42}5!O$^5;&z5Q;4TS+Nm4twaOrPOk;t`luvnX?$#{EP2)y7~ z+kM~QGP&XKGQzL6zWqmNc%R5$k0$C95u=Ikk`0M=>fAk+-vQLQ6?e5wmCSO-)Wkl`~+^ zkqJScmtl~iykqUwfl%yvhdoeZ&+iMV+O2FIUiJv)Ia&i=#GC3?@A32rR}d-Xj``@< z?Lin>cqqJ+*>tBa+;|%?Ms<*Y^LbsP(xipFM<3?@hWWI$G|=qt@1uEJca~XMc3g}# zG~x%A;I!)9ow}@bJN#(_>092z6rT}KmA&P$;j2}l8vx?V1xf9!k7^|E>u+x0a&x0@ zTKMguNp_fb{i(JW`1YtyXHc4Wx~L6#cLX~pfBa!f+vTLMev`x}bOD{)0rt)4 zA8YdsCA*$iF2qRiKQQOQN|yc9A3nXtbP~ycec`ar+hf~gp+M1;3m(mRbfi3d747g)a^vf3>hgR-! zn+662=K7_G%PA^0!ba#R?YH_0!;yRJfx1*?HL=h}8yI;n1fCUGl*`!Xl|21p%}45g0BA z_0~kmse>k6nE?k2IgOWi)5ZT)RA>HwZvaiA8`EF-LULIK`!|RkS#u=z_LfLYcY}HJ zGIDaAy@;Ycd1ekw48T+W_4pUcWnyvaPoE06f33Yee5_lS->X+TZS@>MhTy*6N6r=K z7m0)(Pt6y1%-5W6D6k{g-&Bykm+v}dYzv=Xk2tn2d92U8&E5j%LSPtn(WEt}~D5$}E8po|Am9+D`$6t#3>m*a*qw@R1pX;pkzP}z_g zxZ$X{dgn#0?mYDiKxtNC!6CN1g{;WH7v&+1yGrdu;xjR~R9-E;zGcw0+BUH6ol(uh z`ePMd;zuCk%2W<;*@RBr3pO9DvWjrh+npLIX?M_Ey1U$DRG#P6T-O-8Vb>XFQ<6s< z7@Shq(#LUu?C1Rm4&S4@wk+QLR|+<$*ZFI^GZ^TX(P#P16V%~8?T{I~p3qgtUFm^Q zeOVhUX6REFh>2UBwTV;_AJc1?$B%O>zQf zbtHh0j=`nC5rgln{0A%`f)xp-SHNGE@PrY1@ghF;sGq`*e_V62XRFCO87wb}I>4Y* zUTM={z6|;-sJ4ML>-TmeQ#b20uLUH8DOTLlkDDo!O7r&ag%wA#tES%iHhEx|V#vb& zYqGbU*3Ujv^n@I}b)GH(Hi&U&$xlC>RN0QHsY$$kBtJA}_*RV)%`b99sohnAJV7ub zzDn=8BI-m7UMS1yFmk1yUL}HRW|~-~e=&6VXb5rBZ+?!z^7n=5cb^@L9|pZ%DZ)i1 zqezFjDWE1)8V(WwmZP@4eIdvRf=Hx*??fA7r$bR?(!nPAn=8v(H|O!tNl_57=7n`4@gvr zq#Fh&y7~JnN6OnvIGRdRdUByZ7yf%XI_os2(+)$}VUs}br}#k{Tn8^K%_?vf_5-%_ z1i&h;>p!JeVI+*Hn*tOyJqM71*Di^lQ06Mo2h)%8N71KpU^X%tgw={CFb6tzF=;PP zIfF1L<69%pxbqDTq#&Eh2kK4H0(T;@GEv?4AoPn=Exd{q{NH~;2gO}zT~w5BpwJ^D zD87EYSZl;_f&BK3>~x(#AJKDBfkU|4SYjDHF*&S_Xx6u$xS>74Pf7`5vhSS9C+utkK`Lki!U(l-ZhZ8N8E1Ml++>dIWW7cq|5!ptNi+l)miZZJs6h}B7s5%!X9Dw37UnGh3HYC)oFX%F z?mW&+?@sDRN4>encI3*R|F4z|e{_O9BWW*K*78aZZ=08;4B&Y-8w zZ4&*uW46Z^0gr8aQy(qH`=P%qh=ft>>8g*(YozI+&s(zo@j?JW%rE0aDAi~$-ca}e#B^MWI8N_XeEj9w?eSnsH|T}2w)^f+=8fGePfEf= z7ZE%pFbD?-RIRro%sAbWMes(#_EOW+0nj2bP?ULwHEPtc7MRTD*YKEO4AqfYtJeF( zTJx|ePum&P?I%J)AH@vqGaHyXo+=p1DT$n8Z^!0_w0qP&aYrS*Y`tb(cb>5Pc1FQ2 zowZx-gdP87M}7*zC~q$*ZEfcf$54+A{@G)7O4HRHGhG&Kp?lvJ)X$LfnMsu8rG>H$ z8U}p-^FB_vrtD!sr8!#Ko-I%NYw3OCJ38z^ccU7enbv>58dK%(=OVy_>tEDeaZAlh3rNzhmZ80l^^YtpNAjq*FDu1 zCt1`ZVA`CF~pa@u4n1MY!d_7QNVAec$qM<>0=xHgxdY zq+GrxyuY*{KoeGHPF}ohbz(F5mHHTps9)!`;EkK#aW)c-{(eiPXpvSk_xEfuv4Xu- z{t<Dh&e&Imhl=271&Gau0C&i`MwLF_Wj zAgjn1Lz91rJQjm4iG0=re9$1s7d?iXfCE~Py%i;=2KA@lY#f>U`2ug6*jfZOmnupM8%CP zGq$$qo0i0U)|R}}>rc5e zaZCtjj8?HRZ`WZ6T5sE5S3T7#*D~4s<=LF@-*?ZcRf+g*LQNBF9!*>GO{~6DP^IKw zF28x!G}%&|AT-2gK3Vl@>%GAC1KvtIrPnj@w}0NZPH(YpmDsYl{CZ$LIMg7@qrriu zYKMkI>yaFNTPq^RMRzybjZo!<8I7mz3E~Pze8inz z+PtFW8_S&5vE$p@AA29`6@=i~pybUl?e?fd3gdbu|5lVD@MK z^wn#t%MCsD{_1+7Up`;h!GAdV3lyXm8V+}Ffu))s^b{Cx-#!l$3d9liHt0RxLNTgW zSu?O+U{*#A4z>jMEYpWH-F^#7c{Wf0U7=w4>i}82v(GJwm+#x<;Z#>1L7d$_rxsoR zZs6;!%GCMBo20=qe;@8-GE@|`hDx)$E+Kqy$*RT7>Q{2+23|r1wjw1r-WL}31^Dke zvcVmr1p!Y#2fnmtK!X07X9O1S833J61H}7zf9LNVP!%FkQm^vd`SVgxWY!hlz3l28 z;MrkQIg!zDVWO^a4`WD@gQRKll{|hw(-0>r*Z`T2JorC#aRuE>3VP9=`qmT{@Hga5 zoJi!x@RS%Vq&{oRjy5_3PJHfgMP(7Wm5k1~eF_eFj1$YdS2w>_=98TzJG&DKYp6 zmmbL`$k(1hw*`UC_7~|}-Rf;CM2#(ZPW28DdK2le)C{PPcn*5tnqEXJ=|6gaqZ4Rj z8pSUNMVnr!N2Zk?i|Mg!gb6U7+`{Qk?6y4_m)_KW1?vrVM$!hsp68$?XG}jqY38gu z!_xjR#rJ)x&t|_XJc=MjFii;>5=Z;u*JFCEI*hNtmk?9R2=n&3%;gG;02qc?OGeBpwm!3b-r(WGt(JeE3#(8)I?`t2F<$xu za`7^4wqj4bDEZ>#0MyHXy>-nfVt6-v0-L7R_F=lvu0Q5dhHchzC+69J4O@6E7< zHyD5a2_?|c6DFoDp&L?RoC_PE@T+2$s+b&q%ATD-?2=H<2C~Eth1h>&yxO{k*o9Ms zJbRxnii&t=37ZdIBDIEIU~Q0gF)5XB(#vS<)!)_fP3TnRq8BY6DnUjK<^uhY$n(=d zB41B?9{B)c%3uQM28e+v8e&fN4hFur8Pfy#1~Hj{eINrVH9|xEA%0;D{2_6u9_Z+K zaXF*B&Zj>7qTAKy>X&ONO*qjvMu!lx8P|?Cyq6!aFTMFdTWQ}sCJbZ^66p0}MhZ96 zyEWMoz7a6O$dtKo;zj}o@Ic3ZddAw=h0|z2T+ro+jArI~Wy|-6Bz{H(m za3}39)TKe}GW(ijdTzR~YrT84SD;pK1+o0cVCNv;YW?Z@AHOaMr8dxQl!;i0i{T{a z=La~6qo7+|d@SnUp`}G_1bXACa+?SLhqt%>s&f6pelbu4lu#5zQW0H%U?2_B2!g~~ zfTW^`v@W_;O6eA)lwJ#@I|ZaAly2z;>4q~O+~4=S|x%8Atf)%YZuV&Ie^I=eoj4v%ljUx**Q z02sN}&iin(d~qido+IIdt>*|tgYYYSFKpftPy#6qcZUxV?bB5bj9#lc+{cYkaGUT9aSNpHI?Mtnf=DK zrKR>|=uUWh5pW{u3#KWeFkZ&}0Qz}#pdiASIzNE{S#E3~Md7W7+K&4I(V+sSFA zffXNw2H@-G{0y)d7Sa2g&{9z)ilOf`GblNONgc-eJY)MT>ExKq*kiGRJ;d=PJWrvi z_MyJ}mdBv+C=kSa?eH7QF zmZL>3>`-`iR_!SUA>ir;w>j4j?%#Hqv6e`<{>Skt&9r$M$=;T$Mr^J%$NWVSODSgr zA(2RXC?}%>xG*pDP0*eHW1OYC-$W57rE5D0k-}ev@<94Gf_s0fxi0&HZ-^d-!>^ln zgJe}<4S;v-T$MUY@X9YQr`y!a)vDJL)N{*koo#&HSjIsC3>Bn2`A{#K-2QdNoAXau zk4UbtT#jm6#G}mQ_JGtLU;u@`;x$9LK?;!&+bIL{`38qNq@flw+hI^;lhsBi|zBJypUDM z`Qy$f@OQW6u7++v?6IY@ul)Wawm|#Yq4MBVcst$jiR+0^6dkH;AqVZtrp-@9uw9`0 zpk8^>gE;PAeq;W(GQlzcUQ-sRtp^z(zjrWuj6R_lCYaB%)Dc=wntIl87_Gw9`i>mn z>*yUGVG5ZWQ-c99_hVkZ$L7B1Tq-?B6q9un*@B8!r@rF{uA4P^~Qicc7Dv3UE*gEdvR6_VE zxIM32VVxPVHQzWt&g+jnSXG-%zB^Q+zn*yAu8xsc5eN4wBsjbhz1_ zuincq5h)UWUK758G{TgYLrV?MBVI9(&1ReqKU(YJ^r+_}^ERl5cAPlV?yJ==eN@Qf zN|F|*J}YPgzX%;tbGXbpI@wxxm0)jn_1+8*7UE0xC~Du$2uXT*k$lK3(E-D zjcV_<3cA9)^ceIs+GZ?mfySLsYz*;OB zstXdYO#QpF-=6r^a4K}}9GORv-VUXfN(^U*&OjcVG z4+Hj5u*;MunL^0VFPL0s%#nJeFPUI5u1F4hR3Wv7!l(gWB;a4EV610FqC6u`$BIW@s}EkT7RxuOSJq#e2+*XiV?f=Q zOqM5=gV#T*bC?_XpdA#(*!j-lBd^^}8q9Gk-hlN>4s{9=vwww6)soq*e5*C|EF7kC#4?*VjO zF8EJ(m%X!3G`LA-9Kx>%@y?NrE0#et!^84J4qw%LTnf_~8!U-}% zh6;^)pU;lLow7=C|K9a2u++J1R&LMW=&eB;g%*Eq0(xmB5NEl7O`B`fNqjvR_aKH9 zb`?a0(A1v%xLXXZ2L;5dt~46ebghptse#XvrJgrIpn`KKfd!djA|gbrfQ1j`Nc?Yk zh#UQrEYJF8DR~a;{3rTV>ueqm)Pt=UmhR_ra<1H6813wbD&0POFm^&=+%0Dl)>*!3 zVCs(yQ~cpRHyMG|A^QK)8|d87G*_@bB zA^J1nVcY3bqhToDtTY9NPKNJ@$Cg4V{mFSst+UbR>|qdlv@%InP9)qT-8CcWqAyQb z4<*^Ct|QBr>eo0zLr_p?3v4QeFbQ7yHY>#>*yhG-QB16mA#6)uz@1L=JW)0?iZ7o2 zQ=$FY#+F{nkzBb?690UOE#yZ5Yc zWto^L#k0GSc@r1PV)L&&9qmVoj~fLhW_1o8iVAZunt-*lRkx%yZdlqF9D8MvYPC^J z3;%YqXRtp`G^OsQcVpUiROh|lHNV^pAL4w|tkJ^}CPse~+AT{|C0=L%E7Snc%_6TE z4q(5aun`o>Ku2S{<0MJ)hT#g1z(%1Oibv=U7l1xwGlf^i==20>vpx#)xo+C~4<`0u z1$K!4&QVaBvFe697w(Y@tJ^=AYoB?0$;|j|GJp3wh0E}P(eqFA^s-Ol_+cvKs&hKE z+0Xl-VUIQHVzar)>F`Gn?DTO(~ z7nu9(QT(!q@uzLV*;y&7G7=Hkfp3_jd}%!aQUmTUSIt0AGq}a?(Q=zH)vE>Eibpe( zkB4bu+Z0zF@0-DctlPFb{zQ%(^ln=|GaBz*UGon98xZH)4z?in&$}16^IzoTNBzgv zJo*qUwL4cY{W93O48+`I`@k(Lb?GC7itLSO9kS;VCy!sg?*@#fZxzPXH%y=SosE0m zOtjeXuz8)fEsaLZZ1H%>;9|TIoZPmhvQlL*9dOc-k#AQ6>YtmtyWu|4fa=8mU)+Ea z2yVsi!lM|Uk`)1mhm+BB9F$M>7Lg9x(N9X@j{W|d((QgTb(3^XiP&j0$#eUAZoj@; zxWETalwVk3N};3ixq2xG?Ev~2+rPE;DFxPNee|1l>}}!y5UzBgoBED;j^R~%Iim~W zdLXo)$rA+pufLVEvokgD(ef3lR271+%UAi)3A7bi7{E#GvZQY_;Kp+*bu}lCJo87F zN22EO=Nb>S$%P+)+cRWBiNqgG%MaH~5OM^C(#=VoP@~>*>S;@USVMmBQkF2u*?|NOTEFDB9p5%(MuSHD3zGSU{I zrNRn#1Wq}*-XC$cv;7=fo6{`cI8hx8vBDjfIZ{{Ul;MpFYB9?>W-VX|!;95HtTy3K zbtg1OhL9r#Oz%!^hc;#ez*88ayscXaOBnt^@c*py9e>qrpf_Blm-Q5S4C+~Wgp}uz zU-z!xyn+sK!~&{wY26MHB#yx>`vZKlbD(Zj>xYm#BJ;BKix6Sq6;}_OC5HenwIt+& zPIUFE>58u;$r}~@?l+GIb7eU_ViBYWr3BLF7+8PF>5RK}KC7|0w$dB<5!qyxC^l8YdyqUJG+DyF(LQf6O`9XU?CuAbTx^l5=+d!q)3;rbims2Xn#g_MF2*e6Fnr(^PwNA$$RQ{Z0s7GfEh|1-4%dF|4D zIbx0|fRJ5xE&0jcswlcGrzx6moE^g-a3?fJCq0z@UGZ%Xmg|#vQ9PXMQhNeRUv`b#w&#Ry8$RGoQeMhrm`jr!X5`>n|PL5xWqIIyV-rNRzx`+THt1 z?Fpw5qb-sfiiyObq|FEd%RosJsAJw~Okesc_CK?%VGHXP{Kx|xWTZ%1Z5OZZ zrI(Pe4}jdN?qH8Z@4Ea7{eaSLW56$6?7jLai%L!mlO4|>AjKcFS z%7>Iq(fgN&+YE`$M-}n)>0F}z;GtPyyY`T1?sb8tg`fvk+~dW*)4|$#XwL?XIdy23 zT$iJs8y|c8-F_Q9F=TU{B$y$G?s!haefucqks9#mPDaI&_vGTFhl>u&;;Zm{6g}Dc z;G^lwdd@2}nF#+w<2V^qP~$ccnYM5I@_D2r_0K3b*f17tYg1QM!_zg&Rx?VhydAlA zq+6m;dNWQRNBOTgDna;cEK81hXZ@wtBiZ&H{!Z^7x$nM_(~oZxUO-mWTYP6g36t&( zCxVTdr|8X7sE+fiL%TW*oFlH|E{B)`>|^1PECu}Fym@EeuR<}IAGiS(aSCp1g7k3B zlbqMjWiKEX!?=;w@0V#K;}@#!H>E-!iK13F7I^P&bYAgiN?ADjL}#pr z&jL#oR1Y?Mqr2JuQN#<*#$k-1c6AEZZfS>&1xUQ4lnPet%PXDAEe~In8LSYg+x@#y z{!qZG5hFB>N?Tsc5;?8uMbqVd$wK(#nHi9^@X_Rtk4Krt#)RcPcD}{A+w9EB=kn^E zNi65{dQZ^{?oJmvZ;$8Sd8)JdjPuLw3%9{~p0C{g@BDOwGvP;;RO1cLOiBV|O`XVK z;EDO(vt1HkW)Y|q86jJ)F>4~#DN7CIcS7txjkm6=>4wPFq&%|R(Q@XQ;fRWBt;tfXMH@z(3M{$Z zV`@i2ZA*IsOQZOA$70-(!P-ICr!sOcobXPD%EQTObV#w_0ps>vx}+stPe(dem<$en zy1UpJ^vR`LrsfXLdcrYnQFa$=UJ^L7lP%n}ecs;8oQSLyd=b3VNqCHqOW4od*AC9w zNjE9ob$u^qCMVkYsqAykZ6#Kzcinbl%*3!2#4^pT7H8a*2x-dpe=FsSZu=|SKZDj> z+P5MiQlu&w&=rq%bbh%L5v79J;d%8o4hBArVZDH1|KEA9_l4OiDP`R6iOKP^$OZgG zrJi=*=g^6>T*Kkf-0I!w+vVox2ij{b@TXwUM3rGR0`Usl$DDA@~%9= z1NkDtT3u106 zQo-P`d-F77%~#CodvqA61J?D)NF);rhOJ3tUYsi}bQigH?N{d4Pq)`Z6`R5$j&sC} z5c@m5{G6nOKHR%ox752G)lz5Vo$h4okl{IK&vu-adxbv#%`Q#U?j1~els*GSEMSGo5L0-&9w~amYxR)*tNbK zRZG1%o1ts7P9FTjD51ZVvq=~QjuEQbgSyTUQb9W!*8+mz()-;D{obYEW63O@kc=J{ zN*4P04G5>59GrI^Cq`%@ke^AL;YmY=K=GKL?He9{|FXB!U$i-TO@3>C9Q#L-hfNE{ z3X&+-Pv>tU)rdql^DREQ>Wbm_nA6&wjLhPDh&HCJSgMOt6N-ow%OG3>yIE&^`h|ob zMyYNOrSPI!ir+f<-4qEcwbT^H#?G2jIErg7?XGM}N3(bj+AM1QHNkTQ9ozZyg?;x1 z&nt3fdb=NY*+b>>CimP*iOQFyteU)6sy!y)30Rfzr4LPZavO5A0N+}hT!gdJYOX&- zNWhm`*(nLwJPbWJg{`Wv{w8ReK}M`h^B8&PW}d6iqq-h^hKfy90?>Aua4cpsa{!_bf&xm>D!PNT0D;$B z3a4bCK{Qy?0+9fTW*1V;4>V-}D~N=&bbMjv^}_$6H2_mm`88Z(Euy^dszhgkEVVQ3 z(9tZ9dF5%DWy5nk&vQFYmA^$Z*4l)ocFkW&=!dB$J=|jU@ED6hylQhH28a_E63s$U zm1rhnkAQ%*U9N~PpE5lj4cDNSU~T%arAybS>Q!U)dK`@YP#xmBziILu(er_~j6|^? zM}vB_4hTEQDF?u3y>r%{=jfFE4?)$f2-0nk>z@XZ4ic!c`$%5CIotPS8svs~6%5&v zKnvL@5-!5C$)6EMx{zcf`sV3#gja-^+}SqjzAUYA^M>7?@B`)+`h4f$&&m!Jg-2OiR0T5w7fQ@1dCc?m9 zK7%y%=Ld?;Lt6Y9K!wJ*9N0n2g0;yS25Zm)x`gD>tlRdQ!Mu37zu*Y^2hFROy)U7y z2uUVFV4d?s>_euYc6|%&L|vf@bQ0>^`*d?x+4HP%RcxH`Kbt6a z(cjf0wsnKQMAHAN@}qG{+2T}Y%-fF<0&qHc@aH! zJ3CsiWmL=DVnMF2^XmK8hi4_11TKKu#XuCS&qj#2Xr9}ANQ)2$AfAO^hX8y^MUME@ zU<7-v4Q?;w3=83WS0JEJ3Orfpijx_mqoaz@B;fYdeF@kkaBv7@0rupBH^3d zp3iCR=xBrkbW=tk|Dq12^)O~BJ$f}uA=zsS`uMMBezTx6bm$PZ83gg9 zZHU&0KN2JMa4w_KRQO@!glRmfS9{g;wRR#*_dt}La_w$pGu~?l`UGG6s^NFG#Y>Zw zLX1g@aw4&8b|kHoC|1Q<;PowGDiDdqLt=w2nfHej303-h8TAG|i-BuVFS2kr%wRw2 z`*>H^PTCLy>oJy1;qDE;FTr=OOlW`}yvW=2#arhUZf%yZ~`oCBARO2JPi8 zYu-K1AksrTA-1&(_YHLe5*3i}8~!0k?4d+r7GSJ}yzuzq;x%CX(XwCwo~D7}^afT{ z>c;P~DH!C&Q9dzzcwA2T7%%rAm5#EhY40y^<2tf7OAOILf>yKyrOl9Cavgt>-edGPjn29JmV>c6|0zRSywxs07FPPc9IHJxs69g_QggQdN!xQL@< zNBh_fgRO>N8v`vui-OC6g&i?^^2Zeci-ULJi1Oxyq zHVHc1y&m6R5LdpZylZ!deM;{s5sBoMwhhyt;yL2^B4i&PZ%F_B?ops1z%B*X+Qjk2 z$qoUt@Vzipsd4`wHa7A1$I<*$aMLkP=(Tj8n9_O1q+0^hZ-RE<9*zWVaDNT9vqt{T z?}+LTM1n?7P_F#qM?(@&S%eYim=BfmHQ8mxP5++n&D2Z-o{~QTq}b$pBUepqYw*iC zuXGH3rKKkMix_pX=XDgWy@~IHESAaBA>iTQK$U{VhlZkCR}06Tm$J$nh*0h2#LxQ* zM`gvNvB!Mfe8+VL?;MvopfP=7U9rhyyAk2T|E+a=U}&2+{+v{qa8r5oyBi`?!np(? z#)6bFzvtu*D&*|)-LTXNqoGKfwMH9EVp))ltYvx^Sc(uUcfWsKdEzPPx@YCefx~!% zLF!5Nwe&4o+%6q!^zr^&&?Uq7UGZ&rp}ZN9yuJ%yy`$_*gu8u=J!0~4l^T|GDoP{5 z%L8dD?-$Z}TgP_pR2}!(nts}R?ai2W-RwTxurkr}9JKz4DU+4!xHf}@F>l{%p-1(Ym6tU|D zXN3872X_)EgMm9+S1k7UB!|zqc~EAMCjBlL3m2e%g?b9( z(7V8T^X9`r@FGqcMyB?bIoPD5FcX)I|EW0G+NO|~_4gPEd~G2$tP=|uS)ndd$m||0 zFzRfa%SVFqwvPvh;^VIJclofsG_8ucn?6?8H{rTmbG18Ol_Gi;Np!u|*pL+x@EjKh zEy+sk$osAq!w<6cpSHknUzKK6hjEzEIfYAEll}BmCFwyh+mCVL`w{H}s9X9PpC*>o zrVB!T%gWzjiE{IPk;G0V(`cEoZs7JQf!t?6z*WKMiO`T~uak(`SJtU~jXw>F{CIYy z7I^b|?;vcJ{Uc=xY7y}xneXn52S4tx@YUj;=Y)5cN|Lv)D z0x#!HyvZ3qLt^(wOURJMlH(k`V2T(h{?CHyyBg|{PRn|6&vP{=$OQi^_kgV@Wfx)+ z?FT;ry5UXTC%tipNPpd`fDE27F}=@S-9^rJkLY?RN8b50K8L>!&AYOyjw&J9LYn6- zF3^HKhad-?Ai!Gj5*dH$(}#+3r~sW$N`qQS<`5?>2$dRPYNrz~ z_zE%bkRihiN>vK!D{05Pkx z#~<@0-^{4c610cCA2XxtEFvl@$_oj0#jh~VF7{>gNcLvNE~reYrRz z^WJPp?F$-!I_wM^+lGsVi!7xF8@L!uK0BAxQE_mUNkGw|(qBJkGu_6-ZTz;Bb-awV zb;)t5-aQ8dl%SYut9rX)U%w;+`97lZy9)2 zD-Iw1{8THd0XLrF1Uv@E79*283W3{YV}fezR6~&b^$}w3Q*JlTF;>j6mD!k;E=#RD z1aRA=$9~C6s2FZ`vo@jm_zWw1(?@$O3rbeiAW zzeGQ3Yewo1%5G@powIbVEVNt}Y>DFa%I>6PraPtO!{^%*oe7Y!4^_N>4Xotkh~Y;zZkNk4khB^Sm{!a{Oi`wa&5HHf&~MGULJkMR+5|m5J+u zs>Si&oH1^??tN{M$DckdB$6B8+fp+midOJn>&eNC7#q>WmG{~7xt|Yu@20!`*>8&9 zfyln%K{zu$#kvt!#^oVdMXgG2Eyu6iN{^l+6&=Y;yw0?+_a^V#kO)y%LX%8|qtt?? z0aq2}r4z=ZEb9Zz`=d8|I1G(UW{aNtJuu*^Y%6(lixH_$=kDBchOwJYlXIBQ5+%-(GFo zB%lJbUGmZ2UoDnyrQpiNi%CPlE4|DvbINoY2a!xJtc2T7Vz!r*gvYMUe{OHZZdOQd zn}id_%6_Hip^=3I~L8RswDe$ zo5D0IxeFx3bCNroCOH}l4wrQdhTFe&@-#y(q>o4#7nlzZ z4S~MpoAy^R!d)b~1FNSOcNsY-p%G{TejnV%_zgJeq8+BKNMJ5*ZEs(!aR|AI z{ovF3y0DwyZ>BOe9R=&Uu?uLo7H-a5bck0w%oR^A_%yTkfnNc4XN@E;BQn;x|DZBx ze`B*eD*gx{(eJ#`Yk+s!UeWKQzx&chh?!Ze-v*AL3&`TxhvcmdgV|R@Ku~bt07%{` z699ThacT~~EuIJaqOSky2dtlRAL8s>fYP(yS1LX~19VwGhrWoO_%;Y<^IbZdfv4xoC=bJ_&> z#!%LCY5&bCNHdJfn-F50;zhzk|NlfsG%4@U3ngxyx79<7b3Q|Ds}Rt zPnYdp3Lw*-mCT8*gWhraIxGY?WA8@^xrUW=`mWm zH_g(yC}M>x1L4DbUh$9t520tjz@AM+S@RalM&=xU&j}Pyn_q;yQr^6%FTb&AwC)$7 z(!nyLqS?E{c&E^ku_vABTYntz+9L;tcp#@{@ z!O}19jeh&hJRY;ID(5}1pna{V-l1%au|q}4l4^x6lS|1FZ%1Q;#br$FU^21Haz?om zVcVm30ds^G0zd3J-TPq2qkS6Mnd1D?a|BqHn59#Mk#1 z6W?hiGOC%^chX=b-vw(`GOB4eP@T(WS9?iRG%HTesm9aL$<&gHiyB9rF47ZC@4)Kp zqc6C$c5T^Sc{NukCf+feNe}%_N0%d{vPYfLEhKeBtzxnk`b?djlSB1Xsn|!*&k#gH zQD==>p#6}_W)(dyYh9#=1ldnRF&O3qc|Ru}3og4AJHg6ehfiQsBU#;aG==QPgWV|| z$MV^fE`vMMTwl#q2HKN8AL*sbtH*8(JAjOM83H=SL=LyfYW*2v^~=!XfwC;vVB*W& zI}TyQbP$+}YC~OLZ@yW`gasT=c1C#r-lXNuF~HoGjQGH{k#nMpeJ zZE#QuYPut>n}}l$EwTRP-t^e>wrnd}w^M|S_pe`mn04t_`JgY+#@*2$zg#n){Puo9 z6&wfQGA)rY{7x57pQC@U8rFRR-`8?G%1hWc>07#*N(-G}Cc~MB4Jn;pZvXyfJ>Q>q zoilR+taz1sJ5frX4CbAehz;@0hBIxSGUA>6a#!oXsJnjckTJYbRn-*t z@+GPN``h;@^ViS`(E?EuWn-5xau}`b>pD{Q^E6v(W(3hge^NR^t|a$~;rV;N&HVLl zOo_2vB(jte32)@X|45Wta%W12vKKDtjKfPsG^)O5Dr7goH5jEiPZ&Q~Vo1ZYChB^5 zxYXFU(a2~1i1lPSIDQ<@e=zhSnxmQY(923&BpHZ){YBzLq$JYnXv|4!OBtcEOZq6Z zWCWJ#X=WnR@){EwuXI|!G0a_vg<{#j|EubLEz#MtqOt>oKk6jnA2!WVTzOiRn8OgAZb!L#zpMK_Z>e~z0!J}5KHQb+F^ z!GAb9At7rRv6v-U_^;La?O~y9zVX$$Mk?Ip$vEatyM^ISJTnGK;OkY;;i>dOvq0|= zO#U5HmOB24)Cs6b;*~GOuu5t?T-J>-^H=R`n%qU|11gN*o6yiJuZAJVVD`HWWIm%5 zL(c-Z~ElALPm>NcOVe# zGBjsuq1ez)aA`G6j(gLy(A4BnVk2ts$Fc$Hg_ULAMoESF#)gRjL?r^j*4WOvhBqDH zrt<~UC+=-YaFkZa`D(ld?_}_56LtEcE7Px zAd~afI2fl??p7Tgvxi4rbe$gi1s=gSpbs=Fh<=GRVXd>p{$$Y=yEHLuG0(aBdNNe4 z1N$^pHD!CSplC=M`PKDF|X!t5$2HBR39;}W4xJO_=TE}kou}la`t|av^>X;a?&h%&uzbA%HqX4Y zu2ti`)WG4glYSk|DV`M#Uy{5m*7z3l@_&76Ez(&p+Q}0ZX{{6J-ljdvRUb^L6d0m= z=*vCXZkuva7Lm%XFc8Br95pyD$6vG~o*ss<4~p$b$NrP&wgNhy7>8+J@q%um$VfkU)v=c!7PY}jb zgvwz|)ouulgJc0^wdTgoOtJL1P{vTLWAnZ_y6L2eRW@s4l=QKA6i0y^-5BEPDKZG= z$`<`69pUp!lz7M+mi^<$t0n-8WEe7tI!ggMJFNI4q`CCa(0S6aj%ALa+4Ez)RGf;K zLVbV#ybnmj48uq0dk-Hd9G5$wd3N>5h6TxezUoJQ`f4{C>^S@RToo7R#s!R~ga^fy z{OV}YJqlG=@cVs)W{0Y5F-9}&1ux7fA(3eaBV(vlq|a&*Ap2f%MSsXRqb#X>IvDqA zJfQ=zdaCKz)jTg2VZC|C)=7Q+pKwXRJwL%`19F-FB9TWcIg-)b!lIg8|Ht9BQvZn% z>v&D&j0~U4%|OnC+;Vh$5RMFFvjWN7U#fB@+Xw~s7cTm9o^`mF{$X>;(C?{BOcH{@ zCyr{r5bE~)W|N)Ty4-U#x_tgFz#w^eJ}C3NKpXMX#>U|>3qF1|>C?79t|v3`f#da| z;l*po8W>xzl}~OV*GjL6GUKKvjOL!*dAX*)CLk`~0-kpoORTY+dc*#5OFr?#h&zjS z3FD&n?Ntq-Y3p_<#+nf|aW(qdGH9VB5;b*Rx`pxeC*HxB)kpOqBcOuO`ZDHFrhETc z<$|Q~4l$4b3Yqh8=Mb0NhOh#4*9bAl+QbcX*AmGQ2`f5sP@6p}rfhg9I z1KE*xvt98IYuKEZris*^T-n6=1z6zHc3;qfUp;{Km^FCDooAx8qK z8=Jy=f5j2*5EYxf^UXZSZWUaqzdB122VipQQw2BOAOI`1oIoq*3P~OB zlVhDP1H+2?w%*A{*HykA3MK|R8gA{Fmn;u@oTtCUUe5D{?|<+G3MJJ$+ADuYs&7B5 zItd6#QWzjf0p(HyjzZT!zIFoEU8%XQZ%UVwHkvpo56+N<(E5wQC}$c}rlofb{*M?| zD=4+KQM&44EP)gCk&i=Zu?!|qwotNkUbqW`Bupg^n(og*$Cue_f~5I&pz9+Zva~_c z%rkI&tCu_SL2j2D3>5GvRcOuB!Ej|hI5s)AC?v!qxxClstTd;g6IJ!`zuVQOZMjB; zM}>|=FRg$zd)O^)FW||=!BP(ya>4wk`ZJ%MfDwj2XT=_6%{%V<`SmAp$fI!{EXk=I zmaRoz`Z1cYk})?H-Emq{=6P12?udlZgYPJTvI>$M-$Af+D_V($v8;`qPEWK)~Sw@^yY#R4w(rdv2{9j6IA{)2PqKU@#$@4&i&DGmYlBc zI1L)d4Mk%KcfxfHZHOM0OIw}0H{pO50`k80zJ zcjZKuzVDeP=aoMdIgxbE1XMt&e~yIi$p3NF7X@=P)pXrIVX0YStPn|b!%6X1H!Pi< z%jprDBL!7J_|^r%W$=oKAYKuFqADNhzAw)1ibtch53;P9MkvcuC%{)m1+P+w=8l%R zXyPP_pVj5+@OAi75=BMN40LQ4Ai!N+&_XVxp%?ZDt<4yeWt@Vg$q=hpG01%b85X6;^^$I1{WYo z6~tAkr*QwfbQ59ROt9wv3(`RA^_W4K)7zfWrdTETwui<9lki0MS>KnDDUMGOVc8e%LobR!tg3l~>V^>zCmnf_NO zit2`(k7fYg(eB9Am{N#fSMDIDUR0H>`;`g8 zjDz^9FJfr3*vJ1&b}3r|VDRy8?Q`3o(h8NHlXQOygHy$LtxV+A4!MMlkrzW_V-g@d zo9ps+r;;cfBtQ81b+wpwrEB+hi^u&|4)j6>T5(ztaf4?Z0KPi8FADl`41oxR~o|_=uPGBmNGti7e&3& z*J$hrrr(9*N~-e7?-`dkjy#I#bbx|d|EDJKBG+M(wyQ}!OyTOG90i`(Yc9*ZG-OFZ z<(EHkxnjPTPkS*kb#%H7avZBn&pvHiC3y{5KkrBkYHVT**lb(6d$x<=P7`8o4w~IJ zHa+?cSWoGiaU@4UN$CgM_zjW2vm&T@FEnuF{y9ZIq{|m~db}ao_%pX&2ele##0LgR zZSvAa|GilHb!C^SNKb*O&yf$7vp794ixk@jG$w2!F4!pKHeAx#mv(;w2Jy?Y?O8`R zd{J9Lwi+%>W%1QZdF3gJE{+?oX>(IlYmdra=3+`YpZ;9uz`(YdZ6Tm)K+<_d)OaW& z(|UCjDh2CMy3CPZfkG(@`$y~eV$_ig1$Yr4LkVO;L1AVAhrPA{dYW5ur`))AMVdCQA8je}@ zpf!8@YkCX60F@y^5O~ur&-3ih_jkOn|GdrjOW`8SH+hE@_e{BYa6G-F<=@I;4U(61 z{yg2cf#&C#gF$kff6eft!=B7$AcB0RYqZpGc0gVS`Yh!BukJ;sT{^^3mQTA))qE zAqPT(`u{HK{%1=T={WV+3Rey81*O}!P5bWMGwC5G6a4#Dy5dpU=47P#D5y@hpg&FVZZKz4glVV+OWW3kN6YI!nja%tIPre+$ zhDOYueARBGcAP9@8hQ;}`xLU2 z=Aw95S8(03I+0NUPy4|6>;vu6OKmrf_O+A*UOMAi2&L1j+-TR(UH`!NleX*F;APBT zWUB9|jpJ;IT5ON>VM_kp-k#;WZ;yDyj0sd*;(@QL5bfRc5wTH1U`$oXQn@$MtCsI% zor=W90gYjH^r6Dh7!NI&m2^Wp4_w&@1YlbMb$t+Ap&F36H-uLXUGu%dryR=I>Ib|A zsOa!w(uwZ?1X*VsdLFAM*O<~|d@Z!(Xbb*ur{8gp(!;Tg@Dim;UipSq<1@&;QM}*N zioC!pd0Q7qK3B~2d&&0eF8Td^pyEgGuskXWL-w!GPwy2@g4Ya|)s|lekS~U|T7y7x ze7;-eV&T|1li_kY@FiBv7O@w#J>EbZOcFI@Yydzy;^86h9WmlXzU{d68mwq!kmuX9 z@dn}y6_vhGwk#BEkPcqxylG+!j=M>36w8h_F7O?W&zjJzpf+g$@#mo16BMlNV>=~k5E-iAFy!w*%B@A z3&zADH&vwE8xrnx1DG~hXbJ8jQp=joibr&{0(=IvWvJJadwl67i~LqrUxLvYL0L$j zzG&+#viead87{Xc1ygVJp7JN}{IbD)dp9c|p?ow5afjqmd=UH8z$3v3|!d zSg6P&G2wSFz7>pC9PV@GoAkzs zDGeb3@6q*V&Yjz^Jqs-e1*?fcBv@~J`~@kbJ})mX)A!468G`Hqm3FQDcnkFNwdGk` zr<}215PdQY_(z1xyazKzYHp*DVm)(6^4$XAQs(ozb9tO+*kKs^*c3_l9D_l|58yBS zh2KpE=P0FjX~4=rGS!8oYHu)c+NFWqVAtE%7c++lS!#cS90>xmB_g|)Aaadf#I^aQ zc!o|{2i05i^6jL)*bq&dia15*OUgB}NoPX3-=$LR$Q%=L zB?Gmz^zud)L^ej6xTW)igGD_HcJ5U=ndW5ZfmBa2Qj6$@Xfh0^U+jiM_a_c2Y`(r& ztuIpw8QrP)DL{Re&E`W@Gs|30OvNk7P9+@K>$3{$lzMq-vHPI2fTc6(M{xg4fTP5+ z6Su$Tizv_~HQ%stJF_T1d#Ht5+k|g~v8^9O#uqmp9#?325Q26J`V$l!^*ut^i645P z8HADJM*McAYe;rp-*bJ_gEpLB#5mq`|L4(Y-;l$`$(?k@stX&|uhlGc+w>t*G>r+UG`d$bEyPl|;Ov zRTD=+ittpKfZe$>ZWP;M1AE&ttwfuIc}D+jmlWiXH#stFUWs?Tlv`d;F5_iyJr5WGYQe~{-dk0F|UK_I`9}0o`Kh{#7cY+ z0dpDx?p2!*p@7=@`qHp&^bPFL3lZ2W6xB{NhU;rQIHTDdaaTIJwD=-r9A2v^yZbMC zl8vGjPL=Ylq9JNsZYRNVbb0b$B|`D~#`R-PNf{eNO5FGh9=e`3FMw%LPr}D}?b6wu zm9YNVCCHpqq53`X{`*g+lx|%YI*?Cj=&z>+dLOe{9Khuz>Xo41S7=DG;m&bl1|~`) zw4!9tJ?v&o55!;aP5Y$bU!!`< z>JLi=Ot(9^SsYuFLjNwleMz)3HlqaEG`;U{kIhE4$NLN8Z`}xC3_cktyfb~3{Ykb2 zVcMP8;YPnGkrJRWEF&$WRbAL;O7ZhUa3Ez5O&`CarKiUXQMC9Nxfm4`zC`zr%(ypW z=%WQhLQ+(BmoP-q3+g)N@AXysKnS5KP##lbX0=MVUkkl~5Mst^YvHrC1nwl}KvLAW(Iwuk5=0lFRgP}6yWSOfRA9RR5v7hFLk ziEgKw3_*Wxyt20yUIq)Ou2WS8#aT`E4U35!QcIJe-CsJCD-Jl-}Ir))`nBDx(Ge zWF0J?{%ve0ImabhU=tDANl4oQDn7d5&D5GR%y2!4Rrt6Kf1iYd`xexW9S<3&E|L5; zgC0yw?Jd!pe4^JvlYzi9as}f(`zs!%9fBXAc(p|r6>Cu4#M!SS=zB(0YpP7QN31t8 zsFa@N)YLiTp8a=drYeTa@aI!YKOkM%(5ce-l;dl25=(QfU&t07_ddRR?zZK$qSbjs zp90-eL@|ElG_HJSmd$h{|oh){o49`uL{OtcDO*t|Xp#T;&B4FB+fIS`A<-(?d?#xnt35vxy zOP%=u(oJeuUTTr%sgM~Q9A@*FQ6~RZj0-2D13d?ja>RW`S^2Vua}LcReObafhJ*G4 zjeo9awb!$wPB2wED0X$^3wS_{V0G&eD*k-vi=)Hmjis$`=kx=E7C(E{wtn%{X}vQA zC78UJB<0F&YEmLHGNi0QUaOG?)>w(BPybLAEVL(+#n?^KKvA@RI6rvW@j~atx@GZK zUpI$RiHMHBC)N`s6wUggccOAAOE~KtVb&+lGS{7XZBg|1Qx_i;zyGo}z&*~8A!GuR?Xrk$0uO&O`d$JPn=S2c0%3`ETZZ0Utn#WqWeL6wj@8@^~4ax$sgzTdQFJW}eW|1g#U z%KJ-LIr}8Q|J_0#a}XmOi~_ckPAn9aOTRC3~`;Zke_UuKJX?ufjbqoU?@Y?2eN#V*#7; z?QaYO1P_L`%@!}y{@u9O|2dndaQ_E?N51^0W+kU3x5lHw86@SuwK}QFn?`HHKViY7LsUrsMU2W<{u1 zKWvz(d>;{z`=fmyHk8W#JzIA0UF&_QUm#xXn4KvD@;L}rLQd(FZnQJg@*CwR9^5@8 zu1jbpfQ!&}wiGYFL_qK!eO5SL^sO?SpacgQ7?UA7;fsf4c%rFub z;>VxsUKKO3$lW|P7YR|64e*e0$crQ>XOEy9f3$Zvvv>u{5E3j;7moQ^d=$g;c7pOo zeMv#?c^%s~I$rLN^CGkSzFSPF;BVbgbD)d4}7!DTarC!O?tMR(mBn=U7 z&KvW+cL&!Vc7oHVea~rkq2NB8js#!{8N{;am=2ZpTD>kXY`;Ee5pC7=B>nchH)nY! z^yHuHDJ-b{!7jj)b515v?v^!=?{U)kZJuBB1O$v4A_rUXv|o@EXEmWRI7jCL{k?lJ z0X2=#-i(`2s%h%Qbzvw>W1fh+O5y8KvZ!5$X4R7M^Wg;`AIrJdXylz#ZZ z75FxSlExjGu1Sw`I=*R|E)`p;-gx9g1oumdIkXj`x(8Yz_$2v>@bg;I%^U~|dB%`D zA)BDA7xGDls^PhW|3@Cf^Ek?iXJ_HFT?`6&%Mj#7MnFLB3#qMznou&fv+I`G=S9D+ zI+NbF4a@2{Uez6`MnTzMwGyvSYooq&$tCY>zs>1S>qaU~^wU;ErcVe6zC2J_|5c^m zR7?wZjGhvAa2q13Y5A}TyZap~kL4|fgIMGSS`izNQ(A%J@@UQJweX+0uYNIC@3+ht zM$04|hpR_G@S&{jB+A8o?cIsf8c?wh{waU`5xtFR9CEU5)>LtQh;}EA80`BNp>6j_ zy!#D&ZTNZ3D3fp}2hw~rK>EeO!cx=1Hx*^6vH)Mcc^~S(7d>^U=wg$>;`O)x4|8uF z6=l2ri;rS~f{GwYs)!N-2Hgsh(j5beASK-`7NMks(hWmOHwc21bc}RMgS0Tfoa^!3 zd%t^s_xi1M)>*%^)|o%_<#m{Op69;rD?XpkbzQU<*d3Ooh!1sNioq6#*Nal+LSx09 zd&7kuy91qyw$H!ix7F6r2^EBG31!JW+9CY&u4y{L2ub^ImyLcvy-=ddcPh)uCqS+> z{W?5CaN@^n!VANUHM6mvdJ=^?em&R}dhl>TM4wJu07~C8tIDss!6U9pCwc-@rk1DQ zV7KT*{OGc|1hKLb3^X8cc~tq>R2Xi3N!#hCX0g%0U6#=3=k~fTUDAdjoycDB1?s=G z=t)Xy>LBc;WjNRleVw_L>(UkDFHhGAJp;Sz01BWKO$Pwi1|VvPeX_2~@#;4uftD~3 zD!+0ayO8ZLBvJ6vlTIl!nph~sIaW{@b>+^S)^4{9`v4r9xxJ8(4Hr{Qc*5Y~_s50plqkBhDhZN1AeGxJM0!JolQ3)YBjLt(1Ok z*%DyruQ-Mhc_3YCBMM<@iO7|6eakP{We&Y;0MPdedrXvdg z=@bGu@QpzcQ@01m*>fqW`KTSX%3lw#{Y7rOHYF>dmDI_q0$fZz%yfOaIb5|ZEUoD*{tosOx@isZerUd{tGpvrSKa07LX>a-?iEGedWl#vW zbT5j}-t-5qY6g*!Zd}5<2MgTLO^z81Ji&x&BHr7_Hu2PCg_wC0Q~jiLkL1QX_HMyg zDxIQlFoC9CmG}Fyv^N|!d;bUa3)B0XmrbXYls3j|UuDCLT;6K;E9}RVlv%gp#F&|M zxDGP{kD^{w+WwEj^nh85*1lKK;`2C6iWydM_rrq-PL~SWkI7_ZK=p6rR+!yPnghGJ zy7fMy+&^(K(5YSyVHw-Cb9 zOriReHm*ht#W?mYcJ8^uyAZ-Z0o-L?{8#{;@F=rNdeAfeJ#<)(%1<5D!UyiZC%jj{ zCke8SCjTbINC$zJE_*;I521`$q&6IlyE=L_H>4|}&x~H=4A8r_*ys!L0$t`wRufC^ z7OQ|XSR*HwG?(hjvp?qQw}d|ZK3>cdLx(pmVR zYgmPop5AW<_|WmxIVw=8My;>Hc&m3AL|MfN(glE!7K{D*nfq?}-DQ@Spvrkp&wn^> zdS~P&O2p0O=O-g*-1uxhwMunMoZ$+nkLw_5PVy<)fecIoya9T$4@8R=X6{nVB9(+o zqsz>S&VDKw%0FH-n7mXh14gudzFZmx;xw#d|Y4#kD_u3g~ z;9CSzjzMs*Q!N+@RxJ)EJ+MnlgJ!w*HJR&u+soC8w(M2@*!STPHC_ZlC^p77^|SLyLFgZoWy!CFtCHK^bI- zJxn>WH?KQX?Vgj{RB2oZq87fwvQhz94QvuS*9QvN;XoYUBj*VsG0sF3CR!7{Q{d{)=CkoS3 ze-HBY#nGyYs1-F;N+_0|%~2hU%~2er^Y2$C=mi5gJXfUv11+qjG+=PyXO_5Xx7~HS zq(cGb>>N|IytK@!D3%c(l(DGY53rmXWu_QK?;U)(!AbyMAnMAKN7i>w#A~86U{JF_a3f}j12N0 zQ|NhE>qk{?c%36y+E-ygj8?$uGu3<=(-HEZ%&9#nz&yp@5PZ~F^_(-LduV%N26K#; z49zfQb9}&pQ=wT$O;6YDBDxo1A&kWQx^Kih8AKUa4Y^EZRb#aZjf0CvuvXGH2ZUc1Gyo)eKa@l5@Z(9&Yv7(dS2C2} zbWerFXU$u3wqSoMzhROWkoP4s_>G7m4Y1usC} zC8OLYEu}2ox!zN4rd12YXhyqQUQW4^s-?GlRZwu*#(XP~`wV5M=f*zU1wJMlyjV_* zOwXBJek)aLHD4vr?Z|IjA~q;p%5NQBMci^jL0ZkhkjKXzOQZ2GA4u{^h?x2WY4);)g3uWc?se|m$W@xu zZGsoW>OkiCP+1s2M*Jj`?rnFWle8(3`4`0pD#q8sJR^m>dOaZ2vMN!4iizgW-^(a7 z_U}>>=N@Ac=Cqi%f788qri5DZ7|x9x6Vg&|83qw&&?ObsZD3ig2=1sTs`)Ad;pjj> zsUo`#v7AF#r)Z{}37@%aQg-d7jMx02^@$R4FR#EuyZa&t6h|lfe%JI5dN#J5J{&1> z=B&uF{rYj`0*cHajIHK7^LEAU$*m7{Dn=Ij%RK%zfpcqb+{lNM5*A}l;2=R6q;=oy zvU_zA3O43a@rz)v#D=sNGgs)#R{-R{inh6TE`l7Bk!%io0o8RinCR0Dr91yT3Ihq} zEk`#CKVcM;-kJ}WcBzK0-&DrbeOTEzZLZL5Yg~u}3Nq57BB5F7&>+jOH9SS8iSL|C z%pk>;^C=d)CD&AyK6U>I4}yNbC804zqJyZtJ-8`{9#a-Ip|$!8%b=DmfcPa9v&TFe zZ5x0SiZ{pvcDx;`7=O|x`4vL+U~NV8%c)SmN3P;P2)! zvX}e$Gb^o+CT^QsmE9+&0M}^_W9kB8fY2}riAP8V7WJHHsFP($G>4M6re|%DJ4DZzQ0v`OaC@=26PJKX*J(%=Wm{TM$L6sgoT&Ly!-2*)g@oz;3j7C;scE&e-} zzTfMxYjs%qF~O(DR<+svh;ArFhJw2 zmS9H$4HU{(mf#rY*78FNv;j1atdJ2H%{P1}2#5z46h%ioPBF-kH2xJI@&Q^jOfsj9 z*|f5Ps-K%5s-`?=G5#WeWO?R#?3|*G#uIPPPQ*GUpAART9YV>YQwr z1ft5P$CxWc<;+Z2O~v~Mh*2+Q0k9~b58$cObz6IgR2QI0Lo*&%E+EOZO1lcpD{2~H-u<=)E{YYor2OKz@&LQ#L5bL9zMT^>HLyrycSIC>3r z?yMaDGKYG$38)w~bNGQw=0~LI zB7TG_zDduA6x~!EARR|S9rmhCrQ5;MI}c_2wHj$0b?TD{&bv~6!eG?wp!dpQ#Dt4< zzVaj*$}U-Xc?Tl7%okb~GVkA`k1T%%jf5@Gg5rUFb{v4#l{l$ph@Q-}87Wdb2;k4! zpHFLaafHm7^xH1Yf>x^*u0}L^B&T@GLhmM$uStrSHkQLc-;u|HMJ$gZIhu(m*q(#n zG0=$$$@`%7ZD~eOVwE;6~?J6f{-_Q1#MGxV*_*eO=YmC86c^IfZaex{zZ2=sl=sNU>p57b+c)#D) zL{gLxYm?eS=0~-$qhz7;bc2dURQ7loOe1$ZRO!l+j}w~j?{U4a+825Tx`)Y_jaHT& zY!6u$KdwCr6Z1aEY_ibaWlf_j=;hf4^PvSFn`JxOp0lPz9521svkuc`X@8lG3lN}! zHcG26o+-1A?W5gV4?Mk;pG2i~SGD`d6?wAP+gz%c^63znCFl`bywT3jo)gGzt>Oec zg@e>101q6<8v$A}hC*NmWgb(jF~~EX02wJ-S14s2dj!=)!)x!~=6U`OiN?TmqFT|T z{636E$aQ9v4}He%6w*mZaxTh;{z!S74tkT(Sodwsib+2f;Ejz#(#7a;y-s#3WwSpA ztFO&6T1zWShO_I9$_Z}uKEn1f%5rKDU%LGTER!J|%~&%IfvNZR{-A{ryxm^HNfRrZ zE=6)%#=3s5_q{Np5(YlZJ_(^of@&C@7s{uig2axaniNe%75LwVq=z;Yg39BJrH?#p z!~*V7r>{kX{+I84E4qO3p9iAEYIZwZ+rrvcHNp4;1W^O`!y)@7d_bQN`Y6SwcTG(B zBoH*qIr`x(`a5%7UlCR~y(H-yGlL(7gL^Wry$|-r9A$9mqp*tLW=WM^Hf`Dv5l9((+}p&3_ze zSUyLc87LCe5+1xsFR_L6Oba@**cj3U7XXrP2>pLDX@FjHs69?$P`(1F?=U2ruBj|x zx*%p%r*#J+1|ZAV<2IZ&2wYi(MQ{v@D5DtraNI7S(9@kPf8lJ$y)w+8u$%X*Hayg) zRquS1fXE!_08%_zQ!a`OjBL)e0gvl&Pej+lM90c()2ON^?(;m$CCI9p05F_%c9* zEMXpCel(!-Yrc*rlya>YAwsj!RaSM?0s?1PO?dgxE}J{Q(gi9<&Ttot!rJ;!8?sKbmCG&S-JHk_7DJ+?x8BDf0blQgGrOL5`ca|Eq zD|)y{s*CnGN<{w}guyOD#T_z&AhOu>^yDO)wY? z7s&OH-O)3=1Ifb)sysyQ%|D;~VF@%swynMA_(A9fS)JyOWtUcoE+v7yq}e_A&&P- zMM(aEBs>zbO9&rhrD8gV1n?Fy++%J%2ZH+bKI2nzLc0b#8wdvw8XC$mqywD`=5Qa5fT>%?X)^{T&w;(X_OCLM!3hs8w#!9YY&$|?J3n0PDb+A zH~+!HMRMjl6JD9U)+%`BPfX2oyVMOBA_utpAvo~QVUwr69lM&C>-9i}V4S)Ow_Q`mY;Z?`KMj*5`LzDLg?;`2#qk1&f3Lz6HjL&2E{V zTS4mD`(TIWnw(WM&L0eB&wZeSk7*ZVwxzQ$y z$31utPF#3dT3_cZ^C-HczTUYNKWh&AM=e8z33iz(?b79SFQXWTN-;MB^4k~MvT9Gl@eI85X=a2B0iN~42WXa?Q8W7c63F86T)L11- z{PNGQfp6oUK0a#n&$s{eTTQ1cJ4i3gvRYfOAb;Jl9u1;!iu>31!q=Ogf3*=FYSeM; zuhsa+pVof=;6MKPe~;55S?j;Ohy;)${%+l%oR(eXbP=r51*&wtVPg@ohZ zU;jIw^iTTdAHSzCj@Y|@d&PgD4~BjE_e;b}0^tV#e!VyvD8_tjdAO|S#97+s00b3* zS};?yAQcURjf(*;(pc5<;x=p{bA>~m1#FyJg@ZW@;I|cqhTw4f6GTt4pvwzYvoyL~ zHs_u|plKVsWP85^B!f7Fd=8Oe%X>w_*K`mEjX^>Y`vUq6ArQ1^zYUzJZ0LvZYT_^# zG%xiaCNP5HZ{THJGPQ~_a9PiRjYwUzSqW11NtwK0tulEv zK>B&Tl5ZdnQJx4jd5upB>DfG$$!h^EbUrEPPS-1~`SLKYos(c-n@rz-;x5pjc;w2t zQ_zp^o>l#MiDR4L5DAFKLzLu&c$u0^Xc`99h8|gtc_RGFg+0S%r>YEpLaOSx7|N(v zCs+*#mDjb0e#ap0rv44_?fUbv(-~m3d7WZOKo#(1z$k9H{r;!BhQs*~#T^H+cgzah z!4LRgr7MsMxSxo%{&sn#ztBY4Q5QI0N|0QzcMHDVxabMymx2)bsTaK!R_c?qeo~)lno}S1`yq9hlTQ*Ug|*6I zCwssoBPnKl7^aA=v^fCFaRTI7lsX9FUoq22ZTslaPHsH1mPbJmLF9J0JL?6Y-w8ho z_E?k8oRCdA*050m!hV_!X5YpUfSo)Y%C0B$8Txby4B}pzz?yqOjwEm!WS<RB4+ zjeAo4`Gsd=9CagcJeF$^MiH*G8l@v1G$syk-QxgXU(jiHekTkxoBga9{^Y&W#XOEp zQm{Pl{#qU`xF>PyHyT$0{48NPy)~Gv&r2`}=`+M;)->kXXuWxo`A^d2k?DJBFz3Pl<(f>de@IP^cc_0t9TsZ);n+1rM0-&T^NXNYM=+3+ zH%1Qr6;9SaUJZ&V`wR&35XWqL;KpUwNEuDIr(vC3u#aaRzPo5I{EnREr-#kxWCvvF zW-vkJGr04sPStf2!&h&fmzkk+5C| zw(YK-L!W|7XwGA{vY`fX-!W-7WFt$XtVSo_1#Cb!0}BeYT`*wYygl~3f0lFiWE}BY zAP+2_Oau2MV?7aU$huj$f^g|aa0P)HM0pQxg?auOIERO43g>q}>T;U3N-)9bQPR;r zJhzo47k(X#!0f!oRmXNCj8aM#n4n_oDRFRTnPE)IHv)Ig0li`?EYCN89b&;!Tnro@ zz_%2U{tC}wLL?VVE8cO1=zJNxS{|r$E9_x)=^T8~=5&;a%3TX@5&r$$#}dBr4Xo%niL2po-YJCWCSfN|UI~!CRj=g3ppV&q)$#VX zI5l$dq^&*H)Di&Op{9e8bRR;>r(Pmo48>eGnCON4VU}PFiy?=(_Bh1w7Q zt(>cmPVAu_M`qgPK?Lx$iXZhtM+7k^DWn9}WGJ#Ta2(&jik_1>B627}9i{=E(=4=Ub9dd@#*oL`B>^`7@OgCA9N5CHTqBpvd z(mMYA7N`izmW(GV8@$bO%D%yDK67@Ub?XEFZD-&7duN;dV@-Vj-r1CYt%(~L$3Hte z60Qf7U`U9+V+(rPC#yT*nVU4Ec(;YGqBBdtHh%H;q#gMDn#BN-?36jqFmy;kqy4$u63Ci)BC2H5DypWA0JUoxBm=@;d;hov)g$yv9n2@*U>V;4lut~(5cXNy9Q!ElEC^k_F4?bgW#h_VEN zU~Q1hFM%qw_`w^Z6z#x`@Jr0JwO^a`>=s5l5FZ=rEK*I9j zuZH`{*DLxkfE#?%;r$y&V}>4u%KfQ;)*EW4#9bSWFT3$E^;r|e2VOB}fNv({w#{yfZ{6B}1O z&dJQc6JGNL;{j$DF&@Hj6zC??f0Oy_@Gzr}9rc2WrhD-vqF}%Y;9Q<&Lz*FttOFx@ zBNgXe2(E3VT}B*mzp0g6aKw*CE1hd+;D{8sK8LMJ0CLywNmZ;Lw^v4}cpc7{p_ z+s9x^G=;!3yTU-JQ&8H#_Zdb|aqK)(j=`6+_+c+O2@f& zS-8!*(Z7zcG&N%M{+PoVx_ZSXurA1HK0GhOa06Kg4I*b`Q^P_ahFpv~tfpRuT2&HH z?ZkJHG^QoNB%m330sj-`vS?R7K*NN3qy=0yG_K1VZ959CMUl&(DQJAGI;2!PH4 zrO$ygbO6tH5??*>mI_+BJyFXykm3iJ-5km}5WIMY$M9*a$KJg2As-PbjcW`6FWhw& z^woQU?TK#Dm6C#^*$>qn!#Y^Ln}8@304_QJgR`H5e74wTMp{Nrt{8OjBQ85DF+Y(2 z4Ur?6b;f(OY9Y;Xzvg@e3kEw*5AN6dKW={wiDS6W3|Glw3&30eRZDnuZWKyS|gmqzv* z?5X}bB~c`3Huzg@Q={@tMXg8mjcQ*W_u^kErHAIdX(A9}9R(*#9LlW9+zF!1FOZl~ zAdPqLy4{*(UCZwm{LB6ShDC7#i$?(l=RWnDO7VZinEDW7>iMJuVgC+9!Nu*i*75iz zrT@CpJeK`M+XuJ9(!AIpoB+(QHx1ta)`|)<|V2PzAAUx1inBEn%KIr*2 zFpy<9&`V5v(1A#yywS9*e+$O*{m7}_Gvx=cI!nx#R1KrVzz<~32-kigtKc@YJFr?* zht9pG*qr6!tvLLCV|EX;Gb;(OIBdkPdg2Qp`z>3B?by{WxT6D%tV!fDbV<+a2c$ac zuAv_daBRp)tC$Mlx7q&z*9<-r?g?xea)ak(jx@;>OjGxKIj~zWiCG1hzm#vLu<=^;zZ#hVmq`7|^0dM#hvkJOzEe;f<(LgY@ z4MlXCpgHUrPJ6!rX@Vx`>Rz=%S>)o;Y$$IX0ntv}4JzIH<52q$cLP@mIb?sTEEUMt zX1j0c0l-xCtM0Tz1G&1vVi7E^0ocX=SZNW9r@%lT?*@Vp!P6X$_){1(gWCaNqJ3*W zmuf%{;6t<#oTH`IPa~v+U?ig5qMpNmp~=gv?{44;!XqOW^7}v?K~brit2+X1LG=M@ zMn`S|1B-;Pip9D>&x)w|q##?%8$b=z6&(C9eH@@y#vm0g;22`qBneY^gP>xoHN6D0 zEC>p4E})Q*hJ&qdcb-Gf;E}z^o4iU8?L-XemL7zGQAypc0PGo5s(I<#Anm@Qga9X~ z0a0q?Ot`}j>qh}Q2=ax^YWQ)Db^*jkw)gvqxrfJF+`~bBC#bt|iDgKj;LJ|e+}dD2 zv_Jd$tPD_XzM-KLfPJ$5&yOQH1|LV1jBKjMVxPtnfSRG&uYylsnFZ*!J1dFTUlb8q zL4k&_D?FE1t+1oRR`Lc2m!P_4`fh*N%qVOWpAv+uruwg^@g`Zo`69~aUdvv;PBVy? zFs#sMxYP}RS_=z9d5F5Qp^fP_1mUU9`?ei|6b?~`HgW5a19`VubVx!ZKittdjsOT2 zIH)GFET8Qhi_E5eDO{j+ znY+5P@%bx>?R_k?O6)dY4ON!QLsFHneOQyS1Bk>CybVde>v1qBntulfpH({sQBH28 zw=eqJ9_&d=0JY;Z^*yUjX{XTc^o0so?PZ`yniJ>=;E{0Z1O4z~=_+7b zs3yowQDy!B1|3&}_)W)Q4;WJesK+gaLsv0w2Dz!pQPz^;0U z@|i1=z-Z3E?67c_-2n)+D2$oz2_d?M5}2O;<1~Ba_)C$tn5@UrpO&CWe-sN24!L~! zK`5X7f(E#|Wgu4CYX3j3DO=%6;ah4HnH2)R{veHlE!RZ(KWyxa=#bE-+W!GRy*LIz zDZKYzuRplZzkWjgci{*_$|iq|MGSJTX@rde(wKMe(c{b?f)JE_reVW#Re-5 z@k2ZS^ut)Hpix9E8*SU32XuiJoTg=1@?wy{)Av^ZUE&pT!TLhJ5fpu@r7K)r9INrn zas=HD!Ce?1;V6rPlAEVhEXP}b_?QZ)(Dx%q&=4ECzv5S%eTNpWhoQ-ZikFM0j*&QN z-+T7mDt8ska$B%F$Xw2`$2F9DOp6W4%5Ay=P(R96{aZg%H8Y%nfni1}N=Y}?o4_Qk z>*;B&nnVf-amv8AuI#h+w<0gx;aJA((t*LT$s%a-WYH=8d^%B1U`-4MfR;YcVxq9q z^AQ@qdVgI8akB^ufRO7yf+c88z+eY#tpJV3;uDoWR(gI>y!B-{9`&LLg3tzpvM}pP zd=mzcn{@as6PUASMer6$@rR%iJInFB2(^-te&Y2!q#goklvV+Ux@W0?V%4Ptz`4@< zAH1n;e9(4X54Ryy_Li+wdVoOblf}?p`{SsxE@*BlFb}+k;3Lo?d)(RcHiCfshggq%*nwTxTt71>9&BgaL?k z>lrwOYQp-!X>AhbG9R2iar^+#w-w6VdM_e!=2GVGNd3p+ug-h`Nwqx?MSW>|A%x`L zT-0Bbaw4=CXw>QsYbfs;_)wuH-2(~jf$G!%T9Xxk4=&5m=e9q;=K;#!sCr(paRSjZ zL$QX;#z#=(cLfL^I^jTtgai)Go6;z<`r-(X`3-_0ecB;O&&i!H| z0gVL1@3{hg;qI+Woidv{Bi`Ve%cnA@vZjU6>L9o=a)t0C3#Oljj|qUMbO4QEgA4Zy z4v5%-p28fLPiz~m08jjh0>BZ?pA!qJE=<4rP*1<|Iy01~W6&=1Dchi=z%c-5f+dUiUS)M$93dr!Fx{@x?NmAh? z%7oA^M`!klHw^$9c(+=lHk*n#^@X%y0cV{IBziY@w9fL)UBPM~q@iOLldodJweyTl*L&r>Q^LCk+Dz!Yj$ zu1HOnO5u{CC|x&sREHJb$Ilb>}UFiF#~)v|0RoTK%oeC$$7YG$I(WIn`AWd}WZYs&j6 zjf=C8Q?6#1nYu4O<-p-Vc~tsC5|@KfKui^a3O>|s_kd|R4!w#JWmCC3@1$2&VZu%# zR2Ahn;@I4vwAZ@|kBIEBCxnZx&J8BbR}=v?kAK5|8CLA1*#fg5cyw_}3VwqNe& z9L80)&lPC&Iwoz|3wCYk^Tv{pxOA(GcSeA&8hpth?5_p4zQcnK`KSY%;x77*6+7((Kya(p9aTl22UE$?RHh zR4ykmTR^kRxk51A)1Wu%Od^42zsvW{H1OT^RFa!XvnsbTm`I8`{Pf7aGLw*SJ2BkU zpIh2=H~mS&@L>^OBE%o6lC$%!5%>N)9v_u8I^`n>nMRW;fbn)QUO<6M7q|hSfe^B# z?^Zq;#m=)y0vTpQD88`23pNe?`P>3T3f2@?1jQ*t(3=1yFI4}|EVL8vbxuD0iSh*b zC1b6!rSMIa@w9Izj~5VK>b1gjrIn*hRl<%Qv*hT5eyFo->j{NpE^S0F1xoPw@6%;=9|chzM)!dr zD}P;Jkd1t4xe+mjebd|;^ZlHFaR3-^|B#x>txvclrRSVo$M}0nht1p zn3XC;r-%#Z=7~!gT~>VhL7%e0aA9dpywWvBWg%YCNEd_~-M7@=&nij;OtP5ESbv}t z(@|ACS3XmOYI(~{8tAsQ6OD zJw94fnO$h7u0u^O?R#DKW?K3Og&YtMU@=M47tuT9-su~XxcC4yhGgeNlaek6yYDw1 zeH`cWw0jhKCfV@tHv@~`N&82k$5N)v?awhC_Hxx#S@v0{?4B370dXIr1Z}^TvV={9SW?3p%V%5l=#}yy(Fs50$U&-imdv<$pta_k&)JJX} z1$>-6t?JbW*4g@24OWB2v~-bKt7$t)e4LzZB_2Bl7Z(=}vUYqA0~vP}8)cW@`i+DY zrr}Ex0t0Wzb@&9-1f=mjOac!2PJ~?+2K|^R|!Nci2R@QRXPTs53M$KI@r{oIH(w+NYM(=o*(DTMnO!wAG8t%-Bq-zR}=Ze#4+-OeXKsit;TDfaM=Y`BPvYUKpch6if?IAmcV3 zt8!OZ*SsDYnk9CxD1uR3IXbk97i+JEWk$cUKhx`-W}964aF)BE)?+Ydl9WcYLHe*% zkY0LRjXH5BVv@DNllML@V*`WD+$lT3X3Qtpa9o;}Poe8uujW9pc@%#MU%Q|1CSICw z(@#&eP0!Ql#^5DBe)l-t_bFlmG53llm~SXoX8cxqcze@R+*)@IA7wn6>k|yZ*mxK^ z$80Y22lWJz|FQ)Hg+}oZ8GGF|Y&ixB&2t}x++89Yfgxo53_z9LkPGNpMv7rTLh!R) z;Ekrj7iXdF@>T1Y+lFk3TT{?glm1)~>sK_x)U%_|SnL!TvkK!cP(VrSmv1@=#*IN> za*+|rNgqSwA(Qa4R9xSe8==7Lk`5P8vn9{Flch}h(NJPB6ahTU&C=(9)O#f-Cx0Ql z86EU<%Vdn-Vd)a1QnKV@kbu?B0A*l11hVxfk0FAc-$r)?M@B|I`&5i@+T|M)J9Veo zOU+H9CfeUv@HNIF(nHoB-fdy;*a8TXyx@CzMr^&ye@2=ObJ@MG+<6bI6au=;X|H+X$9ODNkED=tYx;04%Az(8YPHVrX4&HPPJiSFs|% zSsd7LNDoc`&_reo1jO6M-kO}8IHPE5@p2rb8q_}^O)D=ZuWE-d=R^b^JXX%KL@pYO z@4U1KMNIOJ9`*-)?qD#UJO&V^cE*pra0~>Lk1j+!?6KH@Lf7LZ%R8XD@D>cXTn1?B za5>~;(gph zhHvp{aC-kv=%ihh@K)^Fs7##u_9tz1^H&M*g%O6@Njud58_lHv-1991iJCy&T#h~U)bFJD8hkGm*_X;vV z`EhZaqcHxY->u*$SU_rihQ@p>ZAWMUAi7_;kN66?xt-wm zhhtYffaMr}vCIGBfhB`<=&50l6My2mvNwM%Sv;syoGVvx0;2gx6bg*Z>3|D9;XhXL zd|;9`)BbR$2LEuY`~ekr@A)+MOFUMO%kAxhvp|~~4} z7gU#bn-7`2cLidu`#8C&f&Vwq6$P{LD0nCGoh3i>TB%WcZg1hYs%Uo28be{QEb*03 zYe(_lEs#0}m0T!05DSW%x*=#}+#SfLTeP1VWkJN|y4fXhZ^?KUGXI|JxEL|!DhI)z zFjDWdLk9DxGsK^0Hz=Ks8!v}m9FD{i3B=Zi_5w(rUID;-yA1Zq%<5(US8xVE;`5YL zRc~V;iVwh}%6fYaj58JnjgJAE|WzsPEP9SMEZvM>XB8! zERz2BA-?B@s_ z-73E4-t?&H=__0egDz_lU}s+u(v21|ah&HY=+I}lVD?G4nLFBeP?&6kX+oUv$6@lB zt}Ct#)e<3+GgS`g3lEq~SU5h>4|?P-U4Q-ho#o`!U{g>hJz4Ewgqp#A&9#6wfZv93 z^1gNxlfK`b2*oHv<24>h2|(%Vqk@2K^x`mp8JZRy{0`E;3Aex?r3}eAT)-syVhW&F z#LK8^b_{!W+-*947lp=|>dIqex%PWN}ZCDNa~+ncke7G+tJ?tWxm&Ik!fP|4{Pj>s)es!liOO)5F6ULS5NS9qm*brxFhd5l!MyK=kdraE*LdX@{BM&7{}dK}JAR+?p{GM>g- zB&-eu7qc2TYI}4Qa*NptDo5QH=V*&fy{o2^Vj|5ESxARk&3b~0&VreGxgu3)7phK%Z5lp&YPN`m_$AY>m7_2DF!%N7tgI}?{Y=VE z;*RcmO#~--tZvQZ62j#i^se>c8x{O+Zs8Sin4QQ}Ck$vIqnH(jI#qZhbs0I>+L;Eq ztHfn@$RI<@YanzO8RdI2uBT3JnP?r)&dNTd!OBTQk3UU^F^q z>71cWdxeH*Pos&8_-ZBd2~yb&D$IL*T|Dlmh!~Z!k@za_7CCc{5>D4a?1HFK$!dXd zS0dX*lAUr+!&HksIq!Ng1fok2$pv`G0s1PHi)=xGz^V4*tP8!}Z}e*YNd4@tZjc&r ztmYs6_MWo7?zqjwQOP!RX1u8XX=zhFg~$#~q}#pYyVox>5WuFL|F~NDZL>I}gpXr` z)of_tTpxAWWkO;;h9nW$4Gptu-Q$W#yt!eNxuHT_3PFHoB;cH(ioYn!-8pm86CT~y zD#R_M$?2??edSy++#Un~*K%EEIJ9fS_s@y&{W;*zke?`LR7Jw>CEfTLAk>@-s&@w9 zkvs+wj2)p_qG3Xw4@K^8rr2(WO8b41>kht~KW6$IfGSKfcp>DzR~p4=o#Kf<+y+N9 z1d*l@)ZI9xVlU%u@Gw_=qr=gcO1cgfzG9?EmPSQ7`4{(hSL_c+YG+;yUCggwtpUu+ zX5A%~i;89y(RAlPE?n}6CJopshvhp7z2_sl4j;O-1s0=kMmkU}iTo*s=S z!Ec~MEY9e+N^^|O+mD>4xk4)kyD?ZN^B$$-Y{Y;=?;+V9Z*+`D?t8HdWA$3%jl9(4 zj}&mhlfMwUuc1-;8zcws!1~=9alJn)HG99=`4c(ysaXW86}I0OYgA}dA)J*K>y9U8 zP=l_w-3(t@Ff2-4-zvLWnwKRgwV&OHyu?@G9_NP^y+CKXS9;B%J5CeT^h7r)R$R{o zsh!(-gQlnIQ%tW3Th0+N04)vgdS$r+=7}PGCm`s*9~-7Ety4ZjVwbYhrsCYfmImoq zA5x3@^b&K<_Vl%DNy`~s93hH2l=tJmEabPc+4`elI80V+eE%A8#BR=P``i!kw>OsV zbe{V9J-~}%+OYogE!88}LL;Q;nkmkM$&K14#+w*QX6 z<#J}ECmR^$%HGG`Mi+c7Vs+{W>*7@Ntq<1unqQH zL4cO3Ej%pbf}@z{)a$_17k?j=CaW+^8xhA@#W!~G$8{Cs8P{$VV5b?9T>JX!RK1AY zSqFRzY^2q4$6PHAR8H!al8jm5B~+_KXtpwjlLORn5%yF)G^{K;J?`Z;vBSs3StOSX zILvxZ%QRM!htnk^G+Els+d|v_Udwl31-@b%h+oS161iuW&t-Cy z@?Ae`Ko)s-s}D8BPPMA|nYAl)_ZaF*O%uDdvfMWnT>`n%w-C0_xKgHYgAQVR%=ATf zp32Qa3+9=X)xx#Z3+sH92OLF-lp%GpyrDg~v#2JzO1X2EoB7r1nbJM5{a(^%3zn-n z_23o;SGyz+A)>FqT(>*eA%*&s1(6}vyCD~b{8Fo~mhd&%QCq@vPl_oYzAq0jkqV_| z)nZx?MH*#%4TG;I8I_FOu)7_}XIr=IDjuUKz0`w~>q-8Nv6WwC5pIxhS!FI04ry1m zl>Pvj>a%iPxkk;dZr3}7qH=46gHtMP&F=_{&bo|Dw0kqhx-7o#-eikp7AK(N7)j%! zsydb!?MJ>MAiG{xR(Dk;-t~y zSPMyOZFWf}_(D3}5Rc?* z{?7OKxc7vn&;@4=aKWAUlMRV3s|F?PcmJ|4dw8-Om&`-DMgADiJ;?^;LgE`2k?q6G zd8rb{RjxRCGMR;`V5yrAv-GkT!*n{N5Y>3pH3|A!m7jMD6YeTSMrnPTjwBNQ^fRFl zKS7$n@jykh`K-|Ic-J=On^am0incUn1Dsf$4;ErCjA=(CC8+0qygeG{jgFQQWvXWZEuZZ!&l8E{n_gKzdPi10`MWdzj`HX1NAC7^N>Dta&^et} z%5;yiD&+*7f3Ny0WkybtJt|(6Z#q!BO53+#T%W{OfV3`FpZFpSVCM3=~-R!C~fk1S0q3n{R<4S+*c2 zm9UGm1}QXwEyuCr+?Phi;s&W$j(Z|>+%f6r1zr8fcs9~<2Hh%(`6|DBI=IQ#9H7*o z3x#^XEX$P)ze}osuXqq*!ctm{p zCSO28jG?LWE&#UlgTj{lepjn@k|lbGotyV*8x$gRY<%Cn0i{WTU9*bm+4a>L{UoT6 z+3_IER!P0o!e7lxWAu<^c=KH(vo^u0eP7?v&FBR3$6-FHQ#mGOs!Vd59vd~D*PJ_O zB(u`pdv-fjULI~FdiiDqbwTM2_WZ}5&C98Hy>}gj@<*EyLRg|bC=F6S4ai| z5aqo4${}0i=)Ng`XO1D4CDpe$avw<*Z+JgTlP4P_iu~PiF;(*NYvqa&AI(;9-18J9~`{iK&pe@&)T+fu$Ca(9fAq9`uF_bA}| z=_j-~wbHv5;lNwdr1PmA*5^XMy3z&WmQrSCN2xN#aoAx?G{Hik*=R~OB zmqylOtjE!!OHBHSNi#p$?6K0B6y`IB!MlU0+lWNn)D;@(_4_GlEZ^<%t|b-wNnYsa z)lDS1UbL9rCYcwxEfHhsLT4;iIr#x-O%C&SeED`gdFuRrg9)1+ydX+O4?ViR8LQY} zS0vArC(|g^{3t^|`B+*fv*Ln=GJl}{C59R%DRiSkOtqH!S{Rlvr9Khda+kf+d@0Mb zzPnW(F-G>+GE7DPIFM?jcB)}$uO{Q8&YjYbib^rIE)}c`tIMj3byVv6epk^v95mEg z;QK9>YY8OR%0aemuHT6TE498;2Y3Z4y=R5&(g6a9DinXZYF4G~YbGP9G%f`n#dj0# zLQfSV=DApAPiymPTWZ;UArve0I;q`C^>ODNrN)X;?1EjdXQz_;P_zZRO z>QK5CG3_q)`{Uhrb-;kQDg#qX2LYEP{u9-=4n8-ukdC6h6YiRX_c-} zzdl@q#;Tyr!l-k3L^oYQo%61`%FS_y@9A^E#RC5O*ci?Il*79WHknOz&`w~SncPqedIHX; zErZC;p;2u0a+PD~c+$s*1hjo}>0Z=+9psr0yDjJ1jwWqn3duC+`7zjk4?8z!d5<5| z;yQ-67)F^=6{C}}8SV;hAzksU(LR_1jybVAX(5N?EOQ5#ofOicEzi2lm*K$xh5-G! zAec7uzsNcha47%x-%C-H7Lg)Rku5})vbR~r&J5YhzVEwit#*zipj^E{u={kiY=`}Gcc$k5EA2C9zD(aGC~1^=4K zkOTWR*;)OZnD2bb`0sea_OEuW0SrJ70$Ybh3NjypVFK?S?YG)4eh{QRir}QXsR0Ag zcd*Hw9ssB9TNpA2BXa-t+HFbDu{h7$B{^oK`7Pe8N`>N8q8OkQDki$sq*jp69E8!h z|69=h1cSAe3JgwXt&x5`==^BDrb$aKRDxgkH_WA4zQSRN<;Jf#ZAvi69cKXgng&c- z-~DN3V?z?h{#gwO0Hl+xl4jj;MmED6(DKq9qyW zn-l6*TIGPgMv3%9&%1m&^{-ivjPI)UU}Dx)&r2$suoEK;*?9bkSd-5Q- zp&znqf$NB7ZE;jm145;%QUO1D2)UjiY$pI@*3#jkUF`rw$N|nC$gH3QnvU9AFjNXG zc6O+xN>E(@z3w}ZD0Ua@z?|gJaZrU+8OskJok5)C^Lz~}20-;(9uP!Ok7fui$n+Jc zI;p@yalsmzB0m6H?Cuhi&SWxcB`-V_xI{j=xXx|dW=qVNNB9u4i*4L{2PvlBz!2m5 zG7KoG0lQT#2iMogAh;pC%C@)FC>-gaFA8m62mlCX&3vvGYu)e6a7qX*X)kK&C;{gVSzyFc@El3SxAM?Ms1@Q71Se@%L2X;kEJugC=Rkc=9- zSR6+DeCp2T)IUyTSIm5zFOfvE#}G?ZY^ciBORP;$_@rPR(k!B=DHN zMF!}sco4i1`1wZmxkzo=>e9bVok>|6>)Ef=XhC}_Xw#={@B^#~GkcBm)pIvQ?fSlw z1C1I3{1Zz4f}Zr?LF!{iU;uMD2A+@Ub<4GY0T?L#fUcy+%xW#DS0B`rtypG>JGU0AstZ~g8ZzE!#!x{!o61yMB@pmVe3{G6PAHX;`&N6OZ9*G=crK7(E zFGf$DweWN}=H=g0u@B=Qy7SQRe!rSI-!Qa%yTe1i*%F~6Zcf4Bq-;c?2_2^Eeqk-G zQxq}N9{*hC>*%1q)CkT2^IHBusdbnW5$DU8a_1yftapFapJ3elT|JjfAop$2ZT@$d zaT{lXUOHTX^I1|(U4pkGb21d^-)=+ywi6~~luU-s!Qp`(bs)GqQ<<(!ir(HYn8Yo) zb$(h7iM+kd)z@N35%f1Z!Y(o`r1CEJKMARvRF!z%?O6FQc6W)oG?ZJ3ypF&9nP%N@ zDXL@E&>1Sd=j(Xh9FN0`<}nYzRWOP_0}|sh^u@mXa@=1gIdj)aPhkr7i!hYq9TkrE z)7^5wrh=USz6Njk9gGRI#&f7i(LS1Mcr9SfNk| zX35na0ni1#kpoUhP$~>(7iSRVAn1Y004-hn`5WT(0&uozz2Co=WIxb3MlmG_6-FJP z7nXxw&i5BpWoL8P!RnX_ zLd2VAb`Y_D%xXv$E%gLED6SU}o&z`{MRQa$2R7&pum)h()42X!E&BP7Y$jDS=528iG+EWgeiUCB@ z4Y!ngCMj{Ay8;I z;kxo^X5LL0zLfBP?qHqRQp2nu@Jgj4_?E>@=qykD-AEC!nFt^l)(AIO(ZK?gju>P) zwGpTxL zFo|Do@h#v@CXNc7h`B-g%LV+hn6n@PDMqNto&En!m2$q z)$*zkB+NSSq5VK^j3H=MI>K^M0TqVI|?5Wy7G9Lfh1)7Wvzg=?Uv4%?AS1Mir_Xf5J9 za{y$ROx6hscGPyAQK@+-yntXI@$sP?L3#Xf+@>acz&6!k+E84T73!6J_T5;p0X5HY z0TU3<`O@6k3=fdvghA+D8IQT$_Og-DxTU>BlVxe>)1JD$&|^5%xnsO!FP z;j6*&fWcm1Q4ql>x&UQy1vFR&0YqVB0ocwLZ|MsVPBV!FA5~5^nyR{14$u7H<)y7{ zt>=cth|C?H5$7Hy*bdC)p`i!9I= zb)rGe?`RBypb8qeJbi$^drT@SYh|754y@^;{Q97ovG_E90hZ&3%6qu%0Vt-G6xF_^G>msXl$o;`IVKC*;S&jqIk(MsZ$foKg2l;|W;t2F2UPC}RR*CSl3*9` z+yp1Vr9L7Ja#d)eH_os-eZLZa7NR@+pg5f@HC%#2#J3NZ zv>92PbW)IK~05A&d^GyM7k=d9j5)HQTLj2)NS9Jo({H#r0GZz4#$pT6+G zRw5!4&|Y0oKD19>3M_=jr4JNMB9T$hS1N{}qv{7Y_-HH3_uyz#0F^~eT{A42V`Kif zNK3l)u62`$;dTDX(xt$TIV^YHcDS-ITvaG|dn-iZReVn$AP&D#G!{L=G5koK>MBw&BkR*{1?Ad+9NELKeqx3rn zeym~V5y;<#!m1qZ(_v(1MT92E%GKSB2c*8B`0ZQX4>txb?I$tRsRt%r zH6B1Q@|os@F;&KH>lK6;U`Yo<0{O(3%ANM^J1||)i{^2o;HZN|Vfd87`G;FA@O)fm zK6GTN--eutlh1iy_VX*y29Tt$tUukVOYOxbX9~Q=aZ$EfP0VP4c(dWdF#{sb0OEy?f?ks<3l6n|kZ<#vt62!T0{v-s?lFw{%&_)b-9L$J51!CCCN_L1*j935DGX7x z`Zc;a(3MZ9Y$9(Pw3-Aq0d14rL`%MovJb?I`k+Q5ftl5#G~?E!dFYB&m@ zh}64f>yD0LQ!PeRAA@jCcl8*jpx8=@P1121v{=<~s%Qxp8+bjKPx{Eyw$Mv+loaYT zJv5q5b`-CZY!|p7H0ay79PYs;HWn2yC21#V9wA!qRxVm^SH463tcAK{yssryKI&F8Ac{6s0wtcf!Qn_BpQn`&`&kUq9qcNx)&1E1ynYF{LCYP7 zKk_?1d%imXi~20&9o`-DAyLRSGctEIr;aak()!u>z7#O*S=SIPMnsl_Smiy?j&rF zGwgI6zP`{PXi0Qv5Do~gm$V6ud{grw;B{3W_Uf2*VBy%@*im0~iZ9amWG^zYo3z-m zU#x7r-`?k>9J(%WK=CBBdP?EoT8kgIb?c<4gtm;N_dY@;1o&$?B9Y*Di_q^er5kyx zBsc@ARgFSdWB`Qo=QeIS5lNNB@x_Pfn5TFx^2_*vV~>;)NUjc&5-=ns;r{ypFjPBA zw<~#*?8Y$}hOG84y%7Tw?1qajum*WFD|V0n&E4#5Kp2vatc3`IA$Rb3-#-x;qlbs} z?CTI-S>&v}_}Y#)6)`*8g&izIAHf+=cLV(0|xb@s6W z!6yIHc}K ze2`sj3I3~7^$CimK?M_6>Mt;Ic|&(?bSYU&uqrx>8{0WquFgyOBU+LicM<|`RhjsB z`g-PDzJg2scT|OOZaVE@?~dGwgSn}m`pyQ)FXekvOOvl?2104FN;px9n2ukM!5gzN zEh{jcgOXyH)obJ-d~(G6*Eyu!8QFc8dzW{GC1oTOB&@&)+b7Goyg#a5?^wULIUYD} z8X1OGwA%Umr_!p4c5`QJou$D0)%~SYz@uT#a3@vt4*|W!67fN$9K87kAw^?KB)}vK z89YGeKWZHxlOxZIDWnw_uV7w7a%rFrv zI)c1&&pIJ$@it&HnqD_1w=@eZb1v~2JzucRzVqBih+ESs4xm*QxTr!GZtE=tKk4YI z06+z#jm3*J)o$l*XdnCy(39CT^`bkQe6Je-Pu@jkV9O1Mn@CEO%uum^)%$c!fu^s_<}Y7@>a(Tk z3n(n}5p#y<-8teD>XYVoQwPJyy%z}wsFCb6)4W{a!WhXXTW~z0yu!vOQq~khtf~cUjR08s5)nNVfh0zjjJky2N_ivO9#O zR(rC0T-pPBCE(fWe@N6)djLP6MZ~&hmk;#jWRPmt@O*a{ za4+a>LC~#L;}Qb4A764RCsaqtd88o-bHb#k;cb?acH8!v2GhVHF$Wn`^k0{Z7HT~V zzE!|~sd|6yjOVX?Y;7YK|ImZ|lBy2rP(3_A*9+%k zxSXdx2huF(XiT8G&ZW=;mqJq*Uf|eJLZK58*3?s0n>Xh^^pvv$*@&-h7wCbU`e-EYgO6BbnS}KrX(xU1HhJyaU4m!{MNb57xg$ z-?+B&5KabfGsE-eC~i?mD|44#JN#QPHWZyBpUT4OwSM^c`5WIy(|iAD`h{3xHIETE zuKlsh)_jvEM$byTze#$j_{=>PMc!c(5DhO-qzK84>b$R|ps%JPnep@rRRJ8cD?t7& z@KJ6K*b^?d@cU=E-)a8HzUoNzj#d~Vk7K7xQ2JUYv#768U4fXTFczh}nQk$1a1y08 z)$KWN&4Y=Q(p0Lp<=WQ@9e5tLq(1*2;UNd$)IpNmP-0 znNig7IrS!LLF;WUg=(bxP#UFi(IoHKV}9`eXqA53Ch=5SDu?Cwlv~aVZ0;~GZ5Hm2 zRPi^dK4Id^31@NJ^V7R zuR!B4hJJznkTYJNQl%m*I-0?2NNOZikcgY=k*~YURqOlR!=I}?i!I=dyK4DMd2wGa z11*%FZD7M`$mk`zxofqqQ-mBG9>)bi|$pWtNw%a zd=|U$Ydw_|6>T+`r^n~8p1>fvA8$gcon#&knoPNW3j6!e%GYOEg5|x#y3Ouv_;|df zlY8Lhw)Hi=Tw3b%z&)R0hmR~FoN!PR)n}e<5p>@rW%-UAox0{_- zb+ONTs82=lUY#w|dD2^VmzR^*Cg#3;C}F5Uq5(Z97teCH{|W56M1RfR#~RFW*yGam zGDWWtg8!mWQm0#9SkRD@)K9{7)FtiMLaz~#`>Hz`QCiXTmCsMGUD!3Cc~>hi%Z#<9 z%Iwc-JvgK;9UZF*+6LSM!#uo6inxi%@5Nid;c~GtBh6(6+vxmOkBWFYmJ9(tL*-H4-Vcr z>Q8>Rcl6^nE9HW~CO^4@Rbgi2_8NL^q zJIB?k!XYHb;qq!bm?8XkO;&2GlhGzD1+DkNyzOl;d^piG9pUMVwRoLwTMm17)~kojn14+4;vzf+b1^BKrD;E#CUA z-4{K{?X2sXNT1$wE$_3O$2RwX=G$xvWjM^diE|*xeY3OHK8bz#26asPd%@?@uaB=; z)aPGUXl5@9(hV+SzEa<{n(^Mhj>-V=9Np+cc={Y#PCl{OO1u4~6tBTnRY`|m)8tUC z8lx@6P}HwGyeH!MX7g{y8TPbtFu7E9HHGWrCZ0WMOjHw?h|Qrj<-F8%3iX?p{-Yr~ zrzzFxe66m%k%W~FhkoPXT6jqVPm)XE{AOW8x_-c0B|YC1$B~le(MuTo{fGI#WAit- zbTB;HyWJ0@mZ4pey;qVY+Iw#y!ep=R-$s!=?I9DoqG$45bh0daX#}>7=t>Zj z;tuZq$oMi|v&OanLmwm_a;sa=nwe5m2meIYeVY=aq~ z2NjkBeKP*8lGb*?dR11QX{>Ch;*;(kO>7j8CgVYW0^eY(a(35}?f&u!^P@Q(jukI{ zm+7P`*-C1Dh`|d#%{qR|L&kRKygzs(ZZ4+Vepe4IM*UvF_MA2f+-1!A0MFT^da8bH zKwe=}e&w$jC&{k2;&jfdQQ^(wJiqIYvvO6O@+ry!KZih#O3_EjKK#44=DZlQWx~t7 zyTfmn=Oke(y3oLGkH8fQomVv*N_sX*4zv`4;1xo9}_|w)tq{TaKYh zQv=!hJ+KG@Xl9a6e;nCxiwl zDa->#wK3_OES}ZFU38M89gsO$&4I(Dc-4g6yQ#FLl5E>f8kbbd9(6~JR=PAh_6L{P%%s_K?cnfz z8}Q=$kp+2SNwV+p5C@U3Us;Xed$kZyqyvB8PkCb&kAC>Rbp7QD^HWUIN9D{FX5HSm zc~O?4QNB+u++{Qut<}}O;H`unKPxttw$$AxA%;IcZ*=ISLXTsH?NGAJ z-QI09DRK4U!W85gSqgCh$t!uV~Vy!3u!o{OKuVN`_H@beAX^6r*#p+rrEB)lw7i}I7PxIDi>i^Lk<1A8+k zMx*v1cX6rv>e0gFxBVHsXKJ5y$=&op)D7#Zq}xMG_0)8&-9eK3c1KeiuH^SqO^G`B zsy7*Rs%6CWuSx$%OXFX7z6to+=KYFqGJhSC)*aabhBPQbdDirC_Z5|{%}D}pYVKv= za(!&JaOQkHHt;A@m9_loCH)Hm313jK2Y60kM`WUmF-70a@~aW1kz@M0Up2`u@P*^J zy1#cuZKUzwK?Gh8H#Y;FrQh_^S-;q-IZ+{9Zu0cne!_l6#&xVMe_BF}11+EQ zte_rMCa_~;9|b=M0w8rvZr#_Vuau$A!RYD-7&?2~>@bdZNBL*hxIz4^-hR~!|xdacT@yUOiv-ir5&>{sw2yEOt|`JiZs*YnvrlW4piPXFs_yN|+nkcHpUe zzv<_RW&9rEMmFi@zn*-%OBwGb zklA>Q%?&zm2ClB9^S_~-87D_~=6(OZL>ERDZBO5@0A}$luU=CMwzqX)rvHM)9r;RBqH%QjW_&I zUCQl%M?KGoGs}i7m7H+*U{9PgL}nj;iQl)lo9dRl_sxWHWRh)=ZF{hge1i3TBQUtG zMu+n<%wePWF)X?-QmAV{E$MO2Q6Vt@$u~g}8iS%cp;kX7mg{c0NwC>+t#GZ+Nib~> zeUO$tss1DoiRh?K@9S79zhgJVbX{klT6VL;`@i!b1(4UcWc)n5-fov8&pTU%@d_WIh~=>3Jv6)Ht>ouwJ#re-~sFnY7s-s8W%& zP8FgL7|5@qSlK(Pxygx(1a0tbqS7DLJf1fydhA?co z+V5CJHNUan@Muy^JZCVNTe~i#mpk2Fv^Bms`PZvL%Nlx}tVcBYdIE!@)O<8gZQ4&3 z#Z}K>A79n$nKLk_VNaIVXwjLkJCyIR6KIk@+{a*0^Mz9_)1uc|NQ&Y(+Uv1D4-km=k57IP8k;pdGGSFYP$ z)}nvCp_^(U3=tCI{;2G_Rw}$A!KQUr2HF)5Ff`H~uzUT2#U!ggquFLC#Tj#{j>9yN zD_4Tc!=CK!7?+*-P1b4jyEz|w0x|{LD*|DBTm91MV2NEuNmbv8!^7Jh=wDs|!fnR0 zUT5QNf^U#aRyHncp|F})f3$049$2VNn5$)`#XabHax)5;Y<^<{+Bz?j?K0?Y@ zjZ?y=xIU5NbW4TQf9^PuQn&Mrf?)lLR}Fl4py|dofu!!c4-=y*8-N4~}m9ly8uJ>%m65|KVfWHgOe9qB6%9{(VGG)-#ETAtI?0)2ol4GxK4 z)HfECJ>qkw31f3uz2rtqgKH1x84~d zv;<__YPI$P?0jcK9{zD`cDaM<4&HnibSBd6zKGU=xc~jm2@mae_>bz{jF$4vs@F!K>9IE9_?1XaDl~D6_sr0*nyVCui{%l8{vK%%-&*VMilmVu3;05Kd0E zFpyuKS7TsycJJpoJp3-&vZfJ}I=6a`Zvj@uCg1*`>Q^CT<*)+`o7|R~9uo$RHBC*l zwFwG$uD{3=E+q_yvBYJsf~cI>A<21vHlT@`IVj7nk=}yAil+tbk3uA1G{5Fjv~EXR zUe?l!N+<+6aff3G}QICz~}xl2&spT^W^Y4zGBT358`IE?KRO||56 zX9PUHC@YzyD04S8+8$e{$JI5PZ`>%-3cGN6*6o>}iskOFzhs6dcH4{hlS;j_q?do4 z_iFb=rW)=jH%Wrtv=SA?3KwQp$0}LE8Lfm$6#`yGG4v#FRDT2hy3x!4$aX9dQ42s3 zh~Fx{M_qODm8HAeW~(n7O~|lK)U=9`*e%uHL{7@AQ75F=V&>7^M*>Ld@~Od@*@QL$ z;@8nNUreYRYf4h*TS*Ul+|U!_GMTb;XfeBYH{l%=hqVn}NhtOX=0x>;wNA%a++uw) zXWc8>ls_;@XN!og42dz{z@3Y|*ZoIxB3_{*cQWH!<@IkjK6;#Nfb2KD3kvLl*2TH~ zxFYl5yWKm*5*tks@nS!hJQkPyWcEKwh!X7Mm2*?K&&f@6R_OHWH62fOnSyW|_x=uR zo|^^-`xr0JV$CnDcY2f6!(O;IZUfz_(A=)+A}UG~gW`cK50;)li2AI3>h2%-2xRJ| z=j+&%j>U{tx0pO&7mvy^Wn<;zd~DOmV8-)Dr|BS&0FdZ)&Q(4gtz+1MtJfvY%w1m7 zP1KU@hjH$dsVvBbuT5#pWr@d9Bd6JUn7ldIdmjK;CHKK|9bR@sIb=H zI;vJncmNcaqptNAIL&!(DKp&1{n37(DL>06r2L*Bym;?@!ebd#z09{Vl1n)w)t@iz zIj0>cDNu6f)pzQ?#shRhv_PvBrlJmXe2LpLn_6J>?-n}1 zeU^4^9TWL=&iQD64_3`yRM>N_hevgHA=lfWq`?utfe)sfi#sElaE2!~Kq`B}Q&ejn zi=yd5PD)$7Y_dDJhXeBwx@HT(SG0b8_KD)Caazfw6I;$oa@5!BfSy(fQO(9N5+C>N zk3Z!~*>2Yuz4F|5t)>HX!paOtzB@}E*6ulW;nt(wK)yT|^sQIAGd{D=S^G$hvO`g$ zJFjTf95Eju(|&iYL+(}#n`xReS@?@dv*wcOI&LfXZ4}e`$2GR;Yy?-fF2pNC*MB7@ z(ccf9eOm&y@1T*K*yKT;{8>$_Fp0ZyccHzdm6gc+eitp@tDY)|deg;U=96*fnWOH9 zdg{o&uhyTi^eH{>U!8-C{`eU!DWZ$}1;T?F{m}Vai8BS4MN6?Ps7lH)$B{#|Q%0A< z`xTg0nbdfvVzZKU_#7hh6H4SUD7}xZIfb|4wRDnk?t^!Y_> zRs(#*a_hBX8sCVS@GH$pGkF@F!?x`c{2P@XkwC;}h@V(aLzth|~MXiDoR6 zS8}HaQK01jj*FD85#HOSRjAnwWC10m0R~~JCbcc2mi*)CA{Cx?s87-T3VGOZJN?24 z<>ao8C-_7cC3I6b!?#}l!$G_9N_#)4CH}rj5h*kQIoD$vHPjoeb$!f(-boQrFK#El z;~T3jL6Zn<4*1Bw_Xh_#&IRZ%r4m36WGT&h~>{_$G~o5_`$5YZyd zKK-7nR_%5P6;JT-C7&_M2BD@LDhK4$G+KP$le#P)t=MM`H)6h@eu(eD=W}Fk9nb)olO)+prHg(Tm zL3AaDJU-HifAVFT&_cSVG2K0bCD!EQDYf2a#S=Ym;tdR`q7>2m+9UcsBk3Z)$OYOZ zhcLSo>`Vxg6%l4;DMWi}yTACn@;?D>B5k0~Gfz~^+1Xi}eKp;H@Hb(yFw!%G!mh|p zgFn4Yu;K3qNQvk|7aRRI@(t4v)@)OZ@xgb*TqacsG7T0iCFt4OzSG*IwjUB#w*S__ zM_Nv!E7;N$<;OW|4K!C3V7`qtPAIb6bju;icv@=+wXeI4R*vfA9AkuhdAavp#V8x% z>?iETkKfQ&U{f`+%q=H8Y-asbAVNX$^v-`6$Dd04aR(B)MSVJ{y;91Zvsi91(|DX= z)6cCV`Y36;vmcbRM{+75uo^x@uNJj21`RL`hS$UXfapSrkRIA4H4V!23WOp+s|}<5 zlKeVeyS{=F1E>04asbTIbA41^9A`C*?Ey8Q`N6}-|H(fcx1e+T&u|l8Kz;1&14N(e z9cPHoWA*SnZRg|8zq2*j=4k`!QO>1AB~j{S6LyO_<-y-zqEP>{fKq$eX#V;x(8$BZ2>B+9zrJyKV6$_$0$ zu@vly5#XzOe(W6mF61hYF3x~{=aeBLuT2!;{1*=lvs@rtUHd1wLu!p^BmzA@K?E4- zi7LNzOHA*bFuI@kj;9x5RgDe+B?MMilcBKei>2Zg{qmg>#_o18~% zBChnJ^rQ3j2UluX96r1V9IVaR$=w~UwtW%#$ntrwgK?)s+TZ|hFK_Cv$_+S29d3AI zM%>6A(w^Ka-Q*Ii7_JpB(>CaL??wcBQ(pIIxdty=Ra9!HU2yW-ptAopZA7@;d(i4U z{k+jZ3iibqm`hUnZ4Qlz+FkW%KCV0(Z_24(83JMy`1Y;7#d@5WLyP>*?2^aE{9`&X z|G_I&SNUMOs1)h8ZE^a76`nofCLY$!J-M)V;9YeQbHq|1?9KkI(n^K zto0$D{>_J*VCEc!!@c=B=t66!_&DW_;99}T)mR*@S&LJ=_C&vGEg2J~pqQWy1y#moS5DzPt7FjbYGq#oaQ&VL3HwFfJwS_m^;AK>g zo{a>$d37s#>sChgZBOTBW&s=9A@OhrWEogWLmxf#b#L1tl8CX>=+pPYnb8gFlzezd zA1*19XPs{17UzG%BT0ID-4KyJVFgFc>(h1y)g;TYbW*sa-m4~;t`wa>Q1gZ!_LmX0 zQ%ad@_|6W?XJMU6hz`@dKTK89sH;bP zf27Z0*hKa6<)(0snz^$i7IgXXbw2%++FylemxhJ|6ttC@O-Bu0q|p^_X;#=zzz4FJ z-Frrnu6jpDaKv=HZu>?2c3yR=w~JeTuZVvz(yFSmJ1zC(n~9|~kV$cM+=M(7Y6`iL2V;j} z&LB+sP3$2Jn^g)@#IV_{)$o#^jUh@}6}BpP((><@WVVCFt=b-xosPUQAWtVcSf`Gx z0a%1MvjvA=w@1TBRPH)ySiHIFC`C!J9S|xWj_DJ3%Ek-Z%*G9xUo=!0$iA7Cu>HuX z$>V}|d(}!{{;Z(uQLeY?Ugjkr`@vD71jkpRP7g5oeO{xCYS!__n)Uj9MZ4ib0;Ay8 zl85U|!l(`bH65RWViGj1O6&RhdDJ~({YG}JL0_Ype{5dRqiF(hu@ts&1#&g`+TSsY zJKEn1V<>?`m$bqraEHk&5od)y$UvsudH>@J>8MR;E2x&)109{9Y6r`T(6Au8u}G0# zpQBWX&GM2umcL5SsxV?!<^aV>b?Zn42_?=hF)<*)iR8xwFt9<~1B{|9V0$u6wgB~V z+nNw!aY0rjkfvTRgoK^{(QxV@j=VNi(6Cp4P`OFAn2G4&*^mPHYse z!*l$^g<;wL>fxu2eHIc%>6YIdG4K@F8V*Lmuc5wtxfy;63Qb;Zp}{DrtP<9~tl;+4 z>Q>7DVh?Y>fI|)+N3Na|6p@u?ZI1c@fK+@p_s}ZjvJuCT zSh@*;^;@%|^2{tY%Z=2(1tH@UL_W62cwFcWeJ&iIotzrBu)PI(*Q=4(s0YxxDQQo- z-YjiZjwG1&N9UXs$Sp9a`Ubek2Z+-LtGci1de0uwgU~Yam_7?*s**#6?MQkwN8Mkd zrYOU8kaGa~Q@MRzLz*h*#p6@Plm?UcFAsPUbORb){Emtxk?l64QXEu*A2 z?r%zz;lDxLJv(T0uO~WHI?WWoFG9(9qEm}Ic{?6OeL6SF9^KNKXufR^?gQJ_#sr1_ z(le=cLup2{r<1s%_07XA31zSW4bd=(R@tt%VQU)iJPEVeGcJR5bgN|vk0!=7m{({8npEU-M0^=1`;Ie|yVnT(@fzUiy5B)N|?B-?c>%x7+76;4@$|lKgux9fX*fhoFk6Nd--zCkR>x5%b7Z+33akO=Fg84}z34W0BHh z>MG`}KbXH309aQFa=ZRfrXzzFE29{mPb@!Q;O8)3t%)kJWNo`6Rt&U8uaMLa5Uucv$ua?2Wj zC`acFofmR7sR4XedDQv<`vuZSeMiH#fBfjm`(yLV@9s;IL0pP++CQFh7Xx~l`JuXG z>4Eu^EP?Yb8|1*KvJ9*9rlTitw~!^KMsEC0SiGX7qkDHUYo&Fc#O6-lI?<3Zzd*&P zv~vTyxUZIJ<3p6%tC#-MZaAD)DxSD;{#`IiRkZ@C)WF~H0Bn+_uzrm49~CwGd<=R6 zDR!$G!wQW7WF*LFQKrHyk2&Z(2tVIfMiv@W4PkVREWl>&O1g@4C7}r_r8@kquY22X zb8qKn#`v9Kh25U0EYDO}NZSf?Q-+L$SK#IKke2BWDH3tx4`2X~jUm`zJ(ztM?nF@M zj{ zc&uIU0b#7Ll)Bs{&GmLkb(tG~H@)2Yha_Jme0GPC`_(WiccZ0|nld7mPe5xS+pdwY zm86NmL4n+f$zHLx9Upciwm11DcUpi*@BL2P>zqS)(1Q$5MAG9xS1mnU{P0eJ7u7!d zOWKE=@mJ67N_#Yad%bv+VoKLmW7To*d7(4I0;}tniwGR#hj9F5U9;HOw`)6y8{_K_ z-FG?ESxA3L^lx6=7C1M%?6o(cCo^m@d4gHwOZ*8;G5)hKRUYmA#H)G}%)K8(Wk5-8 zFtVEeNA=tuZ;~%$COkFT1a+?$UBdQF=O1$Y&Kuy9&el`VW`cod7u7q8lY%`q60Xy< z#OXp@pXZme#Faqp1(1B(!zpC8uTZ<>>H5 zGyK}mcriCMR^npg2{qHq{X)4jk|%AOp`MJK|K062iw(B@$=x}NaB3NwdM&fax|y-` z!RDX~7NE%klivm?3kQ&Saa3q5?)I>Ydpb4IMiS{Nvqa>6BOwt zo`G}#XQg||p7KwY>Sn#;j(7mYpjPY(=DCI_4QJYH1;0_U7|&$z;S0_rOeDXW zcr9f1cr%~vi|jLkg3!}G>62k6;tX>Jm4&lpuCDXU)-GvAWbE;FsxKuI&Z7QAD}M>6lH_i2YtvyApAZ*v!6eVRQ-pChGee#kMxzV@hBw;t znl$h$kcbp6(#D`@Yo7L)uSleZxld;M_vQZO+*Ym32Z*_YEXb(f&I#%sZ}qD8x&kJeZRFkZ0F(op<0*J6=E0 z5>x2X+ZFfjFa>)`&%o}-QnrQHL(NkI>*d~|m-;I0q$St>N>oaIEf16p+^yo@am(tp zHvFc~X41@DPB2wcdS*z!R>o9b5pYy|Am(M6L*Gh!rLS_>=AY+{4(DzCmWSk#h&Wiy-B=$W=^O?GF`9i5XLady z^s*Z&J_Vq~3-mAJ9=tO0Jzf!SEsCuSfA3Ijt&)k;&+G5;X;H zi~Y(}3xQuLZlnh+@LrDPX$m9*RM1MFCVl+Si@2Wb>S|`_P3OJ zWXU(bJ=Aj&b^An|=OCf|)TW@`PgBdL zQt{;#AX&M#NC18~Z$5piBXDmrHX6|*|1#@6oNY%iy~wGNpP3ztkGCh~;?`X=yRRd4@g=QX=KcMyw#vmptINiXv)Q3K z)%JrTZF`HZbve9Ayp+GxWJhgf*4|pYV2)+*_AQ;`07BpQyZ8u#KWz|MNgZ#RL!nD| zCt#0ST<~{~;w_7QKP98&=jy+PDn^}wYfJdit(-|88FUP~WAo{;c~Z5<@ejYM?+{$) z_F@+(Vsyop2F%Q$FzswCDrcK1EF0bVz@;89VEl!kKYvBqu(VLcvg@WKF-}OkH%|c^N!%nr#g=WR=E7lZPg~ekihoH z{;2Tdc0p->!f9*J+&HIs?@-06-H>=uX>hE^N*Hw(lI(NNlY{r7Vyge%2B(KJ{&Y{i zCFflFa%|pRr8~y1F@Z@Ew|mdz;At=H|LG0juK8kT*HwZsjgQI=eEYUCzw&b9_fOmv zCw%&HluLv1L##%WX(~^IAfkrZbGky--&c z!`!vao&)@Eu!T1sNzJJ z62o=xcvL(Gfvlqz8k=8!Vva?7A`Wh{DN#;$Sg^Zo(zKg(uh+86OwHfrwJ^ok*_FrD z{O;gbyKC@V%|vYNR)%ZQ9D~le2a$Q-e@6ilXAsU1Gp*%KWH3o7!NBDW_IbWFvlnQT z7&Q;3Vw<$T&$9_}(~8`A@(HR$?3O`4cYOT`et*GpqE~n+=p0~(d^U>kEWoy>XP!2v z|D5AZimB(R#LYqRGTp)4?gTgv_k*0_=V7BbRRL2ju_8z5<2c7uX5GrE%pHZ|gOF%e zW!7<=IQt@HA@`STP2&qyR!fYsPK7oLdDteMC03cK;*NTrSs7Qs5uN;@QpZHqzdr<1 z)|x2oI%^Ut=Cp=Ux{97ktfK}xRZIb|m*{b|6@R_9k0=ciJHnVQ%E;}N*-xsaR4)VGcry%6}dx1Si|Q%AJH_6cR~&%NvZF)FF$b9JRUfbvdsNRc1Rkf0cCHGfU`| zIk?r-QfLd7LQzupEZ@kudPI_4vq<4Yti^#(OseAZMPYdyTt+E9hvP7pppl5l=r&ot z*RNBgpUX{S(0{W?KgTPwZ#?<7o=9XWeMB7l6sOj#7v^c##tF3iGZ!g{J$7#)8QnZ;ReP(($|qbR^^^)sqx4 zhgMfX?JdAp&DM$swS-Dy|)=GEx)YY>+Q+DCUNJ93B?yB z9PYr5$4}oK^Ep?f-8Y;-A7I%`?{&e^GG^3$Dp16k={6hs z-(-#;pw7;udZo?(1wX5aQBDDJJh#=#jOtoA;`Oy|@{HqKX^oR3ezwXx9`C7<)-a2; zXZKDwsmxCD<<;Y#lD;jNMbfAT4A?)=YP@k)nQep z-PeQ&A|-I>kWOik?h@&4lnw!C$tScuk7_CNufR_5iKcWhHv%zDWsk52qh74cvk9fPdr zEnKT-32^AEhO*;^HQJ2sX7$JGldhp(U5GhN)7E`TT=MO#?q-N9m5cB{^5#AZXcn;R zk(SS2?bdP7&X?oI@d-W$lAISUgkJW6XFkZ!3-9WDN>dXzYwQLbe{LM*eqw{MS}z*v zHc-8A`27c)&2a1oy&711pO1uaSU-pzbW9(Xf()+Sj)m&B;5Lu7iySe@~7T3dA6s(2_>; z;PUsw{B^!Xyf0EP|5Ss`HojkyDG}0+GyzM5Uf_?|x@Xf+5rZ)gx@|5k^z^S#Z51}- z)R@O#OB2*Dd?PaE`hbW_AD}ufm@eZgA0OG3P*g^S#+I{+77^%Z-L5}M7t!fg*LhM| zsjw-1yD0m3!J zaDHDDTxg-3N}*H!#cy&kHF}D!~N|T$jnK5bp z{0jeFG6!uhvQ_F(hYwE@G&W}lJNcJqNk22u$kA?&h(hmc$lqR0p{zE*uegW|N%QR#q+EqUg zB!h>1s-cHhw~}@|D0`3LglrZg)DNv^q>FvJj>{5XefpLtJ%$<`_J$6e4flC&5(r(i zGy#G0yCAZq+^})BAkM)4X+imu^N;MAEn|zUlg#S{k3@@P++VY5_FsFJIpYSN6_ziw z4s4Vym~VFQjHwDXrYw%-CIhjG@XQ4{^Om;zN-w!{E%3o?iY<}B6`KSi%vm>BBaAQi zFR2bnBS?Z?baPB`kxMrKp;mZz+FLADSZ5HqEs$pj)|x+_A}e@Oy6yjT;zUZRUwDsF5u<+GA3bVfG@L;|Zpp=JG+i^_MEQL^D1~$hBbi zXmVHmBb)n5N&2fz$ymWvjG4EkEMb7I5Ka|#F9EUiqKh7Z3SyLr*d%TBR%rS{X&`g_`PhYQp`{!Z18MR9!EoSA(*lEn7bgFug#}tx=lB zjF)YhUmM)_hl=g)mb})mzpW4+kR1j(F;v^=2E73|oj~Bd-%B|Fb#{O3bduGG*9i`GQ&Gb}lk6&6Qsd^3gKbf_FX=0fUo6Zn}mdWMR z$9ne*-G+CI!|onaze|mIbgusX^RDNdB8S!_aB$ifP4Bx++zj1&=kKe`E;oJuuZs&BI1=|#!>z#8%IljV^Hgl_z|P^pQi%b)l&$Ij!Ja=%}Daf0Yy z_!*X&t2O~Dx%>9J7j$C_b??R#Z#fE-9@l;IcR3F3c+Q zS_`#gQ6_Zs7V-s(mv03O%v^Me`Sf2Hqo{2Kl+tNAvVurTSFKqVZdPas1#rZ{<#_dK zq|#nxr{<`DMcXL;u_IfylA~e?R{n6a;^TF>9e_dqr(kfLF4Y^+$t*GhD^F^Da*!&% z-v`;Bs9a#_lf!ue#fk|abGlTu13yJNYZL@IMm@?1;S3RG3xH+C9*DSu@E zq+Rr~J#>en!)#IJT{x*eT_C*7K(Fn=XO-L-+bO`ySua15VH< z98b>_jJ8*=@{9~PS=}=Y{(PCKv5uXEMiaao*QnMms`5;AytIL9=v3N!r5V&vS*x_9 z!c;{l$-kKF>}rfxReXcns0XTpDbKjtRoCrKn}P8sRkP`iaIr%Rep6f5aU>lA{3Pbx z_9+&W{Sd_J+zyJpU40>@b~8%@ZHhyCU+LrG-S3ajOYveV&XkmQK(ulq8U|FGGPcCc zE|AYP>E*r)h@&xK&zF7iGbR%EHsWnSh*pVyaO5Vl!P4?b!P*&sQJWvVWB9_> zWimkcvLq+bI`$a2sI!DWd0C#hRky`YBTk6P?EgJT-8!e8!s_%Z|J?IjPNHirQf~7w z^6h6&U=|a$Sa*f$)^Cddw$}2D^IEMM*p9F_6vl49hsqT<@$>(9g!E4YO&oKwwYPFl zTU5ysb8ZqTe?=gpm_{Wg9Fh9kA}^;jV*`uOIXdKj)cddPuZeF{?}l^Q=B?FPqRIKv z-~97~JO)zL#hDyy1CS+FpFY=Ipu-}wCW!R+aYZGXj8n$uvhJPrx45Q-^Spy691bS= zb#3Ekkk`7Ow6XT)+WYQFIu@)08U{VyEv0X?Bd9WqVF_lEI8ng8-`MU{zhNka=SR(6 zJP2_-xQWw(b-(=={R;q-KYZL~7b&I^8oF@zAWxygMgPM)qunhb{pxn4tIKY)zS06W zJ=xpEc459@W^aUZFd(3JFDqd%_Fir7W;2@4_b}u%j?Hws%ulSwr4E$DIkPJJd9s>$ zqEv>f#~b+tDcI%K^_-4njb=18!k<$4MZ6!%HUI8!^b1|lY-qT|UzFmsFaRC;EheBnm$jDwt=t3mwQ>`Agzh*pz_novI4UR%XBmn_yNL3q$dZ7)bTzxO(+! zLaAC9^!2<8?W`829zE=6D4VCoemX_23xFIP#0T=K+@I#D^2l`CaFK5-L;@vRr0 z5DeiY-K>GZ zYF9Y?N7455bJ5^CK~^TVYjtF#=lCUD?tndPTH()-?*;bCT~$g+tXvx3%ijp6(6f#y zqN0fnAta!&NVgFR1u1s&QuX6k+MpfDrU3dY6IWvkU;72KgF{as z+gVG|an}(fP=)K(6g#J=_yq*wLv453V^IeqFXv`U>$pT#$v~@ku9rGqZcJH&|F$HD z)k547(_p@4v8-$Crtr~1tykdqR0l4X(e>{Ae3F#4PD(PRdxF8PtIM_=UB^GBBjfi{ zNj)7HMYJ%N=b`{2Mq~=*6(1F`0)^xFi&AL;pLZUG%%XTHRBBU_F!p?V^?(w5h*OEF z&f)kAd?maSJs5loxW2qytj=N8A=SW%1Y0^lberQZbFfC<3K)0)aR%V^e}Cmv`%zp_ zJ4H8toe$U3bb#4{BUecW-M(+quHNb|abmOoCy2vnC##vaT!kp`fOBc4VoA0KbhZW) z%Q_)<9vVE2nMG^OZdG{o(FIH!bBJ=|8lu zruRORd5r#x!o%VMzizp5 zHGd`Po!b`B$#}0fbHpra|K$DVcrW4WiM-r%caMBKk$0eN`}!jpA@c#DCrP)vAQtJn z8w=Mrp8ff7rbW&r?Bjd^IP2IJ28_vt+`tOh5D2 zb5cS2O#~cFI%tZLKK{XF3#NSX!OE$COZwVVcWnsxmL&HzEjym*=JOgfW z7FyeV&Hr74Gn8l`-x8)@FfBMCy^<2xmjO!LiUvji5O`fZDDQl;EgI}!c znd)bbxn@Ocg9&MfKv8F5)HUlQu=zTD;NPlbiQdZUDlIJ$i_sJi@|m%GQ8Gc{;s+r} zSHKB$HBnV?H+OJUy{A%lN|3+O=o@M=iG{LS(L~8k0-)Jdv7DpeTRgqL2b6*`ny5eN z*mQq?=q0fHbz+_AQpewoezrJPSRn#8+$}i8wTR~K#q8^3MLnipf0DRtT#;PQVM&?x zB8@v|4kP_M2IqZ?UKN3i)3*zWo?N42bKB7=a*wHK{*$k~mY04qc;;F>qlK{|jCG~` zH|){eZSr92p@GDr?3DYf(vi(_iq}JBg5nT#&A^_hx3Vo{ijI8e0?i>_PiOtc>D^;O zo_+7fnpyU^Sz~>YO@?wa=rR_ah57bghs4kSVZL{0VExV-`PGI52_mx5wp?tgwH@`3S@CCKd3b_P&Ni zqJ(sjAZ`$eFLCu>%stG=o*x7kAJ#rBX^*+>Rp>AYy9xkBYv?52z8ytkbUJDS_t!?5kS5a{Ni|#6PO& zwzzxwF1kJ!EOGG$TrfN({qJf$^JDlEEthL2F;Y4H0(>|R6ircTMy124JCKhkf_&J5 z{6KC^;E*+pm(`^Zq~c+);Tgg+s6I-BUCxO5A-gm8X;8k!5`I_MC z#>Or7)L}%dXIu*xFZoM`O?yJ!lk2Gy$z`7dW*Z~raX4=;iN#)>A{KqptRpy-NP>F}kEsZ0!`@G}T(gFNAz^@bmK? zP6uUU%+w~;Y7|bumSHyp9|zM0JdPx)ZJj=Brh}e0H&cB%cbnNFwtp?+nlrq4U3Vte z20FJpY71w*kgz3C?6Fal&+^K6V0d{n4FjdxQrdstX4!u~K}Hcrw)aCKIj-V7cL72O zNqPBuE4pQ?{IaW)O*-%`E_*}KqPYG0g0(Ay#)lnetL37B0OJOBu(rPb5m*?~2?~+` z^@TKO9BVh67aBDd5V4>>)QYggf{AvnUj1RWOuo_0YGuaIst+FCk9tkf9VLVsSbCEJ zBD@mA^OvPX;}gezv`B3=|@(i>9|-KMX&6^`wy`l|H1GsjP(+ zrN6_!Bl12y$r4;rzgVtM&BmiLa^TM~M%?RA*}&eHR?Nf+q#+VE zfVFwFoSYoK36Mn}4W_m3nHgeWocmFnhif3#ej{2tn%n=ns{kh>^7Cg&YimYAOf+24 z)@jLkY)6T(dM2DabLBu|Uue*Skt^wtHDy;$Lbjolw!9|lY1w|b*jkGD)aS>xn}>r^ z;6lCnEaq14K6;mLcFq1U;C-Q!82Ri*;f?c_DIt0Uw_Rf(gJJYQ4dLe|{HW;*t0ePH zjE+q`D|9g>$)kkZ$^~n11(FGfgBIQ3w%*h55nh{dx%bQO+8H@~rrFLEP)s&Z=x|VU ze2Frjv#w6yle80G9z9pjXPb%`&iF?5l;ZB$WjFP2{wIn)B?B2mMnarrx@PZ&NZ7NPL_xh{l~w5Z`5`nkG>U2?M)sB<1{lQWHuPKP9J zLPkau6|G%M*Ta-TYH^)q+~neQd6a#*(B^rVQPI*J$z80?TF=y*4X1TA7ObxB~dHqqotSo-#dC=Jz^$LvK|Q60>!FvX;4~c=^n4 zVuk#7(r-yKgW1%an;yf)+Cv&dfwXfw;9d*hX_iEhGcYpd`<82F^0NFU-{#j_d-^Gj z%Kr+Zw!JsBtH%Sv?cn^`_OO{KqF|G9w6gt^lQ5wx`uCH^^iAgp_PEkj8>YDL&5(N* z(T4}MV7CK48K=^TIB8~&7Ur;{aZ&PopwG%kOv=>q54X%6^z`)F#Pmi;2FPX4I1+%$ zMeQ+WSdOx0(|Vfx0G_X}uOG0-5p>(b#3PWO z(788TQw5j`U^q`eL`1YTTf+o)M`KxaPTBUoRPkM-_QI7PTj>&EHHHycDB~ujr2Oda zMvbGEAVRM8@%;IeO+X@%`E%rd_jh#jBK`~Wt@lxEf9sdjmUPnam*>)%%?IDPSjHrb z5cTz(%x~b*Ty;Q*#PvPa`1&ldzyZ!;%u_YaH`Z&(w%QfXPR|+?n=q|<@J^UXzg>I+ zS=G$Ukdo!PuSHmssNaDtTG3R6Xsd$|EuR3I|2=k(!jM0gV3w#dmQYqCc$F=hPc(R` z(^?5aCoZ(5{!Dl*NSciB9KCQja}vR?CjKpmRU}5Tbwsdlo86QByy_wV+0YN$SRX6R z1SwCnGHCJ*A-YBzO}l<()vC&M>{ogc<|{0>s*S@#PA5byEZT)JG5qmpyH4@=*g3() zu2rB;9m?IiMCAYRc!tE+q%!4SO;Ni=a=@Pp0|n9|Cxs}alF~mT{{}0!oucAZ+Qyca zBVJ#CkCyxAa%FyK#q#F-al`RhMCDIMCsbD4&da&AxAsL3#H#eY1%GzB-YP+bpc3h~xz4)}_^ZaS0V=g-;T3bmIK4YuSX1-tP9rESo$Y zI*AHi%tVH7-l*;eAfbp$MSO$d7#kuRn_y$jKP6e8n^B2D9o%=DYBB#9QgmtV->YQ( z_0fa$b?o}<7K3(rYimPCD?_r*U{ILEPB`pWczQ|LDBRRnFVjiDTOq(9F-R^s)vRd{ zZTBZsczxi=M&!S?p~O5nQh&k@`@a3(%S2N%OonQ;pj+ZwYE=pG80yq}j6mNhDQ$l% zN9XVYf3_SaCN*ObvS0@VDjG34cTFA|VQi|gPLg8MVKS>f_V38V4g5)?#H6{e+TcaI zznI`8zR5t^^%iKLy?|dj=$ock4wl-YKp=Teqwmp5a8cujl}_YZy9s%lJFoLS($==N zpAIDzhlCfSn(97aMG@MeN(C+D|sD|AWR6Fss>}pHQkBz3S1d&ga5Iy1FB;O`kuer z)PQ+#2-j9i1n0C(*(H}(-)5LMZru7n=P2G|Mn-jO%g^<+XWsiJh=K;1JXFePLHCzL zffTJVQE!jEHyTb|doBea8r_S!Om~Q%&_A|f=kE;@CKw$oCuPh`i~jrjXl4$^`5 z+g-e;*DvuXONG};V`1t`OVTroHpGEztf@w3SKXzkaPaI7H*_7Np5B$2 zebncEnkDT2^0EI0y7|g#jdw^mszUu%Hf{+_&g3N12rv85wPVf2S!cFVAK>r2?M7)H2~%zj)(# zg4G{TP*7wUzXJ|u1?+1ps;Y*!=O!G0JcA!#w#ePB_)d$+xS(NVl($TWQ_9MLkcP^H zfNgTjt%|*`ccdp*y`^4XznEU)LeH`EXJte_my}6xt-jfQnghqR(#8~lE$a3M)tm$g z@dtNyAU7$0YqN;1GLJwl)(>V)6GiTLO{{U;^CamS^h_d%roy5u3B%_Ui<(_D^ge2M z^%iv>siXix@!d`tDG|3a&XCGjI0f-zAG^nY<1fFwr>Zd;4o@#Whh0dGNo}>|zMNh( zME|H6yei2G>#Pjsy;doXQ?)4Vo}HKHbv`Xh+0q!$iEgO)`>26uLwJwH;yM`RLkgc= z2ymCo1@T2WmTF#Jjm-NBUE21?$HzI*sWmk<1!W^ZCT6hWlkoLxj}MD&h|(lPn=BT^ zfy21*d1CI_bg`BK6m7u3Ixf+m!HE(GK2h1&*rG{!@j&D+;<_}sa4BmzA_SY>6U{sq`p zice0C0Izg5a5&F-0G1GJNBkNhE`L8WM}hx1Q!ADjj>#99Ew`VWa_;EUGBl=7I{3keIGgVhejSFgPP_^KYp#Kpxms^u%e zj)1s&o#k#4=+TdT5RWywEw(?5@3xxrQ7Wg^wKUkj<*E;wmz z%QI2ZfaL``XudwYc1%_d2O`smzI3p&w)~$sAwtsw zi3| zbi1UnF~xL+Nvd<^wip)=4}KR2+8o;z0z>nM_ON>DI!p-b=ZlbPTaE7!HX&waO-k8U z4}~E9=~*W7_VzZddO`@z0F140*J%fPxjHdvIv=xr!A+$W^{{Vs+2Z2p!|UZA-a#i( zK`lZkk`AZn!XuBdbV!9~UENe0?A1Ce3ErQZt@Joc%IDZGB|LxgmJI= z?c(_~K>UJN(`q*Va9B*}6V<%$8^8VIExlrD`o}++-UbSp8F3fsxN4!1Qj5+`5@YPt-47 zJ=nKTU9$*ofHaLGUn%#GAY~y6={aWDbA-LWXH!_%qxkKJz2%OFd@zlJb&x5`S#Ox(c=e!>d4?ym)piTQ$z<{L!Scty)2M1#aV9}q!Kvx^x#Zn1Qbjpt#b{Fk z-Rgx_Z>t8-8FcHKe4Nf1aVC;m7ip-rKz~aXxASLBqy<&~KnzhM{*t69RNq{@@K&~^d%`WWU z*Xi66*zayY$YN>(ip8WuWIKiy)b7yvLg=t$vTxe20y-^cZ>!&&Y^>r2j#l zA7GB80J`uV?Sg?OQ;-z@Bh=>A7dZu)3~1i2)Bhfxn9IEsMf062W`cn6&x9VgAvfQr z7Pq5qtR8n}1bpQyMHAv+WUz>CGHS8_kkDnxy&e|~Va|AYO6%iv7y)^yD_NnA_8jt3 zzE|@!a~jWG82ZUf#^&8!^GD{wcTQ#ScdlpFSnV0^WM&mDAxuwZZ{9_>u|{a4$gKS! z#me&kmpYenCGQ+q5>nmQiUkYY1sgg2^0L8_z|0;p8{^^wfP?O=5E(e3YfIHjok?yxf%nwZ9R zcZj-~va<5ObUm;Wui+9`?X2Pb14Jc8W|gFict&0k=E0VIl^96vs3KxwK1M~o4vx!Q z7T!9`?_@g@cFDyLqV_+H#~96#B-gt}$0U$`ATh6Ap%GJzu>2|%D35nM}Gf?Y`1TZ#jNXb%C`b#c9>QR?! z*3a;N>zOC)#NcdCnvh&O4hkl;eV}{u7Nmy*40360Jb*op!&Qw&tDuvZ_&)S5cfdSl z?efF>%Pl$+&zS!n<+i?;F>AmALGlou}b#mWm& z;D{6hs?%-|Q1q{i(}uC&M;4APd~k5^<&COF*5(D6PV@|Q(^dAsNVl4ZlJ{?bOrA7r zvP%p4tRyRl`AnEOL0uJmXI9UjVs_3|TRk$GwQ$phUiH*FR~^#uKS~MI@JeToi5?2B zp9nS#MY{0|xs+Bz@-PjGN#*bXch)C@SF=i#J;SUjkweF%OPY|SSa|Ol;>;wOsGC$K zMU0f?fg*`apG&m(i78ShDa_o%n1ar zk*80djKSO8H-{++VTg9)d7a)f3JP|oVmIpSnFG+W83t=YDI3$Bp$}T6Qt& zwUN*B3zZfr*$PW6LGhjYB~I*O?6h6xZ&#dl@7Ht%Jt*YiNP#brhF|32ikY0na`&EM zqJx_|7^4#bAIEmR)mMBy@iy+ih9rq&47t)HkfN`n?|?=t!(TGLITc&LZxSOAw+P!) z#5Mgq9`R-D>C|YQl}_RsH9JYkd3FCUm_r{B1RThh^6QPkyCO8 zQtodw-o`7^Z-Jw#zgBjAck7sWURT3f8*n1eI82(MhoW!+xnRxU;9!JDeny`gf+WCqY>;(av*Tp*}hQx-i)l zO_1RAlMgc`JFD-@!-((|MVI*NGE+`1;86F}* zDgG`B&WVrt1w^=f=^x|=`kx=h}B~)o4TBtFVj-% zSlj+rpoYq^$oXVqN_V;P4JL8Rh;mp_UjyLqgk3?(2wGJHlAS)D+H?E=`ST|jEX#i$ zw7P;FYN=_V8&|b58(m4DI&@Dex|0cu^FO7oY!6eOeO*ntD1=0j@r2z8o#Gyux%cMn z&u~57PVKWMwgm~yL5D}i%XtHclV_Ne*-kA8Vzk3%Po^Evs|@zHr8b$+^D2is0sIL)=aDr%|}gf%ioz_y$}jKC3riq zjxqS#TZ>{(lNUiX>(=DZs(c>7;@i_n0mMb1W|43jY=37sc`MV^2e?m;OGK)BAT2*?{LulrL7QQyLC3mm*f=!Qwik z)@jkuGCya~sm=EyAouKB;)VCxPld~OG9J&@zxQTbR=6%<1xede9I44XzbY=-H^wIU zK+>-v_6ZXIm-)@+qm^mZ{q3o1q{BOuq;-s1a{$=t=`UBc>W%#4;{CeuV!w|RQ1oEP zip?)9jDlXf{fooVAA^;F@4OSZW5_85q#W$;m+v|y3jX;}d}%$vpVml_%ik8BiXE_H zKnXJ`bDw?*%3+ycZW##zr1H!#<{(a1Rs=$llRhy5wv0rll z6Y1*elCu1p)rX6T`2&o~*V7|~^dG9+x>^NJzMmcF-n!fq(_1mvZ7lkG5TknH_5{IE zkL=3T-a2Ww4v>C3se{N2=31sL6GZwF=e|AiGfjRZRKfnVYx~8;Xih2ajsPnY{DiCb z_xvT;%>|-U>Nntjfs6ZBMTkb4nEy2)Flq?M{~A~3l@sl*jRIA2)Z`MjQq7IJQ_U2A z0l$l(5c(GrTBMX<>b~&)j}*mphMrZsvCj_jPGW;h;Z8{>A2$4|%EH(K527=;-5wNL zv;-NbmR#29ggpd_nU%nA;~jQ`zfVYBc$W<70L&!vJ)%^JW{wBI2|@3S7<*gf^h!0q z6XstRhUz6ICZ>#ICNQetTj4U-pt4c$G)Ys8UV~vO&+1p*Iktqu<|i8Dj4w);vSf&7 zRZwL4%<9E2fD`%3K8L_)?E&VUaYZK|-3jmnv+WPeGAR2@8t;kFw9mH2WL(F;Dbe16 zvj=9dy)H#rTriZ37 z2*;L%hdJ3{EKo+a*kpFDc-K*$JR%(~S4Y$4-(QVML0*?lmIMO<0W>B)q!ENLp%qM9$rM!EDRRBC%-i60_Mh7A^zF;vsdElPQHrshTd29*SAQ+(E$Or9zklG9 z`9!0`wb$2sWVeCRv~#W0#q}z@^TBCtE3gD#JG$ytBa02Duvr~|MlKQH7x5%V(Sy3l=WnSQZa0VYP#seVG{_ zQClQN+Cy!$vbsnp&l0XZwxdRs&fuUjym<{UrpYG-1qFg$M_J6dgP7tU=7wu1 z>)q$W584+`2A2bzMVjC3hxp@yA{X(s^GEL^%4@yeEwj|F8Y>~ojnC-Cll=y4Q2-DC z-^uQg+xJe+HV50eh79_*@abK3*1w(8+>+s}MiAw9K~5(~Pcqx5St1B+&q=vf@m3_0 zFR;LqMJW(yJ^-9-8?Rr)Bew6gX3bDuO~hYX`DKM^8>q`2|-rt-%#;+$p277xRBLMG1?~YH~g@tPx|LBHV8&3uO zJob2N-JBzSVKa9B`tgJzI3k%w_^dq4W6|%!y6mG>YWl99GrEH$T;E+Y0~g7Iymz8y&Qz+i-aLbrmE z>zd^KA=k<*{h=RbSZ)>@I7x#A^S-UWwJwXSd_ADCnfSk3vS)%Aha2+=K3-|4;XZxp zn}2q7bF(S3(Bg?}U|@jYaM)inK{(CY%j~qi>%M>gy}tUazU>!`5P+Wyhvd1yc9wyY4R z&2+RS%UWk)ZZ3NRww!ZRiZ_0NrbpI@F(KkS6dm{d`JTb?Z=lkVZ8@A?*_mQXai7nZ z=zTDpTLavhEZ!8z;O493Y?H68C{)Xg^w8A&%IH865gSXiA03S~hJ%YsynQeQyvjpC z2Cm&4cssj66U)C30KK*QyBl-kZ1vCXVga}MwOU$Qav~fYs7N}{) zYq!rv+V)3pWVift1=`}mmS%42Df#C(ToUW^_BX8NamqtSh0Uzyv&Hduc+X70aC=QJ zmc6MwViIFYu;gLdzCZf#d-t8lsf6-Y;I&RYrF#MC^`O^my-cQ<{b{mnrb?JN=f8Q6 zt(uro8_K%vnz&J$w+D`&#CfN-YWQrB*!`!+ZJdT374ELo7)S&s+yLRfrq<{v zJT#XmskPl2$4hN~_hW@Ml!kj$Jk5vx7Ngl|Hu!e+4e^5k|KS5>v8fJ6-XZx8_I*-a6kWi`6K- z%<`dLbdOyh>BlOXWH~$y!&o_d+_`_(yalm{hKMn+O#{ml+VCHIH!F@-SDc=#4$(%r z#%F(s;alD(&?+`P$c_c#>iNeaiEYimesO%(ZXJNQC+UG;1m*4knS7OJ0UJ~RvVq3N z#wxkGx(-!?F-03}b?QvuU|^_&X;){$!^K4tG|n2)OiWBs0fXrxdbOZV$ohigHmA*M zfM^V)j@R2hfCf6}MZzyxU%<%#oMpga8j)^eRjOOg=V)eTwhnS_`5Yr;a}bp9PcK{B zBK;X)=p}ap$e|qnKR?jMOhHheZC_ts6aw5SX1A~@q-fd6`8~sX;ZVlsQSrRD(5bDU z%%HTs8tg=-^i@|?X&`JB%|!vXSLOhY{2atrI3^BQCEH4a8u?mkaX;ocUYlWUGBPsz zX?{SX=4WPR7A&@UyRHLtxzMhqsfk4bh;g_xl9O4c&j9i9*#msdw&40EwSb9Y0S0wu z#QQ?78=pN44O496$X}`&R2T~lH3KTc-wHG+we=rqWWsIdYO(!5Ghu!^maAZHQmR{g z?hlkA9CKi#1coo$nq#q%k@)*8S_RB6UcBJn1uF#|)$%c9DIS2QgaX6q(OOCh3TUSP zc`}KbvNGW=Fd56RHEZ`XsJ%E`=9~jW8sR>u5|QU1fl}7U5zzTcz)?%31bc&v z(PWF?rloAtCky(XpC(Nfh~w=L)D_2tT`T+RvrDC^#nwoz zXOW&K0e_&Y#U8IOlAmXGEVZx2o~SRApBHr84XgJzB);RsyI#%`xT^%eWDDGJKD%Dd zdEn;1UM@9hsV^{Ta5Aw18GC$=fW|pSe9tM8w`IU%~WynC;qzQHU(t< ziQ_gUWQgy*g3O;N5agkoi-KBQ<^V$CbNv7PeKl5KBt22AGVrX~%o?y;& z(pF^r)5t%d0z3(rWb?VbtkPtKMM|}aZPvQ1D^(3GHhz$KR|*C{ILP8v+uqq>U8;0i zY_@wr{p=Y{!NeCB40dRNL&7CFSa{aDAE0gLmsu%irsc6bZDQbVFeNJUkbVbiFn4Bo zP1|IgZR{_gJnPkqyndLA%eomWP9NUYL813}Nz1>ZYV|e8H#X;`R+h(PHqqM$4>55H zFr54=|5(4og0j22fc0N2F;Mtn+0W+>`5~Ig_@%oQReB5qnykd0qpg8({{$Gk49nyK zNP;lWM5cr8TSL8G=*n(kN`5`TTy3b^>hT|3$ZytcaD*_iQy4dlP7i_N1KT*%Ki>iG z<42GkZVoI-0K#RRaRr^t1r@@)KY8e{%90Iyw94Z$1Ox=&!jFA2cGwx+sNExI;O4Y2CI0m&=BS9a zwl*(H77;uvSoTZ3vdaYE+ptR405%B0`q7I(u~26s%lV zIl&X954Au8DVthdtZ!ft;rL0t9^&cg$*oRA1ZUL(&ZoyzTJpGXRvD|fEph12kM(V! z5JowM@OY1|l{QeqQDjIR?5E3E=AaDWrg2Iz)(8Qscow7jvgvxzP@n?!WxPgMVxi;s z^t7&JVfNYjk_RwEEW4u4x-T9OF0NT_wVgjcJq;@@E#0DP!{MCwAQO1Uyu{?=&vJw7 z_&TiK&Zpeg`>XGK_C?&12smL|_tnqod}7Sp=d)+xmSo_{visj0MQR;^=q++k;na(B z&SUFD@vco_Bvib^_U_bMc7*q$Ugj=mCSAN^%RMK+2+-=AvXvIYj-Qq+e3rsY1Lqhg z^Lw`LvTw$A3zj^#K8T8n$`-Kr1a6-dJdneyWez3UsHdl=%~SLCLzg&NbpTPb^^lhr zm^iUEet!DD!+7(qNbdshH5S0b1?cxs&-Z3e;1{_MfC0$bPq_D-%w#&9m48qOTXLOq zasdS6b3hPCaGxG7Kg8UarYvu=dlMxZn}u9Soy6y>Tajz?@^SUNdFZ+<>Q&(4<&$}p zkipPb?bz`CL6-ixo_Sk7EAN|=$k@x)Hfnfk@^cme1jMz<`xSy)OPxP#tGG;4>)b;! zG4x<$cOURVMd{|o@1H7GfLSwsZeGi%sta<>z;Ne7%+({!Cx;@0PvbaIUkjt~=uBI8 z)p3kEJW(%b~3WK29F%Mx?oo!`bYoOY5kTFgYL^E+fkpmK;EQkBs?uLE#lD zX@bvy$s9VPa?*2Et_^u9RJYX()r}2yjk9K@?i+SJX6}n4lRU{VzZKn<-T7R5A&+2Vg ze2YHCCDOJ*2~QKy=nj_u{n%1S`)AKFbdD0OyYEpT$+vt1~BS} zE!(^q&K9`@vwS0|?4S7YXPLESK_hde92jqZN@{a`f6I*L&*qyFfjxT0nWdy>at;O# z_!aTEC;J1fxpxxzYyP$3WS;c%kBH$VpTLTa2KtIYVrLurHWadVT*K%0{Rrz`FbOb% zdKVh?NSKM6&k@YJvM2Lvax@K%^D0od+s^N%NdGa5z{t>4i&2ZcBucit=e;am-7s8= zulVYdJD``#(JQ<;?HarMclqtJN|f9y3k&P8k>%3pwakW|&TuL&C`p#HyPl}vh4x^e ziv)7Z!;>E=q?_+|mFfSw=zND*$XS6m)^T zY1Sj86l4f&1L$*>u8&FBeu>*{M|tR^9Vr?liof8x=r`GGPi!+AI5nPyclOgs?sv^j z@ZblXg$A2sA{%Ts?@@w&lOI7@CfT8B{&Jrq2KOf#rn-YKr!CD2ZspEDb@s}(8M?6# z=TFj}4vBLeI@yF{Q}h4md*Lr+A`C{r0t+_13c?1^x!^GXg0x(TI}+NVA1+!~WLaMV67<#q5?bD+l6t?KK9LOe}VA!&GzY275u#Vl;94 zKcQrf3vbOcOf-4KdC)#3qFGE$N*aq%#Fk-+PxmijV_MEuSt!Jp-GH_1uG8%avt4l@ zrpC|LpJLEs`4Ty;F^*ciCFHqZ(lSVY+Uz`6`|1(rNSe?|`HX^<7gz^i?k#@A14{X@yx8a)rGfKT98n|mOT`MEz2&1ESbuL1GPCkmw;5ntKtP3)oF6%P)Pb5vGta}tOr zQLJ=dX4+;L4GapmVh%b$hppn>xxf_Tdce6}3tUa}3JY^V<42nm38C`k^}U@uWxAc; zFw>akEbJMS_4B+I!J2=m3`J#+5ZxR4vtbz#ZsN~%`3Y%qcPEC>&SOR2#rUymMMo%P zIjy%TJzG(2cuCEwj9EQA3+QVgznV+6i&3blGiRFPa&zgx$${bjH8D~5Z=BRfaz;zV zsDR-vv8q84F{n zcpwAZV{%YH&VNXj2+ZKpo-bd0Hmkxnc;=jW4R9_5=R`tIzLuaS{%j2)A_CYD-$}s3 zo#!Jov~2H_jUwO$h2vWsxT?v+ZF_7`CVr#tuzUa}Wv&)875!AQ?b<-ZlhP9Uu=?o7 zj~^?5_5qX6dW-V^mL8nx85rzT)Kpb_f#n}!^cEBmiHg=08jCPtJ#KKE7l?mGXLFSe zOoFAO)l&G16*GuY$yx+@6Dy3kT6f+nVpqpi#Yd@e=4_s4B)Qt*j9bc4cLLPGI{ZEE z`yWGwiN75ZBLrW07<8OM+0&LuBiEPHenhx8#FH4gn4uRKUBS+c>g-|64GPJ(fWwVA z+ntG$B!S}qElT(tfDCL#v(84ZF;wQ?DLhd1!;_OqS#?Un0LX6lLSaDNt{FVg z?%`n^;75^>q`JF5RZ0xji}=^AT@yd)RA%yU);TYmT55ntE;=p zZ8u8{J0GZ=q;5+*y>;#ItmS}9tHdct{Q$!vfdORu2ip$QVqq3febd+tC7^J?v}vnS zcwu@sw=Qc%TWoB3Fq2FLK!fH+_g$j%!{s=De>f=Zl>8r2Ulmr>7OpLgfFRv1-Q6MG z-AXqoB@K&|6a|z9>F$*7bRpeHNjC@xNd9BF&wtLvzSv&u?K9V$V|?G6KHlDIKv{%; zBGfJ(Q$se&0Z&l>B*_a|D)xh-2_`|x`Aaq;QS}T8isW$hx!2(_!|;S0m*QQKp_2C# z3sak^K^{WJmtZPhA^MNztebK!QvArb5j}l~n*w_YEM?z96PWDAv*BQnHzxa3w_!_K zq8bP(El^Hko$w(1qhzOo=6m-JZHKZLdKc$V$v^+s9`~YjgB}wTW{pyj82|1+*%=t< z>Lx0Ir=S+RwFOPY?bbs;#*hTQBAMr1AUL)EsWVhKv6YqTD_PO~3nPxNL`Aok$IGMv z0Rc3EX6t=XMhxYKzj%0fdx0jo`uN_X_&D1*+Ndzj{8vNRZbya4r!Y!s#VrfVaBEr; zGicGD=Q<-8uSR&DGmVH-GY{)f?GDkFr}-kIBrQn-Lg4FVa-x3%pB*4?*3V-@RNC9x zIL&+BOBr6lIL{*h zCm!8Zu+(ja7bH7ne~))M1GrEOQhp4WR0thZM zqnTjb>X-HG!?65om9I2|2L93f$9n6LXf_kKtivDyJQPLnLNcpiOzP%7<;9LMn+qbvkOD?9HyhsXA8XTKW|Z~K-@pykuV^Oh zs>(2F9~h~|sV!F)!tG2Z5kLm;3B0SKFU>m9(eQP}chCIxGDqb|-M`M$ z%IQ0Lr{2Hz%Q<542%7g=_EiU}WUT1VHl(Lx%27$C%~FmgeSJ}JvV&c@_S%9mBqgbt z6l5_)kGGmJB9L=F6&C&3A0G^>pEEJ2b&XHJRdzmBHgr_K4DGK_y*jQwnrPR^8Uz6x zH1H|YaB6tZrBhSfY>ol#VZIOYJ$nBMJXLhw*Gg$>Xuf20q=L}nLJv0&4$4yHgQdXe zFkSv3#Gxei-S!V3ds9X`J%|Kaaqh_npxD+%^eLIJWHWBjnJSc|Lrck3QBBZ|))=4z zc*h$6%OF~oXgTP1}k3&hZqvk!>wCV}TIcnbL+tAgXw7Jx*Tn|msOrhcQi%^>ZQT+6?Vd~GB7Ki0(bF;!>8_(TmY6JyV#;C zee<(Y#%wLO4mjQ1@mI&h%HBN(=pI*L1VH7U6B070Nojx24H6mI)K!BJz!@n3kq50%-Y~- z{hK3II7MDTA)zR42uS6c20AmB1emCMB=whu*wOEeLGM>OQtIR~e{u)&=3vM;2V0S{ zH7ZjbA(EY@P05_>;0u^)8GaBoG2+DqBr|J%-oUjCQ_Zq`T6-vXD$u&o@fjlP;rSuQ z-DE3>PqBSm^{?SVouh4%v8l1zQgL=zp(-sP4~Be!^ZCAXVjYu6=5G_$4#*PUPsL`V z|2bs~C3ZItGNipYosRIWpt^K~j4u0CSPs87k~;kHCBQES%&bg~CIp?lk4Wb~N9BDS zk5u^VYkBWne`8&&tdJ6BzLr#!y)7ayYc@N%wz>9nQN>8tAw;m{m1@|o|{9vBf;GTzdPh)fPFw*~mc*z3A$2 z4vPFBx%S%3hmcA$5dt35g=Q3Hel83BF0>sGrIF=G7nbeWxJvF@@W!c<9zVbbO&gFh zjvwN#i!ifN$lK}c_9ya&>zwrA{>pThmM8BpMrZGTh&z=ND<~e&06Y(nh=Vi)5Wk}> zrvm>1>cf+A@*m@e2om+z<@E5X*4~VXmh&r)^WML)t;RSA6qHB%fKoaxvnLhZJy_{b zQ?YZ&`W`iLJ8w^D_y(JKtEfYLv^L; zX>I|1E`7CSr7$1loA=68V}1RS?RHg_rpnnV4#rGJ)nQ0L9}H@0&HbU<909TdFvC8E zWVv;ABnZ_MYj~Ekz4~USgaaldN)fMP8Bk(8>*m({y6kJ(!E03O8ffZB8PMacH;fq4 zbL})2(kUlJ;zaGM7{w$yDABn%czU+F5q6E1b@huKj&NOtF2;#IHbny)bF99p)a>~^ zyq=2n3k37vg`hN-Dd09582;9()g__ypfXpzNr}W^b6Ml?)1)@4+sKx+80Y;$^px0Q_ z-8@7{-2K_=Ri)?Cb~o-52cY@LbIJ>nEwZ*YG)#AI?F*6}e+X0d9?(!SGP`q$w1+3m ze9#ytYUc+N)s)fkY{jcISLR1Az`&@Au(7EoEQ|no`N=AfhOsJfXqoA$J>IT9_Br18 ziFb#@Br0Jc1V@q)3y_wHk29&g<>#yM5a$1EG@xf0^GP?CGDif`!M< z2dW^u_Z9_%h|0lS+u`Tj%Yz?=t*wI9oLn;DthjsOLfl#f1$*`C|3>A4<=%N6j8~qR zSGjdqn{!vSz2KuabFBP2@t;xZKl778UrpZZRKl#WA!jfsT~jZH>s@W)1h z^>pObY@3BN#_#a{WGWbd$6Wp%WWkL8QZCev7m(%&GrOlv1mM>OQ6R<=T; zOBBD*N7Xv%$gYF_#jfs96jvar5Phi$W>*42TT&8|=pYibBp*#fF(W5nV*#XHDdVkP zosB9i&x3=l!6ZitN#O+!t5LneMTF992}fJu;K58;GUI_fKCfM~evNrAvMMDIY%)ME zKavU@hh3!#y3zm|kSU~L;1fAZE$c+s_Qj3HWg++h^EiPD@fkV+>+_wryEPz%MIa>= zf8^0aPmh3r{WW8)=ESYc;nC4VrLjo-?+>F_#S^zG3(du3G>P!}=Tb0Eso=KQKd+#m z0F@m!)@CyHDpX-1b%13I69}aTfG3j^a^oJa8#79)saM^@Nd4WQ#?wLVvdLDvt6`L7 z`KXuPqzNcCD)cEnT-Huut(lIJv>&t(>Z%)R@_c*J@`&!u`{dI9=@1WG0lc8#GgJu_ z_jHt$h5eRf0H>YtXhFbOdJWLhe#|$=c;A{eqi2BN@&gN}+rv&x%Ye!;_)VBtcmPWG5fSLj}ffud|V#5nxP9=Qi&YpDKn z&q!6wEJO>J2RncVQ;C_hZyp^H5i#;ZL5U89HA8NU^j~WXBhV9v>c2Nvm4#ZFlg;Sp zctBWr3rbZ8;gst|k#>OAQWo(|JF3_K00gMHNhXkf0cXf;n)(j!P+8I++7E;r1gX`# ze>GkCqNz-ru#3I{+r8ROCX^WLfp-KWt5+*BZqfhwGEGTGT^>+~z?|2)#A=hj2;rXrt z&*lqmy@^`|J5pMmv)JvE#fCD4Oj65!Id@bSKXp%$wXKC>w`1D-={n)k7Mnn1^%Y-Z zGD^#SqOYM%;r3k>kNG9w^}$|`T)69KOakqX z>%W~vS7)_Gr4ujtDisq4QoV-3ZM9%uIoMDc^!qJy+l7)X7+=e&YOlQ&R8*;%HdUVY zZ`~%glLusdkW+Y#8X$O!z%Fk*$}=`Ogk$OWNw!nz-t2`>d(7W{O@~4Z7KC2T$7@Jm zftAI5#2?Mh5Ko)ru8J!cduJlDB{t2n3KakSgfJbW+$wT*9JDYgZKsd5H0=(^`Rku* z*)o#lIbpFuW_21u6j*&eqIO1LGJrs!3Zd5tT_sB z)_70&UDmq1&K?JV*<8BBC#w+x|JtO4#H$=5=9v!CWLtQot^MxVoMK<0RWv`6ku&j6 zqGQi(*$K-__JRSpS8@nmm$vF((w=;^Mc^|%dMygr_8G4?QS3+h26lt|tgI?Aovd}b z0WcX%r9#~BmPJuUC4Grb3C=}obItyKV+Bvip6 zD9cYzK6cTmT_X9pjb$STltZ_d84J7^maVmmi@UW-bYO&+&~J zMFM85ufM9}9i^OFkr}vYBkIlAZGB=^g?7&;U_t8EBjDe_gg@-#g>MX0t{5dha$SZ4 zGm@62?m{@cIOGWAUN|_02O>`oboJZD)9KUD3*pixnRX3ueKfN+Izt#%=6xr!7V6RU zeKn|8Vw{44YV1jElN64n%GLGUCJXQgJ_A`GGD`YNaCa@mg`UTsLoEkly1})`*UrszQ$a7h-Q%C+5?^nh+Gb_Uq;>9eQ;1c#u<@ z%xFRb3-LZ$4OBz5M#faW{5lJcX!2zw!`~Omv3cbs*aRqupDJS-Oaf4+LiJ*m)dL{c z3|kS4T@O(bK64zL7g;m-U7!pNsMaQtg-~UTcfwk2Lhgl z2E5KIWMFC-SZaR91-!9Mt*z3g%D3^;KMq+|{w)jht>;sjuN0thQLc{K(*6rCJBOtK z!sq7~ec^5uF7`e+o?4}?yLWznz_dqSx$4b&SfcSnRS=_ z$*gvL@VSqJyiG^pxYT&0m(jt0%{z{!TIoN)q zcayx5{kgt--wBncyZ=OCd3v@C&yQOG?&W+;9^Yd)I2YfakUC z$Ycv8l6ZS$&X+F+au_C=oNlh)>tf!HWrjWY1N1c(K5)_Ro4od1p@I8b?4)IbaMu3w z3*{$0IK8KPztks&3a~qhufk9}nD-d-9)@gIh{ngIaWy;ygmAq+POe$l1;Kr@rixO? zi`AhP6*R%PtR$exj^@@?eAY}B=X;wQ%zZv&#jU9r%%#V>8_2rzNN!2@NfbS@14&Sy zSx z)`ClKa}$lOV>yXj!L+}h1c4-B&OQtLd3*byb|KZ#8NQ~GdTJv1lXyzz)nrm61w~di zzJ+{71E>tSpl*lg3jzY!dB1^Zll6{!;4$nSY9K2&`Zn$1b@915Ug)9f@-e5(Szq(` z#HvrR;%{(#@NPpM;m!4*?m<3@@Q+c?l$0fBy27_=wlkQs_YKo9C}XOg_3|QBQeeuT zmJG&OGxwd|9Y7tndTji!xGn_z?Y<7KxtF)?vuWMoiT84JdK%S>_& z2tv5aXpV7?q8Ub1yUSe0G(r(=e|lm3blzvL7FeORf&!myZ2+*pA3ReHpq4oN*@*S+ z+qbX>A{fdwE2WO*Hu(wFRV_+3xn!04`T%31(w!~R3#M-68i<|`wC#1_;ntjb=qQuO z$5}xEe+qe_u14@uy|i3wA#@l^qHDS;ced8BeyQD$!?YwG8{>sfT|W4}^Va{qyKBuz zG~jx-L$fhTZqV=+d?*~6D=nfA_V-wy#DwXg_hKr(#i!byvKE zU#S(yXMX+ajZjcmA8gnETWJx~4hwC1*dbzZWqh_-B2@bOR{jY~vJ_T6jdg`;jh%w= z#e;yxi%^BpIP>e;^Z83fOFV~& z>SXI_elCVqJtm(tAz_NQ*cf90)At=Zr8DBqFHhei6dNeonpHwks}vMxJoKZHZdB}k zt-!-=!JnF*2BuK*QZgAX4F1O9!Tr6_Oj#ptCDu{=7-mq(%gL3HtLAfbztf;kTm33C z8bU8=tZm7wodO?K`XdPVec^z{dOhXZl36T0l?i$WUIzMG!1+%+l3CJ?w)d7w5;g>> ze>lv!8(uI|>og*2J`0Ilg`pmrEbL}~hQc`^0iV7kK1s3;?*18sunKScpsA`Hh(+(0n91jtjC=vr7HM!}-MlOQwQ1~&>3XlhaL@&_T>BNreu z;TPb1cBhK4R8(s7vhwosxE!V#!DXKZv{rUYO)NOyXixQ@v zya~ryc*%K%b8LPQJYn~17Cvj{R>i80`7N`sVNYW@-LV|q1}nGgEER6z+mGYjP&9u0 zhG9yZ!td1wSyVdqT9_RkP$>rkU$s$9GobrwM9l@MCE%&YMI&q&MtM5DylirQ)m5St zFaA26SuN$x#QMh)KI>vKg?KL^X()_^eK2KF%$JjlpoE(f?)s+w#E|4Z1O;;pk-`)X zEU_srhVlajznCY0&;O78zce*tMQK1u7pL-r+y-|bs|UjK?tH~BeY`MRmqub1HmzJX zfC|z)9UPdYq@-k^8yT6RJ~Z&LUeG%hu^`sv&+ z5il1&?H-bH(IVTv_eH$l_kkI^66*MRN3>&qUyt%|y*K$N7oj<$vFK6_&b^wsq|9Qd zLcy%d4-ubvtaR^RChlmn8QO_LD*=DF+Q?Y@xcKyXbPkXN_26fpJz1wLE?i!G%Ibh{ z@#@+9?QTm1c-Qk~P_#5Ji|TC0wo^0Tk+4Yvk4<4Sl%A7Q1LLnf7~J%zKqHJ^c_e`6 zH-Lk+$@7TB!qQUKhBiZd{A!nw5&Yu1BmjN9JJjYjlNYHm6$9(BN;_K)DtdpeVf zmp;U!ws7Z`-6_MB^ga1n+L~>tYfILFAJ!~?ysmW3lW)sZeRzQ|{fCtnzT`L~u(UbW z6v6iEWh(f)vhW`tqHLDFC*Wj3@a79-z*6qK?Ks(wJ#Es-Xe(oG6~7%P_ZBhAc2s3w zeYGyAk0eKq#QzcTm_kGKLq`Gg?cAP#@zp1A7$@vXmV&C*=wG@TLDT8^EbO}hIvZ(9 zNCf&eLi;GTM5$Kbk=y^1f^g~W&SnNiI$}RfyS!k3;7mAE^Z}EoCR0cGVEh7cft`1z zIn-&$Z2lQ>E(8T3R39xjH=7ad(`N=cAnm~(Yj8xnwFlXi@n3b`oGeg)In(m*$1MYY z2-lP7{ojO2ix-I24viQ6-md~*#dfmco_>SCAfyl*95^=mRu*hVxrON(u1Y! z76Dg^4X(FK+DlERh?z5F^~dR%8DY{aA(|Dy&sa8}m^KGPi2RtK82NfNv{VqUqU?|P zICo;%{D3xBvMQp z+1RT{hLexCf0rb0Vx*Duq3wdfb7jo+Gj3OJeng6kJG?`3#mBMzfN%%k2#g^|dWm!` z7)eddrfgMC6F#8D*v5MLcU0lgng=Q0RCAjj-|Is@>b=<|s+gZ2= z!$~4Wg3K>Kb|H-T z^v(Lt&gXgQpWtVdulNKSV1H2v=3j-YY|stXkt=LD#pHp=B(|F;i}jL~*I*Bm&$h z{ziNGq+3U9)n+cn7zXYHsoY37Q`Z03gV?tiwxkcA*yEYKMgx6M!DxfuwX$P;70IP1 z1#=oEhbeke={k%`GP56n{|7k3i$f^Ha6xSDKx0W{?X15`1VBYKG!T&#HL`VV>^yBs zVo-91eDUChw$iLCetw&QQptAXO{;bK@QnFB`}#G@Hu>y*l<;tZ*GgzX_~oUASzCIZ1SuDzWLN7m!T2z^TkWm$jYr-T%a8Gdp8Ahrp#eXwY&h`DyZr5v zzMl2OGc3($D;IhWLZ_61V7kssp@8IS_@%{ovE!d4oZM4}`ujeM=4zd3MCYq?jEP+v z@7*y#ndO0;02EhnM5#FPL)QQnoV0fK4o$X&$iHWW0W**%H)~!N#?yaasnIAb*ejNY zebbh7u~IpG({Ba(g2}aV%IU)Sw-MZ9YS!60S-GQbFKOM*tQbFMnHJpP;Jr!YiHin1 zHnJrprDpWl`;Y=VMpF}fmu?~!>^H)l9LRe0yF4w4@+`a_j?vGkNjXpUMMa0b3sbX7 zhWaKClxzNs>S+>GtM`j@eom^yjRm;eb%&#_|6(b8QYKnIl*e2_DVbW39e-+F>z9>% zh%>xsoDUDSCKgUVBms@?_;ndq%}vf;ZsgL?fwWVIFVmx!}6--x*zE^MXt25eoE5MLYy1 zuOc6v9vDK3r`LJbe@``MRqt;z-aQ!E8a0b3z^rzKf)m&RK zX1*xguy;egBTWi*PLB2lvQ&Mnh>ev3J8Tu%+@8-aE{2?HZ(GnGZ7Pcwf8Z1FFAN%h zW2pwG;8B>08Xk3_LSP{<9ML=ae6X zDH>?~u6>*%3-1Fw2obLZcVTprUR*+#WWI4LJA9#xP{+6iBaSYypc_xzc6-lU-%n*8 zb~bIfMAc}e-z7ZC6KmVZ2<#gG%{n1VJV`gOrMbEJ(|@R3C+pLhK|v?cLpa#^+_02> zIyFdcizKJMhL@`kAO-9BhnHost}NWx9oB69qPEVY^F9W@LPnlVLwRL33yAWItfGAE z6%CuJ58&93o`$M_t))@MPX+;zHS3nY*ZU(uR*ekJOJE_HsCXsd?d`qW^6KXM0DiFq z&duhB%K-1RluskcG%|j+8ZR;`RJpe9nE|-VqV%x*BqF+N@j=qR*um%&lWLoN{6wN_ zMSf1qDh3|`P@relHVKe5zalS7|Gz%Uf(x$Ah_I2Url z<)ihPxoo=q7AOJ;2ab=8&Nq^(B+n-Ds_H9$oVqab@HB+v=3`=?)ovRHEBzO21tpekuxH#Dl&1puLzdM;T*k2QZfiA1B zUY0@n*Bi@dY}Iy;Wih>kjM680pPr=4J@LXSEtEw;04aMQb+6;C`?B;vN+wW(CUGb^ z+B>{>>6;`~ug$a7a*XqDEQmVeWN)={8(Va8so|Qk4+Ps%9$D7mp0;Y2j?M1*&)X~N z51(dPcSIq=F}DsVTEu>S2tcK8U#slnCpN~uRRI;ZwoBQ0^3A8hNcQ>=oOdsQBLkIG zP$9;T)foPRTmi>S)C){xB_7 zG<5PP<;4{#uIrT`UPj26OXOpqNz5wY$^M_9AiANy|jMy`>O8LByP~7FG58p0!eIMD1OT^9Vi?C2Rln#SPk`L zosJ=n7LRqyG3I{{?sGzBd!V)Rcl|t`foM!5#jx0W14I=jEiIE4QMTlC!;d7@Zx*x= zRW-Bmkn|>+%s~XpwBvw*y_H!Kxk07E2&S;@2?bKuHy7vN!b;Ve`xVbS$dM!C=KO=2L`CkxXBxY7~fsNicwV>M`zd zWuW=_-!wHk6ZDo6!LTGq3y$m#b4UfwNOjf&i`ddP8W!^TBc`D1-15*qBmO@qWC*^b zT;*@H`@np9Vi(l^^a%jfsm~w8;*FVExN$NMW*xr1FVOPc>+TLZ>?a%otG8@Wg84$N z&$eNsGXpZ%Bb0RX>X)25qsM6LN_9P!$>mn5ahPBM1YmZmxtuj(ZTt2-Dz)?trc7ai z`A7xdyr&mw_%&8HFq)d%zZO>JzDa860|7CXS|SqVqPXF@wmmXTCxFtr{+D*ZlW6VkIT8)8zcrp5!@DjR5agg-_nFho(wu%QpeltK^4x3TEWQkmA zK&A=8ZfobQfA-n!xW09TN{zn(1X~3k#AC8HS*Zgv`tIPM^EnZf(d+5936h9|-i0j|8p|&FC5t+_| z7>K`4YC$nxR%|145cGRgof+qVyOP7(XX$GGONx}KBxx3QGdVQH4&VRKU2|MZSPk*; zo@-l|qA;<0Rg zedo=R^MnPe=W%6`fSOAaE{Bt1aO#WzUO`)YW}WB3I`Q%Gj5Az}{fbnFq4iE4K=DU;c=0*9RI9dOI9c$B!EHlNHU4__^U!De4L{$0#50T|vSq%Qr z4}%6DohZK`?#D`bJS6>@GSE)&Z{7-{5~8)0V_spije$GMf5E6VWwx3B6`MI=#7O4; zY?!4gLg{=9BcEneyGY8yh8g->GaDX!(3kXYRNiprj3WAL>>*$aHh|<1#$r+EyQjoY zukY7S3|!wK$>~ytnc(|&D;Npav80kHa%I2t@z)qbxU$qTi{Ds}0^y~+SssWU(B$sy zwBzF24u%kQQMm?!N}rIlD$3ZP)I{vIQV~g-#ND-2FjoyTiHn;ofdHm#9rTq{vE#KO z=%qHs^ciDl=k46U5I$kH2E3*sF;UBfD1VI8dv1b(mSYMQhiy;L!lE-MeA2eb*ww@J z=mTKih_U%<_*Bx8$~dKA&AF7y_y02bEyG?dj(rBTDJ;4GZ3GCe*GVL+X6f(NI57y? z!RXgtH^ZzITdMvLwAaZ){99W{mXyvZ-nWs5TJ-oV+lHgL=Akq`?BJm)G1#Kpf*f~t zrN6Mc5BZ*0yOCJM58aQW4_L#M9KG^<;!?I*qnrr|qY#s*KFNgpVf%#=lsKjYK~fS1 zWG@jOjDRZ9jIy7OY{{wY(reqTLn_I=J+p|qX$jJa16yW>^taw@;P&=n5Edo@Nl?!> z0Lvs-;_0Y=*+h|iF|2+7RlCmdGZ(y1L%`Lj9{UcFiVo6fZ3rOrfGNK%k3 zZuWUrkmlZxawyU8z$)@#LBTzD{sQ*=|0t&P@Q6#4c64uI5(%ERdQyuqq7r)@m%FK0OY-qwhL@CRF z+JY|`Lir|vs4NzT)8elWE2*BTRiafvq$v74C!T^oB4Pznt~1>I1Hv1n z-;aWpG2qzl{*fhbTtvK$`hN*114_sRT2c8Itg7i%o|fg_dFX1~jCIprTdPs+5NT~~ zEB=nnR>pi_EZw#Jo2C<@FmL2h|c%D!p2 zP_Qb_Jqu(?bz>E|e=>|fxmM>HKr}Iq`!7pNmg*sDA6~yeD(W^luitFL4jDGDFAX6T zCi%M}1Hdf&XQQ(vNY!)k^dvAW0>%t%0)lX$YoQSnBY${!Flch2R%(b_zWA6=(&&i< zosn|o?NCJmj=iW86SwUEGVzBKoCk6p6uH-GjTb{Iqbr$;oA@xkU<@tWKM3YfoT3)D z>_zJiB&Mrjj_SQ~2w!DX zm2;*fb5sGHiod_v>+UO`a3#QhJFWM^zD zUCwIRXR1JoN_z#c51WCEFG0Uc*3m49*z0R=5Dc9SCb3~XNU(nUDQI~(@|&1ckE(8`;C}L5(&YxM1qDp`&L5#(n^xb!dB0atUd$+SfANLc zj`;$PYU<$;k~rzV{gFf}J|6Q68otg;ZG3<8-OWA*A!18b%8uJh(9UwmHgQF2o#C*) zh=|gIxpu-5NXtvDpsN76+7X9p3=xle#(PtDt=%-3EJ_~C*pxeTR!H3XC|X;07DG@ReRG(NCZLlq zE0n0-_PLsT2Eo=_<<@95y_ETHxw9Sci6p|LBoTiHkMr(3i#R2OkyoUd*V}7mG&)Mf z;w&3!E;YG7RbX~ca;UGLLd`^Sa{`0erxA@5dH{19&lnGYJ%*^C4 zJ_hPynn>u%3I&iO!-NAVY8wg54&Z=apXDEPO>1fr#Gn)>1LAJPf9Yi^fj1XSu)u%N z{`U+dWp*@CTAqB4pGJkS{E@vvTb7la>2X9qN7ONn>Ij)Y5zJ<8Y{~RAc+{j_^3DiK z3^t$lrGwuyL|gix*4_Bi$t)>~I$Yp)PZK5S#zUgrlIUNeSf_r+;}gA-7`0mn_8)pwp{S@S&NAqObF)B6Q@Tp!I9YGG_>Z=K~e*hnX@1s9KJ*l2%aw4 z?}3ta-4oE;X;jRU#hSFR%tu(G2?2*mxkr}l2Lfs?E^N?5HG`;$4MJ>wqybc1n=`Q^ zI)nciwa39PlcFn+WqduEn1TUVcDm=){6}XJ|Ik_+>vPsT%wm#SFH|*&?azS@4_j|e zwa4}a&pjMf0;;%|j(+eGJ6pRU<%i?w6d$~=+;BASx_YMB+KvJa&|ijPtvc9>%Fw=W z%CU{b3Hc#~5q6PL@J_btq}zA(rFRYW201$>@bYiV8T>Uz@Lo)JEQGWk34ePXkXewH zND)pinJm_;r@lZOABh?u%DF5rNG+iBu0D%h|Zl6cCDNiTcD9aMQrQj7-%y81SK zggT;gUu2auRiH|v9DVr!fE=vJ$w`;%Q**8rAPf>cw_nTb+{ES91ActBd9f-LLCjkmqPP-XDliiWQnB~e^57EP>r?~CTXqn(O@0v^$>7;L*|Ke*Vrf=FK)+tp@qCsGjI9PK=>MCd-*Ws6*umyI1yn z3I&UE+N)^(A;r}%xI{b7F^=T*AcJFoCp4&$TkWr0OwCZn0dQzJ$;6ZP$7eEDQY}sF z2X)V0R`YU60X-G$f`F466q=ls=Yd3}b$&)h1;-mu z*wL`PWA)}uOsQ_!dQTX76Xce^RKE%nVmVP9FZf_(5Uw1bSQu3Lka(MC+n!rxgWfr0>;lPqht)&mByOKEu zm_5i?w!g|fRrnSNKTT-Eoy~RWS#ZxP1T;Vv zwM~NgC@5vzDoCqRsR0mklg7$fZYb*ldT8;kl?akz>b36sk}S%&MA>-ww7+pMKbu)w(<$B1y?D`f z8o>J%z6AKTO_<`KunvH|tK;R+*jOyIbk!m?3$p@|Y=@1bk2KlL!fYaI5%0ldd2X52 zJGF>QTNW!LU-gOevrCt{lRD0%=E@2g(CqX_t+oqfMs^Q(!y}M*XWudginb<4f+_r$ zNx#}b$M@fR210z#DRd7t3u@4z`VA2;^yNT?oFyo?cCGI_`}OB{cMs3Hfk~lCFg6gB zYyquvQQPGe14|gDKl*E9gx9MW?hm-d3#M~o7BToV;*Py0CH1C8g|CYsuGfpg>YrpOlr{ z2n&HQOV&3Dz9iKR!_bX_MZ7$Aks{+|!*p~rdd9szAN$yoZ=c|RQe^S*iGt8jR?TOS zBT@}-xCz>CM3DbY+2t-T3T*8M*i^3v`B<8Pdca%Iw;sUbzms#;N|(xiHbxlEl+qj| zOhZc}eWBo<8UVf2RWpUP0jvJ!&p_(i2atenxma&c#OHtq^3k(7KtAtpqtsF&mLEZ- z08PrV4a=PO31lEJe)T>=u~g-)190l5xY{6Zql^q{Jm%0`Lw{%lJ_Nc)DUr{)H{$mu zs3q3yX(2v{kcQt*Fwx`!3!0b7FKtqhFPLE?^cm0wW{a}Tv(O)pcdY;65+9UL5juatcr{we{_ z!^)2A)i8t-^5xG3(`QTynVwJNaSWZJsKB%?r$y^tHBJsQlZ>ebhF4WMXIg{Vkn`Y< zwSIM;;=FRSx9-ALc(1IZmQP?HLbc7s&f*=+^mn5BZXoW7dWH3;rRbi%Kl)}ya}?46 zXp&!6@Xhqq61&yd&Ya*etD3?JOf!3OtOc-LMJSZSZGM1#WOa(Pl!}>eb(ZmMny!1_ z9q=^WwJp8X3daZ~$dVnWUD;u5XiJ03`vlLg@*l~W>Kok_U$}N;w5PS zgyFd19syd=sUJSl1KyadIecZSwomYNPI4#N8*fC~n*d;NYzm(8) z0wMf}4n-Nlbc+L9!@c=#)+t&(9neHGtrBjGVd3a4@ar@7_4P%vs$U540A^*k4&=wL zflDJYMkkT;{PgsBBTzgub5cdaSAjVw?`d@IWDq|;zk-6o^Q$sY(wTW%G6!pA*v+f| zK#0xd`kClzr5QnMwe2NOx%+3OeDASaG56E`Adq-lO=Sn?G_W#lL@!Qjl5qRV7g920 z#n%KiYcgorEg!oH7e`0%C+~~<3CLJMe-^ZK4%sds7Q2~wCm$Qd7f<~4)0%dC)4K0#GSz++=9X~)0S>m0JpcGye2nNYh*3RyQ)&;dG0@eKD zMjqIPZUurJ0xlDV#!pLCRk9 zL!y*x-H>T5kuo^&Bk`fh34@53=um(NxH_8W+>~FOpNB?)Alke*P~*O&tbX?EJOWwOTjuI8MG2Uu$Oxvk>d%Q_$EC#h%Ku?P=5`4Dv8bG{74*$W8w)cEFRz&>`Y?HJWlpK z68Wi4-?PM|p?gybORXGZqM$Pq$Lqw${5k&DQ~okTT_Czc#1qUR%yb0I!og<*CC z7H(KqEW7C}X08Sg50CWu_DFyB7^A^{UlguI8<>&OIvO2jlwzDg>Dw5D-)V`_3D{iA z`P#wAX>|T>vH2at5ics;8F(f$#-koQN+bBxY)(pQ42mRPi)c06PYm|M)j?Q(*dQU#*tNelnQm~?uec=sC?h`>4o1Q=340ijW(;XiL_>U=kX#I){2B$ZRnNyb7{gVIM00AuC z>N5QL%L$0U*&Hi3YRgReS5mdg38@?k3gYNGVLmHl$wD@Pg zcFLJsN0g|mY`Ti`x>|xIH+8ce)U%D8VJV82$VwN6=>S|^?+Qi`^g7NUVtgj-v!E*v zaHL+4RuKw<(|hLUi9o?`AE+&Q{`~prA_N%1Qh01JlUNNyfJ3KeauNrawfjJ5m8OvX zcXWuAc`jx?_k_wu{r-Pn5(ASwRbqi+)C!%guSOcj)}~^{D4P*&?3Ee+Bw4T5xcm!wE{gM@ThBbV+weN=SE?baP+M@80{D zGe>96%>MR%Vy*S8RrV>Zrw7hlG#!qLmiF7-efPj`Gz4sswE==DR$Pi&U=vja&PRdY zMc4DwwWvBo{XLGl6!qCfC%9~82afwM?nPy4xNvy*_%yLbDk*f}HjqibQTae5iQgLB zczhG~;Q_9L5#X1kx!CIW}dHE~$|MP}Io_XX&({y%5tu{JEf|0xa)yK^b%dko^7L**^!eAtld7KHH_dY7B8Rr1hz2dJt0Z;EAj0?Orsx4k}P=H}+cwAgqNJvQIc!kHw zGKlYv<}*|umkI#M-FOGeMi;!mnf$fkg$M9GPZ-<>@?FXs2=JS{*-TVoGaHD1=Oyy1 zeIzm%m~@w+8^!=k^9n!o z2!4on(yj6WJ-L&#{fYy3=nypySAI)xMs?P}^p4IGQ8`CAK9303RQ|cm^n)wK^vlKk zWbSM!aL52i8rZVw*y1BAM)DZ%cNv7`5;8dr1Pn?n?CoR00jZGI9iADm2qxfn&etA` zz@c5;+BtD^QndPAsO(`j-M(OLBQoj{DN$|KN^Vbi8}KIx`3{F z3#G=98$+AF`BXBemLuBbR;t9rSX@$sg_dlk`xxEMTaw$*FPq=J3OMW2ZgQq>yFV6u z3w)Nd8490IIN=dRpUxPaY!+HxmkGMk{kg1}q~~VDOFy>7)`4W^3k`Y@Qbk}bU;C2> zm!gI`5UzME_pfiu%Hraaw8VAXSLseEpi=K@PZI(u53nv%jR@i+Yxkbl5%PfuV6Aw_ z`SCTHXXzK-DoNn1ZCathzB);)o>VZp%F~`;;5j+pVC?w34RMOVI=!Uo2(RJWwm8^A zlGhKA!ja#yEI=+ibJ{d!Na?PN=_tg2z77@3+6qjr0sOXh#Df;>;SNGzSSKD zwz((kDML3U!Ns!R_~CRyhCBk>57gB@pxrmL@k`P)1cr?-CTB0#XTXJ$(iFe$GWmOz z`l9pyl+aaOtOqiUwd6in1@D4J5HaHMalc$-+sNi+m#iAo)l{D`rMqu)MQSNm>|L*mT#zSzS za#884G%E0)!)Znwc3|p1x>?X2Hlwfpxpj=s&c!lI5d4Ukm+*mV*?~dqLwmJ}Cqa5- z%Y8jtsNb!*WBRC)&MYNf@Gd)k`o6xX8_+fGE_kbPbK-;_VCO znE27orEcJWrK`|oZKt~50OEo06d}4wXN!wlkFgZ3iBOScu?y<>1ReWt;TF{tE(h1$Z`aM zGB564;8s;qTl-r@%Yb1*P!+p8gI1_j%s!jzIE&8k+(K@dnt^Y#vFT2X_P9HAS|iV( zM-v!|U+zy{Mzi|Gzn|s3wgpdYoW#wCXzS0rBUtr5_MYD5)@I%DDwYnJa}N^|y?^7u zEm3zZd|tM3rK6GZGr!SQf1WtBZY8UG{`-z2(0_g1m|9t@0jNr0Z=Z(7=aDkhRZXMW z)(IcsgT^%=K+6$1#7<5M@<)MjI(YxI6{170)`wG^M%5&DhGVQlyZS&WH};?Z;8|yC zyXb3dY|5dhiB29~-Ugv_u+=i#0|RtJD?gAAf!Cn17wJHi*I<}c0b4TZ{KsnR|1o@8 z^Gd&mxK8{j8HG0o2W8vc(X6zBg%A>Acp=)k74s6=*^c%S5 z-#LIgTHo?ATRh}yg%Ds%x8TaC%I!e2r@z0uvlE)!_Y@jv)4~8(>2zwzzSU}8Q_<2P z$9HFIsa}yla4g&smdvYV(%(utpl*pM|B@H8lKF%mV0ee)_i&dDbhVCL9)EP&FA0-R zZ8RrTP=N!>95{WpW3bS&qA+qm@2U%jtA(iJ3tP8DL~G)@njcvr*dVIC?K4AE=oOKZ zra;p>&iPSUKA^g#*1{yue8Yff5Advw`)uD3!TD;H6T1Rr-6jXBa>6M}$Ah zsPWb*Lh<&eyn|zQgWPC)gB_TPIuHy49mzBexIs)O!rcpE3qMo&I7f|?sj1%`E&L5q6Bi%&6<~A_$(b05cLt~W9fS#49L#k0%pw*}L!tudry$eD z`&ueyhhVKiz>=J9_3?NyGc4Dw`!(*^b1Viu+-dTVa@}I_|A;vbUY|sj(-q^S(LSsRb7Du{2}Li|Gl#`_%fqG!l6n{ zq_=97EO2`1_~n9n>_5-4Xgr{%kkdL<|Krdem0fZ{%$q$j;JJ3Tr*;0RwC;EuTJuOy z%(?_b^8kOdN)S5HH6GutMri8L4?qq;;dTCmbpJefVkv8Eg1N>ywqd4H8~Q2e+CTuA z*OGb>I|G6>x`1T$iG$32TU!wFhR4YJ%hGIS_}3jUp=*;7&>*lPqF||Ce&l84IzEb$ z>HG&C+^b`wm>?zzmYJM*dMs~lY1Gg@Y@l%#*C2ax&vm5$^{tSV?mRal2Nwi}gv#eg z{tv_H&VA#CwYmXxFYE^AmQc|m2xa6jU$Rsbe0&LkxyEiQ>Zx_%0U3wp{y&vMlXY-0 zR+(G^`T0#4BR`UdH8@YAT{?fZCev7MSUgH5@HYi;S@*FcVX)$AmomDJVAEq*Xu zUv7;T_VRbr5qYbr3Bbjd@>i+Hd-AaJ8c5a%7{3XGGf+~(fEZMeaE%L^9({Gt@Rll8YqZb=~B!K3wA9=8_tV6rN~(=kX(wFXZ|2b9YI9(HWD6@RdLt z|1Fw-%V|2~Fw2OX@7uqKiO;fc5iED@SVQ*=ASTbhX;(fKJ zLFx?Rw~#A9y2n#pSaaED1w@__J1LKM;r>Y}=~zNhF=69L?rJHdf^fNoL}8>V05GMH zTtLGks3ZZ-RMX&Hp?=UGdHzsl;keu2{p=7U9RrWJy|iRbHZ#PZC&a4{@1|@2Xd6pu ztp?YL^U8nI6l1bNUrKjeL#2=vFxX-&VLNwjY^4ml>+_r(|_B)(}v z;A-}#jdfcEMj*3+RTf?ZvTvKcH~0s+v13|eYv0WFa&h7=!^w#5wO9T0t3R3d>xG^TK&>OD3?y! zjC0jq)r+VQi1g{I)h@xo9-y-##wayUds_@_nj_c1I^(YI7hSGJsXX|XsF0uIg9?8~ z$wEb*53^P+@fwCz3d}XD%*JR)QF!B{e9|M;_q`X449>kGqRavKuCu~)^}nH2TEM}|J|nl zi6s+`Okvi;=W7=aM&NVV_8QtSq0=d?%}P+b?=n=(OnNJKUSQC4CgjP01dLyOQwwgJ zU`uShUBkRG<|!SAYUS``<>i-E#z5=3xYX20JA?@9Tz0uA@M_KG)ubKH@}T#lGk1CsEth7QE76tqsseoQ%G|qmhG|RQ_AR z0rdV-FU$?Es@)hLOj4mO zuj#3#)r6X|=u)oB^D>qsQmAECQ+z1(lA}Nuiv-sMhj;@VK^yH*)vg-%-LQ~AhAZhb zO~;uyyKkfl?j$*O^YLEW}dv+Mgs%`rEVdzmxW6F;bXyJAjUM*5IRjRB{y_f(GH+YQ>Cb zzKH1t&5etVDX)|ld#2hBtK37Q*DEZ$YE>A&>{{I>ch-C6l)o0)S$vEJD|A9{Fo)KJ zv7I$twWn)^3c5l1thBW+0_BNFaB@b;%p>`7xWxFHjLB))aiF5<<-Im=%h`)5nyn5l z&hHD1+W$f8^tn(!;Mt%QE~=`C78Ni(BL)=<9Z)oFB5VM|`~>nIZPjQ*L;#?c z<^vRRA*ZK~z?%w}Z>6gX2Ix|4smnZn0>pCG!1ypEADcd{h4*#$qs28)V_f+xoF_eg z7?SG>=zNaIX>;+r_;(PjNmg$Bc5S4#(!>_UDB5;E2FD|}>hjF=u>DpNPJQClF4}AI zh>w!`GVfPfa0>`M&KVOe{PoR{Z8Jvr)d13Fu{ zmk04aaH_w5@Z*4m0fc_wfsDrvv5_oMa$vl0118YAV;7LE<1|fW{u3N7Dra7TNI-0W z;fC;KYt@%A)SUw7v?`G8esg!13kKwIi%>cgzp)|U_o8iD382|$NV;3T=D8h?wQ}D z7TqHVCG==T;ne|x$44E_@oq>8NWW_qn`n1> zRxP)~$?&|hwR#O5>m6s_XGP&qmB5uV@JGC%mI#$-$o+Y~Z%Tp|Ar0DEy+$qPEa|I= z&*k5-teY2d6%m!@_|n#vw=6wpo&V@6rqK8IWwCUOOCo2b#h#WNg*)TfzDcmQ=rsWf z=DmrgLnmIAJ*{sq4g*KUp#(p7nQ{|3dTL7+?8C*@5`OdfP!CfxlWFr}4#>ePK0!{K9XxvkUiWv-_3uS?xiVYCBBbma)+%_|k zz`d{651oXMLh%<9u*!u~+e!ja9r5v)`9-2iPT#vv*Sf1fuD$KwYIu-m{|+Co0$p=NqfEef9Ev5csr4t7{heQhTcm`CcGJ_*{i&(<5ho z?Jr?LrbA**1c!aViWM!QjvMG<-+}yv3k}d#PJsr=1Y{ljtg6DA`a_8D5?I8cKPhDo zx^GfhOgVe@8I;P^$wT5fW%mAQcXvE}&GqwpS6!|bhBVhEYJ8;pJ5sT=enr)QbFbk@ zR}Us|#K(~^@y17o7nPE5@cHd45Gv|-k0unZ5TZxO=?Xdid~MyFy>f4;``u!&JKYJ4 zF;Q!G|B%?f=@N97h6W=wJ6D<^mS8<27)K|o@^h*-xlm|)EZsE=xy&Q}*wMx5`6FAP z{h|BV_}>ywm0hpUicT5s2PG&}mn0T+VT2I}^a~y6#_0+37va~1VB`+7l#I*wyf?^wBKEH|h z34oj~7(Wa9A5`&?`xK%@@>7ax|7*B&FJWvifexU6J32aI7CeE^i{h#(vL^rH5Qjd6 zhT7ZXAwxC4@STZ$1vm4#I6-c}{bR^=9tC|(8n>}wDZc#qU(wX<=J?M*gYuMP$^>GM zvHD$J{JIr|9v;re*0s^i2`e>9@!M{DH)HJb0{2Ojwa zq54We8$qsGUe(?xjcsEqv`Wce^X+hb`hIY{YLY#ozU0>u)!92KW#PxFSf&s&-kOp= zQ#<|*_e*cd5Wpjqx*trhdsl&H*foSd&y@T1ALoYYAKCSc7D0{ue_x{(6`8%cam^=) zmM=Ihv&J~lHD6nEKr|ht86jX~#-VjQ(?_Os0A#wL1AYk^NZ`95AZOe z)Op?t`?vcvX2lMixqNR#TdDdCK8*(2RT@g2z1Y5MTiwK(!wY&}(1FJA{p6SRG~^=) zYE8o)>HX(te|ey?7^gy7X2erQrh8K|BwC|Ylr)^+w1Ym$VkEs^Fubm5@(KBbRIoNB zk(PGDz4cCa>mHn`a0RJ-sASo$XFiO1IW=bTYyB^0yvP_=pSBaV+JNKX1bbOa9Xhb`foHLRg5`3z ziw5@_Ip2La3EuOCpSnp=7n)wz$sVFc_JsVr5jr3K`^}{L8x)ZKuV>}20#-!jiIql( zf5lA>joPu&1TjlD7PM*#4lQ;~^&3z1z3kbO^WSzIcbyO?8(!mFva2dMudng3ogYTm zuci)9veBPLZ)%KwKU8aG+><>E7=5Kvn_TUkc=aee_U3vye~(^aYQbma-tV;ffJhSU z2VZ^D9+h$L)Ots;olk0dAma)Q*Mm>$uE}%kmN~BLjPoEqH3LQ%<5L7MA7X)@_$u?` z6jwutIj96^`qoN{GP^*a4Vy_1Qj@*ak_(IAm<1ql!-WQQQ<0AtRB!)5wso=YGn<8d zl$%WvT^BsW8hm)8do`4t36N28uC5y@O8xw>_?%$=QH`Z1AGwH-zovx1{jq@aS=zonPFn4mOc4(R-(V zSN7fDZ%hn~RXuV|GcB&5G+f#+gz@pW`Z^$_(ER9`Nz(U1pR|O@eXwJ8hUuykTY!>p z&V3c>a>>`sh$7=|wzjFVaNVbD)l((>*ST2TKHQ!#yfCB7j99GQxW!#iQk^wIZqIJM zHi6W>hdm#d^5+=}R`u2|7KeT1w8Er>{biQkF4(pldQ=44F^r?MUeDTKJj%SZw$ppc zGWfMfEbAFsB_q(t9#&4#V{!hL)RCieabHQjk}@|c)WUvO)N7$2)mxqbg7ms3-s^#%Z-9$mQ?LR} znQHNnLXHY<4M?*(1FG~cWAu!hQOkZ7TJkQJq5nlO>b{SM<%y?}R0yh!#)tAyt%I3r z;jH@7CWR~-28cb4f}~_*GYB{yyEW~3bqr{dhp!`HEF#H8v=j&`!Za`{T0AYP=qd$r za1ejd2SMk{CJnL{e$yHfe=`5Z>b>M@RgIQ-chr*~rUekaJN5EE6cCM9I#T90AJ3im zCAU^TmUo*MTi>*jW;5`vX}_yS)yR+?O2&gQ3Du=Btb~32%;9-&ovq4=B)7Q_=J$}7 z{sxHqUW+WhQ18lW64@1VJ%=6&4TFDI${SPZEo)Uq*TzqyTusdEt(Z8Xe*+yHPD1+L z)JK;8>A01FZD5Ah6oR*{@-ad-gICLqigP z1zS-(P*H9qVRvM~z#1k(|Egf2Ay2(_Z=nh{^ugEC=R$0;VTDgf6E2@gT}0#*|LC^a zEu`K?%hO|*q5=Fi-8J5q`wX<@QgnK?dRRcQzG-Ke*~Bfatxe#2BN!b1q(q*d`pankoeGIw^rfGFSMl5n4@JX2dyEs}>{o!QMtw4x0!)vVPvjZf<=@Rj zP;yz3M~ZB0tl@MR(yV?3}~LmCxVXO?=*3@>oe`{*ub1lRIu35pJ!NH)HI%=*T}AImPxo z?#m=pi}_u^C7s+S>>OjG+|FUG_2}fI*dY`l1iOI4%1mH6UvfTc$6N z)2wcM_>Wm&DI5pH3?{$RxW_Gt27GM9nApszm2(iC(bKz$)QdnsZ;^DJcySi3p)`@}-GV!tN=vqK&r{{&cHqzYlV&QGXurNJ;J#K7sKbg|g!GVy1YDJjV72 z!~sjn-2+j^rX7$|uSNC*d7&6#cz8MHiwyf7FmL+)qc#@WIPChqxVSoz|B9ieIy)kw zYT+;x_*=0&N=Aea6fA+96~Jh|HT2%24m+Y$zC0Udr2^c&$S06J6Ztz~RN*!PN4E9a z@b4Y(dI{|suWRw78YRSqy^X!GefY7lXiB(RV7jXt<==HgyNZ=-nX{f1Bx#`+=?Xao z57Ng&9+e`_jF;Y0dUic&Y`a6ie}RWkCYZ!5p^+!RSC9F;o7MfTVGC|v^48Qd-BEvr z2j(abc5}sp`qmsN=2B|rA7xKH`sco>eLGtQ3Zu$Gsa#TUv;ZT}WsS$H{9QVNKI&8!QkWNd!51F^(6UI)XB(Fm6K zbY$}|^kFFp!?wwqH>ycsYJfFG97-q}>z>>!;83pXIU1%*GM(A)4nMu_`N@MtuuKDEO12jn znTwosKqBGL1qOwIVY&p<{;dABy^Yw$t2dUDZ*XH45W3YC#30L<{OoxX;UIdBf{hK+ z;n%-DcwsFdaeyrO1N{PEmW;+-)E+$);-rzz{&o0?9>(Kg4>QIz-nSfi+WE?Ok*n18 zRL$M01uf=vob?CcqzAE+LuLBQjYSU>@GbpK$DvhRozZhhrX2v~zTm=D7NKNB>$h#V zeH^hpL8u~g-YOYx{zWYqC5khT)gmaSL3EwjbP1T+-c`<0y?FyYI5?wo3X2oOj_mj^-QKnMtc7W5(s74;u=)%!|UmB zgiv{PlwEDJ20km-H!rXdSbm9uXTXn=Oxrc!R{Z$5*%l&K8bZUPxwnVOA45?|u{gIa z%`X`b+F%Na!RLo+NKiMd7owx_0`#w%N79SfeP%`tG#epQ-5AY;rrFoI+1;rPmmNcX zMVoW!nsg0W3{2e^>2bqvVvE{c6lnD)maD!dcN$ABzwcMXFsDxDYrIV&$=xzi_k|*A zK#|tJSN_s-Gtj;lQEcyb+KD*V?8eykaQ5NLm!C02^fsOgNWLyUO7gd#(uFGMAZo&Q z3uI=vyXD@+5ag*qwTSx59{7&I){eRrm^HhkAA|`XoSJKRfsOo&t>aDK$ro{m z?THv}tu2N_O7vSwN@wqiUrLt%zA**nB>w_T3`06IffBqqGAcX!r;uyL_eXk{sB--O1AOPIE<*n77EW%F4P$?uDP{*y%?LX-Zxl;hQCIvv*QGl}*3FgV@tFt`jy zO7{IgXVn*5vn;xLkv=o7H;>9~Bxy9zlLffWZg|$dH_qIEhv^5(7k0~W zibeMYyVnoC>vJJZgGgK#eJ>2sNZ?WUMXNz#=0}2g0g^nH6J)jb)T&q811SMkh-b<= zAMXX*-nOH@Kj7R{E?L3^=O=Kn4sa|1c;lWxh)P*hC>sv}U`ylE-2uDR5=EWI|XdBb!v{gS(X0#|b+cDM(dv`P#iH6;`f^bcueASZG>B5*M1)~Og4zaCK z)oV$~{GYFle6cWuPN7}`;ggbZo0wTx&{GZE3ExRbBxjOgsbnC{hHlhfH4HjYTnD1c zM*zP=mYN~c8dsegfjt}kGz6~RfkJ?qGK`8Uf+B*LyMH=>uHroRL6$lG4a1w7VpLIw z56KdF?h<2#odlB%BodV|{dmyJvVuu*80tYSxXf*kx1F5Fn58b=$&Gg=} z_PRSpR4RV7?{0IcFTmV%^wXenc6gz-mJ5|kNSoyu_>+c5m&uUot2WG~Na)e=<3PkW zFqyYkre3SH|BYqtciyVM40B=I17qQOJy|%K$-QAU6+;|)#;cwysBI= zB?SF*Zp=mbJsoU*W~tF-u&Mm5-b#=m6Mr#}UUHlL2x99lZNl=%{>{u8(iCjfCkB8= zp3iiw*5n>PWE4b^oYP3qe6*kf>YZG@eTcsUr7;`%IPd1R#w%4TAJ?#SY1qnG(ww7Z z1N0M?jGU{>^7AGfEUb(eB>9NPHxTj*MD#9gZ72;fC`Ntkj4;j4@=ed52f=gB&QYiu zkI84LWpZwXo1e{mYY(XS=R<3ggCN7{`mwU)Pj+7Ht`6AIZgFUxD~Q#n*H7|gqCaA) z<)|1?X}nn0Z~nk&`ntTe6j zn+3>Qn@68X>wF)SX3-D|Nup!5`1q*p!&awxj! zp=v+uByXA8st+!TWIxUW4|;}d7xUoeJ_w1KmAB#U{uZBN<6`V~P`INgIM68zro2+! zJ5*BfeFDWapgPR6)SJQ>W1$6mS|XCL>yD~tF>l!?cD z{^Xh4E+$T(F*(M;28tY`1jtTY}^)@>+wIcMCOPLdLt>pIV%C)bp@|U;ceX zNv+7)OmV5Ma9H?goMv9{?x(!10fygQXQsndOkRZ8)i@BL891E!ta+CH(sU!KlilU& z$CXv-;I)&r%7KRi6xJ9b3Ei;aH%|yS$&$#f^-=a;+`?+AuHwDItu<7j4GI2mpa#!} zef%^1JA0;>ge*X4aB=;8xTWgly^G}neWQ(fxD_EBhQrpv2u=b{!)4SlOtBZ!z~6|W zU``f?kU0tmp*lxaRB0SnJcPyD-#kL1g{1b!Q<8i1Uu&GM;VOJ_EEf@POo_&?MJW8k zCu=C3brq}n7r}|Q(7zrW&xe2g>>kJZU@L_Uc)U!VoXX|Yz2EzjiN4Sq#Kaw}4(ftp z@V)wI84E52$4BMf%$j#4+9#**Ki zUf>0#w7|OklgOr_-XY8+zgL1e{o&(0+v4KYCw@iT%#Lc)VYA|WNB?O##^ge|vJZN+ zVbTTSE4yt36J~G{8n1&I;|nzMjgpVH5Q2sHGt(^46F=eZ{@a+raW2-d-(94`>|T&w zJr>_?hc?xN&pFtNN%;CLs_*C>L};ZjL`L~rd06_80zx^M8*)?*j3v3w!*q`bhA49p zcE1?JL$Dadi>~*7&*-FkqcVsIWv5tsle03jJV#r05TtkXwnP~U8Qlt zsSaaZ6ds33KT7Y94P?A;8EGHD>IPiE!zwFT?Hkt+3W}sG)bl4>z-u2}P@s(XbCv}Q zE1_mpM5DV{)|`VNTS-=IB8{6)=e)AXAj`7bcWenMU==#7KQdc=?JIRru$pl8(KJpb zpnw&6VYgm-;9}1c3f6Rt^ivF5-G(|pmiaVJBqvki48d|}AcWjHLE7BP4ZJx;9iSGf zs(&&{*k61iU^Z;Q4~cr6ce7H*wz;Lz8t^-fL7bueqr@8T0|!D8_)x*8s;P@mmt?A& zYBiVGDuVz?p=Fhc6)74rWsFHi)i8KRIsS)fL@RdiG3}kv$BvXueL{$?2OTl2E}!P1 zUM(%@*h6OcH!+6`2eQZ~=64g7UUa`&|Lh9p@T*?$P(CV)Fbv1c#Ffx-Qc%Bg!PwmnR26{qiX4N0ZfezSpXn1#~rd8 zzRK(w>{7Tn+&)G+-(WWph~ln<9kdydP%lZpfnOyrzbFeQ(u`*SH4_9AfNNw#=~$A8 zS)%|_Edw{aTHSK&shpZqe$ssGA@0o6dVnIy;r+k^%QC2Hm$-Y3qos95+xI6_I~z*Y zzl?BjVC_;Xs&$k7(nWYZis-~XfOkq#l12K9g+Peg*NueZIOBy9J8dUhcug{+2z|d} z9SIG;+`HB4M(|}a%rR;wROYl{;YmqOW3hkXlpwXiFbCs`FRA~$`#bc6AZy~SxVC?9 zb_IoqW|x-nd#$MXs#sr_+@HsLl_}%rRgL4NY>d~-^TVxBC=nK8m&5j!8~xNW@E?>^ zpS64ZoCeiqTY3lq(D&*2lHY0jiO0Zr`OkAtk-kyGurd3+*emu!4oU7}CfFT>cG zB;QGLf@{L@bq;C^>3jmI(Xv{*1$+RN3W2aM6w!9bWBXu}rSKwYu=*Xgjg2UHi&W9C zOZ1hhB%~noN0B?ew;I!!$t{AQ)OUG|i`}3T!NuKp8dA$kN~*^s(kiV3Y%8gq7Bp{{ zf+C5XY{y$?+)6##b`OAk7hkxB6LHna#TcrzZhBVFCeHa=3qA>5?F58EW(v!bCAJFR z&7M{e%y@nyQE5|&^hLXz_#OZ&;_bzFVNJvYtC61Ql3mF*6t8aLokNoQdn(_)%F{)a zwx<>qrVqxv01wPBaew@p_^L!PZi`6bZeQ;tuXPkAIwN(TSVpA?QyyF#yv_YE(pFAh zs?sE6q~zi2Z{s0!E<@=*=c{l3fpy$;l9u@2D0c>1HEaNe8uy}b5ItiMH3n?U&i97o zZKxF`s5@aK)QmyRfIq8r*!jA(11uA=EK~DJ<1Id2U$uDl3G!c_7&drfQH{EdvPOH3 z#v8VgniZ1U2bFrW!fE|fhi&((QAJ6gz8ovDrftQsP zp(OkVlln9#q==;Ml?I`Lw9aq2=-Gz0Sob$aBRP=9kCyo3@-Od|HP#w!e-iX>WlgJ& zt0bY9DGyD?`6q216hhuBzw|IZ-;u@N^e@P(--g6QgApUw`M<8gZg^S2_^>~)UUWkf zfY7Ow{Ufy8| zEI?uN`M^w#?$q&)q_O_=T;N_q2!s%cEfn#}@!Ef;;lYm)eTGR3Km8!2MoF1_H;v5C z&#$v*HSXnzf&AIWlTTfjSzelIQJtZnNKNuyj=Cn%YoF(GfP=R4TPk96e62RrK6-b?D*o?4NR; zPonXKJ^p9I;021&DcB-K#8Tv%%TtU?_nVswiCbCStT!@=u{i{84*dcT3=zAzjaPcv zZxr4s4Btb3 z;2<0jF<#DWhQBd`a=ergJ5Z4u`&z6>cb59z-@i?$#G@OH*LeAt*GoD>e6soR^!~v? z6flWg10jG~Cf2Sgk?I0<%(CWx_Hfg{Ylrn}vB;UZ*L}L!dSPB^Ya>`X%RjQirc!Ey zRm37ZGCX{j1e^*`v9Ktj7bK^on5=b$G;YKSjzcsUJ-xg-`}+}rT;}V?oAvc|2J-Vo zm$AV_3ho$?8U3qotik`$TiY za|+;C?Q|r#`#A$TJB}O)d8o9IJMD(#B;udj#`uOtZR8uG^MTOh6}^T^u%Ro<=s09Y znp<8|=-WD}Qc@7k@*5$n7iwYh&UG_`DIqH{{71YJ-T0jx1%>9`_i-tnVsirgr^>8u zpU25BiQjJiN#}2!3$ad9e}ndhKL5HvzdIxL!g^Nq)*s+W0{NPqJ3@H!EXy>*WyK}-i#iQZgO_$l(Fjq5~$~6O)SYYGG|LQtr-{ zjhEB+^LT!|HoZAr8yGCnU2iZSPPy4HtxWnxq#==XUdOjh9P6L4m~%EUbhZzkZ$1eq zCn@V3dhtWEQwC(UB5HqgB-m0?yIV4Djd{S=F+B>xP)~t~bA5LrZ(w-1RQ2Dxhh;o* zN@^=EBKZsfJPeVWY*bYpGcz;eKgB9@O)f>hl;A2xxj>5I3zSxOC)Cu`)CibJa}-Og zVj@xhUyJ_W)a(MJ);hF5@<_*##enEOqn^sx@M7=v#|Ae?8~)=1XYyD)lEfg$bibCV z(p9FlsI*o|VjPx4?fn7Cj7}(|2lEJOZ#@e6wC{_3l`|3m1YL^vEm@lT_%+k+KA9rN z?xX>Eoi(07A40awl(A)`+=RwX1eAjd@qJkmC1GxL5hJ8y!a&Cv={VC`lIKze0JOQf zaFn?bqC(axt>44LM->7!gIOI8g0xbP`gaaS%ExM=hMgWQ(*K<`UVZ!1nlP#M)?#{3 zfTEE2bl5I10CoI6KyEA?6rySwU1jCKLIvOiSd^O!lbjq|t~9`}d;i-?5IL^hnfea| zLi_YGVA_Kzs}4+AWyAi4+pxn|LuzVH53%%YDM{HAwB!;Cc`~G9@!{bE+Da*~5nc#}=mVzT{Z%grno%({M909;lR9FUlcyz#{J6jv!KA@oBqMCi8nhvqRzG= zO@zsPkbrg~1?j^9@E3v$E8>8ZN9aNA2%Ll7C7U26+dmIz&rg#=^fuK`)#e}lB{pDS zC&;U8&t{@0mq|jtfz)&PuAK7Wv}^8a_pcBEZ~Gs_y|A+ce?D@;FqHU!p{iji^Y74L zw%f#9RJX5|(=e-nlI%kq&}%oY6u5D`=b`1;~tPQN-GXAmZaz=F1$Nw%cfrX*U& z1`W7VU-E#>Sz$iMY9uB7zo9z;CEhvAfnY+B6L$Qxa5?1MVFtw&i^mk9pFt@O0jK=n zLFDz_Yn5pSf#cShy&uKy<&R$`&xWj(p1^Cava~LeY(qaIsF`1_^*uRcqhTdzXhgor z|DmE~;-M1H5#v?KxfeDTR#VzerjKGhzah{H*?!!K6mZ*r>o7btWLBVpRUu&XA%!WS zA@}$BcgZbAkVaFc--PYmYRDp3`c)y_(RV0A%$$0??HPTZXr2H&;UMxNqQ|YgGBL05 z7Xk4k?||}jyVY}Uc3Jj(+;q|x>W>aWC-0@5;TC`aTEYUw6nyU zAGMQ47BkK@g6I*@Lds9Q<-D-yHprKx6Rk7rmM~o!4#5;J+3!+i%z}-5f|Y-OL2@YX z842H4ikC~G3OKCl-%NG@I>wrm8$84)L(esMCZjfzmrzw~wLEJ}pw4-f`^^6zFfg?7 z@*`BEsNVjqV_?I@A4>d!*B__XASLufAn}46)FlC|d?Cr@n64NxxVi6HasELkpkj*BF-@yE{3iS=h*FuG&7DHv zi+iAMubg|TTx0udX}9RF5_EsGjAbJEwUfUYhNrCg&-4>T)A;_}#{_t}^U~N9X$CQ3 zu3j94E8!Hu1OncfYGk}qJ``;nes=X@6{)e4A>l7S(lMFvlpsBP+Tr>@ECtx?OAGVr zP+LGdzr^z{$5QPT1@nfOyTptd%s7=9o_|V z0!?ifHWaPjhG9zf`=Rv{PjX5L`khY&;5PI3a26T@Aa{1%)A`=#T#SV4bIM+Jz+zAU zTQsl`3d6wueyno3!xJ$c&uzyR-|0`Xb>ya5s*7}8Hbf{kEHX7mBwXzK`1OCMTEY0P@z z^}NsBUJ?7pE;!%IX|2=C%R2kl%#V`y7UFOf5UTnp3Jp0qx$@8IM<5{A{XT(8#w_H| z2Pqt!n0UdHFGDgYaeXl?e|O$c?rc;z%Ull}|JS_-;RiXC>uB82+%1k$j-DVjq8{xe zF4)@?9h^{)x%W9n@}e(YT-jk3ZJ`+^fE5j)0XcO;9yE==ipXr*&pd`cbb9FGt_jw7 zdvP9>2VY9Nd=oNs!ID*NJv{-Fgr7vkG>SihU9E&`+JA=IMDllp986P&1qGFj!D}xJzi)~(p>dDs+-+(zr!fevmK7{X5hzBm9>q>Z(m+Q#CZNi4D?t- zcKt3N*q=5oJ>7i67>*1gAbd^z_4*~S_@YQ8I%v_~;XM)Q`8g?P0Tf0$$M3TJLaz1# z6-^{q5jriohJsmV^optnkpLBeN&9EV9TOS;e8) z+Xt_bbwf`10{Hz|24p0s99@wvB}zJx0%FR$z9H%ANOHX@KI7N&|NiAO6Deid9aXhe z;IB)ReyiJN=_3z@L?{&YkOiSroOou(9c2Zsn9+HMQW@{N@iJw74HsZ)>w`%R!O4h%tgZ33uY zV0C*#B(~6u^%nvU1N~d#rmA zy&v&3@i@U-$VzluwZBwQsM-YB*DR9RD@&$^t&}9w9VeHc49rGhm_~Y1(i04)_rLqg z4LBs9N~9WD1$dDq!R}~@wH;xo#Vhg2`HhL5R=H<_Kb=uMoKUiUxUg*U%UHY}uZe^8 z=S)HpC&2T*iaNU#bL~va(5pZ*dA8S|T*Add#SrGlqK%$~6a&}A_3ks8RV+F?^tY=G z#m&?-R)j||16~#4t>zK8yx|BeCoe%@W<@;y3pJ93{I-(afl(g5tr?r(f{)D+p~HQ6 z^D{1*Du4QmwJlk=KstEm$;O1vM|8+%^WWp>(6rLz_E-Y_{2FpC*)}g9Q1D@(Ohksh zfP~gXvL|!%O6}C0&Bp-Apv>|~LA(S1eY6`mV{D!%V`XFGeFv6x5TQQ8fd0aQfG|rt zsR7c}zYZW3asIJq7DN^kTqiro9)Ka0y}w#w!tcNQ30?Zuv$)E09*XM~x0Y`0I9{rR zmRTv1-IAetP*N+uvvxRx2BkjrJB>Ak2h5o{3^GF@yX|O^ryEnDvjM7`-Mlz9n;E8y zgIOi}EFQZw)bCOI`4bZp9QiDtw<%qvp5OS<8gn!(d0P ztKT~Bu7UL5eBm~%aaB_6-D~cSSMqD|BOOz1g^$MpaswxW#S004L?Y;pT0z6h`L5s;7$0qO3$Hs?3*eeZSV_=n>h+QS)`?8ms7$_RzyFg!}yGc z<_RuyVQq^$#)2ER!cz&mcuJAdSy49!lSKWpYqPAz%3s7_ljGQHl$kMB(DK@d=y3mr zQVh&m*BoA;0*|0K&91W^DK>r}IS8q(KwKZhB~*M}v}XjI#`QP^Rh{kS3?HXIeU|wc zg%a%l+}PYJjrk;!O)`BiM}y55Hrq0j0=M~GN2HzCi@57?5FzOj|CTU1mv!~*U+|tc zxu$L7-c#euCo^7%sV9<;W zyS7AaR46sR5Ie7WY>OR*UQ^=Oy}6ViH@&(Rwnp^FxQv-7*X+jcIHrh|2-AHN-LfuI zu4ze?B0Obd(pb?{6?Hn6Y*pGG))>Rxvw^0&A^~~)W%%NO==4EKuLv_A((jAke-KWZ zQd&5^N%Glye218C&ze72wuLJB{Pt^`5r%c6EAD=^Nt;F+Y56X$t_BO=KRxh2gDuzf z+7x^EpF>|r{vqSc#SD6(eRD7XX z`mkc*RTJ2*eEPVdp#nGklJVuYJ56crH4o*}y+3}WaEB@v(whA&!cN-Ab*JEZZioL0 zc7AEKchoC!$_SV%6s@BfwZ4b;Wqox_lLbx&c${?FkWW)@XJ`2KOPRx-92r6t#_9PDLD1e7J3s22nfb(V;&kC8&AW%_>HY% z979-GI50#d<)|5SUPP3X_i{8wz5t}D<9TT8!Yb{K-+lV&)2CurzYS2GwUBas+Q%H{ zqRLdOiwa_ADC-)pmwQ{o|L0B07K4RA)ivsej|`N)-MLMEd-$LzrFigts`MOTibqQS z!!DX^;=uw24~CTu7J9cI&F-6L4hzd~E~S(Hj*8)UqFf_4XXs=mBug4YKi*3+eYSA3 z=r@o2v8m1QylM&Z*9V`&D3c&S>AFvwqghlLgoSkzOs|h=`8=EbCMD++ zM1>|yAdL6wu{i#;QRj2Y%=foENbGhNNZJRVb3AVcY4JQ#oERl#s~U0GhyP<|QB(MX z+h@j^B=M1k?30A6(cBl7Y_;1!Xc9)-FsL%ME7ly}3QteAEN#HU^*?s$G*iCMh8gKn zds#m$M2T)Wvy}+-(?b!Y(w$eJ#}PWP!|9O-Smvr zI6M5Ym_K#iB5T(DBV)Oqf>lftXDc^_&K85pw$4E5+8S_+&-taj<_La7riMvCroW&t zChk{^9|35pUU&!3?%jWwaz6JmJAdeqaR~0N%9tMd*;QETA5O6RPipI0bdx=Vc_48&iHeJ34ZjzB%BP@i1_eH^`vGThNr^FaBS>dv zLRj)6+B3z?zkJTsZMeZvxAXqwU~9|H#Wv~k%RjCwdDmWgeG2+37CgWdxJA$@5`9_4 zQlRWb`EgF(OY|Rof++&k682F|**0u`v^{B(x;~ceTM-c|H`^ylF^uA@bd9I;?Vilw z#^&Fm!Ux!2ALk-({y=x<{fcAQ8=ce46Zs zL(t`M+cq~hDG9Ads(<~pF=fOjEF)Sh`zn!jw`EWKx#F00!Qf|}BVj2I#xAAi-8$Wv z4$~f44mV?3r`WLdN~-OsU8BW&dx&QGP-E$nf?)p|!|6NaG=^y2pj-Jt=8!yKRkQkt z%X(s5LwOp84}8l~%Gl`UXlSHFkn6#0rHsy)>kKrjU0Q8GBKiC@Zw8>fo}17?nS zsK3CV&1a-cC}-1e7PoGRRlsWVGSG9yF`LB)?w;n?sCAsE#F%I@KLMY5ik2opVu;9 z*ul;YURnD0KXD^J>Ue`jgIk<2C~2_kRNaoxee$`?KeC*Taq7>%excMoet8N%^B;xa zOj=(Q5=jU))k=aLU0`?rvM-3*zsonJ3x1^FSxgmuIqB-L9NBa>!O--)?dtt}+)0up z*2gE_Uom|!7any!=sO$;6R8;U?$$+r?2gD=(>f^Qati9H5ZC~zPM}?Do38JP7np@z z8LONW*Hr7Wt;b0*7ws7txi~bbwPPUk5N{W@M0Zp?c!Q#Ky)6AuQYBbfc+A{*W=@&c z7Sf6oRF8A4;k3(~UQ^<>PZ=*0)OBiH#(7lqfi%KxP24=_x**&IT!z6wh0i6o*CqJ*+2VH6OiIOmFml|+*TKbP< zaa`QNv42z!wR(N^tvRz8r`=o!f8s5LCq(2ke!A;Re#0@V?XfH2otEkI*pGJxtSorr z{1)5G{f7L=BNeZ?i+Qiq-ER76BBURldtkgG%SEG@gkDa@t;~={^#7R9kmw%!Q@}|mu^PO zPfuSe%U5HkmX>X(0>mzoLCZKXJDFq^I%yxPV@FdeZbesSaq+<|lv~O(BLY7EpCO9> zy2jtIG5sLT0qR(vb0nK2@m}g}%+Po2@1KBwq#V1fI>1R{C1R*AlgnXmw{N5@Y_C1# z_&Bv$Pu!Zvml@&jBkk%v{>>N|t9_qxpTPV| zvA9-fSdIEmuCKyCCKBB&td`v{*r0E;{V*4Y+q@xUJ>X z2`u6<@w`NrC>1f=obsrGa!9=nrUOGKhI)T~l`GvPna&-`(`+F%MT(?eHrNS7gE~dp zKz4T1K0Uq}EqnnBkk0_Ns_c3dAsA-n*XH}K7LLoa11xE#EFilYF_;s=9Ab9joZ1=W zQFT0bu-O=Hr(8Ad6We zMOt>#XqkEB`}e-kT!0F-^jrIn1VPPr0Q5}{s_K?mL99%cOk3h%k=x$CCWm2dCO-`& zm^lCfFD(v9V*w(>S1WF!npsh1wXyxPQQlO5Gk*@5-Ajp?Z*=j_>2keaKKsl&c{jE;58Z zW~au&Xd<@zD`IF`?Whs$F{d?oIZb!;*!vP`&b8ECXH#eCalyMl!Ftg_Z-eP`B@vU2 z9fx5IlS<|0?Ric~WxxN9Gn`7v&ri04LPMWq$i^G( zW+5&-U~MvUb1%|vK}+nqLHeKZlPErF1}TseQp$9F9JE(^bBd4x`GZbDJ2W_V z!fUQyzusud#Xrs=djmke#gHH2J>d!&PmE>pL0P$B@e|96-A^VCa(VY#5Kh)?BB2j-_%!T)c-wwrBA9RI3NB9y8mU^N;s0*B+Tk zEIx%VKld*Ld_IT4L!Rmv_Cx!kppcm-0eDSChV6tD6nDd$uyAm02@BI<`=KW%Cj&#H z4Bnj^S7XnE-!dgcbZ>g)c|S{iDkT>jl-VP3ByM0d5NUT=VqQYBL$+}(_s{S6Dr&dl zy%Q4w5qjy^Hy7ktWz)4ypUoi~<@UaJcG{>Ch7{Mgq*gNy#!|_LLDXVJht9693_QbHRvQ)xZfkseJ-;&K==hd#>EtvcvK98OT(=%$ zjjiLiN7*2oVeb1e%1FtAJZ+1VG%MuyD0EhT4P53TpBXtL^)oV11M+1y2jqm4bsIbp zciPL|1Q1!BC@sDE=_3EiQZ<`e+99OpO;CsWfy+U%wY@GMRzLoR>6rq6;O0Dz#;am> zqFY`D7sVS>#+Xm2}*2@Xlk3N(L2yb zG#taMJ|Oc3c9ziTj?&|kjh2tRwkZ}q71onNZ>hMY%IiqRI{BR4e(NwZGHX&gC}_mP zKKan@!Mv!FR#3=(?DFcrH^87#=gPeXrqP{$BLlAbah*o*$-9ChqJML^f@`hFsFOH7 zLu#?3#zR27?&JYVNW+xj7Q&-O4wPeB-1w#S?g|@Ihb4p}4CyJ0sbK8ATo*JSWCr7r zzyqfaeY(+N|7_L@qS4C%IYC+q`pohQ2ATwx$#WK&=vSaGT&eB>ixFNgtBkn9r$gU? zNvCwfv7*N#%LS?>gy|X2d#wIhGzwFxo!7thZ!tnU^T6aSZPtnpQQqrN_CrZXNNjB9 zxR#wluN&yHy)d1flV5D*w$;hB3CPJ-S|UlmnFVQ<{o0-=b4@%u(lXD1HRv-d&Sfm! z{?UTQ{4!m%3&*H-5QNnsGF z?)-$6|6Gz5Q6g=_gjH%0+3;GN=c83^+G(ABE&uo+@TaG3tc5J?=h&qz8U7e$Cg?Av zK^FHS#i;B}!!TvuY4(+GaCa)Ka$Aj4WMyU9jeyha{{8#KKPC@07tmzEQ&?MDdp&^> zO3h;B91svj8_Jwez%8EDel~vk0L7TCvf4S}S2|N#BBTnIR}iwX1bS40?Q#^mLSihU z+>URQ>2v$-4&z`$Yeq)Kzo6FO=8A?U84hqV;j@EV07Zp^)Vn3!h^~+=unbPuU*6nT zhi2NdJT^9k12-qSo@8=;)_Z_XclsI&gvGOUEBv>FzN;?1djBwV1#`UQ#=-V;y>^}j z%V<}YOAy64iV(*Rl+Unj-(aQ`kt$h}KokpF%uP-|*!QjE8s%gDlY z`>85+USUM@-hY9jl@r$cjw6!8;v>9HS|5k`<{#tv&|+u(?`vn}aV zrb6llB!XOE3*EpDldV-P-hhO7u{)Jc|9<-f1zoQ4V_0Kaq|c}6cdbmV%>9|n5FH7S zD7>%h$oo=If8#LHEA5wLUZ7wv1V{32Cnz`HLBe_pfW?sNU0i4D>dKGc!QeZid_=yg z-myy|EgiYt7R$LTHO*smx%5vBiy$LhD{Or%gr^2ao%)gLpYLHRyS=uU&IW23)|{xi zXmq*v#M*=@Gm zs!Il&r>C1g-{1Z|W+!Gfph~eho^qRY%#GMu_MiVZdJW`6${m!Pnt0JTgX7~7`h6^g zA6?(_LjvU>rUO+p8BWQ|p19!7D5hD6yq197ejiQ{B)!X+KAB zvZT}_0Z7QqM}%Q4iia*$eS;zJM%aIj%HiQE@aaKPlwE}RL&~rYs0P_<0{tHV)|=*S zW69f09cU;yOK%6ZcKW*8w8wUP^!J#qJg<5V)}wTOYB+ik0%N+*v9IBtMqugFm82n4 z%5h?-(dkc| zW<{Eo)nr^;D9gk-IY_UtdG;*6FQZTo6XmBqTl@Iy`b77B(mc*mJAKa{$6#V>U|_HT z%3DKF069TyZ`JvOVc9VE({gx)SP5b3F)p%w>MnC0ayM6U1D(ck(d5&j({YI^kzPIdHn}}ET z2nz_+>!eDtu2&dv;hJKHjnWvNjDKer4p?&^mDUcmg5*_ z$x*lh-Fj6$Wc27H_7N3{=E_QN+q}l)m&F*>zJ{tuEZi1IIyCmyO(QP3MXsD-VhaNR z-a#=y0{-j2gE{0!d#j;;73QCKE{Pt1Rl;e4|&4J>;f^y%01?i zOIatBhys=&o0O-FDvxBpOB0TWH5=tLxip$5)C6T#z05z}H)Wqz9nbSdxS_R&z8hZV zd%eT=`(u#;KCTmw6W28hvuhV{#Z%K;b2LHh<%knFI5_z3y?ad{+nlqrx90+ko=H%#6q^-**MU!F5&s=&3OHp4L-ZqzV3ILKV-wZ zrmg#K+3F7T*@i1Vlk1qZ7~Trjn5#@5{Zz$G6iiHPVY1>|^ZY7ofQ~QY;k6=>IXHCg zgcoPP{|Ag^zFJVXM+IfnikYiDK63T`CuP45+S3GZ7k9B(l3uTUT^w<^Y9fDl*doY^ z=QEqHVvoEn@t(Kzuo#9(zg~+%G`K6}Gg}5U-vvzZRpx1-HN#(|d-|r|z4@d8^_kiB z?s^2x-eAtjcWj?Su2;y(uYZz>KOw2S@IyPR_E2T&voEX+aWTx1|NYkfi>=nwHo$w^5` z>|Ymtvc7VR^lkWMb>{vMguPGgZOMct&iLFhPm}icpG75l*3KnGNYMb$*s@*5K z%TaDQ9Dt!(f2EYVeHpbYUlY0`P~O9=nEv&v0nAJr+J#Xwoji>@JNAK5p8o?J3jzGB z{!BIazT%tvCB15Eit^es?lqdp)zlP@rcUaQRvRAl^t}25&PC)` z2cId5$~;HNZKT>l-9rlM$XUs=NDbuzVz?XaEF>qV3I)5Eq_A^lFlV&y=t*=DD^KHq zOeS4AHU+ZQoDuaIq=X@q2fO}kpm*nG|1xZY8q5h%5byrY6_;FIX21WJgwB^~kqNx3 zGF5e%xy0dT)z4HiTwSzqS_}C}2gUuj)zAxj2v zhuo=;c*?;s-VCOURs$geW~a53jFfg@fmF3@%vKk9xP6&xvRLA#x+xbGq9X(bUH}-P zIStY5<^kbbJZD!+De`yT2AQ!nPvX9|UVS&|m^OVV5j#fr+tz(ivpP zd_%Jr(j97>HWkFJ#%R^n!O~qb<*i((TKJ?g`;~}N6-{!Oo@N&IT`n%v#7kr2CdLn> zBVs7AOv8B|2GyGr=1h*E66Xua&hBS7C{u_*#_4ROKS{-ce>MSFuN;hFtEF zl<=ijLr|VNDGbVy2v{M!26{>MU#a0j%unWHZYtZ^JwT{GNb;;&=khqRcXo3#T>h0M zB^TY}w?5UhVfs@XA8yCb2d)PxfUoiWoBwNPbN4*L;)*FHgQ>Ebj@rs%&)_1YX8d{- zxSB$$r*!_+6z~bTUC-qd54O2+pfztdaM^N z@ZD0DToBFZA9lXG^pd*569UD^m94$PI<0T+{3OpNzeje5H5^h23A!=!K1A;GMdh># z>(#}ci`$-DT~yR3SID@?en}B#IFFAyO0aThmdi9z!Wxe(iT5Yi#g9&{_8#m%N#)hh zqwYNH?D%Cobl{%yhi;G#nm|M#HeYW9b^V;@OGn2?#KgoAk&*Nq7AGK`>C21^MLBi} z>1pCyrq9U)v1iNc%6+e{$NlCI-1Pm8TVzZ@)L8h-=&8}(N7F|MVVegXLzg#(?g}c~ zsrZEXd|ALeyyqQ$c;FM=iA96Z)IDfsm~A&n2S0~rqL_em(Hb+>KqsMA%Bx0Zn{qtEUEn5trBB_hvlITuBS(J+qP zx{LYE9nHT|Qb4uZlp-xfW4nBXNpoRMU;EL8Ktddq$^ekzLI~1^hoKjj^{)O9)*c?>EacLMQ*8b<1j`?;w!dtKM& zGr303)|iHeOJ6A=hkYgw>~<GW{Sgen}QRy|(VCCq8!h zG0#`p6wJEl1m-H=_cVH)c-wYeu2ZQ(jZ^JD_4}A+D!k@*o%wDIejds%OYC6>)7mF# zMp!sR3d$0oVLN%;Yq8pX6J$93Z(NtsV>vxfoZZ1I(((QKhddQnigO+?_~`(+3gpg% zK4mdWZ<9E#a$7b{a05+deqKs05Uy08z)?)dH(Gi{s0_(`9WToB0^t?yYA!zYQM32* zdTi&y;#@gqK*;i5R{!^yIkel)ZELVO)S-9*nRYET^iqFXO)FBAK zy`j<@<%rQP!(l|NIRw$}i6=UZJMTMj((we+)Q^Z=a&G#-m?Pd|)jcjrN@ud#9e9V(a$Qbn1@G60t!s@dD4_<|kL!vEnC+Z3eceLYUWyNET zzKV{HE+rSr?manwYih7w5T#+A>ERI#02u7)m5yTb6(%v1#dC>kk|mtF-)l;EJpUww z03rBHHn^&Kb^5!(IH$=&064!Xsg{dYHpKo^Vl)|a#Ot%axiS2(@m;}>F`1{}mDQW1 zYy7+Kz0mR@fl-?Qmg%B$WVU$Q#knHBs1qrL$-~3rLM%^0(h>saqd56 z(6N<>eg7~`8i`!v&I`=*Ru87WNMr0DiT?0m3iJ+b(Ef$%)VVed3@DwUAA?XKe%DZf zIsrQ3Tm1Y~km7DSQp}j(v8Vob*R}Hfe`d7MOZv#ISYBp6oC29XYSs4l5UUuH#fm5y zt6u!`3u*^lAk>R@|5#m0;6<02#Chxdd})O6-W`#gCTUft#%8zUWW(G%16u`kMjf>{ z|D2Zv?R(c>J%r z-#ec3$KDv)(2#MY5|Ri_1VPz-addAwgw6J#+PNkg3r)NoXT1?<=CwCH!WvbJ!-3Po zBANcW0zkZrGfkX(V|%-W)@j^FPUsM8tin20G;_3awS3Gd$P8+8muHH}&S!fv0f1Eu3aPkd<2S~V_|gKwT1 zniFV6Asu$%hfLPZZ`9Um6*V4djrF*oOjJoY3VG2v=@(&jUzso#lz&# zT;Q}Xx*{pO;;4|mTqW_G;_yr(m+xcxUz*O~tY0GXQQ2b^?l1FIc9(8WwURNud`f=B z4}~(nVHPd3bk$94cKWOl55dTNtsAtLvfzs=2o?i;?&3D6tg8HKlAMT$e(Yp^eqJ9G zMK+FrK2W?UcaZZal2mO{Lt&*RC<`LnAiyNS*EESW z!nf$)>f!-QAa>q`g;`DcnT0O!2#Dzx z#A7X=NSA1HUcTBXmjXYh*QE=4@43Zj>GOcR8i$nts3cMVm$o}jeiB${JNp!AoF+o_ zz245k`SHvneR$ma3;Nl{rbw?8W&xrx$2XH3jfTmfB>iK5d1u z5fL{7>yB=uA$PZtS~m2jYDET@K&3FhhY$l7o+6oD@KXao8L9Ko{MJ zpfk;nkUaXOkBDdYT?=mL`@(s({Ba$9PW_%yqwkZE0hF;7&D?*e)oXZ()Ln4URqF`s zt8akrH{z4ggwFd*5K;$lmfo*15y{%057RaD%!T=xGzc{J@=M2ynla!@_P5DvS+Gav z4w~iEO}4i`n;?Tm>AA@OIPJ{ZLAz*kPaWg0Z(@c3-laD~4$5f%k?DT@rjHl(Ie$%t zwsduO=S>&GWAss_5F?^W$Z$f^RH6T@@h!OxPn04pRk~Yp**pu1S1sA&k7y_Ms@6zi zlL3b1YTIk6uFcjHEUn!pez@#Ykv+$~c5Qr@!)D~Qca65&QK}av-7fl>2T3NYs$BpI z7jUJ;R?XCM_LYlXD{}(Ianit9?IcSFlC#MqxJhCyWb<3=%Mue~1@P3%Z<78m8}uf# z9qIM!Hz6nm=H6!6okN_bap)X~|2rLn@*7d=S5jq}VgZP_sF;E}s)7@YK%g#W3MBZG zx%c9vv;DNGw(4Ile?Q#6D|EDY*`9Ra_3U%XH%LTuW$}~SH)>M2XT-jfpAoBPaf_S> zBBwXwL+=)^PZiW%-h&+II5Fr-Dn)~dB!&^oc%X-iQJo(&0$qG1AnRtQ?k+Aa&VZ~J z+DwbZJ$50EjEvm!Py01rj)}yQXqfa>&Ep4N_E6f>mBD2-ibTzND_c_TKidM!n&8@@*H@E4hTaI^|mR3OiC? zbAFNm&4OKx>GXapiQE)iL#^Xguc!M(-f7YZdumO5`}R%6t-;t$O6scCn`QfMlhn0l zjANt*z9ts)M%+fXh2kZNeeNlIV)ndSxGXVsa3^V4TdFstr`g>oINNz8uVMpKA$DNC+>Ed^x#3}ls-QCn`(MD zBs6qBZ_obVuZ)n;LOF?qBlt{& zY_29Vd+2`iLtTLx!2z+_*ilvD>+5ARg2QKJhF5;Z#Kv|nc0upB0$rn>_V`tU8a488 zlGgUYHn*zN-dmI1=4URqtM3+Dt`p=wm9V#Uu%z+7w7=8lHthIRCaJ=~4GL{U+!9c` zOW---h=HAY=tLVCt%}CE%%;~R^?F6vZJx1le;BRZ@|Zb|lZ20g%QV7S^&uk@lWyO3 zT1Lhf6mxWEOG}IW*4*>J;~m$!d#tSAAw%Q^$bhPodIuoxoNu}7iSQa%n_&6x>*pO} zN{Tr{LkbBAIwr4?O^xetDo~z`u&VR*LnXaJnzM$`%(H@B_Xhj^mw&;h%wbOruU@_S zAUZZy;B^0Rl@w>LK8k|iv~<9}@EmRBOsR7yFZSOy6zAM&rWV<|I+=OfP=rI8;|Lt|_{**R858+-KU@Er`VpMV*4jQZ`>+2lqKy~;P3KflE zfVpIV4~p$>YiqN+QDV{?vx0ygD=J$Ck< zqj4CWTlESi>kRDzkMrfL%qIhF^7f_H`plc}j)NDEAX<0+2$X{JM_>b;I|5qy&hYlu z*4z>3Z(m-;$1fKehKuY449cv?N4z!mU`y5*#>BvofsP?D)93m4$}kSYaB56I{2d9!kKfGYB5(+6AR%mC)-2nW%z-g54^Fk%#>o zpSuEVjNuyyhJiUGdw$0#*&WVf}A zv$YQ2u78BP$sJnoN9-gq=$u;gL^pVm_}t0&pyB$;%()8T`LKcZ$t3QK8+|Nd37--ji#oDAIS&3=rY&Dm!C1Yzf zN7bsp?`pL%@Vi#+(51~$I6^$hur^ERhuV5WZhAVlRv)MnxAw*yP?iu1yUm#NiJe%4 zBr4!;maupRT-todmLPrd7I(%(CpUB-O|+KjcOo_6I^|4Vi&$+&&^{UNX2PurH`Z3S z_s-VDt!~lqZ<=5X+J8{QJ~0O81k+HPTjI?L+_q|G>+7v<>CU~SgzM=uf`ekU!y)@x zxSKi_hXMk(Zaoni2BPuS@Nk|c6C8v$%R?PDc}3l6CcEPo>uMpU=kbj}G_9@E`8I+t zGQCG%+UXYdT^Xtm?B}Du_72h9cW`#B+NCHoe(P)!_GB(;Hs)t)s z-5n*{n4b?HaPLeO;VEjT+#G}eUiw56Fjx&va5%=To)bDo`$ft1Od6hEis&n^+Z9hb z>xkt4f(hM~wqAr()y-%mKI@RT+s7k0B}FX3IrB%mK==OPp)lzCWI+9BVnT12p-MER@&$@cIzsZ4fXYVWXdZ6H)JKbyY+orE?XLwtN z+!N){HGg#c_gi0kvd*pI-o1OB^qgKW{B*}m7#B_}2AbRc3U zTroITB5c3HV>xQ%!A^9C=scW^LA=E4+;gS+axEBEW!OWLf}PpB-^4KJ-5T27-phPH?UF<^&ysfIAKYz}-f`?42x3e>H zWkw}8bk)~6PK}(7DQL0CUlnW9g_WK_^m_|&0AcH6wq*4CUYlAmNgoc5TB{l5Z`XSk zSzVb3I;T4xes^$Q;vb2%d2W7X_43U5`S;;>j93iy)&_rwV+bdQmvsclW*qddXk4Cg zcnxm6C!kJ@_eEp-H9Y?7UOdB@D# ziDh6pzGrM?n#jp^TUEo*zqrbLm1gQT;3xh6mg(`ZtdvxU7CQ?|RIhPWP)$;jNPeiM zs0I1TjIHkhZ+)yluNz&Qi#@_y<@wlo~Drxzs z_tEFQIL`C>o7pP;_o9~G$d4VDQxrUU^r`$i_b0psk0;s3lSNiDbm=(;d{NNgB3REy zzSmz_1#%R#`ihOEwLEqQO~)%Y`O0>Ys&upCR`^sH&qSu(c_5u<5JU_R5D4xNPC&TT zc0$43boSq|g!LbEbl&^(ng1|$BR@N2eeAICv*iV3#7)qS4tLTZbeWj;`MbPcAc#!Z zwmCFJf{6D)hZPhS77a-YDTR7X7=htL#_PvBOSksh#m(p8UeJ*Kvk&8pAtNOqBO_zL zE0ONdn8$O(%1XdyGU;Wd@uVGyl=NBeino5xn7TpZ{#=LyHqw*P@al!n=0qCGj%oXYT|zZ}x@YOmH!JNzYpc$Qj@V06I7&VXFE0(Fe%Tnvf7NwG!6 z&hPwHo)ejrKxe>=mP0B6R~eP2r;V>EQaYqNJi>uq(~eERX)U+7H!3FPP9PyUx$3;3 zMyV;i@EQ_a4|7nB`=tCw+v zp&VVDgW##Y7`yr73r_`hN!uF>1L3)hL{G6N{aH#(E;ZnCHCwN+SpQvgOG{#@+_H~p z6?1%?(0%*G|5`|wC0q+g|1OPwUqWvtPj$(BbFD1i_SyMYXNM6XNG~+3TL)%J?R|ug zd3F2DSuz_T?ccn=TAiI-Yu9A7F+upA>q&W+x(tRY?Pc@uaqL1u@^;hj6v#A3uKRf4N(k z5E0=GZmGI1$L>pJ(b46T-Mi`yo~ML_gf}_r$I8sf`JLAO*);I>FlsprD%+60>5jRz ze!QN%wQqv>r-J@ARJvhC#iFa@c#q%m_A34PkipDmv>j`zM!LCl~Rrk35Q(Y z?|=5bSpi)D|G>-;2H>O;#&;2jm!kEJl?irM$G4Ui%>6%3bcp>`J=q+lEDYfu!KS7K z`45gC_BF8ztNZ~f5r-o&iB#E)9Tfb3g5ti^;1CWEKMs<-KVv;4%GT1?;BuF2O{T>y z2Y*I9|8V`&mej3!wZyk{6*t=FJYR4+h&;w9rAp6`>1pO5{>VxG*OjHv9y_9JQpMH= zrf)A&qm?E8EXmz>ev}dO^eqK_3krIs7N{BTJ8w*#yVpYHRLhn}uZL1JunJyWT-4yO zJ09|MEqdrkMPLLUnMhgrLICWx8Lp3X{-RoQPtPsEoP$V19$EapsdWfN_j-G2 zb8|DI;%bccm#HIZs*nrFe#c>ffI09|2+g9Y@7Ra7DaWU5ZkLlB=sefr~ zp<)FlsVn14)q?v!o$ij$FTc+q255?j~YRL&a3w9?U8*jhGw zxrO}Pp4(@a%lmG~hz}jFcNiz0olXyAg!?{(?jhxW#WyxyATcQ1`Yj%%?1s5i_MRxtxLV6IhRL2O4>TTbMw%}ZHh4KdK_?U%@ zm=;H{%QwvB$FJU4#AAUM{xsy_DX236x3hkJl#OD6!eB(@ci)y zpKv8Ha92U<_qwklyv$%`<@fS)CQ~U(oC4I@KSNn~=P1o|0Y?UxW^tM-yX!EI;O>7+ z!QzM46di&XG+Ou;XIM5SnnnyJC zIWNIGoL`Tx$;>e~pFG|KTOOx_-b455g}a`=?P%tspStLRMomYjnjkTk&sPBFQOl3I z*{Rvi#C2@B(hcfOhQ9ebdLxF7{S80HUSR8Wn=~dQdN;|T34TR03;S8(z?Um&oS|Bv zQwhWpAuVkf0=a-GKPEW?@s7g7#EkW%`=U%u@0MGP_#Uq}=&F3xDAYrTHlEXRl&eaR zmzN^{>54wCqB7NsAs!oK*=6%d&d(+XWMy_#=i>vi4S$p}YDa+U*V~A`Q8ROO?HO|D z4c-O;3}0^a`-k)?+t*2_!WO@kwp2e0oZ9nzF}-9J`g-oehuJdYf-ZQ}vfg_%_ zJde*eMY=*~qz9eKwickv$jslexrX$3xxsw{#^u}vW;d)Zo$ z?u7BylTv!P&B#xBN`5&jOxZ@>rro19V_@^^b=L4jnJ;8J$e#juuM){znvT4^(A(p^ zxN>1;p=B^<5&8$;n&pQ8N3)wUj$R(rmV>n1>+3p{niwmOrtU zeC#f0shtMmE*O61J$(LcwqdoYpOwiYF{OHz8e41khRv`>(vry3RxJ81rE)j3y@+v@ z;zN5^JjH$&)%2e^U?}8|_r0Kq?Sv%21^yVUX#Ypi%NEt?U)&OyEG;P_q4D_w3{P?W z2UUL~98<{UDUzpR2qO}Zj13q9x%sc8Hw7k7fNsNopi>SVFNS2sN<{MY8zy8rCQR_V zQKf~I9iWFG&j5~DxNRscWK()qo5xmP_heB?A9Oq1ZR%W25{?j7!Gd*k92{Vk! z7oFf}xGP8sUpc#-i|QJbMj>fwxA{EJ4o#5wMjU(^5y<#0gg*4~!IYm3zdDXZx2Uc6 zH_grw!hS|FI02!kFPh#={>Ybojp4K|cRyI>I|K<+JaiMZ`dmY~LSWJzs+8$t_xd4$ zo6Eh%fDw^3g{j3aJ{`pJaegB>thL18LSpjyQvzyah<|FTZ27`=4rB5X29e zwLh3JUGR|Fj6Ge`x{efvrQ8E*d$v%JnfWT6=RvYog0PoRS9g3atzx-$1oH>W(;%v= zpvsYWU<`4{w3UkHD{Kp-xRdeQ3JZ-QaNS7O+BPqDKvi)=M`_dA|pFg-$t z`Em_1*mhCTai0bXxqEN(w-0&p*My+%+_(^r*vT7#g$xf*q}x(cA3!L!GnA)iNDsuh z1$7LH!|cl>m6lXnL&O^lBmfHFt2s*`6d;7fsIu#o5iJ-75lf4B8XAEUDR_D>lhk}O zkobAey>{WUV`5?1-dL1|H6_#>w z{|{Yn9Tn9V{*4YuNH+-50>Y4z3Me2V-3%Q{H%Nz+h@^B#BhAp=C7sepBZzc&zkB@t z?p^o3@4c+$A1)o{oPExI_7k7L+tEQeX~24Pz?FgXR=g`6w}F|YK#qijD=wj9sG|mP zLHTs78~}a(v6)Zl`l|nXaJagYK4M3e^xFO6%E=Q&T;V*R+7$2JfAUY=`QuJpMaHL8ak(hlYhFEQ9xFOJW+TCGp^ z!oRa*DWLyn74k+Us^v$+&U%ir69?iN5kF4v7l9;$yJr`PM%(!D&eJ$hT4?N^0ohM5 zzPnpdP)OMz^$*%`@;qrd{O+(L&)W9XD*WBdV$9T(cV_aBynE^{pkMlEk`5DOP!c`A zM04`wWz6{>errIJx**p)MOOM|y2$aP-scqtr6$^Z%_E$zARmp~9<`hR8wo66P`;&= zh)BFNr~>}|IB5O9l@bvJUj7s)T;84G=#~GCYOiw?RSh(nq}M#{*>eg@Y^I+6*g(Ka zX!D-cQdA9^G49-UJLmWQCo;8n&2_{4=PyBKP8%r}FNeh7GI2s-KnVf+Z_MZJXu<%q ze-zGaF(~Jr3xFjN=mp&@{S{CD_Rv|1>j&`HLdcJSr`P*G`hV@l+`qxt^9^|2EKffG zV!*muK}gVsN(j!_N`bNs@Rh^5X<{caD3X1v-BPU759pUKl0KV1s|PRG1}T<-KJ9#& zd6OT~xF3hW11o*>F;@sxNx@a!n6BuyF}p1%w|aJ_V=iL+_!(1?)4dQ3+zOg>+qM23 zXbA`)57c_5^!Z3787^KUcAmbE_^3PL-|910_^cw&JMfrwYY;vzptODv4XSdTO9T=E zjHwVzk=Qgsf>G4NqF=hw zbS>}O@@swP?s+u|D>PCtnx+Z9iIDWlig|TCq)X)~4%{_~S>*W(4s#0Vi+BTpIXR|c-c1LtXV7FR}_|Z4-UYyN}&p7a6o^^wC&0hZe?ZXLfnvvy4P+y|tov8so zc^V|47SyYkkdu0{g99nPG4J;i}RuWw--RN zpa9@CkAz&<5ZM}{v<8zne{CQR{T4_DAQ*dzg|mj^L>&h}M;plAZQCOPhrU(=O|!mM zK#%u=f3}m0fw?^iJhud?+Z*MgXSOC36Xw&3GPs3oooa=vTmKw!oLRiI2^f^=w~I(vJlve0wG8d8frFX z(Kk3@5qUzkJ0yW3G?YQ^u4mo7XHmH9x19l3*uwsV{CvFAF z;5gM2)cFW;tuVH+j<^^cvb^i-XxpI%SuIl5b_)|=&<-ae@R}v_hg}8@Mo-76KjOzF zCMuz$wE1*CL^VZl^RtV<69~b&L^u5qWs~}kJs8B1A5hUY1$a(xoLC0aUOq-lh6Vr! z8&RD#!dBZFI$vXaaJOpISshta4RC7t69F(+WH!<(f8ucXa4{b1gVWT0azclx)R>8u}d8L z+Hv|OM7DonHB2m{j@$J#fBFml`FK>VC0lpk<9zNa{ zs0ef7EZ53+x=WSMfcOlCqHE3D;%OilFp&1ZLjm>99p~32)jMto-H;!@(Qo*_)vX0~ z0cIMRKjm{?4+`0duURR4qmY$}JZs~1_eB(=`E)G*PPmr~@5}I1%BvA@$I%y#s)cLi zOyA^$r@#_B>*JJjbKhgCu5t1Nf7>(Y9||ZC*NlXGDH~#o&X-zmQxz|vNyvcvz(eXC{XKm=zr;@psT*k9FPfl<0SUJV9h6U<&X3m;WOq5 zB&4Zpg;&Q#MO-<%Gej-7S6a1&{{HWwoCyPQc?yEU=`w}I9Wx;2z2F2&&9gg@4$@;7YMC<^kRTaSz~!Od%z? ztQ2B16Mr=#E|tbbFyp9@UL;g3eEFzCL zwasNYrTww__oE}(q3%=C8i*Y)iW6A;_ZQ0t5e1j}e>L z0hONE1cW?yZ3^DszwPm`Di;%OXDE$*jxgTwW=;-M9d#Dd+B!G7W^uoK0BlEJ2m>p5 z6=Bh|EZ|7+e4flZIs99nbhitUK3W&1jshI-1K z?9fg~YM`*-%>YD0r>V)9<_pLc0T}JO&#_WI?C;XHTEypCq}(DDTn=>g=7+IlNP2bw zl^_@mC)VmoK*cm{^9i#F=fz*+A1^ja)vu|~4Ciw55Lw{;6kYwf-k*VJOe=c!vM+-l5O%+58_fitQZCK(le^%nW6&Vb`2Vu**6y-VyRvstD2zRNQRZ! zl95@^P+ON=5nt}`JZ!l0*!F^QDt?oZU8|rI&`66Y8*$mk6<6SB(yn#55Z2$F{eHTq zXJ7ai`Dv*-J7?cC$ipT^f+HOxnA#nL1!|~B_lMhl&MCZ`4*s}p^yYUwqbK*DAk2dw zoxq)|1{Rd%$C9l7V-G&yvQNWXO?ILV8_H~dFtYq>=F6{ ztQ&s5^Wq3j>XSIkhavtvK>utw@on*^x4U0JMnI8$#VIDR2w5o6W1gwP06JW%jBj%0 zD$zkVL+O%B8F5Fa;@=m+%X+-RA%F&B*>7r@u%g*44pxm*Y@QVAh~&YO|~ ziO)1?O%*uc+#$JJOb<@4%~{!hkO&k?$x*}rEqFBmfO*FRu~!>9`{%YAOaR7TxKMn( zODF4XK>No}Wq_ZSS(fYIrOYXeC;p*)$H?}lrha(r9Mb@+Ow~)z|J(5E`MLrvqGfoS zVEA8gzyTGov*S&@PsUa&Kp4d05-9uU&LszZ3cynK7Djz$*TUkjKo%c(?)M#U2{Jr?H7EV1m)2`XPSp=pCbL)6 zm_WJBMN0C*np06)Y;St0m(GpX3JqAv5&%*A8-GM%n4pp-dRi$yB`iZa2A|8N=(HuI%89sGlP-Z3sEq`NRV}X1z)1jo#hgBiGVN!j z30Bo?dGoBgprwJjKd34)L+7sSq_*u}ym=aA;;CrZ9?okmGwj!A>^Yw{f&2o|^L?3& zvIc4LF{zPGEAJ#$BY_-Hi2i`yAex7QXF-C~`4QiHtV0XH=lH{gb zN)@)q9Cl>DNa(Af1bdcFfPxc%jnH_5s|6kIUr+anY`*Hp?( zJkPFRB9NJg>+?DOr7<@N#2eWC^y-;1?hTh>Ng*<_v7HM6JQ}GGsKQVaJ1MUtVyF`U z&U`j(%&2e3eiU!mVR;Yf`isaK`)7bsiTC}jE22|}fq}uy%q%J;^$;OW@IMB^&r69W z1ygYW17gzF2Y}@c0fAx#4734cwi|eL_#qsx0j$6t*09iM0&|S%o9g!q%Ku(4*e5n@ z=BVUy!u;Ac9UE|Aiq7Mxgc;@z#d1ECH&Fr>U%g({Hc?NXgpv#q3E1+lnmw~FLqxW3 zwh=XI1OBMrVx5_N6D7I=>HE<=r<-yiy_)Mvu1E5qg`^Xnh;?SDRLB*!_bA}K4gKdnx63|!w-z>ns3D{8sJ}b}~K5+C{1T|9i>@E^gi6{L~&p2r| zl%=r_Y!|#oAvIoxYLaTDDi+mT@w1Eg>|v;=%v!YyxS{z8`frvygA=Se4JC_@=MxAg@dcFUKZHl(Z+m@M`X+*~ zAINX9AlVF5k19Sm2#T+0PJakvUySjK3Gmqd*CGaL2DqS|h*pi=5l2ODlHb1+8VBzs z0JF3II=WCi-C)`{iMMaj-hQPBKmg{h-z;zGBAi+B?V5IhZ7)Yz^-{pWV?ko}TI+a| z1?R?d(OSu{1!j?t*|R;NA`CxcFVE({$fCVb+*7N%pXU{|?cU8OB5)k58c$Cta*>c| zCBW|PFbBca2E{T#qeC&3W0>R}@@Y>u^U_yF@dSneQ9*cua|@73_!P(X)cV`jA%TKb zd!JRingSJzRFsNx2R~Z{9qq?UK}<^nk^GMzDbqNY;iC=+CJf)3OexvCw|)-7?k0~x z+8Xm6o`XT&%feFos-$U*6h4-}fEdZYbqV?rR73D^B8k;QrK7v5mpm>DUP#SYvUm-& ztxf7rFF`@j)I>?0C%eM0rVaR{Ico`kJ!TU$5~GpK?*R68@w_QB^wTT4b^wTTYLl@( zuEOfoB4*S7)E|cZ=l&$-^0KLU%`v#fmtJ#ip#_*5aQ4$6IzFkgk^SR(s7A`)0Z04? zi8Lq>H*Sh`>nmn=T^F1agr^vg6oEI$Ag{{ry?oA0KiZ?{Xl|7boVNfu_p#9 za1xBy9+xK$qiabE@y5xfL@TfaD!U3tH9*G)_)v1|PDX+=6dH@80+ZZbM^A6@TEmxP zB7%aW=*a0~W#M3dG#%8FDHXSYO6X6S(&g2ULK_{FFe)IkMYOe1h=?E>YPzP^PuNDp zQDgM549g=B?R2NV&z=%w%P1 zDK~|3R1)T`--CnAg}LARt%KA0l6_xr4y+eu>7>?XvN4wTI5UD*Wq zx`n;9fL<~KUvEWZC(rZDon^5$rPJ+aq-0r61El7S8ZUeE=c=*R3M>vRr%9uz*LQI=F{dnc<-3yNTJPRx9*++ksz?e^4~MpV5tPOp9PXl zSmu9po5TcLaV7|B9}&D0dcw$6ca4Ca_A`J+4z!8q_sV)Z15bKLZ_U^VC?ANm14DXy z2{4&w`~D*UuqE;=N@p5prAkaNF@mmwp`?J)D&U*G$;{LG(}1>}piqFNmJMf*OCYP#Yv_Ido|z;5cKb9~ z`T)pJ_4jNL5fu;I+wH$v&VpBty(}V!N}_O6v-QW6hpg%cKy9d09mYa)Dva2%~Ruo*D$=tM}nUc^H+D zI@t7oGDA7w6@|^LC3UYknom?Vfrl7^2K@!+@t+y9^f=H){b`(&#A6nIvbQi-?(O1I zLvHmK4*whwkoZAeUA=mHQ~Erc?K@q#g^qsOMO-U2B;Tf9HomvT=U(z?x-@|w!0W}N zZ72tdMonI-6Bp_1SA6wZb(3~;3PE9dDqE(i6o+X&nwe&a3ob0FO#4Po!&^|;O~Fj7 z=83qsezgYpBgvohx2~v>=ZWItrDG^LMF3jwa`;yhsdXj?nMOAZJbIKox@BhSE&%Z` z0Rv*g${9H_FR(jH&uRev=XK6wMc_;n=Ct!m8KBq&>j8+gkP5^t@&XD4S{SV8_iw_f zya;l5rXqy}B(6`_q!OlW%L;{Jb!sArO5NV5?GLI+SEI`xgQ7e-&7tkb!{h6FmgvZU$iK zDigPDKE-18G|eq@#ttOGe*pMiAQ6sW9vzarf11uF|5SvT6o(k=p}ng!gcpLo=xJ~i z&k@zeFHPt(Fcg02jpP?yYn_|$=`El{@fbMiyhXx7336as-L zC9{!=Y|e1cVxMF0Ge15z8TeUv-WxXQX|h>^o#4#l5c(&SQD_MN-D!-sbU9O*OrUzX z@Xc!R!)(D%Ahk&Mwu)>`aozMvxN z6rTZRwqTNJgotT32= zE=BZ^gh(}hA-e&=Epy*0$om7LSq%6r23xBgJ~g#>XKQMV>KU;O2%y#X@lO|G#;&2T z1sx>cC(rOIIJD?53#X+9VH&}FHzRo3i6Xbm?xCcz_R}RCerjOz$VmN6W732hPJ0rV zrMyQ2>v-{{=4(fe&1#Lqs)db-foaR2X5};rY-r&dQvhR5_-TlY^-<3kH6G!cAxr6S zycixqzGq^vW^D;sh#tfP2e|-u70}qZjeGHD8k`FeK+Yoo#hspQVJ8ih_G$r2qchE( zuGPo|!tz0?)86g@f!=H}ZByJfY>(}M5F;8MrV zUmg!FFYj}T+01?$)DWW*lsX9@3#wMmYVZ+*K$hzs6iBUNK}0*siiBCE|LG7Y@1IjiiG+rMS1@~a{^CO(EuO$C- zb6lv~!`WtVlguBt^3oH&DE*?=LQdC9r9!tht_XHqCj3yW#okIn_=*>XIy$T(&^;xUXP$Ro;Fwn4Kbc zQl;^b`T47x3RPfV>zCrWS6>03>g0k>`5#Y4B@L^oF)83UNrZai?c4H|fNmNhfQdmH z39sqY^JymM?Q&{wI8`aLE&fxg#d^6tm1wPgg%X$Qv)Ub3p&a-3Z+}VS{%9k*$ulNV64dv(B78-m(IX-(91H3&~oDm zqkN%~=C*>7kZjc6-p=iI@GjvL*x%A2k^*-_Q=s@DxzL)2>A024R9p6|DQ$LmcFpQ0 zE32#3zyM==dit57e?BI^P5h_R?V*gBLMT*9`U8q`!OBA8P~`D7O`!VGv7>QONXv-| zhDgp|0V^bk=qZni^TmzL%*DaS@;4GPCtYN+U=ue~+d?31FaIfcz4WN6_W@G8>AjJ3 z;_$d;nFl$buHNPL0U`kofSi_OF=V?Yx6iWxTn>(Y8AT+T0CmO6Cq|Qi%V)JGi$lRH z`DNg}K1qnwJ{DSC4rvemS`N)k>PQj|SpNbCaKLujkeM%iL zvMqV9K4R=HDZXxS`Y@BQSnD$U8WjY?iE7ek-yPQaiiaXqmDqf2<@o$yf;$6Hd#&?# z`cWTi4Qt8OdU6T4^ToB4CZ&rF8Ag^?ay5Q8g4NGNQoeeV0f?_Lz<;H@|1{0}%A!&i z$lfLGs5H7Jq)xabjc)x8q=^U6w8W~dBrk-VG*f^F$X8NaW-X3@_)$o$!`^J^1e1!A zF0@@i3$7$|gN)2jC+^8Z8IGnCaI*2f;f{4^_|>2cKMM$1*ijvUFzu8(pLD6wa4NzH zel6sfxunNY#^40cFs;DCMO6tf&6XJ*PNf%emFa8uw=E`vKKZ_1lG?3(Ed8Obv@z8H$)`%n5xL)o5zf{|Xr@pLF5Zs~ zM7KKRd_LLPB$OhVFV8}8-eYJ_6_OPlXX3cVq~Xoi-v*fyL`^_?D3U{dC<9NkcqWyx za(X{uX8XJ^lYLXA2#HK#YOjyG!>Upsu`aB|#?V-F?s-f#Ts-C>;rzq{B!$`@mguRJeaOOqLw_l~TC9?`zPt=SrL zA{9-I$?GuSu_Yc@UrG_1X%|~ki!YBAWcl$-e=dB}rq--BaLHb;FG|?|R<3_az?Zxwlo$ ztVX=}OJ=(OWf32!SQl`&kM7j)4FOo(fJo6cqm8S}^%L=Is*>m5m)%r#FYZkWa#D%Oft@3Gn;Po#p1Xoq)Rc|>X?~GX;OqmUBQy- z$#1dGD3d7r6yb%Qtv{M7MByo)2wgSMdkD-)TyIZH$cw5%+hU1Xc7;pU_<^(S?&Qto5+_(E{q3+a4%QeH_G1;7V#3zGdsBW{araUICk+YcTM#<#-apr(tf+wMtbJ03pqHds6&;h4z* z)#%t(vnyl1mnStn(()-Hq#1af#x)iqLD~qa!*Jr_%h}hWU$N&@O)y!V8TU8E@?Khv zBPF)JkgrJbE>)6X1)B#@8(2M!;onawrnhlRyua7{t&MZH&oIYJw2 zye@Uv(o7f|`{$cRlU?ghqVSAdVA{k0W28*u&T-|Htkn75-@DXT*cN0SW74O(o{;>< zx7X(hwhIl?K!Rmx1Q1zRn3?TetHD{r7TTrQz#GclQRgSk^muYhFOl0$6^>Cdw#>3l z&9~zRldUoN_Iq-Mg{j>-FE{F6e(6yg9(55!dnkdT@?D)<1uR>KX}RTCC-(c>D{AlN zHi(5&UzbXib0M+i!97cI$asVoi5FG{p9$i<<3gU#={v`^{xM-$h{+zxQc65fk*I|Ko!mS>zx#YRS3xg@aelw@-XaUS~$ZQPdlFL|@N z%f`zLej8x;(@8`%aPt1m5TOCcKVpqdUQ}#si3dp=_%PndCx@hWt#r>m03Xc9xl<2K z`IQ+(Zu`8LtxV=YE~)jacV7Z@aHTv-tT-QAs#=DVztXQ;h&Ad!&M|XGq^y~1pZ_Wo zQZ6Urwb0-p0>*m zmzgTWXws`RF8^Ni_-`QEkmY0(;DlK(>ar;-qi7?wJ(E&UhzCjlceI<%;J;*KbKI6*vCP-Vht%vx z{-B!6G4C0!*ijd9k&?EAYYEVnOT_u9!FdHVF!`;y#9>udxxS_tV;6g#3^%bz90R8=j`c9L8TRO$%RjR7+BD!ve#y1>)T*&85B_|*ySldpp*8{zl(MtQc8NtQ zyLTz8Zulc2{OZU$weitm*pB;yT41At(_rE&VmR?agpsjJ_~S!}Z(f<^O}1CeL!PkB zX&Gz&F_+1B;Fw1@9*uXJDf?MHX9v?%f&SatUq$$n-GfKz#E4c*)&nNT|NUm?L{1B}dbV+WKmc_FTIHNSWXl&cX{{17>{^je6 zSwTvi<@Bzr)Y*JJ;YfC{yCo+ro!Ct>qm<}43Wk)qwz`58zLlFBjy@ISA}u9_(j9cJ zi~+Bdkb(mG(&HpjIqR)-9lC*J43Ma7m~)= zcl*3}*I;(e{h-aEA);~#36r_~Umq?!J_pKb-rq%EtYF1GXg=HH^V8JUfE+jjwbG-u z2Y7XZ$-r4hE}36`_|>&_#7E>vQP8CHZ7{*Wd1j=eP|{e4JW;<=#T%pCKLZ-IggWoP zOIZYiUdpe&r?Mw&39{y;`YSX`zf2zA*VTCx@<5g&K}U>g+zb2`VJ$Ut-;~Aja&k3g z>4Kt}R4r=FIF8WTY>gSJcgn%?@phmoFi~%EZ1@A2$`2-xHZ^73dBkwj0OA|YbEm66 zB-B@<_y=z(4!3BiY`#PiL4ATpPXeyqq??vNXPdVV{M06ul2Y|tIto9{=g68|^Q{h6 zF+%%M)Gh^V=#k`;;=d;IliV}OGiFE|9ZtV140;$5K7s1H3SzuQFPP0j0aG*%wD@O0 z;1|B$sXPM2!^9#r$G?M7fNdC>*VoVKASa1}URTWl9wm-)RwzgG!MNf&5(BR}dh304-nSH^Ogmf0 z@ChROWqY!>=kH!&t@DL#A&pM9HfNwe6}@m!+nF{`3G|^9RnE)?hWrZ^KCghTz<2eA zvjMs%Bp>NUp!`$Vbb5;U4yi7!L)`6ZU?;@kG@gIUC^Y0*w5gD{=!UMPk5rAFb$Ol% z=E?#&9%QPiQW5J^ZG~i}_g;F9T!S~4W>I4*hj#OtH;)Af?z<2FGChV&OaM7|6CnK1 zQ#I7M-@@tz-!MGY{Qv8SM|dJ16qR5^Kca%pph`G6{3Y$X@vhXQ&tk~e(;=e80yn!# z6dP-d);6~(?m3HUu=gW?aHObS)`g`tgVxbbdX3%Sx5ItjrK)10?bS{@3@i_1nFUmA z4BxGSu=kUed$p@=72`phXc?sGv$095n)jWgUptBZOOynOrQ$%UOdDUug<&~2g$*l@ zirjH_SJ|jZTD9_%!-xn@tdg-0msKu~ zkETlPstsr$OI7a2rdmLm`k75kvX1LQ(L8XXJDN~c>_fP&0Szg_jrZ-D4`#a)#cUqm zr|hKMHYVCS+}{#9UOp0-M$|RFLJ#twrldjv{>-Fdn`G}z5zeylUk+t#4s57Shahs8$CGOSjh_GX?oi<~2P{IssLK@;6%AU^o% zsO=pE;HCow^Z2SNUTG<2Pk1{~9QB}+oK6<~f;K1vA&|YOR*&lJ=G*W;9GbMnBG+oj z{rl_YN^xj1Cf@hrIiCDU1kGz!rS%2fi2tqR3MNAR7E~0;?W+Ht83tlQM@M1ilX$Ba z_VMP3OS!)IXj&MCUHxov4%2K z%B`RouQtzT{L_7Y_wKNvsI-e)x7s%S0g_dqN5xVsa?|!qq)tcR&~8@klj4RUwV)&E z{P*U^mY9&|NeHt-P`|7&laZ1peE_N!l*d5-guzS*wFlI3U;IU(6Drf*pc9$aP56j8 z{_hZDhPxw1!C(I0d3dB&a$y+xP-%H~ycQWglhhg#;<_S(L$nO2f~=5Q*^HcNQN@nt zW7guw MBzIkcadI-BH&)l4 zVGolM+;xAE;}|GgvQO)K7ALlB<|cQJ*E{X5{}wZVe3bR}ZrPcw9=LfVN=p(Rho+2* zh9(2J$PCY)|5}*z!yA)Jzmrc2V$5z>a`Ky1O^SIk<1-kQa_J)trjHz24G(l``Tf+~ zSv8W_)XwBqJbZF@iwrP8^OUm!a6c0bB!1!yA3IS&AR}D=-q#Pf*-&N|D?P{rt{35v zy`uP5gtbS95|4IVc8<)4S-<`TEK-uhWu{%zk@$3n$1WA?j+2FvOCp%YGq zI@PBM?iQ+fA?&RgY`@^jS(xInnx8(wn|&ZXc8GB#P?zN?7MU+ZWS+(eDgfZd4MdfD zt^|Gl&u*p(DBnyk0pXU7h)E7#Q9UW0v*sv|B#!YDIIq))-c;Mn@w2e}e*b79%$pP7 z&Hn*^9nrP`#00AgHB4@Q((FV-grhN`D7XM z)|M5G-z^Ws)F8`x-QiSLAPZVfewh9GYXB;>pqE3a%;1okEhYpf!MmmS_ixk4Y<^fN z5k9SrPofsJQ=AT|JuEFsh6fh+6;BGpV&XiS$c(c%{~DPwK2O*wY*J7C%5z^9baexL z*y-p@g>Z^6MNE8oTUK!s)+9X7W$yap41>dnt<v2gziqEEB|KY5eF=7FCM zpx40bZ#fB%8>k57XpjO8Ym@))J|wrtiTR-uV`5;1GK&>4Xypj|qg7er$Dy?X*KFOJ zRBuW&xcL3oDW8tr#34X3o7ZE6sZf>fir0rG6 z_Xa;r0)q~)APaRyA=;a>ik|+=%p7sczT(G_3y%@lH*#cF6e&viKU!we?;I*(DWwF& zU|psu`lO#>ucpK*4Ono#!1EcNtOogX#3wxM65N@2A_luTh^cie-$`-vT$|2WV0>#~ zABUzMhpJ&=|7QNHfVxNOrPcJ-W4fuJhvjeH1~an7(z0C?0nJIu@hb|h9De~;XUyy| z+VK#@><7L==4Fj8>v_CEGL2>r!sF5eJnpiyv!6EvW~XAc>CAxQ;oYwGaq~;A@_Hlh z!?fn;TBia~CpPOK6A}(q>F;Z|+{UBu+HsI_A0h5k)c+0WMdetD#OY|hrX}%R%|()y z#A+F%v9hkO2aEE)LKdb=>bxg1@Tr8GyC1m%mzsAR<}8pulM5X?Exc(A2mmMHB=)dj zNdtVWp>eNS5p$~O!yq1zw@jSX7wy=)0!!rnw~>X@RjU<8dGj18^9XnS0^7{x6oByVW^{fcklBR&kA#R95bh zKq+M6 zrX38s8_L2mXFwvR-DOb_hg@*dH@!Rk)z!-e3CTnZMljoD=x!gU5Evl4Q)dOzBB5z7 zHUo~FOH{*WGwnaR2uV%R8kkYG(vHoV;Z`VM!7aPhbqA(7L?965pVDu{)Yz;V)6H9q zx)keRE4Fp*3$RatvRHS;c}y+?z9bE5NoI*DlUTfkhnxgkc}{;Z`KeDcw()5+ZffZ~ zJ&)0KG!uH4F^LkIC|Xpe1bUG3E&dF0?wuyppl;~9*}JZAew~Vj(~F72SNf9>K28_@ zcofnfE9mv5I#dx{;smaUkI(UKhq?-V&g4Z4s`1@L){eVtA%QM^>s<0uT|3Z$pD5gnq*^0S!YkE&&Q9|wbFNcbK(m~ zrY4CQm{eA@>K5uXvBp#s=o-BYCb98Jdu?)DZ!I=Qw;xgKTj)zGC`__$?~JcA9w43aK*sOu{Q5Y(%=G^zo4u7P3-h7|&T* z(L)*O$JV~^FAx1t6w#ZNyMHaiT`9-^0&#~Y zvjV}9R*Lctpo!LIwDDE@{+A{JRk+wh5P@mgj5SjsOxndo>p-;z&A>#~!bR(yx`j)4 z;<|xEJp28{bCy0St^sHK5GSL7~Cbd4Cir1)bXqX9XNfZ$F0*7b_ z5J`?stu11>+tUOBoD|TwH3o=Uhrfwt)Xp@ZST()v3MG>T0)X3f-J(OCOKw?>g%AN& z1$h!#<81eXJ_Y}Lhr0`LOjVUmm!;46Sa}so@)q?nLjXrMmo&{E@f>b%OKSH!y?i!3{QH;Te+(JP? zOG#a!B(Vl>>RbN;k@k}(j5k%|a-wnMPI`HpA&C`Lp{=VJ-D8jfnYs1#WDXo>=Al91 zof>(Xk7ctyAojS4;E;NN(QtNEl^Q~@5Qvl}0G~OfMUQRv#Fw5mot>SOAFhF5jQ-X6 zzE3AD)Gpgk&8RyJR1z{$QjtK;6n0(R-PP3xfbDb$;9wJI(K&1CfczGfg{9&+Tqx(= z5QW8I_cq}x`wS5Nd}DHGO|m|x%^O&wqB1#F=$Y>?+QOe9R-$bVUn*dg@Q+=bM$bvK zqrsgjn4%>bKdtqyQfsOE;Ok^g@q2Fz36k=lqRO**WW>@{`4Je-Rk@w|z&JqZfQo9; z4x`A3OJ?;OO4iUW9YxJzEV4^KS}vTga6Ufnj8E3!Pfd7Q^Ks#oB61RcaaA)%Def*5 zAHa6Pfs^LubZL5)FaS%(6v(WD5v>>~yqi00byQRgKb7%r^pc^VqSiNQOn&!MLnzd` zM}(8cqCuR+S{+Gk=gk{ZBA%~e_k0}c)-CgHP8b{DOlC=Ucqu79PA! z^{VMyu=nR}xplF)T4t~~>s*b#n0Fjosn*DuY#h%X%1f65>4DUofvLERGDXGB_c5Mjwe`w%@ZsBkuxx=Vm&ycS^0Ej#>5GqbjfMUc6`=pISYZ+kFrlAMHhxFz zL9;ytr0#Gt;|-*08ClEmw&_#8;%p!)S+8r$Lv>bxWXiHo}a;u_u4VqnydRhvC< zJANO3AGX&${tkqQJYws4W>f%D+Ke9Ha0WBNRx;Q@p7BNZ-}P2vbcJ z5R`>>1VhO<`P;>ZTW-&Eh+?ym6wZ8^hpw{yWL?0O1GGlGB#qYu!7pB5eNovmeBF9~ zEz;A_P>iO$>G~dLZ>%j3C?-!Ehhbu1ME&jv8~{TrI>0r_!N?vH1)!)F9fVr40VK-A12+n4a#?3% zwiFZi6)>UtfLoLgxd!;rKA=y(d*o#5oC8F@oi2;DtGNYuoo`BgZU+uK&Bb|a-#xC+ z(8$cXnOUJa>ZVDa*M17RLs#n2&dh*W!#!5)j07=IDKBh-16?;s;3=#7mtDK6*Xiqe z3QVR_NUJ!In+-^;E=8xObAqFw#VD=I@YxzrE^XLRtb9L1!#w5;io+*>f5j)J<^nts zDy*hCc$&R$J!XKOJkT$lMNynVUr@BPgj>(m3{7Y?qE5Uk1h4J{ip%6@u-abX@K1Ru z773q$tXlEt5@bs|23Q8yv<^j2rH|ca*9se#tsnNtYj`ngX>sTs2ApzY-LMtd%O6Js zM}1@ENXW?M7tX*ytWUlMiV7@4Kmvp?Hq~6YsGS|_7w7YiLtpy=p{PfAq25CE)c@0o zXSL=7-)!Y`T)Xq+5!k;D%V4>^u`gP?+fv&uEDQPXuFj1o>y>|i^u zIHAz~9Lh(JG}nZSLvQc=#@bkRD}KI8aT=30Z9&tr5RG>s%0F!a0)RtHlSMF!&(3VT z5ZT5XAquYufV&R2SQ7OD{lNq|l7<7Y3cQ?3{PU###Q4w7ZN_R+icUwru00ToR3^;Tn{T>&~ zyPo^~@onF?eLtT0A#gbiSDfdO`@SDPjy9ebF=9;^b1!nXX`u*2p%VWrzIa*QQVEl6 z6X?A6jRqy8Xi`v5)ax8^$sp%6Yr9P%1B&7Ta{|$WnGeG)$mQ@bJ)pHFIQU$MDZYcw zJ7t8_>IKX6)ts4QOtgW*65+0?xJsvKxmH2v9}m973Ah9kvFS0WbeKgGwQ~VM<`MG> zs<^NkpBwVCet1pN$dIC>iw~Y(W_Yw60nK0r_AyWF9nq3a*@q`ig(ND`epb8omisHw z^U%=y-MC60h5&&*U^qBeWvkdKKLTj*Et52J$FE_wVt<8!}Uh& zYX3V6x2M}iBw~_fJ!;K6USSsmkH=(v@8x0{t<`tD%pTmc@|Rc3Ihvc$ zMx)P#L2)0*$Q>pAw!#@Afcx5oW#YIrE)FRO&&W?rE~s8`?!yF0OjSN&#pLAWO=0?I zSk;9t>UaQd-sUw~|6(K2mt^^@=5cf2!}=?AfWgo6wJ*GI;Y!t6z|MAcg__aL^ea=} zyOciejS`#x+R}1&Fl8F%CnFOihe4*9sn}UfEm#P4tMsrIPQS<`u(bw&i9;q{J&St@ zzWbFPOT@AvJEs#5dK8q9i2;bvE|Ybs&tg4D+?xX87OLNyS8%i4b~|fH+0t|#_ZMJN zaNj*DA_h4CkX1e#_YIi?G*5j$D<4KC!%Q zIY-Ijb1B{s<%drjOXdFBWK}Asp%Lm#?2$y!BRJs7K700z#zCK4p^6Z$-?q82Fj6ge zB&u$I+nSQsF4n+x&@^pUq@5%8$&HS9?-g0B%Q!@Z?3R|6O!9rmbOA)=bODzVRdSu4 zo&beoUSIuUc}po~@_x1Lw^{}Uu^w^J$Mvg^3eDyePr{%F$2nwdd)V?sQ|r>VkBV2@ z0oBL0M@x?H#(5&4;`;iNyX0b6yCd@@@*fW!4t1%-l~F-LsCrhcaTq|-qn(7gx%*=6 zw;SR~n=QwOZq@HTBt<{yj3U*s^ot{C1du zX2i(Eq*XaAQvdKW%BA*JD(~}bDm68=l#Gn8LeH(s!R_h7l%rO1vuTY3kQsVBtk9RE z6CEgT{~-(dyz2ciX>#_bt#$1iSkaEEt$6g4p89;=rPykg%`Zh;{HdXWcyqBuE|h|X??H<|e^G~aReZRMVCqiy z-XL+zVZrF$Y|?rm*3$|d)^EI-nlDY9#TL~WrRE#AZu>B!ePcUm(Fbej66~h-n7JVO zfGu9oGd7ftmwnbj!!x8j%c*J0tiXvk%5zjh$jzBnaHM~KGG{@(b(^>MTL-NdFOA;x zBYw9v;cvtI+m*=)g~$(_IcuDTaZXM9&ZQz~bcvI6zObvOVvlu5kF}=eG{$Hs)M&OO z2Pcw>`|t{W#5zEpP;;z(SWMf3F52 zvXg7+{ zyvn%UQFGY^1jU+6NBO5?AK`D|&vd(nl__%#g$uu(fvn)Rtypeyzc{C?C8*z%pT=&4 z*yS%O&-UlGCt|mw=VA1_Vi}vQHnFZm(HVisgu~b-L%lK^mZPMn`DS(kA0>-xc+Rn% zfq*5;aSw*3M3-&kmXBKD^A9$C56tncAnwOyYb<<)t!Gua^C)>k&*H_ZH)V|p9#fQ9 zv%Ej9{&)kJwUDklG~@0XRx(G+O*>s&Zf`8RXuU_?bTK(d&S7g)IF;hWr#Dw_DPHl> ziG~R)`m?_wTShFdpz#~tI=|02)D~(BzG1DrK(+`kx5h1G>})rQ?*G}3h8!--K;+Sv z)5Qk3Yz)ZKYV$*?JqRdhTM+2NxBxNmk>Iyy=V=Ha9zW;`gSrG-fbw6c7|YkKU~X<6 z<5>evHLd31=acID018*|0B&IuePp^DZY3iY=FbZHePY}W|@?KUP)=yj=uit?AYC%SAv^#`Uy>D19_1vX)L#N3c74| zAi9rn^z^J^fe|euWq>tqssnXVTR~z<2n``#*bC25_2arUKZJd*@Qx^AZ?8siKGIGJ zy?j&WK?{;r6QEM^HMHDz$gBmanY53MjTPp@ge`o3k_4Hw2PAi8NVcb{1T!TNu)bMU zK|$BtERswUV75x+3S?cjEr{b}LE`eX-4Ca3MLHT8#j_|!-fjfOK4(*1C;v3f5KDUO zcSjBb4=6zt#UBk4T6q}-W2gcKabRieTj~$?t64f@csoPKKv7+NwA?;{MHHNB8Avua z)gdfD{mq3DS^^9Y!~=<)z})ot^C03=IpbjCY^>KG>~)5MP^nc5!XscoLdvunm@K2d zzgd4^+u{myZpwiZ%ijV67Skx_oTKs{r4P-BgdXnh(0xUAYueC$*;YO2RG0p$A)<8; z&MZA&*SI>a?#A{gGdkbdczcuueXBHQF}G(^uXTHrP56?h!rYn;@lG-JQD#%GQcx+K zQ|h$ef_&$R)ajSu>#?eyXDoF)SyHDX;RkikGc%PI>C+kU*{+^vW-D#drz_%TbPA3s6~{UfVN@Mz)Cxw3 zClx2aK3l3xELCaswAJv;?X^yeGm>AAwn|6r5uRA;zB1Wt21Jb!Gh=9k=CO2wEeLhV zajpw@5l$WQ*e)Gb3JRdRoZ2M2ncAdCbm%Rj!@YRS?zX@C{?DTFOe<{TVtsk;LRa_c z7%94Kw+pD@+?140WuEI+oIgr#?Lg$O=*223IyR!YECa|QzPY`>W@6=CmKU?ji+!6M z_eFfUz-{mjaoG)5^i)=vchc#LiTSZZDx;;LF&SyBqVfRWRW$^6PXenvgyj!i+!dLodBO-Ng_Pftxn z7m>cU(6uiaII4GvT#0c^XdW9#^Db`LSpC3VCc6Lgql1KsZ_WYemecI?KL`fI0e=hC z{bCrpC#`$b2-FqTHp)+(QmkE6FegJvkZa2&2YWW_4NxjESClWnz0FbDkm0JD+rd+&4&nX7oD=l=>yy^(t#oG-OO*7=$O0|mf(XOi zo6nr4e@tV9#qz7*-`jGc*gk%@xyI)(wpMfoK374*Q?ZC_iCCHi{+RCfG+44jN7L8z zbe4y^|6{p=`-BzXvg?<$g9qL$%M~H_u76vrA4?60Wu6A1bm=qRbhXQRqkyfov-s&( zRpH<=o*YCgDqu?~Sf;f}j%tFYzV`Nmr?n$5B}(S`Oj|BU57o0CQ|nW{+6O+*qO20; zqhuN6QHb)1>O7!0*Xsdw1x>qTii01HJ4dfJ`A-brwh@}O!P<>@6HPf*x{_V>JdXz$D>imqUVxI&@_pi9 zak_lDnkFT;RjBN9P{HVoS)08G^bu`Gm^Bf#E&_+BaJto->K!9`9{y{w_qHti=(g+# zc2P0GOpK|T2`?PJJ?Y_P(b{JGEO(Id2XH$ti;C8J=HEg!Y`V|Z7d}34=-6J@mOWUa z$9^`JDYxrcd$MC~JBE5JR5GjQb-4;ASrRraX7ec)Sb5vElasfl~ZFfc^du4eg*Tx;o26HG_J6GpMV2Rp`2#!N31qK^tmfIn=R{Gn2tn0N6-O65>v7|6))@hzT;5p9Xf)N~9H z8@84&dgqR8eOnY>8F^)R*LRg9GxFBF%oEL8eQEjXFJ~W?{;q@Y*kBP!Lz>il)di5a zf=AL|9xxNcGWEM)SrMwPdjI+y$QHEoadDNjpkMq=c<*Y*$jU6~Npkh%#6r@}b(Lhc z^6W1!+hwpEo<#g>G1eOp(%t6GS(AD@TGL~<8;gcjYz94xm&psQ@!@+w*L@3BI9Uj4 zXj>7|K4h%Q-~Bp%TB8u+>21L4)N%T+|v7+oZ4w_50Huq)=!BY~mfm zeOQn%zD^PuWv!R%CPBD-?t3Ihd;3M>WXiM^t(@E9Do+Ty%k8`0&z(0(JLxtT+VE-0 zOmFcuqwi+i&n+i51dfREOa3-g9MaGrR=G75LqNFPF?Ln5h%kCnh-atBP7cUH-6JKZ zVt`_jqs=N(AQ!jK5QaGvm@UAnI5smze1-niRfGeKAb7qT`xGN1>4E|A-x{WEL_GK4 zxb~ZSM@?lS(#%w-bW(M*xH9DYv-{^da!M49R0D4gI5wPiz4Paj9`-wORS{%b#1gZ{ z!%`Y50|SA6D@eXzEMtbPj{W>0;R-o#`H2(=G7}7rWI`YwRFMEF9w)Su`0{KqW_?+j zO`(}tB03*GGJacb`h1Rx6)1Xvx4Ih&$^Df=dU9?C)-DKG3N7J=p5s}% zQ1~KZ1Y1J?cTd7^<{P#1aSEX&zqVL@b(q&}t(FV9m`WKiw!m#i{QT+e0aH^A`;`?H zxp{kB7dcvAPR>DSmM`0hAjjoleg`2XMCmSX0y3+m*q*%OL*a2P^7)?L)2zbjUB?tG zAN1`il9`N*-eh6PeOn}V|IFlt7W>(am`09ktU3Z*Z{A`Q=~LD3J&RI$bt*#4SH8B6 z2`V~ECI?&TFBLygQC@o@D?1e+f&fvtoIDH*-PX02N7Vr8V+z#ABAC_0T3*~~kDz3D zI$E=e-|()S`t7Y$vXq>JR&X3U|LT-1KrwqJ7pgyZT5+#(yDJo{5c2gHp)dQA@3`7= zw>f5ZqrC)bYp5f^3fZK$N_jdy{vZm*A1Xw1$s)}RDBy8HU$O}k#wZ^y1Ic*l=EcjG zZy+MS`gWC)&|WcxoB*k4Rj&8zx9>(qMhc+W$qk)#bRYCho;94@IdPP*81SNHns+Ti zcG@}I*VUdW_mKoe_56bW7E{3tFh1^nn=rjQSMMtm(Jb0haInvDuiL!YDOs|jEzdO7xd@NB_wLWSfPAA`{2hBJ z;3K`{=$j%{6o%;>FkIYzYSCu6qOgB6cQ79+87q2Yw`PnW*Pt9Kem`_bFkko4(41R^ z#+a+YPy?sw{*wP(4M9uRcT>38cpjf?GFOU|EA|y-WH<6{w_QhIov6g5>-3rzq2)dK zPMYq{$8MpuE(tR}np)W%ByAvZVPoiGt=0P+*{D>kl{?^}qGi+hmLPc7WN^bjINDH! zuz@gzs&BNOF0zSuO6+mTX+fB5R4bXSe1RH+* zu%NvC!0%C=lC-2GH1uCJzjyf_c%ur)Kb<0+6KCnAWF#K2u?EkHPRV>4$9~pN_1(@i zIS`OPqQ@YI-B|#r!!6M~pC?b$Dm+4-ISV%y-gF#LDenbZ8oZ1F+DGNxpCvv&(Voav z-5cNLty?W9-#^zJ>O#d=Dku-AXnBW=BS*4wb|322O(asrZfyl}y*>%T4$Xu{&D@6{ zQzV^Vas{1vch&c44lwb!@%_xrEsmk=X93YDX4vJI?z6Y8>9q&7@bm8LV+Z><(|v?1 zJl-t2zZ^7?r3{Ik2lK22)YO79N1G1kxZ^F;qi`N2a|Ri{o(pll`&%61&Uicby>;AS z%QdP6X-!4g!lHlF0XKhF9pU;kZRr~r+(b;{g^L%Xa;8a0NHDfXjvggIWjDNu&P&0r zipYd?2xz17;?Wzrx`hv)pGHXG>obhxjiWabg4du&`Qy%`l;#n)D2@Xmgd?i+;p6A; z7cN9_KYB5_yC3lL27?SVZ@8zQp0wy!KmgtV{&|&3g*(LSqErNbOL?l?9iw_e^_m%9 z?W0FcyC1%*omVJzRW{Sr${NvS`YPw2cShQ4cPQ$e`3q5=6s@0v*A=fnO_fx5S@QBs z*r!O9^AGy&HQh<-;kv7n6JA992v+zv=Jw`3<5^nMLu}pn322G|huxCtV^Ey1jyQNE zFQ$t7)uXLI4i;Rf_-JyX$CBKv8+Bp2;rxger!ta6uYeJcUg%x3w{XoCT&z)tF1O`V z!;+x}L@w>z`Sb0rRR>5593~?@D3ZaSV$v28j4Y1!;l< zN`xMC0ahU7lC!kTH1J;joMzM^&V|TRG>6=1_1#~+5`iPz#9n0<&gYudfb@q!RMc22 z&%m;S^7ZG39qCz5W7dSa6s)3E=aCjGWgk$knUOX;)If9*A1gP6)W4@_Vg=punw5A! zq5zQ$;m7?VkB7ey^A9fB5ezFg2^@I!TA1f;)>TGH*HIqTll}P-m-1^_zxk8$hd+<= zF1nDT%l0RF`z5=m@owzXv=7@G4!=6GONOvQw?C!TOA0XFk8aeJvP((8VpDai zEq7mxjcn^3LlQa`cxVOAIu(qyJu9f}1g^l-;mT%;Y*E zr=c26jBX6h+e0NTSR@7m1`FDYohNs}g3I9Y3~8f0LR|}aL1O>>Bq)V5gs{t5E(PZu zKnN^*@7`0Ssfyrn&hPeCEGLmUa!qtX-IxFZi2E%u8dKeTrqrsB^*mSlV9MHV4~oB?wGW`_tmRlSW?|ow)-TW(>2{U? zM6A41$0)=F93y*!mJM;F8%S`n0%fTd2-jIa_i<{tf8pl*6ZB{Yu$S=O8<}(QyKfnU zRtls|Y+0K-5P7B)i$$Zrx}|$f-ar&lydBmOC5Qw09w44FPPMR|@HvKR_-N6uYpZPO zg+$G1!raiZ$Hw>A6d-%y`rDIyPoE>8{EiZ?(>}=%NPvsPm=@V;4$W|wB z*B%qF_&q!?+h2wt#+G^_n@akad{p<66k97Jr-h&_pS ztW&v>Gg=owkg`bAf*-adYoB~M?utd-))aLh2IJ~1kzokBy-$n|YoAAo*2rQ3 zwh)7&oEBpx|Jt+p5axLuQ}g&kSgE6FM#sXs-shv?$-No*WQ0Hq$8za1` zGCRY?^}(IV^Nvf!or;15-Cy5f$`ZMS6sQbu>+L>z$gsQC$T9TuIBCC^FmQ6;S#GH8 z(JjGljvC)|>J-+7BEv(x2~x$2%AfZ>1bL%Ldi~5^S@KtMSn zj9culyyGN`>RBZB35}9eLK-|A+~P7A;`gGWRY%&BbJz`AHXHS_grWw9>G`&rS5R3{ z-x@0-6CCu99%lSH1{mAnyvgyTzcl(+u5^<$=%>`~zeuUubu7i%*FW${ZS$fgOSqP* zB%UgL&Sli2XRi513RB6cnnT*6h#pe}G^{j0?#ZAbjPyD`nXu(0wsxx2kxYK9jYbF8 z>pp13DWYBrXJpHUF!ex6Sv;0?B-6Rh>|GI=Y9=Uaq_>E%&Y$bz&kiG1wDb$-%=H2T zY64zg8l5X95e5O^Tm{iAu-d2Q(};KRPtAAa3+|zySW{;n9|q(rq<>?0SJ`sYn@T2C zJo7lv?;i)8!I++)5Z1_E2jA{84c#a1Mm4+^F6aF>fRER?=H^C8<=1UnVQqPVZ5)Yk zD-vTGvHC%FYG8$x#_6Yjk$$;2{PP0E}Al{K>+R*vvpm)VpDd!&|JD=Jr0 zKp=w#$S5qr?kdngb|g>fSr9hfOL!LV9IVso`xx@%(bR$hl|ylyy_MEmf=@Ggk?H8# zL(#NaS6=<-%Z{M7hJNPE$tN0O!l%gBB3u=(w>{?bc+SX_D95Qk-`*C1Kxeybv@!=b zh(}~wS#s?1DLI^D{5>Si_~QKvixOG0pB<+=Su?enG8l!+O+M^{mY=*j_nj*k@9SrsbPIs{-d!3 zdih{s2{jL?)RTryv@Jc(7ICcp5pr@Pd*R$VR1KLVktCNyPb`&JDC|H9&y0_2elc?6 zysx}2e}Q*h`26g4#A5cNBjRk2>x0`Qbaig3#)bf24B#^i^z;)MvzFzwL?V$XIn?QP zn3;QzX$PxWV^6(9OaD%59TZ@O?^Nd6sh4ab zNa)%`MDaaeAN22ZSjjGUS}<5X5Q#M=Q!u1P2f zb>E3A_pX%p(^x$aY1*U))sqZB<;6I3#tUadeJj5aaHffWLIAMzkm^Sl-}Qmh09O?l zJpLIp;rO2iM;S9jd3P@LrT>{BWbu_w<-!kp?ujB7c1=59W|e0Cm(3pWd5>K1=FeSPi}@XnURk0^ zRiezy(LDAC%I`+81wq_T3t)~WlXTDJ z&zIZpz7`pD_$wT`P-U}2<%@UhPjN9^^v?j&VaOYQ(x@AW0(m+MzfJbesr5^#GQG`u zylNI1lj?B zOfav@&RCzt$tx*g=ud@{S`m*@#R-I&HW(BqQ%ft{!?+|~uO6ylF`J6U0j>_v+jHgo zF{?oh?EIkt|1n5GShTYHYLw-w?QwIB+alrN;n5RGi}ujBY&*)jy(c&3+y2vQynzJ4 z9r24mhjyxZ6cJ-u#x6d9J8*Z=3sKdmgPEhY7>FV-J~gB(Yc?b_Z@g+nq@UW`5Oxj-B%PY{GYcL)fA@E7iiqsU2n zHy3Ph=^y*7@q$R1bf?GDVsCD6pd@}q zW?Bh827xN4VYWqR=7$liH^0(0urbQ>@zWSOQDN6uL1_^;*TqrW(TSOO!R>wGo!+() zJrR6m^0d_B#|4%3Nxq9R044~U!^xs)J@E@BP%-s%K`?^WNoi&) zsfq3O?XKOJq=fT%gjS&`y}ShRy&T$G+Z{)|Um9u9KRucvsgU7p-z`I0_Bx`tn(=EY zTEAu8KbVmBCoQ3SwW{S*#M;|T?e%xJ>}amm5Bo<T|B7hKIymPAlYWPv$E4%+tONBZtQ8+>D5@T0}5S)$}>dz7Ns+8tKDM$Rf!#-OZGBTqW8P$~cE&R+pX> z3pRf|n7bYnG{>rJ++`SF|MULVn5i(KolPm&QhW0qMgBZn!dRDaU7d&D?(IJl+hX}- zeCk)7w`NBc8T+Ab-M*&#U&zRc&bUJsr4v#p=T4ps(k)vPIF!i;hsrdU4=1(w1!+2wBt4gC$h zf2yiQB2iA=KeOTqg?qTvWuvR?>53s!x9YBTM;LtvQ43THf=ruAi{B%z&;tt2O~H+p zY9PgMo%kjfu*RQ<#RSjQo=xK&Tl+EuDDayS0cXO<=-eBI{}48K!CZSHeK2p$EQ<7T zEx^VxVyuR)IX=6~()=)yyZsF_%5+U?B~Ng%nBOcj;k6JxV4de0Wlv5K zB1PXB($Jr~HF_N7N6|fwa;doT=X#S{s^|;l%wmO$=KvJQkC)ca9K*sb6CZ$mP!FgW_=Colevns3xG~1+-SGCH0fH5!vuq-yJe-T}k4;`{kq1=4;TB+O<*Q(>gxy2;V zP%}@<5x+e1{Ib#8)egbV!bSMBA)yD4+(6p9^-+-URB*TZOmC){UVZjV9nzVAD?iF^ zt>V9pU{3}|4fmky!Xg1|`#Je59h8EuImtp$&!~j!q$MD7fe0OfGna(43MujSoE?5| zN`7CZ*u0a+lzc-K8LP@NBn;2b5%9?ZE#)JwG5;T+6E>X;>5HY|>$TdRH`gKuh=o0m z%+j0Gf->Xitc9oKI!Q(%;?K)^F@Zmd#%s;6Qp);ib737}lNP$!SE zB=^Rm;Vvm-5^33Ib%@^Vvz~J06M7tUF6_9(eEhgMw)(`9-UhScj%b=+dC$W5$yf6d zffp|DD_)L}^HB{XD&-YaGbC& z*3OD~MfjUl)tvfJl=s{G$QgWk>vTj!Zd#5ZO?W%6Ke4PJM_)qTqcSH3{=NK?Fmi3? zZo1vznK!Nd*weCAizuwi!>yVXQ_~TaaNF!g5!2ZSx9W@`o#yw)I-BYX5nM$>#E&%|2dt ztOLhDiENR1`#EdpzwVzFG0b^GRaq?u0LQ)heDW}}bOe&6&?TigMq3h>Ms>Vm_C*8eNq1fYe`T)^iZHjNlc}K8*Z7Y! z{pksM52kL=oQtIP@O<<|{YMa|XR)+>#Z0|4{&>>p<6_Z09YaLD%;TI+P~m7ww)`RaPo2_==O9rt8Y$I+>vtQ~0CL4y33=yWal#!u?VWkPJdz zL8*qLmTu^6jmbh2&n)ZhWAD>EaOrz%fvWRW4e`OdIzO;U_&4H)WU5Gym^{jiL3JZ9 zl(nc6cN602!{GVwbACSa-J3Q3ZVA@L=EVfk`o+nik^eC^TTGLwewXzMU03^>8EJ(5 z)S~k}{GG#pGCuf`9A#i-m4h~v3Dg6_!VzE;(xv^4w-=~Zy<|uZ_K(-knqSS%_BBP` z5e9yKevIk=>~lV12UNM?duyQK!pqw)At8Yhgp6Pacg&UF{Gz*7o3>(+79~|Mcm^>j zT(dyT8Rj~VZw4`GQ;2--LbwzN90I6g4vlC+Q_AHfqNsEDVWc${OBx^>JU3ja9?fgd zvA;ijsiBZemAI9><_(zyyop>90-&~NZg~X2xCOI0kbYYzqOu9bO(E*8B$UTT3?)He zBcS3D@(U=xAfPBm0og-wWpx!GV4pz^;c_v^Wq2Q+=li#BfA@fHR2y;zuRo9c-DyFs zZ<@G3*N~B!`K7|WjI+Y_YrEa6GMzwHL0*a1KyBvmJCXbT#=w#PWL6wS!q+dx`gG~= zFse)CtjuA<-jCw?RS*g|d>M3?{#qHo4kUm`%dc<${_@`krp=27Z5Hy5S#eyye(%+* zSMXQ{M>I)LiQ+J!=AEA(3Sc;nZkj3q7V;!0l^qM|EmFKHJXsOEYTXmp3jnts3BOJs z7IEs-Db5@B3}YV%G*mwxt~uY-F6b~!weNuSHC`Xn<+5?<4*$MA9>-@l8aOd^_{H@C z>wDVcf|!`i?*kq;F!~=w>=4OLn4eC0_{)c%ky`OB{sLvDN-79lMJ_|yFl=~{<|rn{ z?Ry3M4)P^QIFcI{&jJtaUkYc~;n#VE1F3XJ&fT7Zm_Cv{w5N)0J z4oT5EH9+pgd2!T`&SUZ{&=p1I%4m2u591*7T!6@*yLgcil)8JMgkAal6`Kj6B`jbB zGclzL4BQlli7!)ngJrfu^m(m?5-_cj5M?3laRskB`lc*&Gnw|dTOuu?(n1~%sg1sN z)%Yl;e{w-gzlF0Kvm#QGTyuFo({WqB&OmI*7KrLPUW_T7oS= zS1JSfZ8z=i^uzE|M}r1q0%&HlIM|uRdr+6P-QVBm$?mg?5kY&__Kj%QE`OoMO!Q`| z-xxZ6+28>N#sEHx!a>b;2vn)qY?~aM>IVjN^pQ-76hp(`a~kT7yiJT(uQqHz*s6hl zF)`(l2NMlqzMDnVo=!OT2|wB|mD* z(#wlii+}AJx|u{vsI`#hKCSJkA_#8g4+Mq7KM2fZ`&QVHZN5-GXxkO{C#@{?sapU( zXbHfCn>PTaGlV2|r%s*}c=*T~dUIx#`^klX4{q88Fd`m0ptf(QxQl>2hf|+NW8org z5iW%7`b;0ks@pu?bhbM=fL{b59kt%Y#F$3{qj76Rw;QaW4MrJY)u6g3iP@sz?c{{f zB~A)!obD-KE;XGg4J-?teoqURBJBZ}F#5*z!tj&-xVq7xGC1_FcfkdZ+NTASCPSAr zH!-o{(#4CWN)>=<(9jzNYEm(jW+m-1udoX0J+l6}^vw*!eTB6x2N5)PI1)3u7|7FL zjJ~e_@x!6FGl90LeU&JR_oM;x$UI1r`0e7S5h-s_ z&~63{^bDVo=C^?)p6+lfz6u_9FsaDGH~)L#_Z2)wFL}esU7ly|ls;VeS>ML-<6Cft z;oikB{z+jY4NgF}&byLw6WEL`gpDmWt(**;XOdZeuD<#H*&y(Puz}@T`QsPT4}NFf z3%JU}^hLpGSZEBA8*_|$wd;IaBA;En_it9*>{J5&rY6UjI6)B-($T z)%jm%bx;4FXLSS4%KmvkS@Ld>7sZk%bn7@_q94!n7E};(;jHX1=3p$ita@c&f(9cS zn>S7C?I!A&EZXRbI8=HdCQ|1|6{TDWPK@oJB6 z;MK^h9+|39z@3nIw=P8PgD;T-A&uUh11vUGq z^M${)KRj20A?$PtyegcBD)}iKStqPRK)k}TOdVG$Nu@fIM;;G0VH3=rq0!p7>jlFd z5;0+AJUu)gX^$XVx>4jNoTH<&`vSX0lLOc5_R6C5 zg@e$=rjq^n;Yw`EPOopp4W(FS5I=gX+u^Wk5Pd{#XR`0c8Lo$Dm_EWJpyx0w^bv{b zK&@>B9E_G1^}k&?7={tJavr#H-d|UKAFdpp6)f;^yI)rx2v-j8+_GC)LatmKcpa8s z1YPGc5FS`so&V+)3D z2o`t+^{*9_#NKxsZkxfsoooQ&js#nr(6is>nU7?S%ZOK)GzYUr3{AU16lyg4Jet^* z+(BRv>@1?V^ph$Bs_-D4U#!adb9Jg6 zGhv#50|YcD;^}^!13#RD0^CW8f4h^3?0rjcC*klvAG<$tL4qxugF>~mrblgfh5(^r zkHvn2igB5#xw+t!C?Fxs_7lbZhxJ4&z^dMDB0M&0i~KXZ>B|>)D$ zhQjC1o((`CGNtnc7IzG!8KYwKlfkv>G3EY{#Yv?p=ms$&XSR2VI34fcD_@J^- zxA$NZyTb3G-?I~h;V@#v1L2B_1|{&SKHCWs6irXdvCbCwrpeu{RmN-A#7WQ4SZsUu zIeL57Ah23^B$EqP+d2OfhO@p<;^T}6twizyDuf@fV6+4zKU)E;w!DQ!21qO%yahfl zq5?jslzgWXAUvMFv~~B#yo5=uK-laYOlTu`;?s+SQ&YZIRN}zND;ncG6%cGwVPRo# zbTlg>K>>mX@({xy+M^sW5h`LHHjgFbBt2=0)aOgifpc7S6|9RA#?tk}zad80NvQY@Hy@lBm71c;iWE4;aiJn+H;zCdv{CG97j{uiIx$8*G zLNt?>1c|81QE(|BqV)nv3B}sG!;es1g51C1=;Y*NFLB?=$!XU40Nw!h=_KU0W){+r z0QJ(?vK&?+{FGz}5O<7!gFd7lg5Cx4`Tf1`&}_E=NQS52#Jx3PY&o?Z16^&g7EL<5Ti2g**k(R!aL; z3=}2oxI@~Tn|nhrYA`WS=L+HljO&d=wtvuj03?fngQFuSeKrFDCnAcFmyO($5QU}< z5V*Zn_w&7_0y}6uQ*m2O4!8l;W-y^2nd`>wqX;J%An`x$D$3;zWV0gj)O~xl9R-ftsvaYww>O+L$DesVtfS}rXp9(2sl8t-26QT zZ5$9!+V-=u@=yNu+hZ195G{nXDJXLvKC=&8?0+m@)cO;+(R@dM0rOF+M%%fSy*yH? zzkYxHR>%S;@FH#U_x`Xh*?M<e*-z$nIr$Tc**2zcQ2xrZd0x2qCqB^5K#oWl})<{(XuXV9#FT892`o>#OH{4 zWB3cKo5a`!_1d4`Uu#O0Ur~bSol@H-781yoxdYhwd275SIeXlIyzYhI8vY`;gdlQz zrr_acP=M_mFD~g`fhI`{#A<{u)b^o3-NxRYfgwS_ZTL_)TH!D;3AroR1SqgJb5(&a zH@5Vhj&;J+ATU;fIiMF!M{7LpLbx0F=1s0^9JnykFGwmZOPPhHN5Yr;IilM?k{uUo zHxJv)HLD#VAaFGZbMh2`8hkk0Z~`M}MT~>x$S3e$XK4+Um?d2hRN(9;BAh8i5DaXG z;)FGTB00#}4aX7>}&`& z?*io81L#zW^eQ__{a-?*ipC(2Vw#_Zf6Zqy^-3&&Dt-DLz+w|he|Er4qoMt;$HXnF z|Cz=SH9i6IY)P!@HehzgfBe^uD$}vDMFjl*^%_qF5ZAwcgx^~IuLHX_&gbAE{p;1x u^zPk%9PsO;KdgoR^DY0I5B#h;Ae(X3)E4we22~prN>)4+PsURsUe%#5&=bpEsl$6_l|A3^2m!p(m?3fe0$gX?W5#B5;M-DRou{vhsjagV( zShTNQz8R3YFv=NVGPVBx*VT2&L409IUvDiaA9BSv1$v8G))Z$d`gV=t-~zL-OtMAXWPUc< zVSZVx^#0jmb*}c#@p0R?W+mr{R^BhVGZkdCA_Rx@ms{wi+*(r2lz8Q>-xC!M*u#al z%3k^`jUW_#=TB>R-m@4d6`B+@G>V36_4QjWp;djwM#-*y`;slHozuoM z3{^L-<>ch7j0(PbQ%<{m*6?Lwif!o7e-0tiFjR0a^B_c2(=WQ6`~Lm=bdZlv)xt=< zNhp0$UPfi}=f@*^A32k1N_e^bF*-@-jQK0ILMVM&(=x> zTO_B$%48=pXuhyuvbx{Y0Z7UsBogn`ba|it-aj(Y@GXK>6?#*LPhqg{=Oae<;h>nBkSP%_cDDu zq5At9TY)-@z`|hLnpDg0E3ns84!ugSe)S#}k5vu_dyJ5pdruU;z);9vzMvyj$-lHQ zBRnMFTLfqK&Yst-y3Y?wc%>JxnjVvPmu<_*MpM%|4_3R6DyFHT8+JYEdJ(uf-8o$C zGSwwkmdGdPT-Vf7RMv8?N0nY&A3Bre-<4pfirPWrM+JY55%L&bU2fezc>UqQv?QI% z8kG8Lq%EOGfccJJ{eE2~#C#C&CN}nfOovT#kZ-V=u;D==A&*wUqeo{4Ygp7TbNVH?OKR@o5_L((8jntJtc=%A;$S79U^{bH}nsP%Se?G&OY^{TB7+-oc8Ptdp>;alODALQ~$aLW%8snQ#c3ERjGiYaL6B z@1G$WC;5^GlVBNUte;cj6A9=%lag9P4j#(Y+K*@o@JwDOdf6);7^`-gVz^x?TF)=eCMT+T-oT)QsYaj=;dcH0l;(eOw_(&y1d({Mct? z*}G9i@>=cFcC0W8UeZ~92g$seS z5$;JBA~83nyo-ji6i^HP_=u}={Fa)@p#Ni40~!3y5)0FikdVnZEx`+E+dlP3=Pn&D zuZkfvKgE^U;?)z*}_f81D|(H(C=<1Y5~gx9p?w22CZZI=7Y;WbX4JgJAe%5a_cc3fA< zbKBgaFY6;EB+!N9Cupju&YKK_t}n%iO1+^P*z*}P1o7K^d-F*qx>Lx!p?%eR?^Ysl z-7O{bx}|Zu_UNR#x=WoB3^W04lx;#Pb|VsBl{s`JiXv3M&{T;%@1q5jx|h>xtXoqk zm9)>tJW?0qVn`x^`*HQ z&kr+C5{V@3`t^JP-G6ChXt2i3lI{ij1WuFmI->60YsB>8qTAp=gRhHauI3BZh=>Se zD3o7UCBKD^{=?|~6+u$s2)9$W%5IQgCCJINUU|6Nzzq$h%Bu-ovOZmbm8@J_BoVEM zi$$c%M32H7xtjUwN@vb|@|(k?43n%I{oE{Jleo&c3|4K>Xz78iTeKo9F{g*@1U<6` zx>ULYv@GLjlA7J;y#?!aP~4E`U<;%bEZ3hJeWYk>+wMLoe($TsG4NYiTmFw8W?lngXTjE34ukXI!Z)Go?4K0l+v~6v{ zPH9E+mzVj|D04NN?*?W37R)Q9W^4MF3+4xkNUgTff-272OM~=T7b03eOvdMBXzk|u zcv#w?z_DX4jq`7d7J6PUhe74R1?SYRRl1?qm2?aYYDPm=an$xX+S~+zj9&^|u`jLBUtb!<*?s)*p?l^k$q1vv6Wqw`k|{BjEj{C* zbhlM=bQUe^X0@{^-A*g&XxyA9ZG+$dYo@}q*u2lRC|^HgSWPOZ-o7(lFQ!t}g_hRg zgiDvB_9TQF&ieHd?^aL5>q?euXlXeHWrj*dMn*0!C)C)6MN+8MM4JqUarIVzD+Y(d z=9cf;Mkj2Zz~A2=;~1LJ{Ap9@Kf6cc$bT9~#Lf0-0h!2whLG;m3+{XJp2ft}?vHYg zjhQ&U+MOn&ANz>sUBWNlS@qU^Z^-K8TefHiU48v(Df&!ym}^{I+_a}{$S;lLGF*~M zF!gIF{2@KIMyQI^a4xPZVhif$+I^9GuXl2hoBP@}y{5W`4vU-Lwmp30NQ&F>B#y3G zWq}Y_gUy7Xwemw}4XR448waOcJo*3Gnut;Brspja&t`|j7g1f_J>V&hkB$9SrN?Hf zN?(vkVWaUayy_{8I2Y|Es2uPP{Zlp9F&jN7zGwgbTE-gDmb%d(WgbItdy_4sMN2O} z9s$>QJUKDZ-ZtNDi&+!HZT#r3mRdhP-e0@_rw6G<;udM{O53%-A;F=(Z-0e_g{^*V zTx!7j{rKmpUJo7cn<9>&ep>7|?!wUF_viYGc#L6BRc)Gc-(GtHg}k zw#NwhTeMENY|+SN)}^_I*{aBmV;)U+#G=&C@%;Vwou%zzm$Iqf3Kaj#Lf!;@d52>x z`K zaX*8i8lCMw+U_&d`}(SPwOi=ch9e0!h768)T$js>;45jTt`-^Wm=dmBr@e z9|V7gkV|TjyPYa_Je7Mj>?uT8Mxt#i=8sLDjXhUJf3DF=UQ-oN^7EZy^di$!DpWr`Jh3s(y@;3fX`k^hqD*|dB$8pwqEkyEHAqc4|Y*qk12*{#O>R3 zerbxvPSKlX3iNwx89{ zab4+U^}zM!(6!wWZHlGT{kMu)8O1xae2$NI9h^E^R}jHrHW(MxhA>l4OxlH{%=Q_q zmbi*7H?0~(52tzx_4}G%R!L_}eQS%X8EG&&f5W}D{h<|lZGd|Gac1wbKyxbx!ELNb zfv^IgCGGvb=AO-s)jhJY=cLM1|a{6ZdG{$ zIUwKR0*ikA$Su-kw)#Kx>QhWgqwby^%xfnZ4XakW&=}LrEhs1n{H0#pw#BN!Z}Hsr z=4wo$l&vbMz1EFnrs%(viE4V4^&Cy1}`_?3PGb` zUC#6o4{&pzzWR8dZ~c-gnwHw`KHg%zPe3uT$ZziJR(9ByXpHj86AGDVDA~9mT~ER5 z;_}EQE`!B!KTeB_>v8PkPc3S{S!Oe8^(9S4%OLYo9C-%V(*CLZ4EKYu(bE*X^M?$* zu+AT2)V5N|?fZmOl?hGHc*Jy|>f+X*0T0bcSyWyB@=`?%qnna^=i?)Xpv;y>f1lD! zC09E3_yvuW+qHLZ1&^cGry5>B^~2YC*rq85R#Fm zIaOF#IIWZq_`%q`xMr>;*#v5he9GT{|1FtfN#4D0-xn_dfY#O9)Jq051@BJ-GX`^x zSDB6L;L)S0sQQ$Y6w31WxD|0+7Mmz)((S(y&dOFX(gGA%Kfis>-xLsmtW)p%mdz{0 z7stGNR3uZ#jty}5@yW?Z)roq~$&SARfgN@GqhbVA7FsoWlvkq=EkSGZ;zGf28K2hH z`sZ)Jj+RYP@?Y}Vs5_&ZoZ3_4uCyp)-h--EX`Jy{t8bl3wzOK(ZCEDz4KC)i)*mJ- z-R_7zAvx|ndSu6r9eKy6e8W8Ln?JvKBT^~JTvF=4eoQPer65>c`vkfNBSHlFm4Ya5 zcrOjKxe7ED+qQ183vC1MBt>%V9tK$6?J)8Ag|#Hn?^dC)29tHjhAj?PQ^`=8G?K|g zB8uH+O7@Zl+fJW%6d-nAsG)(o#J$d<%dENHIBL~i9c}P+00?gkU~O)!xe+k+C%r{d zZI2(C?^?Cjl#b}F(#-C3Iqc5ACqFTH;{8Li2c}DE$KsiwbLYz3Hzxe62+33}Uq_pm*3~02+O_Ly=jD-aAv99Rj{e*1iL#!bh)0 zvhBI*UaRHjxA66Zwzl?UpMGQgRh--(}6lIQ(Cg_OHR{{-0;Wg>~ z?tg+ef3@RgKJbXeO3Jgg^Qi~Hsz5eoRnjH!%az5{;3UshLj!}0tZvZL#bd`?Geeeq z)5dy!d3(4O9hR`D3yI>mHRqAOCV1pQ(~=Y~$-%8XF_AkZ;v4fShvz(3KgWFV^MFvu z&xl=$qLG#RH|6 zZ&teylW*AsezM<%zi570I$ecK@@SP~!z@wfi^|fEW1?Q7sfa_V6C#b>7lzm)#I5R# zYyik?K!>sa9L0+?ZqS$QnCsILlHE|` zHbEIMFGCR1Ona|u29CC9ELP9>F$PtOBW`Z5hiyByn8>o+P`i~rYQUx}6Kb|6zNM5w zU$?zSa=cmNitrAc?-xzM%Do?;Ne*_JzDjSgc^>+h_|{a+kkm(O>Cq0)n(%L4yM$hf zYCSQeB3YY_A_w*ZN$?u@ys$9VOxhfC>dirZY-DOcw-Y(a=T!6BEPdzuJE^rIMQ-T> zq1#Lzcl;ceYX2*>MWtC)XZF8Tpn6LqyHokMx2D#-J=VC2!&PooSSB9{9JS@oQVzrm zR$V{vN5te-OQfVB$r7Ri<73>tbn7^P#ee-5nj0xz}w_Mse_ynO1IIV=($5y7MKq!KVUb=WB7dqObQI-&K!~Z zZGcrn6&4lWSMmDDm)PS@LOqperz;{io<>K1A*^owY8Mi|eYJL?BkmOD(mR{VB?2u? zw0n^}dpERZH%+M)3ai)epLc>a&xjpM;%J||Xkz8=bWwr4+V}eEV}7+SyFs*2Z#i4xfkc(0e<9PaZhi06=bHDQeCc+{bv*tz53}lSVLSM-qcTRvI1LCzcRvsv~*|x7X|Ew>G`i66wq7ai>&OTiy@n zMkuE50(JWeC`|FFdX5+NrZ2m?Hsc1u%T3>k$QQGIFP^D0*r=B5ir89N3oZ&jpT+urz*|*P$>3)G;qIdWlS1(>qv?0tKQL%DW z*ZLgpU|!oE1Up8geXsdo%bUwcix%-(*}6}=^YO0d4OP|>xqZ`lv?BLZ(v|J2(IsxF zghfm9?2^q;s+Vr1abZ~ttt+J6={)(92)TgulG$x7U@P7{n@OYol^o_4tZxUsxet?N zl1E!ij>ce7vE`wDP)^eeW*Uq%&-o5Hq|{s49KGDj&S0KFONYmm=SB-KXDewVGi&sd z06|ZYS92UB{sl4n|BDiRuiDJuXz$Ke8`oRvZS?o>F2t-9d+9{WUT-(ubpjXvnvJ2D zN`uP4V;)?LcOE``3ll7Bsbw})DO-2CF=84p#3kZlxpV}#{X6r^eesXy0cj7p8*+Kg zLpRx#-{pZkrg`Sc3O;!f<0 zp<~kaPj>xG1!%KN8Grk2sdb|cG&amTy)RPnUB^ z7nOIf`_8d?RDiz~3bFoy=Nj0W7rZi>fETS*sW|y@*)!2E1E+Zpb;n)!sZCrP^&d}T zxu1zwPoBnQWaugFSN<7JP%mlG#C^`Ye7e->^BI8$HjREICik?Ro!|2Q9ITm4=7=if z5i_gXyKv5xkNee`8EGUt!T8RgOMkq>k=@t(ZtvvE$Lk5z@5;xAUz8xrgEpjhW>WUJ z2KMN59+$#vhb+OM*%RK@Y-zGi@5=>h$AXFguT}{hf?fQ5OOk>Z!rYthbjv|OZ<$gZ_(uX83<+r&L4vT*-Tg|JJf%EJ*Fu9 z4p%Zh{Cs=3C|6WfP0E%e;tab4-_C-|3DISytuho(zDsYX_+&UvqK@ZtWFlB^Dco^* zh+pPzCt})~_$ZZO+gto)VzoVm%LNn5<;i;e#mV<^`HVuJy%O?u3#jU9vQw^!B z55-Eu4tg~fxI}+tzIyWgUuL_jE-zU)6-eh(1vmcusN+W+w}46%q}&>E|5n0 zmQOJB@iR*xbw$_y{b%w0RsN?SJZpwMGe29p`0$8LN0r_TS=M8$NnEHEiQ}_m5*)^1 zDI3@9*5Gw_`otRTVU&>CSm^eMMTPxSiPNY5QNDQod@oZhv;^#lTXZR{|GdR#-5kA5 zZ{4nB-?=k&YHMq&2C&fA;aU$5(7TcuzH<`-3N06Jg>EzaGiqyV5rTq(?kH`Txs zEDh%T!;6;hZ@R|pNt1SX7HCT;Z)s_%VQyJbS=s(>CSu84YV}{Xv4l#7lV<46qSsUg z$cl!wL|Bm4OPqsSg*~}+LxBG{M=PQ2+1!}Jzg@=by$jH4F;P)fzM$WZ_=78#CPaPo#yBY2s|)IkW63bJe%E3 z4Q0yg5|=K0-D-mJPz9dS@Rqe}b1TidWKmmR-!~}4Ak+nHJXh-mpYriqu=Z+zsirQ6 zxP*DHseT6bSK3UqD5ya-!nYh+GD)}TX`Q{Ty8 zONw!;3Aw+}&V4_hY=0L%VD23CU)MDM0Qd0D_qQE_`}WE#^_JUdZ2-yAt2vCD%^SKYOaA0i){LxBchIUd zchY_b7T{O==C9>;k2}3yPt^rcX76;I#38Rgd-^mEXj#U=2kB2wOAR^7K7XNVHhv1{_1Ym)R#)?Ll=wpp87WlF znbE_v&2%^2N*1QjZ|{NElPdwT)7MbkeUsKQ8(hq*CYhL~qq{gqFF;(OD9D|{fgYys zcLb*=VuF*ZGdFSS;x+(yk^u`WPbiM{3E;0l%}Y}^TKS~=9pwp zYzeT3uBO1%?)9S3WZA}8VDHnW%r2@!0Z>B}CBeSPaUYLi+t z8Qm@%r?tRInQqidLGVUXf^V&}qlM&5Y(FonGD?ZW;#QvYJ4>^Dxxxwkai_GprE%Ai zGw=b!gTP2f?Hiatj>%UNFAOXe(wjp$4{K(tub*Y$`rKZ%hBa(EJyK@ub((HzDmH0V zlEbEi8oCZwbuMJktk=INEvK#z3^>+#&wQCyx?CABxBS@M5_i8~{CWDB?k@c7d^ywU zN7RP)%q~YE-xJU#wpaaza#k8zMT*MqI1K&KvbfbSvguu;FPKXkK9nPp*9httBs^+D zHoOj9H|~GIuYgbd)xVNhdS8phzc;>~&#K_ec2)5k4$FpwWUu~jSWrgN!W)}*{;Q3R z#B5Lh`%>Z0uT_pX9WVv2>bb^&q#){$qrR{p?r5^L*|~3b)1mn7%3+FQq{ugkNDG2E z3JiJS%l}3gqJ9bev7T92?){yq5e@7 zt=;RV4kI9OQ@(7Dhzt&V$)w z4BMt>H-U+`(}d?f*b7|*L$$>vPfwDRh2e- zidP01Wl7?MBqK_VMQ{e*l6-<6w_HiR-GL-$0GXTMLQXdz^O!!m6ag=pKGnvPwQNq) zARxbh&JsGBUVL*tx^UpEe)?&!ToS^=Sqgy>MIDp>;$vlv`QdZMu8~3m@?%#@rWMZ! zvn#T&oO#S)1n&h$IU#J2nJW53r`mZy2l&*atki`Ief$RqsxefRt`sSc9qn?d%BL1h zX7e1+j`=7uy%kY@UYo4V32ww-aw z{e3MDUXB!?ccGi8XL53LWlFBfyakriM-i%uVT#uE-ya>b2dIIpr^>juGW@))rAoH+ zK2O{V3WflYu#5(9{L& zxnZ>~7q#p3aM+A8M6DP>w=gb*KsIQ;^)pjbXP81qeG7;`df4iiTvHp$rDxP&pOD4o z^)YOJd*65fZVZ0Wg;3Dj5-C_41ire=i@>Ref{nbOSIyZu+2(?Qm|ym)y@5X_1!emh z_XnuAC4npr+k{@yqBLZjFB(U}JHE2LVFo~r0QYpknX^9hRp7Ac)RmX_^V zJ|r|a6w|+x1osnE^923zTcdzZGY1n0zuG)n8D^68lLvQkamg{>Nm@5_0Huo~t9rV+ z7Ayb$7CEibw>67Nu{rnrh}4&LD$B?Vz#+f-*{=!HeesJD+ChmLpo70(zQwN-Mep#CLs& zT9{8gF4e+vJJaEuNih>dqSkLXgDL96kn+G)fQy;_zJe)}umva{pP65qy?jL=umAE@ zG$ZJhPE-I)cc>{pG8x2bCw%ys#YiWl5!Ys4!Xy)h64dY!5(4H8zdj#h zmdEpv+O*(hCy1Ekhhc{B5_~KehUg-vli6R33BXs|C}*m&sc@j_tV)>nC`zg!6f?c> zx_vjD4-Agfd*gn+(tN>}rkYkt28l$5TuBS$jf|E3W*HNSGHT$EMg_0^PNNo;HZCa= z9Pw=$Tu$5H9}7*k(3fP`QdEK{@5-*y(GAgAO8)5+mO#6u8T^Zt&KXr@Z&)|$7dhw3 z#TN;4K|Un&_L>TE0xL^+6ZVpOzMVOk({d3vf{L$7`Htwth@x6&z!~)1eHR~2UK(x8 zPNg^N-A1ln7deg#dVTtJI2PB?vwrV3ELz7P!Vvombai+r)vx*d8bfC8N- zH8jfN-R+1bV4(R@zrNbVD6n&*)Z?k;Rogj?ON%CRE`7vT=#mi%>*~ZmPY>mL_%gGH;j#Te2 zh@dIjq?rug0weqJu73zrGPQR-Y^*6zxMQzKjVsHyJ0J?aKCw~*Ck7eZ3bY`N-V6y) zY9P47axp#5%?%@QV_)Wr{5F68LLxvObA{bg(-ZUjd1bXz--mA7O5_XlmRs#;@>GLW zHMU_J&PpXZ3sywHI?8WxIBB9iZ0f^`d7(M9^q6e{gvCfR%#_*T z0|##J*|Voo4u${6Z)H-OUg19$*gmoiAtC>bpwfolhdI99pk0$@s@+I~%=nyTb5Jz_ zJq{t~%9qgUhi9OdLAFDO$D6|rQ1)Ela{Y|pD*dHrla5eX z`z^`SCr-3cg3m#|o7CuEMvx{4fLM{RunPUAH56@m{rVF(&<$&5XPqpW35pA++uMa8 z;lXct&bB2a|H8+A{<%hi<6PJleUewbTx_%tl2_;T*6kad+)Tws}Ht$Wr4aUZt^P}y&u z%z&Sy6gRKCMD3ETxvZiM!O?=U_X@7`6+>7B3kJh`o}!qX!hgJVdTtUbSF29Hv_mMW zOz8DgShj@JL)t=uFd=&=5foTPP0|v2ZGIqasR-31Q}N{)c0VrQTk4scJ<9zuzaU@r z=W-~V!vAu;gyH44>hqaSkL${D97YX3nj;isbngj#jhd7*NkInmigQInWpdDwnny(Q zqAqf(nBa4kpCglcTVB1q0rB@|!V*afUzhu~9>m1Nm~3Cia*@-v)V4P(^YZe(&bauo ziWS{AeTk5{IG2aHA&vZNpqSOdS(BJ>VfBrjkUr{|RZ_^8Xk>TDQvJ+At#4L(gbw{w zdSDJ@L8%VI7Z+PEQ7a*=i)N z4K7SYx64v4JxX>b?9GVgZLKc5o%GCmA$zu*Pb@h96i&IYbr}>#!<$;5P`qvdBOy+&^Z zQk}z$bguzQkZUKiKI7A&vxa9VaNIx?`=o%yjNN&m}=hZ0* zf!{DIk$O8sqR`bXY$||X>MY4PW}ultC8Z^!>eVr%3V>=Gd=s0HA;x)jfY3Qv=caz+ z3XC$vI&Dk@p<#9aN-P>ugQBc+jgeXRAD*wY(4~ zI*zvAFgG{Pr&~A{1Nl?T@}vU)ON&$#LQ~?Jwzufhp+Vm?%5Fije&ypY^_QP^n|IZ9 zr!NN>bzV?Pw!8TD{(KlB2K#n-whk>o4^tRzVPcQYd3oiD<#m1eQG1S`vgA6R>lkA7 zHonYOjm`jk>mVuTolvx(&#S_q5ReD-DtDXno~hCY6EvlXHeV#1d-)Efve+S-x2CuT zVjb;zh`y-9(GfQ(k)XxJ9CNR0qiD%?Xt#IS1TV^Yi#vXOcSjns@_MbmK)yrPv@RcB zj8vvCR8D_QK-TtubKzPUJe~_?U{7Xv@)0wo7MPFXul{&JCZGE8=I4d)I&95e8H*|f z==V+UqV?-{=Qu+%RQQ5nXscY71_NSeo?+_w6SqjFwHWGzY{D(~8V9qZ6W6pgm}i^- zy1UHm8(!9mJ$d*y?&Ph*0zTf#Mw23C0y}Uzv2pp=dcK~h4-ZP z&PFk;DwN)4>DxsWl|th8jbDN zjpmFN4?3?)2y^L7jjCF<8$H`fQP&2BrmyJFRcujy_n}IxNRjFb3jhn;7e~#^=KZwN zb8I39j>$Ps`ml%t(Alas-9&>K7K}r!b2OZ5JSbw>uk|g}?18@z{|u zJ)Iwxb0$RYs}^?BL7cakRed8aB{UwHgI~Fyeum5xH<-e)OpkIx;r`qT>>C940u;@i zY7TpI@OEyc(Z3vs{&=@~uuN051y@cM*k!7FUj4INVUVkSrAy2K%t5bq0sre)uG}Yh zap~Zz^3(AY$krXSwY8;JFo|iKJg)e5AIVVp*8}X#vnNl^rv%m9F+=veT-9v#MMWaRBCCh+jKR$vfBgB%r}rr6CfDh0nzm7Dm}Vi`scCTvmiRX z)QFKmgn3kMyWkWObF=wE5ujU4FPO zl8Y(MEGb1>nS#NU6$#Vq?RC&>LV}uL(<*8r*+a?NA$;6&UX#~{0Nv%cJH@lA?8~VN z%N=?t`{eVl)PzzGFh4h(Nk9+n3=9YhDKHP#QDqf*|Dwm(0(Tu|NI|M~(+e`vT6!FY z0!R9b&G`b{`zB_GO}8 zpcZ4D9*J9Ln_`E4)yo}l1&};lK0MUCP=W1+aa|n_2}TZsN58QYeTb`+IghIF?KL7r z)|{jmaCgmtayymd!rYK+x`Ym)lI(^%nRJ0gL|zu%xNjC^dlh@uC0n;=7UK5$829R{ zoYbY}bsK_VU*)$T>H=!tf|Jm7wy`iss*CCkI}%*YX(PjGgviha22kFF!ky7}wgx>p z1Xd#;8inIBeDdy@yubU|5VTMyKQXirB?!u|9*Z)VAIf}A{V?lhGG@qz^F41Ub4L;1 zl7L)Y7%a)?j4o||#)R`6?N#IX=)D391a&b?dkW=JJjVy}g_A0hc??mN{53Gh71^Co z+q#vh&KG?b6XqCu6yICYibBj^@)jST_jM%iP3#_l2Sw)PISMZe zBD03G33gt?GhpgaithKmMxyk}N#{9Be(K7>-?Yo@cpo2JLRp z8p9wboF*L+)U8364cc@78Z-Shh8nq|(_TvnNLFp-n4SG~ifa?U*Bs`jVqwS)t1H!P zKl}#K#*}}&b4Ea+U6@*F*`m34yK2SUn0xv-$W;r=<1C~ZlQLHU2G(FVVd7>G{x$qn z#_E+yTl6YUUfN?!8SszVQVZRk5GvvFrpDyv z+3PHNo4vi+*)$DuU)okCM+7@N`_vn^VJX7J*&xHrOLwS30(%A+EFyzIj9g zs)08)1P#os5fnaG&i#V2Jw-8CrgkO0+T7fX^KSn6VGn!7A^_sPI1p^SFUvxEp=`TmDf&vI<*i?u3PC1zveY9XPVf|-&Q!svfiOTP8&e*wG>3^!Xoiwj z1N?!soDv&*I~Z(WH(>tKjA%g5U&mk?k^~VXD?w7{Tea9E$U2aiLm`&%BYH|qWjk7B zE(pMx%TjK;eZ~AoW{Ot45!9lrX_xV0kWQvA#no7n)L?dTH6(D6&7%DUe~hQY*&@}n zDW*hUW=g+8Ygm)xSM(9-nTTZSh`!5Xqx`ECpM~iV)QwM?M#Bq>7*n-!GEm^^(Ci4 zG2BkgH@kGsN%_nF`{aNAA9?X_()&MOvk1og-=8Q;eDNXazrW9Xpv=!(_}%E z#11=qNjEO;PgwFlXCS=&)W7M;|A!}5J^h&}Z6hz3N5Mk;W(qb(ESgT$2FD6U&MYAU ze~MWp`V@_gjVHm3O9nZ)1EN?{Fhv`mnJEsPKLLtB7i{)35K9uF5O=Vg$-*IJ4#ge= z3rhMBV4SE>?v?w@cdPzO9Z()%$!mb?tUN#QRQSvpKaGk?WM$*%fLF1HmLUyVk4!5_ zfeIEqkO?_+`t-GQc@Ie#(2dK>JI%zhAuF9{SAbn&LFdJ|k!p@i`Le2O=Z;rGhCq6} zWG~CfTA!aiJw1l!Zk38dPjEeAh$m$D78{3+dTA`%%93c6n^=CS&1q3M3zkfsQGkxuvmX>ZnK){J6 zJ{FOB*~lxPSMIVHh%cgL?U{U({7og`=PQ*{S_T#RJ6Mbk_QqTZ?Sr+*A|g0D2Uq#{ zxU6>~$Hjp{6D^QGl298kv*rLMj5KJ5dpL}(MYvixZX)J>va-}GvDvWMChgn$rShz) zm!Y|y4SMe|mTzuZ8VomHh6wjg+6_3KPizgf$Cp^j@7lY!d$ob({h_(_H>Qss?cLq6-(}l#qyXMo=d9N2SIiB=B0`(}_2*4e z7fFti5c-+-s+S9ciT8SoW#-}nhbalpcj|F*6_-j-2UcJ)9#${<@7zcD>R);AGU_c^ z5QedW&|$rmAV0sWq}|=SUe#l8CxFy%qPpOIO)*7qIQ}VT6_A-c6>NzmQ-A&UtFVBK z1QJ2VFzDafzG0icJ=37W{ie}$V0dy8+)+<|0vz$bA4VVkMDD{w_UzhqAq9Z2WJ>$z z&*|9S-d^RBR;zk1e9|4O=S;iw znDMCf*KitF;OBJUE&60f+D&-KYCZS7*`<>``z9xMG*TXNp79uy5CDYw$ z3Aw)4G&Iu2=V2%C7>j`wq12bs#fOF(#bPe9$oVa&FY2V@50gnFpCJqhPFZrA#+b=fC}Lg)R+U4@3}VT8@HQp%cx%P8mf@^%deL_FC zg8yhkm9}%mb!RVHQx$hc9_Cq^YvDz0UGElmrCM=`0pW`oMW5|QzZlyL=3QPoX)|_L za&h&G#5%sSATeO$NdOsBJ9hXNwYs#xHi-TtU?cgU(75rB#f+8_(((wV>`6@aL`ZY? zlbEnVO6T&XZ?mDO+mpvo0pjyrD6fE)?2ybh-@_*7*64& zWV)C$H7hCs(`b-R#=U(|->AT1baD={#SEd253*Q-mcA+&`uolXmE=(w;>rxoU+RyL zHhdMS8cjNP*5~Ik+|@vaG<$`uP+hzH`|YYI+a|62G8KD+X=CqN{)94j7qJxid+#j`V*NHWAAZ@Fc^b& zbURv2!lua_V3#C7mt?iA-&gP7zc1NE-oav+7hzCdfIq$ElYaW2ucyCv*JD#h`Yu5@e^M-bx(waYzo{j{=#mt!I5W6#8R{~-RZL!UnQe|c)9G|kB|cXStEn88 zL$}21vhhbNj*P$)TOrmv)cu(7zd~aX?$!fBvE9Cl3V1gCLDu0-yo|l+4RLHi+TRkt zwC5u4v4nphLhv(?c_*9q?a-B-LKCdgz}xC<>IG6?umN|1h*w%}BG~iyjvv8Q#oo}- zd8Q0ue&NRO(yQ64%zz7Y)Zt~5N~AtWY;OXBgRw9}e^**1ice05c|U~&{Tn^8M472& zfYjcjzxJvzO8PHn;4_88wM;s5*7Jy!;k|qBqWBezHKUH+>1ipg@fdffM!@7j+Ds6{TlcK3 zfeVn^X4t2NpLD(X_Mew}y4ePK+9RSF1h;0rXQvKyjT#^H#jn4dS*6# z+c#RqFx{8Ce^1`jzHXr8pXNO5U@c#pV4^=*lu)kiwV;<%pJj6PZbEmJ*K(mAw(H^V zjBjrnc&>D_-pt}N+nr=oVa^)NFfIh$ zGb>;QY}sBV!$hGDL^dGKBw=pV6R2ukFa_24LTexlI%g?ZT5}Foo$mPj;@A{i5&71q4Uz)( z$7G#i1G~Wu)DumE(D-%u?pbJ|(ri9K`HlNFMci`tU7AN%XFe%4)Qx!q`G$cijj&_B zy+(6szaRf!WPN2+RBiO{&=#l&C`yWSNp}fIH;5piQi6mM(jka}NW(}YFvL*O(x4(R zLntYYQj$`F^xenzzwW*3-Vc6qEuAxSp8f3ot9_R7i4VEtrC~tiO~8(WcwY*2$U-ap zY5W<5H$TEK-8IFip8e~C_3ph^+1(~qUvq|`ubl&S7YsT}NgC>{KAuXH%q%xNbNZ2n zF9DSkD~1vCOXC}wtJrS&7Svh7TbJ>f(@Oi zMyGlnr=g8xb@0pHXh~yR2xCc0gop?CG*2H+Sz|MgzKSy$i51d!lUBxNcfIW?HVb>7 z+IK7r85FsO_$2=Hl4`j2W4HI&d`T`_YO-CElg8Nub#==Zb=jEgrn$_QP%)1%A%nYkVmI$C&ERD@+Q62eg1quqwx*?R675)1r3_(g9K-Da4#9r2Tih3} zSy(8GQ8S~cXk_}_faKgjv_;rB72MMXe*F`KC1sIO;>$J(<{&qEz{LsW(LI$mpM;YGotH`f6IOQ#PlNTg68!BoljEm zy<-XDz6Qi)@+(_nKlGOQ?lC+R753*Bx(p8gVL1>C2DHdym%)1R(To9xaLW~vpr0JF zl3Lo!8u79F`ZF^Vh7zSaV}JbfDH+Yhqpt_FV6I&+j3a*LqZZ^ri$y2SFZdq@ccUYO z&^uZa9-w6W~0ttETx%xr@)% z`C&)vv#s9ORcDSPmVAw<)+gnHG+PYH2N^{P4DUpKFFL z9*w#8%j%?;D`0f(O(ir$TsBq8X508!@n&xO zi=y4k55`+~yXp#R7mANW9PfT`Cw z03CeeCF<8&tWaOD$;ilA|fNJlf_DE@u%6+w0cA-pgVbkAlcXH|eS4Y!; zrx)2XI&90;9Kuvx&3ts@NsYbtsk(L&p`es){xwOgTz%hZ7I?dgu0zs`tG#EGSiQ2) z!qhbNbXsqrL2K%fl?A7Cvu5g|_%TsY{)NaCwd({%brEg2K^B3s-8I_sRN>YpZb?f# z(c)HDOoMHj?T_9dY{CFewP0~!w3w-ziYO@a=2^!^NaV1sjk;**_f3;l#AU(^fwnj! z2eXd`{h{njv(2^F$6IO;6)GrgU!BHX8R@SUcG%dZfTOKrNW@t4l9Bnbhl z-8;sc6}-2e-;6gpNl$;NzvTw6(`M80p$?*8G-y;JLsdRg0}PWjSNMvOc5t-E6;?L3 zIj;tJy(d$KW;sk;hm40g6a%vf5h7isC}Zck>>LT243onCl&-d!*~DbcAZk>c(iVm< zywUJmc8e=l!G*ttyGa8)a!3lHo5bDbAUZiVtEqQ3^WCYoO~feASFg2Wv+fvZe(nEO z)G@d5J%Y+$m6yL_%>1@8|H*58HhzmVNCnHD*2eAFLCr|9nW~sl=0M6gb#u-Zf2Lw8_4Q(Um-h((NP>x6RJ1 zU<|r{m8ZEa?%89{D;toicLb!`q$A$rLC$9weC#eaO9dSG$(bW!>wJTGyw4pXThqmx zLYKuU=;p*UGON_=chPX2+nG*4mY3Ubo*oNw4RmFs*ar2Ni7P%oBf$Cuv>PnebcOE9 z)zQ;a?0gjd{2p=^psGnMBv?tYKJi$2=N`G~5YM|L1*LNKGj!r#JNwGi((WHVeX#E# z^KK!~|1vHp!6;?V$%J;Gy0qhljGdd$t@Gqq?D_N8K1rq@GoYr1)z#(HzoBcd@98V| zfyk@N2EqyPvk){mr(d2vKaiUq;Giz)z_T0+@C18Zh&_6DQ^y$8P5PSy9Gl4RVpcH!VM|NED8ZhXawzSwQjqtWQM= z^scX7|>{|K_p#yy(Op_1yf&5 zfNwNb?J>N>+cCmO&>T@v0%V0GQC2k_Y`ms*FAu)6^#lsj=D-vBu|7(HB;Fq&lIqfX z9s;pzcmPsn^K;W&ah;Feb&@6J|A1dfEaZiWjnn(?#UZEA*BTnkNHa}R#@E&TE#H8B z7(kEyV)Hx2HFK9YCBA8%LIh+yCS{*x*UWmI#NB117JGeWXP!S9a67mylem1y%sac+ zlhFP=#ICa*ZR;3=wF%BvnJyHnLaouUzWcA|x-kes_y_7Y7UvJ)T@&5E<1Blpro)f~ zwHu%07|U`;-}*Sodbmln!z@iAW-}vS-{PvxLobcpTG_cWM{>qjUz_tUPvl%nHww7O zv4H$0K}QC)cYWjhwst5?8t1d|`@#{LJ<=Eg!^#tBxbm+kOZz)QA2q9=sHb0~NAos?Ul`gHl)7ZE zgNBV(#34@NLEbP$ucC2e@T#)Q{2`9B_W?m2^u&s%S}XM_@&h2Pu4 zZMHEJtAj&~TNb&tZlFIttEqOmP2n@V*&0m&hn*%nrd(oK5GwSRc4$ke8anvZXMZm$unfZdwl0qAry$xvbN{!Fa*&zW_wuCl(aCP)1DtK!*2 z|9S9T#h$#t_LBW}ARkIyq$p*7gg_xccH@uJ%1lxu4gTFPVzu&D(&F}&2YxQ>_NJJZ zLstPu{ntm^-?(w3)5a`i$>Dry=>of?-NkEf;~|Rgt$u(h1%l0r4a*Q@a7g%G)uMRN z+&^M{LD=JgqH6?xljK=^j5@hr_I>|UKg@k1Mo!B%l>@?)354*|s$b`o+Zme6+W1ya zU)IeW-+)nQ)m@mpD&vKHvbz;1s=M|v#1HfEaKl#LC+dFBD{+(ZgbiV_jE90LMVZkI})KvFJn5DJU<*KN}%Dgc1fLTD#sislsC1OqLe-gE57)_3S6 zy5j1*fmiIx$f8K;N|ESG^JbP4-d6;JGp%3${9yQrq}D0O{HR8EB56V1sixXDg--ri z=u<%osAU^uyz&5zjxH#!#htmkI?*82xB06X(|C(lC37b-B0@Ce)h*i-7@{cdTZNBI z#rP=N3NP)x+kZ)+z7&wXA4{!-anIOAAbYSec3b!7fgV22dhdAWOUQiW3*jQlgxo)d zCr(F|xVW%~$eCP)`AD<*fd0a*_Wtv!Ffc4Pd7?7%-XU#icD{v-%;?NhqJ?-y!Lqp_ zsOrW`1O8JW_NqW@4Ei&o#&`wzw6=M45>k)ZaNU8iGoRB^=tKC-TQ#u4f8y22{riY= zFaK4&tt_ZXV8UtB$8w6Js&Fga1Va{Tr>>?I3ou|Z3hSiOw-h4Pdq(A~ncfib^csr#y$RrCbHO)O*x5DYYEqODDlyx*i9liy+>FPE+^5wGugku{C! z``)xtZ7efna5eaFqrP!>Gy)!?-_7oZNFkMjpn?4p2LLJ#^EQ8J%$D#g%VDSN^P#Cm z=*8G*Ar|tCzVLhe^=rY9HGGo%1CqUiOnUXp0>i6IRIRNn^9MH$s7%ws#MaJeo%$t~5N;@cGA1sDt*KCwDZO)&(^tn@ z+p2&zS~v)6-gTn7;;hN(^_|B!)A*c&Kg;{qofdC;L>UXk#JOLg!|g2`x9^_J)Zdfae@mb>8`xAVR$Wc^Dw>R~ zH~~TVWGB+D7!ov>e?{XM{PSOzfRl!;E{Rm8$fW$HX_ezbg&z{_q z(-Xq$bvrar9O171bqUvS5v^)$ONPrQ=hs`pZ{U1W&n7wico>}Lckq{T&q61TfsB#aU)?_ zni;>7BD_hboT?Q!IY=iMi#6x_ZjJ3FYN$PH}|{d&+r3Bq-<3!n-s z==^wm!6c)#<~3Aoj)9V-P2eI)PA9JWC4&B8I^)HQ?7-T^|5+MJ?u`N&0*S(76kk~1 z3w@jEW#Zw#cENNbQT{m27%aQMcC^eu!8<@H*kKeEsBSVTGv2Gr(m^?5IzD58w1y34o^Di8-CChB8+(1W67|vFhN$D%4g|F9Ox|w#ko2$ zF@Z#=sb8%Vy!Y|h4SyMwwBlB@;qaDSu;bfQ4zeLuJO4KQt42w?>f;&*+1hq;ZsVgb z1Y|R=3t)+;rg;pTie)*na$RD6{oRx+2DH!mQL=-p<&dO)L*Y_L4hJo5K_O+pqPtm4 zhoKG0t(6Yl5k0)df?%peTFr2r@UIc3hPaGkMI<3{gTI+wk;zCOJ30cKmF_uF=drg1 znnubU&lMwR^H1?b;qV1<_5S`E!}Nbj9A#51WY=~lMRDO|{Fp*uke$z+JI4fEme4Q; zOm7^3aKt0g2tX!<(+aWSHB*amd-?tShUIC6z)&%j+>dy;W|#>N$+T~5M=won`fbT< zDz2%2b%tHxM;s*?AKT6Dr}uxUx(7?|FZm?a&A#nB&{&L%A0icD55f_}Q*6W*Bq=AU zEhLs+*2S=r(l|xNyj7YP&LGoi!+*Bf@62xTbb6R=SFkvdM>vc{F{Ot%9LZY5coiRD zZO?&{>cd-T7?v^iWfamKxaB&hBE?we4lK2z(X21+199w|m49-STRg|;$TK8F=%yFNbi0`;c%hh=s zH@KC+)#70x&o0**LfnB{& zJ>ILT&W@JjnjQJ}Wj>SkyLL+g7j9Rs>+TJgCCnHm^9*}@)U*nmyefht?Vu*-R*k$# ztz|;U%Y6dXZtg7yNu3%9WoV`e$uVMRG77p!z=2wV-Hx+^?s%u@+#){^Xr~S&M*3ey zJ-lNa&sor0y>KPaFPuLV_93usv8lA3Ik%)h8=&SR&<8P~PXEy6GUxB4|tytj$!CQ~7UqbQmd>y)@ zt+D6Hjj*}Rt%I4$r`>8C2Ds4pqFtZ^&@qmmo^IM4CQ;utX$nuP#Xdc#S%29cGSSNaImVb_hDP+`8!~mF-!DXGETYq*EtP;zN z7G%X99-bFka2~{(+AmDxWpWaH&$!RA&w#Q%uWG~00;+-!-$4Y!3qjLJ&Va;|<+p}; zx-E@70s?wDI_Z3H_>nCEoE@Tcl8mYY+2@Gzh5H6rWR{XaVIBEn?0a!7;CQuP(?GBQ zVhYzs{$+)K1++ywwWH#mb>bDd)6S-0d&8$VGxC6NFS(uR+fD~VN>%1oJiXv@@f zgPNA%S5?(J>$PnzRwsuzfxc;D(d+vuq=ttLyKjeo7xo(BY%iEp{;o)@FjM&Rl4)W1 zLuTU0AP0-=L>!73HT(OVYWzQW1jjYTfWPk&A+D4IlGiJHAhIS7l&tMg==kAJ6B1ea zb2>KQ#G{$^Xjh=9ku?q}goWyvRAt$99e4NWjB+$h zc)s%T77&MugNid$TNfR^&fP++OFuD_`NWblx;FJNdxiX|e#z@5ul==S0$sD~i{w;# z$rP#44`of4sg$T`rdV7ohHiX0`%vW^^{LQRV@0X{sFLTEu_8T|oR;H*x11Ms&ACEJ ztZRW{f4nh}Iqo_=uYnoocAHsGAH*$_t6bybe0`$_HN{Nm-{V=@0iX}bL%{jw3cJGl zfKfo1 z4Ie((cBgP6l%x)1^w>5|OE*Na{s&2O0hQ2QQ4EemWq< zZCZk$haIe-RdLq2_hVz(Qg_l#8n0Mkl z>!&931w&kk3P6Ck<{bTR%JQdmF9F@&6Te=-QT5O52xJoBD4pylVII?^G8{gN=txm9#b1HVoP^lbqd% z%`I68J@81w`iL52%Y&fDqG5Tuap7$}B+(3+gW~{|x5DkklW)hiuYHtDa8*ln?Je=n z?!1j=_%7aGO{x& z73Pm(@_{Q=IGPWx!oi zt88U>SW-OQ^X(N z*xO!tn4WuV*#%ffBRDGE%*nd@0d$xJlrtU)79&9N&u|b}mpYica}2YwHOQpdbTLT! zU!$${CEzeRwz62A5!6WH(Ug4E>;vJxC|lzbCr(5mkyrD*`>?*JB_JI_!*C$MM^t2@ zw4oJF2zGysT`K)Z^1)Z8MDfR04;`5lZ(?)eKkE`IZSrT-Od>SzFM;w_-^Cxk z=Pm?ILm6wub8mV(+I6Q05WN6RP7Fj>m{5S%90|O2Ar<Q47aHp_ z?`zSBre6I-G=uv>jq&VLwYTKHQD2$EilQ=j8UXLHu5{|(gVV3}er3^3HXUu){PcJJ z+F3Rowi=TY5+rdhpi4x5x3aXxdH!eX>(-&2gTyq+uXEy;-bUN9J?Q;hQ;|TCZEtd= zOZ9I2%Wn#mh)V3Kc-Qj`_bH^7+Md%TBj!bx&43Jh)a7A9NJWM4E})p5K1P7tVL+rP z=rkf6tH;dZU!PEh>=c5ykuMgK^H0eb+TAVbQy_eRv5Vo#Xjb1VRq`Qh8bhif&v zcpGg6!=ywzoIm=UQs{l19@~evM9k8D4am<)4-RPRY&$S@71+wYEJ`d8W*Lp*Pf*`C zo@*ESuff!Cb$Fh}g`z4CM~X&J@DQ|yCg4Ew?~UObMwG9dSKOE-Rr5mZB8WVaycLA# ze{G8Qo;ejEym`o`(_uSSI({FKAaGs?q1dt? zBvBc8!W-(g^=tQ-p8#IMsa<`adjzujyjOqI-q~2(Af?W>n-1F^E$JNNvAa-Cp%3XU zuNtX9ThN6v?Q&g-Gg|Tai@e57RBUIvIoO>@$<8a0c%6Wn?~JIU z&Rl6Sd%_S|*|Rj5QST>QQAxbJ{!OXFp!&wk{vuuDo9+eN$WW7>l<)BAyh-VNHmB!Z ziSx zr{cW))2d|af)qy*2|0SE5w@_bz!sMW8Y{l^-ylKLhkG{H8`KAG-j?+JZZjbLurR*4 zCu!bACf)HxvD@Zw%t-(DZonIxqpE2H^=Z3LIU7KsjKh2sco2KGKZTey~yk_&F_~nGyR*X1Hb-SC)RHSCEhsn&-Tqam2)sT zOOBHw;X=vl@vh{u`VoNo+qY+&^6_YqL}SxXO&NE@;*pH;=Q+PM0d5SPI*y91E8`Jp z|4&cw3kvEZ=1M^(aSD{fEkg4-4y(J*W!oT$g&pE4Ix8l^iTomnUcv;`F8uN>{Y6)9dIfqsC&f zq}pBMeue>)k3I*?6|=GlDCZPQSm!XwxhxE31aATDGk!UN{q>cgtE|oQe!A{{ln@gf zc9y?OI4Z=EXn6gJ0LL$9u!zS3>SM7Mkd(+QB4UIrqNqV)YwJOslPK~qVGN#jSaut@ z<0z2xBrgpW<1Q<_~dQvYH;s>!gb7a8&(=q2Z@Se}Ow(rA<| z)}(E;MBU0Mw7mO#+2FBSzM$LTJz9as@fEZ}DyzoXK{h?1;`eVa3YB8$2(tNkcIJD2YV5z(ycQ z)DTMpqLszBjltIjiwZb^fEVle1MD3RtmAhRyuD|S9wCv8UDKfYi&|Ylh4GgQ|9lR-6=D;dK*_IMU~S-Vg88yfgRk6eH(|xpCm19rzlzvHn$* zEpM?e`3`=7mn(S}a-i=G?y+n%e;(-d>9lm6^yIb~8Bv@=NuoZ4SS@^eHnW-d2bMYt zfQ2RY-d%8wU#$O)Xc-}gdjlLG$s2-6MhaxVE8I1o0A$6FwB+z`c%rk?Oh_;jlC9D5 zp|I>StX)eyLk?1E+pp^Y1y-LuS|}S!0#&m6(xBGIPvcj_2|x7BeX?6NDh)oY$J`pe z>nWyI>}aM3Q~pGNZ)bRyH7|*7;a4;vipta1n5|=eC_T=k#Le@Dvr9gBCrfnn z(gY9hkLYgpEuu9?jAIoY&G^meY&-9~H&?`9%VGP?8?M+hhpvf*W4^et!5M?}$=+K& zqQ$>fa|l{DHY*a`Rpg1-{@;d&F1*ZW705b z&eV~=@Zuihir%C(JDIMZmn*^?bBS+dWq74Oea*7!o^&89dULQx^HI`oMtyZY#`8QY zE@voRECqE5Sk+<)_ukkT(sEeu>3r`q7#t4C>pw1bP&!8)6{*QDbJjjp@$Mk zT$Jupc9R*g=45v;UWrWVIE7sttuJ$Txvda<5=*H25@8sXBW;y0LC6sz*8KVVH@|Wy z#mzxrUBRv3NU;W5H&cki!I_aC5a2r(zT85G5Tz6D+XX zj|;f#Scq9+A6kOhJR0;^`rwN@jY*Y%jL3lxefn_91;Twq#{B?{QKfG zCsQ=7sWU-Ev20<$XQ1qwsWsQ=dn+N}%Dk{#S!v|fu4SI#!-e^(DgqAKOERRVzJ+_d zm?>(i8g}u-n%}ax{MAt1-boHcg6nD1s1+%^o0I*t%X+<=1JSx1)GXvFwXsW)7#%kL zc$rA(*F$0?Qow^0_&m^3+jN&lfs-aI0^n+;up}ffMRV6y;1!=&3X*mFw z1yn`ySfHC}hYQ|?r<8S%r8RCxjrEp32fF_UoD2Dge@D(kr8>7gF8Z6R}H|4BTy(K2uC%?F!c?JPQ3EO=HG63yRHS((5zb4XU zTfC$ugvfAAL!Ntk*thcO0DF)2xG zjT6NBM33aU!V6--EHtZeaPaq-*gI=Ka$1&zsXYSBw3^`N-62SQho96Y47wm-F5P*IvV*^ z1O$Fx{)*Fe{uwo;hv>Jl?R75J^og;X4)NSRb~gyq%#Qd4F!?+qX=(ZD_~Qk;Axd-{ zSKlCmpg~?!wy8cPZ61$05DahLq^P4c0s(53n%|At}@R7HqU0L)HYKc_$4 zBbW(T)7fFf>|Y=X;@}cxK+wht*Kb@)^LbitPSAcWD?6x~i>f*N!gvRxv#AR5dmir$ zjx5H|V0pPys8fRpO&4aW=X+ES7PdMoxUy5wW}E77fw1{K2(S7C7P;rD_{TDJz0lODj*Y)6FDcH&S>9PPE-vD!bU>lD2ki z$GZ0B757bafyxWDN5ocQsErrE(xKj@R+@l()wW6IplrldQL49`sFm3_9O4DkLa;}} zK4Zj2(28}^eXmMUQ;kkHX1H#U$Yi-NUJd%_NmYY>YTmrJk_6Rk*g>>fxM7MvX?JiPcw|4tv5vKUKz7>H!|`Ab+_4lBFH z($;=6(DuHo?dN3t31_yQ6kK8#!6nw-WF@6P)25=0e)@d0iFlffedp4TcOO zas5rE?dZ2lYT8=IvYVURF+S8cFnM+RWg{EYf%~(~`Ce*jk)S+8tYtM02OF-RY>$mm3 ziW=)|ZwWaD0g%MFY^n;ivgNdo!9#o*QQYXMnw|@{(}3!{>%1T2YH9>|6JTw_C=wh@yxPYupDfVK499&t8c#d^a5jziM(uYy3VFgI9U>GLpZk19gf9 z%8+cTnrebdfSFY{gW+YR^?~JBr#aNNg#0~T<7~s=uO*Trap9FOY+AObw}sLN zO5Z9OBq-$Keh{#o&=4{bDY@KDAlm$iFkf4(tVSCNDG&Ns`D2Q9|4)w5$LtSZJHME6 z1uPWD&TYS?P0n^3cWu3T4q3MR;gWu=)5iWofsM(?^=W${LLjn6O&K?-$%3f!u3qh~BrQ3~*UG8*gvRlPkZ&+jVwE0O!oU%_1v3k~T z$NJyZa#EKdsAI9ckuvUu>l5T!k{)^mMp*mL_OC|#kG?y^+se95lVMmZ#&)erP`a1e zFObjZxjG~r{6_z|FLjYa4+ioxzfIfVOPsoSz9nD|_4jj~6t4H!1C1sXj@}kwxn{M& zDTDue*yO3&<4L1h;z`p^=2neg&AaU>az&n*O#h!9He+pBngLYE_H(t>#+>%L`F~Rj zH*WWkQqn{PG(Ob4A6EZo{+6fMN%(}1l4ZDKX7J4Hsloq7gnQF0Co%~i=|MBlp2dH0 zH39C`kH~)M3oT~D_N4!x!!5oFP7vNr+|1}pOqRI5OcX=MzbE@QcQhInS zjvu|fjvGUx0vn@l^C5Zlhf2l%GMcq-PB6bb)vT<1lZy$x#Wa{VDXMzI)o0|dEA!EY z`|q2p&NM;yZwP6Pk5)z`R-{aJdc3gyQu|4pcC~-xFt=-KqqD2G*p?O=5v*;zW)62o z9(abLAWiE=0-re->?sw5aSYJyNLbTh0kihKIgnHA2QMKaF4#P4A#h9wwz6Je#pD9a zV6g`e9?g&Lh-JrvJ(w9(3awXJj0peP&QEoHQH16wUMT&&zS&4>$MD{?2$4!j3ra6Hpq0aVSWb;nng z#!jZd!Pi^#A$uSOETJ9Xkl=vu^;igG#S`DKx5cKMfK~Hg%t&G? z1SqNle8@})HVwbI2H+PqNK#TwSeg9f=C~vG-^=jnNdgCJZMQ3_cJ#f>7vs?kZ@5NW znk3!IQ?%Uk?EjI8%}m%=EZPzzdPaHkES|^b6krEfHLUJw5AA!bCTI&W0xS4MH2SEE z1$i!3#fGqV8^{a4Q(7#z^p4NT7({zNw+Eq*kUh9yqt=f&rxA`CNeBUsg8xIYU7GhzL|qr0LkE<+&c6^Y z|K4mh{Wy|6$+&G6ge#VR_uc>Mt)JcTdV}hWZ?3*}xyglhJG}fV-E)t5l*Nv6rc17> zwoVNmUj7}a-ipcNfbgsdn*i5&Pn&G_+08a%paFoUY7c!}UJXOb7SDBpMvD!ch*o~b z=?wV=PxWcsJwYJh6OU(R1y`PSyY|>rBGXy!J|<__8uW>@5;mz7{ zmyC1$UJ~!_sy;T(a(qAL=}o`C0)$7j$i7Zpr55rC_lWiU1Dk*+d0jwc-A;ySm3w=R z6t!w}5XpzSh;Sb^y&Dj&6SqOA(*}_8wi}=b!Y`rG0lZuqpovibEKeBr0;H!&`2VpkSXMimH>TOa0h=oWZ>_$W zpgwB~J_bHf(8tr+4yHAK?Xt?oCW|68Ej?AA&__}yYt1ZhE?Mt|pu>LGXc{3OUF}5X z$bP!wxq%=Q?VA2uFYnl9!l3>5U&oD*%QXwp9%xuJqaiOJWLRuwvI@b;5?*U}i-%y1 z9VfEv7y~Fi%-Ak`jNUWnCHy7#g+3z7JF`?GMQ0r=K5Id~wXoBOA>tiOc;}_6sTO~H zSUV9=2j~P+J>iN(_57SQQXlE(VBz0#xvG)2z$Z7+r{}zITM-v3K6FAyT`g8lj6VJa zTT#NG)ltm($KA;tt^!JipXkkrv*2EyC{a7iD5B75_XG*T&R=&s)lqOw6)I7n@R zpAe>cQ&uzeQ-O>wv{LUA54RIo1U6+7s zQ?)W&uJR?4kzdHR^ZF*Z$5XRBl$Dj$5Fidn@;L$6==lZ-1w1oz2(G>|>kdkAl(%nv z0MVlc66c1IUBwm@$3PAzg1`6yU9r(A)bn)J)S?_RaMpgC-LO`j!b>U9H5d%rap}z#V9h}bA_n& z45p#s%dn%m%SKaEGcA1@%%Jfgyi|qV<+F%Hf50kP*#4*M5iAPQlK2=*^WRN*!=`-L z`qR5#l(edtt>XqTbPtzjHA&eeZ`t4Kh&zI>5EYj7`Gatx*Rg#r6E!kYYVdtYMGp2QJM1>}?-V(9HrRBbJzd1n&-Ak%o|t{kZ`^on|17or*Dit7{v? z(H(#{G9m6>uG}xO^S+)F3HUf$Z@4S;o~0KkyOpg_P)SElUR6l1-`p6mUnVxdLnP zrO1g8^*XW|?OuGRTa%ys0T%+F04y;Z`9|d$8*^(w)Aww{U8gQ)q0qg3)gBORmg}N{uGV3Lx)F$~h&H3~|nAmRx{aHXzbCX$gr+G#F$j_vsb%-JI`1 zL-3HrBXbZ&BtWFdE7BE6 znRljkVv7n~yzv;W=ezjWhRYvd;+TrD#nk}_;JsN(voV>+urc7|1RXCI<xTeOU1*N@RtsQ>yTTo@UM!n2C#KLy&93 z%FC-&>%MRm(oNh==Kz%h@EHvXIs~-uE8}nc`_W7mH2J%JB99PL2M%tjFm9?xi8;(Kgl3u6`K;S zRPHDbdvDq{j}_Qk5J!jzRSjXO5b+bxlXek%0(P%~Y^4G{2)Ny^@G;16{K0i6wovL?po;|C$&PuV9B^3R&vI`kb6)-4GuC8T zuDYOGSLn5QmYgpGr1W#H@b81lb5lgE5|QG?n)BMy@3BB5ppuZn{EK2qZvOW#m}9K0a;xgl7dE^>5c$P_Vl+ z+qe9B^H|(H{a$kL@(Gvt$lhH>bm+7%AQS~jl0*`R5KF00u^3DN1_DSz9W+|p!{Dxu z7{Zu*^DuLPR0c|F|6Jlej_#m8#6>go^kjznpTXad0>2$qQ(aEpVuW5!vG-MY)o2(b;v;&)$)Zr z&g1GwzH!;&#F`61tzsJ6N%{BRHkl0w>+gv~#tjbO*Up{;JBOa2T{qn>sMHXFGa~tj zt#lhMvza?1%ll$kbzce2sYuIQ1hT>y0#RN;#KUU{jUI#D+Wx6=XT}-; zX{DA!%e6MahuPVEu1h5pj%-A-rj8LND^j%VzmFTt(-z0S0vdyAUa<(+wJ`w)6s($1 zJA!OyXgW&M$95psCArfO344wh^ng(bhY+{GY6%(H3+EFAjqOgqDKGO^h;chN@S0c; zP$>!CmZWDc-PnYqB}-W&6qWL%bF@JBOzs|EsV(#?J)4!tW(TqV6*HfZ9X>nb{cZ> zG{>D;9<7h5z~0o2&+V1%f|NOH2)IOs)kH}Ttdf#a`}=#JI%%Jp#H)9=<5m-sk{kwO za>zX+B+*UMSH%cX5dGLXRR9z-`S#Bs`S!xdh8xCb-Vsr&)?j>>}< z;wIbUyN~}oH~tHRW5##R=q!oE%Z_gO|h_qtMaqaTh|2tm>7HE3ED=dJsy$uq_nyMhA%^1d-w7Ndby{t{kifWFvuL-|0VuW`2y?i$q`-Q@REp;g&KKz)7KH>=l_Og&- z2v+;NAxR`<)IE9r7UqY@Fna2G6z)^RC=5jH_#xr)fAUSgx|xGg9{A{8VC(9#fs4?b zuX+yR;#V`PPOk|g3{6j@%Ig&~W0{If^qJlF6@N)UHIng{6AC6oUnQ)qJsO7fk+9;F zt#1S1fMh%@nzVdm4rD(KKR>_8hez*_p2N z@DdCExyJ2F`PHvuI-nnT^?fU;0mepn+D_V56l#HXAE+a~$;kn|1i4W~3)=6(_zY|b zjlJ$blPjl!(Hav->h*CyHs$}}mci5cm_r_O7{DHy-m01mn2s!R{pDVNXB1-G>j1{@ zF{{NrUlm6Zv@TYf2eC_BiRZeYk`j~seQWv%0L+=3NOg_dBt1(7L>#opS%pqedaapg zZtvxJmTUjJ9_!7+W(N;!XyKn&k=TBS^qsCKhWsT21j;Y3o`Rw^1Q4mx3OVx zZU$mhyBkBmdp$>%m*6upn9%tD#*EHgSF4K}phYm#PC7xz!5MSGQE=higIBU~39?yn z?NeeOSi6}IFLb! zPS#JnuNF*P=18H0P0FprcVLIZEwFKLK$PzFrNODfaAdcPH;fsxNDik+qKHO$@ z*N92oBDP=#+?U+_PEa;M@HXsw75s@U$91*+PdsB$8Q)2b7N|T#^=w_luL=#(C)m{K zytJy1Gdj}ARli5jTW8O@Fe84|nH}Tpev@k{u*7a( zIEI})7tNUjeh#1?EJ2+;j+A&*(2K9J*}1Y;c){{AI{(KhHlP1o8n$6UD{E zBoCTmyD~X@sy$@YCDQ~Q)rosHZ86-t8pFNLt`I&bQ z?lQuO-)IvU%_S}I#kM$M)zLn)%hi@irFvD24S$D40oBcgT}*DY7tsNkem8FoBz^s= zTk~C*Go(PR?bOqlsr1PEHq(q?L~$xP8y8W1z427WU+z-ce8anUGdfOS`a%)uvn?)+ zRHFezd$zbYphQSKsXPkPFT_4Q*kAd9{`QI_DA-W8-x5#(2;aZB}KQa)0&PCAZ-P@CEGJ)MU2>(UsoeH;_XISmDjV`1=shX1sz##uwd zN~zrrqn>9;vLM>?WR(PninGnp5UHBBMxVw<7*K0*8-eVax&z;~GO*_C*9Mrei?=m1 z{mQLN62TRyR>Bsa?Na_vW(kpSj`~`0Oiw97d?HK`BqB=gu^Q*k1t>ez9|>pPv~ei#74QhtZJpcu!VKsCXYP@(P7*(EQ$ zhfC<|Y8cTVVwo_1YQ2h1f@QOcp?Z?-3N^}1+D7qU7Ho1|*><{j60t|)3v`Cm9-?ZE zmQ_8P=UXlt8tEZXE8yj$whYGVDoIc*wzB^>A?f~%?KMMQh1EA)xc3?kNu7-j2R~(8 zO&T_Oi|&yi;)tLdLp5%8C(oWmLt)2(COI2YdA0HEI%$r4`#ZmX=g<(c;(@BznEsT@ z3OQZm3g?kpiBr9MscnHEj?OQuW!k^-F8a_XtNr={*l+HP5~0%6iL(C{?}=fBBj2hQ z^#M+93#Lu=f2}zdatVw+E0^ zDR)VWoFWP9e?)BmDoN1Q49)lHW|PH9iJ8+evn5>%AO&=mTV7& zW!7Ip%*RHYnF_)UPySoINp)%Ej#0P_b5Vo~x_1R%S0|wyOd98ZJv)AD|8Dd(ki)$C ze$D$}z7moCwsL>!-~ee= z^9Or-idG1NK7z4EdgO0w9qX*wLLN*m2*ru8K2ZvQADnSE7|^t%jImjr%Mx@OKX3B> z@Ph#jw%enF<7H-RY3G^$j;Qv1vy}SMUsjxqjJyK5gJDinE-9DTum0hBjsxr%pO=57 zb0u^%Yom&LH4ikA_RTBVK3)Zhzt=Q9MccCQ_+KzmM*fCDBZYF zlE1j-TSl|^(GeU@PiCg!${qFJZ1JBb+cfrsSufE5^-cXh&*AEq8K*7%u=vI5I3H(Q zL`|KV@|UlWgaR7tm1@gltJj5c=T-(q34mcDCMCte*!;ifdhd9u`#5g+iXtUKW>z9O z$d)}yb_s`UlD#vtLxp5SW=7c#8OPpHvdcPTlfCy|_vdt7_j5nbbKmzL*Q={u<^0a? z_x*jopYMBpn6V+;fD%SMu!1wwoq%iuuZ;&$`sQ%P(7w}dR|U=puAW?+w{Oyh)u(^m z1)6Nhc{i$k^@=MX6Q8(A1aQK4PSCZao_9oLKtwyzm7jn;7PY*_>SwypZ@oN}u50h; zsaP5az)VCO-smSqz_A15;gDL3!#u?G-AD9!J4Ja}*6r>Ny{UZLJJF3e9k!gIr(MLk zEP^~uf44O$o)3veQLQS;F&~^-v;A+@OeZ{Pyv=aslMF!h^lUeTHGXi7J*)SI?viv_ zB?FAb`P-t2YV^0;{E$O?P~W5)x)^u#C+GNjZrR+5Xj6?hl5{_MLt%UpQX-1blvNp@ zUp0es`X{QxtxhW8x~b!$hgVWBak`yWu!6JVyjsJ}hx*=+!yJpB?JtAR_`nP`6wO|PK5=c-#L3~UN`E9=SzryAJ-l3u0JQ&$(l$^TQoxFq1Z`vLHb&NJ3N_^||7cY7&mMENns26wp;Zv=4pJ8ER z{$O+HpU06 zyRsFzyZpXH2}^e9lZ}G8m z<@#;Mm8Ho)Q*QaL{QrD#fO|LPRW{)zh#Gr{PypJ?&KAzCx8t#?5b&!*u&As2Nw#>; z^7n%&QR8BTKf5cdHk$7>YHcFkRb~dC?D|50z>)8o!@pmwg!5G75kwV~jjx^{vp*Ch z(I*oBll&6y-G{T?#1D`*0IjDJHAJui#eQ*Hl;J<;os^&a?^ThDxBWJKv}t~$P=q!b zO+H5f_2umlR9N+wOu3yvl&2I^FDYhSKf15zYw2zK3EOg00>{37J4MtaZSgVtJqWj& z>fePgP0Y3u3#Lc-80nz#S&=Q|pMFltTF$0cCENsga6-&hb>7-sC(bq9n&bm&6aK~9 zh*!myyzV9?zkkxT5r%I^o^riX$Wi;SjXR-zG2Opeh%OcjxS7>I6ok9#(2bMR^7lny zB&Ai{r@M5=)92I-CkIVNGr?{+GNgI$xK|1Noh749fBcvg#NVT%{O}>yNcft+w=Cc; zH1jCA2NTjz-tn7!MFyh;ytW#ifw?m~J$-?LMq4|xUPM;!F1`iX940A>x#vP% zLj(OvYw(&V(0_E{OPlE(N22%`@QvLaHiBbSs+B6=3K@;k#fh}iD5o_pWQ z{XGr|MvpK(mF8({zIqtihv&v=Mc&i&tZM9MVDw(5@I|!Q6fuo=NFm^AQ6iS$my#-a*PC0dNlq)DrTbw-SKq?*`G=4~Ui~B@B z)H~MY+xua=K;Z!D=(bdCy^ERS-K|CA32`wY_;o`qA^%`67gb4j`HE1@&v^^Oz45F2 z_&m-pgXA5o651SOV>+wjES4rW*FpM6t?~r(Zzg;Tn^z5uLQ0^9EHNx8-;*h$) z1F$Rety?jc!jKY#WWD$E?(*PNZG&t+%iQvy1KZL-z99d9t2HR& z1ipTD2_YWt;oe#}(9)^k)&kk}#V}f7f563`Huzt#@_}sW_L2^e+LMb)a&QoiA(|}^ z=zGTX6i}OVL}H@@1p?vr`yV-NCHjwApj}IQyz_GBoo`P~&1Mf>)METjvXNNY#h7xIj7dgf__pRt(dvwaSE_9xcQV3Ns4vhJ~U2bxec0vplmDNS*@b^ z8A7>g=_iuTw5UCc6ex`%ZxYl>ODqD5^Tu6lwf`1Ev70adeL1h8(da%QZtlY0!vX&O zeL{T2&hanpYBprCrwPP5f|V6t)NS=U4El+PjSW0nttN(X#q2c-W9Q5nF*!l|#sU2& z`n+n?aqm%j`om=uW0hZSE(Z=o7H>K@lutG}O3wV>cY^qNXQYvlEOv(%37;_14d!p9*GOEp(Vb0+h=@?~NQQ??2}Ysc z)oxr+=5XvMY2;lv`15~zbK1(BH)a(J>yi$b7_Z<7$VPna)L;DLdw*DXDA=+R)iIvq z93Xe}w_H5X=FsFwOgIQ1!fZbCw6nn} zOtAeBd+S&)RDAgO)KU_AJ5s=Dp1lF6Wk~I)L_I_y#UAT{wcm>N+O_lcM$WCW83dQV zvDrQ@I?_S9%kEjdGB2=RuwW4R82Uct%YDVN?z9v5F-%M!mywrGz4&^&?OaMF6AUNU zu%*eJ44R12O}Wo#pQObiYzv`k+Sb|G1nJgYHSSLHJ01`h@T?<_M_yT(qt_8zDS^5t zfodRbY;26QjDgIF#^HUwo8)lW(O6^bRSJqXkRE6ZOvD4b6h8AXnsJA3S7NutfNVp7 z=gx>xr_33z5ozj$P( z2`WJwDnPEav`15|%qCsUbq=Lhtcw4BO%`4%!uj!f$;?MdZ}ajvVCA0&pynI!AYG6b z3F(8`eeE=oK4O{5@%}*Z-x$pJ5OOzmx7|s20lPI$D`xdRh1tSZ9ze&n{QT)UvI`sC zQ()=mhs(J@Z}BHi62`rd6Yc;bU&X1kn{o;=C8l3D+AAR11NpKWV$!mbd#_Jde-Q1< zkwQ4Z<7-Te4_4=T*z{gh&e%6Rxp2B@MDKw|y07}X6?1lSw%x?G?}fyg&(16%90U7A zQF?P>355%DdUDm{6^ySi#w6moB8k?@bnrneh{Su ziV%F?;|+Yx0)rKK>qp2Tw2w8haQmVDzgt1H;_ebywn9L{VB8wX0)k4{#ADAt!K_F# z)J%J9td%xacqP*92Ge9e!-QIk-pngC@0VJ|&9JRN>Sb)$0q9XtVZo5nNC@Ls8oJZu zitI*3qCczOk#S~en2pq5oK~27U}_BhDMYb3Od#(o5@?lRIk%!l-(|u(39)?Na|`M& z*^?YiM+MdF8EE|Bl4PXXAEHhy;JPNC(Y^G3w^yZbb}rX-%n$eCde4cxJ!7*|N=@eL zHlnjhN8Q=N?x?p!CCU$_J7KS{A& zzSL%rmyllU_w#!XO-xL($?O2xZvYnTIb{C3Y(B*KQnT=qNaIR~n@Ia1hA0Z{Gx@hM zR1LkMEyu#{{{p&+(cObv%5g7YugrDyhA?;pW)rK<#72)GJ|TE}JXOA)pHHkF{@azO zUVe{maXemrO==afEoBY@FX98Y+V3K!ai(A9ssi$Ko^>?wgnBQMHxvuMlBT(kdl%Ky{OvmJV-RK&;ki$Hc&z<8~Y zbeh;GI{;e=X#}j&XW>n{0+v2vFts+fEz)R^{oPTfg&iaBh>O#~tQl3)<4e~iyebf^ ze7>h%<&pfDBk{%J=uBwAR!&MdNlqE)SIdh#M=Ls>8N5$Rg<5+j-~)^g_x4 zw&_=hN#2*EO5P#knZljzvmxdi_mu~`oRQ!0vH9+Y>ua1QVx z{|yuy2_rI8?Fay&KT*-)W5aUN;s{pd{HH2LG>$0~gUZZYA<(<%26 z>Ke(xN8hkFid=fvJ+^MDLsp|8iXlP&^A@&esnO23pr-HCDG(>@bPJH>tRbw2`BGlh zLgvS}BW_S9EFiMT)Fl+<_j)*rw)gL2MjoKiHfP!Rcz6_^o{&R)Qz?hF1pJ%7Ct=7E z`C&~y>ppYDTh>i%g;wM`sNE|pS2w4vFk@7!Kl;hZed6VZ%%{%bpr+zv&&OWR z(Z#lB9gqq2l?!=U6OjH=LRo~4WpGXKcPQeGTSS<`KQWVG0 zEa*$TsxvF20THJsh>9#^;?-IPv#>hCU&l3cPUXxfcTW{_#0M}q~r z6a4>AS46687&Tw|8aD@ePWYGE{K|7b8xj*1EspM5za4q@Q(5-hT&sM>>M;5OI)+od zw?lqL8&DPd(V=stjobSUm0$99Tb*&=co$wO7#X3z>l(D@Oo{EgYhqe0n$Ua2v@5Ow z+u4}LUuoX3=Mf>kl#IWo3Y9%RTC3lOzQ4$JSWv|+utq+t`&Fo1B0eVnKjbC3>+^kl z0nlSO6_pRqmD`Wg3k0VW5PJ#yWV9b}HOExr11i4+LVh($0bJkB_V(V3&OYp~^lU!e)akHIix@Dq3vsDNfyzGaTGOWhju z;=hvVU**$qs^gq}eHObbkznM(Cx1^%iwZ(+vfG3y1wkw&PzNJ0ANM|@sZ5@37uaOR zuH^2f4?FC%AACN)Lmu{jnSh1R_=Zq=n$GR3RVke))32&r^ZQ;NZckp#Cp{po)A77k z?p1=g!yRh-%=8C!Kw--|5%=UFR(dkxU`Lg$d36isQkBalqe;e@TQaDzDMVH@!n*FDWAu3-9R+ zlmyev(LBs(lh4RCWC)$>d~keL`?f_j4e21NaRpbW>FMN6@2;{(+0Vvy$rdVn4(qk5 zmP+J~uxH=thy6a$0n68P1rhcCOuWU9gV!^=2C3863f_t5Fgv5Na~N$JLc<(q1(LYM z^x?+Wm%FRo?7cwY5seYpG+rXg5X!8+`*zd95T3DBRWKQAs^{LSR;`Bskg9Kn?W(Vc zdu)FtQxWrvi=!gfT2thWhJl}yK(un8^L|MjDT`~q0rh}EXVlT)T&qJJufN8_mp?1p zZH*YQeX?o}6)|m%gDmRuc{NpxrX=F36MYS>n`^;ZTjVc>!O18O%I+`4_K4Y}xQJxI|G=Rx*@K ztW4$fDo!QUcf+F@U7%ac(=C5= zW%ngpTK9Y$>wQ^Syqa@=PeLTop&$M@ajgs}hdPDEs73GITx%IRtp7MPIXW!L)HPFl z{G#c`$i{+5;!o}uCBE^=S zj!#Z7`yd#(uamHqcUo;7~Hv5ZzJplITffbc|5{-e0R$T4#2!uj(PpifcD__(#XiKJj_ z6@z%VQKr2lUemWC3WA=y%UffvpRw)ctAPt@JbzCxs=*ylyp|nPd+N1$sB@^~H8=?u zwD9XgmtJQUHH5h@g{Nn$`Q{Qc|J|&n#s9BPAj3Z(`JKTw{(;T`e(MElBLfNjmjXy;HK&nz!LqoO|pmA0$as zLmyk}V!&N73Fj=Akl9n159M$$6PwG({-imaK@L>j*cUIOpI!uBydFjzpSlqR*c(z& z?xnSK{{qd5vk9SXcX>&NY?Gy6j~Uj z)J;vx_pBhmJr8(b*iwG=qX(~uA??>R+XHMZAtO%|PZeGXb-&INT_#Ll>=tTE^9JsL zNrYj*hCnSL4wiDzosN11vOb?3a+y&#u15R1L7s8Wn;Hau$d+Ht!GZKY2HnP_e;gmQ zNV?`RRV&BdZZQlgw_gf68||a0@(*EQ@mcJY+?!mDUwY1jsqHxjTUt`L#dipp?c0V; zWi6&g+CO=y+&&y6^G47VvD{JO&Gm)(I$aCS*z|gUJFBsH7)1K+1Cjq6XzfcL0y31Y zyAcGsv$G;Y-VB%g?|g0kyI+N2H-k6V?Y_d?<~MLAG6DglyuAF^%Um@tp^|j2_>l74 z`3Ca~47K%da(yu8&F%4V9lbu7uZ(^7Q9T(a@BW4I5y67D@FwpEGVQjqk{rF_Te@#o z$Cvw|?p2RZ!Y9W0#R0bxET;9?KBs1Xe}CfV>Xr5OZ?S0xCNH0zw&Au??e9wNJ9d(o zWx+yWg2nZEd)t}cvM2pF^A!*vJedNUS!@M|!nRS7Ru{yHeh2pgY+Y*wKV1?!%{X*Q zvS|&^sV;DSe1E~Z;m>Z$oFA#SwG(VVmkXrCrHhn+l1dZjT-~D~0}nu(4~EDuG0F z$b?Qnpf${(;Q^2|Of1S)MS3ku`l#yP6BLPRaK;JY9o94KL(R4!yd%Bf$6}fuNtVp1 zE5cB1?)G{or0vPD$WyI#c?Lq8DKWn!TmEM~1q2l*QKdqOi7VZwP)#vt=l}#@NBX3l;dQ2$o1N+T)ZC-tBmns4Wh^Y$ zh?OP?u8bU8VLsc@!sX;12o zlrNUX$QF6FP2is45~xnwJ9_b6IoZs1wN2*%r-TbB!?Z2(~pZTk=yRc~&^{;qOi->q8C`l4UmPm+CW z?LEg5v>0J~qXuoN@~>}N&dm&>`N6PCoBO1|mBae%^yGJqdvRLrvu2{@q0;v5vBIag zfRCkTW~8Tau(Po#lsY*&B1Ku6TqV?g^u!$8TV=I%PmGvp*<7A>pFUVlXXs%NUC7#g zB7bJ8z3K6)M#6GBQ_>=Sl~%YkKkC1%ExBw`%Vs(H-t?ljnPorSLRy4Gtm)|cHL`)E)TOYJ znUkxVr!8(B`TR>^UV*Q@vXqKfTLka52&SdG$d!q^q8~=uMn%|aJYNd48GdE6l+HOU zabvi#5q`_JVxqg4pzye?6y52f@3!n$BAcelxzrKQhe~??{__wo1+U?dqvVb7sWMs( zD_y&@e0^Sa_flAKxu0*fw%^s&Rn{}HUGKmV$PFYBDdRQ*Lg1}sO`dDTH7jsUa@>_ z{jh`8{T|+18!xcK`VfNKs}2`)G2W6V+r7s2*lCBD6~}tCSMqCo$mF+HR(>bI&E55U z9CYS7a9&@sfOG0hKO_YU4Z$qhewgi%y#SycCY+kjEPEGF7mXvZ5)!oS1f{u#kHRvM#eS zqoSfPXzWZCi~(3VtgwOYrS(Uvq8JHCY_?JX9})^0Dn1_ zK%KCr!}iO9G@(}7*vMWh&^GIq`a;MW#-Py1&uR?}H>F^g%^#|)tVD_X9KW#k@~U|@ z2uN$$?&|2Utv(fuW=Z2*`U;Y!^h`jkKgaCArjol0xx#5f*8O>4^({8b*^T0K)nP3c zHmxl?AnS)2-_2;OwbUAFHSG%Yu)463&sxaI$?2v*K^MTTw3r?6+M#xVCO~=uB8U=) zvj|4QVdAA#J>(n9g}=p%!u8bML_&ds5fX>mXcQxhbdX5Z<=)VSRnQ?hLLWS_2$DXZDLCvo{e zd3n0nag?InoGm3M$*X@e{<1c{i8lJtMQyos&ZTz|F7iX@M+Dl<@SDU4m&g4NM78Bi z^-H5%wE9g&wVO@#DbcI#=;><4>FOJ{2l*EdR^@f;l=SIjm@>oI8T zoJov9rDKLwyH<;rW&6o1v9MuB>*(Q_*yg$q;m2$#5geQ6gY_u$Nj_pmGt<~A4({uk zWPR8kmN=j(j}(T@Ar^J(w5}f+y`lYU-&1l-%ecC6<_FP6oJ6YnYjH|=%Qe1w=Olbi zm6-d$Ro}P}YpB`lH(H(6Kv_%&?870e8rt^B>8VH=rAXi9`U&)O;anZP_*1rup3+lF zL&j{+a>o@ejrKaArR^j8*r+3(wonFb|j?%KTrlR<8?f?x#H zI6z_Unex21xv`mmi-UutAZxwli0zQyg-15Y@FqL23}K4uT7A@^cxdR3Wx+d&HA9!O zsLR6(hqS!kYRpt#Zqbd?-QA4{2^On^AX$LAHMNyZ4!}ShsF|zri(Okjoip7q(nV&K zD}B62PCGebfTchoS!L{|`)E%%#2!XB$&69LW}R`z?d}WyZVg$oR>aLUUlWt`HUzV_>Gan7cczgV^60jf zxvXvrq~JC8ajxoR-cp;^7^W0;FpeR0(K&E>_ylh;igQpCWc*op?vl*8%77nZ%sI4m z;D3RAz&sbQXjHwfm+ zXG^plJ?^RMO_L+u5pF$VAM{nMUTv9UO`#j0o2$AxE`HDf+k9pjYvc`iG()SW-jIvj znB)N=3*Y+^B^@`D(XOQ(#MXPU@z?!ygY<*Gd($p0yGk)!!#CZNUJuYM#MZ2#Pa^je z4msm-W&~P~=Brj)%sC2lTaLJvUe$2jlVEU9tg+v9kxS0^l9Id^^nKg>@C>jSxMKXZDoVZKbQFN~J?+F&*W=u* z!nk$t>E`VN5ThGdIPo9Y%$HUo9l z+#-dCg`IH1@zoSOI2D9%S*~0nmU-QJF5;nF;*xCmkM~%dA1a%Ip33G74fS6f-&rD3 zNOEmLT5cGQg3Zp!t>U+34cnv;XB1`VYmJ4VLcj!@Bzc%>iF$b&y7h$+I&?_J+J67; zF8X}5`po!XjE{chCVZ+G+jW)AL4rBu53PrM(GovKQ9299*xnccC~#+#e*Z8Mgr{- zxALv1sWO-;NCTpdCSZU_u3y(>D+sj`bgCn_u1802lab0~5bwSSyT+7CBfS*N>J6j2 z*I{DGCpg$p0aN<+8Y?2SH1MZzrfA<~_8m+hcI_mmn*SxRQ`LB?KI}w&Wwm{^g;PbF zwk2a0&#MMF8s4~f$V(!+iL}Y!tVO*?N8RT-9fnPJ)_*W~@|bTCXxX-2U;kBCtW|l& zVYjBq5N?Q}1#MvRDzviE#c<3phIobliOETLA5P47r)ibjB`J<@19(|}#LCM047w;G z_f3=crnP{*YgM~)!p>t(Zi4|=EHg8+5rFbmgQSTMkkqbs1R1A3aI)vhTC-zCff%8P zMYto4`fj7IAydcm&q8a+7bk-B#j)G};Sq={wfzDSZMDo-U}=&F&s3gQJE%5EL(oR# z_gYQlZm?l^y^oKt{?1Vs-i<_|D;|p*)Mwzz>W^JL)2+B}eJPAZC+}{baHdq?i2znYc5B!BhyypB z8pG$dst1Sl$!X*J%& z@bga}rby^tkx6!NC?zr973D)4NQksxC8a1!u}#C&K1ytl%bJ`}^E*GC&}4Sdm8OJ- z0aRYfYgm{XsZp6c-!8x#fu$Gbjd|57@zU|D&ZEeZKz2x+Z$}p>Nnec`3%|k1 z`2`t%mnPI=w5s%w)gX6<*g*S>=rYIBK%E^Gj*AyDyz?#XI60f02!XrU1ydR2%?!+w z*&K0tRopw|?hLE%CL1tjtE8SvV={`;W4*Z~nqn?2t1F}O`kyS_I9Tw{PE7F+>*@#% z^CbRg-iw5r(LxsKIy1Gdla@AWO<1f7BK6wBeBKiXn?goVeva?J-7NkjI_bol2NJ5UOs=KXf_ z?24jnHXH^2fQ?8FvS3IkC^T>U!XlaE94zM^|8L5~6D_dhLZ%he6K+T0LkCoVXylj~%F zkBg@EGE?liVyj3K2=Cg8(m6fq;J2)gktkUVpSgW5DtXx(#dye)nx5qVv5xv zjIeY&uS!7Rarh^MtI)XVlFd*F0XRY{C(w_Q#e!knB9TZE5rfydpRRTt7UqPR^x}1= zht?WsZb8AamZ=%$GhfhxY5ZJ=Q~*;(%!A)f2wZBs;po6Rcx!E2q!6hI%DhCkSXgpw z#X)bFH_f1O@G#b~UTrrvJC&R$Au{SE3H=hbZmMFQ2n8ul({P3@d=0snzL~3z4|-*$ z0!Cf0{&8Qv2-KTg2J)aMZ%V(V;U@f+X87Q{<6dMWBx1I%JYZ}w zOS95teV0=jZ1v(Q2IzcbycV8ncT>cUY(Il`xQX|s8Q}I$OyA)XQcW!6cU{;&u$q`# zQnwMj0E>8&a8=HoVZb{(q;r+Jr@sD~$W}ppdxqikiB;eQqqb1n!p->87lrm@=PS=I zn$E_rH*5w}97or0{NB?oT(Gzl{@^OfbHlgU_omae3lCzUrJ9?Uvd{}-BPCIt*^uXd z`=LCFPz5XA^zvrU%xf*P9qy|D4iYOj!0O2ywfg~83ek@-DL!2DJ5TQ3OVhUG~*(pK8J?gDVKnsApMybj*HkWX&C{sy^Nr?@iz>P z5r$Xg$f+g+!tUdUjk5eL=VuG0cB3>PrOdq~1;UkUa7cP$BZam2-Z>OzZhw^xZaXX& z%m7r`y;uRe5h0gUO+}oZrXsC8E#Keq?@^x5K*)?iI%2XuY(dw)5aRq^{xZ&bUyqs>^kfi~29!=!@YEClVu-cNw-V7tREzrqy`u zCM!S7R7+D^TbtU{*RNlV?TIg4Qk&}N=(waCb3Wt*cDFb&cV5`f!j!tfvTQupu3*Gw zsFk!L8Wr4^=q2!F<6M=X2_w1cLzZ(~yVnjoWFEve)}8(y$U)J{V_-5Qwqq>+8`E zpZli#*moMR^s1b_*iY|hu*^oO_?!0$opg#>H`QR;IlPj0ult`&b^DN!L{qLRk!U2_ z*3U4BHkH-qVsK+aZ=WXe!~RATp7`3J%!V2zN`Q*AL6S;g<#iraq6W4}?dRV1Z+yOp zIdcEo&_pYsT_rZYlf78X$?Ae*DmHo_%eupCsF{WQ?j}67N)vTPxcblj8!Rt$rhFWS zBcpJYTm|zS1!@PEX+Ltl%$V02N0ECSkDRe~8r0MGi=}P(XolDdQ@B5(*0uR%U)0D| zCx}z+{M@rgMaWNSK|m8G(%IDd^QYz%$Z9WX_XBVlNXGie2wLg&y}jm)|D?Z* zCZm&1VGFCFsejLfscmIuoirL|`!J99D+n8cAf)gb{3-+?Q)xs-3T)H!^A;*@QHr13 zga@&nj7>q(Ye?pQ*tm!fn!_5HOk=mUFuxO0)~JklD@$>!U0?h5X4XtPk z_A~-co@d_M>Uh~txg&Y-y%xGJA+KI;EscpNg;AAE3~!&9ll0>cwzNAbXN<0mQ+Dn0 z2b)TX-gBdB^^~2Or(ld4AN}4;A1K`9ey;2FDsa|FVz`B7p5Z$_VY#2(s#QDc!< zva!(HGl#xlt*h?Gd9~+~D34ZwggccbDHoSrsrjX)%NP{{&J9}kGp75VIWDqjxOM)y zx84&`_Tq3TCa#)=#PEx-zju}`hEPVW>fG9Cso@p6rPt3anqLsEUJak0P?D5TmO&0y za6Lc@@7`&@-=;deagX6j*Or}G8kBkd@PP*aVNr(Bi=#5H^IN{gd^` z#fKCew<8}ctcTuL9G%kvj*kGlUhx0|(LyyJ%{xCZx8mof7gk%8kpGdL+J0M?`%N3a zh8DG=kTy#$@~LSaw+)wzcX#{k*+~rbUK+|$7NvACy=UI}VpS?JuWz~cYP|1BlN16` zY~;(iy4d~C&FXEE+l9{%oA0|BmmKNK^gm5CQOh2v*~--WDYeaOwom@m+e)lWACW#!hemHkTf%ZS@GoZa$k--WAxL#JJa&Qm1=c$e{e|JQY%dN zEtGua@0ms;j$e%P;z`H9)s^LuejMrDJ}?mTI(NMyj*(fv_K+L@LT+=;%)zx0!_(ks zY5J@Uzwq$PmN~`}9-fP^>rT{_HMe-vg=-XF8j_G$zjB!i{cb|yNw#V3T#5Ks;cQBs zb(PX>2+{0Aj)ZU8RUX6`7keALi#WxN*clkVxHw2AEzZRiI0k#s@F<{DeGa$2y-c~w z`e)!vFm#)}&l!rF-kN&;Lfv>g7h*l!rzWitdpFp>jIf~sfQKL6Y^j@WHcAqYY@AN( zN38jkaLGt8keLXq%jE1wdrj&@Y!Xz76-Kdhyw>xs#KAOKG?6pdrYZnN<2n@f*jaoH zkTI>;i>uHva7J!e{x4*RVjtx4@H@NdHm|a;opsE=7IlfiPQaOS{xL>mlJ_NnE2Migjb+j)w?hx zD{CO+unhu{2fvcV+c~42X=`h+`>?00j)FQv zQ~zKOGAC~U{~66#Hx(u-Hp=N(`L-i5=FRbU=KX!k&-UH-I*E-eB~uaic`xOB%35CD zBEC?5RBjR6y)wot7V#BXID-;6Dm8E&UWrjuZ$a_PIY(_ zS&dh`j(0ME-J-_3-);Kg##KeMCizN$q}R4bDJ_GfC#7{|QtXCvzwbhtAQRJC-E|VT zv4x8oy^4BBMlBYl@To?k07&b8_7BuL+ z3w`L*z527KApX~ac(SQV<|z!&klhea=e*F@!bUE-LwlS>XUvTE2(kXO38ep-Yd~R28Hx$E48KgX&ofknn8Ec^wND zSFZ??w{WhJXk20AGyl`EWS_k-k&7pUZkgBTQv;)1H%oL0Uy9NjVZO^T9Mrr|V~Wm8 znzl0bYG2rLd_32A_fvN@KHW-n-p49m%U4&Z)tDm`dF7~y={a0dSlBh5lDXO4|pzIUwm4X1nm7xbQB+rLeF4q0IM83~F2z#=| z)ek=fce&!Y?(>{Po0g4KU5I7c9VuuOe?s3wb#UAZUa%Tca;MB`amBU3zAE0uhb|F760 z9e7>A9z5SQ;ebkA(ju=l>gkV3JH{+utr~Wtth%hc zkZ%wKs4A2T?cyu^7uy0?RXdwtPkCDk_N^Fys--TWD+xPKjhLWN5T*Al?Z_)U$7{KE ztGv~_ZOVPxq&RUJAQyWc`!TWT=zBP&%6xboA4A_9ybJqo+T*w)hG;F))xQz0VeJVGI-2CGTc7r*t3wY}sBP#@&% z56JPO0e~p*lA5BjGE(}x+M6f-sixYbh0HAOO)ykAt8KH@K8;oj0wiNExkd)ClZvsV zhm+Q@?CorAskbYEu&2D$K6m>8ubSdNG2-kdlmD0#EJw1B1Gu1akNx4Ph31 zZTDN#RHoeDx}II#R5qj92NAZD$sU)Zo(yZ({#Vj zxuL>HCoR)NI`!ZPzOKLYC=s9gqcFKo$^T z$MtpYVSE8DSx?WcV>a(52W89RtL>}-lieCYmI10y=jw;(2Y}y$Gr&OmyXU^RdEm4AUXfsyGm^$dP7jgOo)Rb84U^a&`})^*XqwC0GpV-XhS39@Tsc4 zBZ_|%v?K(nkx{9nBP@O@kxh!cvas&-UhlSZ)n`u_6QoCumw`S9k63*B>X^@wZ7Jaa zXDp!>Db=k8jfI7UMFa#09(8uhdA?!m24i0|k=|Iq<>*b_nio9aX8eqPL`20c)ma>_ z-|8eU6<7WdX7*)OOPNkQS*N3j*>)!Xe#4L9kR}0C2;5&2-%oN7-^rIbuFrPuLVFA5@@(H+F_E!Zik*Xl?akU-iIhru zLEBl}4;?Ok>Xss-C4A^`)hpn>jkL;*hQ(^L%l?;K#|%0yaP8FZ<4A}|4trnrPkwvz z+@poc10jc7>Y*$5W%CIZW<}&BpQX4F#lP_)XUy==*z9Pzb1^RRYsTVNdIuIioeNHn zM-|p%&TCfUJvpE(F?CR;Ja4p6Qdf4gPd+lBg6k`AN5s&-d^tR}Xr#G5&hlupva{v| zc1h_4bmMXrm82xG36N{IKVe*2tH1iY7N1FO45E-&5DG6o5tSQgs$7Y*T^T<4@L|yt(iF4FyQ_RuXc$A^r^9EkH{T1K|xJZ^rr;d(a%^Q0_#K) zkg@c)RY?-jV2-eYbORKBvb82#>nigM$ogo%G#GvDygR-ehgDDL`e*7X2yz@XXjO)P z_!;u{t(r74f1x(&l42|b*AoL4+YCZ3jr9*pj#%;>oTJK|X^=yU_>IkgLKN!6Xls(n zkTWnC+N^(I*WQK5OI12~>hCE&hYF@c9~eamuEze-@$Y56uMLh@WTx__n5}ckC5p&T zJlX{jGCq2f@MI$wHrhRN-B@s&+fdV0RHCw6U(Whp*UABPpqC zq#^AP{Z7Ecw=Y4A+E2f_YE;~5K`l;AZQ?r3rHAqtz3$&ba)i_@y8I=)2 zlCo!+C3_}&b2#=4$&Mmhh{)b#W`twqWMpI{aS+NTn`A{~uj_gGUe|TIez)uTU4Q9w zE4Po^an9@gd_A6z$Nh2FLF=5az;#_}@I0K)tm~XiJ2xN4f*Q z-G9|HAZUlMPP3GCmK#-<5;{^OCG+J1H%t8b zdZ~nULt-o_59q+u$cI#ym+acl8!pogG}Yvk1=)Rk&HLeN#V?7D`B!yic8-s$C20CU zA(TZQj@A7(Hhp#Fme=X5qWyScJnZYY2n@3;>~vH~C|-O#FhCKZ;$`@EsheWgVqOf` zU;bn1O{F!|kXGhvp+=yJORjw|*K|d@eRLr{3T4PZ_sRb5Jm?eO1BfOo!!`QWbcHiQXd?G2>K_yi}m_+I^Jt)NoYO_eTuDM&(M&32r!H(PZP z_V2v5f8ON5l(Mm%sW5M7o85Y!rE7yY)Ixizyz_$40{Z>?Kb0M(cc!r@TzITpW&pUyPJ3$LEIBtF}}jd=J~;rH(Pio0@bWJ&*O1s{cy zjbOdGx=Xqpayz2ot3B^^>*TFP{Z7N?(;&ZX?Se5}^8R{~6+cID8hcxbxUj^u>m~^y&$Wr5Ng{{cz3C5EqYIiFM;xbjYf zB`nur*Ls*LlZ>9&N}R&)BuXlM?aP<&w!UU{=d+IK;JO3B>Bo6&XeZ6}< zn%@KM7gwX2Chik1W*jcgphzR>vn(;f?_IvQt4q@q|~Nqh`Du~uL~sm+P# z<`$!_FdU&$);_aFhdtGfTLlEm(k{f*Pk)S9_JBhuy@C~gOMpOzu0}9jYa*Uj*fk?Thf3T9`9d!KH(e2vp z5AG?(xRT=xu>Hgh)OGu`12$C|sKh`nGx!`ZFza_9+y)6RH#kRmUXBRs)kI9F+DU5e zy^%PEkseTsvMIc#yax7qq}EqRL&bb{9pkvZz($+S$!9apM3PiD} zgg1m}dEFW$kE~1d%5HMAT>p z_2(bIabV2A?^eG04iuL^IY=t5jU|G7&d%|xGwlT@tn<2CM+}T7wbRyZZc|6fkB1J> zTuI3;kG@$j+`6kz;h;gQ#8~ zBRpt`uO`-$#XF&Ht6MCSp)lJRpN-c;D!3iKR=ID!9ZYmhW%98k5q9WeBHx$?A7icq-x?7k3N>H)tkh5ASckP8<*e|a1BN%E>yZDHFp+a6u zCMWT=o|8HLp#%A^r4rnXB73vXEP=-8(`)Kimqx9LKe(ma)-du-uX!*`GzPi0O57s0 zYnJRQa3TwH`xt{CI-0^&OC=>^X2|-eLTt`ocjrwIq3MSU#pXnQ6GHu8+FM!E9C@x< z^B6~OroT+;Mu&F=TN5YFzTrw|6z_Yn^HS&C);)e8aec|sk>5+O$-0zXAyGPxIMl>N z)pzyZ#*NAguG73PSAdnhURBy)uPCe5_vZv2jT{oU$#b%{zD z)^!g{9nn~(R$UJWG$NbZnvb_4h%C_A5{Wg5;~%e3++w$$65M)4P*<4>bT@m_(N*WLnCr`Mi;mmOEZw5B21OZz7XX4&|=aIS9k_-n+^C z?*2O~)P@T8UXpH-kOZ@_;Qj1TOCnVL(th5ZDg_5)&ux5rq=1vxv!}z{{H;x=k9KOg zKi-a+(odeGz!Hmh7p02%Us5nq(dP5Ziy{c?9-VG97p<_qTLAf2P^o_~ZozS4-$ba_ zI$1MW2Ixt@80m6lkIAX>b<(cq2DyikqN9+;Uq^~hhzVbmMlbeuDo7b8ee2c_ZsB~q zX2kC@hF7gSgKXmYrSg{T@`#{F_l^f)r(4gHx{65CrMhUkm~P>cmgbePBkdu@*%J;z zBc2MUdyiq6cJ$k4sQ38D1y~(PY!fTMaT{XnwZ8uD=Zt9SkL=Q*5)(d^>V)sl`Fn14 z8c@;ydfB_fLfeX5Ukyz$E>>ek`TsjV()5HI5_eW5xX(2i(LZ5iM%hca<*R8d(2Kf# zrqon7d$4Y~q~R6w1v_gEX+C_&4jhOQ+_Z&zpI=dNZ9>u(D`~SmbO3?`2Nsf9>jmzB zk6pX0`au|^1=cSE$vWeE$<}VyA@&JG&?1d%NNQv1+J4|iz?n5Tw>x2U?_?HuJzh}xMSwk0O_XMOAkU8mzLU|W-(H4 zT_WU0bMdky=_lyJ`gO5Df0PpZyHtLo!O+VO?~Nh9O5Oa$K+1IPnDA01&sVp zOpOliJJu%&_;kgt9zp_#xj3r+)pM6cA4cANT(UXCZ*ErNV&EBP^Xt0M{dHk>b6w#IIuaEQ7y;U?xrDr-dx}X5> z0vuB$TTzMF3>+s+hJ;+qEJ+}OPQ&x&qdgV(1=-4@D{@G6$1r+Xv0?4tH3rp|L~E3h z-zKdPy?XoDgP{Hax2->WS$FN^^4HJjVq8yjD<&9{Oy9jq%NVXznDRfz7q~b4#18@u& zOsxR4`jdTjd=UqS`gF;zGnQ$^{vDhfkWnSI&%)=yiXOJh-7eYY)Rvyy!3l%sALI>H{b?HC0` zPqk&+g<2b*642m_a^7u2*MxHA;uXx3V?bl)fA^8GlP+n`#mzR6pA29?1nlFCZ$6fb z@w;XTGRAWz&4l&m_>b&Z@0H|K{W^fRf!vP)P1En&?NWDU_<dHH9_^)jjBc8l;*L_r^AA2J>7Z*xew}0ez^Qv)9|O*co53Yv_Ka{hOKMi< z9RoC`xu?@<6L=8i=|ze80bk?3rOU6<8E!^Hlr4}PaLVd)en`SjRDo90^|z7|$_~7K zatJeXmGM=qCH&q3+>bEiN^Ib%77eb$3b8GDzIgNr|9dsVw+joVV6_rddZ!y)bdO4e%?iVf%;M+^FY=y; zPga~mmB?Guy^^kAg+f`aY^hF@MuY0Ru=)wqUD&ZU=4k!N|6{PY{B??Dr~c6+3f=1E zN3UM@-eP}ex;x>K-J{hObHl0QiuGBEo!V`qAyS!bBc?~>)ImR8nTjas&ynnX4MXu@ zU;T)F^$9?@1ip>)@omO$n`%i@sNs-`$dM&^F9KbC5R z8lbUQTV22N0N>1W_WZv2eN5?!3U29wB#QKia=s%*5qe<`8{3Gx{FHb3FgMwk2iSr~ z;6zPLRPhx+jXpO)bXU(fO~D)V&0_XSx4W?c%WJ#m!P z?IXc`+_$>@x-y%(-p>BT4165ZMqL<@lsB$V-*pMymwQ_LfdT3F;kWep`ZfKelvtb- zxnf;$L5pDGj>_V@4M<0*-{LpRLIRQ>=SR1KMPngfi zJ8(YE^nU!hTo!0tUjpjG!(K5hy0U30@oOl*c5;gk76q}qtw=jhi>`BH1}wcOv%U<` zC+`BzAme;>r%rY3GgN4P8ZgbjUqwzH#`%=-P7YD>Zi1R_c#H(r?_MnJYYu#qPUKrA z1^@$CECyPaip55r*rHW-L=H%+N+?5VtOJt*+ z5rG<`*WKWh=qFQu5JYFFaHCs9p^N`wr5s}%^(@?h55ThIY;z(uedU}vPhS89`OvR> zoVVsVmL;(O!SVPl9gW8z#n$j=Wi|Hn%GoNQgxv2-g2_J@1*ImMSeUTP`A%I zQBiW4^+vbEx@ce2lrY3?v#(@aPP3&gxVZS)vWF#xajQIzew7lbu8<&~dF}m04NR3a z>rLf>QH!Ug)X^F{Z9@k9l${tcW@gRJGC^KM42TvXT*LN4X^rWgw%W@6XQPvBl*Iv&7lNK1zFYHSw$jb{ey+irEAnf*4fsO6t zlfzxl)Gz`;$yk-|u#D*guRWu{V6x>+aq%0sZr!5x%@EF{&D)C0wEgsE0a&=|5_bUx zs{QpleCqg9s*{cwV2_?9^xxTUTYwAk&i7yoVQZ4W$ccGy2#A4uCDrRSQis3tu#l14 zkTmN7Xy{b#CZV(_&9CBnD5!UhJ)XLxD87%v3FFp%6>ER>)-3u|iRg6Hizt9K<4f_S zw(}c&GKi0_72bdjX)?unog@#}FghKuxh_*Nxy0RqMzjNgR8?eDfaD2lJo-&!D5ULv zWvxQK@Q~d8aZtS6&Nf@P%+J_CjN|UZ(V!<&0cU=Smy=?13c;6x3m%ph(9%`bHXwTV2RsEb zg#)v}WKxguwZma>H%%fr23yRZafb>fA)%{C?cH&ArR*c(Ey{$M>3XW%?1K%j_nf+K zj}tiJ{(erVd4I)?KiA*yGZ?@%g$DcM{YpsLOJ<7M8Z=5M&6e^8?6J-mqKuVEf$L@S zvS}pG!%(a)lQFsRi`UetjK(z|!#>MbspdP@&7mdYDShkfb6JdGG$R+)BX^-290O0Z z5j&3vKZ&{VDv~U*bR;;G+~fMl$#ji+C1zAh@_}r|eCp+Z{mKOa?aF_*nVM7k=LHte zgn)?r(C=6h`w2!71?s}QM~zLM@n6o6@-s@>Z2(tJKO};?JiWJ=36*K0?L=krW(lvu zzqTZ@H3U0k)^%(Z*rjW?Th@iro~@SEK7jV8%_@i>GD%9B+Ay#GFDz>`{4T5@2E;~h zjolt_Uxi}!>(tas9_h`^(#{`dFF>IjCH#Iq!Qx(11SjunKs#!}@qI>?yqG zNx$z*Sn!=QXz0=B@DBQ5Q6BYbzvC0_XNL`&Je}86hq9TbBdLyhAN=XnF{nQh;3w%# z_+Gy7!K*>cGPUNInUupYH`o@4pjgw& zMpDu%JTrVbdRgd?CH*>_8`{X*zSU28H3)~x{dG|5+dhX=-m&hy?D-v@O8?t_ z%-5>BSji2K@vmr!c0Q@8JDJ(3MPIz7Z5fkRF)dwg&b!pN9iKGWR$0ozzf{C5(OBxkKweGcNc>t_y0a`D?L7Ri@< zG;GW^nF8SLTyq(RKr4@84aEBIrM0&SS)jI@43E`18nZ+(DXsmrvqrO8Zt#Y%{``dLfBL|klIgL%#<36K35f>RUi_0Wcj zM?E$zt(gBVSi7%fJ=_4=s6}P%#q;My4^7g#(GI|JsSKvw?C$I90FV>y-ej!{LyhCu z+#}VWB@p`+9|teyGm~o;Lg`4uHgI>KJUl#V>}{8yK3519cu$l5#4XnASFG4==PtF- z+0VQS3uB}+-P>_S6o!ouzK;RBCBEM zMBIHP(LP<-94(~dQN21jV+vkfyvA8>m`TL^l^SQ{eV zPMl6nbc?=%3NVz1nOTy0D8!)w8wH*&3dS|Bb;Y{Vx~{Hu-8BZl^T9 z1^@5K9fXF_+Gh{3+`Rnmo4L47D|!WR`WW-B${04qWQ`~5Q~l?I@>LjV1V~y(g|0O|M;To-$^I`Z z)_{G>5Gf9F)9X=R6q8mQ%!O(3CI&zKGK<<=%hB^Zs)^qJ*+g$3_J*%UlO{}3r=q=C zbFZzBZ0GaEXC7Y?Zy3$ba-~~<#sAj`^94}nn55LmGI!@R)0Mnhfmynyq}Bm5*_{C6 zf2_8<$#gAeSJ%G`NV|04nLB-2&n|6u-#CrvHb1#h#&Ht(lQKXN*c)?QBPp|?drbae(-c!UOAh2_xd&M<^g3?)DEA`7OW$ZE;R&(E(6P^^_wV3Hih z(ko``ma=rVtBlI(V+t|#Hye__cg(F#H=9pR0k)SS#eHW;zvRdte0A+pSY{S#V$r|A z=1h&4403wIfNFDq_~U#Ni(+&YJcNe70ga)%44_@ERr7OmrIhSyS_6iLhA3?1xYjy6 z>`QjRkKjD{%tm_k@4gu72yd#T^+d&?uGv^U8FiI z99TNl|G;w9BNgTq<5<(!I{Ys*s$<}$HV(&{&g$Dqn4?Rfp6qcI*qB#qBuk8I%iXbr z;_*iUD4UNUwxM8dbRIf|E?Qk5FuS5YK3UQj%98S{x5v_bcHx{Y`}5}yfKq^ki*-BJ zy35qGd7w$LIN|8^SngF|+UczaoE|7v+ca=2G;pkg1-57xp6q4VWP#13YX{vh*w97B z;V`Wwy&=@|Ejl{72JE#7_G0ky<5`8#UUwaQaz!eDO!Nq9`DY&=BIe^+ICW{ALW0rTszuQk5`|R- zWcLqQov`E`)#>#GL5#!ugc0tFNsTE=FP9gM`e<&ijTen6-;?+BI(>MYyf#97uWqG{ zVr&=dC6oIQQaa)XrHgl|(WISd>&(mOXeHxe8m86QDw|9SbhNVZuqe}NPHBV(I=ZXg zo@4c0X*c|_yPns2GQ|e-Os4ah?D&=``PO7yjc>{e-`WN7ha3PuARWg%(`m}{*qTh* zco(}-#jV;jf4^xPI3dqtor0=tZZKJ=Gfj|+kG<`zY1gO`MXy&TIK?}rzgW}nL{E9n zlME$KKeC&ERkksQ)7UQI*i{wdnyC9VN$9Ut2~Ne1IsgaanlKR`yQ-NbVW^%h;f3H? z1w)MPI&>JKY9d2=Qs5cfInVjPn0H}EWuhsDdE;f(`jP36Wa`P}TFAhttf_i+`zO|- zdi;L=nRfCx{_?S*YVORuQ&YK;p4KIWBL%DFZI0F~@(N-ljH{z~nQUR50%9O;<>Vo}E5um@_UvJ#aE{!QQY?*D%*Cn?E(d znzH|qfLVH+3YwzSqz*k86Q@Rq#eoBZAsA}3hwhvKCr}P-cYhXB%*_zltZn!2JzuH_ zs{{Arpo_8cSRJRgDayXN0>Y)O*ecOv*ZWu)3N|eYIA;RQbci2d?MV`+`}3Y~$$0gH z*DvK1aCu`qlq#-3Q4hriR^gV`4%NFx3%lk4CsZJlBlnJAZOd0Yy=ABx8jZcyVJ%tK zKt5s36mxg8zVin*y0Dw2HB`roEM3XiId0EmK)eY!C<83%ue z?mP+kYg~hemSCz-HA|4lZdBZ=FJJ(E(@K|)mwA5ncWI!oY@|1mf{_`%GZfyxKj0hb zS+bh}pi)s$;hnu^sFu(AyfJ3$B(ao#>l9+xC2KPANQJ2YOc#e?2y02CuT*@s8k$I1 zkLKWbp@Dl$i`A#cCdKq^GS3)d;fei7d8WOpP!jZH3OEd_RkKaJax}Mz-IGOkVY!rk zU*Zeo(Op$8(`J$iQ_^Gp&wzJZMj5{-pEcd|yVFCyrVw2|G2#Qjlp4q4@pyT5D35iT zjN|fk7(x}4Zzdgei_y^2m!k(dw-zK5`sDd%I&DXx2EDqJN8Dq+HtOO#Ar{&O&4(?X z!^vez)B{>k+ZuHDw(BcDY#e5NsI;O>azz0rrwBroD|wA%OUBPw@uEx9fi9{AX36IG zlfo(hhxu=fxlYz?0#-%aD@GMKD@PpJZ>-tlJ(O29Kmf=YSw;S2@sc{KXE}*I#jM{y zyfwzY-t4ypsv7g)mW>Z<#e4W>z^Rzlm~TlbH?tY%G_rd!n2yjLL@v4$d!5;2#_q|N zb-=mYfjN3;{%pa^dRl>B9kax>QS0l%N}1$)y5;pEG!*5I7$!Std@7dNrNlO!ur-z3 z?ZdPhQo0w?9D~m4^3ji*a68)5ZVH&H;;B$MLQK75SS;h*e&Rn<61C6LnykKlAXE1E zjEXAz+y7W~|3-wohGdsw37#!9H5s=7oAUM&HV=aA|=>azrSf zD)KJT7ygB7T+M4fhWiJHMQdGwVm!F+O$2}U9QDw*SNAl3Z7s5ODb}s0CbN02mGLhF_1dyCNXkA= zUCYABfx2epV(X6T8MS!k71c?u*S?iWnbABHx0I2y)=$7I2EP3(EZ#K}za~YH%QvkO@aV4_E8Ps1iC>X5X%s_JD-k%EB^~l;mqP!GljNbJG_BiG$miaHCr{ zM6fHCT+Pm{qn*`4jr=jq3tgBWFX_FNGL@1ReK%V#uHa|npC*rRF5s^Lmt%-<65{BZ ze#y1d{tYWB8G}_ve+M-00N}zs2+d5E>t)QCl$oJcow5{1nd^r85f#fQO_*B`eFK|N zC3&^Z@4q9+SeyC+)c4g7Y(;?;j0{U8W>nWpj_jPAZgmvBC3a4+19!&JzFp3?d++;c^PkFgov3`y--?7l^NH+B z>*{+`?X|zvBw*Zj(bG{`;FE*CW4JwFrs__<*moC8%cXEL-D3FiiffI21L&4 zM!3hIPO%vj&gZK93RkE!b9$=U5hWy7uh}7A#KF#-Q@t&o zW!$Pyx8yh#G(hpq_ZhJ9I-CEhvM#rZ3I*q z!bPS$?$sgy5%2TAlEgjo>v&ojouYfS`Gbf+swA#%|7o+77FYJ=yHLqd_oxXb z&~WSNR%2FIFB?rI(M7}JR((9_H2ufvep!(xw0m&s)>cIAy0>X(Vz=0iLMMrw#v6B@aZlya)6G>zsJt-}_h>%6kzbssxMLalf;U5Y`0Mn=3Sd~Hn9 z0}3s+R-{L~whCHdOdZN&Z0rxQh(os!>*V+??&g!-bj||3R0*@LmAtDSwL>!LQPVt9-z8i_029XTtvr z3$9|4T*`pX!XnX8PZJ!@yA8!958tMk^>gKT*A{{ zZl)@-NRP+9CqWjR18H|xKBec|W(UV;yzE~sFBxpQOism4{d4TSsQg89liPZ;E!=ry zxxA;97lp^(I?3qOwC2}z^P5L)f4wd8)+r_l@$!4`t<;)Yrva+Hv(5HcV&v9W3SIVQ z!2{~*$g8eQLsF2eSK(XDINxyQX0|!Gm>ki3Egh%=d0pobbif3Kl3m2vIVB+yl&Jpb z#983h(0+~$&jg__{J1Lozp)tpXb)wZHC8{5*?Xw%^CrsA!yWn)tSjT6Cy?9_`Y@@F z)~<`6o&<6!Gb3XQ`5+baGyvxiwRn|UiEW!LB2h9-Md_es4!BONISzVUgsT><(5fx;FMxjo2gSFTVu)HL58_sp+urKb_xJ;LKY1SvFCZgl`mFk-H&0H_A zsjydrYicqFv&pWJI!L5$bU) zG7BTx%QfiH!{^x;xxN>7?)F60w$$!~wNckiJzW|(jEcORb?y3yi2+f08fo}Y&KOk+ z{Vx>&u9S4qx%WW_0~B4TO1x1U>h8^d+VONC)uNU%3P?)Y&;r-Bt7`bs7nqM zJsi{2#dbV~Bi2BALJlpj3=ZF%RcU}V$iSF2#WWQymI+N@pUmX1n>AKCTc3L_dKliim&c6xLmsE>D&6WX)H+L zTMaQLEbMd5L9f+BrZds=isYq6kVw^&i1Y2g<%V!_$q9DD`qr>n$o)sRBP{*&1_OR8 zE)+GW`UR-V9Ej;AdD9!D#Bg1qR_Gd z3t%d!f8p6dvogBC{RivuGU^4^GV?$o>dmYCkqm~6fE z0c9E)!F|kVpHK|-8w&EIv}0*DoVuEvA6??*5E|$x{&*nre!6aB+>!uq176xVMciWS zhN!E$6Elv^(8^okYGq?Vrtja5*$oM6Oi|z$N@8{x2-&|%X@aCux^1~k1J|h6LSck8PK=t9KS&UtPWGV6A;55R2$G%in|NDYir#^s zjpCMI1JZ63QO;psz-eAk`a2YgE*E&R6smkkDI3))>Tw2^KzBn4wYFldrRXg)75FO-}rR0!y@M?%TsLcA(& z+%wUv_z1P&yI*Y_unQzA>=JF~=mS_gC^p1l4@Z^59jG3EqRp>+0%IoX(sT$E;}lK* zI^0>sj`G(;9y4OCn7HY^DNLLK0=hO0U4Z|@1AQqRh1E42r}f6W|=6OKf{3C5?#ATuqT zZ21$_<}9O|A{g1?GVeW`(9nQA1dBp{D}t@$e-@_Jai-HUFvLJdV-aPt4I8yyB&3?5 zRPT9nbMtlS-BGIoP8zeMg|rMAT_h6=(v`ce4(iJA>ffs8+-g6QLDW~=Ru4Ox_r=BK zHPC2!4B_1;RMc_>S$sSsp{5P7Ae1PjN!}=A*Zcw?1U|QB7&aFie-!#+^oVq zs_J&Xqiipe$1Uv}u+hc71Z)HW%xZX8QP(}lz(b9-NJZl<0+XgFcmFu6K`ZtF^%R!GyqxoK!U-*JyLlTffqpBC6ashsr5i$*E|SvT z10t)+iO3k6-Cz+pwx6!ycYSx>YJ6dU`Q5vB$q{O47;HI2^@y1cP0L!A&u>w83ng@* z_89W7trMJzkSTFT*tdUEi=1!~pJgi;Z>p}D>5chQez z%|)xRsk^B)7SyC!Up>;zg_+Owk=TcIHd#q{9zUeFi&?ybz%`K~qhVE2<;{tujGEdMEwsMz&1)q) z+T&9M9{GZKwQ9Z+Fe}y!Q+qSCWt$?QMpRUgcz$HtZj_2j<(59=0oLze=nL`t~ULN`Iv2T)LmwR(-Mp8&3U(-GR2l3;3x^0+*mCE*TFGq=7 z2H$?Csk%OF*1e*{Zu-|F0MF%KAiTWoGOkM!uMSnzoTM?zNt0kpE=5~m{xpI83yT3> z$wd&-r>uy;0#czkMG&&PScg-)*#|N2zrXCaL2OJtdvF0R6D4ocP0HI$vp1PVfgYr1 z)?A64D+;FV_kwW~`7;utwwK_u%X)e|;Pw0ks`fvtare}~7tmk2@XYOV7`Z2OunbZy zB17;!FfMf_JbdEph#;-+-YINhD#zf7<;+o5IURlqm9*{f9*uX1b=! z%_pI7=I29;z;6NphT^wRq8356)F=v>^u+t_e=47uPt5f*hrz42X&^1N=CSbU*t20L z|JYt?t0Up|RI{j@iXa;RRyD>6Zzy8?tMOHp&ke@2sp<-y$5Nb&;TM@^f3US!V1zC9 zVC1Z8k>NEupdc#job^q)vOF<5 zU*rW;&bHK)u*kJLRvq*U2$brjj17%3trc-Ql5XuK}1DRg5vQ;5D5Z zcZ;rC=2@~4w7>U1bLPwm(Aq$+*HltbL0o)=g{W&ji{Mngj|jD@C&9LOW~P{A z^0w-_CFvUmCvwKq7R5gQrBkR;%r(3)^LfIh+4SdPsv0|$*7~!+Eh@owKr4k|z3*7? zo|we|h=?8Gd}E67@lg~XYHa)H-3u~kXL3=>{O3oPdox|tmVP#*6xHco|7b=x6Gbn6 zHS`7k{tyaT-dz}xkYxL5HDYgO)xE>QdIJ__PR*%$1~wbF zhdrrp-h$un|27Tv0|cL#cNM_p`!MC?0Pf<*f-IK;LgQjse1$JOYB)>eloy*zaD!Eq ztuyuT%+-eo{YzGSef?&ssS7VW{cW;!Qf-8M-5)gT#l+z%EXT>lmv0YwSSFbMPUaCg=_)DkpJrB!;-pYYdT&P=c|Nc( zd5ugFdCWgo)nWVF!sTNyh7utsn2?~z0vnr>A(H5kA_Rr#15(<+u*MC z3*1}CTAd+Q{g!dN1Wj41>Xx}YS8*nTDUNR3@!E=58=bLI$)}{6r&S~z2&Y?<>)ry< zpNWHw^GNo5Ao%^F_BXadhzOgG4hqNKx$6A(H)pZ+TtEA=%^xrh0XYh3cjFex&5D5} zT9<@)3E1;}&Xteos+Loi$AE>TAI}{reIrS@cC^Tyzdx)(|S z8f~GW`s9Z`8bL)G-33>52x?t1EXTJUyZdzcy3q4%e1oCrmre3dsI296O{1TvGR7Wj zvIlb=NTm$`6I;~uLWGr3P=ez?Qyh8NhaJ7zjvFy_ZE3YryaW&2lJ}ohnRXl;6baf< z+^?WBV2wb0`4m#%%@glce(-w8d7InIOJv)7xjXNm)}F9MGPdg}FO8SaUa~h0XAvs( zdEfU72&yB`W^^V+f*P`Yr}a(h(xwOe!mHm|d`$K$_>}Jg2wv3`UL;$h&FiAvHG`QR zg`qi>fc=Yi2x7uI)4c$KLP|s@>LzDO1AeoSf0=_pnT(c}dzw#QO8ioLNFV<`<81#n z)}|b{%{l@bQdhA4%`6vYE5Pm+c`%Pntm~V)Cqd6THM!EFl@QDK*uaJ7n{IsTipZDv zn2)C&W<4hE*AQ`^%4KvJv5CQvs%R^P3(UDhKckMcfol&9-AkD3*4h7uhYA&Bl5+0v zIlh~#OY7Fs^5}}r%Y%6@C!F3UUZK)rOEVB?Ws;|NknpY=wNVM>Y6JT5W8_kqFuB;H z*onp}uWbu4zYG4=4l+0NRipe^@^T1?7No9ZrVKsMZWWXQ(f{oNr!W&va_L&oxqJzO z?JWz;T~uWi+r3Usr#$j{g{aH^15gD1vbQ(YvLWW=4!Sg`qe$lG(o)Hh@ctneK8z2n zVYi@W67lisnO@l|e1sty9b4z=T0Bh#bJK*ok>50=`QVyrNSLvmu8bpx2==FO%=ET#RxorA*4Etm~ z@pXM8)Z?8>x{6a(eC}^f=tVD%)i@bD{`f!+>v35hGfxF7R=$a#kKNnYenFZlMX-$l zQE{l=-Ql0YI`lU2T<&WJ+gyMKuiuu;4N41-gg7PX>n=w917#YK1KZSJ4>}L$>ot%z z*uGfcfd&I^(h36PEBPt-Z(^?90atk*q96YPs>G`R|5aGGa`kycl8yrB82u+l-fbK^ z)U^rCe9KQU6OU)qlC8258Ls=-P)4p#UpaMqoCR}Al<>OZf3=78Q5GSzyHP}Lk-d%# zpa*gjUhDT*4b5?$eD%?kUtM1EFXL7WeaN3*cc={5a2S_N&F(A~4J`G__SU=0|5SY* z=>mNN!+BA$uB^xD!m@u)_8KNNRZ`M)5IKZ|+DCICck#3jbj}i#S|adkKO6_}Y?BZ+ z5S_m*8l3ziD0OQq$7SRy)5&2Sk2oMoV-F|ycw=}BqK(XC&LyZ`)=4N(eN(c^UBOGi z;C1CdSs6Z(ttW?6g!%l1wv4u4$G{>r{IXl23M=<`2o&fN~`Eg($e@(R}8Fa-i(-D^y^MO->R|OF^ZSw z7^AaoBihuoNy6j(&Ha6~1Mjkk2Hbb;KU@jHyI1la)x-z>W!5}fz#aVUz{(As(DV@I!c-c=Xs<^y`E)S@3uiwvF(l3l=iRBdHpd!?c>H=N2d`0?>2kj^4dcs*@GMz<4(bi%*kLSh) zo@c-qPAX5YfG&9dH;2pUzP+JW9R>#`yUUN5E=~d=QS+MJ9L5Rdb)b8ynCdBWoCD)p z43vxI*tRO$VRmCPGuLlw-ZZQ@K0fY8A-RT<`tI%0`~Q9A6%_qk~fxg zhV}3YZV&WAV_{@)@sX(yaN04@dC2efB<7R-ddcqW;sJQ9zEv}Bt3sZH=tpc1nbG>TNb80tR48a*sJTEWQZwKsM>4351>?8LL!eaLe5MCIYhK%r(y zO{_oWtiUaR>4cPkK&zW_s~$WX+Yr+$8R0pJONvVNj%L2Vf3TRmKZBuCp<@o z7+1?in**A?BP9pEKs;e)b+u%QqY}bf|7q4x5$jrF+HiqRoI_H_F|_>0v$(i8C1vGS z=(HJHVp&y0ASkeSBPr=9g<#mL7w?^*yLolGrvPtrm;tXy;W(m8nfF!Fca8fRr6VQ! znj~BPj$%GnzixcMbARw)Fx+X`A^mV?HsCicMXgK66p!&YJ>F;)wa?$$UdH*(;U8XU zEC+iHv#>CAYoW&YlvOZ}7z2aAZf$1RkJnkzMWx(>lI}#JdR#S&KXbiC$~j|IhP^OK z%&RCs^pARUWQsTn@S$AVTcEp~l1B0B0i(}U0JquI9N z+ji#9^m<>1FeVVH-w+OsX{KM@T~i45-qGTaqQ8(pXbiu_h=*S-Z6Odf`42!78GyP0YohPo&n$~|P6Q|IsSzoP) zi%Sb9-MLZJ^7=*F%Qtt@zexyP)zgs@VvqUe(RM^CR1p(A z&yFFZ*xrtF1fgS zH2oIfGSGZ>-OWTmp4iX5RxfcKfb+Cv>*6`wkkXd zIkEWu;6NlRGuS!PX+rbBxoh_gu(tZ4{=xk-L8^e%BQ6`kljC>up9!mb1sw*Jsz){^ zONPH4?yR(4r!K7V+A@ubibAgT2KwvgPJXB)hoWc!YZGo`q2~phBdr4hs@0Qr2Azz}F3%vo*O*KVtTgdd5H;GE zcQy6tWF1M$QCoG~$7;}cJN<{quk&Al_U=tDe#9+43JqSAurhgjy6m%DkTNK}xE1pq zyelCStKj6rUC*^vP&oj~%`9 zf0VFIV;!KqL_pgLweaLq%7;BiijqRMd(s9Cj_lknk~zDJ)84B*knzyUyhgoc*d6XH zT7J{H`NYEnLX6l;CGrS}eJy9Fd(!`eO^%VpI;@ZcY-OGmxyD9Pi~$oz$Rcz9+3}L} z%0g2q;CBDU8Aapchd~XtjATW|{#CTIa?2<9Nh-#_2Y9J(`BJpNwEwDYEl9{+wQL-G{9ioZ|{e&Rw}$jvJ&vEB=aabx3if z>^pUxVjR;vfTA|@o+DhleeHI09M5g5K)t>$AHXoIF=q;iLG3_DoEZ2R+-_upxl{v{#(^14(lQeEwHP-v9yAIah->RH>&h71(GtGB!R;^=U$uu8x; zCHa;6*VhwLeGAr9GXjN{EJ>LwO-H|VT2;}!5l1zSU&>rvJ?p)Q7Y_C<7B(U_RrY>= z$Wu3_?NaLK6n(OJ0@D4fhhHm21kYRj55C?yD(dx(9vuS|Y!FdIP#kI~B?VMaLAr(+ zx+Dceq(MqSQbLB3M(LrXJ5{hxH~$J!e*t5oU9W&<6C0! z#!peg&5}kYO#-)@4ksKM36qiy9wb5bfDJ@;$)MQC-tD>&ZdK+PCXfddWM04YX28cm zw3A7RZjm1NJ3{uZlJ$LA$tjywp*e-|t zLq+*_G7%Rmi27qAs!J62ZnhSi+z6pu%CR65!WHjV5d~9yemcTr+#lGP?7@+MA+)M#tW6mFu{EyJGl-j+Vk- z{`K^Arg4;b##4wP1!{7EvnUAnkh_5j(UNE$=H*lS*n`2@)nUk%B>s(GM$o zjr0<|F2je{@@OX5a9*hXdvmN^RZUaoul z_HVGS;xQ=^-WR@Ge_x%H5dZYH;nW(srz>?lym)%9S883nnyoyYWrW*^yF4{HQ9Xvg zi-c6)MT6#LXOrkxPHoKJ?n6eVdcERA^=Wy0R{6>DrgDQ4$M58Jb;T#2O8xC#g4_y)eHx;j@4XZ29t}VDePqNh>pG7@g!s^ zZg_Rgvh_inU{(HR3i6dwK1GC=D}Cd^;LvpQv~32|Uq<0y?p^5jNEq(Ypy^%K+Of`o z3e0X>?ZGQj4SO}CX&L+T1p3>9Wnnj5%lxGzZFfP7(TBZY`)tGW(zI|ojBp!Y|6z(> zl;+CYsoB3ozqBXe<(rONrECDVbvD79*zQNH3WwD#kLm|>oX=gj|M!hqD;3WAjx|jG z1aV)_nUq!#y!*&V^;&{mi>mIQe}^xa(fp0PjcZj0NvQ&ImE=lw9VhmT!Z+5q?rra_ znQ7c+ym7F9iYC&t8eGZJqB)U&zy7S<{ zJJT~3Q7B>pl)`dx`08yT`po8JPN6#{#&x(j5*d{%ZBbD`CS{sNFlxi|_KA$_X^+UktBM##0cwfma?NlTWzP}S25@ftL%U`a zf4;8D`Btf=Zi&%V`$dmun@b*zv#|zoH5x$;<28xd%E#o;Zu0BzeL2a1kP1BcNcv`E zrS1mep=!5DGn&argt2K;zrzG7!^V$%<`Lv-*_;VH;Ry;7u}>z>hej{)Ff1tZ*@UXEjnTwwj2VQiS>f42LRy;@%z`D3xM8yBBU1|lDn zhhaXjT_?QxkQ@@pCf*sjQGtrxOX@aGwk92$oD$Ams<~=vGj%WZ$tm5=n|SF}iF#Jq zt!HfcfgPP(OyB4a9c+aESvGf1g4_BOZ2Uln{vQ{8Q+Y(hbUyxg^MomNEv^hhPQg2Q z{Yt9$d!^kJY8hAEOqm8|Vb}Lpo);V2)!oP{{?t`i&eBG{_1qCa% zJ7lrc$8b4MA<|>dQt@7GW)@OXmXranPULbnoRP{=3*(p1C*#;(qB~T$(*}8%D8kch zB;jB zYE@%KJkm))aL5>}T?Y!o&!~FM%_K08PvI5?|M)LI&o0`KA5Tw_Rg%g9KLsrJED$c= zp?@BE8A|bVZMsvH@J_20k2T7vn|><X+Q{H(AXFT6rAXro#6K$8{SFaDWju1L&YV~%`t@F>PO49UD1doi@tCMp5b_TW zRy7JPC@2WQ;XGibDG3U`_@yh_NbUXCkY5$^eCz9`<77kgmwNl-$N{x~bL-vGLgMQ`%I)cgjrUo{1zg3L+e(vo7(8r=#lj`0L$@%{%@H=f{me7K z9xf*Djs3sO)9<>&2Kjt2pE2HbL%qbPxw#W`bG6TTCm@!~zCvvUQ1k}Si`})Jl>FAv zP=CF2iD#B5I`K_^>@-~6>$*AW)77O&9BzL}2xk1Kwc!l*R`-=At@1B0K7lETl#NLI z0Eq+0Tux*Rozwq92T$bldT&rW%x!o=>K?@j(zA+dlm0YisHK!hoqAwHR)}6RpUp9T z6ck|QJ=9OO{#+OGjqnU*8bPXUP9m(Cmoz@C)Ro@3)^tY;4W2AM8z}nnD z^02Uo_q#PCGxnE6K@Z@$Bv>UF|5$SOShbFdDxv@GUg9cXqjt4N=iy* z2`CPZbHsVKHS$nb7H6;6df>Q!e|+e&^@RouR;h^XW;A5?@_?hbC%FrD@*Ivr_MXEx zrx(FS`*7RT!6(t+NUw9FG9>d)azlP8L=L=K8WFS$AAupRH$YeqP--VhH}54By`5{-~@BX&UlAU+O$tv)w0O_WZbvZQ&AZo%uLWVajVf@`LR6 z+qTEzkTonLufnNWe3m#!Q@y{T36fZaI9gf=a1wG-EqOVL=3)~Pq9JD2he2OwJ=GES z9%8%h6RjgwzEj_5{8P!uldO#(I#OIxx&{qa>3ZENN zHn_2t!~qX8y9ja9i|Tyy=8y^o3ZemP-W+C+D6-^+P8nt8Hm0X-?I8@bzj8t z)M`74a5iFTw(nx+1v8TDTnSV%v7)61Mj`yT^AYcsS0{em_TFA?Rd$kLr3`qcGYEy$a= zPHPY%fwUo^Y-jXJeFHEN3ojtM)xHpV0nz!6^Z6wC4)Bt;j2z<;ftp2`7bP0EWX&_6EHG8IzlJK@7M^! z?^gz24M4WhguRx>OYVPv)sfFrI`zv(24G;c{J+~5cz=sp@H+oZTG7hBO`Pq|7-FcLzsfaQ0xiggOsn-YeTsbLjnz(Mj z_|gAEg30%#6sI8jig_o%&eMhPVWN02nE+GDp;D{c6ciL)KqDQQ84p)DaM>>odBMQx zXP^|rfAFm$SPg7|@JSktAX31PSGPtK-Zv7G(^uU{d%lyPm9~dpAxR93EkupudH#G~ zUo`u(t~qpDu@PAfFCl)C#-$_RC{XbGuKf#CkY&5X%W|qs*Xw@ei~ILSQ_M`q{UKgg zy{}OB%>@mu*=3>$q!LMbSiy0aA-6a0giq)kbWzNtkVa=8(^dg#I1o3~+cHDrR12Wl z9d)pgJKZv`>hYw-DKfY9FJE@`Df1U|e|C}7<6;e69L`@ac05>1w z-IU~;+kX^3xKGKFtbZ}qu=z{-w9}eYX||BYg*Tsv?pjyCBjQuSiUs|Mx{LeY^{8fg z@i;WhR(0$%qeRMPuPhukreuxlRY~kZWF=MO?Mv)nTrYva^g3Ba4nydhT88WAmI9u; zqjyJBGB$qrLhy@FJ8!-MIF!JzQSHRSTf%5)@|R)x_gp*&Zs?qPLz8os%mUS zP%^c*b7{%ne7JwZb^zXXlh^!1%A?)r5*1emnCV?Uk%9V_BXaJ}^l<8l+pb-uJ{`m% z#?Nz?CHs=4DBGg>&cINz_{&y+;~}Px$lNH{2BPZzTi{QKmQL`0w@gseFUNJEEh)Mv(BvtMTQK0S?akdgk)dzX^QtoJT83W$@A_uQ`#_4A}qg(kj&=MNtODRHlB$ zV*P7ProYTs_rtD9@J|W1?r3)&6?;(5{_@~9s z&|trKniSkPRR!r!mj$GtAc>7`Q_Q!9O8j;*E(Ry$n+zz`0!14+|fH%KflAU_-@g$ zXSRxo6dj%vS9AT12U7QvovTtw)sYHNjH=0?CUHQ^CXfcC7hXvc!FVUoK#GDQ<9;6_ z{v??cSg9E2)pe~QuEMYAtL=8n6U=Dhi{Sn+*C}$N=a#BA2|a!4xtgo)OG1G@HG}V5 z3Kr88SM`d>qGdZT>%)>2mIx08Ex0DHR)9J|2SW@GeVJ@8mzR|DS>JF);QjSg>uD?EBwX6}%5wqPjF!oh_?HTeH-qWVv@#! z;JC=>1aY9gGka!5ui`htyWKDkH`t3v()^l8t9>mRdgfti-zfQqF@+!pduBl%bW!%# zqkR=)^ZfShknR3P$Mv;!{Aia}L%!Py(%QyI=8LWR2gS(y8(&opqoCJ;KVA9({ue7=oK4pzZH(y)(Asziy9u)Ba46K&&fbJ95#My{J0JW}avn1L z_@_S}o&NE^m_)Jg_Vtr`IV<&G*vF@XcFdw@h^hst#e#DX@t`b;mg1Pb3Bq(p3;pvX`(P+64D8(-=z zE!G@9jzIi6-*Mtil;Ae=2>%^k3`4mcPf615ra&vpXthC<^?u`_!BrV_nrMO$)|i|UXGDa&`$1* zc+a@)w=tS5>o^;s!gPuz8l>JEskW?0>l|aq)g6pO!f&q(t>#XVhg>&WI85y$)EdA3nAv%Y>*Dw}Dt8>Q zr6G<7PDBbeF8^$I@VPKtoowcQ-gR@goH)WtOjs*}Ycy0Uw~5htt%cbg zT&=VP)r?NdcXt5DoMV_Z_n>^NdZfg{ z)yYMX`9R?q;!R}hE9qo^Lo*S$X0+E+Qpp!Q5+jAoezW^11|B|L{(S<=#UCN2`-s}y zUNwe35Os2Z{5R|0!k{uJ=`3~KcNa@qYYx`Au0g*-oWtaP@$-%osDCGmhhJu9q<$hr zE?S+XU~1^oS3HA2Y^8LSdgCa6&9t@r%uD*of2}pLHOGf8!l1xJ#!W@#TuIk1``Bx> zrMUe1>XGtCVU;VnbQBbm8|rPx5TcAJJVo>fglO6KBAe*3U@<(ox(Kt(sx|pbiEk$* z*Fsq@9zz@?H$0{GK7Rvc-k%i$Njyzn)H*DxT2@{VWA@}_^|tXgOt&EK+);!{HvQB? zczer}z}^tA)SC^>*g%~y0bgb<{mGN0Ep_ST8-#_p3V#cme?C#T)%Ah=KT~(}*E%Z9 zO1;L6c0bvc&4!BO;$kc#=AU{u`6S7Q2wl3h^pXZ%>s-cu`q4^fTJZ}K!#|K}EO@KP zY3^nfJM!}&>kb)ts_03?Rwzx}WdU~fHcT9G9#ZZz283G#P`if!+^*XJ#6a{5@)5NV zyf&>L2CmBeMo>`rR_}=^`;Lw{ucZQZnV|jE_pXBF<57HL4=t_Oc_eZU9Ybg(Iamtl zC@$Yz4wzwWpBFEyFhw8|{SEJ7ll>DJsNe}keG_CvFUqAdV4aLi)bgLj-wwflg?SwK z028OmjH=&*S8$U_f-4h{hy`sQ?>>RC6C+srOV7#8-kd+pv@BP|#oQoKQS<LB7%ZxgR1en-@bPrE$uAJtjV}$h2bobiX|;SA6(y zOb80+4^IPOM3FF)-W!+Crpq041+)5e@Hqum{%PIfml=eq9Zk>9u{SjtKg94bM$!zA zlZ(i&v#Wg(g%}o&Gt``6M2ZLqZ#IznhkSoetvYLCY-reI5d44%d?i9*{?Qc7d;;hm zXxLv7+^t~;X$`x>h?NhRDvV$Ty60j3PC1BH5dC5pR8gTU_F#A+4WbN2Zf-PGB*`Ga zNhKeEVdw9<3)0PDz3dVV&YCFnF~qM>7H$m>8_XDa|vFZ<0u_dw@|+K-Kjin4|gfL17lIWjUb zxE=rMNl8k62!6ODS?;jR3AR8eRqV;^^uOSC6Xkz8Qp?(}7msa4dd0n-%dnp5`t*4C z5RMt^Iuhz8P#v(Enwnbw5T|Z-nvOHs-~kUzp3;wDVaS7gn`&~ugn0{Cr!m!ZY;2|W#1>7b6*p( zO_zGp?Fz??G%?Mvz%o0Xzz%zq_XYZw@s{97oBH@G2q7#tK9Q+_fGq^_gjDw4u7$Dr ztzeRlBWkH#ffuy?H=`6D@FTJ&yP_ue>XM=V;T7 z5@bd%O0`&-YgRU=*5os?^>LRxVGtXNeFO__D~aZM$WWevPgHs5Q%lVV#tQpIRAHQa zodDQBY>yuvsAf#J!f6g}ieE?iAC*Fu@~&(2zhtUUg=QG_REm)*=Mq`4`FZ^02?20C zZmNtI2Wtt3k`OmKNnC6gaT&H}rupG2dDbdv@;8*#9}y?P^6lnTR_vtY;XDk1z4nbl z_m-ULgo_gFJG{#5ozZ-)n6v8+*VZQ%N<6#Kj3UAnYJB)FK{t?Kd|Oi3seGfoEu^Aa zjmc@7Bu?F`=n1)(x(zlnDs$CD z2~2ZmXC!=1(RlKX`ucjquEa;iG%>`GLs<)R*m0z)(c+ZNE)PObcY}yEn@fla6&Bo5 zSnLy+lFYHJa-X}U;}1AsSy%C{^UvfjzP_y{&*^X&kohvx6MqW z>5#KF!Yp@0$FBWQo|u1EQK1gIHI32K z?Pcc&R_^1KVE@GDiQ!@4=|a*x67<}uv9{e(t1wp71orieAV1`L_;I?@?Yln}Upbc@ zSlS5!yjpgHo}R0kU4ToMe?g&$tK@bFIRcT8q`YH#yuwW5F%{$RYxnd9_2|D}^wg<` zwa)ttLu$21)Ab>5pVt^)t0F<_!JAGTlSB_+Su60Jq8zVj_;8c55hPH%ScQvkdpcqn$CRGhcaNxyB8zT zZ7U_U3Xt;D&d<9NDJ{_$G*vH{)kc%2@-;hMymLD(k#hbgdt;&7Ck_qC=jTeALl+-J zvtDp5=&l~AQV$52?c6IW#nxSszDwj3@ZQ`KR&L{=HRG zW`Y~7?8<>C@B;|s_(!|F9Wk=jXDx_}H;B0X#B-AHdlY%kH;5gI{afetjy6R9_cCXT zk3Rp94r=V-R+d0x{JfhT5^Z-cc#|BlcP*ol?>V5d+_p1{$|3V0DhqqhSuW|K-K`_R zKm{i;;#V2JMFAGAm=IQ(NS<&{4e;^{;}QFBx;ASE9+ z*<2?cu!t}P7k%HXgS)LdA>@q1)$9`J3zxqo9DBszw|{i%MRhWv2TA|EIN=h0tu52v z)wBo?!?;Z#@tXv}Y$zEelW)oSgK!C)vN-;mBe699i-9GQ`PYEY3J-t=4u@lal+h0$ zmb)_ibRcu8J?4E)4|yE(vNoGRgge{08WD!!nt28eCij<1J<^zCe2P_l*I)c}kd5E3 zQ({~kId5hCot5mr@?UUv(CgIhf2HC>t3}$cwaz%HWTB8E;)}h{h+!~D_dbs}Xftsq z0p>*Ic~=g%r&IW7q)OsW&Ec*HggN-d0?@e%2eq_bVrTU_qe?OZ@%tF>D>7HL$zG9H z5)>57H|s<3RN26;{B3;prk=ds7hY)*mPgx8xB~OYiWuOHY}N=JToHq8O1;77!>73( z__1C%hWLB?T4S21z5LTt@a=73+Q$SU(VU`J04-wJft4AK{!9H6bMT|J5l8P zlwj^MEYYGRHUR-uV*j`eo`@+9%VSVE>&if`#g=DV?EOLO#6*oqed5A$834IJ`#R7X z{-Xk}JGP-}ZeSf9ZHRJD^TX^i%nwD{Y%Wr=2@hvmtn%g_M6+kw(-m>myfUTEpqFF5 zfj`nV^4jx@rXpYTEUU(R9c;#mI0qssAqIP`y z1p_wWp=;Y^9+>I06~qQwe6WNaIAv+R{tOo{RWgt(Y*2%<+3KpOL=Y(y4-b#FURQCR zk3_5ZZSblg7P7?L2tR*+3imuKcl@L#Ny!t><0;}*k`4l72m~e+X6VOcS0KT9JnEa( z^}smjF&^3wI}Of_QXm0iTA{`MuKUi1wE4jIYV$z+H6!)>`QH*q9@fE8xwX^pi)nEs z?&HsOtMgTP1k;|xY7U=7Ak1{07ANUH7Gvp4CvGA#Y|g@}DMO)QfBH8Z@qF@q;68gg zL}T6W!Lg&e3|Db22hh8HQS)V)cy~8#c!0}8J<7EE;y8?IY^d|P>n z-sMYIHF1ZB1d9|y6O&eOS(FWDy?*2B2*}X`uHJo2!~a5U#tQTcX;BRdo7-UYA|ops zuoY^UX1cE-5D@X}X2O0ceB_(@1_lIh{@`t%2GwO~LFYqF&WZ(YDOuStFe7OKCN#zU z@ZdXLWLTIXr_jExQd*pS^!_Rck3QOge^jAsT!Yb=MhHsaj@o&zf#O_YA&Y3*9Yfq! zg+Y{{^+06nLi1L%?_pEz`K6ce_FsAaTNrt~0$ntsh3nznZ*I<(6jT{n^7ETA5w2x_ z$}l~r5We@iKe%>*7?h#FxE)8-L=F{Ouz?F_I9QFh08z~W$mAVQ*N*eF_w-0t?U%>2 zU+4l8=TB@Epi(!ZNRofJZKz)Lx+qJdsAWMjv>?`$&l@x(?IZF~`x1#jqwDmL!%`rk z8&=5N=O%oYb12*57+%IrBYM)r&^J6X&Bn<;C9?9q4M*dtw-zK4$B&<2%#w?F99*)x z#^~ItLe|Af?%~OJlST5lhv$tFPmhHX@yHV&TM%!i%fXk@2$I>kp^BswFr|16aIc*8 zE;wbiR*3`-`U4OUTE=1-$JE7t(Gkh6F=vwcb70`%^8FPD1v$;z!xxM`4hy+3+MHv93 z4_h8Wd^_Wk>hMISAEpD!wC@Af=9<68dGlCBZYz6yKBJHw1sf3U!8kQ8A)a&l+N{2j zVRUl(UfbEc?)7rL!EU>2*C-CR{-8~|zI$!tO%Iwd8sQDdGbzbVp9|k$i=cj~DIfF# z#mzC}_f1DYW5OS4Ocy1DJ@2*^GUzTU>3i~>H*L-Na8YgQE1bukeWJK}h3VL*XXwLm z0V&0h{I$yU*|Dsgqpx&?xkoVMzjGdi9AgbJJM`9S zZp}loyrV3(=)Z?eGP6d1=`|G_Aw&FK5kmp2c}wBJdRST5)6nF9XbQRdZj^Gdv@-K& zGW6>e6Deot`xd2=AM<_5FJLqof7m=)rD+WfK&w|wnVTr zoR;0zb77Bz2X-%1Z|aoc6{x5#Vw!V*STm8GJbC59gv|vy+(O{>*2>_Y=DFdYod?%? zdvFz&x>0nx^PkV8%(ik-YeW{m`b6uvmhEAf`AY5^dE^`4S7c{}-7IwJZyo#njAFDT z_asb@Jf|0QJ`+EVv@%_dVDeOYd*;mrauFS+-^2Z`ggHBrnBfrSgcms7{XW1YSAPw# zEw*Y?>QD9;oKdxc-R!{YsD%$#tqJdN{KOplev;rz`xD1(W#Q`PSlR}SDzUie6wVw! zfxy$%gr#3;od zvEDQJ#CxnRNU+bsdUGk}>!36>`mT`m`2}TXNBoio88R$J3pP-_}NP$cKh* zGD>Z3v6%kbvzf^KC-AqLm2L9X>!AI8C&KDW3JNh2F(|v#U-MHD0YMZSl3jGJi1Bh;$GgV zv|$caOi?D$7!=ee7*cD^muJZ~ceYTl$yl05i5eQ_xxTZTx#=XK{H>)8^>OG9j|_X< zkRoMP;W@7%T9s5}0p*X)3YIvHyuai!S^sRCWfZ6dwI)|~+^9pUY)r~f0enu2*bLW$ zZLj{t(lt)07@jn}=x=}gRE4nmdg+Vy>8{b=evzJg&uBxq@c`xXaQoZc?9XeA$lkk) z6Du;3k3HV;zEE?d&oE^u6J)3>m-`~sZ?pdTm?PJ)=&#>@hqyP|h7I?5sJHF9yTZN; zPi`jfZxAFnmu_;*Qy*3VN_@chl{KVAZYIFQ|M;3=#iqs z%|@S(jL_Xm6buW5xv!%R_M!0I3vQ6)4>@89nD4kBxHwHknHYi> zG%hZV14d25!0Q&9Q#KWCy=VfyFx-&YvJL#HQF}BWEJtlV;u~RatG7;vKPNN~n%7PS|JlXThtH{h0^b&K8Q6yAF+(FFx zri|GEZS@1u0pU@skHk|+Fm>myOSc()^+(za;;pE(Vl6AO{KLbw%UCIF$Wv2Loz9x6 zu9MT$7-}}|z$BGs)W+7x2)BY$2CSni3dtJznDf>9@==>g^CLFz0r7R(F>Mxh{308i z``}d~Z`5D?-~4;~B@5-7)%kokZ;A?KX)8}{9A8qgHH!|2y`Mp)v$tDIU%4_&D)>R; z!|CgE6fA!@XJwB+L?FI2N{XjF1xkp;46;P5w+WFlZf@1F@UYP}Zm!!<@Ec7wh2(?7 zpVP|s`u!lr=Jxh*h;bbp2?`7h2a6aP3?__SyBzaycY$YnE;F}n{mZkj--f?23we8P z;v|id7n(!0Gh@@%&p4PnBM|XkVknHeIEo@;Pg=(eZ^*9AbWJAmqhUY*p|$xfbDc)yFn03B1N-}?vyQ9bQFJR?*7n7&(PFCDqT@-QlQ$9{Z6q@{+P-hh zeXn&~+r_&zG=N~%A`*8zUrP&9m$%xoonWe}tT9Ec;Km-3nM@WpNAXwbS)JFRF?T`h z%I3rq{zZlG3)7axT;n6TfxN~-&!K$P5>nm;z!ps_WGe~!a}MxEV)|qTFsboAJRm{< zPsaynMw`A<>#~DHk*nuGUXXCv2~f^<1COB1Dl?CMK&y>WIY(lB5-ZLgFBZb{M<-x@oKxc5QQ%S<8Jj9!5uJhyc$PZ{mLiw$bu!FCr> z$m{$TJ1^(cBrPBDH|WM0#`a!%n*yms7RgvMP5t`Iipu1vJ})pUjRG&(tJSsGD&{&v*$t@xb9n0;M+E$FB4pZxemAN;?>aPj!>Y(dqahv3ALjUh+SVD%L>258too~*;$I34?P_d-P9*M) za{3IDsczXLqe!4kKPm50WzRrsmIz|M6JYt4eirQCABQibo| z$EH60!+8YDJ6NNFaq$FEyunU}JrceBVinUOFy53m?iQL=WUf$YCFyHuIjX@DBK=** z4!h1z@%OPYzC~JAH3M5Uca}~ogTGg3m)#~Kz<9Y@vy4C1Ca4TiEq&_5zF>%%efPs{ zi_wR>t4~e{DVhQYdKq`bh1`QezBExvuBFe`8IaE~U@oFSOKoUu96d~xBE_#=ZhO}c z1Fb)WWzu4>B{f6LmCFI60Of}FyFsLQ2#!nhieSUt;e^GSWfDX!9~4&v+~z&JQr~;> z@SPtgZ&tII?D7l#or>XHK9i-b$xz`C=^OumF<-)gYd;Ivm zvzrA1A&>?EM9q4uIY8v(&@Y)F95uNlqoEO`+1%_I-TsdDboDFD4C7~2uX=(qm zQGksjhi6{PB@co0ErlWjfFI`f)Li_{>M*nT$uVh5tntI&F~dXoWb62Tt`%VU&VJS< zM<`zP!=zg`%ll#cWap>DSJ7xB0a(YshY$h($3*?x+3QT7vt8++1%M*@eyB~rq*goBC88;*sY8|$ zPoI{LFriQ_TJ*wGS4;4c_5G03)V>JbS~AezW2PGIiTLympqJ8sOsAx>p$8Yn!`R9M zBTo8k=^j^-Pi4G5B}|d=5kvvM$ca}iC9k}z6w*?GS1`)aV)bD@R0ulw>j_AlC_3d} zBj%fqjoc@@a}5C*zg2)O@KoO2R$}OTFT&WIm*r7FXYO;V_2(MJ=zm|~$Ds=VO9_D4 zj5@Es_F_!~oD#ij%kUeB?vsH|&}$mo6f$h!E?TF)ToTghe( z>mvU_IHTY-KwEB~NbWQJwDgh!QG0^85EL4u#^&#xxq0{DQ1;@Z+0~wIq|wejE1 z0_B1W3&Z3zCa0yv%+JisJf1fj;M7?W;^1i48o*=oLJur~OO67ER4`0v3Ao17*6T(9 z&(1k(EFLef;fjAWuQwQjdF3%2k35tmu7_NUaYeH8>os^S#G7v7MHj6}dMDH6awn!s z63Vhhv<7z<3UI5Vn@myo^u%&I#;NX46g)1a@A%E9BKYCqQu|@#J>ONwzBrsL?9*2# zXD&4!B=857^*gx7;hDKccj~K;RKQ&nOddxq^$CSprPo7=Qo%-3|ZM zdeH%lMpglrlLRnHjv|~%dD~-4Ov3eCwj==#()N&m+axC2v%EeU#EnJuYL#fX^w6J= z{R(X7Y70=0E@L-W+1l*%YzupD+f|ogAZF0bP->QEk?VaLLnQtrl_m6zA`#)X>_wm>_$B7lTIZDh(NE zCaVKg;{2hx)Ep)Q-h+HI)E_f^A*Hp#hJP^9a%LDE-S^&>$mYD6Zf)bBrB^yS7AUS? zXCsneXXzhmK~w}26g-jeIW z;vtVO9eT!{>VEwQ?sa?{u)yGlIqEkUOaEVRo%$EOw}7U~0Afb~+qv+N+Y%8@BH&0h zm1hPswNn$);E`C#52XMzX{+}>Nv@40I;Zf#R}l*T{&s8l(h6mKd+`b`WC)gPuoZJ_ zUPu`})pGoV*br}ky+i1pG~kQ0FjjHil~-w1`39K8sYO8GpXlfi9yO$-vt+ zO_^~p@=DZAtE#bjuU34K5fyL<0N(pe2S-A#tbF8)lLzqmlPoT`f!rrO@(fJ#>+OHt)GHLI14$y z*jr?PFBOIJ`f0L&09(yrxF_=tvT5~AYXt>22wTU6)*uUbC{cW5Xh zKF+9}Cda)V2*SM<7-M-EfkK|rtv5IR*>kv zFrg=$AxS*Ol4HNWcF#mvQH*`^tcZd0e$UaY#xi0dEKY*EZShwvqHJxXn0N>x{&IJ$ zTsmT_vCJ&@L}`l2B1X;0Q_F`7`b#J?&=;m!i&KJF&kiTF1G@XBnvEP;r}5b~APMZo894%^l@f;>w052Nkjam0=9&4+`3E z5}7Eq?QLtvRl`Bsm74$_4P?ng?Gz>NZ>LqnkOIh7=Oo%yHsK-A+TI!|5QkSfw>Tyi z9ij!dh7vBKKLX^6I1H{1}laX$fjOi!tTHgRIt+g7P;oZkfGhHfI=>Ncu7i zQdctFdjgS4uyhR`dmfh~FsWjxE8E+!&W8Kd9*&Adnu+QY zA~J59WT9P><5Co`A&a2UpSp3IbOqxtsj1t0ZKyEwu{Ox5)mB0iwJwo7@186g0KE&P zg8)!Jm=l4Kh2KPNW+_5OMIGT8fC$^Ayosy^s=g<_&K5ithX$P;cD&1i0I(96+*VHWH==6md_j4Y2DN^;Z;69<`1gcHNK3Xr2-`fO8LliF>yM zC(m$%G4=I=Rh4-+_yWzwc*GbCuAV=I2)jU)0YLgLBTf>FU$`M`)M!y6)jw3EPneV* zW#e>k2!;La`aMaKxz867aSdAm@vvOpEh4c^(! zZEd_I`)_StBxCPG(WtmiQSR+~a%Rw);hF@y;1@v!n!?qkPX=y$H{4>Uv4#jJ5ukmNma>oT&(*=C_XacFWR)7K z_sX~X)`u%OeB-LUP?Hn(h2LUih!aYUV}2MNl_T^-y{JbiaBjmU{V54TRD34P4U)a5 z>;ZAmercdt0|@}ui_S2OxH|Eks|9FXW+E~7Z+U!NYXg>8Jm*&@Gm#^mHeT*=Nqx_* zQ$-Q5?lg91BR)2K!-4i-ZD}XqRRt2h(4o88x_&_idj$*S(>i(i!KXewdel9W!3;f} z%;+dDTon^MxUn!vtYV;)mP@yA5$QX)`4@hKg?rsAA|E)TrZ}a0d>y;#$ zM_q$~>q4_WX>M+AL&!R^hI&5=k0C}B5D9A!j~4!PFneN-oQJG-7NEEz=H!!)v=(pX zda|5-*$+@(N5-n#cG=L`+k!oAF=iOV8-EatgyR;%j;buq8$e_Yu1$tAY?CN&oP6gc z6$*5KhDicpDHRrwfd6T$%RcR^pT+PZxXsp8P}jE-HHcdkM6orv%(bq6lnP~%%GIgD zr#zt6diA#KC((uvGJKFGadr_z;rJB}4J0(4DMYWRiHz9LHYuW{Km8%u=nbk2Tj|Qe zC)*TM^Kq0kivUhI_Y;O9h2!a$h^qEVXoAzj>;RO2)Vp?h!eS0FLkNaDvFVn zYsF$4Gr#+b;W{tG40UGAc}%M&X;|tmCHxdEXZlu3grZ7^0k&aeWzEnEmQqj<7yw^v zuYS{RWhD@!PL-=+MN`CT=neTEC7T+tbTg zmG=jC|CK(LI)e7s6hvkc!&4}27g9OgQfQ)K|vhTXf}a~ z6CzQbT!P^X!hyv+#Nz7A7K7L4-(h0jsQhWySW{w|NlxuhN-oPk&H2##3QJ}oGvJL=W5zO(9kBM^XyLazW(9odB z$jG?$znvx?6TQQRz>^U+69k7gzs#t};NZF>0x87l-<^xYYBLf4crpnM8Ks zLZm&mEA7?R-)A;AsHhm@Q9J^<-zIH{J7$&f10cbND|tiA6wo=eKNoy&3u{XPOp^#a z++#0}@ul>qU7R@)V%}(m9wrtc>FBD>oOej1q+=v!H>jGCFkwhM{N9-H!SOabkejdv z8xF{nsLTG=pD_Z+iFmw0>Z)@~Wx~jamLNoV5?~gdw`cM>cOMvoF|xDE5=ja{LYT5) zOBn0K;^%r=PY<7-{O>&+KW!xs2GMu6@Jop_aCl+$VGM6-2Q74tQw>FLy7P7CXe8JC=%xNI>fSRfs+~-nG^n<{Wd3 zF;_ybMUK^I*uW`S38!BRoHxuoJY7fkqqb&~UXaQ6WGT_aUB3HJS6eHda}D~SwgT7m zJ2-E@0RFptctpemSPVqZ&DpLP96nBa(qW+TE*D{*QrRgg+smU~Wim2E z51{TxsN*Z_q8bXoTn+`l57#Ehsy0Xaj{ zJTTj=20_C|b;`m%IRvR)Ukh?z;Jk3^VAYDVlzZWNQJ#k4>|v4|YlsKlMK3xkX9S({ zi>^0)V#XqxRa5erUE=}00XGBOr{%YY89seZmrNN`hd{GgJso-HR|Gv$7pd0v>Mk#)X`Cv*nigkX&~ zG1HNs5bhA@hENBPJX9LE51O+~4!*kk=sCFIZEY+X&pGK;tMkM&P0Xb|p$`Z4ODmOS z;swN(%DvzJ@)jAtU8z>Z7E}x}0}pF=bh^rC93eX%`Q{A?$_z-}V1GwNAv45E@4;1H zQLXQ?twq}p14zP@&;C`7vKk(M9od;gg^ULlM68V$el!PafC*6BK)&>+_1U7BIl6$Z z{Q%7ye+iFw{aJj8uCDh+Ei?;`%e7*^2rN!TnjBzKGLIH`*30J7%uHwKHN5AwE6v`# zv%o@_KOStYkaA^hc<+IBfZGGR%Vc{P8Tae!(B#jW-Pu~RE0BUU8vHUROPc8;z#&2% znmgI&89+N-2XZPZHZTB;;MA`yQ0=K@mG=O|j4={U4H6Q;3u(2cUX6ANWgim?w%&fm zfB6=9GPmFV965=q@Y^UKvF%7L=1-j*pB9cEI}-WfesiC|4wKFMLxUne^jbuX`1}aR zgk;+s8pGaPE*$~ojyZVpPu9qU(tlhaJ9aDzNOEmp$lbo`Qz?}LM5o;IxvZc|tBYd^ za8r6(qV1(uNqm|(SoGk2JVu6pqSiblqk?9uQos)Gxn{mf$~8W?PG_aN--49CPId4l zQtZLnB&pH@vqrBOHGLZWJ5hdEtF zLRaTLBySe?;)jFkJ3j>YivCYEfxB@G*#A6?&v+;=P9@4t z1YX;*c_#kgT7&y*gW=4Qz2x3U2hp;ACqq<9wU1-1mOhZT$d-H`n-sI^9$I-4R>j>J z*7wIjqSk}AjC7sMC}#ECi6K5@FL}onmiLT7c{ZlRy26%DI8T*a69I}fF(*7cdf{Ys z0mx$M1-u#579&3pEI~sxct+kb@>;A8=NTQNb*?qcn8i8TOR9O!OYa&9!0I zt-i*Pu0mu#X(qyYdk?8gm6qx|9{JerVZFUqqb5Hc&{VI915I_hYCnla@>DkY`-_EN z)KuLh-K_Lvw&j{i%}eJW(=g+&oTNR?6rAE`^y6sFml35uNK)TDK1#(1)*f6~Q;-`m zK~I?Q_!luTtO(mHFm4Dujgoz z#EM(hu8e!EdhkggQ!?G&1^OIVJEmrD<4zZ~Jzmx+_4tt7?4gji>c-KPcW&HZ&KrN! zwC}=&p4AF0LT`TkW!@GYXEo940%=jBX90_Hf3CV}nloUM+#3%1_&U!O=c;+=*Z2al z{K3@WJ9s;XL0_F`JZvH$=KkfmMnCADbAEyJ$kEFv^C!`$I=UBPyuJmH2*ueGb{!E= zZ~n#w(?FbG@m)IA`XLgHW4>yt_H9X7YqP@{AyhgmZq-jtw1x0iJ&jU(5y)`z42u%p zOWisN6O9lUf0^l_iu`IYxs)p*I6FI=L5l}+dU|e9VR2x6n?o6RP$x+N+b`WrYF2zv z#Y-Qg*p(u^Jvvzk%l;A6a=kUlYHX0){;E{`dt{0c*8}ax&WVfyCMBi`MXg!iwo)Gc zLI#plc1RtZ^-JFKzQI;l-B=?vJ`2Fp2Z_xL+3WrXq@-lui8?5_LStM+FQrGC`bjT1 z@;@&0vTgG5^G^)BbP2X-m}hF{APG=lU|?7Z^kaak-ZR)g!3IWPrk%daV-tc5C^(C!sp5#&bUZIL2Cs1Kf{420te!- z7AW;Za&t|`y0HCJt|bCq3%XY8JKp4OTQdvxAWos5fUI0deH(m$JP@}Uhh4-~sr>I~ z#$<)qzY0KuhbO0_<3gPmU7`K2kLDBU3TA=V=Q*)Vc@?#xHMMffI|ed>dA)ic<FrKPq?s-%p3*yQwDWOENX7n2Kv$V962PJQK>Z|nz?ya)U(7o zi(<^?Y}`WPB)fXUBTIXhnKw^ltpQrX0J}Rk2Vi{A zidnYoi|o>^!xHhA;%o+F$Im9m_+LL#1uOkbjFDF^;Ndb&!8EL7XE0tGRY9u}Zh#Ve51c$X64izH|o{ zqA9@gXixkx%yNxaN0Jc~zEaCBWZ}8-8?G9;%LnAj%9u9p(`(+JC$Z|sl?@#x`3Vf& zG=u2HOXtSaSp{>|3rr%a%9dBm@y{r}cRyi>pBVF6V_JAX*sK24Ndu?d43SmyNM_h? zwDx-$X9a3piI<(=e$rDqk^d1l--g|=V}9_UfpQT>(-pmtD0}M`J3MD<%G2cMm6VT! zagmYbH9P44@Pz89pCgh0Z99@4Pg{gjrs7h?qa)fGKZg1+>YW;gIYjl{M|S|`Y^~qaK87TJ~KZnez=H%j{RnhkduNaeA;fSBg7BC z@fg7(T@RUWee0PEGwJMdUh)mL+r6Jz_MQ_U|L~juS3+lq&n+YCc-uzL=d?*zIvThO zZXUL}w&_9|v{d*PFMO;gxyePQE9`xRnz?)~%Vto^hjS}&^x<0t_{Zqj4=lgMqJrFM z-cN~@S{bcf8;Pw;HhwJv>c{hPydLy?Gn3KZ2Q>k>-|g!RZQ+J&A z&GevrIu@sj`bGnb^-4PcTm0lEfAp`rRU9^8B3-QKxbYdFRvLu5EGTC5@RQ+FYFiq_ z3M_jiDNEtNvzGU&{C5Q5^tEC?bCKPvW~D`sx$DWcy3_aCAhOC%>5i-Wu=jtd`V z(ulcDu~;k}1yO_C%htilj)tNIL2ZTcv&NBcR1X~oY7&7wH`+mhFYMmN&Xt>Wq|oh_RTdmxRiz$!yM&WiorL740RZ3TIfD{r-Rz=P+tw zv)n!;n6ogqRoWDVK@XMrS?W>mP^9ZQaA4UI-KXT0@|GvPE@rcDSE;=hHYS1Ck%YaV ztTzyvS26`iy<^kwGZNblW!Bm!Ot%Z;B(H1P_mpL zEQv9wwuPZuI!WuDo$XD&J8wt=fwBgYMMPFmj2FlqCV;tJ$gt!4V?E&Y90*7sn6Z_0 zm34|JKu5a>>O%Sjd%!aIt7wO!S#uB5^2UqO4o0!!VuZ#2iNP=hZMw6~~IPeU_;C|}}MOQ^)oC1C!9fl^ErdNuGs_q|B(h3aer z0&2KC@#w_07v<#wGo$sj23|9nJ=HFjaiShY1}+_f&ae>&Ha>+1SV1lFI4mh5cKB|8 zD2UV6^bX1S5bo@iJ8)A z(M#A{RxGfr_;eu`dV$T%-OxyH9jw)MANGu~t+mLYP2O%a*`T-wlR>42>#f=`>-nPe za80wzC3*EL1nHJGo=XZTCuuI6bqy*Wn1Tg;HSC`{FKcRldI-HZ`=i4rqW6;0*>a!p z=1`$3!MMBv5%;-Ez_8)2G>Rw%mp@Z`VD^2_W~0>}1~BRM<<;Gf3p7_9Q~!GM zqzww{O#HoS9XT&0C6DCs2R!z|KIe-1+@8zWzXwL>I`SDQ>2E$7|Mijikiw&Zy{N?K zS_&}5!Y+i0ug8|#U^@>e#>?4rbm$sAIl&Muy72mDypRL;-Me=uw~+SlqKtg9`>nP> zFlBFZ?vZ1alal&N;9mE2kTY{byie&6mg3Nom&9hI9>#9^@yMW<&+9qNx^Rn%LRs+% z8?16c`_a83*_f2%!~K~q6fy-B1=QIa-o&c4@CfbheFMNjd7KiiYS)FVL`H)&;tl9! z!-e1(J(y^VNtmS<_c~pxLXj-uv0xjX1DFTLni#IiAqE6Y&BOEnWc?yj(mN7(>1p$_ z%Pse!g3UY)-I>khuHBybwj`dH0OX`@ZMwD=9g;zK0N|=`+6@ab#j9)w8If&Z+>OOHvD@1|)pMn7-$C;p z27HkSViDo%R)uULOO`C1cRLz6TE!h;g z0;!+P*p&RYfP>8}A~ICq2R#WT1B z?Q8=9&h%}*MUE@1**c7xKq!{AFFNp1!?yL!_Vc5{z)UZ7cz2*h)G@Ui;|7nFdeo!% zz*#wG*av=omts%&A)n#3hFuGfG43Yqe`%C)bpC72d*mBpNckIONR-VB;i5ZBMK$A` zkdt%C1p^E$=XrgKq-#6BObO1asxQRAl-?OSW<713S@-+8E|o{w%Rf<||1wiKJwC9P zPkm*gE0mVMD{-s%&%mI}JNwa`oGVsxcdx7iJB`V8m`heFFK>g0K5oiyd+7=IzQ69n z%#YUVv#2E7w)!`K`Y(Vy1gbOZe77^kxVf|FYlih93*> zEcE%MP^mI`vQq9F5RpV3>UV>&#)D_2K`}FrF+@4#LQD2WbjMAeC=9T?a#to3zURE` z&*IZ63I`oqkoeBn6Nc7>!wj5}Qj|kA6#@yXB?7)X!l+>8k7c=SCmsuN=n;|pJo%nv z(`Dd;txt${*;!_thN|B;|$z6j7HGDH#q;9kVd+5vV5`f_;rBb{e0(QEsATnwmHG#?MM?PA_;C(91L`V zZC0p@J3xaJoM?rZ#RbpJEHHkS;gBrUDUk#$j|F<6ihbQv=JP8VL#G-tW~tm)uP##w z+B%wHn?vv6?vB`jCXG^>$6!QY4d@NF{k z92A#XRygRG6Z|$~%m|#H>}aIhQj3D~ZSnB4QDHNGNBBO;A(>V$Z+m8BpJiIUdRDdj z->}6E!E~_l*UZ7_-AOa7b+#x5wer%4gvGN#0o~&nbi=9#3cUd`Fzf(ps( zM%$T;Zfz_g5t51|1v&dLgpwp4WPkp7Su@w7kSoW%ru+z`+zYZ{4BCq&JfY&nNe2!d z(r9Ub1D%~C2_;)UIsQ|%ot5(r1ly>^#mCCeYk8h7&7_-@$VfUtiap7yDTO>&W8=c9 zFTz-^R*vYjGj0!^k9X zf>^l*1@XdYjON5jt@`q)X?RqEgZ+~O2W8tvW|^T^4?89#8t=!$)kskOM(=a|C6L?R zjJs%^r964+E_qhak;Oha@))kDtl~yx02fJqR?iTl?L0IN7Z${TW6;E4u9IDLs=80q zJcQh-rF%r$NExWZcdS8Q)1Z-SgArzccqr#EiBhrjZ3o|4Hs{8BzmFgBR>b{0}b=8YlJjX}Hi)&4}C-{&Byw zv&6JH&)isZb2|@$N^Tz5;04}}LE{^9{1stgT?8H*59Dc!bfgA%G89hS)`Yk%?vNAI zw6tDJKRYfI|O@| z&J|lnjl>=du9M^wl&sqhpP-NY`N5t1>it)?wO)S-B(FUa*xhys;}vQfuqldWVNi*@ z0({9=#o4V3G(}a5Hc

TGqa<`l5O|j;xdU&RI^KJ1QEz8?q@j;7qW-F>-~N-t$H~ z#;w7o0z+^|BIbF59%xn4uKNHTuhgQ0ny?J|;EUj5!&l`-Ejt4Dw|0)%+-(y`2Nt@; zZi8CBT2;N~n8Y69^p5SS=jx_yIo%LFja_!7wiUt6xbnJ@aSAu?n&sA(n=bxD=3?lI zr3_wTivRj5v43N55{zrO*9SpW8%pFaZj%W)eRQ4vs^c3@OEa_Bi6l{vcyk(}!SvL7 zX$hfBjJqp?{6=ou7Oi}WdW@Z)#}JF*z!i48vda2t%jT?U_`?*}L^J)|R~-t(Qj!1` zjm1@0SDSCmO?&*+v-rH{n&{SuX*ynRYb3*!-dj6`jyS5eWtiPoN#3<(sIyv@j*q*t zW%$EDPPNfyO%%IXW~z|U(b;*00NpiHU$#RW-cf4AVOZQ&lho6!iS?*%Fw*u5-P+OU z_wcmx3-ua~@;%==^4+hPkf-Kafkb^(&4lxj%a$F6sO9TrFYSom_S=qq@Pj z$5Cy)`wyU&0MzN3TCt5`7bQMT^Nu7@?CMAGI@a6%%%&|Rf6wfOM7UOv-nQlqV;8qy z1a$zXUg_;~-s(rS=Dy2qG9!?EZIRiCLsxa~MKJHKuWkfq576<~t#tC^po1%cdl6!1 z<91?A5^}vZ9WVs1aBhZ(hzQe#6QrbUy|W0^X;aYAiS19l@dpPK+#BYfp1N2783r!K zdt<@w7>Gm~XwROt3l0V(hICNh6pZlY%iUgJbSK0uUZKR~3vB6_O9aVtzQw_tAbIyXU8ji|b^=7Z> zv?uNJp!M@C3gu3s2~xY2gW;AzF@(uQ0|SyQl;!ulY78dzOJW z?j2)$D6F_ar8^!sI5=ow%+t7Cs%up1UZ{&IiXF>tgXfS%G&GBCUxx1W zcLnW-zceg@pV1eQ@#p+UFlVGgXswI~y50a}Z*IQ`b?UiUFzyj-gBlEPn;&s4Mt5gA zJ+yrps*&x>AjdbkB_o5|SS$7;w*3J37qi)?u2S*%IGwdIG~I4+gTN>jCleZfX}}Aj zy^dw5*LEP|8QW-}Vw*$@*?zeb4bZSdw4Hs;3`;)1@9Pt&)@oq8dTpdhnjxQ{TBBk( zFr~VcSW{r9`tQ{%A^+3n6fD-y3rymp^EOZ-ZU3D7?k-letl==#>6N~y56={gv{finj#G?L11Na*C zS?HJ_sr5*}K&O@#POwSt;+(AjseJ*C4P)SjiJ6`4H^&g=@h1!iHe{9zjkas+pw1ky z+FU)Y(mRC=oyyW`eR{rEUg?2vAH z5?}!K%KG)2L+h5aHtWsf+r7_Z3Y$ged1KCY4Ovc@X=3oVx5C6`2h}%*Ql_ee*zv4~ zNPaTVUZ1H z(FOxIlLZfY#iBonC-3on0jC2iV`NUjrTcB#Rnc~eK0Gv3 z#9TItv3u}MxJ$8&+HkXO?d!}R1%iR|g@c89`Y9{|Wg$@hQMm^>jo%z$;6rE>hxSm6 z+j+x?0b5r*q@q=gOgoi-W{L+| z(qN3>S199fO;)nRPdC8QHjNg1D;=|T^-H+9t1v%*FX!y}Z7`ymC-i)O1WtCi7(uza zU`iG6a2oj(;XqbsVJzygAkYRB7Yp4#yfU#h1+!81*=<`_GYKWk#g{Pj`r%J{+`oih zh^*|57D%1PVg|`GSJ!4rxzc2f8@X}h4y#0`ow;d(rGoVBt(~E8_sE9bTN0v`Oee+d zYit#wyT|j|fZA@MyL;0_;k?>b^($t*OT^*G^}!Niw)BQ>bXN$MoD|L=xhtfB)yG|a zckT=MYU|cu3A6MJ-c|RSXytcFV&|oKIEEB8wvx;g#(J6d!KtM$8S9IMVK~V*l0EZv zUlr|@Zd6zk?U(-h)G7lDFc(n{jzLv}w$i_2rq&4dImb*}7{20)2^33t7E?oJ&c5YKQZM zu2Sp1%6?*`oHqR0K*30X)yyV~h0<2MRlZGaB_vr~Q za2fqxRJ#z~#$Bt302GsrF7RJO%tk6OcS6z*)?sXt$0!?u8HRzU#^<~J%VeVwd@T`U zDSiqyDQvHCXe18I5hB%<*#x*ESIXrezKDiKp|y#%)de7}!|mkhStuvFdTp~-JI!F1 z{SxSUt@|6-w5%LwxiohG9k?I#V;d7-x%Ax>)1}>J-umsKl|Zp@eYKe2YnOoa^shUw zJa)oA>XT&=#0oVF6dS$@kp6iG*m?fD#D;fep(_!+T5U2bWLW_W{j~vNOMVBt0GMh# zACeb~Sg^01#{WZ;Eimp5_@rxfeW8jONUwT;Q$g4Queh5QUCCT9^CN)eB(JO-ffmDX1;gpsSk-cCyHuDEiR(U-cy1R9pTH+@ ze}Vlj{cj2apM&x3U{a-q9y0j>!jP;kpWw{`pRk+yD`xY>x;-=7KiLO2j2W_V5jpQ{ z4u7(`$e3@|2#Ab!>MzG`jcjGQ!PiddEwnt=P=}oK`nOjR&`Yi(#C8r>RosmM`*b8^ zCp~f`9IULqU;(PSE#f+To{EYpYfIBS8E;s*yAX;jTjmmOpxbWcAsXZH3f8%=$#tQFl9(lKx zD5iiC&jrkOxmR()(9Heek*TWu9#GbiXk@zXxe>z+xu3yb6k3WOt!L4`LX0w;!R!d~ zDwXz!$m?iVxXQ*Gq06nUbSJH==m5^y^(W?G<1DX?>sP-#(Eni5w+NR-PRKt zWg@#%)UzZ)7I64W$c+rhsd?>$JP6`YcTi+!sU9aEkq%okat9KcH8p*v685rppD(1| zd|>&BTvinUzSjbMDXP43>XpkXi?3-cNyX)B$*p%Ltxv>w8C(b?D!U-j7|Axqs}Yu8 zOV%;q=l;7p9qytUOu zpuM?i{B61G-;8+P@$SdM=-L3inZ&$c(XP|lk)O^zykh?KyDPyt<+B?3nTcMv>)_b! zZ_N0*yj<(ybKzP0%>trlPo@pUDx*CzyvD7cSV2Mor<*m2aeYwBqg)&0;2 zw!Rmd1to6RiVDCzQ}fJ3n1Azw-jo3mM1#7Vls)paCVlhq?=2F>;CobaMPBkE+Z^D1 zK%yS&S*8oQQB$Ne`?`V)u4zZUYhgBeOx9ZB^Qte>3i9(0e46PZvWteEPHa6(d+xkP z;?2EG5(8EKJ*~l;^n-^EeWJn&=iM$$3}cgV7yVY<)4qGz(3zjiUgqPWR<;{08Mpq~%B)!Cm@^ehAF1#e6b)Yk6%DaPRr zoK0-#@d##Ka%(JC62a0`GP zP$jKZ{R-EtOv|U~V2_1=KVab|M3mG12zqs}*)uA!hUy@;*NJ*>om>hb6Kg<^-YM$AT|kWBDE(wo7sbl5jB>tsfCi-!b*{I>YT3DWfB;6jtl7w-D+$2cpYj*F3 zp}X@#1B{o8)yVlFJ3Bz@bB<662T$+ZHygVHLK789M#>|DEzMJoW2#0H@EJ58?5VPJvp#e*fkm0x_{v*RvE-$Al%1Z2&LW>JE?o>sD}%G0}dV14~c z{3Y^rd9+_#$vm&l2O9+pVzAWoGNxhcP$7-lrd6PJI}-Nd@>VhY?Lhe3FCRI>!;J%0 z6p5G&;n&cDJ?#3|D?ZC7OnC%(c<{sfsyFuS+t;(ol$ZSnNgadWX_zSgync!A&A|WX zH&o5R1)k@B>;5h5GWqG4(C~Tiu6+Rx&7{YU2@=F+3(CF&igBu~G%pj)lHaeN#%~ww z(U(5&LA)>l;6@CT%!EN_k_ol!WvKPEf(F zhS{L{69xZfXwjL-2FaXfMFX&y|9<^CyLU~hR{v3Fmdl|A?`7Rak3}P4z%k6;{YB~w zJ+%~osnk|3F@i;og-+v50Rs+SUcF_qf||4L4qz5>7`UB(zce505qngv^u&MsBme2K z+H3-N!rq2YQ`Ze@KRV=5)=f4}YkhXB50xBG`T3l`Y`UnMZ*egFPl zUc3v;CMO_O;H^4*^qgug=ATF0`H+kYeK(PzzLlAg5sQP?H*VFzMaJbofgGvP1Y7dk zlbBtgmMudmP9BimxuFULEpJzDpa#fyB7lt{51-#wp7L9#f$qxh*Y=4$RqP8u*%TlGsiu!e zPPQ*fPfsT+%!iUkiCu3`PrDoVZnO6_0Efd0Ta*;n!vpfXg$9DX=Uhqicen+`-8=vK zdnEoZ_j9(D1{DTiyWf5S<9E33irR$zB>r99Gv6g8C0oUhB}qI3ULZIxsBh+?YQusA z5)J^ahn!Dl0LaQrwt@KtVeL)izyP76rnT{h+Yp9|o3QO(rv-6R|HY8y6Cczd9ChcU z)G_CJs4sQvR@@0mH=j4F`xoCittit9Eq-uXnIF$;EZm}bRyz9+gReV)8# zv{uHEqelfuHIvUNED^_&uG)_Vj!(k>8vKTzW^VuCbUM*#u8yL)L)2-wjskPrvFQta zBKq?8JH66Et8%;*DKWS*OO=YbDyRd1U61@5Z^wWC2t{+@)=`+PKt8*uo~0NDWxLZg z4j`j~dCP`mNpe|v1<>|H-q+VRDzQSFuHB`q|f|JR$3_?y

O0DFP% zJ)+;v3IjM&JC@AvLPI=c3*SBuFlHsS5y;d2er^9IFR3#3&SSt?i4T4pIlF`I%NX2~ zS&?Zdq^8Kaw4>%FIRDP|?h5yQ&xeLyQpob2ORpU90|&u|lhe3ic(}OBrJ2s7uRH(W zOTk}a&#sz8^IMMsk6EX|CbsNyx_{r0o*CI9puxku#S=| zx9(1T07gYR(!tcsV5KitQ4iG5zhA$AIRam*n!}yG$Zy%nxw0N@UTsj^qV4t1GP0Jx z6D&^guV2~w|NR`2{0zST@)pS$>mEA)&zo-+k)8T~y#DXKQTzP)QMAr7U^ODrFWjAI zs68NuToB4NR^4AR@q})M<{vtEGU=hY#h%{Y z3FuK0HCt4x3_y1WUIT2#Kyjpr!hAU;C9b6sTA>Xne8;ro^#B{sgdL3h%o!HgAYyEa zs-+mZ9OM9Zz>U~OP^Na(Kc z;sXY>1+XO(yh0MDkOP)5%a+jnmv^3-KFO2foT!HDI^h)QaWQ(?{)3w1;cu6^q&eYg zGwa|&HZDd#6g{cn$^Grp;!WC#diYs9=ZRzS(CCI>y|d+u=5>i6wx7 z@q6@o+UuT&gO(3GN1h<@<-_Bc&QYt(}{8>T8ssfdTb0*ohx+i{Ys% z9)Il_y}Ih^k_hYn-u@hlgZjU3{-Px9#K`ZPZ+S5Uh9l$2V8XTtgM{ITF_fGhexTD@ z3-1cSAmKr6_K05&IZ{v~xBqJ)3c^6m+5^5+5Ds~l5S|>Pu_($`N|<~+kjCh87g5jx zLX!AHvI#Sg+yY+OzLp1v!3CHT22UP8j<kqB!j8=mGBk@7SXUR?^PlC z%l@Y=>}dh8#81k+O?sN{Lxw+*9K83f=UEfB*Ayj@i(|EK%izhLfSb8obGTUzv10|z zAml=F&l1iyGb!Ap`eRElsE-!bimwo}?4(s+E`zGsl+vum?MzE{4XA^8-la zA#|8T^%z=>CFpARlk8sSoG^s%50{)K`Gp=BEClgN&-=6xLoxv+%pf~EySC0)0nb*- zg&Duk9%9vjt9Hp|+sKs|MiemMAEtK`;Ra+fpLbI>5+A?LKL9*EwFtUGcqU(?_3gvp zh=v|B9pVPhQAUNwI(U4aqYC1}rTshIY$(8~yxT2=GqIhvsIN zIn~U+Cp(-u9Lz+|WSd$ONY*gOQ2{T57i*wfHtr~h^d56iPU%X{zM%`yKO!>RiX z*f?9TY4zQ!RpkZ63Oene|EG}gWheo%xk@ybS8K~3h@pEKM7p4t7O-n}Dw&0*R z)4(7`Rubs|XQ07{+m5WiHxWztmg|FB^6b;!GYEQWhC;R{a|MLzfbnyJGU9d3IUKsND^jQd4P1gioN>Dugs?ML6P zKZ5CK@@a#RFgk>3Wn%6xNMWf2e=E$IFksW6NbX7tpBkcYK@kMuB12N;?nj1;EB z-gXCTP#i|uvb=h=72H8hCNxX1mig0QOPQ(ON%Dui7sN!2xCip|h5e zkx|+jRNx0tnH`WVJs_G5z%Dqq;WR9Ug^TGZL8(R?;rEs@`sixw3d~;~81N0+fOwdt zh7fo#z=~o-clmaX9PH13{}616_|n0f-*QNZ*~>FJKi&0?D^1021B#xhmM)EOYyPQx zZXu|xKwNqrk|Adge&PV6s^7uatdhs(O!>|XgipEaZqVo{2ZH_X1|PJT@{1eTVRv#K z^_-FeS|Ze{cqX0VgFV4|qwm-3Pu8u`SzgtfkB^3dD?shA@{~qpRHOz3C-Kk#EN)gW zRIV#o9O;3a$KLGIt#;0w1MVUuy%Xc164B7e`>Ypja|gfOVCgNew6iK5fSUi9GYzcY zBk=s-DzCw|^$h0UNtrYjxD{7m?cU^^*xSKMPFuqw|JM(p{nmCV@@igBuFg*?s8SB3 zhhDQTB|x*G!e77c+w|qA4M3)h;>``%ALH6vDGXg{v_oeH%d#n{sH{qn08P>cv;g*= z$95(GnCDrGow-$J4JTA}7o1}S&YpF_*VDVSCvN*EDF5mG;5TOS z&s(o)_Oji7-q@!9|J55~H+HsHR6M^0vLeg31$(He`C~6c0~C;R?i)a|{y`U`_xq5+ zHI3bUFaR);Tq+`f??cHm3FS@5oWWq`!)<>D1lNgO*VEIhSdb7Guh-iENN-H}fBYB( zDgMTw>r75`Ze>H+2lC5+ED{?zdK40k4Tr(fkHgTe&ZMOIhu6q}1$buiL4^wG%R%6t z3o$m*id!&rx*BhNk=Xh5-WCrTgB37p2Eo{B)TYvEurzZ}nx`{#7m33yyOJ$-OG-+d zKT>Lcf(b`9-w53>kn}>g?tTffPDX0$6XL6?QH>sOMgMKQBzfwN6$^AEVqaRLI40mU zj{#4O*z>H=YlYr#+X-YS`Q1CH^SgSD`Xh#8i2~OVbw+6nQdnw=pI3X&E2I!W;LT4p zi#B`I-0gcy1nOHB4Kp-h-j!wz4(m^_G}w_|4=^{Q^+h}niX2F@fb7Q(CJ*Zv2C@6y zb1FDE_n#hVWPwK#eo_GvPK~2ZrvYI6cuHYkjy*Ug{nV!((}`wAa2&+$ac-!vgL+3% z4&kBWi#Z4q0tO%mlvS1>y2gpPMh$!`X<@eMs z(HK2?13(;Q>oo=d>|CKs$0*40Bxg6~1}`qwt%M*W8A>lyOUV516Gn9e_>%s{t!-@) zNUV84pz^$e+jRF0?$=`vkIbvLISm0=7F)0c6~$P^W#mMO+j&f5f9bSY(kCEmae*X} zMV|=6URUt(se`6&T2PfXyhi|zF!DIySQUX!mA}b}W*;vCZAd5~08M34MYF4?G!AO8 zEV69!Q`dLEd7-slWdsjIB~~OP0Oy2Wv-dbq6`#f#P04ZR1Hn0gSZ!SlXdheoFc?<= zXv(FHY$#iUbYL~4nmkR1zt0|`bL6@bf)$j{pFf}a;rYf(RA-|w8-XAvYuGYL#I1eY zjo!55iiFx@Z>!a%nqq*+YTBJcLqdvvB8F@p8N^!fyiUrXheum-+P@Y4oWC)G97v@F zDC+i{7Uyf+X5(i8{y04(Jte@6D$vL)1KCSp3@t1yN}9EpUIFp4qD2SGxd9dgQJ#J< zy#5FOW2}D`gn`8CM7l0sM0>dk@5=$HcUiG9#L#j2gIVyKw}FTWV^mISZUo*s_r{FD zf_gm&D7edT3gncO=0(pASwd_jNbrLs<(c>s?pdD>_$a}E|M5|&06aEk-e^MtDAeYK z*R)C2-oA+RIc&&G99jjh96SJNX@g+cp$L)*Ho-AfW)tSW z8KAGXL43|vl?zLEatj{c73Q06_TM3Ak9)ob)3F6)RF?pW3`6r2>YmI!)))1Qs73FmTxd){Av9YF1TCw6xhck_P!ABJ`24rSqm!z8{7fvm}nx+Vxa%7i`l zzihy&n)~x@Rp4L#d5vZ60k;3~#>W46V?QSg>3;#0#-{$igPr~vDO8RB-$T3p?|dvb zm<8S1zkmOC^w2!i>kch~H=(0|R@hm}C$7Q5 z>N=Q$ft6JsD+M7;r^m7*LH*$=`(s0A0+>lICGw|JiK zYIJxP1d5QTs=cN3WrCGXM+NS{{8hcZz2=aXEdy)f6OdLXao0P_aO zsFaqL6PnHn@csnO%bjri>Hz?VeR!4Gg4jn%#z5=6?@|?Do@d*hVvpy-jgdi}Y4?Qa z2|_A}^0cHSa{o&M#@j;(e(~1>q>f=?4^G=5;CJO=zdth42ql~9nXCls0&xgqib|%? zs%_cHM+OjiG88O|KM7Lo!NSj)k_6`g+hhr}27Ws*Qi?wTpI2?#SIMUqzMDYxHA)UG zS;$gH2DIR$5LE{A-{as%sLH&mSOU#6`ZY3IArk{hPU@E{NLEK<|M~`4M=(Jjelz+IMKV?O@NGepNsJmsQ)!mq;wv zyDuF#T`2-u33qIe-Yo^cq5Zo036WZOXYMk+p~MfBp1zO-oJXC)s1o}&fUS^=xMWbG z;eo_9%e6ZCGQu38UvYZ3Z#fsT8>=py=(Ou9rsPO0)Gcdr<@?y&oPfoGN79OpIJbG) zd=K%*8dzSifd~jWBK$YehXeASJ=_KxZR?MH5e zS<~bXR{M<2=_*{@fSip5U?oAE$8`7G#0SubvH0_WJV>LBltpHkRtP_=FDo1-2*`~{ zb7&%Tkj3Z~anwf45$V_<(NRe3;pXB}LPz+@x!}UitK&y0o}A7kIA0^EG2}0W&R5Qx zJTrH#p$y(v@e1JlSq_pE_k((TDdwv_Q_Pn0NRCq% zQrCc9H)bi^2oYKOEvHGgrk<;_Y;E^yv*wi)%zE6Qk?9GXfHyJ>3~XWV!K;UZ-~QXx z5i7zo7PY}V3#-K3VkOhI)>wez0;?;Ms8wcMrlzK}ty3VlVI_|Af4R~89QschOgA8X z=+0TXM!=j)im~Tpnm?BtOZ%-rgS&f8Q4R2HOg^4+Jt|E|6nOj@@1tbz-XEH3s*L)F zQ}y;vJ%9F$-|1nYN~V~pL)6S4pXKKvTd1Nk?IqgmBCCBR$;#l$mi1VOJzlFasLBzI zIt`Ra?>!@+=5om@#(p5nu_{}+KZHUcsEc1-tZmQ+s(WIjrWH|g(?&tOr;rT8pY( zH@!a}@dx`Op#9`U9`8i8CUCM7>TOX2l)ua5i|%=Yt)o-K<&40aoev_9KacvzRW)qVncQo2 zVr_G+x*uXJ`=Kw{Dh$0hG8Pk}IT=85N2S`2O&2)V{pRK)3Cyv|>s_0hYxT6C*(If4 zU9&n54;n`Va3vP2<9hz56rEy?#7O9&D>Xc8quz^gdFQD8ZDe8=RPw`SY#eX>v6^30 z)9Q0Z;KgSfqR(_(idW(&+zyk_&pq=)_DX;D-ch5d5g=WJCueaIipIuijkEF~WfdxB z>fAs$vEoK{i9gHay(ckEH$=_}c`O*L(B#p{-?GYn#Nuu<72sYl<3JRiO1xNR*%b}w zYyF3*fpfB=f4Pd#*3XNb%ym7lVtQpHhUJaT;Sl#aE5)1*B0S>Ty3{AEae^TlLxDL0 zVY|@%P~eYbh_VF&N2pn?r&5lSSaYgHV^!6L*s^$EG+m{ZvECmM+~i7~K19APNDn(@ zu@a2{H;0X4r7zJ7RpL9m^LceX?^CqgqNT2yA*4Db9zO%y|Ka(V&sSv$cM`26gV*0r zlVU{9=VH!?{CY_|6`xwzn15^hF#x0dS92c40LWG$_h&qxd60OVJjDxJRM4Rintqeq z#Ixm%uv?9LH8qG$zQYrIu!2GPFc;7)F#P8;<(6-m$_Jl-WvWZTV1@4sG*Ql6dc01& zXB@Ak+^?g4xJY22uNh~geQV1*C?2Hk)dTzR;fM#ZYGXB*N8lXt(>XJwCH|P2<_975 zk0dYh9D05_sY4`hC}tdrlEt!E-&v(RVHv#lFo?x(*5TFY4y4i@M>*vEu;mPr5k=(; zHwd@O|9lYgZ01}iM9cP}-sKWb@D0{pzooWTIChd${>}D zzg%Y4LcT(d;y5jOJ!d@~Ecjq2e6ebjfIyLy8ZOAuYV@gT0@1vSg6D zZ_(;$NRBrFn;~JoVo`~}Eck#d=Oh_PEA;>B@Dho0gS;Tcu@-fao`*=p+}H!MB7Hb# z2Y_BcW=BAk3%eBW1z|!BkZlv(yf$Gy&(E$bFBd>Pqq|@iNF8&tV0(ABYyrOHE?zEi*Gy7a-U&0PA$`)z8~`sb)lN81)*Fa#DF;L>}<~z*U%M12FU%~7KR5!?*B~1U?7hZwEc3q zjEGc-#>Jta;n39u?H7g!T;W0}qv``SbpSP&!W;wg#52H66YvedZHe0C1q-wRxM4ws z*w=C!f==H)KpzD6&udV3u7INP{OnXmg5|*j2Xs)Y?SZV9OCfd)Mbi#G^78T}YhBj} z1)lBbK>x9$VB$RrBBy?+-sz+f5Q4u@I~i{SmB&QMCDc}>5LC1j>QNKu#0}o35Hw+6 z#{g0Rlx2E9=ZXek$eXON5o9XuTfmEm1|iZ?juDWBjOTVo8#@VL!0BNF=U9yeeHdO$n!uh&xaa%p>*2BnC@OQCz6KxZ zcdJfc#Go`}L4<~!CdQzx6X5{W*$-F}Y4@eEGEiNfui(ilC|2O1wiK>~Q@O)y-qNMN z5%{*o(O-7caf6DW(JAO{5TCl*dS=Dk%uFMvaZ0L~^}>a;KCEvOm1V5mvvu{Trgdt; z$s`HC226d^2Wpzxt(Pf~P*{s7vw5)%2`!J=2#e0Z=S`P{HsM)y>uPBq9nO zTGt2f!#e3GZK~=l$q`MQCVC?A90mfZdueNx>u2X3Y9VKDuR(!EH?ly`h46m>g^b)$!dQ@^mQ?-qViX2>x}M(d|6MF#lzWKUEv&F>&eLh*-^m)qOUJ;;qK)#rV7*= zbzN8YIujpkF>GVC{F1prP~VXMh2XP>={SPd6+<;W`cjdZzDJep8lC0uG(QI&7`j$? zCXH$>Q;F_gI@N+#(*rHt?y~)fgIW7s*RI7#b;(}ROOoUe%gD-#=RavjnHSl>trUGEk+J_bQ6Xt)E4_Rx&gxI64{c_~X}`}Xng zuIfzBhYuZqJ|LgVK37Oc@pOr;JfBJLwz3tl=wZ8?XoU}7Pq;@(GIfnwoNUxxt1f>= zs{Cc;x>MqGl@Z}pdC+@EU5zg#f1V-Q;$oicjN0_GS|S&cz$b}>Hd~0t&DkTO1GHY= zhMqh3@Nw6Q3#m135xfmPzD4D&3d2gf#OwvO6TzNI`F>W41tldRjaCPN);vs=eY*ZM z9Yqh`vye6us}~^4qenSh-SZhlGD;JyNqWl74nfX4_5C}i=WSjE(+G!Z&P0CHo7c09 z%>5LrXWl$&u=e2IEk?Sj-uu#iy-8LRESUer)q6lw{m1{~l_G^gB(r3cnT%}7-t*cN zH{v2>%P5sq2q8P0%eD7Rva?-#l)YtV|DX5s{r!IDeE;{H&pGuuw|nngK`>z-nL}tUz5Z^*wnzT9}XD2{W8IRO(eYJaPS?adN z%VFT9{q&u!>|Gj6ULHcHlscwMeMXtR85QY)iON6>##<4s8hM#m<>vCBbB3wzvE!OC zTbuJ=ZB@;Qsf;=oawJ~m;cpnJ@T8s(dzqik5UIMG(Vq&Pl>MSId49=kwklq#qhJz% z+$=)!@03sO41Sl*6z>fWm0>WXi+^|TMJz4ME#ZzKVc|41;9F9{dzN5Oz*n}fuY{=b zNGgrU5`CAJH){QQ$~ZES+kkSaQbjl*o7pH~WPZ&B%c6T!?nQ573td5yRg4{7MSu7p zj$eG|+g13Eo%y5=rWf}g*DXIYky&zTHxMw6Sm1xFKyX#bTFt#8NQtkjhe~xOD}^tk zsOgrlikeD`tD;CUsz3^#^* z4ra!14z2Xv*NBgpv3-~7p_z~)9zov3E;EwR)cbEnnr(DSOTUZ9!o8>aB1C=&rt`(d zrzlKO<5`A>u`GcAsZgElOUP9#-vSmr3NeSNm=K92x0t;|Ep9+3ICVfm{}f;s5IN4j3Osgo>;zV-7~Qy=v${oWDhT>r*8r%MoA3$`8g z>65&SY{!tR&h3pw_c&kn`%=U{$ZVO#Au3yY%&3Q+`c@5ibh?bpuED%RbMPru+%p`k#FV6S z3KmA>LR&m~;TpY>#&t{Vu2Y+|B`CpkjyiXnW>Z4P;}p zgrRe7Hi}gjrqROMh?1c_@hV{f1C!D;fu+}miCuZ*Yg35FFnOT7Z}z52THkC|5sRkP zr*8>7)Q&>BDm@_&Fq+OOQ$s0qx`@pqVGs@XYh)s*<3)1$NJKO;jm&4rf_Y0sYcPk} z=9a7Ic=p#L@s{>$(l6X~BKs1hGTR5+^Qo1V9R`-c+VkzX@a?ofZ73r`Ym*dxq&oV` zwduRO?#tvntXNjcmQM<_ed^)j)4OEcPM&Xh)c(-R1g=;7QrkI$OExeGi{-pB0m>LB zsBy@z%L`I{sw_c%?)#Y4RuPNFQs6(2CvmC`nUTphHaxOc^)qEt_86-4Ks9qrIOKcq zwY9cBi0mzW=DR0p`LG*EkyX}xMANXL!ihxLX(sr#^w25STFH-m+P6d@m`0UY%>MUd z$78;c>|DS^&AG6Dq+8ac3v+VTc8f7u$B_uDBi2DF8HycByvG#1zg~6X58nPIP>UYRS`QtyAv7#)Bx;zw~QpWIj%?-G5^FntA{-t)Ej9Q$ccrQH^`%LPcD)D*=9wtqd zcj4JLm;`6s8^W0G$b2*1mfPxcH-d)kP(0cnQ*wl>uru~_N>uXp?2bf-L&N|aew?F$ z2H0yvoH&RbR0kiX34(2dRKy@9s* zhNW4ge>iXP2tzUCbmj3$P5dIS(HBf#J#TPisc*H;<8*2YwjgP?bBHI!<)uusNC?Tv zc;ybD%<8_CY)O~Q5vb-`aV^4XA@%Cgwb-1>ul$A+vhV1}YAVhShw4{Xoa@X_NC(4{ zyGnj$>D;3N9+Qx5!1vPCE5y>Hquz@>cgT&b6n>HS+ME@Aq2vfs1Qguw{?1HtQzzf>KTHI5>_Ixq_v#28AMC$s z%S&D=Ddh>{&rY+nl>rsR?AU?%sp?FJf$*F*;aK8E8365@hTYQeQ@81{QC3 ziKyk(?irBNS`8Mtm{|2>;7NB;#M8U|KF@n8Q5BgoGL)I6;Zl7TU) z9VIebE_L~9*{9i?eX`@-`EBs+Llw5Z-+ILh&WbnJz8^xn@#5i$^JDD}H0m)pW_TjL zq-o(>wZvLD>wdxicIb^`It13}0itlfR)4&uv>lI=y_eM1o#Ci-p}NDGQGtXqc|0mH z+0nrCxB2*IT_sEy7fsX)9U!Gf>b>iBDwX2yQp1*=+Rw0Jhd!OO(toLv*Ec1ra-k0u zfB1TdHg$2MpqZQ6&Wa)hg2%RLana;@j1_E__1LEg^hDMm{+>O?v)x7VFHC z9eVGYWfmxb^w>&1oSjAR7#A32}kaQROu zYlVovb#qaM&tcRS%W2QP<_t-&W6gb*9xC+~Wm{yPhbTMmp02Ueu}-Eh&T`#J7&_{j z6{u;ji9i}GJQqk0T!)mG2wr}Yp)3Ck#wE!KoDJzdV)goC_WpE!>cE*%@2 zTwE%_3V5LkL3n_idIJr2U_hO#{$L1KnBLoX21{Z@gP=PIhaCSG-L>cI5oz^j0& z=~@~Z4^9n1%vrIqkx{(65nKfffN+%7A9K85R;MBGGS&Zy_#QkcP|t;71TSi#GuDCO z*|{M1$pJ`cu<@FNjk&pb%o-mho9!8MVd;r<21F7iICcrKi1#lHoj_mFl;PF2IT2{` zZWO}4a9)q?IypHxL)@cO>au3=nq%RYkcETHhT|@^QV@@Ep{?Q0i6aHCh?1IGd6)1C zhhtLn0KM%dSNe zuHeCo-B+GTfksCklGl@P`qbpp#6C-(lrpU&!d?E65PcwoG49**k?s=AwX(Iq8Fi~X zjp8&Jpfa4%1DEL?RfoDosxP)2@<-e|XaA^`1VsR{U7E^*Ab_Rrg5Ieqk&T*u6bRx1 zg`7O)8F1NLlpZyw_{(2EhVX-9tz(BAfX((Q$VpX}_}Ga93-((_t;Dz*RfKP=Zl7P*CIWfR@MX z{Ze`VjKki@3qz$S4u_Gc42b{7oE*V)-oxU(MJV36H|_mX+G4!H zXSlfvZrqrPZxj?x24a|$-yKdtl$a|S{THp8*lGdEJ}20(h&13-X^DJ`rqr8z4L;-l zd1akTFq6w zz4w1!g8$hE1^catonssOL^pquV~79qh}Cg?FXHH!2>!osgb-u^+Hu3$G?rb6UgQ3E z*1i9~Q@HjqjBET~ukm1M^g~58wP*;X(Sd9mVVr^zvI-A;_SW(0vWsl-{$ZI^;?|7~ z+PP>(?rHgD-TQV~(4NO+oQ7f?Z_aU})Q>AF6C3&xh6-`d(L04ts-I4%Cc&5j*jb#QPn-=pKRybg)N9l0C* zncerTL_nwgzh4Pdw*V6UDSw3Q9_iuniRg2Jf82WJHJaM}B>1-hTfj z&;S0%@VA2?siOl+$Zm53mcAxDOZv%^3ns7E`1#ME5c^?PIG}Mg@P8jcKtNWNUKkpG zXR%jmBDT}wIiz>aqP5b&s&J2kLmG$zwGkv4DJj1Ta3GgSNSa|1@p9FD?G86i8EI+l zZ8uIV-(TS%_?Y=kVYJn6=NqJ?8Ygv8W{e2p;cD0X*Z=CXva%lTpMmmOuijfsnS~r^ zVSA8$Cqu*y|JLxUWQ38C(cKnCc(ERgvH2jgH)l$5pVB!~s@?y6o&00}rMfmL>1lckEtGbL#NBfsyU|=>djeqz8$D*f( zG?mlP=ecWl!mH@`Q8z&E)IN^;MXxy}VD#eG2c{S1yAnQr{(L=&J{1=-op>2sZ;er$ zhG?t{e8ASp1{c40UZPJ;6z=1MYNAloZgl^1{B#J=#l9v2-ukcm5x5F;-EiA(09X^j zft)seH0N3lzJuuMI+p)AP2NwHc*2&MU_(@M+o;{w=4Y!6*!~h9{|Rm)9mF>f0#Xze z7Z*Y^4KdK<&^W*Av}gpSfbI)hR`~M&_d;(#1m7E2-BZCTgF;PI@&B@L{4l1(^KJ_y zslOpzPkkF!bMwkEc`}5|5jmXDnHFhuM1j|li*p#Wxm1$-{4KcZjVq@!+~1#bG~+BL zk(b2QO8Ou8oO&WIl=z&TMvCjm=r64%JO)1@aT7%O4e`El~zjtba0jzuIuo+ww72A0w?>LwO z<`qmE0XHS}O%>xu-;%=%qs{M0WIyBwL;E=NXW|=ipFh8W+#B=(ZtNeWAq0X;YfH%U zt1b!MYhbISq?Yg&g*kGV@OI{~<$o8QswYC=;Ps4O(22TThs;79!r3X$ZQ{edwU3ab zHaOyV=l`6;6u%{qjcJra&@;mL(Fgbm%lCEC(0)+8Lx_%$yn=!Tp~gecr-;g;aj<%$ zl0U#VOYiz)QBA>*CbQU-y`a=XKY>_G+cE9R9pL$h`ZLvZDKg({Cx_qpJQuqJ9lb2b zenswBL*ZC49mGCRCrrovA}|20gl6xM)~3*u-1^5Bj{8_?Vo4MV9mIQZbTk8SI>x>l z$i+WUtI1ecut0gGy{Z_*ZpoLcCMm5=P8E@INFO@Qt%lH6SAPTD*fl7Y5Ro_FsY*cA znrgNwwtg1CeQ2D#w&ohNAjJBLXM1QG|2 zJ|B18q@y$b;{uC?RzzmMs)&m7-x>Qi($d(LuQ&L=xWQb3oX5?|bpQ)u)gJ;&#sz(; zqh;wR8-rZkjkF)WpYPb>Rwe3Us3DWe94{4sz&#VKa*GI`KW%C=jWJTM!rqj){7XQV z)y3Sjn`OsGQRmswBtmwx$6?#@UH?57Da{XapSrmT;>`EprZz&PhA+YHEC+7+LQo*_ z8>j#Q+#NaubC+Iaf?{i6*pZPDbq`LZ-;Gd66lQ2s=Zj!b`2qXWTbM@q8vrH5EjtP! zmsP5|!)11y$ow)f@h;lZ8ANKPkS1X{m>Yve!6G0%@U_%-j1sqE03$hY-Mi)58mnY( zYpXm?$cLJM*>D54K%r`9@6J?`^}=jTxAsIlQVryt+SA#o@;=8LD2-?tBhmcNH=qg~ zua%$M5XC((8eZj4@bQl-OG{q_NuS}m0MzlC5t*Rh`whUH24cWm?>nf>jfszdkwrAZ z$xd^-(XGmW6Z)>RX+EUif*VzD-33QMSJf93Tz@p>$qT3x{m#k|#F!GYRVs1~FugP8 z8mK#%rvW2k!t{$La6nEdg-hMgzg>qM(gwg%-oiCgjg*Z3bINoMr#t=yVnY}PhI+lE zaT`|icCkcjFDR3{Z9h+X&xO&y37?gb9?BdhB1`8;<{@4pKO7$R%$BrKH1exBF`>^1hRBN3OGega7-m`H(*!%l8~^L z=*p}exd3RPMxgdXzF;G5g@bO$rb8EgnR}80usdvvSJo?LNOommxg+Jj1ah|@ka4Vy zy|2I#-R8Xc8cMR)yY!#0B&MXi--h{r&T&aeK8mBN(gS+(ZyFH73ALw-&nvnZhJ>1= z*>JZwx;yPcJ0uY#&*K0e6rGgtrJ$0M5^7r-QVp%||MTOzH5n9qzbMy*|2a5V|UD}F%3`l-d7s9P%cEq@sAGw!~JSC-WwJbH)= z+BrIy2aq`#!B+8m`8Qlu>EP&$JR02kD;+!rnZgg;x1J+Pw*fc)4hE|{U)8>d4NUo@ zrqrpz2Q=L8Pi+~<$WmT@3<`p)LX^C!X!QSfjV*IMiELTK#4-i!Dq7_biHn;ta9N!OaPu7p zj)7Zo{$UI4iwrHq5?dz_(J|4Tf9m$eV$9qJx!=hz!OoUTRuIC9&5;J1ckkC`KUT(+b`==rapHC-LN!whO9I?J+WYSV z>R@rYyR57Pa0?CtpiWq>q^S4knJx+|L)_1i%xpPEZn$^oo)<*BxH#@^ZMpN6((MGJlYWOSq>3>k?Uu$FPMIRbAGWe{VH|Dmah&x!ONxLL(MnIF4n*kzX^)uAx-xG z3OqDUy`%@TRA0YhzTlo0VXMlQ`_z2BW?L1foL+7*3RF0O<6wKMxMy7FDfF`Lz=99| z>Lc3b&zWaG&F#(zQk!&!I`1%|9#BP~gn90>Xu^#`s(}fgeEsCq0rVbrZWFAM_wEOC zv8cu`nn=KA?JVLrubc*M+aVu-KmS3_Kv;bIs`yK(-Oku|!=Cr9iG))Ao|(Ik+dNY6 zr93hf?6%+H5unpXUnIKAUb0Bb9J4K-aQWe7D-cH=g@hv3@(L7c1J)Fjl=Iu-WF?32 zgT1btBf~Ns@C=k72_8;*UOmdMAuFq?sd@<|Dk-*D@pfMOB!x;*=O##3S)Hfg$uL+# zjzb#{JIEbKILJZk#dKQiZhV3oO%G9mA`<`S%JA~Qla9oKrqI)l(t9LaXGjG$hW~fm z8o@=hf^T*fp1w&G1nB!6U=PSXps}kVg&wgH)Qi6Ga3CRH5o+e2Ky!YCD!FPu z2C5^jszpYKzqtxB>mT3}`2>>PoagNC8oqguD;|vdapV;2c-$B3Hp;Yxlf78wvWE8X z_W@|GwX+jy09Y%?V`5^cVA|r4$3e36Y1c)3d^9o=Ceffur*YK*Jjz&?hiNS^?6RgT zY&i%Ab}R=|(a?N^nM1BW{sL$e0)Ycr^ezB$sKq4nrA?cGEoGoE{rwTp5c8#-z{T2k z2`$`aWPEaIJ?-W!)S>g<;}AguA=NF$7AC%KbCDX+4DXpL7Ch)RFKt04ki!(W*roI} z9LPd3c|L{uxBYGBWj@*nQ&OFLNx&rsoj>B9a%$p(@}}bY_3x=yYyBgH+rQo*?Nu;U zMmwi|Qs7^Gnv|ugUtDCrI5_q#IK)qYjcl{n;!n>f5!2BU!VhOB{`1`u1YRF0ohOuW z3o$|33%{K{H=qj&!IK85NK(f39aPgpMvlZl$j+A5%BD8JUWiegz~y&?>6YO-E$t^l za+cp<%e1|bZr3^3*SF-JF)eI+m6YF>jH-=Sf2D-j^sO+3k>rAf9YHM zHmLb$O8Ql`e6&Q9&OpR-?qicLNXKu;$jIQ9fiPDiep*64+&;_pfXT;y(re#19Bg=YDD>C>zgwMknnEe7PNeu_#S0b!Rr>kFBxoPVVfzTKx3qRWD(Q=(-#u7R@@SN2s=MmWDp z9F)7$=oiJJ$$3IU5=ZQ9VEcd0uB7X1^-Ra^Kdb(*R~y~D4_RcSRUXkZQ$?65qeW>) zL(!d4F-wUZ0 zXkj0xk7mY)!hV$%>PtX;LDA2y%=|^Q$m)OF2bSV1=IdbtTRszdlJo8ZTXmZKhe%C0 zz)+s-=9>{_j1-7C{$jpGmc}Ei3wVf2=y`6@Ybx>qM|JV}_06YBTyNcn@`O?MajWnN zl5G!8X6;!(fNE6E4L46u&ktkrzZE>%+{1q>10X&mOsEIQbyc+ST=IY_9V-iq8G?rNPdx{SN(h>qP)ExSZ zhiOH$O1}B!^$d?(x$;cN?fQJ}Ytzv>x@MI)^%4u> zE4sPw0TTS`x*_XeKyBmhZm;Lw2}!Qfb!{9EkbPqvg*IwS*CrcbbYk($BWEKSKLS)(@klN8xTKcFjoJ5iO5(#}hKH*Z{BxG}AC9c0%5Uj;YQT5d7J znu=P~gK}*7W5MGDHl)drt)M=@E1C_7fTwF$1n;DDw6$#-u%5rIpty6_knMp1+%udPEwlkVM*kM$HtfaLb397R|b+zNt9B7+QV~ zmjRMxikCic1`2s&mg|NL?}_MwQUC$+YzSu#0M`qjv}S@aS*o&Oxo{?@A=dF@^^8mY z+N-)_vZZd(m2HPH4!t+uL+EM-)5Q+&t-2rd`1woGke;`dc3bjv6XeS~D0n~;31@iV zBJD4(AJ`4d&&f|u_NyGk$TxpAiT%#U-sUTCy37tIIqQB)s?nODK5SlfU!f(*@?+C< zk529{w|qO8qk_ zD%)2XX(zOTT(rHE)$Q6lqebQ)6~!w7KEeJqr?8r;69dk=W>A5<8U)x#z|Pk-mm#Pn z;)+ovOkgF32K*mH@*oOY5oN|v=Ko3}?nvIhZ_8DCoL6^W^1Zo7WBUOwh?Q$#PhA#Gg8phb?z5>>*y%4`)MSk_O;9NPhciNt-9dsf-zQ(o z?bsE*j;6fqe7$#JukWWjsdit^P()IFonmM@AqiV?-ro%$b82&0w|O}J)~8%~8ENNKo}Z}~k(!oYaAm4S=}1v=J4SwPI&o+MQW;{0i>W3nval?A48qXMvy-W_21~waXh=7&cq`;r#=Wc}#QA2g z>?m;Kq}po4F)Wcjs(xSOquC@uhm8;;kqe>C9#jZ)TI#KM)^H*6u_I6>IKT(dt>tm2 z4oq}ZKp*J-RU)&5>3i>>MFqQh?Jr~kj9+BaW9~*Ire2%@Ztoq)?r^QaD?1!SunQNo z7{s>CJfrCsupu5Z1XA?oCa zIox<@kK|+e_`jH{-F;V8k6OnBovZ%6mtSjy4SC0YD+<)!`_YFYj|^)OOmrXOdS17y z!z^-`{hUn!t>|&UOr_|1%r)}VlhE1%pHH%(hl+^pkJ1GGr3SSLd@1r-Bb^pPqMgc4 z>EK|a#;gq@g|d>de=n#=Og@d${(icl_tj8X4>nP%hJ|RwN>3AtCDw3 zL#*)Q8Y}B3{yZM&drE50r((-KmNgcRtW5l9J#vHkZ9-#9cBTcp{>{&EAGmyevAdsk zIlKQU9DXo+!AR&Eg-3brQ9GfuwPq2I?K;~mwNj4qgtIEaIG^aqO7&ZcY=F`Hd9uq? zSXcm1K*t1WYxGjQyeVu$b>Zf(mIgz+5AF+GKPS9yKIzUrdg%4vzMsw**~&}F3oCOg z0onJkH$gf%GJtDAYY>D3nxRPIB*Jeb+^+>V&7p&{JWDzPW)@~SlikJ*=i~vt#>k9`|u(ATa0KK6iSn>(O;NC z_8I7zAZ>*AF<#8>V>Se^b6q^ z8;r!LKV0n6!5GXr+n~MrW;B_YnIm7RKP^JrPIFg}r$JK*A=px0UbW$!s_| zIGl8`;oUi&FPR>2KzLptm>+h9BIwJ=-b6X7``!I~0>el1eSd7-J+2EC|6x*mEA#Bs zUoj0-uGb-6A{5s>I_lFu?b2OpSXsWr(O`36bLE!VZAA&M(KvA*rO=Dt#a zuDIbtr#GwIQ&%76PD6J#u-Oe(Nq)W*(LAEK+Z|?bOz848s|4W&ozI~lr@p}NZJBya zec$c@HEC-9o%;4pa;Zt^y#$g20UDQ>&P6J!H_w78Z|SgW za#A|4{`Ee2BNUp`;+h~rQ+Gz6ttJ+n=itz@xI3(){r;TPZQ-9SIcVdR5IQLvH!6ds zriMJMnOwEYqNoiOKpTF(z87|DC)q@38ZLckWn=r9cUP{rSnBn{_OGsnx1W%UQ?(+; zPQt4yN?L0)u=jhcLVcPu#?#1?$<@|&`5JL!;V+_9 zAs3>PZsCrVaiLl%0=@qJLhC8-*_$`M=`N_URhsvACG?>97C96ik`UdEKGIRAv~`-_tq#6*YZ(#&b4+QaTH4%OjZ zFf98XwrIIquF-`oJRN$fQQ$#b#l-L5Xwl+#a&$d+DAm{|Uh%tr;p<1S{&L^k-2|1J zC|B2d&Kn=@>rP$UDp#0{m|q#X`#^-CW$RVpqq}&uWMqHxHyhus+R%1(N+tcg7`ao# zdKYi7LhzH@vcf`TiRn8Ju~ExX!Nb*JJ=fWLW*08>Ffvg!>Cg;jW~Q3kKQP0?)36jk z8oE%9`oCv9NYsB&W|!~%IlSGcZ^dWrm;P|A)BVHu{y7`bUwkjj-ZbptXVR|C5ZT^F zY^fhT#&hD~Y4uo?n#<_&xFh#;sR7lRiw!5+6GI<5Mm~etQJZ0;K&sZW z+rE6%v=QB_KLZ6_yJor^UrdSf|5D14IF%k8uS6N5Q%F7mU(AHh{V>fSrI&)qTLe2CFW)8kmQc<^{*!N0MoNxs~m zxBuDqhUC;uxwB%d>no!b)vorosXG5erJLW3-)xJ#OzjQ0hoMyPs8!Ss->9MV5HoWY zzA>^^;rN^4LD}+oe{rhjW7-?^DoraZ^|I_pk`T(9GwKdJ9=C{=tBvYzZmkM9U3K!_ zXx(paUMzESO)Gv9a?twH@`CS!;V;2*I-YMzWn|=c_T_kee7?eI;ECyx&6i74^%sp( z@v>{P-Z}pBIF~R;#zB?(0-4AUwbUlMdY|BbI}?igU}tna^M@G{X?0Uq*92A{#3X;T{%9w4`0Yh5elwmSb}B8Xu49gO zRq<66ntV(AwRdz!Xxi4Ju&k=n(XbF2as8`Eg&Kve40?tkV%_*&^sh2gd`R8==9-1Y_4h`@TZ42PnPlr0Q$^n%UgB4?q)}^|3 zbb%~C9lZ`VWQ|=;HsMDQ!F*Je$g}1c+W}gFHg@UxCHjex$tpOytv<6!5(qpK+bLPPLS}F?jJLEv0)o8(o zo`YZvVPF_@=6NNKTYH&re|PYHo`yV&)D;Rjo+1w^`2DQq7tKriVT_I(gt-b2LX(tJ z_7t)n3{F9xh24T#PkB*VT3XmG(A%!}EIF;asKp6%_JT-de+xzk(!m?*UJJqSY6n2T za@;>QrG)H4_C(ekXhyQhx0 zd}hCTi+8Ya`&V)Zj9U(w#D6DpI;5KK`swOb$FEAGYzyC4M~bn>Q&L^pEgo!lByEp0 zK1QJqwxciuTk#g116vYn;dJ-=PZ!*Nza^jr#n}3SLtASx9Ub0<@ldSTgB}ML`GD6# znmNwO!Jjm@b4JT`TRgy=KTrqGMy-ydc-O?Lv!HfWvGL$VZG8-;X&p@JkW@xV{pH^yq?rRZ8XxQ$-%@7#BrNXI1^D|;0AwMcTxTv;7 ze{@|qN|e?8<->Z+3vPfb#Ard7URlk#6>G#mhgpvvQVe0vPvGdVzTdGm+Fn#MSmgQl zw`6I4&CRVu+Sez}&h<6ATZxtQhLatp4Grsaf(cy?i05gkg6tipeAL*&+4$=c@q-fw z)Q{9O$La>h58RKFooPxv)okJoCVP@oXMP*I<~XW%$wWII?l|mh3%ZUj?uKms`Lk|L z_q&3hMYi*7vMgcS=-}9~G~ZL;`9Za5BlaY;Nu)c$ysc|!XEe)FE=zVse9%2PIpk$n z*m_G;cZR3z)uCLCeSd4dkR=URJl%`7$KClR4`RJ1(4_|6+}R)Q?k^-^U41z^T;Er` z`2ELaw*mc@h8HcLj8!UIG5U+BpmAVp=6uamSUPjE7 zhldYr9S>r#TX+ter~^!sA+6nU=@^gG4{bSl>5`c^>vf&|(947lJ6AZoRK`~DXpxOS z#yjvOBwB)ddqc`xx$T)Ib_#`cb{20fPsK8nIf-|xDRw2W#tC&NFt3GUi&j~WI*$8O zGlYGsNnHQ6l)O4KEY){%-2Io=5;TaSi@19id%V0yH&B>%QpC|M7d6A>t-VI`y7vTi z@Q%xC;AG3Abt{8}4!e~Wa9J)zbwRwWCNbfz*?1`Cqq6;b$erYI*y>;Qc9&QWaHz}p zI4b5gIUTje44xD4=B)OQ`YNIaJ zUHy&M8I9bxhe-u~la<=_VUn_uZ3p-Eee}27J#!uHoXD@#0V{sT}zx&1GrnOurq^I6f;fde6U-2%a}J zIAnR5U)WpkHmWLZRMkCg0i*pZO!JIut1_I8h0CsH({Gu7g|SvGefra96gSS=Qhn$* z-d?1=@R{vNCM%1XNxk=?xVPxD;y*wK>2bN5 z!?;Qtkw&K3Bv0RgxFnD6cH;`A6Vc&R`f#Uu96$ z&UN{ye&+bjQpk#<^*3xZXPY z^+DJ_3$$iP$4Y0!b>poXyc0gXoo1RvftT?HQA0Y3x{KEN*>m20w>|q(($QUao8S8- z>nfDe@bI%;iDqS~ik^_NZ~bh3E>X=UQjYX z9{0_6qJzyer7w1EQbo;_ebqypmvaz&0Pzs{i7WMK>-iMotO)$F&Lg*sITV{S%dc|Jolf?{hZMtSw+BSb%0W52uD+1hv6|g)KRfbbyrY|m3B9bq z#ZYDwXd%2%IO}!dwQdy8VjJDEj8!USUX6M2wuFy}n6QEMFzPbb$k}wjWuj|@4h$<6 z?x$^Q6CDuFlefK5-J2CPrHpz@m9RY?CeXV5d0F8JTBz++k&Rt>PIsbcnWtR9W`aoS z*!hJAsnb3hy%l^ZPXEe|rl%*i4`i=))%-d8Jj3uQXVNhF)a%iXkJ|5Ji7CYXBX%KI zqkOCV#|zf+vl})YBgx6b{Z>@-$5n<1HMWpj?>`q#6wLkQlJ86rF*8HRutl?yk&WkP z(|Nm9o3sC1I^K+HJRf}L;-;=B@tORB`=lt-1-o*q(D#q=UO5ZLQ)ai&G?*69Jo%iB zHFGn%T_xLecjCI=g|pl5uhkHRm_-~F9wvC-;gZ*XJMit9;BS9|QJU)+LrA;F*V95e zBNC)Y8J>Obdh?jd*2QVnk>Uz@!c^vY&MI`-EUm!@m8d5rgbF|Jc77VBq9(K~S)>UM z=+a>yd@qx=;9w%psM#3%=URen(~+S{#kJF|3ThFZ^2Ly&Mlw?J8@~L0*Y#FiL~leB zNm&XG;!`~Bx#o$NlwM6^Cm-<&vceM~^%gcJ41iRh4FBvgf`bHKUlpiSsr zl%lCdq2_Ip{%~H>Z`&<<0r$TCV0y9YjGB>24 z!+9q8ndcmHerVe?<{O6m&cPBC*r>4_^HVd)-0ERtyUdyD+r!9q8I$r3TceEcwIrbo zoYnHmlrBnm!#Syi@=kHjw2Ak2yEqlL^Hu>XBbwNy+L&1PE|dQE$WJ{ta3M)-8c+Nt zlQlKzO~{@?#>HIZu_o3ra7YuusvsHu>rUHc5I=dw9HsC0fX@uk_mN1!m2svev;~MZ ze+bSZghUJLm?P#*m@nplss?!uqPny6L|i0Sho8vM*rD}->-PMena=WOVjkL3w4Dy>C?e+)Ve6o(=~s*9 z0m9doy2Y>dm#x_k+q_vt7SlblyOb-i`|mJ~sRY)6{1bptpPkL=3X-Frzb1@RiZj=r z4~>iM^wuU8S-QX0KoGXQ`$)W&-uvy_#S-s?A>r!Y0J>Ta-GX;X)F}0d!-H&wn<=M{ zh#kr&=1cYG2^_CYKCN5*ASw+VU9T|QKMZFG3UWICV5P=KeY;#g*(Bw|ce-2d{`Pcnp;pn4O_^B<8SCj^^{tR8qWcjJFexq(la~p&GKxw;A#3sMi{7G0|I=U)fclKg`Yl_!D zXKDbnEmx76=~NMDZ{_yr?W~N`<|3fAK3?RwAnfeR$*zRR=+jB<`@&>O=QV$eYA8<% z7<%7~G*)%9SDNI6ID{J{82hD3x-#RU+1c;%moq?s}mx^EwRPs7yVp6&3w26 zr33bs!j=6mEkU3=$@~SJlgW@uR&*NK54>xl>O-BD;c2fv!}#-(tw7cx4_%pbE$7@S zZCwRzvh8-`f^)a>>)%c{`q!>U{G^}pBffl#FH3cFut(v^$EWu8mHSI(X4fIhNr6sc zJ+E37b@s+(t;eBhD?(Vd*q;IZqu;Wfd3E?GXBKU1n6QmiW(iCbmQi6%#j-La4V+2> z6t?tSS}jJ?>c^X(e!N$HwcabZjhr!kJ}a`od$m(A%1+WngXZ~7{m)w3pW+#>uCgv` zCbH7`B_#Mw>})=^vBGAfi>yinuN$16h8Hj)kiI*@7qbl)YMk%|{#lbQ=6{fcEfQ7v zymm!AhUq+;y6ZS5cPMgF@d7z1Kk=3;axhC(+?Jg%c#DoHFpY7FX?HFlU{U*-GTQWK z+E;lQ?DWD$=wOnU!P?2%hY!Od-l9QTn%Gy(Ir(>9Hq%kO- zzr;JnKF~+i&FVzG+ZDb@&^oIe{%S(8?*@5%Kvv#|TgMZD+N1D7@=IHjKPe#~48>HfI%C>h}s3CtXo@avGUZI|HnISVfMn=B# zmFoQ%8Nttp_jn^R6sM^kt{!h=Cl6jH9TKeno~8PZ`^C7HVsIZmBGsKw^c{%OZk3&SGcrtTUh=^=gbAbNb6^ww;CR3mXX6XKO&_ydIS zMm`#(mi&}kaRvqo6Bk_8?*c;73{X9H+t`s;8@kBnJ%l>ZD z#BCUorQoDOq@{q+#GvIg-%}?g;ItS5XbnPq4E!mUtJWkm&aUnjOg(Rah?@7%?-DmK zOMSy7s|{B;+=Y1kAfRR|_T3@K2Mf~Dn}2Yjo%w3=^54@GBL0FNa2h}kTvdPGSRpbV zEG*NY0>cH{>G~qP@~%hLyYXC!;&|JLlKS|_BtNpHkY2>^OSYEjNNr0km(7ml&q)V@ z9feNoIi5i3oYf~+HH(-O&`cU^V_EaerixJ?712opH+(P*Dev&ElcQZH{R9_~zIfLu za^;>EFexmNrjuptusJ+#6yzoiQzUb4<1KPzASDfB1zIj4?0xo6) zt&L;*-F#G|JIZ4&bRvLc5UJN97>^Yqks57rW441ukrXSC|h>x?5X;8W#Bsj-`1$h?ZcC;(i(eyL~cnb>L~{1|6y| z4!}k#z0#3-Hd%em>YC`HQP0w|j&6SezLI7J*6rH;+VHHD{7U>n-u2%PtfeVhIoX=) zemz<3zW4Jqe9S9GW?7)z@6`Bi;}rkZtI{>G7p^nVXGI~Ul)}@W>@4fB$!-=aN^3iZ z>n;CaTY|M?61PAxDyWsM+E}KhCVvdkQP9AW4piSvIeSDLvr&^1RSY^LpNpkZsv1@) zUzoUz`^qnMEm4P_GwzI3dr3<1K8K)0C!*J^ajQ1wAM$loY?}X<%wJEg&SS)~SQ47A zWtInOV$og8w4nk#UGypNmC|(&DWcatbmI0BX%)j~&u-&V{Sd~>0G>61Fp`vl;=_Hd zjNf2%kkZr)?`{bS3~T@w#($!i8Bbs^142VvKx-Ey$KVCd4N}X?L}FFIvL~}?HK1fb z5XK0kE42@dy>`vg(``udVVrNKA$9ENRQ~mirTe987S&S7-Gkwn#88+fJl>`?(&GFE zqZi?1HGkAWRzsps9gD_N7a}C)v{_5(LQ-cfJdB&*9F;?!omOm5jZ9V#(xGntWFqz&w z4HYPvkC6J;#y9pyz426`6qrqeQVB!iYl~*3BHEYOW9ZKuXhxea=A@xX6J~nK5jS>( zRWRkz@WfL8>G8JMJB1|!wM>MM2g%-;jCT3(FUk~QOt}C=>8lV&Ch2_q%?smqk3OLE zr{zk+OUY)ubWaQ=~T5W`cf zM@K}w0}sSBXwoHNBKANPV71Rc`HcEk{Nu+HutpA{A`?LjcNIwM7SJ$%hTzH6mP8@P z4C=`>z&rDLXUMPwcclD5Om%Ydh}K2qn2&WRu+y|KYc_jkMoT#*rMb(jH0ODCr~j+M zC3UW{jJfRh*%utKXPjiYLn+S61&%=^PgWM6Au69nrM}x${sMx*g&!Bh*fek5ihw^8 zAvIOl|D#|jtu3Nno6yiX_qAMI?Yk61p7&%Yx{FPBAuUg~zG8DXrAn)!fdN^?idJCL z!bp4$o+=m0LD))!ZJF6 zd>h{&C|?A|qrZBeNnUBAc>zbY1qAk(J6GsmM$+E_;t~$zEAm*|IM)$;u|1tl#JC z{yxv^`T661-Sj_%7b9;*od)%vq|1D3i0i(~dvJhvu(H&$>wx7th*DYYrY9F?~Q z$eqt7!S+ABFxuo3!K!+miJ$fLa}|K!-Wp&D4Mg_^_lmwEP)V+Z#j({hMR|=04AjJDJZxw+^y6fs5_x;mbJqWp} zLw^CmZvxO+0T>9VVnt=r)QDR&VhRchI7+KHV(&lu01V>nx&7th?!k7`OE^kf7XhLg zw}vln!R{`#i|pz#PRkOD15XRg%qHuI@2AcRKVt#jD6QRHwQ-{E@;R;38f)FnqXRZJ zLadli8^)!=9%3>kF3H%Q2_~`h8UD*F(+@Av)g@hm^9(w(fQ2O6e|tCk6Gb@o;Dz|W zwGk0cb?F2TY3%*uQE$opCYQWjRytlOiId@QOQ#9XthF#^Z+qP9!9AnJh2gzFf_pZ9 zuY7TJ;c+W}f5Ta>cFTkJcfR=hm`#3@;bO0pUDF_L018we9^B^hNM<#FV5v53A#{6R zhuczG3MGsLUZCKaY|ZyERi#UIAnb*a=P^;QLvGx-Q3qnhAuuDS^atb78-t&pvp;iL zJpdKvUoe}OM4>KsK~-C7;6|hE#xOIH~M@jF&UFEXkK)9;3?zE%7Sx~)`X zN~sW4q0mc|JXkNk$(%Y`7maFc@*+^fwBgrLI*?a}E2S%Zzty0txQpHQPI<0%yeAwj zSb3E;R>F%5kG&;}VBX*3aUr4Qt+9jvGk2asKCb*uu@UJeY%E6UcH46%jz_&YX6M{W z{ZTb_4m!U@0MW{~6NuvZ=6iUJdFZl~lw-Xef6livhEE?#cA$n!XXiS^X+1|Lx+fTE z8Qs2PTCBK~#W4JqX2bb}hXA|~0OA}Vj+YIt&vvrSM?^&UfUMY~?M@0Xwp1Gv-yV1@ z2YDKFUj2Z+V+8Wq{y{-6*Tli?3PI#U&*DyU|H8-6P=tJxHC+Qy0V|5|lnEn%LO?>R zJmS?qJ3E{GT63-|VI4NYG)#pMN?E%n2?97?6ueOmgP&&LvU>yZIqMfDC?Y@=sG|^= ztJ|9>`0yaUG9Qi14OBnvpt_uy**G!Ht9=D9n{o-Sh?ia23am1%vd@r-?ctBM(8zsc#qpp^B!u%{QM z6+`AP=d)|pneH3NW83Sz;APEe8CM*vsN8wff8RP@I~C81tCH0><>kXHLJtQk_QyBe zec<5m8U20%!Aj)uU5ae~tga_tk4SjHe|>I6__N+=Tls<(vt9pUqo`jP(uS5`$*FKv zR7Cz%_RMR!%j*R8z%jv@JLU~AzI9g^KgedoHpA$)3zQZ<5eOu2Ts9(-) zyO7IQbe55$4uH3S{)0FLVdthGovL}OL4FuEWCHq6o$P>26;a+xXraB^v}N=kt-G$^yO&d$E` z!FZ#8{#Y?uFLasi*)FN=Zq&><-VVPuQ-Bmbq6O&;5<%q?LJNdij%*xSA)Bg-kHV1* z_pi60R#L3DBYsigZNAJODM#;{_;F)O?5fbU>(LIwDv*DEB6hRyl(L{CA%Yb@V#Z{) zvxuIWjz1Y>VooIl;^|qWYwW(iXW-u=j2MF?qowP)2>!a@MW?qUZ}osuDQ2jwZ?ijm zi$(;v3+M$$%~Q$>iOw)upf9gZ#(a)qaL;> zo0f6X%x-mS<|PA<&5a`Q13fP4!_se=|B4Uw{ED)B$*XH2>C}c2^V$|Tr|Dg`Eom3azeF9B1b%xbO3^{Y;<9y;DDhKOJZCmSJ}Rxr zeeGdVgpcoSkyb`Y;cwWsZM|I!Dr0>tZ?@IZ8#;)X-Z+C-R~jNZnfZ>2X8qmuaJ^mJM*wd(Vlben}mwM;pla=WBl_y5&!i5W24#GDd+&;~E zGiX)*t!fK?u81P`;>|QMm^JXx=L1P;R?NW7O(-=a=MNnPi)2$2Tm21Hw;BV{{OPvM zThBz*OfT_x2)vIgzxHH4<^uiQ)V`IVkWVa*qbEikt@e36Rxwi2+wGHdEXO05H?__d zzb3}7U}KL0zWJO>ewHJ*Wy|&WawWTO;V9GuXx(Vpja=3>g-r3|53qnH|L>}0$vN`i zQpEJiCPb?fSgpm}9t;&}$F&5TaflM^s>b?6u2p;2shIhlvT2>EC=Vde=->M(zsmDd zmt@XlEYt&oD%JnQvVWr}!mue7N^Re>f>s-1{5>KKzmz@)63A4Av2tcxVJ_LdjzoWk zTy{}jnj=#EdTJ$w=L(;HtVe10n6*OtJ9mZS>0O?a4>|rxvNyLzFGJN6Pq*g9f&tOm z(;#$)-A%OkrQp`z=9&26q&!EY8R7FlksT?xy8h;7cim9EcNf#RsJG#D$;8Z(cUgA{ z!Gc=(Yr*XAsk*I&8)6QvHMb|%TVT-~6@=6j^wx&BWdAHhrg029GuOLu;gtk6Ll=d} zjxlG-6|?OEyuA%{maH`7reZ+9Qq#Bmyndq|xdov;!K3?0RvJ6M=t=WoHg3IdN`G}0 zS`kv^zs>wEL|AFA?@r+B+uZt4^XKj6dWhMz+4h`|Tcg$&#HJnYnd8-HY;%XV6%Xvb zS!SlR8Hv;gGje2Q%+{8sWRN|!qwD0y%KJXvO7Guey4&!YN)4~XpXgyw)-^sUqp;Zf z*C2&_KnukOZ8OvZD8{Ghe(|I=Gb&QLD%?4FV4%zZUZNPg>!O6V{8zyf?ll3Mq%S2U zG=RB=>X$WtG8UBf%bV+~Yo-!;z@*y)SyNPUCtBeMXpIt%@^y-%oZV4b+2J2t zn*2zsqNZ14-uVZ$lUPWg#%!GM2S1rlmxs+{Q#7rmCz*y&k3@{)Z|YrTH!@XPKK5>4MzAaw6~!ZfsrTJP9HM#=Hk3KI;~K#zZhd!&N5-+ z7}Dr5BuLNkLnF+^B(I99GTJg-bc@@lxZdDVvKtk<*ukxaE>V2t0GVk&jox}IS{zq> z&fBK0w0&&*kO3d1QFq}P%H*46==e9B)NMtcRlt6JVJfwcAmaz1`vU|DXYkv{(wYfbiiy~#h3CnGOTO4)zJsMwUyfST@xi%1qyqkown7cQ5SHm?Kn9{KKly)+<3Kb7F`@!~fZuCqC!+ z&_jRE3hr4sKF$7e#ll&_L2g$26Bz}&#MLYBB%Obp_8TFReSVi@{FSf`si=Ree0a~m zJG{SXsyR3KT#H7R1wPuM0?-`w9BFk4HW)Rdysa;vOoW{>sk0WOM5*=K&^%4=@UV#& zd76CY$*ylQy}7{4JqcX^xoA$HggoGcr*Q((<`lSh)&u(*wN_2B|I3@y9%1cWQ#Uqd z0y!T)fT8MRM8rcOp*{rG+!wfx>;YQ?Hmr=$^UGJ-C0j2Gk6cKWktnnGw*N*n>7ey~BDq?ErKOi)_GHaah)3=Dmr*Ns zijN1$+M=JUv^*ZAVzv@mb`o}49!~iU|J2H5Fek==e^t(zM?mHhSS*HxB|(O3P=EC1 z=?f?AhWDrdHwhI#2?A3O^^+74IgQ0d&)Yhsgcah4{%^6R;nP6V$O2DQ*yjTk*-pUHwe(-%J)NteqWWXeM0DUr)}*z ziQKp#ld}YUTGohw3?TQi5fG}#RW~sPvLq+ies@HtP+{cyG|2$3U4taDXZJZ z7YJ^Rv&-dkRp6KeBy#M`w9goR4up8jr|jG182ZD|y)CIt1Zm1!y^ElZs>1=fH;3*L zR+&GJ7+4*5z@O%APtPO3AmSl>I82CXsm?mE;2V-~E?EQ@uJB)mK5g3XARxmwkF8ys z#%SVRv=2PBTpx_J&Y4|!vq)=wDUt>IcR;*~_tDR1J8lUw=y=*OQ8hL-d3+hT z5Cqj1OhAbP{qq_0M&wjfZWn)rt8&&(99&=Zz=IyU*c4-t7yxj6zLGvLV=M&Pm(x>2L*hG=%rB`AU3OMA8UW}Vl>wH9O zy3RRgmBiK*jY=WqIwl?$&&IjDywS%6V1X^9L6{>0E1!s=*$1E>p z^<&Kh=guCQ8Kgzx(m4|>&T}!Re$ow9IR4N+AsyO{^d(Dw129w8a}z95au%K_M{}ph zj7kn%qH)`L%`QT>^FuHB3r$O+*H6!+n&iVp3Fu?4e=f}y-19Kwc=a>F_o3oG+U%e# z@SBgAyueAxIo!=qc>8{rZ_WR#siI|&{{%{VjW&7J6g!Z@+%;>zoW#Jw$*D*w@sTl# zEk(gsfE>_=O<;|+{^MnLHe)PUsQWfc$n4r1Lw7oBhnpSuhdUHYCBM-9O6iVjpMG7Lvqz8-KROWX1>$dJ*C!*DJmLun zfJ=*k=lS2cV;_$jg8Zvpn3?a?(j5l`TtA9N)%bAUG`{^Dh43;j}7<{+p z`x7NUhhL+i`yxjzytZp|kW?u6~p<_rx)$eF-RloINDN5t^Hni0HA@)hMII(C1W@o`{7RY=9*a{OU- zP`GYnS20%N&tv3~&-#9CoGv$2FrJmHH~X68I*va(D>N#~5Ahy>tvfY6y$;ezIgVjU zI4$z*1`+b?nfuhxfqemrQ(U73#@>}Hz`m^5tr}G1K}#BUv^~GBMv$#Ad-2K!*H~J6 z&&=suwYc{=nmMc-Orn>0Cp<}G7(1os1ua-WO4xXQB)qXSf!WIAMVvC*WQ85QM$WEK zRn3zTk`o{Eo-0JF3YrbTWUz@8k+Ak-XP~8aq!~) zZxft%YTXlQV-0?XoXAGYLV&w?Yuj;YLHj;VVNsX5y-MKf)UGhU(v`@TXD+OF;O@ab^R==5?l8@g{)Rvy z_gT@Rog5#I5xwSW9AGk3&srWm`B_Txr+?2_Gp^r=-v=A0%hOgyPh#`nWA>$2YysM| zt;esrOe=S|1S8p4FJG~(s@%og?1=~&J`#=($zf;_JWO3GoLvxD3pcegvKhhKX`S!0 zJv_YRV9)5_TCqCE6q1=3vU#E~%Iddq_VCDfWtg%{GD_r`_1!IZXF)^3(%Q}8qVVD) zww6$*R!J&m;ZXVi$P2aK@x%65n%RVbf zhy|_kyOhj-aTWLSvTSUq?z-vMbVk=WMfKofaDDQJ={8^_Y)cWBGNWxQPnBf|&W#*| z_*;c9{gue;%~&oo$j@J5S3Ao#7cwU(jiPQma0Tsh78bDO$w&7Gw7XiA&J`j$ou z-QN&CzO9K;AgzuF=!%ry;NwG&v;W7Y@>B@qy_?EZU~XPHcXZ}VGQmzRcA%_8WY@#Q zG3IlVHOZDBIW-e6 zs)4?$7NDykOeg*Bi{0j~So!28i3LVJeKFxHMR@tq)aKpDduwR+&_yrZR}x<`W_Z;C z#KYX8;0K~HZ*_+}CSP8)qj$UNBwhAL$9dn-FjcbaLDJ3yIga*Df@|?mWnRp|pXG^s zP~Z?e%4-n+?(ULX|bZ>sw5)PVX&YP&4&%|ZehkMngfNSkS(B*IRvt+EULCzYSu?=jfQkW4i}(c@mu}E)ED4E zpwqU`GUH*wd>Z%eBa>t@!9{P;JEXD6*GgOtY|oY0>JEKR#S`(E@ax!_&QyL{pfFc6 z9pB4NsJZp*-OI{vEIktk6SJU^b8-Zu01)h}QFs+LF$?nK)r7JEf@*KhRqU(`=3dpv zHwZ?QmyGeQR&s|hJ%uS->gc!s=^=ILfGkzRenMer%2DP?(Y08EvRd`oR1Pk z-Fu466p@n4%EOY0+Wy_UcdL`9In_eb(v)W_-1=uE2f!2+c zct!FuXx}iiu!P3M1gwme=LE@k*S|hHR&HvmMh3hcY*8EdH-#o71S5QYQ?THcK7Vb- zL>-9Gcwp`{>^6MGhnxOKpvHQaQ>%2&j|rexfaB=3>(^yhCema{eSCa=fyBe&e!}zS zD~mx6fSR_Vv(l#OyS-n8S$S8YJ9?vK^Vv_j1KGrPUxJ#MJa8Q8UVCJN?baDXEHasr ztD*Onhie&EPLAR&G0POe;|v5NuC z{E7dKlCgruCo%`GPdTnW(as%yR+tg=wb@;bL^RcY}%Ep%y zZI@7Z;k(1J+@D>)Cc75%aDv=zH>m}O*7A{p>vJ^AznOu1X2jVk^V})6IFKg!r=nnz zUjBku&=vM7$x>xcF=h67(c{-Kq-(GF1*Mf{sRHahpSX_;s;K zr>*TOi%sHLk9BUf7LWCC^*ma&=E!&UkC@?LnwYID5SVW;8K_|CPOI)VEoiYG9XEQ^ zH1l3t78de>go!Nr;1=gWmX)=1lB}!2Mh#ceiHYA?Ka$l4U&Uxgf647y$KIK^Zq8Z6 z%R5j|nK&`a1Fpb5OPlkAJ~pEz@}9D-6C#uQ`YNa~B^U^pJXECU{Ff`}nwX?6Zn2y& zgj<`ZE`}}L5KQu4eEZEx=j##G#>wHuN$U5~5>-x)5S zIqdXJJ9Kg0V)xp!%_J*>F#VZf;NfKoKbMJQ+n2ee_vH~+6?xxJk4I{5`|+o#MqRt+ z3g-#pm-4d+f(Mz8K5o=9^V+9mIGTF-V{=91@-6MRsg#`dEn#dfj^C{=XMSn`jPs}E z;Wzs+F&u3QVNo-MAb@t?@w4s1!lLUczi>zBiN02HB%8cUXUF&Yt+V()@<-yS4eEf$ z@uLG;lJo|{ssNW8^+KraQ4B`f$7xAQ4;ON@$LOE(<6N?|6fd!_6093wzaYogqd9CW3>8fqi5!h7P9!|o)V()H_ z_t2LF{kVSdj7UHd(8@4}UfNlimKS#x9}F5K*l0E=y3O0K4ZDBt30=E1^oia#&Hc`B)pi|c zlZMF0xS*-qt;-uaC5?u@k4mfWu%Aw8v+d2bJliK;3!vZ9`iM8V#zJ4{oasaZm-nbzKPWaV zFVh_ws@yIzjb#yLCo5+V3@i^ee@XV^~vWZXwl76+CJ`PIZyS(ujQ9v zuz~2Hh9`HCWLjbJ@TbTSJn3t~tmJFdw9nU$_2+3A19zS?72G;G_s;8UK>EDV3~dAV z=Db>Ue0b20x<`D z>P_;lQo^(0Dax6bQLt?PER zJ5+ykDkll=ASf49%-e{r9u^d}`rp?)yrBEaceLDcOdUrh+lIf|xP0q}L0A)4qP%L$ zNd_a=k)625g|#*)G-s-+?Q=G(gmZ@XJu4fuW>hM}%;e8ecu@+}Ii#Ss>)f=$qSjYG5sI8*{r)n77vM|QY=)zjXxI-&&pERP;PQr=^m%qnqiDy$NBQe zqtxXMIq2`y?%om+Oo1N(WD)m5I#=yoIs8}q3GzaEh_5!5u)W%KGyb_dY2HMt6;|SH zj@(1n$DZ=nE?nHok~4dPAS(3#i|PAie=BrgG$J zj;-f4pFb~DI25F$%;vA%;^7zBjmOGS@@y$x?v9;(ER9{5pszdbWJWj5EVQxMZ;yCd zl`>;K&arj3X`{2WM4wgIe|XEt!*5gfH$$;!&_)gY?r<$W#o)dsWq3`FhVlNlfO)eE z9OXQn>*#kRdfZfPLxyYUP!5N6lRdBF^Y5Liumyem*CfykVeQ=NWD9%Phfy{^&z%%- zJ4<6h7{ge0;??j?d3F0_NeQD0N_#!@J0azG|M`&+H*~tf(FW(Cumy>btPJx#0h$>5 zYdD(+-M_!5kP_7y7hhR0zxcPzju;B{Col&4%*13snSJZzILK*oa`BkYeV2(}h|pH2 z^OYX~Wn8)`+8!c=xlO|__PE^=UVhM&#wO}lN<8D4eB>siPQuu1lueX|Gwwe2 zY~{KkG4bl?RIp6V?$q8(S#! zwYeQuP%tv)AHQKux(VtZYk;pdnCogo)jdxT-WZ8!iIM^#zrpNoJ3wOquWWhjw1h-LS*3pwHeUYziY0`C7 zt~(B@x*P+(q7aWAuvHI61h71l7YmznJ*;3^2^-S?T07$y=F}nnc2=-$Y2J0U)%{ktZtK#WIiH;J&}#9bq-wjo zm$EP`_Sd~dwSC;aL{Ckf>2|N};Le(?mR7XT_1VLZyNU{r6sW#Nq<@~T(lcO5@E8d11z`4eThX?5km!~|Ax(x#M5B~P1$&J)9in?vT(a3CsDDv_7Eqc+v z0^R-kV`UBMk6w3e#MlcIIChwyJBOF5e09#k>!^~?KR8&@#3amZ<^IZWU}W108O2q< zhPNKL!5uDL<`Qk@)4!Kx7L2IUlNgO-)kMTRe$U>x$S!D zS|kNs6TR3%uC@|hyGumy2;EldY<61JK#`BwBPlzD?NQqw&$YuE(u&!`N4FYxmFWj) zS%h08GtDMUnX$r$2aCc1^on{_*ppA8GzH%**&=VR>uJ8%omwFuqa^pc^{h3UGrwA$ z&9rT1#JE@}?YrDzW!}V`SrxDSepOck;dd6Iz1>7I$595D+JW}#CkRG(e_cQ&{0Oz$NlCwD>08B0<2>DQW*!AV{_+s zTJtv!zHAKXH}(iM$CMfx#_TTFL+_`)$V+@#TH0RgmVE?~BRX+d*y0+P9aj(1gCoI{ z>Z#9%v-0F&1FVgO=kXp=Jb7)He46X$D@C-yh7eG3PCMzgmElvTR zsJ}g=qE>&<*Fa5;ro>8Uaj{0sv&$rXPB@=SEbf)Buq9jiu6wb)#@;pNRNtT>b>*`Y z7FIV1P%lr$L!3`;#!0X>?glR{(0$EUWad-_SW;bWerb{%Te?CGDkn@$g~9ckwy2>{ zgTmZXCVJNl-sSa~U6Ctf(=hC6funx)@SuAC(e38yjgVN>g78w znpA(GaZ zHHoDf$|6=zXqP6>WDQja%X$)VPLtAIwaB2=k}CMYzs}>nIp?wy{ykcfa`9fQ$I1mNNwQ41e($z;2waK9kV3i9`)y`jevc^@xb$& zKbaPYebCbG<1{-+lExm%VH9C(by|q4LFA04`hTL>{vJ_lp%UANsR;%N6e1K^t0I{! z%x|M5KubMBUhg-etGL-$(cKSbAT@+cE`LGd)`%yD8?bX2CEPHFI>A5o(u=8{SEb8u z@EPjB4ID@MTAjlIg#5tFpgxe2^AhL+L*tE+IKXT3Hvp3wA~EOn9p0-R*oZF2$f2E- zouZyfC7R-9?pFe7*Z6R6-QYkuNsJx{QEG)bFg+gh^a8w^ zc~^Y)Z$voe9@Vi9G10LOjUq>;u<-7c#Y$~B0gfM_XRcJ#H``K+04M;veC2m{Qoe6N z45$|}xG&!YVX_par^m~@XcF^ffv`1$)docWJe7R`R#Zkt1`ixlN+FAzf;rQ!&Z~ut zD5K&Ll%uL9Ip$*@;wc~w^V5_vup`&(>4^@kp~ybtsFSb0h=44S6Ig*cDEc$sz%@M8 z8}Qf%Z=M@N0NXLR3(yc*uQXp4YQ+-b^!LAjcI3Emqjl--%&zdw*Y<5}QAXe+LBnrDlbV)>uJ-|LV9Jm%$6)#B2XiXv z?f+w288PE-8gbKbgCiOU+1`wKw|bFVTs+y7mt+MvfCB?^lvo(ypR2F0j}~&ocGur! zdXO{SCPbje1%t>3fTz#xvQ7)+P@gSk{_n88BblXH|E4EDn1(PCe? z8oU*RIW7Ty{aU~yb@@e?#HA-!nc7sr;G@(Q0!4lWgRXt6zSP(sgsR=ZY& z@ESLq1HFmMuoz!P4av*EoGF#Om|ZpTeY1?vF8z1j9M}K0F^Iw9u(?k&L>orGonw*f ze*6JIN7>pnZhYY4b=T#O5yl9S&N1yxmFA97!Zt)~Z73=!IXt@~dWhgJWSL!sh&sDDbubZd=1=DlB>orjSA}wZxH4^c0}< zi~P55lR)dB5ez!3S8D+P^9NLsjQi5%twu0Xlvl1~W3@1{zJQHB!xpm$I@6MpxpEmp zPk+tL&7ngB%?1$mq`v~tf;?&`xf2gvh5KG88|*r&61Fs&Rwb+EWC>XVu})1gUvBn4 z)|iS?I&+dXFOQMD=qY`N%O$oXAHqAW7ND>N{8%0 zW;`t`>jF5UaU^=~XrpOsUe0yRj}%`A8R~%@&|jit=*JkbVe&kB5!Z~NwUJ_c#N{IU z$wrnO8!oFIFLxLQWAG(sBNQDZOKn%bczLe&rk=^eGu+lFS*7`ebHUB|U8BfS_Po2I zV5;sagQ(T|pQFfbwKxw(G9~V-=uKaPnx4O#-5lcqe6zg2Pf~f>m`C`*o2qvx!48ng+Lp0qxG>nlW1@Sb{8U z|K%yQrWZhAGFx@d)h&7wFtnpyh-HRR#UlT`djpyLAn2X(V*#a^M*jN+r}|ysZotVx zFfjr^)&#y>MxLLF_>qIhR>m=4O?1j%vLayIQVX2d!FwX91Gl zDi=}cLU|$1XQC)W8ylOL1Mq>#wd?Qi|7Y1SE_p_wA;OwQTos>`VN1wK(u5b7 zNd62a6Z#7-mNwKxPYq$922bGdLPU7?E^?L5d;@4|X&@>w0F^-T5S=fJyht~TN9bKWy&s>5@Ni;Ai_)ZoXno?yXE?=#RFpb@zWiSwt}{vFWX z^`VELfD0x_j|tF#BGX&~YBe^`8=MXW5daS42|0bwTY|6p0YoW1Qb!q89J$g}*Xero zIrrGSL1zEndP?2JC}F0e;&)vS-n_+;Lhb27x&UWux$YTyjOJ}!RLS^MJ;s}xlZ%J0;DeSJTchVl*9j*b8P zUCMXPuU#n{OU7BP!-JWC-_`0i6g&-ZV1P<8z)u{S{bjl5RU&2f6FG{iakcBVa70oc zueC6Gp~@+)tAJA=YZN{np2_-D6NFI59--9H&51;%_ij&biWwwM8D!%2CIRNRcl^74 zeo&-ZWeoYCmt8dFEs&cb^jFWT8D49RPwocP^qvj2TpGeVbe#5goK=jFZ>v3af)2|x zw3i#9(d&O(ztRXyEUmI?!K*!c@;I;JH?1DHzpSnRX3!rTAnfZlsP(urjlZbJM zr`RfhX6=ELnq#F2c(q1A8*_tQ7_F_anq$r*(d`Z=k1fNeXD~U0xMJ#EqciZVy*Fp{ z*%BlV;N26jtB0ySxxtVRQmxnXdD$AI-L~4;`lqoW7Vzxsy!8dvOSKZB9b?2>z`B0jL=YcVjX-^WtvGY*3Y`nhT$P zNOKPHy}}5vVKSd=;Gmen4Maz1hPE5#ys0+?y}&RGuf17TF+V_3d$Yj`Md!QljT>1% zY!N>^+gGoiex8+r?BoyV;Qs>MIjONSTQpxc7;0gkY$0m;J`Puew0+ffq=DJgpD$oPy|=RaG=TPCq_1!Rfmkc zK5fQRL+$ReyCy7x%0#5<~3w zUFT``s;#UDpfGRuAS*av$*I2Yu9^M<$oc<{sU(;#{k2--40sF^a&R4K0V~i&3eWeKqhrMcO&B%vZ7vjcJ$lzC?eb!J(&P5XM^Dy1P}; z8SQ*R!rKU(nT!l>dD+oF_C6$6fA^OGD0b#D#=z3i7~w86Gv6-;2E(a@Pe0TbiFDwH z`RGO6w(7ucs1O=hJOLKfbkXrD9i2xXfFb;|hkcQJV~&&b z%h3M*Y3KLLujBl#OR|{VfhWoOd4YExaWCL?AM6ha2A`&)PK8 z%OYNo8i@Mu@4kW<#g&6koN~;;tG!A5XeD;Q*sHTWPWTLZ0NG;zD@fWZ^lyK8E$0Ho zgx+r?i<=1{%}o5D+ilg&ij)s-H|`~uyfieBfA}x@AtDkuDeUcl&oW3^Fw>@I!&%OZ zj@Fds_%EzeB4%-thg@0(r$Ff;*WL5~DQ&uV4iE`7WiUM2&^h@=p?ZylB~8gJ2jSO) zxhWs8%|EFm=va6A#pX?v(>KjWoizWJ%qXl+-?l&w19#xRueqqLdAPqbL zAmgp-HA1AYU37y;(w5Z#1t<(H8>%_f3wM#M=P^R`N6Nz}y%z-F>*Ej1TP7hPT7W0X zsHl)2RnH*(GbjXCS#PRBfK5_}T?7UkNDsfMt`-4F4+cNru%UCs_Gc*WJ^OhWAu4Wl z2!xG9!;-W4M-?sBM*zOFKbgPXGZaPArDT|G#E1?PKPh>(oh z5aBvg+EsCJO^9~?=T9{Odix|REr=zClyZj;0Yw2g4~T#}suoxn-J-e(wZvOQKqLIR zYF=LRgwSuJ8UVu&xT%w@o;0N{USpXw^E}eUztok*Z=DueciJ~*Z%Y5EF z>-8C*etTn8cEqoO?_A>DY>|KYDxC1IH+3q=ebU{xk=^V2*t7{Lch z{!$5&$jwx7LrdVl{P@2}V^%Te*b92$SL92GMBd-3c~B0Ys{Xi;BlP zDzmZ(K^&_0Z0_TS09B~fd4`-?HA$1~EN1j6gv$>9byWVskScHAlJA9Ro_79~KD^z; zp_oDmczt*`I)9Ocn_wK^Gy|_@)~2H3b#BRvw>#%3vqcnLArT;WhMaUmPW5+1$PRZQ1?LgR{Ip0hK_pUtxjbmQmy@yfH8J|TY-pKA*u&jXz&I6D#FO|``L4kRhar(lB7 zMPOVyj@T%q;R159Kj3AN5ALMm10#T#$ zf!-`ocXLM)p;;<4=;NMSLS@LDN1%~@R5q-XxuVjnC@9`v6IH$Q z?>DHMwOctyXryo4$(5rd|K+*D`iAp=rd|4NK-es0%x@0#)Ha};pHo@!`%+N%UwFW& zOLK%%lK7=H^ge|T5PQbF@==COOh!HVI01*ct#XHBxAX6USFn&$W?D8Gjei3q^q)#| zzZdVLH;xsS74F#QU+ojad!{#%sf2$DzGpSua7OW@Y;2k2 zvbN2I-xKcezZw+0DwwdZ`Ep8_F@qpt)h8T`?CRn7f(wH$9g!3YVkhj{Kt&&=~yfP~gZ~Y>_3R;JWHD81eONrQ!!y@CB(99_V z-h=S(j>`|FFoBTg@g>MPrJR@DP&j)FLMc4#{5Qy~Z`4kEz2EpO2U`rFo$w`8+PJUm zW>Hb;=(_bbFIuF)F`*1GU9v*bf|iZ%R*8VtfJYt<_P6?0R?=sMQmc3S=68PJUnALn z(Onulouh`K|HorOeji`oi{R+3oflwFqMYjX#D@A`jmSs#^DelR!Up+-s5-Hfxv{D{ zs$6&nZnz0$e(?8YiSf9j(mBLK!4mkt5A4V~1Wr!s#eqMT80h-XKgKJ~?sxjP6coS7 z!rM*%E#(v}C10W(?p@+)m*a(%)zi=UiVkpM#1_G7te_epl@Wf{g>CTa+&ty{E$3^EH8~7~Ba{ql;%p9oL zAblB+Q5dS#2yAxp;yj>b#SMDUmZdtM^(x4bq+aY`nfDO-e>5U4_7w+;4QSWhw!h6B zonZM0l@Ex`%=_Fr*ku7;t8Tq4Wv1`e|r75Rs5|K zW!_xTtS&y|{sF_THtuOBt0!Kvek=jjp2TC|CDc>n?hK}!s>(1D^Bu9d?W~*4;0Tq_ zG}O8E!0{W$PmT;qNpXzUdJdaMBoWlt3vXh7wJ`GqMnhP3S2L|)Mk zul-qS04NbuR8;tX_|OcM_45{bXCvqY&#r2!$*WOAbGNQeV!&ds^VV)Pzfk25TcFn^ z5C49&>6VMPVj`)ceSo}YT3cxjivL{%-#c!}r;cI_M(@689-+7I|1%?G3$5_pfX-OH zjycWPy7(2o!k)ZZr*qjyuj1V1c3(FHrd7ApO0ir$tA*L-$f@YWujk zwa!ApE6{Q1iP}uNhHO>(Bsuhs&d2{a2U+}PlsfiJRh$LWmS5e+Z7oV$axF^p|80Q; z4m|JE^g<4G;Aaf3Sd*1@Dm9bZY@!H^irESdz^pfT*(2Iob-?glMn6z!R*NK0Mhb95 z=-gqUFQfD8-Me>*D-SdIay9}{Nc*pn^##)ZgUil$=i;BC_=4U4LN<{4+HV&9YIoK& zItcA!#p=qiPVa;w>J}iOZdcC2#wPv!K8zAz{Z~fcxv$kwgWunxv)GlzXCN5RV@dmr z=?(YUf6>2!8`cbRvD?l^NE(AIHU|F)mUu)e7H9>fWPV2S8!6%nX0(wYW%fgm<@uLz!rgi$;DJmF$4^ThwA zHv{PiWEQbNk4+MF=LaLm>Qz!w(w1@OiD01Z=mK!x8)#y8wYwYKWK(-6sS6W?d0FMny8d0^Pc7nVBhc&^&=pi27^l>IQGs3ap{(! zvenU^S4QuIB*1CQ3NU5H$$#vN1E8CCY%SO^w~x!*Ze?)!bvoY>>5d?M6krSt9c&~G z-h#|8RaM}BMa@>}AQimcRNpQeF1Y_60l{4pjh(-=_A?Y&d8w~%Ye}Aae`+u9Zj9(p zt@jM){o8@;8Q}H`a&dPNI5*UN(}-Gr-?2_E6+}e!02E?i*d2f(4uG8YUzVv%UsbLd z4oz9Wt{L&iJ%`yyDzMVb$|_)jvkq;FU!Y2YDAj;1D+2w>R!X!r&)9-YJsgM5uPL_r2ng@yJK8rq;GR;F*6#JnoM+`62ZNj zmHm{QG8=K>v>DT&z4xc-|MUkM4j|5JpxcXq@QHnI;hMe}alJw!e7elC_sq$z*GVP^ zTK>12=k1dPK)O`I6EZ-lFZdbBVteJ$>0#}sm`)4LU|1jh>*K)2Fx9u>w>vNqr6ZYp zp`Lykl0?62p5YhpA)%-k2M5wUh-pyC;%~?4!3Qb*5T1~bYv=9l-Am)DkxqV|os-#H zkdPQM4rS#j!!7`XG$NL2C8}y_YVUTx!{!CiZ#pbAMia;>;V6Pb! zY9$X1bzdZNS0kg7NpsiM?ArX17`gVp45+sao&I9`)Gg(74ly&N4G4K96JPO#esMkG zVw~&B6{@VpAK6EjuC_C!A$l&MZ}HTLQlL`@IwxdPQ&U*a|62y9-6iZ@y2f%+8CI~V z)P&r1sq8uOtm?UjwO^Y*Y(pm!dolmdAA3FM1K`{)tl{(^($ zde;~bSYY-@Z2RO%jBVfM_tkxk-Updq-XZUvI4Cao}4K4l0GxQ;kP|caYRaj)W?g^bA9xV4B^Uy;9t%YYOxccsRtlRhfmJ$lt3Xyr636-6dWV>%Gdz8$OY+5$S zN*UR4-}cN1Np_O5XBknpaA*I{_pRsi`o4b8KhNvwDZStC>$=YCJdg7@j&rP-sn4>7 zLehiyaaWECMmZD5gJ`cEtABCAVC)|VXez&s2?}pXijUV@ zy7_SH*E%tSaOQ{Y>51Ym+Fq<2bo$8CPu&7g!&or{oz;&nXh+giIDKRnd>5|U72)+E z8_}Vvwlu4GNw_+-)Siz|BP~gMN%&tjWFoN%9(3y9{?v_o{{aTAV*AQL1pz~^@4%D_ z$VH0u*m{lKN3(G1ZGN0FCqYH6ePP^2gz+6rdqDE4-x;9)RRD((tY`hh_b4vykRn-s z_aihCc*vu5nm^I8>_Sq5v|H1@{aS`tY7XMkUg;O;5)>bw0)WR9w0-x28ZXfdk8(%{ zTr=HU7C~MBUBQM)^(T_w_5`8&K_RyZPNm-$3olzt@A2WQLF4}(EIHhU1YkW9T;>NZ z0=*~wlUJ8pNknT~n^x1IkN*3+*>6_K&We5F1@b|mA5dhGsi@GfPz3TCBjzXgSbao1 z1ObjptNZFnc8eQ>#Wm)-rbH3$IX34>NcF+I3&k+h#glT78nmA15*!Ly0-HHOjro|Ta3S~KO@v@% z`xSy(&lKs6=Pn%Dm?kj5N`DimOn27%YY5u>_(9w1lBtgnz_|1gN|4@z+*) zH&1GI;dH=+i52A14?#EZ0q6jLMx&<{nn0T!IP6$+>XboLR$2OEA+#ojaE&B=((O{f zwe$Y>IWa#wFj$ZEE+8UaYwP}7AEy3bpgAbFZ9l-*ILdPv zWkqK73N`Uc*$pD`2itNB0KES~XhcR?=j3oB&QdSM91?K%Fyq#~Ym_AW#>XA*i^yFk zyFS|*WrC;kLT8eEwM*+3x*`wD9M|#blLAm0Fja2Y?^WLJzaphu5+&RP!2iGvqi$T- zp8=^kGW$d5-gj9Mg*O&(2N57=c*f6PlUweMyC;rzFiprWZ_)Us7N%~2z@1W5T~sS7 z6j?$x@B!US6`z&?FVQ!FpDOQO0y9#rKOkMTK7-VJHwaaDlpr#t+RDlbT|J77(nRdI zk3YF~D&JoCRgq0M8!~_eF$@uM9JwL_79?rRwfn0G=N|q+J&!gBPL#eynmEc(6MpK{ z`)!>q))7m6X86l(ABUh{y}CEBu>1|pUz~vX3O&}O0nreYRmUeJr_5*{F}2!I&9qPl z9GVid2-S*SBa5kPrzqEmBXmemAt->t&WhP<%JX)7X#>T3fNpM64_0RsBY)OS5q0Tb zmb}>?*Y97_m+axR+&{6~w)>o#0o|{LbY8?$!@_utiww;>J{PQB{2x}2X3cF;d?ALa z{dhD66_XQicb`o%3|`0M%e=MVY$`FTu5U045INlGX15iV#-aJ6^?*d7wp z@`@kmjC$EL+y=WLS3wz9XZNHR)F#;u@Ws-rGftj-WNP;P#;K(l1}G4b^$|6PkPkqd zqT-9HdH_6-=s*NMW!%t?3A;S*L$$ZJ$E8}i1-6boJ0C-+I!2ZZxnxd@iQKlk3z3lp zcm)g$WypHWBMC1|?_osx&oKS%WLT5S*A{3A(K-Jk&d`HjFeFcD&!phYtzYRW@-6>; zsLs&$ORc1}nO*p=*ZTjSlOyD>N#k}_b_%d&5%xg(n=q(}1%et6+v&dWHaKex@hd88 zE$IFCLxk3+0?jb^=^`yeQTB1@IjL7Jz+DRo4JGhnGBY!U@z6*b_Wcz2z}Ffj011`; zHAUPPS)|-%vsHoOb)|lPSA!$55Oigwce6DsU17deu$IpYyaD4kYN2-u zY~AIJqH7V=_o&5tryd%S-?axav^BeifTMfKXIB`JCB=E)mec^PM!9S}# z^z8zG&FktGxjiP{j@=#;)7pb(vB|H@B8WJ z>z1DqMSLtq{{Aw=-|Gf!;@ty9r&%|$6=)1aU1y6^I(kdg;`V``^haSiw1D01yP~Ms z`(K_0#0|drVrf%vCZ&NmVxz`^iTD_ktNY^#xwIyR0;O{x3Ve(jQHsVwZh&TsLp$fF z{;y%QJ~@s3zSWRh0uAKaefG_Ew7q<)OiUjf)Yi-3dNX-4NclC8pc&Z=3c7R5F_&w% z=T0fN@o9JX`Mtjxzpfxx`49A4SJ?5zrw<;*%Yzx0j6<*G4fErKg=Zfy4r#Y06p?Hf zeq{4__&36vf)j!${6(yzDW-2K;xr=_Pz}h87KJI^8zSx){Csb& z9(39P^#=0=*e&NZa8Wu>RyMxY9yq8i+;MyVd?Tw7PXU2svjyhqp?hzni`TJXUKi_g zxF7(NBHsmtINi*zx|&x%@tX=v!{ zzEt4orJXtO(q(Xo3Q^VG4Vk4fMz*@Y-;HMOpJ<8X?XbXG?Q6p zySB9~FHF-Rv{9#&C7C1J>}l<8DZEuU%a&*0i1xnNvIU)v0fWEV4TG8o`PB}*{IU3*;8u$i#;8>nh$F&n?EE(9oEmEE!vT>xqw5mz#Ms{M~a;@ z$n0_jwVsfg4^*2km1)ksN31p!(5^@;$dSc=$9aCJyrXsxy4;PooNBWINq5FU*cJHE z$o>x$`!)~jy#b>`p6c!TTz~G=3Qn~kEy6CrE_e!>dF~&2R*V|C-rDx!bcbX`Ex~Te zW3o_cahlYNeWHEOrwjgIjzz+EvjO2tpsg=rme8HHe0rSlOh|{ILay|`x>P~*&Qxnu zC9ol$19J`}#ulKxMyNQ>Kx~1$XaWgAZD>T%4;@=*dabGvdNc}kQF9mcgnOY}Z6ERf z{{ArJ9XaecFrO8-Vcled3h7dv;gX5^GtvgCNsZjEUnG(9wbokxSU{|=)+kv4g;k!8 zCNRN7wNx|HZ7+GDF~naFm~*{(P+DtosJbnVFd&1qdvn+uYymt%730z_dKyV!#L7%x zJ|ZNgIbJYSctP}>izPi<6;HALLgh-S=%STZNL8(;wbQW^MN}6ENr}~e_W)dPkJ5W{ z&Btob_RZU0na}E_lB7wHqQcUDLfZV}<&L9ua1Pb5ioOR{YRo9=g+261ZIerM^ zcEE?bt9EDV>7w#8()@2?@K9QfX<@ujSE_3iuI|B@!I!ZI`Fg_3T{-J3dAgZKo^jq) zqZ5i>xH#el3tF|k4*$206u2xZwoIbhOYx@6gzQPwi_Z8yC9qt2cEHcEcL5;8FjK!x z#ZE4E_V4f?e}-VDD%07u)0Dp(~68i_}HBkUol|uB6S6%=SV>Wra97z zUEw$jukGqlz}xu&RK;clIoBAEsu5o!RfZ2Ud&4dM#jhMve)?8NUYt(RLH^51hCrE%lu#S&)`2G z`GX4#bMuSar$?sN_w-=$*)Vt6FmsShI`If6Y3 zwX2_nQPt3oYHuYpUew@gQ0ZlcqU!Ot2+7-dfBqfzM4&l9qHnd4gTP#8s`3B2t-#fk zTHC!1Q!{csYjraM*S^ismq55pkI~3`q;k}nTy1P_)GyjA^7d@L%7i9hsOi7IDk-w=d>}>3#i?Q< zxICLPR|)>A3TjlB%#fZ?dPku$#ztV5RY>1|g5?Mj-DfL9(Q#;o<@wz^r4ne-FJ_#D zNNY=!@s}s3q>z89ca`a!veBhXhvW+6w=+u15X^Wn3%dp!$dm~AZ7o=mbX1|T^_M@_ zrC^=X+h68y*uvB{G9&mwl%mYq4)=e)i)iI>(e)($YPT}dB$d`%bOfoudh?6kfBo!B zbUXL=cPKA627gg~J3;feN{V8xxYDD~OW=a>4o2myl8t>4U~`Xom=!tqDKETs3J;m9ep7!8}vBf{1#AVJ97$c(BK0QnmJ_BQ}`_?kR(VI2Z*QE-{ zfBiolAlT_B-AtU7jf@`o`|u-S36KpS-y%>Wu(|3_?x{tUrKoFr9=3Ase>a_#k=Ta2 z`X*UItJ0n!ujSb_Fk1zyL7y?bK*rN2zhKRGfrbi)g9oU_ag;vIzv3Jxua>1eA`~?Q z52IMZ_kG6bKI6oPBXebH$nO4&%C01%izxntYI)N8a9|bQb_pYq1+`boBiUS^XC)_ii$tr zZfF4!ntEjy+~~O#F$>l*gZp4gDJP#679oLEzzH2CbZ^RvV<|8q|^pISb}P1`8w9`K0s{oh3Lty z%p|#jj^Y2S?JI@1|It%i7;tIfDNF&+AN?1`8tqp*aYX=X)XfBLog4 zUiNBFX+@!49;_eAnsByFftmVWi_kXsv=z;drlu_UEj964qQEl2UFR5hMxRS&?+;1- z{r==HG#8=(PJa0CA(dl2jDH2j4--?wsk35cfcyLf|3`#;0v$uuWO16SSFf6ETUlG1 z50%|58nj1s|Au5u`|a>sr|V!FjntXt1$EyPuODUZt;QL~p~4aL0fGBzlJB!&o*(2$ z!M30~y|W~9u(Y-w=T4tsHY{RzV=?1qrW(wQ5C3h{-w9jV_Bn_n0tQR(Sk4a=+kjhN z)-W=u{r3jcF0ud{*Pbasvhz5Fb>N(PF8l#wIyON;a?q!wG0J`6kd|iFkl8Iuh_}LR zeb&f+B8I{Zg`iDnu;B>bSnFFOWL7WaZr(jEey~L`pY8Mg{^M8MdiV|9Kjy#4z58Re z;iB=COn}3?dCWu@K`Z8;vk6mhUsOTv@#8~)$(%pH5F~g8DK8PAaAC4|8Q6;IzBM%^ z!-STDO@y!+vRaL{ah@X3CSPqszLW0=!+>YYO#%vV-vl~CFsXobo-5F z=j6miry%W0V1$mGZ};1Rj)^ibg^+W`Wg$@E&3|G`^MB8J7o4Mg-bpsq`g8{RMrDgz}z``XByyn5u*ct~z^Ugwqs7Zjf-17eX=m`(%-m&A$mxoJ#Nj;)a znXTnLuXrtwn7c1GxHiKAUuuE(TBv1va7~tyh*Cm%iC;E{H4|@Nvg`eM(Eh^7`8Q6h zdyALchd$4<4?bV{wEynJ;hd2vzn9tl%a+Jn-e-$*Q_ftqFM~i0>!W2>r0?Ki(;zmG ztK_!G$P#+CMZ|I982q1dRfz|cL7VFwSezCK7L6L<-*fx&NBzbf0=22_)ToR$|=7=|X3=^j&z`L}ON<;FItY!J2)~_hs`+w{+DlGN2 zhblWMED^faN~HE;64cV?5V%gEb0#M4MDd({h~4fWb~kk;E%-HT4@Pwxo1CPxWPygJ zIwmFpGZy+XU6x5L0YO12YcF5$S*%(;O>sRP8x=*Y{nuUa4D%=CudyBeYq}C)1%x%w zixJH3U@$;+MJ^ik4y-pcb9+4i_p>&~q+*45!wqZMWiM39f<5=KlEmU1EuN zeG$=?gEr9mr%J1U3VjZ!_CtFhy=(Ub|E1ara(bcncJ*8^4h$Thq}!6CQl1hni` zo*0^$t_a|Wo}uFB?d0(5P4eVYPtQ8NqXc(LAyC`v@}o$?HH6A&`WdG@O=@)pf56~sYEu_f!*YT1yeDj=1o+%&y z1~RAM_I4H3mqOQQb;@s>GY6pMP7Z?vCMN>Ij-Ni5+$TvwZA?Dyi)Vr=^k?}lusvK} z!-Sm4s-$6ZCh@6n-uPeNvYQuv{raV>rlzPP>_Ns$m9DiZA_C1qO08Qz2VJ{T*w%!S z+v98rTQxnKAk@h1@i^Z`VTQlV0Hnx5nSinH0oHvEvBO zjO#n3v9eT)X9n2FdDN}nlLdMk&LH4=bIJk0DCNg@##}DGaYxE zLTRo*+p-w6@xbh$1fH)+OKe2NGm9_&9B_l_31uhGZu{)y~r3E8IaLZd2>`}5W}&NEox=7vIq z-*o!~%Abqi6I~@~^X2?i&oh@b;HD-=ltDY--RlAKkEe_LBdl9ayBCU?M&lTDs=AW{ zQ~S$8A`$FR>j`AgCcb-M4oYY;x5}g&&%iK=c=CGNn`B(L!h8nBm)lF%`nMdi-4)|2 zzZ@)YANS?Ubdc_0D@I)tJtDI8Onou5TD6-$6A_-Q2xA0akr(KaF1k#$GLFw6%z2;` zbHwB+ynoC+@O!l8GLoDbfi9Dn{Td08X0P*mml1q(ycqVe%k^SL!sgAtON9|Sar&IB zp<%)}4bVUs59o-Zx`Zs}q7@mu z;ih`oaB!L)ETb}a<$OtpRf(*Hg@vmrWBD0y#v~UUIi6_QbM}4+X+!5^wFurCiSa5x zg?O%`bM7)Y2VLbeOVw#=sd=%>pGks;=1n4?l;sy4ENtq*JOTJM+oGF{oxjfMY$8St z!#e+ky?-H2R%cO5nTDYLghEl+a0IV0B+jWF2jm?9yG{3Y@uue_Jt(@Q@aMvnE`aVd z1V*d0#v)8M7Ee#lPTvsf??3mviH9R!RQ9F3`r1{P?uo?R=FHGASN?vEsCLj|KzQma z3z5S$qxJQEB#+h6!XugWaHeRuE*oEe4t!f`*VIsS37 zcY~ifvPIc~bNj$sCl(kn#y{+WD3uXFA0UqhuBL26IZ0ntc9!VekL;DPw$Vp1e#`4e zeol{O0Q$9qw(1QoIP!r3==C*d__l3=No{RK3Umft>L9Hhoa@#Bo%FfWLMW6<^ONHV zE*F9}5Q2bhr~>ZZ!9f!;uD;&@k(FKIZ7npMpwrL{=uqNz3Ab!*iBBYNxs-NcbK}g0 z?cz=NgKVqmp?L1hcC0rW<^~aAyEvG$#*rhYQxH)<+9lHOX^2$UuD0_67v}@?StrW< zfreG0jN;-tK`y!c(5?nuZczvwu#oEp=8Wsr@pFTk~_(ROx>&rgp&c}VYN(>+ei{oZk+dc zrTF=8NRi{PI^bleWNpm>0+K)&T_&5R`5j~!EzlUkG2z>|Qp`ZU?E!+x zg4CDJFu$ToFzQJ7tkLGDXyc+mra9vYxqF+-u|MnhkPT#uURf!yw!3zCo|g168nsV# zAY~Y$r-d{T>WK!BMm+`u*bKV0!fGfUo|Aeeo>{7KjAHis5iQ_Aatf#ch+BLhC^;-3 z9WUvhggP!1(EBT9XXnV^I2!srg^Mp(y;;9S0k>>Mc!6FF5cWEU)tbQlr*wEeQmX2&x6^Cn{#zg_k51c1c3BA(*6eL#bPBnf91Ix=EJc(b<>v5t z8<^ZjShK5Vzob1;ePgRe1_T-}(Sy{0kv~dh&an)IHOqj-h>}qd{)^P6Z82OI`1r{9 zF`k}Rk?dq|WEEevs~p?f)~lYkL$B>IAj;Dg0rV_h-MsfXS{cv3TNds#vwbZo0FfE6 zC{)!E!UL?Z?$RIiSHfB9A-d>G&Vn25t5OB)VL|K}?lsH*HhoOUOL3x#Xl(<{tc9W8 znMndWZb*2%rg2;OW28Um?@fjgmL|-XXk~UPtlcn_ujddgc8N^eeQbcx@q6-){21#n z6EXv>_3B}B$kaez2&_#uoiiUBKa~hyFWegne8V8^a|K$Xlu#ujTO2X6OvQ@anu}GY zVa{8vK0(#k2A5itQ)pAC0TFwVBs!qENyQPzdVzIVv zXZ5D18j=9Ss&EKYem@e|h2KaL-7xC|hT>3fr^2X9QdClnJ5ZB@j#PvSKZwqTs7c~;)trlm0{t-zrnv*}O53cYRobYDEZm7&N3tD1{+a?K+FqYfF%K&HaJ1{a`^ShEI z^u*L1fZ_``eN+S*5-dmgdVbL~mkbH5YlYTGkkfJ{dFm|~g+t`i77!_1wtzcP46sFJ zRu)41L0U|F39D(CMow>aypBZ7pnbRwRhGZzFshgk8yZVNzsir^UT4Siasuw zd4~KW5F0q+Bbg*Pz3bJIu5_2z=jD-+C_X`6;z`?uC>4NMVE6u{p2eHM)R*V3z{TF# z@u@P>(s~3+Fg1a&vCtM*$6GMg z&%^&OgvHji6DzWpczH>IJ9-q{S$TA@$S^?t{6+7W7$_e!B6L7rW+-v-+|)Oj#ryeq z2bO1~C(4};>Ot(0+6Y{E$<)k+{Yj1IyE~*;Hl|{vS+ccJ?_M8%fi5D1bf!~dzP77V zZ62_jGz|=RGQ`Ub^Uoa}yfjn_WSCRC6+H@kx`}$}kM}Bn+=iPa=PuD!Z9il;#*O=m z*ROY2Uz|$e&I=6-lZW1ax#e+kX1*DO{gs*{(ht`GaX;2MuToo}v>VZ|x&uj83Sqv! z`XB;K-6}P*6|Tig3UV>@V)~h)z44WJ7ip2TIQrkWA#<=HyOZqpA}I-GOJBVEl|2ax zKYC#+;=V#t!5~98&>d%|cW=dT3_mlUG{@ijy6^7j#7*5B6oSN%_f8qM`FTwot)452 zEuq>WAFnfCd#?So;b{Q6 zI%0__x;Cs$b@64bH(ww@5SC+rygp3v;hcdyt-{`n>NJZq-;5}Y)=9_#SMB(LaCi4E z&xa2mD3swgCfvWGA>gx2g#-*D`H8V>$ap_1k~j2Umw*_6NZ0h1@K^7Bf5>+XAJkwb zs{_ViO~7h)hB5+~ax6`!9kOJurhvlO>E5lF2*|22p1ZT5{yrFuC$MUJs)uq6c`-go z-c7av>ioxC;XPWM|kPu{HVh^5UsgX_#NAlsem&pJ-RfBwnnc$MiAk zF-cXP_OTynu3ivJkicQ{Xhf+ufk#e`BD25I1k>u&)(O`-N>YEpP(ZXq4ff8^GbN_@ zPRF+$zg@49b+R=}gK>z>r?;6(w}q;7C=@P4E48iUL5gM=DqD>o0wG1gyHyp>&i&mb z|BC?rmfc>5mYmH2))Y0aF0mkTmC`CbyOcZGHs191$He*t*L+h?+v`x3I1r8x3HzfS zrCwTbx||6MbLsm7807$y+MUG_J?#cDL{T8Ym_>3M!Y?rK7-5S>#m2I874ua%0F~?F zSad?LE1wn2vu{5u1o?xl$)g8LZfmnzS7TE=o`W!e@9O;Lm0D^Iu_-lA&#I(t@w^oJ z=r?b$JYRDZCeymL@ksI(sdS+A(vm`kF}p~6EPwx57fZWPPUEd*dtZ`|tRH)C#^ZW4 zVq8>SAO@Xtni=M?uIQM_Kh8f)lD7wtHowF5CtNUo7e9^w@>b|Az}dSYvR6s*^PfVZ zir?Pm9mKf>l(LSqZ{Kign)C*K)2%tBOlzWp@b5`b!SWyZG zl@B%Xk^Bf;C#^PyBVaRGxrwj}@{I4!`~)mq$;U?$u`q&ucZ1z{)P?H^q4Skwk>hA} zpr4)Xk>jNjQ7HXM(*(pVMD!i(5fm?P%qiP+_X(gG2a`+xO>GWJ2C4Ex-P}0JWwzS( zKMkox&XY=4mq3efJt>pO%qv(WsE>DI!S#qQR+a6k!^539_Ln?f2R zk!#Q*mJcMmc2fvo-g6QUR0ZzbzTLhFIXX9s7ztaaIRc}%1Q#|Muumh_M9oNw40@UN z?Z)WnGxlTe3|{MHfBONjTj2wLMaQnava`C086Wtwz?9#YGdG}c=%qnq3N z0&rSsA;i)m+Ij9cQ9A3e*rzZ?Onw7RjXepvnE3v^%|A08n=pl*f;jy4k{l}8&|v02 zB25iMOOJez2`j62#0$3kU74)i2yp<7amc$!+moQ)V5gFh3VG~u@g$SPmdU3-h-oqO z(BlGh(+?SwT}nzy3Qu&bz9IenK5eX)TVf>jkGG?XKOC9Gv$@LrPIrKWsz;Yd&;=?2Zhx%%CW z#Be9mBD2FH2~^~fY+l@Uc}=5o|)R2nv_zzJHJW&y^y1_Rqg#2J-r$ zSxT*ZBn{SRRVA~1CHr~kL3N@ns&pZ4c9fIZ0}Q8&440~5+}lxLO4=SQQ&a$g|MTBC zKjMu5I|XSrkS#gS<-hIwTMdla8;f+1n?6Kt6UgTicB(%%s&{|q{CeS|d?T1YDbVT= z4Sn%rj5|W2xc-AogMB2UJM%vT1%7~&Am+?D{nT> zfzZi)Z#ZWN)g^~=Es*g2mh#y*i@*nTP<}$Eoex$VfC(cPu+p=0A+U9-84DF2%Z}Ii zP8+GJr)$YcvS+;L`W}^&+V6r0W7B`q65~yP;#OdN=Nd*$;ayGXa}|mRyYwGw)D|H} zP*D~fBzerfYg(J-$4Nj5p5I84{>Pj$$JYiug z$J%qh@ctF~y6UDN3m*+U1p=@1h(pcb#fo`ucq%}NSAu6tTWAr~y9Au(#j!!Vj>Cl) z0zT{q$xlR<)2{~X^k-|Z^bdSf`;ldR*W(Y~*JU$0tuR z9vO*8!nNl4eP8Rdn2z16!~C=Z?d_?-!OOhPu8nC&jMkoIEb|MfhPmy}m?qM~y`8!j z;jpaCn%F6I|75xJG$#Nx~BtZz{C(HbW4p{%FnUv z`M$>}^a{uYkshzGBOpU0SL`!vs{qg7zHnhzTHwTVuD`~TAG~^coY5<8!Hk~hUKZ>kzZ=wuN-<;MW_LrH>lN%Jwk1T@#FGB=yG?( zU%i$0^@9!y--jQ?aD7~=GrQ+B%WmA2Clu-Vrcc}p&p7`=TKdTFZor?@N=mfHBtxbV zPlOczy{o|R-3#=)Wy^Lv1Bi3)43K=o@g3fFlh455@A$Y=b!g7hqDx&0Yi|8jeG5t# z@)Q+CnFT|0<)lak6p&syPMJ+AmZKd=_ZtBVc6dXc#;riDKA-$=1~l4dKEZ>9K4^K?6`&E67>m4 zL-g`kuJxri*%^(Nm!h;2CWmM}+c<3YfAeZKHL-GE87Lo(PBxzN`~Z`24`j_+yS}B1 z#3ZL~>q+#lQ8FcNM?*4lgmyRw?8x_P!$)_wEXN0&TfOm8DbFaAw~4aByr+tq3?3T3Cdsv;p!Nf0Q8uH_CY|G zue}feW^nKNQoMfi7`e1@RD!i7QthPK^%uBokJWlx-=Be$WEslq9=rqg6@54iY$r4; zaPh ziqYzQ-U@#t-?*079B#u}uU^szHB4Ys)Cj1WsYA^dh~&-g$GM*XX-4Lt-Vz=2F1MfX zHrZ-nDPTiUChjeTZ?AN9Q&bh`c#g|>a%o#{(`S5E0ufqzHKMBlaf0c0@ZVfb8v-b| zed@EAZPVhp`A(jgj)}glH^s&Y2m4R`e3{nz{2&!LS7M#VfxXK|ibcdh&XdiUVVEY` zZ`mBqV8f4I84E#A+;Q3ID&%BJU(-Kzy?bOs_4+CjhV1l~1{+hpoo&jH`tz{oG_xm@EiUsS5EpNo; z)&W)|!pS;jpaccovu+!^^g$T_77Yxdj1RMAu)P;YkO+enf>ZBIy;sXSL z>1ze!z3!9fmm_!4k4-uWUH@8)M^G-`UuNDoNyE%?O(HL3&&904r`+h&^JUNj-2Dh!FS_VV zpwDd~Z}JONc?Uiv-EH*z6+@)h=om`hh4PYAl{Y%jUSedPef^b#QALp=PfQ||?Do8* z?&CV=i2RrdPEYjk9q4EA7E~TV1i(Y(c0n*U4{_5(u5$>Z z*pmyN_!?O5E(Qg@4Yy=Os{dP4=lYfu?xMj`^;Q2C(oYBzs3wLze$B>)`$!Z@n)yW@ zUp)y{q2=hscjH~drACe0$%DhA7oWq&5C!taX5PrGy3QAFnlI<@=iwKD6L-llul)1^ z7h4b2Vm&!b8@sTR7gFMGruN~!?b;viB*VDZ{lR*W;!{@&z^Kjnkt!;kZ1twWJ1G$z z0ySiyZ9tSxMfZNlaZ{53GNyHUpDWO{3dWcaUZHj_N~t&7_==LdYql_K6RqKU2in9V zDp_Y>zK3VT$!tUj1H)?{m6Ql04Sz(&0HtRn0IHu0(++|8ug+xb>%}^*yDGTKEBz%L zEpO#e<~G?j{i{Pyh)($#!w2R+h4@(>kCBK{DYQmD6=jqls%H`Pi<~)i*FZV}jZ;2KxrMM$Opl*P*f*p?~Oy z1i4yfCIh{xwH-RRl>*w@wd`OOFqWAVo1e^FvUz4BZ|cCqK;M%~`|g}GxhSf4bfA1g z_AqoJkDq~0<;Er|n|T7zGeP|ltCL=9v#cyhTElC5L$Y>~KT4FTla2De^S@C@p}EPA$@N1nJTeq0`^{-c+It^jsqbsE#cN1oQ~bg>VL^ zV|5VY<2)!#5v(3z#3AA{U{Hy}mGE^WUPX=lZBMXM|DDP{C&n0(tjBkeg2|y1LSNC{ zZ=6p911;KPdHh($!9B2l`Qan(`S5SkQ-X2YtixG*V~Je-{Y-rCgL_^oRm#CPdI6+R zY5L2KN#R@4-I?;m8F3%w5sIcL`AC`e1 z3ELr;$y=8cFFzwMp;XnUx6tAAG#A)8B=&F(XPj$af*{-iGy&(KyV-5#y9yGY5OW9M zh#_4^m+kU?ldy9#fg)+|*H_w)7Kn6J3h@h^8>u5)=fMdLfGu*)Al}n!UbW*bFt55g zaNEvKQsPgD93jSq7t2XgOtQO!mua&vs2ATuntc%#lMx@5h=mfQw^9R(K5oxO(;|(K z;#d7`WRk1BR(z+QC3F9swXCw|7f|}N-rw(4pk-`Xj2hLFEfTY<) zAQky)4q>3^hofkaZN8h-0>e->1<0ZA<52b7b}~&)KTqrx8fif>0>kG7fG=kG+X@Oh zA&%8>!(Rj@SJ`n3VuoyrQ!?Vn3e*W9-V?iN$*7AB<@Rl##eY;uT7J9CBoG_0=rtA( z`>P5R9%j&LDp=RbP-+V@0k@5k=g_OhFCx1fFs_-~)!MeG{rL`gZ1;qU{(ct^Pp2=G zZ3;bPdoUacDIdnDUc-Di(K=4xSs>PJd>`qE{;FGBTMJL$2LZ0Ks%mVAu0!Yh(@~nd zPs}QYv>Zz;`ir&KZa%YF?0}DKOYQSpn?P5BunR(Lf)Q`|AHK}5cAMP@3EwtY0p(^9 zZ8w+{=lj8R-HGLqP`-*Z8TBc>xzn^3_V~)s-5k zUj(G$I+Jry>N`>lg2jn?}qrL<%KW8x$heKnX3-n-Q4NGjb-P4o!%OZSUwn zFyxMh0U!!x1#&R0p2J8b<>uz*>YfX*UELUIi?Ukx|56P`bwwwJ{oP)CBHlJC;KpW= znNYWIq3GAWC_4`sq-@c$Yx>l5yz*Cq$lWt+Y=--ta-NtasXOF|`4*daReKD(Hdg8k z);=q%=tW$=mjLA}KNULn*e%@JyW_RG@Jh0HaK&ytVFLNlwR*o@K;^`N1}vD$@<3~J z(EdT*K=Pzv$`{_ce=ic$Z4*nR`2j}U8_dlCY%!(T4TxF;2!IIUb{JtT9Pk_Imm|gH z#NG29`%{+GKH_v7GP`81N`a;=r}6Y-8r1OQl;yhNG`Jway1x^dmyOfCjMw}mef}2N zmz@={c^hr%*!Mx$d+S=+=Lc3ox@rLA9d zk)$vx7p&JRcm*l9CUmh+LraYpaKveL?V~vhL(y7K-w9_J>7pLr^CBmdd*zB?x}ixtzt-S1-Z>g0((r)JNc{rbHhAcCHeD`lokzK~3~_zhaeU(46+ z|C<1vC`(X4UGZAuZf|c#0Ij5=$QcUm z;n4stNbJl4NMjLq>{sY=`$6GjBu~1Nm9q+2)d({FcMP3kDJ0mQkdrw%hGKW_Brhki z2^*6wz4ZvY&|r)4V*b21C-4#3GHl>YSh|HV1RS??p;47p!3;|;-UQLKw|->{t>IAzOwCw=yj1v1JJIN z0seeycao&85AdNy9qF)WwWjZJ%DtS@gs8Y~QRYFX-78X-lB%*S6{SM|ig6N^X;>v# zIEDaHfJo&nD`)&ZL-$y~d2#I+JZySJd+GD`q)F!EgL;h;ijy6pLDQ>SPmls6y$`E~ zZUpqX>RJ~b?yinacuT5XGH=E zrKtC|^fGa=v1TCOUq@w@4BD?s$|)-oA5pAov$T=nKOj1-az&|RYW4P=zd!|);hf3I zv{0AJ@a;I1BByf9tf+M>1M!LC+*OAQlVHmLr%goie^Oz4 zHI)>uylGXw8jvDNNl9n)eO=g9mzK|SQ!_A7mXZ;beLnw%A~=iWlaXR~L|}9qT36Qs zoS|mHC^h*r(#(&IkG}w<32<*Erlx`FyH}VGA%;@+CMjoOcx-)*2cE9XR=JTrrY^64 z^XA8HopB0lyxw95p*umT6<5D8)nH%VF|Az7_(X^806+;gJcjXkXE&&8M3&f19H2Yr zj_SoHUYcI5enO;XsBA6$K$TXNq35R*o(=&3eM`jLQ;%I9pj;S zD0h;ZhFBXlo#{BvJXBy@D=5l!;)QApo+bTZhrvgVnb&g`7l{)JjMKi@y!)*6s^Y|h zm>W~~uZj&w{d^!XU*IOo027X#yFT&<1OTu*C$0YO%OCw>gGJYQ^2L4)+H6l@`w?P0 z^R$+Wi+IVsccDf1p1u}qpYyPDzSs_SM_b!oK1>C3XZnS#h|7{CypuD0))`-(W?tT6 zM%TQ+`A%G-aU0AsX$c!o`A}H5>Sx+j)qmsR_iIm5StJLxx6ey=ofY$P__MVYDPg)+ z-rx84RmoYk-tpl%@pENT-sgQL6bc>UB;3=d;!U5`1SS-;u>O|WelC6XgukTk z5_wjjED%rE)7BPTJuh77@aDpGO>hO4HkJPv%hT(3J*@85p~m&JZ zy&NiNX;_z%-M{C4xzv1ND)xuTu*wU;AoI~`#<9}h9h51(xUN@!qtlr^4m~*5=9aiK zJE*lYL3;09`{u<0@n$#XmbQ=87T?Xdnq9o^1Av#qO66- z*y79AqUX40jax`j&gC--wI@Gm7SKe1=M1RG$5WR_&c{2|EQk>j=_r&!2cZavl7Wsm zW+7lUz4)VPMs;&F308NZBWAz&CoJE%IVxrPKo!ye{3GuE6zkpZ)`I9l{NiPW+|Ox1oc9*Z!vN5gUyXsD3Y*-{Ln|M z9$F8rN{1;;l7jZP?F+j4m$xdk`F*&46(9L5*8%?#RkyWFcyJ6j_EZePr2wS$eFk;Jz;urtJ0Na9kr z-7vPLVsb^4Yk$-$r{vPa@%6^bmm_3vHbSh1+`Lte8_0(+&T=Xfs{>2}9#GCallI+` z&DVutj#A(VT{_|q`NR)^G!#-P$bTYS#{a{rtMmVgJ-cyu3`h_DBf(eJ$3sfe5>-Z+!)>Cln>&;NSp0&u84neJ_Io+c^7=0MPyrL;z_^_QTkOaIp0! zN0KPROJTi%$r!_eI%d zMTH#jj^%Ice+8Wm@Eg#)+MoHR@8y<2XscJA@uXpVh|ujz_SV)#v@{(ej9oYUjr0R4 zGPT=}@Y)$Ln7O^(?p3C~ey-nBo7#2L5WEz6SW{obrYL=*SkfDJU1l^2zfJmy=JwSy zk1b2uEY6$O^gBuzhU5JQSS1c6ezYoQb(=h#G~FVq>SxEX@swqG%Vq2nN)Z>?l5HI~ zu~#)$Te048^ZI&8<52!UTm+`+L~VteAWoJ`W@w?GX-$AS^zvWb@SkYMlJwT^yW4!B z)vYr8>k`d!C>b9x_6M0;NdpfG_4{dIEfjj@#rj3&jmLN3<8t%z%0m@`m@^r{Af1kF{7Y?}%+7qXU+*qW#t7PGLiQ#Z5(St-a+B;Q0b$QXnz4e9Hs?E49Eu{hr z|IoL8zABmQnm8G3J44Z!z>}98f*5vzqr8n+BMBt z961YC{nw#<L}4ontaryarUo7mR4UvJIP67?Y; z>G=GI=+~26f0iEGr|R^kZaI@~>6tg!sSW1SbDEFmV)!Jq;~&^qJg?acCKII8321xV z`qhkmgXNcsZizUTgq20il}O98W-t@%Le z{LI6VO!2Agc{gW6B#qbEs}f>u*Lo`$(Bgc*T^N1JLFiEKFlo#WU1WYvGHcWc)p+K*Qd{yeLi&BR9=cGlwT6*wZalJAB?yTDhNcQF)}CX?zmM zS2jI(9^scl_cmPmxB9p^+1{PRbwKnm{4=g+yDKxt^7i(`h9DCU#nI>s%exEZ`#~>l z*o&v88={l>y<4%vn0@l%NvV~_)1R09&FI{z9EdrQ2cd40>Sb&j9K^9B}_57+Ll6;$H$m`{26 zeQS84nVspuaWmxl(2JyVIaDvNs6;f{m#1ZZ2>+@=s)Ay2jjGhu{$YY2F}Yoq`pHJI zs+%pl1m}5+oZ}49CEI`_C~FFI==q$u&(=#0>4%j0I;uP(T&rx28K{q0_s%svC>D6; zF2GAOaqeE1V)tv;l&_nNT2^RLccv3novt<93NFlr%N`Nj&t2{DlFeFIX7t^i=uF?{ z=KQMMeU=~{xsIEVz}w}9N=R);L{d?UzI(xlcjB`QXq~YU*YFbkr%Py6z-5#!UV}2y z!b@YeH<;b%6iI$;=?%QT1g>Brqs^`t;J>P$YE33qod8%0LQ*MR#JXMoM`s|Gi z7iyWSoL$x})uj~e2Im#2XQ8(!ebHm*2gUxEP=7MmjKQ;0miZDP`wyikyFBjReDFeJ zMkecK`1~U_ud5+Sk46o$W=9LWMi7GzqBn<+9tUO4r%$RWkq64XM6vuK>0&4`f#hC^ zq1Dcsd13Bv(o4Z{s2LyX)Hg@Kpz;IVT*w37PAf;*AC)lRk~59n+mm=U<{5u(wP3lU z5akIWhhh`gM>=h3NFHXLY! zCho?i_M@g)+G+nkzWy>StMv=}MlBRU>6S*iOQn_WZfQgj5Tp?h6zLKq6#?mz z?v^ekq)R#^1*B__iR=Fy`+eShY(K0I9!t3IIp;O6agA}Fzq6xqB7IMrz`pdS%B3{T zifEywP!mR!hyZ_*o9J2X^d_G*>9^kBM~*V_pZfsI^It~{mIsa3;PSBWmB(Cx3(z_e z{MSLDKHlGq*=cSTPc$%8SNDcyao575ch^Cm4A4!0+Rh?qQbxu{E-tH8Hrj<K9{fBycGKa&ux8zWnc|4{<}fu>%-8jf>PWb9HTc+ohc?iuSwW-6fd zXJe(S(FkgXaKxj{*k$YHjX`J_jd+fD=8y`@!e@c<055t1eryyv0*LPbYoZ8*LPf(( z_E*gNiG1;7flm`379LYaaTHm81#y8e40O{JhY>sps~qThJx;cyDv`Ls1Bc*Ze-L7# zfy5O0laR>=Hl?E2Y{PmFdZA~D!mllA>p((Wau=F{n5)iDZ7_~S;fJW>R=0Qwr6|73 z9qCILFza~Vd-TeX-uHxLgbK$J zz5Iz0pAtJv#58Sbxq>8;)KR#Cm=>z+b-qius+(eq{gCi8$4VQfRLysiZwOsLeZSB? zo$XjFNg(fIuCDyjg+KXT2tMaL?z_HIEM@gRqzMoX!m28yS19;_?)zvRt?JDbRnx6* zir;pxew?|-=HBBY5JH}8wxAX-V(zEk%z0*i%CWbAVaJ_6@9S6j?%kuYh}h34nlMT< zR{)WRwrBemr4rFp(0^xk?B8nH722`cXj#F~U?Eh!*(4xUjg~b;fwxnIKf3h#&t<#w z{US?!VtgMA3XeC_x@bpPX0&kuNq;d2iRK+rEjG0C!cf#TN^Nh8dJD98n`05Ci&)H;}JNP=v8SeDE4%Ac8s|{e(5z=JR%q4 zmwc3>{_RO=(!`UNq2Od!DWsznzWIOq2>yHj9myTYCCdE&zE3IPUH$E*sj+&(j zd}(LsfpSRUK0TH$EB&yOoP6uo8+a-u@29IGW zX*M0WD`8}DF9GxfB{oy!ZzG{e5q}`Etzl~$YOSF$S;8_p+Uv0V^5;J45Cdn0>0QlD z=f9WqD%le+Zk@t1m^h^66&%HXI%jxayjYz1ksnP3h zYHaNN^mSx`e8|SV#|5M15j^S+SH?Dk-XwZNceM(+tvG}>Fot85>m|q5Rx}0+@ue+) zB--dNbc(yJQ~WGtdDTX5e1J?XY`d?m_ip*gz51bclhTg6j4B+ofoY@?YM~3st{C<7 zfk|T$%VBuf!Ku<@V;VnNT&tzYW<^U`!oZ^9;{#z#Jr*W_W`gycc@x?=I@hVwl4!Yg1%3C_5-XrToAQ?H-u0mgL9j1%ZfAvc^3^03 zharu{r=gpg`Kw5ubLs{3;u^YlLEAbT4(fFP5k#};2g0E1$kAO^Ht;hHg*v?$mpD8U zxVX6HYoJ4C&MHrE=WHTkRX6dQ@#)2jMtZ!q+EVA%x)z}^Kd);!dfQ$Kt=;R%2GK95%XiO)6UZRpP=I8pAW_txmgTlYwfQvCoo zWVH3az88-^rZZI(A2%*E85vf?wE0U+ORB?G9$wDlA1pUHo%T4z7Bt6Vy7$7%`k`lP z%|1B@4Ls^a;$!uDX&n4cg7@W zWj9|lI!07P-P?z+kLaa3XJRA*GdnDrj>J%iCPRb{;No6sf!4n_G~30}R1p<15wC@M ze2*hYK01;vFZCoE=7z~y;exZzduV<*e&fpDzDW}uWn>!XfCKoqFQ~VDa=*&w-Pzyp z@%Up@-@{#F&$IZIpCJZ@qG8Xa>hK1GpBlrbOcU|s=kR>7zqVajKpOP2Y=rdRq=tR|6Yp!ES&=*!2b-Goi#~K zPd7n`#CuAhQ~`5H3uM?ncA<)@CeqK?V-4@@03$e9^wp}Gr^_e#>|SfQ3jaGXF4sv- zjQjg3!Z?l2f6XU9jUai|l!P>_r$FkO@M@7* z=PRv2zpnr0cz!kM{=-I>e^Fx&s>zg+w=%Q}ZBz8_Z;YyCh-N3nC+l^!1v1-~WM1h| zaZP+bj7he#@Ajdoa28F6gBCp_6wi3P&xA;RqpUqb26Z@DOSOVDv`|Zx$n~yolA1iK zgN2hUCAEpftwZtDI}ZX%t|E0uYl&GG0Uw2)+|^L1k<%?Yn0}nms`@B=MpbAF<@zW) zo*QSl$;&mE5@wNT>hdtTOjb}mUpMoWhhSQfZoX{w==<2?FO=7^YR-4Ytcx$3Y`gsE zh#oDD$b7*M8wK+E?T!~fgdKML3DVJ35LZ`Q2K!=1sPW16QdrTJdBlo zT&#oGi2(J6f4haG)4?BOJ|#uP3sM6J%X&W?#!2Jbb-v^qY-xviLDBMCO+8~t@d6Ts z22BGDwN{l_?$B=z^QhmYxQsd5AgOt3wou$cX7u^_LuL7-_EZogXJo*p%zkE@`yAvVLEC_|!YzqiUGX&fpb(T2~B= zc(pjgwFxBTO-8N#CfOF>P(L8jk2#-ptSS9^>$o)-znaWPsZF8HhHx0`{tiaI{e11| zz3pEwZ(*(kS)b>5F?CJ`X^yy!nQI3Hz2+A_@scq=pACYZE&;pb;#JHe_uKJ{Kg3?v z*BOjs$X63Z2{=8_{9I4b){_&x`|f(u+=NnX=zXgR|E6IBtvTCAZ8qp=qSZ@@Vj~c! zyacr?RpjZG-|(tM9r;5t!uQ?ibvyjOKF7a%7o)%E8e+P5T9?qpd0+2+W)S-xzZA6S zzqm9su57OAI(V>??sNwcP)q;TuW_Aj3?fZz_>?zO@#OP40E{i_qOUx4F3EPD_C4?@2Okr$sarP^7^vUPk0n(lfz%P;#T>Z znMuT@$5wuDSU;DiU`5ZQdeQeW)9J<0IkxVWxDwYb^z8W=;IO?~{|M z`9#1D_1VJSooX-uQ63a&%i^gc9u~WCsP=6UR7GR%(32K6=fI^m^Qg^!tDkmVk^Sz9 z57b4&MVv2`;(NHis+Ht~S**Vml_yiTcS|r&#Ck#SNq6kHz51nvl*UYC><}4?_{X;8 zN2MxEUcGQ=xXLeXV&o_NS1B}1Y$t#R0*$uGkisWHC5-Cd&{Aa%agEs5)-94-K`m}) zpbP!m!;8uM9t>-N8aoXZhZe}DoG(LoKs7ZJys2NLYgKawmj`oV^bD(=Sb@9$#KIfG z7NAyuL_R{ut?rn+XY(rCny;$ZKT|PVwUj;{l5HpX;m28$C(vRn%+)C8XQmCvOJhVZxS({@_+&L z#-PP5zFqah!}jmrBQfLs_BU>)Nxr)(6-rs3dacP%0PUyK#ss6MBBF;{1;B#1wl-0f z2CYY~ZYdfdlpflt;xNg{7C8MNagh7%KjM#yx3FY<+#SC>Udb&wXz>JybEn@f-%?bN z7yy3ghX|Ufsl!nnX#I1{FC;r#v8+krmQnEC3i;v7MwL{AWnn6L-6T@>t|d<|266D! zxz>&uW?Wp{g=ZdvHz+7)*PXHQ)RTKn8ZJl3c=R}+p%ndiO3E9@S+U|g8`k07+eXp! zT+5JkxZGxnZOM4k^HS5%?ud#`fU?gOGTzW%K6h?kFVV^g|Du`l^J!Cer|jS5$;Sji z{x=S0i|L{BU&OU$ctR5zD^G+)ddo~1i%Xtv{&B9I{)i1vGgX76Pa#JlRyDxAlw_1yt8?mdu4WKq@=P0zkvEgv z8Wa}T-tX7GiQ?6(krTB#I}vYZPkFtl1g5CYIw#hFIE`p=x2u_$`y_HCQdI+}b2JRx z*yAx8&`|7Ho<52FPPBYdwYMn+IBJ;p&<*cv5E2T8QooUC#)AKnPLZoBPfA*O3a z%TEwF{ZJPJZF}b>!)ppvxBYh>HW-mK?R(GQgS=BCzbg`a#HgKu z{;=ZgPvR_jI#)N7at__9DwD5_FP*=8F)CB==?BAu67+_`jtZAmf?Jd2m~O$64#O7M zOu_>2?RAf?-w?yayIbE%8YFQhC?!~aW#RB+0SPnq=SU{>0P@j_o1Z8tOIM0%>iqgN z>faA#Zx-&j9hu2+{5mVfT8YI_UKTaZVrcloD)+}z>b~o#Fa{b%^m%kp;){SEfpt|z z!~1{xZY+LzVD?4t{?qk$qB(8PR?j3pBt1M6E>p`1XAl+uJLc>4uYHuVX`%sxfv+WL zWczov+#e}vccB@x@n3&YP5yw_?Y{NF66U;)#o24T=4S>OsG`q82%K;1HjwmhD*H-` z;bXH0QRVBDU7EXHX;A;Ze$mhP2MPLFjj8|1o#n%02D+{Lavu6_`<X3(U8p*zg>hzvi-4arG5?~dIOHTsvPX@4DtO> zeT)th?v5g}_L@x(;Aw@fiKpa{WPcL(_X6=PH6dB1X zGKMdYvt4N*)q!)!FKrK5VNRk+A9dFYO}Gj(b)Ynur>1p1!QILMlD6R^#82Tz?%H1J z0X>%KANR>*?kC5}+$t?LYPh0B<*RL2{4_+b=U4H~QNsyL%je|K<*Pe8vaB@3b4Nu^ zHQ@MPJR1!k4-4A;B7)8I^hj5<;_j2vSXNs^mG%o`=Kz?sS$te!miD&SZCndQ(&<%mp#q3szjDv;w3GRO zhyKn`ji7pJ#@y^eSK;(hqDqa?Whajl&Th}&v@EY@ZAoiatywBJT!V>4enpLJAv77w zQC^QLKYeetM9@f?{LEAtnar=(tA8=ipvP@@*9nu{Yx3%inGfMKTkW(lRlkmrlP|a_t2I+e3NbJ8fpz+`#h^V#xR3g|75e?c75CMPe-;!CP;-_ zj(;vV3i#Q8o|mHg6EFHWD6o#I(B7RS1U6#LwEe`X-1sZ==yJNqNrBe3f2(#5!8HSdg z+3!0&_VKHyCxU{?yQA1opFNvf$X+GbXcr8`JU^0Si<75&#kTbkOGbeBX|X%I3gsM& zJI-oyuadUz>;ZpEhEsny^~C-`@qXR&qd$nH{wQdegfOt+E*+(;I6uaV3)nTg5o*#( zzxuL2DA9Q6Z%4&$)6~{VzFR>>>a~6$VF3#K`MHGeDH}a66z`qI%^f7173>ljcphlh z93EM^9KUy{*>4k`*lp`M$XY3m(AXW-G1OQtD=iYudlpM(xKval$P)XSEZT}cI8C7F zPyc;;j&uF}dglE3h0esJezCUDnRxeo<qfOvo$b?jYk^O1*#prQ ziu$Z$mUIm?KF3)yh%G+bPntC+v2yyd{RhK+zm0Hvm9TkVtmYTr%ef36l#12NV>hO{ z#2e>lKT8w#o6s3_gr-W_JjiF4sP}bpJSdn+r_@x`X+<*dLlg6jr$`Q+o2~8KLtB;} zx|4r4=;>-Qh4*=5icZ`7k_;B@z0nUK2P^Z0^SMfBglE%t0ky?@?NEVABeQ*Cd-lB~-7j^nEuo;6uaAZ9vd%h>sy_feP$ zZ_CfPiS7sgruM{ONd_Sv(IclaYZRVmRbI=Fe#LgXo!SsnG1lxPHay5MR86S^UXaS; zgJap!I9|(WcOqsqG4aB_EnxO}y}f#)Q~g$Wr+Q*ysr2I`Xo%dQ4Y!!b4Jz=QGWaOH zyYR!bSI5SxM4U+lYp=7M`A>(n0Ny8~)a788jql#B&}fwspPsJWH`!(H3Qz7g{*z&E zv^qWxY#Sq!6V0nV``#~R56w4Ky4lW^wF$;*q^U(ET zi2^#2#o>r8gOGzX9eKy5ty!9UC-yL#NI5=!o;cHFRtDvc@0}W)th3WKZtwaN*&hMw zmqJIah;Z`VbjYDVpE*cDR;-Z<3H}yJ(Kk$2OxP zoj&$bW;jnbb$eaO@9Cy6^nB+cfu;Iktih_-q-kSv`6m_1%|cEF+yJrPznA03@WZiZ z+F99JseEv1Z`|_2mt7NjiF#v?;$gnOxw^KMw1@*yinj`lhbd)u~v`#Wu?Pc zkN4NT_^2_YR|3&eh(EfOi{gi0s+GOrW_x)%G+m6i596LAhdlm5CQG}wa2*Yv1*^Q7 z4@cBI_1!FHw0AVdEZuK&ldp~4ZR@?>AmvQm{#4?nRiNJedD5`@$5rj(l?F~$m^?|G zz1}Tf8Zq$jNPYOLgd&enM}ij~4hc7lw~A%m^sTHRYE8ECzU%N-G|Rj2F6L7)>_!~5 zg-Rwc25KkyCa=^g@Ev2Ps?*3&T#xz@SoHO^@@i8OdQgNIQ?yxz7$G^Pg7h`3blJ!y zinmPnP<&K63P=LY9;MwYWOgW^;i4ytN;~wIUb)VRr6*yF@wFAryM?bVZ`&(HdE{-_ zvQztzR0^etSUN{m%7^)SDdS^J8m0$$4OG|#y7CM^1iZrhUChN*Bl_MHHJYLZJ;#jF zMHiDG-W$Q^5@M82@~&h+4=i%O)%$z?jdPlWPSF9IU{$;xdxyxCG}(6r{w^PDG{Wq$ zT5RK$KlhS3qAf1YeSq zuNB&o719;n|L*g#AdY#VzFst+tR7HlP@Ea?cvG@(qIx;HdizRt&1x2s6Of?P3g43A zF&=rrBT5!`C5;VlC=f@SeAM@{1^xgNZ|7}V24;3~cdHwRHm{z`T$WEqYi=ot*wj$} z9HLM-X_RyH3g8fm_vb}zIH{SJI7G#$v9dA!1}z>LG;oUhO3`TN_;siUc+KjKL{yo| zZC?V4t#_cw-vJio;V7jNOTDO>0*6H!V(_1ulhnv?tss;c8`Nxui(ev zo%6&Sic883Fk9}`Z3H!dd+z!Y%h|Z?2sd+>*5dL3b)b9q+PeOCZtHOQjb>p^Z8+-j9SLx@BERiy5j{>Vue(B1| z8g+ENE6pStN~YEhinre{Qs@pc<|YZRnwQF-Yw`TKwlOJ1U~)jcJ^Nf5CO!EtsKc37 zIi2l7u~Ib!=t5fERCD^yqO`uO{~TT2t<0~-kTVeWD$!*qnJv|=WO?6b@g>Zj)oXwx zb9OF?VvS+QzDPs;+Xq8RwKURA)=-tJCY3%ycmz{BTq!7NQPJ6zqosO_V~Z0uPFDAI zjX89ct&19sioQ%B3I;EYs7ig4C6lv9cB{T1RFR9XR7q;)T#pjgJ^9p0>UR%+=o)zJ7ONL<{bIq`jA$u9W;bv^m z=9ukx^Lh5(U&G7uaeYaP2l68%9RW;oU4<;;64~F>+yh--8V6ZZJTJ^=EO}#d1 zc(>$kO^n4I-NqiBzrW5MV`zR`ANhyI3XQ4+BR2KS|DLO@CHGarJAeA`NsX4+4X!LRfmU{ zdQjy-i;5TcJ(g@(&Ua}OBjYBpKTzp9>?=qU!bo2L{*?bW~Q2*YZsm zJ<)MzpLTy89{z%g;Y(L(Es|fA&7q@W&cT;%SNxe!BUWYhGt&=-=q`e`iBjumu>=&Z zzK2;VjPnI?p_M-^^-~$`1e=5sz_>X-I?r?d`B|Q@R4o zV5k;b@AQuOtsOrf=LFvgYdzsp%k|p~jD4=3`?$&HT$?*EV%@LHi4So7F*Mx{XjtB$ z&3k5b7a8XiA>JaYx#nJHlXgDN;KYpo(NTJTQvBh2K3JE=n}v%DM^2qkZxq7-QS?63 z7iYe=D$j;Ck2&hJu85wRdlzbGQma=XlT$L`9udrNwF4+CcJf;~--d4D14=h}e) zL9)thBpbcb8T>7E@>l@I^JIG+@Hnj15srJ5Xv6pyLoDXJ!7uMMup7igo}lX9>BHFB z*<)e(^&z122D-&<1;Q8DV16@p&v$`KcR9aZMbr6V%u*7$uUJ$wO~&u3Nx(;y1%QPGn!7`T6!amfcF0hao!e zt(2C)&p0%8vdjz0;iI&nmD)Y*+*C>gihP0TiUVDRh5pp(%DE)Q{821pxDk3oSy*&K z3r@VErJdQ-jQFD2(PBI>6@RuBc_3tbl^U20jeMzz9A#3UzjgWgzv z{3^JO%0EAjOHo#s2J1TAdu<-0`1T^K%Mpq<9{h6hom~a9d=8 z6D=B3#I9CyJok=hTc`vjuFT{5^==Sx*QYIfQ-%S`@GH90m=K^)Hr zrc3l@9F=~l*ndeAW8kIt>5XDW(`Nm7=!GD`=6vzS)BQod zpYfzq!(Mi{p?Lg(si6d#)0wea_zD4)MArF90FL*+IY>z-9yG$iY9hvC2f*BLAJsl8 zi($LSUxnXIU|@xIm)HltosqZG4ExE0JSq48oVChxtsza&BGOqsgZ~nPcEYK29W2)k zh?N_-wVm^SkzuCO(H$UI=tfXRp@BETcw#}CK3{$uhL%h-W&PE&o3)`&(!YL?75p>A zUg6n+lh2d=P(F!ZUEqA9r>$m}_`pnxp!2<(^-3gGUd!R>xbe?7PDEj(*kOju*w}mp z)V}U3w4010Z9Er@(<1<$w?eC90nZ#@Pvp;q;h;4HmN=~134?koEH;-_3ewK|?N7c6 z)Q<&Al)o3X&@(R`4)Tq6Vy}C1mC)cW&16J^D;oAvqE^Pfbx;7&Xj;^R*k|8nyzEH7 z-=BV2clpLn*Fw$T3b)@!{x|z5h4tv~EaEge_+t)J-y7fz5M^vDl1!Im9O(}SS!N+1 za`Mc!_SyNpeWs8Rwh0mfjjSC*Nf#jpIdSo*_H%lj+aXv@GVh>fy~VW-qh~Z z8~#?CSpmymoD}Qt>T!IDKRNm+C;@OG7;7tLH?HM<9m>M5xrrF8x8H76h%<%!Y!hdC zZ~T{~va;YYOOf-Jdg{Lx?i{Zf(rZdp>4F2j67DSJwIz>BcFc@q*V?G#eVB5&ik2%W znHy1V@{ah}POgwWLtgR5cBfhRSke`-T+WTNyVA50E4^&g;5>R4uB^clMZ^ZOlrCFi zU2~1KUf|;2eWI17a&vYQ*_8S^5JT?rKvutu;XXqAq|OHM`w3uBmo*0^siP&T1|dF| zW+^;8+?=&!vbs7t8&m#ZkU^Yjg^A)<=R(}0et)lCuaj?Onghrne2I;C(Yxw(85F_tK zLqKT3(16Jh*J}Wv_>hX)(X;<+(Qo6XvCH9y%N2(^A$CHbKPs-9^xDrORh`?tw!i+a zOkTWnDVl_=MYYmwBu;-(mtcHVmZyMLiZ3vAOiJ@dN^=p$I?Zm8bYMI_tatYqeZAkh z1U_x>Scu}V0?cm?cH0R(aoE9yf4o5Y~>I) z;;4NuvUJ(U@upB;y^+Yq#{KIVDGU6%>QoUKi@73R^6%Xg=s9JlVy$g~&2t0xwqHy0 z002jm{~DMSfphb^qARP-E%+S%Ckm-QvtMt1Oz&My^7ns}TcMrzxTt7FPn!1g*rz^*4110TSq8gd+jTN3x3ng+X7fA_kJbp(Z<8%hlmAGI$Sgi|R^4u) z_r+f!N^zF&qguwx0l|$nW;o|&G8L={e-cTjfa95HX&Yo0vOvYh$A^CF)ciIC3udnq zj*C+bBC-k%s;E3af$+{xar6SaStcec?!qCmZ=Ioa6EUS55Zs7n6(ZB}ebdRr*aWYt zRg1!)uo9GaU%@K?ce?@VP2+rWVV_h~GB)C|%Y>K;l6H}Gd~6V+^XW3kH=(Ui`|01U zhPM6**#`wYMdBQ^tnWIbsN~A^mX&Uz&N>VwCeERHiRCY8>CVn@@c7A0p6Z3f-m!`7 z%8k0vjTD(P^{YH66)C^Pn_8Tx9INRwaG*T89794Fs95w8{9>H0ANj!^F0Me+l@bAfk65$Ss*yrL|QQ zY#iVTLxdhz2mP^k`Jc&W5>Uuf1P1-mp}l%XHiTe;!&!)6k{=D*mG_ex?0t0xZrTLO z3j<*f*;Jd__Ji9Nn$$Sb-AL7%1N_G*%l7(ZG-5v@NF#A|xD-*F0qS1-X;-bTUsVsd zseEnteC7*L>tQ;J0se=x2NO6`M|A^a$s5C_ZghGWH&1Qu z^tTwtYF0icUH_y#*Wc)ItW@h?32z)VjEhX%o^qCvGTPxQk}+ofIdoB(9^z&st&M2x z6=2k3a}L6kNHyrs99BTqcZ_t@zo-`ew}7&q1~PbCdpHfEvV)-}lG;>Muu|AYF6QF$ z0Ew7B90TG9-|(s>MWX_q8Z44Sb?FNOa(PBZ2I8n>4JdQFuSKO{HkQDjm;tPOQnxi* z??g8?le~KJXZ!1+Zry94AV&MVvswGfWiMmW#Y)wN$ESvircU4UnqjVxo@rdTF0ps< zG2P6zG1*It7@|>PWPNIII;dWcXafM>#l#8f#^vyp-hvJzwC5h??mi~T8CCfC4 zaz=mWlMQGtZb^rV*TeDlL5s8#{H&p*ipE z3lx1DyP9u*o9y6c$>j&JTA^egY3;_(S;u_YY^jLG7^@oir(bk4GzRLAYF7!N|E>&8 zjG*uHd+DBkO%K4Z!5bvwvjX@)joSgnJUsu;sH39)x9gP^h%@!wVP9I%(p!~Mbu~71 zcdk5XwK;y$*`IoO{iS9i$TxH1PR0;Lm4w3#&l60*}pF+W*9zae*(4X z6B;sP*$XE;R|kq?_|Z4g%u;{ZTdRcl_zP-O z8kBvzYXOn(Me>IE&_vD~OG%xE#LfAg9CB}1MjjX)DeL`N6lrJX?ug8c6PbFmX(Mub zo#x%DU~(uC)1Tq^(E}k?sf2R6n=`t4_gGM3Zg0Lx^)HJTgz&36V~tz!kqzd%uFA+u zPaGs{&rju3i^i=A<`=;lg95JRMmqV2VaXm@nrZbcQVV$fb2tAZ-;p$0V-L)#mt4Z|@I-D!ZdGqwCi>VUs}17yVR4 z0=|Gs7kyQ44CAU5lpT-*{#nf<{U}(e`IN(GYNRl4*ZTbAwuY5ee13~{A?5y{mi}rIZYzz` zfwKEZ8tMJ-`tNHsTGYMF+jZ->A1gLkq*+bc4x0 zwv2q#@d9>#iqc)Zj1LlO@6?ik*bpfnhh`?q1lJ@kT7OM3Rrc1>g~49^ezNuooQR^k zF7`vv^?U}j36c%OY4b}EVO1HleNPlI?50=vG;m96q6nY;=vV32-xI`Dm#cgV`GG9Ub95O0sFkheOcium${5XVMW84{(>BzIqR!FLDH`h@klah|%Z& zcs3$X4uCpgLN#3hi2Ur<3X5KsPmb$$jmFb5Q503 zF~&+|)mM!a;{UYn`p1x6ZgYGub}kFvgnJ1-lK%<$SkII=)1#=gGc(EDe~@-CU}YV50IvkLa%yr znX3jAf{;=I}%oW9S{tgq{xoy3)PBBg0$f5iJpAc^vI~-o%Wz)>zNgbcf2HrLY zIaIMJrx|z{l&W9;l|_?6MgI%0l}s=hv!o)M7cL*5r(SlkmK(uM*DkjvLqkW$hA0g5 zR3s;=Qt8%`a}5%>RJRC7#7Go%k`PG(ZK0#(6o`wttIKRDb2C)vZmZF71{_sfXSi3@=wzF4B4efwRR3Z{HSvu^=xtH zln7epYJ*{%R85+d4{}(1FG#%b>hyy&)0#il;?Cdo-8z2ZjN$Psn)XhCIJUaKBucGj zfO0OHYY1ABuG!#3p?(eBJ@Fvxn@PP{uP_nn$ZeYH=4IVb%;9^hr3jj>YbZyHz2zf2=!kX}as%*br1SkIjlx!2Yu&H~UryhAUr2n5LBDFiQoH^L=lAa|V}8Ed;6UoH zhpS>{Y44+sguwm%-G3@<4ZvaKl()UT-3xpqq#L$6oZkj*i$aqYG`{Dhzo7ad`1}9# zVszQYNUSZvg=8pq{{0h$HQwdr;$8AkxD;@H228HW-zq$u0CA>_BRW++Fl9Rm+G4tR zoIBYeFAWPt(lOwMLTlaMiAKNNI&-8*B2Q~$)c&U^(@vqOGCh4$#WPI2q$Jchj=F$6 zFnDEdl~g`51)IuC$XuHNJx!D;WsoR?p?|hLHo>Dl6EytB`jj`swk08+5T4gDQ3T--TbC*2EPOq@$L@rK8r*dp$}o;--^<$8h=&y?oflWqogWBJy8?x!T*TK?|JIu-I zC{5DicK5&HKmBJ4k$z6L9CehNxkz=#yjGF=#;VOFjptdNe_omkku+S~ISozQG)q3t ze;7OQkdM-4%xu1C7>vK+W4sO3;1qrUw5rea^z_3OQ;aPNX_*HkvK><{QzJt>aU8 z|M}~F0$(E){y#CEg-fSDY9m+6OmHHfOi zvO$!I;v(A5AV1&M6Dh=EqN9gaW1}-0=M`1492})D7=#u<;ziG1<7A0nRsV_mUJM(D z+V8O6VPmb|wlcqN(PPSb5({K59`%SBR7LK8$B2yeUq3XYH9kiB_HvqL*QT4Fx#Rta zdk>nhN6iCS=KB-HKVGK6sNNI}`{45je&PWClUI1Ev_%*70kC*n|2{l4^z1U5 zOQ;>(8)srt-ri9ib_MGJ&+lFM$h>85m{a**PG5p$e8S&q#4)fc1h{e~mxS$YJ3dTw z^c;95RN$C=Njd*!#d~z*TNZN;0Un!8us_oQ^5Ope?T?#(jeNdOqKW~1rJu3#AbaAU zO}Avy?GhRaPdxvlcr3#gp1<>%jdDmnN+k``tbON>n_+@~MWXagqZL6mbxd*7(xN~% zn5d+rWXN$m67;$37`!BG82UT_3FxBIO((wKHPHg$3_6yck@*Io=lWJExjZ-E0ge*q5=SBTH9-9=H6r9N_Q%D{&Nh|blwm{W1 z4dNduP^u4D@FF~5kWbET){}{4ADDK3<|~S=D*Ij3#U}Qp^CG7NTO|b-MZFYX)y3932YJ3qkcnHMnvaQ~TGtJe#Zg)F=5&PbZN;>6H zo6^$2K~<5+*+WU)^^56Py;X+J~z4^=!lB)jD2q+%2itRuLj$?2LWO(uOQN^Cg-4iEo?QT;P{-VTl8 z6ebdOuV4w*h0s`pBU*Emol;)^e^T^(CyV!ZYP zt`|Tj%mnhJiGDWPaCC-j+<4;(OjEc4upmlDjm!8+qORp;Tl=UxMckwnIc14?_t*8K zpoZz&$Vm*1>bu%p@2Q@w5iQ@q%sy^GPF@i!qs8<9-s97A-)iZ7_4mXR7{o7ke%zmf z*rt}|0NlQ6XPf$N0f^ufgjhFo)}p}4=TLu9-W!LbrR1EixV-R4 zPvGx#6YuFr^Zwsy8UBDA?OXul*yw6cKUA7t*ff&Uz|Cv|VFcZse8_wCgN?^@zCGM! zIms`(wg+Laf*lnfi712fGKcG5bHF;HLc`#Zo*t?2*@0<^13tX|p>F}dwEaF^kejy1 zBds>74;ldhd1loOmmWW7gemu+0ijbXF)H6W58i75%pmzsUMzvEio(SqIiyup`I4zF32^pN6CYqlJuYu_n0{mF*&NyN z!}T@%sE{;;=$oMBl_B5`_zlf0yEuPkXKUQ!_w;B<&3j;dro4Y&i$IqV4^0qCOzlDw zpW&CDiphjCaHL~1Oc`D^Bdumg#aSAuu%$s9!_j#Yz`j<9`&xY=s(zd{svm3so;ovi zJTF02r5`#vrFEJ~eUwL?=MRT-wBmmSAy=K8Q!w$F)VDI0>gIUqD5C!+cO+_~olzqK zc6bl_E zWDx$Z|26V3K;48$BwW@kkLj%Z$+N4FLfXy?FB_FE`VYN6;_oA64PU^K1JZ3MWMyUN zw=QCNL&<`Ml%??@hXPp~l4%MGj;uj@4E9bKvK~jHrK~0^15c}vMc+D12Q6vm1t7Gxc2}SIPLWX{J;zt=mehBMNldU0q3mf!Rxg$nTbmOh^a?ysQeF;mZaT z4+{2BM{;(5MUUjL6#&^k5*798Swgb4a))D9w%+6xnq7d;{WCims8}WCd4$+;_$HGePL>z%#X*aDTOw@~c4ZAlvm$kUC_U)q zG|Mb2U!J+ADm}mjk<4Yw=g)MtSZP0)<$&`guxaFr*ZSCMM07yL{}t?vM_?o*?Zz#L3B4e`1%|U2fWjQ4is`a!$z3_pim9ED5{+g%+mBR_T+aTbS--uokST zfdg$~G5RNeq=BXSU;8~qJo`wQmQF)1mFZ-~Qrpgc!7fblU94q(?ywtj?wI_?S=_iG zR|jO_(hax8kIxWbcKggea)Mf3UcO!-OUc8dPGCL>0PpRZIs(;FJe?MJK}f)~4=!S6 z;d8eN+$^U3wdWU`vV|;^t5fJ%{|-?q#q_E=U7g|Q4L9;$sKgjzl(zlxL|zjm;N_Yy zT5^SldJAUvFe|(QL244fcj*g5QfHRHOCm;DIKJM_xC<9+7&OUBtu9B88&zR7fOpQ4OUTu=ZQ1?WhzhsVe{T=v2EE$q3{g;^5x6Z zYG8CEr#C_yF#i21cY^!23@~Rg8yXra z=)P@G70FFB9nt!(9$dOO+y#isf1K4-bls@D7`4D>+IS4tBFdoYzdc$p)vjoMA!&N2 zM}HM8qv&Sh;_p7kAs?a#+G_9N^zE=QK^#QD`k{XdNf*dNj=$sfJv|jUwylHaPcmnwjhB7jJra-)s^h;U=7F=a`{?l7&{TF z-2C@C^IZQ`dk0L8LI{oE#dsMKW(paTNNlcq*nKb_>%RZd-u?~2$U|s!(w8rnAS$c^ z=JH#;VwfE~KGEOJ3wh0{qYg(n@m}wiS_*+4>kI15to>I|(Pkb(w5zY6Vya5%Jp&(g zY}RAp3!Xe&<|~hzg1JR=KOjL|7gn#!9WB%N9;dQyaJNtlB@CWER3+)zUkZjtAa{rA+s$BJf0cn63p_ zAX_E1$O-g*Dps;{vTwII_bc2thQtvrGmBp}uMCcd^7NU5(q`x8wjc{?cxtFHG!&1?kzg!4_7W;J()@CQ}u@3;JK_Vvl%Ut=!RlLZ_f7Ope-S1R#LH< z@<5Oa`G`%aDmQJ7NoSEM3G#>JeRd`Ln=7>8ffV@2y?XHb_wR$PHj0I96o9anZvL5R zvP*zL@myi|I;g0&4^NT9^r|Be3X+qEfBbvIEl@8ix=4fD@t^iJk*^7V23$sj5-c>V z9q1Xo%||af3b&g~3XPzr#X%BM0U`{}C+%4Ir@#xh%Y*TwpfD)hy5;2L`c(g`AB>;G zz;=T3Pnvkx@Gp25X(RWhNMSBJ@Q>#4z^Uqcg3$Qj^@ZO7^a&4@m6db*-(d=muoxnz zM_#hlTX314?qB`(FW9+M21!i-B*+Q>(~XHrI4rp29wYe=o@a+QkaTZEO_W~nZ=+r5 zGXJlSDrR(Xq?SdU6afb)hMKk;y!$WjF&UPjPp5_fk`-5E38w zEt=yV?tTxE%lcZIOstfPXj1Ih$Nbnr(g>QjYq?Vh4x%u+V9yJ34 z@WdJ$n`bczq5Ujn`d7IPYD5G6i2CB*w_{qAl0YgN4RUbOkzR0sWQWhdc^D~q9x*|W zmKW*_4{VpXBl0oakd?@Ezmnn-ybF;sf$sk&G)31mUzm!PR^r|hvnNjmppjCGz*4dV zakCsO2vXr(gFUG6|2Tdpf3pJ}cGqtg85tQDNMA!T383h|1f6dPgVM=EPBOaWPVecb zAY9u05|W(dxDVOlb7kY!?+o(WKG4c>yqx}~Ybfx49h2Wj{}VGnmo#U@6lS>>-(RKa zWtj+Zh~)gz(*6AfqTg zS23sIiHU;9GIZNf>7TZ$zkQ;8OYd&L4;Dscz=I?_@__083-3AxwUPDC|jel14`yB>ks;;z7NA&6HrIR5_-_ttSyu3i7=h+^QzAOuN8 zP|}u81w@bG|24RS^F5S=j?C1BsCqL(N&iu25 z&CJ~QeO=dD-}PPJwN|CWf-f4zcV5AMzV9S5aiF_;1WgIXtCnh?_LzqG%prT>4A0M0 z<+}=WXeWU!wwafnF4cCNj`vL30G*yx4@f!9p_(&L2u}^3l$|ymi^Uo))d99I{}lIx z>qe08AJo?aWMoGPOiMj`9?ii-2;9pG8lINJ&>SP@Ess5d-e1(0-O5(-7S$KeKuOJPK4^V@Cl=I(5bqDRm?b<5QpizP!jq&KQ8 z{5zZ2-&WDfN8UbN^&qs}Mh1D?sC3}Fh6r#==&KL1CXf9|P#z~HCXNK%-Z6Uq33l(5 z2q^_BxWeAUYUnU=5rdm46K5x7#2{u02`MC5evjYvPPZ%+B*`Gt`2AJ((01tsnrM&u zi2gms6C)bGNLgZ@~dh-tRFMj%qrE~|AhEcI^y6eR!Z)P_)~WQOO>_mWuj zH*1@4)uAp z@o;kD`z_Ii!FSZ$=G4K#!G_j7pZsZq;^PCGnxr6E%;;QG>r+PtUxNanL>iUInh=UL z8h2&y^vV0j`7)2rh+l`YGx$zhcsh-MF}!@kAO_diw1QvG}%1FQQANRe-7%qs!E z5IsGM>{CTP{|i&Y%at$J$q~WNb;PAf(Jg<2@rDsom};vFXm8aDcv(6_+x4$ceG~E5L=I@hw)}X6Q4*106pq zC)tElP5EF?)`L{m^h)2YfLXtqz4U^=J^SuTi)?=15WO+khmuxjA!zGf5i4$|N+EtS zY}!@Q|CXy&!v?RA&~T4fXa-{T#kl}SrAtl%Gt1=f%d;xpjm>{a*F)v9hvwwWbVHm8 zuS`Gl!23HO5P3G!{pVA|SbtcbuPutrS>~&2Y+DkDuR)}4iJ-;eXE|6vG{JDlx{)4@ z(Mjkro%EmeNfasqmjHJ4K1_;{l9LOsImmSG+!cp;b?Bg-R&tEvhUE~i)8arXkZp-6 znLnkls6&5uuuBt=PjWHaQ86`p2aX-T#ie zUoReTn7TC-9tTruO>7y-nM~ipGo~Sd2V!s)wM{cXgYQo&#u`RAQ(&L}@np!n!1ub4 z(4RW>0y+mDgN|AuUEq6v!l>EUpOI)Up^5_-Gz?1lsexLUIs%iJ_N`8}NBF1!#4zx0 ztotLLB}cO~AlNkuM&p4Mb;vU8{`k(!5c5FsOmXC~PKKQ!DPVw}loi|HTi@PTW>3bJ zRX7WvP062S_~(h#7)4O)v@HLv5G0|4cXJ^b98!bXTn|F!0SBM%J16<-9hjWnZ%Y?^ zb5wIQpF6Go_zukaN8pY9+;_I_LmH2C7qOfuv^c9Ij|SMULTl0D^9Ms9fAu%P7VyV2 ztVgJf1xZo1A)C(6r}$+Ju*q0ayYNRC88I{PyO{#YJgcbbu!N*OdHR%gUPzvAk|JF> z;I;*m|Ll)wtMDW0fIa!(h>s6I@f*0br#cgJCVRU(HgS=W>T_oG`5t8>&K$@jAuTUt z!7gi7!ce9=XT?9#x-libU>8j66c11g$wL0pm5s8hht-SLq>aitoK+whT z5UOtJo|!`$@x&huAi}-J9leYnv}&1zu7J3$N;H8O^)^H))YEK(C1z@Z04okhg1ZZ$ z0AQpEPhlB|-Z5ZvJ|Ny37`AZ`IEJi3)<6D5EM?FH02xU$Ea<+l*x+qlpC$iG;`aw2 zJw7?Am-s^gu{J8p4v3pbzX%9fyt{hdLBN;eODn&R*-vQu4~>VcQPa3%PL1RX4D49~9@Te*nb|QJQyBi1){tkBFym1;Po-A4tRfn40=%comR;N-qxa zE@0}{XI%OC_^#5|K?1SR@?K59J zSU1+JaXOq~w0x~La; z4rpdYY}}TYq?V@(wF@Cqd1Satg*sADFN;(GLJPaTz{W&=)d18dNEeS5e)zX_U z2+58h8Wn`fMpnN>(?XDX^uH!2BO6;6D*yK6s9}2*OnADvyFhN@1LT@+qwiQ6>brP~ z!{`1!7;=BBKU%Ij-gx{goIT$HgT}Gdc9F(Hqn68Ny`239^4mhrfZkCdn$tzad^<=& zHXGEBiYtl=f>r~~;sINd#@R&H@CjI&Kv(~A$>lPcYSkRomQ}DgpWTj9T@1z9ppGr6 z(U!r3;)&z%9o4l5qg!9V7wO?Kt}CA}Om2z~RT>B0TrN7{5#KO6AB8>VLqq->F*Y%d zjc5)T_&Fq^?#6QDiAA2}dUa#I91}a)<&Jn9$oiL|5$=8Hd1>PYMywo$7-rfxJ@hGr zcGiBRJ)$4S?7$Kq={TuC!S7R-QgSlsg}3pw+7t9^f<9AXOzVOi9COKys;a8ZbyG0J zTeyY_yxhqW&V6c9CEy7jxOsF8-UD)MMQj}coo8%HsogVz(7h*w)UZ*6&XP^=CH`pd zfuY7{-UE@Mel#K{gYubUShrlj;r5W2cz{3G2xVsYzgoa$$kPDz{%W1VpR z=5+`uvC2EM#J}fDy(4>mKe1do+1$r2gVFuhdwYNK^LHQBq)8Q$bxe)9JW6Dm1ne>f zyCd|7%6Ma7#9)lA{Fsoq7`39(HBE#JA*D+In?K>^49vUHa_Hkn(IBb`<51IIpY8bt zpb}Z^rk5XU$!=*w+nizGu~*`>hK&7ey^kG%sRJ+Wez5pl2t#|vJ|gZ$(bk@ZVnL_X zjkK0bq7ivk`0iJz+hPyDps(CQqJ=r+B!y$;x4DFC4fMps_CaP_-gzD}PG<+}w-=uM z_WxY_TsWC6CYMURiDyO>M8j$Ma@Zp}>n)^foF6KPBTH)h?g3R`28XD;+^W&^o9!~r%= zkS!`ygBCLr*b7`m+RJ~@u0{peOc&k|PQ6IUdt$dr&yYA56_45?(nAOa( zgaQ(}fz#CfJbU5X?lG&u($XKawe|?m7ARU zOf&X&iCkXL3#!4g+NQ{aPNQjlYT3>*%8TFzafob4&)|KTCZM=h#`=QnxP+k3legpN z8YNDuC@Y7W4ZnFp&B(a4&cCeNc!AmX1&e&*hKuZAeaYcOb?Fn&l@bpn*xGPk^kL#q ze9b}=TW_xmP5xKh8b(zWpG_WLE>r(&)A8z<3&e~&{5*=cE&zD19!4|;`CMm;UrfM- z#R!SntTQjN@jbwI9L(z3+M6Ff%cIq&&(RjvEo+ zUK?1~-qY0URS5_PT#a#%AVgvH~twSC-U$`cJT!krr- z?01RJ-+vw)sZ5b~Gd#7k%Z_=o#Ao8+D9TPT-+i#RiJqTj4Euav@MZoKe+Li z_e1rkzRDQSuL#aqN(-^b3PJ2(c_JaTatBy-9TE9-nCdu{nVGd9ZDE{waUDhtP(B_y zd7JJ#yD@~RttxN%!H~_v-#Yem1F>`>mfeV{=d^Lm_a)EuRAwwDTU4|AO}3nFJEfZT z{iCNi^P!-((w7zn8?xoMIGqh6u$=(xKS>L6SqrjGR^R;|I;g@J1F-^T^zp z0WC^4l^W;m0SFf7SpC!xAFl&}MX>yFIu(cDeo zRR=W!_M5wGNe(yXj>}D@<2~t$ADKD^Jbjh@rQl5zPuUZ**pn=;LJE1VD=jdZg}GZi z6NnIk2czKQM_(J$<5|`9PGT^aM7@-|$x&AfB)yJ3REb_mRWgMUv2lcIQ-+L;jNDGi z%g%E5m{h>74L>9fvsX#TwE#hEd0`&=r7Qx}`@(~Ia8NZlGc@7|CNu*8MtR|=v|*>R zy~y5n5iMI9o6vR5T-K<#aj!!RO+|9_9GUp!lRx%;HHvToZjqIsjeMOVEeA10$*^s^ z(8bNWVM)6?OCE!e_*!W8Uotxv)*Qj%qdw{cqr9`V((3h9`mv|iK~g=KQ#LZ&oH*x} zc{N<}{l*;blKjnZvS^St~FvU8qup!5l?;)g*p0ksfN?gT~)yE(^Y z-1S_!DZ!4xD3H+@{`~pzxR#}5ZkFp?(gQy?IMkn`)Y%Du$`nw5Z7gS0rTplv2yL4?aZ}J?Daq3L1j`<~m&_fhsI7CM z$3klqS-AWksxd|AYW1WjpOj{4t(iG=V@qdADeUYG`fr~v#G3;Z8tFlM9CXxK2b?7$mIIs1H>Ec<%-IWm*{JNG2m$uck)g7kucF6|q#aC?qdsrwFKFrt%^ z;@(7swl-YXT-rsLgEQd4rASY{-!JowN)7OqPD2{->7(&dERgEtdc9p`Guce!FBT}c z;$1~~;XAK7);fP9K^j|0oFtx!%%*SRMv}D7dwwM%WEA2&cAXrLN-C|DKJ55#43H?3 z-f`ABU*$XK1SNbB7=LvQ^m|SX&TSsMtMNo#W{lSLCoeActPN#yrbMcSJ*?ZB<$(S! zRypu#r*hev{FE|G^(^GuOV>OfXWsX(zv@H}_ke0NdSVb@=eWbP&#oE2eFpP|lhrc@ZYN<`L)?pm}V?U{iMx1+QNSY@NGv*C8Y42n*i4I&{SLPN1murL-bWEnKd^7@YsU=ozs6nzcZV6Rg=kE5r^=eQ=&-c)yhAG7Za z)9|B@q|A!Mie;8MRiF;w6c!d9s(K>Lp3kI$6$}&FhY|hE=94ZL%O4&ayAYg&PX)4M z*Dw>v72lag`)VQol8@X{P6&+v~v>p^XHFoJ6W+NJ--Cn>G)eNVC#+umxIo4o+o@FJynfTUjV&!^%-tQb*Tjpfy`;e4GpWRhXVRei zhL`7xMEfz4zOaHcCKYL>t}XcGGxloc zCio}WXzo_vKo&sjQhb}iiTPn-7>JZ6H9)BE;sW)E^AHpj!l<`CIKjpcec&O$)YR0B z5+ckOE!s2nXrdDgFVD+NK_n9Av5M#p&)CgqIB{TEJ_Usy!(e<<*%cw=PTAQdEa9O5 zquel!-+s3IKsuzb+b_ZZc|RXET^qbV&&YIeN~pp*;Q4ZUn0sF~Xj#>|wXtLsWbP)) zq;d=jNrRB4<2Wjw`@phR*^V; z`JQK$MD#mf<^NimPuk~L&)hJOXQHG;A9yn&C^kBcMkS2>_g_3?7r21qo^Seji6=XV zx)%JSerahUt&EvqBSza@4`^yRW|AKP?yopVXkT4(Ei5A)8>f{gs z|9WppD*5KVDxy?;R+K25999d{S4VokuE0!*Q=8Qw?5zA04;{H{8Cp`9PRS#lGjIG(E|%t_hD3 z)~f>Cv+#^uE+3+qA=D*aMpm{2AVoiLkqWbUJoi5wfL11>+hwo^GQ0JVE1C4Oxy5?M zUmUK@HkRMMP)*l~rM(i=Ri`#%pksBVWc>c)f?w+_VHQLw==W&_9I{pNbcctVu|Oc{ zBq^-^Xq9mrG@USoy)+E@4$vr6VMm2e1OO1jU=9z%p-x@TilZ=AA%9qT0P}1SpluoO zwjW1aS7oap?%$pR0HQ2_Pzy$w^w?cFxr-$@$cUyjG%ULj!)^|`e3#+RLNk)8VidST zf7qtJtatg>#;J*k^7Kh3WU|`x*`7g`fz~Ou?T7qaX-RubTeRyM5e`e|{63 zGtk$c2ZWK|lchEYnLX+iJjewMVC@`y)S?(3f#)>J$HBdNUte2H@a%piMDNMwqm0FP zN};hK1JGc09!&+#>!NV^4P1=UM#G*l0~9ERZjha`xV$AA=K%g$R*-&c#x2Y&L%V|* z9cUYAS@{moWPgsDeO6H$e+%rn$<(9D=Q=7NCx}G~DLX_yvHtCE3~8f`l87zKOa3$o?{QG_rlaH`8ei7j`Vm?}M69@ADhl zd3wK&4!X}bSoYHOXm4srk-_bz_I6V1d_8wl0~i|~Y$l*orR<)$q6?OnR>a*^YETP? zcV!*egS<~44E&oNsJmD>o%m#l>p~-Bv3Gcs>1ZBjHgAGtsm^S}yffJ+n%@R2+V_zg zu&$q2d7crWP$G)f!S(d%vm(N&&g(>^9HxB*Cqf=));DT>5e_9fZm!+oPN5=eI#h1$ z0G3t@#-@10ruHc@DHlCxHHYMNu4d?8r?1uv?E=4QtCEYx*FZHnDv7AYjx%BeQ#e0(jZgs;O$BLel{O@93Chu z15g#0Li?INr>R(DP(7rXz4QH*u>-dXcg;Chy0p2W{gfs}kC+Oh;2OQVo@wFw{rPLg zy1mfPrwDc2?Cl@i8`X0MhHfgXu65;nP70*(5aF>X1kZ_cP>B4|8vQcnKoQ8L2w+Kd zTw+*{$XMKiXHf|1Bm!eZo!b~N5x>n*A0eGL3dDy3`CAv z$yK@}$BzMMjXYJ#0XE#xp9toK zW@O2Q#mD7!XvDkW-}L0_42qB~MVaNY{ZwahMk1?=XXjue+4U5I%zIqWM_Ib5v)~t) zi#y4g35XGhs85@CpU57VG5dyWF7%t@SFuAC%py8eC{B{0=+5CxjpS_o*K-y$Lx70Y z;C-eFankIpE?HR2qYp>IU%$QwRBUUARLZlcckhFuG*5KMtE}5w_HeI*VXSQ4SphD0 z5Zu!|yHbV?K!Ef}>hpn29M}}(w40yNXm|e{8&+nhW(Ch}uk>@lpDefgF?r-#GP^3x zl{DOuWK(6Y(8xr|v$BV#!_MKZyP9H}MQ+^tHkTpxUM4XZbQ_1<<{_6Co0b_CoVpT7 z)Lm<)`5xHUh4mVyEmrGU@G*{4zhJZ`*GfTVb}DlwWX0OxfgF!`md2cYt&R613zP7Q z)aPL87MkD2OWN49?gjE%zG9R~q0_~#n=3aNi-28K(_y8cDW{gQ&@_!!qSZ;T9ImYJ z@*ZiGDHmJA8Wn-mcVuT5>Nh6%(m+KRVn5scne+r#b`1pvBQ8>Cs32;3Pa;#Coed+Z z@m1tqPdPTmrt_`xXh-p5Y12++H?PnWVIo27kD^2rJDaCFl7_kJF6vZLX#UV9Rq=xK z1l3xBblj2TPh2&5+iSv4b8fG}@F1M|_OE4pua;Fsv+?scSB-pS!aHwl!AKzjZm!aO zCM81q$T~Dq>Qzx}@yNN2mB_5;{q695pMr<-7PP-q_hnYwRk0~Ba8oaARn)?RI;}(H ztWce_DDlRa!lWZ9o-mu0HxI%qRs|>Nr_?ug^p-|yZVik;qx%)kEMEc~pB$(nVHpq+ zKVEVN0)p0oViWRWS>>YNsx6giH70sk^~X$EQ%8a#q#C;2F^N*e*XKi@Nmg3`othWe zm0k%keYq*t>&W@cpP?T0I=cs1YCZuqV*$EdEw6yG_~#?WtUYBX zbgt_w;d-w$(iuQC_r$<5HJ&>j4 zeRNN1P_xo5BkT17@COKPNlP@GruLb>4s`iO5o}(urHx*WHvrR=?=oZGBEO|XP%$zU z!6-6E(Kz^K=~OuYEHA7Pmh*>f3m+b$yS_It)4ew(HFW?ij0reCj+F`+=THOFk@x_N)kWP#c3@EJitL;{lv z3H0SXaZ)+T6*Pk`ZrZ>m>k#!oaJ-nRsD$kTK(DW%q2T~Q6XmA%a9&{#;s%g$!UI09 zI+!YRsiqqg6;x|{rjgQeat>in*EA6@hX(Sti?xxFV=p1h`0}WI4rqjec)z9;Gt>dT z446%hWaf&(hxwhOlKF#)5`Nj&Q;dm~lYNsAC>Onxx~{Z5wAJ&QUm1ls$rAda*ePwt zLe{|nV*IN2>S#!t%qxV^=+0o=HRAvo;kCOEES$QiJ+0SkTcdF#R6X6u zN|mVfVuI09c?Nq;_zi<6YQwThX3hXcFYTR?e#2**isruTPTmPQzd5*V_o|qywe%ak zt<|iA6^R^|v7ur4BusjOr ztt6XGNJ69}dp)tyM5CSD;`ZR`wEdbh!@^5^W71Cdt}kA1a3z(ZhtO5%=s&Y#rqOuI z&Xp*So?e9hc3?nWK+)|+k-d_nRc3xGP5gt8U!6!D79QK_OmF?1uIwKyNwV-RJ>6WA z!o z+dn`0t;awPWJ_OP-xVT}FGae89e5&iRve)#BVo2TXHG_1dIjMf9x(5Mfr^KJf_Xgk zI2N*!1VrrWK_Khu6f*)6? z{>X9p0u9c00!{i0QCT#RrBG z8IhHybBh~5ca>W}8pXvD;afcPY=6-W!0eXFWUycUuA^2^zU1L2_j2dm-WfL!ZjB~uJg3=lu)IleU7so>6|8hng&;L zu@YaxwZnA)KRRtCu~wF3w8Q1ITe`;cqhmB~_n^?$VE1VHZI7xg+EY8SW?EacR6DI^ z8u9xQ+YfNfUAwvV=5712Kw+h|{;>|@0AUxwt-GDZA=5?DgPq0^(?y*5?RWlV9~4@) z%qiSdXRmc7wPW25zq4-$uHu86YTnS&ohvj(QQpJ z&cpmUI}Zag)JQN1GP|>M~EO@Qm_41VW*3Dg%O-@S83)4mZvL! zl5vUST1Rr}FKOHv{A+SxIas{7y`jRv?rymQ-hJWm5$CtM0=oL4yQ#M-9I7X7x;>a* z7+5AFmx#Zk8y_s3^+ZQN_hzKmza;0xG4_4zUMWkMghcEe$-L`^kSvzDZm)fv086m| z)hD)-m&wV`8Atgc#zC{p(gF;L_2}*6fiwBL_091Uuo~i{-sdA;P z@;s736Ah~Q>_WlAd)eEY5GD23e$(EMQP}$Bog!*@G1#k^|pMZZxLG{xB z3i^3oS_3u1iG9d0g+>Imz5zkDJ?{0-FW1^BTm+)bHsSPetTMAx$bX}oX;??Ox`Q`c zHFD}RMMt#f5d9#qbRq6>VpVMlY^IAr&Cz)RB35oK;aoD+R=^u!FbOM8z-$bkdG~=+ zqp%(ToJua?SyLx4v>UbnQxQ^k1R$in0a<}iMidW0p8o^633y&%$3Zoy)Oe6Fe>{=n zw9FBeN@8wo=6mU$^oRMtKf)_z21S%lF-Mbp=hBBDfAJ_wqbQY2v$(0mBtz3B0pax> zy36-OBk}0AOMG>b0Fh$u+XCi`eh_rbq3YL|->|{;lK5~)H!gUL{2{%;eT0KB$vAqh zH>U`A-3C)!?gCBe@2|w%OcBq3<^WXAGE!29H!d=Lu_M*CZN0I$0VL4?2oxskP@j}i zlxkDFO>YEY)zNWXB8+FDIH2J(rpg)EhT&?HZ=S&`x$*DJNPXRv zJzFFL=u9B}^OXMaH!p6;0%}1rV103T5Tu%IkCvEzUxi{IzI5eQwDOu1WNJ}@p22D2 zB%~Uv7636B7q8!~;NJT0wbM zZ}J`Ts)=A3^WWTXi8MG^6Fg{C2SAFO1DX@n6lqGw!Dx^eXazC|p$JzsO^#jwAh`vU zFijS=hv}G_X1amQ!2uW~P4H2}qsqV&qXak7UJrsLecL`+{Tv5X&I`M}AObN43b(kv zUfc{K7?g6u1G1pl5^U2l?1vh)8xOy}b5kt^WZ%?iv#6lyox((Jt-N+Nz}K=K+W~r% z@1k^+72YX{#|xC#m;y4Xy;DvctH%cU@Um~6xGiyMEZz6M5_5y1@Vlv1;5{~v3Cv%P zzvu(R0ilg=icSyiV#o%pnH!WElWbg6HBF0kh!UXMw34U*Fg|Pm;)mhY-w_6+_%3mC z_wqQ*_t^|?G|z&Fd_6u53T}S22@}j6k7cUmE%u^ZMJ-T>l#dyvxVHa}`fTF8pcBK3 zss#u@7OU$U8wpTaP(;-Jy;>p;xTiif^iXx-Nh?i{^E$F}!R7n?xqKe|jOp8S&sQYr zDEt589u-F5K~Vj95W4k>lph;TQrw0Go&rbYK&6$ZTjL6p!_Pc4oo{F>DS)3no1oT@ zI;_=#{+S@Wv!*l(*cQc`5(l+FrWaeKr$+-){*AnNUhBk5XV1P@1HsHFWS^Q%mN?Ro zrUdmF7#*sxUCoA2wImO!@E5mHDWTKic(ud}1dlyje{2AYS-#qpfB%*qmlUUV$lOy z6Oar+=!UBxfXw{!)e#vANyZ8OL;QhTgw;nQO!2kF_6+bblX{I#ew>h?Ulc@+@oX{{+fc%kKxkwD8KK}WgyXODF}=Vh-v`FoEwMbLOzE8m@w zCG&oW1n~o~VY4B>)eBlmqK$Yc9$1_LF7?!lnp<~%eAtD|*h);5XiWRhF74)E0D(W8 zDZ%u42L?KdfDCs?zD;Yw33Qw0%VQ0Azzl?TwaYOAq3888e4n>&o1rQed)DkM#{~ z$U!$BT%I1<465JU+l+tB_=&DbD8QlK5P-F-0o1rCSc3S0XRD_)>|){}r$ftcmkvZm z+V2w?<(p z+beFFF#Y@SJXL3RK5_eOjCT_IDPcyqPG!3@0lpXOv4=1G`$^No{*$ph(5T3aeGg-E zXWT);Q1bPtoL#}$t@boWIcSa3{L+Kk6<8kplE>blQFb8pl@E1stEZd*a?{fCSjKo% zz^&oq_qH3MT=ECnbaW`QC>zV)1Kk~rz+4Q^jq^FO%GP5k55Z@ zh7VJ>4B@0Oz=A$kC;2YUsbc-J|3p7r*C9AVP8Xkyv!>$rd)ll(qNpEG`aN(mM&d53 zZ0D}Jp`)kGX66J`f9}K-LU@3JAC)_jie3G>1XV?j?jVdOTO8hp3EhXz8z&{)FQAn( z;qV+3JD#eOVAzG>f83;_6birnm`;L;5*htPV^dS$HdVIdno6^&r<8cpCaqw_a);7t z%dMcL56XV84z)Y^X|wOk;B&e$CVF}f#MqAg9Uq+h{z7!Q(SrvApevwTu?36wuNw!W zT?spx8*ZHT?;D3L1@svfZ_dos%c6Ns09H=v)X4C}mb9_G(3N$ks*ktho~thA5#+ zK;*%tM

2+lGdQeI{wK50p~x7Ld#FzDEi9f`S4u#ug{PV5txf}fw>`D3A{#gK!5EecOq3|zN39iQ#rRM-Ls^CS6etzM&Thjs0rI!A>|pm2MLQ}-~tDb zLJ0Kgcp!Ewrr2m9g)M(+V?*)Hbl)6Z6G&>%Ikufp zOr51wmTNIo-htAcS}?d5>OlH|H@FX?T)DU@>k+1|&J-Dox*fc&#vDjv{@S93^*mYt zn?Jx;7`YxW_-6ucI>C?T0MuTpv-6+4t-@K4WtIOoyg?h&$OP`65aaut1iH!h@IX8j zF&y#w^*6Kb^dPII*Jo=XAMwlu$6kXP&V6Z4v~<3kKLwAXRSkmk2sh*dw%nooF~9m{ z^N(7>;@})#m#jv~ruE1NV{o6fknd@6gl$cr;jx^DfMp{kIr$eqH1w;&516@uaF}(i z0-}W$&5cL@0WWotF}9{?215h~8tw_(;RAc~Z1_zlFvf7KI$rYrxhEMW=79eO;V+#Z z68H!DhJSvS{QnQ;|MN;jQ~n=BF8^b^A$s5qV8vmNy`556kVi`pJc@Ro4TrFU_4F7k zNYH}Mg%h!#f>7nm6V|V&5an<&T#2+Ncoj^7AvhZNAwZoZU0o`f$n$VMq(ZL_yMt$W zV+pFUGchQn#7MtDn)8GS?-Wm8X!nH)8+dkvd>)$Llt3>#v#wPC8mQdQU3&o5acBoH zseK(l`pS2f$ST0=90(N5obe9f;hm$R4Dv$)o#r5eyQi%T_ zSZYCmWR}Yp=v_6%kfil(brk9efl!Ht=3D&s^Fy}nC9n}E`rRQXypZtpSRq*I{-E0o zCZHrOLDdr7fhZO)fY?{`^9htN4y7xg@GPWIOVv8$c^CyXeL&P%LH5H@OwPff6eWmz z^Y4Eov#>V4HX zd#Gg1M=~}@kv{~f{QWpWsI@Q@Jp<%g&zl=4$vRq$`X!hF8i+H0OzZ{vV}-c+RY*f< zLdI$yvSnY2+=2E*uyzpA4T^~yuwU|d>JfRta*GaKm<{~UOru2DmmUh%cOF_%S?}|w zGQ0zFpm~tgv378)1ayS}xm@@O6wGs1Kc~sZ>wa5~&~ea+JCnr58SMJ?zlZzaQi^UX5mP6AE1F`R^<)_3cVSZ`2`D^cSIMM^T8Ju{P3;#k?k?7}>GW%GMgM?ebgze!bkpDUI@xl&0 zDry>VOpd++Tm7)exE(KI2^$=75tZ>s$0UY><8ahG4-#dFo~{gzKg94j0h;Am;o*+Z zQNJ~khIBUZaL~#)PG?pGfyCq6)m}#D>9bX!bhUsa1WM>@0ikFCZk!vc_^V*C7s9nH z*;3AdA~WCdzpWn)tRH(02%0PA*P<;eKQF@RgoOQ@qqV>yhFtjC>4tJ3DMF(zOF{xO z$st49pGZL*QB;EjoY^Z#(jK-g2_c@Pfx@_=5r^v~bE-q*RG4Ei(Cj!LIo4VfG;?pw~Yt}?8 z;OafGa~P89hEJa=-%XZ}2SAk;fpe(J`*5+tg63~B9>TC<`0XHS&5tz6*^vF?c3d2K zw8Q{5=Sg|SuSKvcHf9OzM>}fwK#@k|7C1{#rdMO7)?y#=KEOO3@rqx{p21#tA12+~ zLJ!orWxyK^K+rH>F%?$?mMFa0Y}j?RmD{wN6$e5*;LM?m1ZPZvX>z?G2Ct*dX1*U2 z0}X2ipago-y}hli6ySM3aO?|^xGw`obl!a5Urb$~>Usd@>$f%w5qFhP1lS8P^#e#k zP{#UWJW2e^kH7`qynq8g9$_3Sxx!=G-E>Uwrzp7SIc;Mq_Bd4ZisVbt09~>s6hNYd zU#RKm-yfWa_jE^q*-Rn65lL14oThS1}owxsfaQ!BRc zQtb|2iwXem_m`?Cf%R{1ki;}If_;&06+1+)d}h!Y+S&nuMCAe;-t>uQh>J4^_kYnF zADnIofqmKik1zT~_BQ1SAe9)t0W~KQ;zN*2VIn><685rv%64iMBHI#R-XmDk4Ea?^ z)r`8KuKNR0_`3jN=DL{cT6wIPv24i3@3%;yH&4$BG$w^8fye;4YN~6fe)p86X(N^> zWLFZ2;sv0JPbV#({}E(zoz+OkA&X?=<2^YE@o6a(-BGH`t6av2CYQR+xU50?trXcv zw+Pb&H)ydWvrXfBzdKJ^k?qc3qK_|^FnV6KZ#w%@9ZdD`bg8K6!>@;PfvIf1aT#W4 zRC-L96(FfX(J3!I~Q=)X|} z`Rb}2%Z|k7!;i=PfrI1TGXSP{N*8KV7LQc=Bc0{&9=M1pjXaaZ0KOm8St?oKfDEfJ zSN9BvWZcb|hrCaoL(E;SR||*r9#DCe8v{^zfjfhWR2;K=&Yb#!w8`T#0JL(zGcT6U z3O!cpn7G81dr!^Za(2FFJnWq3LHyKtRJBHGbZThAQj< zZngVu617+t7*T&@03R=C9g=9 znD%I!lSw6!uWQo>iUMbCGgos=upQbS=71wqh0Y385lB6{5Fa3Wra^B+`4-^ExUAfa zMl(jgqCE)dtqX$iHQ>M;KC?vh!I8@@XPNNWoTL&M^FBAUj9X8T{58X0B7S5fotMF5+ZuY811C^LE&Evv75Hh7bw43KIuH60$EiI~9&nbqVduwn zJO(15cSNhEws5+P$CV#IB86260Ea`Y7tp1xMeY#mJ3uL1#U&@B*sOC3 zZ+fIJL#aUg(S#?buRnZ^avh6&BoxqUYLUiCS+0t8R*)YWlFX0p?UtEMKZfHiqb;cv zYrr0#!xvF9N>h92g@DsFl0;cKMsa_-m(Mvmq4ZEUZ)6F*(GfpHy z{DMmRk=NkVz0mDs?7XvutY4bvo$l93;^|0k2(5Y5o<>U2K&|{9*VleDt*mmP*kZ8P zKe0=@ha~lheQD;ZLZe=w($}oQC7O!Njweu2lni*yJ+D#GEAC;Y=eC_-O`A9d!yfW5 zFj`qm)JF+SebwAUELrf_$sE*8C9aWO5_>K%;%L$M%_r^~=><}xIyTUcXb{Z9 zl%>}dW%mI&(Dn{1-|(W>FjWtMj81tu>z?zHy*MRr=&NKC#qP7IkEO4K;N_LRz(jJC z=7H)I0mlG-$3?o8-Aj;buZer8N!=ioX__!a(KzurWSGwqiUxK@_jcm_Zi8gXqzbT| zUkLWh zvZ;*W6sw)!tX2jn#v1QbiB5s7@ReNPlz!BiKzJwiqFoz@rju|7GD!FND|CuQz;IH-t5#(v5Yq2(n=;;fIR5 zN@fi@PS?IGMH5WQav&o#rQrc`w~FA;)rdd{`FmlVJkxIx&w-Wqx3T>BP6#ygK8y4r?3`XYasCCs99VyJ@3=dw!> zoUi1e0@+LF&wp~9FKGPX;^n#!$aph3W{+S2*5x?P5D0^Sf0_*4HhBKS^@#BX^ z+5!V8B^O-}ID>B=E-h}oH+7RI)}S4!3VUbh4%NQUEdnOL7V>u#j~BLcp}B1tDgC*n zs3X-5c6!h=$ZzN%WYg1`8f6FYfMVua+S>4WI5U$P-NB?jV8a?v(ZePbT~6rbO~s-N z&oZ!K(H=DcLA^U*4bLtGZd5{n14P9j##@4NKD#*-(Cs-li28m6R(gK)cuhQra8MtK z?ZB3%rk@CG2<-tJTgzhvp@S9`NDly}=zF=bneNrhiSab3Gc;h423z1b1Y)Cegr3rx z1y?{7yX(-HwH66h>8*B-VMOzn-oC^8ymKHKM%34oMtn%fcx{p$ntSI8Yg%OmZBG_m ziXi(yJRq|cJ5X+IfQw698bhI|BkOXZLstKhxxHP&%trZgvV8bU0*4m38Xg+$So z9RCb+neJ$n%PLSBRgLfvx`p;o)KUn+_`&q4c_duxMK(EqZ~xeuA1Wr zZIFl=A0D?KvaDL*9z{vu#XT^5+R;I^j`nwp_xRnN1IDu6l?Fq*;bv$zH9sKS&20lS z7`GU+HljwMPHSw@3=G2(R8!fvn*d3;SI#=pN88}woE;#c_~JMt!wUE6YFk-bNkCFS zT0quVG9&^BAd*)9woFRP&1n;jL{5Zj5bf#lLI>;inp%8GIzAU9cbf)3$5sY5?S!KN zX6(u#=E(8OTGB#N4K5TH?FM?GFc_*xK3Nj_vb9NCSuo9DqJ0i*Z>njk;H> z!C7yUJfr($nw!Eo1$R>^Wdl_x1l*Z}4)GHVd7tt{p*_JvFVRLBPLf_sV%z264dA{` zJ-qA$mfLSVuDd)!WLoKEBL8tp^&e>gW!r&jjd?WoL;`n&5F&KhhRQ#`p+K3770F-C zK2_1%e!0@%3@Rz{r@nMNGdET*>>zqArw&>_Y|p_&ts`Y}^xm60kd7c`&w|+=(N??dujduGybVIwMPd8HKdeLrfLwmED8uTb zjH<)GzT5ajYgLYN)m=5UdRFm-^*Oa~wGic>N&fX0l-7nazt{TQ|Lt;iDyOiQOibTDa*v0*&al=ZYDDUE;`JLMUg*AS`rJvCt9Wuc|IM11rpF zbL%%3idP4qkZ8$+YLnOgf%6fAhRIkfmIE`qKNv9ywn{_d?3W7W0DCS!f9g205$>64 zCTd-r+w*RNfn;fDnq~l8jmHkaCvD@xwxZyzRT13bAIbEP!~MW866TH@#zxokoz+A-#vsY;A#cspZI z9UK|8E8CTdW(YWd@~M(6Dk?->Dd&Kk&HZ#N@ z-uvGedlP7=`#1i3DpX2~ND1QV zg+K^e)D|~3?$tjA*TCKOkB)T3sLNY$0O@rzeBr4jxDI{Ck~+lM3$DA#h6+C}KJ`r} zvth7qdylEqiFTAoaD~K4V}A69_4N-cjS2PbgCS_EcZy5UlB>HGM|R-W&bDH^wPs(~ zrxqS~JDFb4q9i-HAx4gL!k*Miss+Jn?Z2?R<={Cf5&c=g;Pkh`p-r`RjtirVgjTANA?OhOwniFrnBr#b$aW!JQ?NC2V#NIbZx|i z)m!WwR4l2bmU2X+&fw9^bJ?1_bdOHArSzSh9WGe$QdkK3iv0v?JYo7<`BLzD0mw;L zil8r`qS#y>2)!*sJ*?~^{qv1FqnVp>w|u37#=KoCj8*jyJ-eP+r`3J|D|TJMQlD#L zWw32YC3dqdg>XU!^Cnl^;*ju%yVnxxs)5+WO8!_khvS2u{z&~x(U(0tjUr*;dm1-5 z3;XAjr7rN&l^dH3EQjQUDQ({8xqy|MvgV4(^w((j#G$L#&q+M|f^?WQGYf8T>;-F9 z9PSmfC2(_Sbd3MX{rqyPcvECo+jAgNC-q^C#s}S@h*6e(>99RreVp;(!-u5B4dPpk zP)#(JZq$FJN$|n%pmGSV{tmFmX}LMB@9S`aa!CZwM6^SgE#8bj8EY=Cxv8wpz9&pt z7-_mqjYb&m2;m*?um%>gCJn`N|AFTR_B}@yU9I@vZBxcJh0?d0B56uxe*kc;|(N@u9qw z^Jfpw4@N16WEAtyoeN?JdF@{AilTWzGe^UXvm)wl_Wi571y(1R2K53VqT3G*~t zB}|XMT?Z%$Ny_eemtI@s-^(6KzaeI^b9Dt%xA1VsK9Tu=?WhB{EGveD4#uSJ&pivr ztUVMML_S|P=62O_4uD#*8_tDJh%4e{IQ(GDCTacY9*5Sf~y8@QG%sUhquf7Cv6Djftpejp=9Z zk_ktjIzmm#OzdAiL9`s9 z0C;ozYi>-o4#QX7=ko_K<;+RUaUmyGMK(BgrzU^ZQkfro0Un)M&pTUHm%t&?$JEJa z|1&?-x9dvHEmNs7SCFu6){SbbE)e&#=$(&Hogc|7Jcu2GHdt%I&z!J#*4#Ne-o9BB zKK49zd`dyec(2}vCw8RLaeoAidd5&sA`Km7n%BSryF|m{AM3n~u`GS5j$x;~A?jtLnl9u||XF9pUpTkS@WqZ9T zS0%+76U&~G`l(V#@2PLEM>Cf&_U9cDCa78)Fe04tIm?{iQ13DK=uXm=Oa-U z!S_5A7Q?`m$hs9*x1P6=Txnv7#4~vf%V@cF<`vNDS)63Rej)TOk+(P|Z zmusrlCbO>XtTxkBPBnI1N-JDIZ?^8L zj#yA_bi&5#h#t*^Lify@Rs$JyYMZn#6j+k^c$pFrz5uIx_w3TQ&i!RgS()ClqKS(?^J7G`Pk4+jp2t$ zSKL~^2b!<4Kojn>9umN1FEEJ_LjMkDo45mD8S)to6sE7@Ip#2VDBS$($8C zdRQL`@x7!d8V?$XRE)9mO-BVL(fTw0y{W%z$|tr>W#%m6zlM6d;*;&;%7iLP6xX5Y zN|veV1-VTwC6R80E-mU=1%#mUQav&|9lIS0s?4lA%Yj=ffp%Wz6+zBOyEXi^{)5v( zIUcD)hb3m`8Ke^0bIw5(ZXt&1SZV%{;ItQ9<4+#+77O~`x*eI6cS%=YH(L60RpveO zwrX+c*0sWyek$TSbu)D`O=#w^c&Oresd~k(;##cV%MP-jkR~B-2DY!_6W5=0fmrIX zp+4pTVH;bBodE%7wZ?eZ@C2r!cIa+8!q#JM1fL4~ooA%(9o4hX`FE==Y%Obg1J4m` zL1+?`T1tHnJ|ksKt5?U}!Ivcb*7-iLG&pBYVZ;qQ%Y8{Yb(}IE#{UCvQuWE zvFG)=Njt6VI{w&V!^qhFUC@s@6_Y05yv5Z!l1S7(b&HzBBQf4q)e8+?*7Hl$q+yx6 z+$uo?Rj$l}v%lYoexiQ6>gk~e(qf%%gVYj&h!`QaRsd6`b4oh^J!?oszi!BTuE%=3 zOIjq>R5kP-(d*zP|3onz(a&gg+aPLYg>4?1FLv;z{vn@8t~pP##r($F3eWt&!As99Q!Kgz3Zb=XpHw3 zfX9B$LT2sz_wVQXw3AK(^ahot%J0IfAdHRFV~`(lhTiN5KI?zk4{1HGJk3F=OkqOR zg@iDAW~Ua=J%|O&atIigq_?-^>M;sLD7XvAAC=J<-Y_^5#Ej%{Kzkx{Uk85+&2+x2lzPjpR`5VxT@gg-_)&jAo*&qYkK z!3!Pi@75NicCm{alQJXPo`;Ay+4K7G#jy<4l+$21oRZV=%-(*e8ada?%ITWS#`bH+ zNARA@2TMH7I&)C~ty<2|rahFC^3*g~Gp{q^kCy^|x@F>il(KZzxSPIiaZ89imn`9w zR$9$UkE@c=HVvU~)SxA3;hG^LqM=F_ksAFFr(&teBn)d#a;AX~G#4!PV!(WNCBBS4 z3Ju%@2WCQ7m$9tmYsM7RdlaN&uce#vH4A7jWzFaKV|1cCwzi>yzp~6BrxQ|OFZ4lj zC&@-WYV%)8+=jBj@o+Wc6UWo4dsMGv1`-3cHuM^{+a4j`=V;7F-<8?;49V-5E7XZp z^7D1-qos}9m1G8=?#@`A3|8E~yGKEH*w4*=gA3=!~>?4q%r*6K25oPVBLGmA)CR zit%{{C-}aqn%ge`mgzIIm}7*h^EH%^7aeRASziu+EAdg?NACu&adVZ7#^SQ2C*Bd7 z&~EC8^b1Ge*HX8YbMYN`s=GI{N}d~|^F4#b&9w}VVx6aLOX?6gushp2wOKr>s0t2} z=Hw!|v@4S=1~Mmw_B+j5+TpU!jR2PIDq@72dQFjyTFKtjCBlyw*NvhHd3=+ss+YQ%M=uq^FE9jlpfVn(8G zFd3P=-CPf!UYpb6kE3hjukSWM54OqZerSCG=i-!x1G3yI^N)zv#uY>p^uB*=R;WRT z6m=Mn>h4WdJ@08PdP}eOBhm2V>63)p>hsTRUVUorSf>&e+NZLJ&v;`DNLTBwF|+)2 z^nBc=8UoTN zTh*k$2%}MZw@bf|?uXQS8Izc@GgSMusjKIlE=`!~UCDL2$0a`*+ghrV@f-SyL(T8$ z%^jAbm{gJ77Ax4d?UxPHc3CieIfV2@cOQU$44$c{S3>vaD$kTc@7CpAGUWBY%cQIQ zBX%)q?k*;$Z7M5IqosZ2fk;8LrTz=`5Ia&1#5D65MSGo$ViOQiG#reyf39m5V{=(3 z^yz|WNGTWJ)I8eQ=sQmr+ivUEVpYliFxXHA*vUH`!B_Jn-~a@-%p=m3ah#zGi<}iy z-4vF&_1(uNALw3tC1+X8A@s(P<$-nbo4TyCN1eqk3+)gsHICO3kile$z2@{cST${_ z2Hly3%=XG>L{qW%X(DJxJ+{+TlmAzh)e@4h9d;I2Rzt2=Q#*#ajOg7CbQf@O6Oun{&jH5O-8pEUbB9!! zE%Uz&0qr(&KZ*a`PiGBuygLzj;7R%DN^Dptgn|63gb&W-`~_U-M~I{fZd^O@I>BUy2qK7qd~bz5 z!#qnoh-XK_`8y5{J)qxRKXn3Zy!^8kz_#o?pmEy@bW+GmeOURCgT1v^poYqh3MM++Ms0Di zT8RDH#TApwXQ2cu!9vVi$rhieZKz#5HlmSiU|NZYj_;f1W=cea#6J==*rny7R1*pr;-IoVYFo z^k4=!9FXNTAg4M?LWm70!jkp3uR=P5UiMfoFeNWS0D{J?TV!x*_Saz0wQI1|%^*59 z*sm+UGk8#9+yC?I=j_OK$a?~}+eji}=^X;1P6TP#4E!}Dh=}OX*H*AYL?h71KK<9C zsWZy82N6soCaAyC=70Vn)Or15xHc#U3ZN=JyoyYJxhSOPx_jb<8pGkOKS~h z%kATzXfk%-d-SwYvK&^-$kbvdtgpV@sYo z^)F2G=Qb4%Xq_|gH4NLR@+hj53n-XLb={9@cqMii1fRsN7C#W&a8M3CUZ{ep1uE>mB$$2 zn@oX(-5G|AX5Z#8K_igtb_eNAY>X*wiT+!uVlss|Rs2N0gT2NM`F3O2E=0Q)6j^OH z72fmjoO{UT)i^&h^XVU%FO_Df1BburemUmdSNsoyrec7lMZ<%Y_T2aySes2? zCFzw(uNKwi><3%KIwrrP9^me-pnAI72zC!Q{hP?lvN`0YC@{k$nN(2Ok!V<0C{NgVwPOjXLBVe`;N z@bf-DZSY^Oz_hC$>ORNLMHoL%r(ZQM_sZ!BfC;|E5XsUTghMVS#a5Gds6#5u9RCi+ zjX8}u^}HBR2KQ4KDIkv`Lsp}=d2u4W8b*I5L+LBw@7_7W*!Zq&rM3w$b^O9;ZcI3d z8#_B^Z_W9F5Ze%dir&YTOEVWg4uR|95Q6PQdiq0oEwv!h>OnfGs{~ULF~_o!4ynL{ z%~qP4uOuxiy7*TRO$OeKV@^(cr{IHpvjpf@X^@xFFUNw^Oa@Yn`#i1$Y;Ab=fJn3N zvY|>bXu{h4?WZow1z_LJwWd5T|HoryV!0Bh#!s6(a%uR@_4WFPasr49%`D-HPa#-g zkGGIgLU6IkPGimSv-4$!AhRBcxJIg6r!|A8aF(35@naM5Mo8k6(lX(3gy8D(WM&TK zQ*dMX|L(gtre5a_X4ex|@^XQ4-sbWuR`LjLs_s$;POVOR>?}gnnVX@vlENb+??6PM zN1jm!;FC_x-P4=9o@hm{QhdFnxM(F$m*V&K516>*RE^-R{My|1!qhx$xk{zZiuNSz z<@oxzkDNNpMw@e+6}gAzSQYh~dSXW+q70=>W^#jSnX+Du;-h(ItzNZE7H8XDt~zG6 zIp+Q{@2D%4&(OQhgW8LUZaZ5OF?rg=7h8rmF8NH9&2w=RbtB_?&h(VoWKp~Fo?+B8 zZOSjs7RFC`nTBrQjB`imxiaMvwhqtIif_5~^q1OXlncC#6)GLEc!jg4uU>neDKm9U?7w0W-KZEq$*+eYxb@22NbfEWI#gBpfW`be{npbV2 zD7kq2LdHspQrD=(`*JIa6US6vx+MWGh{|{6s^j37>u3f!_KLon>us@n+_>3 zfBwYC$^U(Vh}d3d4P*ED{N%H0>{0Tjn23`jC&}Bat%iq({~#N`%bJ2I!ji6nSxd#I zHRO^tf^BX9e12 z#5m-V*t%OJm?Eo?-hYkmQ;S1j-MPC$!O>judKQ`;Hq9W}FAqilRj}58R-pPbvaD41 zmV3JnfIn^23e@8t=YXB;UOo4}@KW48POFH7u@MqXJsZLP?A@rXxSDF#IZ$8@aH-j{ z6-2#P1rNI#gIEm^Gil&u(4wp$jnVeood<)S%@J5j*Vs6Chplo!@jnNxG52a$Fk0cKzzuNk@50&$8i^D$ zfeZGp%q*e!HVc3w%mX(!w@ti-d%aK<=nuzUgv0Cnzk^H_DqcWvE1r#D^>;z!^(!(? zZC|TrU}nb*yEGVlh4x7Cxk4Ema_V1U0@AjRjAUt1u0ZKSS%*Z^(h-jVDL+?9k+^lB38AQLG`YtY&rN*a?)dDJR6yPm5FAZ+LE}+=;ce2 zQ$D^`0kjAW12$4mEQ*pZdaV_V=}&6!W=Z->R-i<1I4Q zBkz|+zf^bihmQ00Mn!5`Tke|fukr%6`s$V*j_tqOgFRpT!pz0r6-VivWEi@vb_VvF zViQN89yuN+X1kUxk?pqxUpTK7R;H}l&+0=!Vv+On0OWI*9S zMHO%m&#rlU{j65G{%eEJ1AxW{+h<4S2VF^rB4sWtR>CMuMR61<%KfhiRpz3zw1^E` zUPp8r8trM{GTPdh;cjGKvohvJU*rCwgq*2pIPWcT36Nv=v-{xxm(CA|@E$2JB0-gQRp0yEa~HJFwTwV_nR(o zF-cK{G%S}DUthtpErc|WEVq4mzg16}EzyQH)2i2Y`g>iyg8i679Abjd(lGv+}jOtUE zXRSG@`j;oK;0$KKUwTzVObn3qAt463R^deKGLh`ubUseOn@pPRq8Hi}I@d1foRRcsvx2)%?d~`jUu$QfRLstKM#sOyV6{7!$Nv#3=Dn4Ru#$LWe}X&0AXwO!_O&qtO*sg*9Jmj7QTo8~Y>hn4Zl%y>>e6 z_1{Rt(dFVYX?ZtgU&?4;3z;N+o{Tbfo!!fazQ;@zb*K<#SN@pJ&-yquw)fiF>f1>A zIgstL>+kmHkutI+O`=QBwO2+fHeTp-FrLjdr%26`4AbIPzt3d(C2F)w{BzkpQB03O zkLq{md?wM@9d1kQF{FqY*0j-5Otc;6+M9f>`rJk+AW~IFPo6 z27UY;SeUZUMWTOrzjRsqF7cx9Qj4}flcKQl@2u1Nu$dn}EX(hEzhXiC(m6;^s#y6* z=GkDH&RyN=H2jb>w`YUz-;{R?pt!xa<@KZbgf{d5XynwuM1;|gF=dxVd?B+2mNS+h z!mgeMJ)l=Xv5j+0Gx2WPeRF*#zyDHe?m`s3w73`SP=27eM(j|_uGb3|VaKrnOUFm1 z#e6AwVxwFv@}e-mJii9%L25+ zIVY3LNL0hFr<<+i3JMjARkWKSjqL{w^(KS$laf_cSo(lzB6{KN8{>=C-$k2<{WyL65HVy7?(D2D4d|zfbwN>Gbz+7FS4Za4GdVqv`@N7%u5SEf=P9!DjQ9%`S?$eAg8((yq|KQlV9P#wO+1Ay>Ch@2%XrNr&lxg3XZBm94nouez&!e%{e46ma`H-TNMdqlnv5js?~0I z?Zw2p88}U~B}Z?qycZ{z1dcr!THEGtyI;y#VXRFLUn`OeuqVXi!5 zV{?p;NZ9W5@FQnUF4%);o5+b(b|h!Tb)S%r6BA3(i))8&+(`O>et862<&(BiuA95f zd@JbHk*5#;aaAI-@G1D4J=Sq;7rRH7n#e-7FEygwDGvuI_RgeZ zR(Ck|8lKm6cPbTBwv+oIsXReC<(_fv< zB7{m4yiT}h{`jbY!i6?Y!agUY{&m#*+AeaP<~%$Jk|%?(Vs= z0(4wA?u=Bh_n*0pp1FzMrC(!=N!Gj8h4e86QcjEU@8LfsWb~9idYZQ)gQyS0-aOo) zJqF;oqF7*Zgu>Ym3SB;NHe9T5^Ve<^w}r@sSu9vrg5GRr4}G)mSZg}!-#zpjL1F3n zmtY{XF(vJ;-8ebDC)L?Y>Xg;%)?KEdyNt#X)s8NTjm_EhX5A^>F2WiU)PqPTEjr7{ zA6xHFo;FY|ef9qvrQ-T+==$ z-Nr#1+v!(lnx-Qk$90wK$Mh5_NfdL)pYPp}KVLXQm>ccktCl29g*iF!2y{lYK1Ydh z3}#jWDCSB9R*{#$`--9%SR;ZGk&-{}-9@*+^=ZA(vn-6MFP_fuIOlic=64x_)2 z3DZ8`EQ&+FTFPE(myQC4}ek(|XH zw+_F1m_{_hD(NR1y3s#G-bfpk#^mUg8>Nyp!f!s^9x=G`U8o+g{W?_@NvHFj{R1Yy zwkwVA5KY=8F(Ji{m6mH5(wa$@SWx!OS#PfzPV_eZd^_Kh&eQtgyPol7UDgkiXFHZI ziaGg$V!gcW1Ly)?{8-n2$fdGdMvSjaR!;Tk(ek$%2l@;qPQ6i8*$X}WzH3oig8LJo zeHoSc%Ec$P;K9qSl`~B%kxtsdFAMhgkqR%A%M`U9ilH|u!JI^;^V`3EO7Djv2BnnF z7Y+!bA`k!fQ<9d9&Xuo`1C+PKS^q95s(%JxSwv8g;Jk9WH~c0|%_z8d zGs84OVY1Ut>_45wrY}g@s@FKO!i{ZLTDCSUFMPD`_uMUG{v|RZBXa$#HCNI?+s~af z-CzZY$|`gFmqYdjW-m2O1s`~l`kmWsruh28KaaMdu8j#T47*-vsLu9^hc5L%-y_n* zlkz@|-Fl$6DGP=EFy@+(&@xh;BQeRc@g-_gdvN8ZKx)AWHzqzz^PlG=T*Kef8xfmH zNtcs&HM?RdZ+;UJkm`NX88E+49&Ue7D^_!K)?b7Jss8tv!+_Or+!k7(L+G1h`wgY_ z#B_jj9yirB#Co9O^| z!tU#7noH~a(Vy?zkq=geU0Jw^v`r8^r$x$|+H>CQNaM7KJh6Eq@fi1e)%Prhsd|r% z12BgMr-@Z~{N2~ljD|ld7bj_Hn|J>31eo7>>_@&6tAvU=8GSO0Ef=o&83WqUf#t!$->)|Q-jklib&lS4UE&>v?EP@fL@6Ud&RoJ zM?!HV?3Do88gwBV%Ru30^bsA^0@7Lf8_RO=o#e)Aa#OayC2a4hfsd@sAQ)|k$g=*sV0;%>tWyv3Ppagq7lHh`FPN+RHvLjzB%S>p%4UU%MDz1RWGnD(ZSqj2X=)$tIOrybF=h%BqkPdQTLUB5QP?y{`q%}(>*;V9h4CE zAe{ZH*wcD@fPIkyP*#)Gz+Ju zJg&$+?;NFcA7~ z4jjipG`;PikIq&aRjPS9V;dsi0xTC(5ol7#v3(k%b_&#BNIx|9l+HG96&c%_1guET zRGRTjA((G;z^Ye5tN-))b);lNUNV^>r|D^)t11r(_&g_Iq%FLALhys|AmffKSkXZnq_8V z{>tvon5q4(+$NLCzA`p~%RW?>JjPP5EnJZ~1<RaywKrN5D*#p4@7j7C=w?N8(daQY`B?P8wb}b2NxH^1p2*a>(-YYq{ggbgZ>b?H|N$EcKXpTx(8$M1lI^9@sW}Y+oyk4m?BK zTR{TanLQTq?*QK>`df}&Z`pDVVB9NzU>sVCYRlSpf_+$8>fTSXZoxQR?<7Vf%_haf z`Y7YN=@UizeA_8Tk?x@wQ+u!6oSuZ+f?8H~W6O@Zs(!m6@a|H`CH9Z)15dHY4^J1BEFTZLYA~OdqFmm1oL5De{U?tb<}08Ptpiy&!Nv- z7NU;`zCBb&mMiXtybdk6)r6&Yq?xVHph23TT<@gQgV@0NUeI56c%=s6c(b`KE#VCN(l?uSbpjd7_l$w>+XU2$<~?sY6GChO`r`%h2_+YW@M`t8 z5C*ZR-Nb{A(?71DUEcs4q!PsD=u)Os=^$iGg7?IL-osGgPSoo@K&qH8R#;C*nxRj_l4kuJU;odz-Y-WN=5M?5f(% zC+?C_H7nxi7`YeRI$7vCgitX!b7QSkad~z3MZb~RTO2iaLYl65(z=w8Q-0rE&ZHs9 zO?HLU_3lyNew5gL8$w@j3PTcFZ=$=#js`oI7kZUG5tVX6D@931#m8A>&z!CkGMkFc z>C3STjrQS8#|m|dEtPLquK&GZdO32$XW#VD)BvGCAuxcZ7s!F?;-e>h5vKS2-PL7t za>at8hReiq=8VN4w)o0*kRu|0H7otAWiGT)H z{Ajt{lyf2q;&r3B{j*lHZC4=s)4e7jDYEZ5((n-ID+4BOJKO&VT5-u0Mm2 z2!Mq$2tYH&XCRm3VPbmG`V-2inOX#{WF#rkd;$(ISg@623<7iM_T3GS)&@EncS;j2 zv&SgeaW)(BG6mWWIbMxYo5kNkZ=@b#QMLo2)FqHT1@A)+aM>J1&ZE!XU&L)l#1%P( z*$v-$vShABp^**ErgSKe`E|xnIHRn1jRfLbgN*Mhd?+188H?r!1&fQ#8Ax#9z@C2U znFwwy;n<$ai!Qz6=?Aj)h1a((BI&T1Da~)Mg0$!cLY6wwTYexM;Si4|t9ryK1y&&= z!KM?rM13%+K&223kmYs>&^tvrKoq-C&dEK;WV#3@S}$Kp2CNQkP4(0!-js1|@?tt| zvdNci%wBHXmURA97&~Y^P$#u>rD%s;EXIjirLYKJxSo44iZjt>21i*D*7D43|BQ)+ zFQnAs1{>;$T1u>XXNi48PIvTdv_p0mR@lRtYqoDYDl)|pzalsK&Q?P2%}q>gM>mo~ z^H+w1B(AztwouB;lux7CZ>Iib?CpAv@Z^y*dN)7<@KCM-D(0|;x_ZI&eV4!fB{3s_ zM$Db|7}wo$vH6$eq;qfA9sBXAc7xO}S-muL@-On};Vz2(7;jv$eZ&TNO*XE=s}O%{ zpn^7b`s?YgtIPWeo0sLy>U{Yv+6;;&nZ=#?LI~Fv+<|5Vx^o84=he-M^VC zW_wEYRX(h(Uy%$;(|_QCLM5$xLSc+RX0pC-7uO?f%TCReD+^T$$2PPzUG$u=moLWr z(HKIse_eRB(S31+UJbKf2{Lc-6O1%J|63cwYhE`WU!Q)n_Pit!{UN!H3#ANa^Tw(r zVZE87nV=oKcKsygDC|goSs(iVvE~g*fGE2=PcB;dpN2&9|FljZP)rp^PV1)6lxyVA z;Z9;G(Vp3L0iMnc5q&9LR_ksbLwV^b28=`SLX_w2M(n$@my5iN;!PLhHcIEUWuIEz zHo&0umstO~C3DNk8AL7+Ld=G`@%&T6Xpxai(ewxNmoL+Jo(w0x>I3G^VLTlA zSo9|}XW&&H=)@W{VTH{ePpUJEE3?+f^TK)?e^%Jy%6-fEyHaZv=Y-*zAO1+S%RnSK zGtWnqy0pau3PBn4^1*{fUdfsK+UF*DwUyI9YM+~ivJ85olpXhB_1Xt*h{j&IEp3JF z&YNZN#XCS_wD&(-bjS{AX?fO71m&aMI`GhNYTgLeJpFy7X z3%;?-e#j_{;?`K#otG6rTKb@XaahcXH}3JhIO|jyyX3yxK#M3NN`vfEgJbo(3pzi# zaWHME7b#b4YY?g-N`!6l(n-gMBS@TYWEB{6N*0)_u5dBj32*6vNE0eSzYZCqTT#I9 zAP=|`;6Gtq_5fn=V^< zu6$byJVi!5ZK351pK=s%zf&F|YvH#77n&U3BJD`|bWdaUT)F>D?r!hW8^m>W1E_@0 z+@7p;k)``>3=}1@ZQRj5w)}*{S@z9oF$hkEcA(*phA{*YmU)v~2!_&<`wMp-~O;c3`$OfW(DV~T&4*~%FYWaA8~^JGs%Y91aAxgpp;&EXwL8m z0eb4tk|CX%xge~kUb`i?0Q}O$|5t{te#8_sUY#L8ES+f)?@)Me>{dCDI4zE+->O6? zPM`&ecJT9E2fK{}GKP#B9R*m5*Ayn|S9VczA$$=^om>!9q#2-_!}Z%V;edxRMzT6! zfiL^tV|2}W6V z%f~;xoDMxSkT2N0`FVEEzk1oJ^ErHDluMmVC`9VMn;&9JBPKRb$IU_KX$Jv)h2W+e z;Fv_@Tq|-9&;ITNvry%};}rlTk&FPS)Zb>gn7+ymPJ)iI`?db&vSQ8jl^qWY-#{38 zwdfDg2Ka?(;DM1NBNgnnWMp2QZ>RcX3{?KSo~8sn84EwSqGrHBb}!zoVb&5Lj|uq6 zO~{^Q&INAafJ;LG>5XZkqQCj`zd3~vXGWH^s_ONW8y#iSCia00!*2uBFlB|Q4I+Y9 z?%(}UM?5wGCz9S4_^S3Hq)~)BxOheN=pv}V3VaqvpFTJi6k=Z+qQGHhVLE+O!I(EP z($gfdb900uEJev7yiZ%>Md=72eQH$F75vRU>y*u7b~}{#9mfI!azj^fukcOZdQS8m zFAcJA{~^Tq{BZwtR*+S9&hvh(1)mqxrEPpiK;D|U%LEG*_esyHr;j`z?wPK3fj#n!4K0w5iT`+LQLd?$HIqluIu{8GOBP{#2Bison z*71iYfP2k)x@M#)PouG+pl#+0d_v(6zFhU{)f+)Uh?fr{Xx#If{&ArFO@ft>V!zF` zceO(A05U-x99ns`D+E~c{6SB3Js$x~3%D~99(L(jVnMQ6-;uyfWHdSTN?(OVcPz0X zK7<+0K?$^ceVDQS zdJi6ljZU*4?czHW)4g0r7GI`qJK&!z1f4YaMm$`2^ES>F*#cKas~rv*X&0MYect!> zYu61~y8$Qst$Xc^aA{z0owkUj6$)68rsn z%Hs?PNzrfeGuoy>f8F;}t`88jLt@$x#9-kCKQ6l{D0GRCqfULd^Z@hEKu@-9(2Tk7 z0CrBa8Fj5iX>)!*k^_a)*ZV>R2c84sOWL-Bt%M2hqfa@4IoQN*A?8!rZ+XCT^@8Ee zDka4$6KOjH1;YkJLg+P^dObfAk-ASQ*lFH(nPi2$FMg@uL}&e4Q)~RdnY*!}d*k!l z1Qj4yw%%^9wj_m0RUB9tT1hm zh3fFdDE*e(x^dMcf8Qr0#>#V()r&!}J@2REk2)hQ%LlM z+r?CGapu^{p5BG=7aG%rr&fxHyVu)MnbZICgDWf+$Hn+p{I>P;>+d1k)P*Fyo}edX z#nVR(cg2y=XrU;1$!4F+&szz{>_9xo7sK?Hf+a&bY_C17WSX0`YDm{Nvk2A@(=?v zy&~uDk=$Mr2g@7xz$S3&??qpj?_P)Y8_V-{o_cMv+It~D$my1!2ar&LJ?6>VP9R4etcAo`c&+Xs$=eqTBqtF|`#@&nXk2LeEuFM2lS?pd)k$dWkBlgZ+> zT}-6aKWJg6l`jYD&Dgoi)sRpd6d%k^UhqCWyQflnW{=L%rd{tk^KdVr4ph@qrxIhW z$5tF~=V=)aMqBY$cb&PCohCFKH+U4>6mT$AzTNRd3EwB}hwC?E421~p(H!8>7pm3j zlg->XE??aG(d8%@?sQLy_S0oD?H6e?OWJb?N&HEz27>_kn+eOSG1TZ3_FHAF8nx_W zs4vNEqfW}V^FKf0)p>_VvSR|IoaK*h)4yz>)%b2m`6@v zi7E-Pu~?+p4Yfk{w=u>PttG0@fb3`8{Ug}}WYI|8C2e9{OCzN%Nhr-42}Iswpo;ym z;NcX$A}E}OH%{zi)VgZ)c{JlF89j9u47)EszbMsCV>!?*hLuo(y7ZeAxEQKT)

U zuV}?ld?3LttImjWVMVFES^no0oL-K)G2sMw=6W`x)@WR_~#h3Zr z2#z2&&-Q13Ct7a{e!wjQJI!{~eZ3;Rnd-nTnvf1&Q{0#}ADT@UOa8T%&uMZpl&Ql0 z=N(H8@}1kPDxYx}4H@(<-l1%Ve~j&~D9!3j4Z5es?-^O`*bJ7?w;9_b*=SAi@)ToC z>X&m5&sHO(GDa4#8NMOr_AOtIHH1SRXj-lF#CNa3L1S(r@{J+|HzI*=y{x(&TCZP7 ztIzwL36+LJ-0^9|kz{0N4FG39|7qykPeXm`&hH*;qZ-96t>m`3@L`p3Yyzq)d#EAu zak-_nO^Xiz>QJ(;^vk1Ih@>kK<3RG7`ClXIrFH&?WqBo#NjODWg?(!hs%z3KwSHZ&x=^ z;9DskV=A~NCTtS2uBmWxgHLMLvCI`3oj*GEJ%8uMwug=fd3YQU9Uu}PR!NL$S6t$TBeo5%H+xP>}w^TFrRQ(*hNXY3~6pIM@~F8ytu=@3<43s_8#NrQO} zbLl=@Nqv#$5+DnT>%KFvhh|`nbEA9_38&=m^~30kXo5C)&lMxgeuHhFQ;w_egQ{(- z42+V8yE>u7A9e|1E`g6>^gSm}rlx&%*Wgy=^&#Hq4+qwQUq{fZZ+lMl7m_!soL6ww z3O?^WT`bA^?Arl?9!3v^cGNYbJ!s1lMl!OmwwcnWjg?BB_~-K)WnxZDXU58JD}Uw` z@Qev?RYgY}z?GlBCm~F82+Z!aS1_Z2hiRL#K?zc2{H%aQ`#VQ8GPFccstE3I%a%@Y zonxapDfbD*nA;pZdekMU52A?1#T29JP+t%XF~{+}&MOdxz9T&&qX$$o)(9*F;__<$ zimB&n4o;r?ar_f7X-Az_LJUspoyhzYR=yXx08eI~{{n+k2dL;<^e;;HcD$egSWC8L z{=?;ay&abkP{f{+kn1D5+`(7@yQK561y}BxRxfDS^FA=%17l-{qRuXbjs3;E>NSZM z1W+fBx^bZl&UBY{GrE&o%(qjC@mUMKFa!g$MI{8m~_+C_ImyIp4`hvp|LDk|q#l8$}s`o-IgtL$03Er87r__H$o6o0rXYfXz=qNPWZ`X@{HL z4uCGk_NhpnbThZGAV)z2mGP1d`O8u-L6xYS+G-#<$$}*F6v)#WBGM~L5$ZaaOLIf6 z17L9m5|Kq2UWG+PaHg{?`g5Q2lFsEO<0>JE7Y4%o9bE!{oOAJ7P3~?3$U3Z2X&Tit zwL{qlLIoc-oDi0+tFl}pRZ33@nUVcX5}1RwgEP|`E#%uf_ZO{Urk>*kx1mH_u?}#4 zWi3|Ph7x;rlVADBP3|HOCXml0dEeZ8T4|VyZv2jDj8Kdcgh$=h3-HTXa$YmweDt~n zkMqLQ7pw4jt;Uo~=UL?H%O!S`-f#GQ;iRlL507~Lx-Gfq`|>_iE7A)U%Qd&k4K-72 zwtP4LNRmz#Y4LZ%3{Ee3W%O{z=jN-5+9{F16AKktywlpk|3lSRhE=t7;bMZQASEeC z!=|Mhq(OSq4HDAbA)=DfB`FP?l5SACQ@W(PQ}WKm`R;R{>#uVR_S$Q$Ip-Mfc%!gL z`pJ#}l7gM+CQ`Fd0+}6{F+e8Z(CtebL}QnC=fDB@5>oeYFOEm{Nq$tMKEswO!}z#h zu>|zU^(%1J`gH4PGQY+qFE+Ne1<4kxJD5LaG5 zKzcoc_9WM{(LmyB;ase>tOcb%gdgMhAQ}8H3gvE3e-a2sfpNc%fH6b?f(Xd}WHb7g zB8p(_vxvFO{Z;uKp)qj1Ik8Ncc&(Cm2eJuKOvje=r6e9CHJqJ!tg@#1c;Vz5QdjZ} z-Ry? z5QiXCopV4~r(f_KkN4Ny_2n#|6dZy>1h`HfcwFUnNTq`WN+Ep@<-Z>VkZ7Kk3zJSw zXXM^JddQwCb1=^E?b}a~w=n3F@66P3JJj%lNX#}aFm&V{Z%AkC9lU@&_{&@SxR(og z51rn2e6`m6-BbFJLba4F~3pkjoDPK;%IP z2SCV$v7t1xajX~C!JI%exiKe+S_Vd3UyTJZmkf8dt@|MOZ1#{>Id*!mMQcbH4bBWg zb{L=tjgiydoztt3HJwK=mCQph>A8CqgVE|c7mb!$YQunMMd>eJ51*nrYy3&>A_f$s zZd9OxUOfe|S|SR8_i*xn{(?;oNvIn5okDUzka8N)?-d;Y>5OYb9r^DqaPa>~y103c z&^pQ7cS1@Y&-ikC_GQZj4B$c(9>NF=X}9U190@5YWfG>V3E3si)nXR6$lOp1wU@khlJT*00o2qB$ZElc3f^mU6)VHGu&wQ%@0Oi+_8&69C zR=4_}iu*907oR2+|6U+mM{5jqtFasrXXsy5Xwk$tvm87By}s9dTaCKl^5KtT44|C` zffF=LM^(L5{#5+$S;q!wn7_>@^Y50=$$I!(%>$i1hjcnm^#|F+O3Sk!HQ#%xCP@2Q zrqLe#Sj+%o>nUOoeD(=?W!QdTAq$BygT;Z6*Gbr;EhAk?QY2hMYJ)K>U9BJABKC$AmaI9K$>E{m6ybl%0JbnIG zOX#&?9K8TXd;i8)o!WJQ8&0{6%g@=z|a^$UGq z^lApOaY_L+HU(_--vc&l39xIOg_y2T7GE7D6;*6Y$^(xoeup}Kz72y*`>wc02!&aX zi3U|V+A9?;5MqErF#fLeD?6su?CI`~ev# zG8YhRc?h}fGqvK9ldT{}6$nv>Vv6ekbE3yK%y;me4)5r0f^A&l@IVw8t+|}k_dG5q z{tGL<2U?7@y-^W>%-F0JI6wzDlra49H9)h3DUsoshQKol>BST4#(yT`r0R-hwe|Qt zRgW2piJv?^vs}2nPUN8yF}1nNM9j3472xc7%62U@>E_$Wl3xD$-vxt)SY8ez(gJ*mv@ zt`iMav=Ly7@o|6Xm=&@o({Y|OR#hyRNHR%uHok~M|L{^H5Drb}Vm#ssh*G_nt&FQ( zo`4(m7X;SW3WC5E1^tUgVC-1?;RZ^B#G-nKGD4hQs`b;@H~F^hABTBA&C&OP!r**N z({Y!EIq_uXZa@!!NFu>K?Ca6)U)9qFyJ785(grF>K23O zwn4vCu4P@*1`FD_H*!%?6lYkWj7c8}5Tl77yM5O^M%naL+63d^p9BL^)C7Q?KLD~* z4FqeQ0*g^iYwLYs8)&*L2)Y1*V{e;l&?P}n?=Ox<#}#{f|MivjrAN>F&B^8H%)lJa z3In0@K6CK5N!DsGd5doJOB^kqh@Vv@AFa?5=;UU>68yiknQ3yYdAbBA{OwNbG_gmAAzCQnwyXtsNAX9~0AcvfF z-zwzzFiZioqEU4px|bl+35dhS3(?^KSnHNj2IdTBWk8ZIT=hLNa?yKt0z>n}7eKF% z8$fqvmO4)Y^9NP}JHs}NeNmzi*{{L1H`w{iz$FTLPr1S9_0{cO)Jq$1Vu1A#2#SFp zz;ne=%C6*w_8Kf6@I*Le)mSYjn4xS4e(=GnL+{`31r|phsXX{b0XF-eBsZFS-nZ7f zJX%fUvjlK?RIuM8y}*H7hCvLeZb=H530-x#AFnTX?+s@K*FA#b0-!)as6V{~uM8A} z+|}Ssg-tyS&TmDzU_w zXdl)#!K|KjJV9nkn?vZc%O#Ov6^Npldb%3k0 zy9OCD7uXCLo}HZn0jr)?e0@6wy6eI@Vd_!pdWjG z1hXBa<^q#ZSKeGH+1tZbF~XKwuxE-AfO$=B<~e^HGE{>Lxzz%eDFZ9(2yl8`QJPGF z(LSvEl1~tmX}ogbBQG5QOrG%_%^czsDCw#)C(j=dLZu55+#RyL76kJ6!vY{UF1zFg zC()0AqcImiS?E2(h#3IInR6fd_(ZOt#J};;cLg7qNX|;1?=r`K@B`(@i-zg#yqh90 zZ$kP-44MK3-x!SH2iYm$oTjk=h?zeIea63b^-9iKr3^|L;pI$e0WhdR^-GE z3F!)ui_MfTjiaE~vU?nurR;#Xm41gr;9OA01j^Yaxh9E>UqPu6DUy`9M3tr&sdhev zRXB;tcXu8B81f;pY74?~8~bq41f+&-DIfq#ZFcSUX7BcJW75?ce8ykp*3$dbi>yP?>)|0^BL6u?=u}j5H=hT9pi}4!W z&i9PelIZh#W%>KSlP{nE&`WP!e0KCw{#00@pp^^5LHH zD#b*+ z(ow$b(wI${jGDi)0(gw2mS(Ki8;6+W=?t;EV+(Z)a`~@{G=pHkcaNeUQOfaitgt}f z{nb`NCR>T=CBMbaR{7o2RUpWgOE#D1JNOK3wUpU8z_SX7&Mo7#?E__0$FX~ciqy7h zPiN4d&h9D4T2>YS_DUEf1_2UM<8#H_WoW%a8xm4X9a#)qKB&r9C-GmLguM19377FsRZ4&rg> zN<~e_n46pX6ug(_~`ypX^s^0&!AiWOw?1ErZjl>K~^>yNXVe@>a?wL6c!L51_* zMd|8GfYTf4R2EQSS1vxu1Nd`_BiCA@yl_p=+h}q4=Po!Vk-8-`ofCAD;A31Oa^Z5Zo6B9X?{%vG1)iyDDG8xTen_|cXX^5 zpyN>zLmU^({M-e2hLBif%6HDnpxC@?KHQTj<(SPaZVRiv%JTnx?pSE=E9Vs#wW;EK z9}urGQXbaexGkf~kfy1A1`Lshqd`jV@Y3ekY}7~WKKH8GBY|v&dBY8CRdA<8VV@Hu z#r}e0R6+Al?J^`g?|L~N?Rg=nN6tLtiU#Js*YiQU0@BAfrIcR5Iv0Ie9A4Lsx3yTB z$@=7B#2=*Wqzq<3D=)1Drgw$FU*+^{-lvZC>V6-{l7O(je4V<=WV?2JRSZXS)gA

hVY_MxiweC!Wsn%w&e4~9DtYbiG(bb%8Vv3+ zPG7CgmSyL-h~?a>+l|wM>F4Dg7knlMB?osG(ft z0x!YAXaX%;z~|X^(gIF?&)l`j%_78t&L{AA%SThr4hcstuLV8M?#;m`Pw!pLv&jxS z?(zR%Ancx#5yHm7(9_PPu~xL}$zfs?vRg!I{q_+xw|MbwELrJUO4ie*&*@!IOCq_%!dg7I)PpaaP#b4ff-#j7~cNf+Y1BnJhBvU zaMX}8GC~O>PoR!cF>AAjsp)gZ`ispc^2wS7Y6-m)DUtb(lHkh+8Iq}B(a_Pg0DF$l zAh$&*XM~iE4I2=r(1%9~)g0L+loG!ati(`>8G-91v;PwWhc0h~<a*OF5EW%0!pbA~{#*YAr2vR=Wt8>_#b*_dzMm}UBLL_i zUqKQdn_7XS82H3L4+G%@m$I3WGoyl2QwC{0ss>}JI+r=*UXyuWs?TKF}O;>nT zA0F3(+Yd)1VoTsVm`elWlf|)~p8Wnxi^{c?mERG^VI@>bR)%Ca7#J0{?=sEb12y(9 z5U)1~_)kNEZXg&mJ+<_Y2sbaU7ChU$$TFXNB4xYG)FcDL7#Y3UcKt=jhmBkqqaqIl z^z(hYi83ovml2`AQ4aqEIT^(~k*Q2pxcf6QM9y%K(mPGoCxMRw4=ZUKvj1MhZ{d6k z2G2h45i)Y{G+2^<+-`xTOPiHr`APE|i9x}3Jzc?Y#gCtcQU7~sFu@Iq7&2=G;9$vY1cII_TlT@bzA>*JMc%e z!khHUBegcb?z3Vy3YEGZOp^?~W~%LMDXc!tEUaZ?Y?-kBWdE*`2X4TM65Xqh-{U{kkb@maHjUzUiYD{dHm8Jx>`8(gr_&X`$pJ5?HeFqasWHy+{onREKFU zGxs~N-&D>;#9yo3haq}9>FGNVe&4GrWdeo`!1ZGcJ%WEn>|b51mHhi-;x^4-W%HX{ zheVN$5&Zis$Gy2%1AIr%potK0HIl-XULQ<*@aD}MNb(W?j*1U-=6EcI4~YV!qOdO7 z&lk?Ta+XiM$$2#q4&Y~3uz^=i6nFr|Q0qA5O)^p{vrCv(eP@BI3KuJKUZTPSGbA!# zyleGgcEe_^hA_sj-Agl~2t;$UkJoc$;d|X#D}x3srvzTJ++k84hf?vVsA*_uUwLR= zFbWGRAV1a^^YdHSUV1={^RyZIIC!O1ln(o8swB?ew6=<5k)M|GU-6hGqMJqU_Fxk^ zo=^^>t!D+&I+QkqIQ*zg$8N$&?x!Lu7>AqpjMddbJ3G%!ZEUK;@IrrekbokQh2hoL2gt40 z{d2ksH8sLn<031YsMH@8e$#_T=P#8xT*D=9f1-1pu_B7FqOIYIRt!K(Tcaw3-e|=m zW{)$=E8A^kH@?@FNV zTiedAq_A|)e-tiL`o`fMot0Y zy0WJ?Q~1A6i6lIXm>m6b2R@4RN#01l6q zBxA*Uws-?Rr+I#!ArqoI2WDf631M*?r z-Q9yA{1CpnV$czUGq~YsM(1@4hgr@yk@yaqQoq4eF=4IToehp(H+$&+`PbjgplgZH zfB{5R6(v^86kbKLs#q{y9rnDtG#`ys_+$y8-(Y9e_#abW`tcjqckIdmd!GRzrE zLEaQ%;()-QQcq_NtGAAD?B?cX(U3alJ@IJ{tAf;I5&yvo4;NQgx31J=;3M3ehd}M> z2qUMXd+OzWg4xgzM#LXvZ^XR_0tEUR&$efus%>mpj);2-W~Jr~4k{dIu*b$39i{q^ zaqtib-@8Xc4{N^h^B*kH;dOO$4;YYpYe?qqxN*Nw-0!&g?fd1G(TnZntQ>OP3!dIz zz8MmKx0gSk4ttn$7+uaa>>r(q=kMFbXQoPQY!UEa8!Rl)Ffg?3&epXJ6j`|Q05z&# zhE!L8&GcjU!k0OvrQt~_p#sS~A}hC%8EAx3(?rs~1^xzhs;vXAz(7nfBEpxU z=?2wBjo+}PrPacQ-xewD<^}~FMW`l?s17TXl!N}+O-f4c_V6Qru%&=A&o1lRaaom3 z7pCL$=YGM`U$7=MI(z%X2CBIZw4ZF?Ge^f|I5=YWQelNgNB?ped|hraE?18Z3brrB zlh5W`Bos6>0+Ry+1JIrch38NQb$8qDzbBY_gxn%i-0^*pj7ZRnz;CySB*ki)jDbOx zA_)zT8R?PWZ3y{vRmRVPX9orgixudp-5;YhCpc&%>pMK;Qa)G7`NvPFpYz)aa!92n zubUlA(n`y;0K+8Vf+j&vZJh``%^)L<$#rb(hYi6`-vpyZB&3Gzrll=jMvrG8OE!Cp3MGXTx^hGU=;Tq3g{P@TioJNRd_x7qtM@f3)Y3i7o^u_8&YL02 z%kPr}k^_tQ-P3HlS-C15ctcdE%AH)QJOzV;)12(z(y6KS?435>e^_1pI#-8!SvEQF zfrm30*BgSl#(T`$(|$8*DdHizVM$DJzo+w_^^4`S?b#Qq(s_raUe{?~TYxnWMvuZVt=l~xec!%^@UWf(?2SkRqFRDIq4lr!h=w_8U=9_Z8{L$rMId%8b) zGq7wo{;?>Y{ryYrBpqz1dJBlg+R2d!h(pUkeP&osx$#WA!yoqQB{&vFSd}wsz$uw^ zG`I>$ZJp*)!^2IUMS z54mV)ID4v?CYS1M$ zD4arrf+ABd0Vwtcosi=laYuW5!X7YVbSqHG>jgdr*jqr7uvpUw*v2eC4EGIhZ=ysb z1WK@B>F?>mTB23Xj`#BNT3I7twGHPMplH=r(PeB0ZqJ-DdupW#hSOn}AKjRI=v$Pbb=C6!HTJBx|-N$qQK#4^U zFqsdz`1oe7fF?EQ&Y$~eR%6xaY>uuy)p5^SV5YFnO;^1d@0Mt`+cVdS z_sGJiJ-|*wV6kb;BOO=|1|1{C%kA@nrTW@xEcUJ+# zA9NCuA;B;|@K^speWM||eb|HU+x)X9C7!XG9RUZoIxflQH3fDcsrdV}YLmn;nHN*m z-iJOeg+h+`jV1CimyQbXz<%@Y1$dzoalLMb(n|l?da51G{ABK_z9>8Lx#w$GUnQ%> zLcF(Qg{GR{hd){(ld8B;2J6x({WBw-!7{(TLfKSFz$|}yhP&lc847-$T)+yyPK{Xv zo{S(J=7y;RflbgitTVyCTc)Q9dCO}AD+>m@KSsNk7;kP?0XWvR=XYFoxx7;qd|BD5 zDZpF&SYvKvFC)F}Ja`|4B?SlEo=aS&mzUB_teNw#BL7_OafTfozLA*nbiPUmwY{+%U7f<2#MA?HO1qT{mM%A;5e8j~{znJcRli z0j0NTcauwd2+LW>&sJInJAy*D^1gwL{HQ3m$kbGIJzOk{e+Bf~G^ev5kP)v~uTFL5 zR{ZBm89BdPeQav#MAK?b=2}e3?J=Rv7}z=$HH`bu&y~wk%gX*0Z4QKD7awaYT@9;W zxVP>l@7uC??^5>c?T$BHk6azhcl2@cakU*1tNA!*p2+~ZyRMeIVAffM2~oira}@#e zlxNG#^VKZ@ZJV!A-F!dy*cTf|3mF8%hRWqJ&X2A}>=Liaae%+RCV9a(8j>~e{n$-w%=9z2=V8@ijc0KR{pp@pi-X~n zv1lgV{MGjK^V7O|;4`qeN6+1uz^$U3c-W$QP!ki&dCO(#j4im$MeCzumBYxn7NfrU z0o3-TG$&^<2``%lPPcMCr}ic{Zc;FD(%1b?vguTVcFD#0@s}CgKB=kNwORs|M^|B& z(P;hw0g@3-B?XeB0htJC%sj+9oy<*n%)F_dFOo3{n`A1tdAYg2fP9GF#slEcD{Ha6 ziJbCqmb?Q1Tm%Hc!QUJNL*M01!r?-;qP+fv+1jV?s+t~|qWj>%5Vl)|RY5R8r4@=U z;3oA2m_&3BTm#!#U>e8{9GS^rN~7ms^dK5TB4um))0H8QEG83V_*Aq5vx}EY31Fdg^YdSOtxw8lYjKiAMA8_TItm#Hp1GZBn6VL*+yX7DotG_hxmHyg;x)+t z7kApQ0#(ykB5B&7TGMUhKMag@mvJ3K9YjAxLnS-{wzm{Eq?+G0ju8+(H z0q?jWVX}GV?BE|&wxaeMAUqTZ{wFFbu9P%*1c@An;B{$ud^FHA02IDA zxOu~-OJ^&wXIwNWFvSPC7iHK4RInH+?WCkuhvxt%0`GhHaf)tPY+hX?uT80L+bEzrp-%-s2!OueMQ>k>AvRe!A0U zF%FNe1!JV2uz|%QhX1sbG#R(I+D|~1kxQ^$Qhnblqk3A zUrt8ojvR7V)aQ61n)+Xp_2=$fL%B=2FsXpXHA{^`Ro2EONOKLv680O*bOd11Ryh z*gZJ*$2EJnC^E-9F2qDL&poBCWM;n|R5SoE}N!b$zy}$~GK29_`$7M3#$BesMW3xS#sjD4y^o+We+hmaEmg z^2Sy~JIkovTDP(_O6@g6?{oxuj0`?3dD_U@5;<3a(#N@d>Z-`Q^1{X7n+Nal@2k*^ zl1tG>Jmhaa5AJ1g3+ZBBC0pF`ITzfx75t-r?&Q)jo*?PDV;I<$F-@~Rtq!A+rn;^Jvvg~8QDiU&mRrHLFBQyA2?N?t9y@~%#n`jd+F-=^)}m=+_Z>X53)|-O6T)AJnrJ0{oEa*Y*=u(FLXSdHCSo= zNP@CLnw9Vwb4+YAT=lUsaQ%@qE@L|qUx59d;w7(#M@C<4Uqz)9 z0~*$a#?&XBXSZEpr24~|n$x9%N#By>1#zT=$k;`s2g6^|-do$Yl9LP2se9SK8#|WD z9vW7SN#!R@A5o0%B=+sC(PW?x_NGmU;?IcQi_ba?<9;Dd(UkaON|%Jao0SfXwO*gNg$o_fi>`rHmTm~C1;N$&Rw)lQ}k{Uy zNz@8FPEon0DI_s5B9@l45ypSdCT}bS8(yIodZUO(DTZ7-daJXUW03KYN27fU2tXX{ zS@aAhH4S`l%fdU)UU_TSm3>S2Rl#OU>=1zK3=1~&bV}^$03EWc11tcZz2eC^&~3A+ z@g$-?sf@vjCZl|q#m}K3mmV3((G1v5>lCoSXy(PtagnUz3=v;daJLG3su~+rScF3HK z>lS+#ME|9*W-!`dsWh@{D(~wm)uA5^R1{_SmZl`##OKnug0j0#_$6$dGOhc)xTY@t z4Nv43I^I7;#L%EdRFC&Ep)a8BH2@$sNaaFKpqu{hwGOYcB}Kh4wenqe zTKeXsnnRktn)o_KP1ASe(c||o@xxWjer+G3}qMITgNyN{ksKs;Qi z`kb?nb;gO&HqN?DoxwV*qk1xAKrTLVY_d5B*w$1I=VYjWc@^uAm?X&x21;D5sQw@F zAe*BFLKn>K5bso6cT?BGmEy^461mN&YLT7P?nVvfjTe=tEsIvvD?xDF5th#Z0i6-d z5r_!cWF8zK)&32{^)GO7{e}G=ld`kpK*LBOA*4pIlGsdna8;7Qs6_MN;(4d%5rfzoarW_<}q<8Y9<OI=Gs5C^ez+nHcG#c z>jZwaAAdVMUpV(?Av6D+&Tk1@5oKa=3;_aUpoR`ia2ivjI6_n@JlTuuuhBC>!) zZcjitQ?Ind_w@3z1O6{YKuC;&ZZo&SY*BGq#=S5e-5nJfaq~y{V^`$IG_)te=iBIM z!)Dll{<&E&c2zRU9PB((>2TF#ddk`Cm6)ItG7)3H#uWHy*SPEh9~XwJS@wO0kzeu! z77J@dzQxh>E2>d!#!|6{lr&_exRnf1uS&pJsKmf-;-XEpq)~tr*+YJnT%cL&zv@CW zL=VfS?aJ?HG(h&5bPaQ$-o>lGFsPPti#Lx{D5) zj%JOeEc;n6tSgeQN;)Cq{~G<(r`E!k!YQ#%v<9#rJQ~u{A%-zDWcKjNBiMASlf;|X zWAYSoQcvLQDtQ8lgUbKA=Fbnfk@vf^)t2p&bGp=kipR7&#)Ni-~|ILiRX5g}su5tegS7EWSteJK5`RwM z#m*;w+vW1C+Vz==z<4h$&i~e^z50sEimTxcRwt|n)2JefpzLe^+iI}qrhzMrc(pM- zg{w3dJ^5@IJ8sEUm1Xf0Uo;vt+?Be!%$>l7L)0iT7D@cwMwgkQP7AC_tc}#~*Bg(x z!c|{eqnlup@i!IB4P@AI6 z@`=cL2vR9Q)$ecUX;)I8c8AF70UE+)tPVW%!A9(7+6cZF-H*zTPAq!X@{}`V!~M_K za;(C64e!s2HN$f`qq4xe6ZKo2ms@A+S0Fr6G5PHv&tJUAQW??vll4tl2dA67#yH;K)SoOvO>vG(LlKA>VU+} zBIRJY`J-DY^cIWbbt*TV^wso11z(*!w~2^I{~g+z35Pc%D>1m^nj`Z%!Tkm)>qM(x zMva(@lFVG(at*E&84+BzbQ?-M!j8RH%>A{z{tY~#_x!yMX7>D`)PD<@ z=&iW{Eal%Cetd7N5i+SYX*|0l1V7&ua{=4x#j1^q6Dl!XA)vY;;!&aL=LKVXZXPP7 zp(guLb*#K+vf+}(MToaUo9u?O@k!6hO45h)q9i|j|MJ*UrXL3Y?&UqZ6=Vt2W#V53dZN zJ!nUbpb#_>XnEI)R5E0$ah+2;ahH5BOTR-T4hA5c zSg%K*K3yiaes4WTFD(D>CZa++b!mMn_=R_5TomhCG|aleW`Nzb2G%4w-}vT=a%*L z#qY`KI_Nu;eD5Y;C5+lsPX!_~sL-Gt4YtZ;aikp8Qeg6kGB;>|I>xaCB=x;e8I z-1af1X!OV>Nohd55_d!~#KggM)Y^L!_dhM(Mjx=qw2_BcUkXN@ZbN?ZDpG4b%yAsB#w#p zaCU+xkX&-%q4S%80qgUSOADESG|xZNE#RKfOdiJf2}DiFC#l2pcPykW5|bD!PA#O` z&jMQ|e5ivSB9x49R&Cz3kC0FQH;u`Vk(B)P`3cr$!~DTg#}MH6fMBa1$~kg)Y-TT< zwR0B_0r(d*~NR-+t51`zgAYcWpVY=3uC3H$| zxo4k9x4ki1Qq6?Tx;bk#EquFh7k$o^Le&&AMt3O$ zW9I>j)jNr+>s|%Rt0n{2+cE3v26^nMhi=S)K>;YgKHhuKFO?n%*eFj}SXerHdt1S1 zRo_O_9B_AZGurtdq^}&PB`PVH|k-;ouS1{12Z0%d4|Yb zD)|)psaH%owb%eGZ3DF55)e}k$o5A`UVslw8kv>(umEr&0=+7&XM=!H7I$)TGW`wC zWYwp``+Rk~8t??VHZDn$&*P8lAID&!pJ0NsrX>=Qi)MjK6P9)A4VGMJerTe^%W(#6z9WW{DG78%om{DM1}S(%xF3b> z?yY02JouJ?%oP~G7!w>ETs>;W_LH-+YlDFyLi$ab51YV0>L+4frAz3il7b|P0#qP$ zI$fNS(ic!q*A88Qn;`CtDG-s6X~3A7U)VI9#MbK|+PQ7!_!Mx1#5 zVO_MVih{yx%ql>ybMAKtCFDfAJl$ylJ5IMKjJM)=U8#V(M4VV==jyHL0=3RMtblOcLOpgPE{Z3zH2_+xlBEFRvU4)Q-o6XES;X)!x<-w@lI zmEQgRwx(Kw=3irSyr+^GF>}!}aZ}Fh4CadPMrrXfc>I58edg3U1{}VRRrB72$mqnP zQc_RQNCfD|L_|bNz(|pcS5KQv2@q(uW>VtgY2?^oqf^C{ptjt_pOB_dklGWo4#b$Z z=J&mPT%mgR)J=>**5qQP$60OE`^+6BvY2Q*l_*IkmP^S@ug)8yF8)tdm0I~xCr>y0 zJ$UEy&EA2RbNM`TQ`v9vs^m~V8?Tr*_(kHz`@KCD-TA8p&lDHV(rcR{T`tB zX*#u}?$hz*7p6{izsZs{94-a^q_M;>UQJy%OzR^<06} zGL78z-w}4q@=e**7gVL(`eb1EkToLw3iDI9a!xB(IhO>5lGHPtZ&6UK@HtBLse>Cg z-^5rLw(~ihG}c-$(AZ-*LeHFA$gOfh$VGdER-Hnj0{!5x;4@LtZ)SZ2GFy>XSDw&0 z4BDL*Z~`+MwLiN8DZ>r9YvBm>(HtceWtB|xu0WA9bn9T_{RIdiQPI8uu$77hFIu-)@bs;_?&;k#m&F0 zD714GL0ikWM0twoW) zc^2m|OR)dBU0pL+y#rnm1~!&jmlexF@JJLre(Uv@R8tpHb6Q@{GL}TN9D6`Z0eqRZ zCr6fxfk#ImATN+J^c}h!TCz`PScWc0UQW!puv{t}sil0dZ04s3uDEw{CKaz#2z3+&GHY>E-*4mNI4@Jupu z%STx_=O$XP#;N#E1!P-mwO@-99~;$uSy)ZMFeB_8^8zoxB-m(Mt=_$Rw*76#vsK#ilM9bh7y16D=J|HEM8H2?2qpR87L+VS01 z^QExpKmIiP3FjNQsR5cr9KyQ+`%Z0LGjD1d=FbIUMZKYG9xn9=9CX|8I2Ky(9bdQ| z6ybU~TGNk;`kWNdLc(ub&~tlxEC$yoOP$_SVHe-Hzqp<|$FeEpa+kv#4d=KQ@8aQN z>E_L)TVM1bDi>6gzCcrL2n1Tp-_5IZ3c&yu$XrW6MOs~38}!oWwB1>tty@MYI+bvD zmxLiqP}74*L+tS#Qeuu))Nrb4M_2KNk*WCF$I`@v3Rq2|laP=+!($ejV=VM=b0cM8 zDd~r4Ld=TlSN!gd86EO*YhlZR`%Ry3yOy53qSBXaeeoae65H3+^`vF`QP&c?KP%2R zFm28)0WTI~XQk3xH?f_l?{O`bZ%9pdCAip6#3-H-grrwk5N_85QUFwEDZ<9ZpmW-S z#m5L$+6wm3EJ>xhLha!gA3NUD-HmUG$s#`#kJQL86dfYM!#@HOe^MG66kw^t4*Un5 zp-j;9=Y3#Un>J*&KdPn&IP&*T+kcIDuemy^6L}uTs@77r!-f{az$ImYr1UDu5KvV& zgHZM5otT)Ic;MVG$sD&T#>hD4d^}9F0kZvYwm>jDinO%!^5y>zQA%L>u)|5HQRV-R z_7!(8$$2O5i=I(q-#v0kdt+n^veEV>?(O%0jgJ;sc)k9~QhuNPy%?gD-$U`cMslI` zo8K|UD|z=oKYQgw@ietazPWOF#$oqttWN*!Jk87`6Tv-VO)*0Xvh=4>a{CP+1Sz#zqGtH;Y~HP5PS-NK|YrB=ejp5Kk%4ly)m&a#4@}B za_-O&_V^N`jzv&I8S;~pi_C!d^R;x;9104GhY-i8&}(%dH4NH6K@R#as2)Lk#iI`j z4~WqT++Yqsp`Uuh11j@2pjr9(VT20QItE~pDk&@52E>FIfF=t(SxcoFb!*vhRB&8R zQ%x0>45U6exY%`QiOhWpE@L+c1*<`l76=s<(o`~X>oLKQ$dW5XnXU$iEjlrvS448n zuyA-J{QHU+9Ro5d<;7e>+Ub$S&$aB>4_E@}Q7Fao(F_z)Y~r z%E<{9BU72UL+&P1KtIu?_L$Eh*Fd=t-NuF`@fd(PToDl;+o{HyoplSq3sq|Wv z&l(#Wt3jK?;&tKp+25aYQB5k&Vs6fj%W*ZF&pYduE@1y>6RZHLS>iMMiT$P@|2^}H zefn^WsQW#nY2YR@!5VandBqg~i1Kzzsk|??m;Lp$&@F!k!IiX{nq%^pOV&o!*gKDu zdaM@C#=6NTT(5e$&z!=J+d(6_)Q$GrR`o!X9`N}GI9PNnqJGW+_H_RKyv5Yg+#}vr z_1bj1ogw;X79D*%mpO|g}PUr)r;Jk=vQe@lEzy(?G^ zOX%nYk0;lyrqnAuN%CYZf_{irwC#uq`~)FLAEZ+2L{~cRfv$58&u=PF6ejY3QVIWb zM`UGVB-U5@Q5Xg}!LnPPF?Wheh>O~*HWFlnpbSSQ-D_fkVFLbiPnRZIHzr-Zs6yDal`30CmGthw{&{D%gt;Z1P} zX;pVgG6^L>)?Urad%-s;mxFmhhJB}OcWcl(+L*-`Zl#Y^{LP4^0~Z(Oy_PA>tV6FVCKo!n|91L319NPkSQK6M}J z=l+bwZQ8E%#*dxr8b0y>VfV&@)ZCSCKzm-qANt#-ur247;4Hi>R&a1;t zp`i;2#Cu@?@@mJZCFoa5Jv1RxnN1;>e0;HKuH%-4V@M3@DbeMCQt2{gs~$GD8Ok3| zclSJ-?y3z8ozDGJ-@}WD5i!XgIu$lz(ENRE6@~FI#i%}YlKjbsMk_G#8Yi#+!xruf z+sT3dL)ceFMcGE}s@MTa2$D)li%OSDgMbV(G$v)1|V!kK5Dd*6Fs`wFQ5hvHlPUD!Kw^P|(Btpi0jaYEj_3~P58t4^N4Vph#ChH+h(BjK6 z%eX(@P9$;CTY05EW);dMiIF!2){c|x<79*6|DFp=;WprL5)D_+?kOV^D8|qd_i9)~ zlq5#s^oqLKn?e!iuD(tW)5BR7(AoGF#Z|A`A2+f)KbTtTU}6{+P9^FS=Dv!D=l_P8 z+2J~%-np5}jEAPWPgZy5G-B6=dzYrx)(^kt2+}E^fFWL_t!w3z4(2n?>kfwX&xT3t zlm2~@-lkQ%zAZQP;EvDD&k_s;;K#i7-oOlbCYj1L@584)FWv{c{D-7F!aJwbj(2Eu zjfaxWe;ddsoK1Dh^+XHS)=pO|2Jt3!HEG5dy zmftXz^|&+^rn8Mrseh7Aw7FO#b@{jPbCKF5A6W(;kjihnsQp8`*#>@d9a&fwYFv$n z)vG4MdKfY<%;u)CA2T)wrRA$lx$ToJt2n9nCH3sdxMhV7t-R*qy7@`@`8n5YEeq7f zWIxIjkcp;CmZ2 zr%ea88Rmd?5+`r0W#v)Gx}skiB$>>}O|fhG5D=_9)~Da6EfU4hKR||)N#P+Bvp18_ z@Rce7zEkWPKR+R>{HY)CngC$m+m_6%Grj=YIKWrR3x|Vep@|UH!&6GmsLqzEYMu0) zpn4*49N+^$5b|<~=i`O;D^va16^75?4*0~CT)`HDp@1(!()LT?{k=6c7MHFVfBe<# zhFnXg@uoEw-07?J(q6E)Rvc|dZ|zuip8~iN#V3m3V@;x1nVISNw+s^XEtB!vdM`Hb zP{u;NNc^*6)AH)+n1Cjw{k2)2|Ak3JujxDN-*yyaEITVT&~PFKwJAZ~RXyk6&S%=C z$uoC{Ol6b1?N|D4TLv9|lrGWQsBwaoLs*g6?%Z0H=l-{q!xP63y(L@l zM+Gg6THjQkoJdemKk{#3Vrt4C!#%+qZge@a^5mZyk~fcN>fCmuyg_;6-)}|Cz|T*H zCL$%olRt$&A+*J0}eo6)-UVBK>{GWp~_fDkpL7>`)uF9hiSyDNh9mb?pg zmVeo=JLr7%o~5WI$bxdT^$7c96QHEJ_V#N84032b@-i#e{%4M^b!+6X6hBe3cOh7o zW++SNBgcySoAqKV23#@5bf~#9j-b{X6oQ)Xa;QwPy{*~C@3_O|Dc>2Fy}L_F24C7b zQ?h|}vTghK@4i{`mzj&J2gh3pUaUt@0VE&u>hn4{{%l(>t}u5N7~E9&2MzcZK|mfM z5m5qgVk~TJ8(_`x2F$uIhtSK`LZV=x-&G|wzWB<_gX@cFAw&Czg%K;z;|(xC2P@XH zR!EPE>Xy^`>()3$h@PHl(}nC3G661B--mbPelbOt=63bh3(NK;Cigw}?$j!08v-;X z+Agbl4%jS}4bO>-dHB68#owF;nrh<7Skd$3zwXd++EFLNx^LXS#=N>=Ke<5a=k!WrMo;_Qatx1=9!wB z53lu~-(ZziqiH|#9weVh3+H?;+;ugRzoc9GVdAHIaQ)t=kX^|Z#>e?pF((YR-YW!Y z9lG$nc)$d_VoSBy;*Qhy@->Oj6*87=euGbMOA^n>u@P5B%Mnoc|G4#_zBDbi;v>cT zp4_z&Ow&Dsr8PAM58?7mvaH1yAvWMFAUcyNIgQn(#)vpN7+zH(=kXW$dbz=&^>|Br zI>cig&@XOtP-->_#Yh{t(&d z0-Rx;L$K0OxYzwBO~i{~;><|UM+{4&@JdVd3DAk~KeQZPjtl%RbV{S2#2xN&0k4la zrXnG1?_AAUhSV1yLR#MW_+2$jOq|~&XUZ(>nn?PhsHOVeplZzXxrvkdewp#w^oU>= z-LQJsFL>pn%kI+vL-TyZ{7rY=KPH4>_fx{_%#SS3{xqLWwlPG$%9&8*Fx^&R#TO56 zHT)XurOMuNCz3IqMEx;b;@-@Be5ZMz==Cc7+0F z)YI)cN$%lP@;(1|cx4DMnxc=g`)O|91`sAGDXGC!Nl7WB=_PwsAYNF1h?Vp=;fhBm zP95{U-e?B0C%& z7M4(6eh|BFZEf9|rXf}!wlm-LpjCpBeaN(eCm!kO5n#!u$LUW*AI_TGwtCLOK+!no zpS>%~A;&-O>vhKY2Dis3U^KdEEm4!dqN16DQ-iHD#+obzYu9?~M^~+E75;rh)XNab z6zg$xdBUq6rgA|x$_^%(lAVweeFtN)!$Di*7;0u{h_HPzi)9L&beUKc4g3!eWRus^ zqn((X6k~`k9o+6UNtY^4ER|Y1G&(3{$HCC&C9c7o-g}{Q^oN*o@*rvV4R`S^Q6)Vx z9%m;li}<(4hj_esGr#NWoj+FDNQKl(T{DLjZ1GZFW-}{C>(Hml+-swh{bI2hw9vHq z(onq40~Zeb-n4OB-0fNs5w*6-Y%Z|V@4ofl1C1T3C|}c`3RP9RWQ-;)=be=`j4SxJ zxrvwq!#aW@q$vWuI-U6#5LHJZ>n7(2awHgU#tR}B>=6d8#X6ZJdbz1G8vJlEv~ zLBqbFfcBXNA^`r7(D8w_V~kBz#kb7J7em2+T)q<#-5siWPFZHRFlv~iFsbUe`Ve;M z#C~D)9DM*@9kFVz{aWSW;Z>!thQH~bke^T37S39SG$|mDfAaNbO?$hn#tj|-uLy~W z>qbKn45DNwz#Hvvu)fMue@;ziQMgjCh{q7AEj`aFc~;LI$?dCC3ioa>DxCPaERKPe z5K-W11EPs|Lm;sCRS;vw4uz=gk3dimQXNBa^Heu1f4(prI_3Bsnf3JK;iz~4Xy^}z z(q|grWgT@Vr_yrS(lM=HpJwwilP!ck)Ol)WEmJH_O`OsE8sJY##aYhjd0z}4qiK(U~Br~CmUJeTqr)*yW5|CZFCvEce3fWK2jK25##EPl^?Hn;QKt{QtU)fnC zxjl46IflShzwwH^O{+<8y-gh79aeUvS=$>w$miLJq+(+P@`G5dRo=D?K& zGEw5q+J=U6cmk`Z05qlzN3=+g!%^LpB+-?r&P~p1brx*D&MvkLw9JC$tat*zfeBlR zsWo78l~=5%p4?PHhcfJPO`+~zs`UzK4L!>|b^m;h${3b~IqXGp)e>qt4I16g$;qN< zi81I5Q_*6?2Cxe%#>78oVau+Mu8aBy3&55(xm6zJ*gjt8$xPuR(uIkiEjwJ5*5Ezn zBnKT;!vc5g_F76=hBrD3dM+uyo_l>1ZH*q!SKj56>Uel6`HO9DJqD)Lzc2PMFc??b z72;M6LF1dbbmz{UNPhb)6-4K%{g70f-6W(dMOuoj1DBh~+>ZXQZ*62&!un2#d+=)0 zk>bCbYOC$ptLhNrcu_aUN|J?GTZdzx?eUap4gx59iKRXdHF_CUDDx_)5a$+8erMR@ zF-e<9U*UA2#&`}BiqbAgh&l23wY1m@DPapSg~kyAefGN4VHLkVe)PC-bNO{nH~b)3 z7~PtRiY`vz!~*j=_0q340VW|Kt>N&9h{^9!559r95@&C!A0y*E@^`)AyQfaoR7iV1nMzkNykE^nl1*hVO`_h^m3U5@vTr(HGIbpS zGCI^b1#3GGS?4O-rreLujf9EC@NX!zO+r#}X(Iu{ zyo-sL(|M-6yv+UkL9J|X)zr9NDM~8m^OnxGY*Z$2PH;{RL{K>ef}99PGzt|weALnqS8s64`JRMCm{>YPQm06I0l8PnM{uC(E1{*QPfQ~*VMe_|H%D)N5|E2O za0qGW!Y{zJXyWHqXNr=-&-LM$cHbA(zXb2ZmuD!{Tgbf~&uO9*f&8#~a&c_z4v@nR z7TmgZk4!m8uG%hxto%cSmeVEhR(>P*Rm9NnE@IM|{Pin$xAN?-Exo0h@1_r?Ew`(e z9lp#w`1dC2;q`Pd`32wwH3|QF-%hvb57;#h4BUcT-G`rCw^aWw4G|ZOIx#~eujlU< z#_^g)DtrYg)EMzhAM}h&@*r#WLYb}k+u2#GMIe6GXBW0DeSp!{#nGPq@2mE+Jb+{B zt!$)%3U3k|g0J_cevyyIbw<6+7#Zv=B+3<)o)41qLQNK9b?;G<;(G-4s|L@w4cSYh zN6QUfF#kJpxBpGq#mTNC!^8D1OD@)Bc~UJHNM@-EXH~zBkN*||0B_ULokPkT#D35N z_iJP?Sm%|uRjbwN-!b{5ZoRaZb1Ujc{(XAUxgo}R~Y;%qpZrVmoQLKx&5krp5L``#gMAiaa;;4t%&u+6Sb^t>ojj1QXV z)!aR0bahM0)|;em%|214!cF5*yDxvnv)GFg^7rbqlrn3H?KhuPk0Anvi8%Wc%2;+u%93KiHx~=N}*+cM+KRF zV`rs`!SD21TWG^W=$h#kaMqGG{u|#R^U-@64DN`DlGr{x_@; zbJmQEXqv=x8iz}!MldNJ!hH~NP>@ec^yRWZ-tsNIFn#ZwK`DP#5raFAs6L2wa-Mz204B~3q>mjZeRVo99q~sP3Up(2As>Cv(V=?%nCifo`+`44&yshP~t3Bvol*{B*f>WijA*m0I zh=3DMfTI&L(_#%X_`t^9v}^u^5ri`ctE-8ss7lG9&>OuhM~^1D*{a(w7f0xWoem2* zUM)1cxYgrr36uz_vBf7!3L~a~ZhDbQ!yX^$Jp^`sd(1*)TWZz-iJv$Ps5EK%fK|cF zpdgjZi;iH^{AM=7w+P0s3R+`bebZjPLB%s?C@QiYOzFQi~Wv%Dln zOe_s_IZkD}7)gB@CW#|hh9a})4rmj9&(Hfn=oK?XI9xRhC=5@!Wn;Uklyn zIzb$Co8<%3(X)m@Dq`(2PHwm8-3lng&dcD3+2C7_QjA!N4Lv$Jd*>c*JEAQuIUwlh zJz!Mcg&y&)TTvs0)y(NxXBaOrwTAa|j*nZQhV+|E%X$U7FR*I@C~q#)(V)&8a2G+s zyLJIP{0%uI1icx_;ol*EozObDbaA9ic~}~gJ&($Bh3na$BOA_a6C_6D0i+VN`-6J_X{8gk{jMAF(Z- z4p+Bm=j?x!-pfRhSVxbFv)K?iaAZO$Y$&0$y!US|K9aL6AxX!`Med#L5md?@IAY^6t?`|8Q@<$*TyE|R?N6Dqmcl$f9hHO_pkS2I|K9-t&_W z5w@9-a=Tqx_e{e)keK3z2nYzorkN!a4O!|5jN>1v>PL|{8wsq9{@DJFwZr_=bd=eN z^gmnVjbiU}ABcIws9fYB-5_cUAKz^;SL372$1ea}MG86>76&iGL@lPIbQ3iDZ-F|% z6%*nfAUczXEu+Q=;+6;HKBa%~Nu~6Hr#w;X(97W;muUFUGb7CbC#!Rm1O|f{FB~$f zLzo0mM#sS%<(+ua8$ss0S4%T7pb29-+Bc`wvZn~xobwI7Mn>q9H66qxT`pAkVDOxC z^U$Y5H+KB{s%k*4iw=amq8C7H0lqUaCKc(!AvKo3pdD{X4YJ}wpAW`GX6ts7Eg^T0W~gS% z>^(u;!ZTpma8crKUu%+zYqL}LR^^*a3*!|@WYkLhV!*zUiXbh}jpHvI9UTF~1Qi$P zJQHVMf4Ef*hk82BxwOx-7@0#fu@oH`H{BJ*lc(yD#G&Wj9T=z+tB_BVak6i=*mnk19=05uH5 zO%N;E8Y1esNx34=H#3D=+uLl86%xrGzEmki!mb^UF<4+J+P42ahkESv%ld zI%&2-wc)FmmkT&n&?jKoAgkDCgccQcE7Kc2=jxdxTV;0dOY)=Xxi^WzO3?ipN4&jJ z_%!oi9$CILMj|EobLE8;aa$s-y#VRMf;}Bt9a%@@u{Jl};RI6zm(J%}D(`gIJUIF|x8?Fr|Fo3tPvL#yh<8wLashF(! z#A_u76O{~sQCn)NkjjDgK1?BrmQdjv82F^D$<$G8Wi=iblH}usaJj2EqMqJmVIf>F zGXuAa6Y2AU0EW%D&>k#6r6%$(T<9dPw>}oQ5HhgNEnLrf*7`&Hn^Cdse_af2lWh7; z;{4u^gRsE4!O3kGCAo>%f$%u2e&)Fy9vVuUwVpn{xabGIH`=@F(>{%}UMBK8ahgF` z>IYYd=K5L!jJfGijF})IawayB(0cWr#5fkSWAg-0bZ7n1ZIEg;I)++Z<=S9S_P5oV zhS9&jBW{{wXrFN=jd|#W3zZBVUdxz7-rC}}v-W*ecR6@4j=^dpteU0yO3XrO>tAe` z9l7`xC??!Hr_7iC4{p5gDOX4vfiHQ3*T%Vu)Sa>gepARD`HpwS|Vm+OcKiTLp2?bO2SS&;Te)#xW%RCM^?2OO?X8T{t; z;@a4TLf*_(QDMUnU)%Oh@a5$I<88-s$f*kgJL zhaa)9Y}f-O32H&eU))22$`xHHFI|Y`Fz#bn1n*Jssd^kpDfpwCh|HH`u2ZU+vF(Pg z0fRv~|6k4e-THs@>L3v^LgG`;x&2kRm7-UNM*o=>qkT2EwwdnUeUz@0+2QOiCUy=I zw__fD3GW;1oXf`W@VaNI*pu;GAB^l>v z2v|_2#76skN($k+{nvA(S$^rF>&oblcb<>OGRZo61kAS0g7OuW6UjJ*48jOTs|uJe z|1od+BHCuJf7D301JH3ZQD2`s{H23GJZ^4o>nhl9jC29}`}@8*`A2YB-X*ys`aVPP zD+_}+7?{>0Uj1xX8SrgQi}7^(4UzE9n}0W5CZ zIu@S(zm&)r+R$@n&;AAzA5j@)ZEW?=g-?xZ2b1fC#QT+AM+t`~#9V7g=@xL>g4_^KdjB-KZ>EnGNlB=)$rKe8 zN{7_gKA9edZnpQKEpxL!pcNh)DFO+Sc3eQ3S6^I)y7ya1+3&-j+&r7LSMH7!f?|JZ z5aR}JW|V#zm%rT29K01TH|yP@t+ifQq)gcWEUXi)WQV-;6F!%nZz>VNVW5oh4x*80 z-6kd`7D+*E-BXdZWqHNxxSsqg{L?2k^N{5tTi{Xu2BeMHG8-&2gpYQTB{lCbjl z$QkxF#U0sFUw?WxRY4l~kA=-d>a+>B~qh{1TEMU#nR&1nq9IA+0 zZg3wwkT`)wywrk+_C;G4pz+Z$F#2?Y-43{lVSy2dr}*V*4LlSMxSMliTTyJ#4#I>~ zPvji$wwKne%|{+5D~8o^2a-<%ponP+MXzKm_a)Iy(|8uTr&4Vx zu!NUwHTNGjSMYufqCIJX+dVuy#B@qbtps=jif`ln zHD2;;mkKxf4YquV2Km`1d!hkzF{U(=T@ZCyfDx_t`k!D>cGcC@;ef|oJr170YXDl5 z=k8JP1z~?MTu+_EN-$JV4$iM!;>=85)Dbw?ltV{-*CC%uYnUN$dYqlp<{|~%DQ&C- z2tkw6@H8H`x|Q&~shSFX~zlk>a>&)JiK39B7( z>n5uAU8m6gRhT0pc&>+k{CFy)g;uS17!0;azyQAc4j-*D@xtSj`yrRv5)!~dH~lbj zhYe<*>894KTTIy1)&4TLpb2&3e5Gj>&N`8Q5a9q1uzH9=x$k+xz0Ggd= z%oY5$6Wb{YG@Ns{e!t&jZ=ST+ws5((h+`PXdg&*Eoj>&P@u@%OYTyjJG_i#Vz;WA# zv>e9IA$HLY5GonNBW-kGHc zIhh~!Ki<(v`12Js#Fgb`#LGK6>4se@8d~0EmBSxaDCa#RPuZu`0rt#ywEWUQ{pip6 zvu7WrplaHDk7~o5tCWH-H0E^pl*wGVXZfNqE={PUG zBliVv*IEV5+VZ@v&Ov)0frEpg-!C~)(PY{qx-^1c?zWANu{d znFux8Hv5B~viyQw&kDt_%l$UE|F&n?%<^tpvpOH^d6NZ+_UE03&s}Vs?RnB!E?9Ji zl32De#8}=-dUS%)4nq07XEw~^;N(P2ODoFKU7EiH&rRA>;&R$O%!~EGmS*No0$t*; zD?-H+IQi>FDJPu`Yj?ThhA1@#ZozM$0d(AljRc3=fx|FpLL>QXv5|EZyMJO3mJOkQ z1ZjU}1zDA^SWzrOKWs=-w1A47 z(5#k~L5%~0SiFE4)r9v*wVQz;*#x}u#t?jRAEe8K+y8=6hRPe55E&_$ zICFX#)BouwxL%Mto!>^EXd=TG8_CS;Vea)s^k=~JFHmuZ0csVRA{wyOmkDR7f%v2A zgoN+Fzg~4%K7hGe#23WxtAJc_XchAkC8@ALJ^-{s;{c7|H`jf;1|3@cfH)W1Rc1#Q&4q<};K_)# zb{iEW&iBA_cnyBz1n8(8{zMtv({Cm8BjI>5x0>oLJ4;N*fHIZi^LY22y8fKwi8!d> zg=LJ|SDf5e{RFZ(6_Y?|Ps|cGNhcH=e!B2=17=lQA{MjKUBRjej?YPcrFe z3uU0aZFVRe`r6RYdicf z(Ls91C7upbCiM0X&7-ltfICAO-ehlIu70vdPA&ZQ?H-g)ZeRluP`V#jxV;1y2(+fP!FxJui4el#U*49LRS^B(IrPm)E^H2v$5N4)=9I3+n)1SB$6&e1i~8n6#&qtSI&iJ0=CYHkoBZ7@>SJ#TS^sXcGoE zjj(Xl#whzpL}cWzWR8NfS!|F}%akr^dlopD_bBB>F>XS!f`UZ;*(o2fZ)CN~)zQMK zb5w_a-1*{v7Q#j`l?`s$?d_qTDsO)}QAPefYalGl;~c9lg-Ypq)L)V(z&29x)te?| zZ+R(c0F$_wQ3dy; zcBNDz3gsxrde_)aZcA!kBpxGw-FecV8;P&JO3w2ve>0@Rau_-UXlO|Qz-A~MgQ5lt zQ52t^(HWqo)G8XhN~Wb#ILv33o%;wM`CML`C>9o--{Xa;8X6jAhLyp>McN%i&}u=y z$fNq`gQWAnp1!`frGC+2;?ldP@wj1H*)MEZo4!wbTC)7y8;G~H7C0C#I6*vBVV&~bwD-3r|< z;ryzT4l<|#R7l8WV0kAVZEIBX_MF2?#r8+&W|y}C><7fl1GZGu4;~O$T3SkbK9zlx ztuXoJQ{Uy>D_mdKi!Z|k-xxe-Y=B_@^s1=FJy4DN!&L_v=nuz(>6JiqTmcQJof!7A z40V`{#k~V6r)M>(YT;D@dOj_>{`=<(Yd;sCI$;Rx*E)vYH~J%oUyyz?dUbU}s1H2O zPd)eA1WsndV;ZGTCZjqyf(U;D>=TAQ#wt=5x~|LPugfs6?)k@!XH0Adg5;|`>VbsT z7-U(gm@JJ|J!dIQuF6L7--6yzpLd6ItzWG-l_wscStH8s?ru?(^R+bpc$-BU8X6pJ zZSD0fNI&@vn+=$S4icUv_w*nzDM;~rHz!pae#P2|qj6w+XJiDgzHQw#?iVn?QyU)w zW|eLG9H4oy3Vr??yp~ZcmEj*HF)yv}e~cDa3Oe2CRwYkagtu0or(A?Vt`{)clVGf~ zTh`c)jF#6Faso+^$tLDti`j+iQ@anja+g4<>HuNdzZVvK zk-gyV4(Qi79sQF1l#a|uhUrfolegWR1h0V;D-NG;ZK6yqpk!^Z+WY$|`HLFUUr{kJ zZ`4u`k4*s+YVDN1;{B-a)ZX(?y{B(~My@7-JJ3o+c)=@7kOnR$whH?@kA7l_{93}9 zS6l5?UQ&m}U0nU4vxehpIrer5GG`bH4=sN`^nv2sWdHYdOrP2DAv_SW3%KOhr6z%b zsWS|*SC;FtdnMT6z}v zZnHIuApD-@Be-8}u1<^Rx5f|)U3UwTM| zXa-$C2lvA;^4a8UaySGC?&^$yQA*6yN%j(6 zCBj6OJ8*-&dx_@vnB%#t3=9k>Tbx8Eiw?<~H%*#6-RZsh+Tr0?B#WjYpglM@Jces6K?wogSWY%_Oem?q zJ55{LEmc8khOaa;tx)3uxb}AuNFF#*KUpiN>n(|-Y8L&DlE{a*D_)z!9eWfSW{z#BNY2Yu@kR9UJ=rQQxIE$O=pN~zdKY`#8HQ2@FR#s5BEw2@Sg1G4O^`9tXF zGFGibLP-4i1cSDDWfL{G?)2*Q=A$3GlD!Oq;sNhwIQwrK&Z7-=b;)?27(MN`z0@Ed za{l(9JiEENH4g#z_W>{H?Zk9xsI3T*)zhB*E&Y|cEEZ## zsWQINloR=msLdSx-@L-@p`d6_ky{ykFw@9-p8VSB*@@ZOsV~H^jVS&5y7Re*i8^m+ z>8r1GYKN zi|D39e}5<;TWfL)Pi3ISW)BY!tF=LZNlc?yJv|$d1-lskBPikb+}+(3YP!M)Od6r}S_5Kve4b`^j;=)E zw)L}DVy{%Jb^HY?({a)jkk@jcjDKHDcV*kTVJ~;&)=rheQbP_A{Kt(ix4qFw&etcm zy;pwV+yQEFpv&GvcxL4g1E*7l3V>V@++nGd$rKNVbLY;jZfuAk@>wuc}8+Z3< zX_)-8L@|w`Sn(i5jesI0@}MT@d=lL=PLy;(x<*+WcO`W1la2?)(*&kJfkPV%G;@db zRxZTPiXme8u|mo8yCbmtvbZEqxLB3a)ercA>MXmkA>+90Y^}H=qc3;Lv-(;TX2GmZKLFr{;dh6-g#5)A- zge$;kB;eT{UW90qk#2=h@H7v2>njWZ=YRst9N%YWvjZdS9e`h#5H26sLao*l$XAaR zm-4iEqoMTkykrx8`^01VT{IA{jh4|k>cC6Ft&{xx z!L`vJ_xxf>(59wx^e*@I`86~)P5^bKIJHSy znU={FqXX%6NOuacjk+*Dsg&BJr9RyR`j>C;I(*VbOurK++&REt% z$AiDkTo?cLFd{p)0b%DD30_iNBW$_bU^`w`)}ZegH|sTn%GkjCCd#@r<>#i!7o`|8 zafa7^(%>L&B#!FfHfnZYn3VcVO{--Wtj@aLySOhpBZ$APc_w#beZargg4*b|{a^1V z?Fp?@vG^G>%}R(}pwa0?`XD1n_;P@52%sB0li^~8Q6a~5JwGa8N+d}RayXgPewP0) zMqu9_%~`zB!JqK6SMIIjYRv@>t0_q|EO3F5?XEnQ=i3J5E*QYp+Q^`6_XZtx;>+(^GYXK^Ru2X1 z&IBGFUeS<|Y9pbs7}jR{)a*mzc*&HNK3T2o@2yW#C_1R-YLh!R>qKVC1>n#*;~MR0liYmv{!hz#2K>4n?F275!iHL++R50#rR8^Gg>JvEPuR>aSbL$VoDQD zzj=0RBkY$eD7v@jJa0Bn0-tVtW&XcyYR{^Z_x;!g@%Qfmo zOq)Sk(rP>R*b6Xs+5zTKSD~poQZ=@2b8y^10xndl0EyE!*Dz(e3;LnDsH34fp`@%U zU%{oLQ?pm;q#8UP_$h#WUghQhJv|aY@Ebe~sBe9zd__%@!;$}>()_{IeH3=M4FX(f_I)_OPbtM`QavWgQ)C2q|#!m_@-+x1R zWUAV2(<(ShAH%|IRkOM67FE`qHdT{Df{tIVPqqB|rj4UkWabZ+z8AM7jjbm-pS6f$ z+vo};Ty`To){6~7@fvOMPI$>y0+Q< zvg5h8y9Ca6nt!SVnRd3gXn-=&rBdh;!jK26pBs#K`ak;0-in!88=+WHLL}r+?py+g z&?$o!0vGrdS*0-W1X#I(q_L97D6R136`&?6-(H*^bzTt!Zlv!%nBuH<@UOf8|0!@| z6V-^_44g}ml#^?OO1V%VNkrK(14v^X0IUEg(qbQjB(RV96QT_%d8p5c&67X41%mus z*K)QSX$CCuWU$=>PsmG5VJE;-kjJZeN5WnuE!DDXF>;DkjKv^=Ws;X~tJZs~JvRE` zYT#luktt5jy!Rb&F;dCw?DZSNo803in@ZZXm-}p!ojB`H*CI zh;{;j)ak?TuLMEbyo1w>&Q-}VdnuwcAhZ?R`A0OR@Y{1b#P%FG&S@fyjh(+8l&khd zg>Qr+6})1E2mB-|N8aa`Z#@46b^%k8>sKAuO9MM3&}xW^&WHu5JC9$y_#A2dYlBzF znW5r1#1xClS^VK0aS-IJB|h*+M_@vxiyf_6sMvpWSwjuWk1Cn{mwRw(e- zr1sPn)RLJr{aY+nZ!S;lN);K_M3N^a@vHND7ivbtzC zOR8qPzp^i>(cgE@tf6xu5E0tB@9rFcfuVmEaj?7UzBI1~F zf;5{GgXqM@4cIa30Pch=;KOj=W7AQB+K}zsNu$d1QR`%GnQ~a9xBkB;;v)MEAxUbC z3kkPbERW+FKu+3I%|RAt`K}S9u_;?R(rF!Tegt*GOrSB}KS?_;7j#3M=hN_&H9M5U z(i2D&QB#{&w_iIIZKfxfj$9tZT^i4@_h{lw`n5dqxr@ZnaYXGgY>zo>Mcr_)f0RlQL+eu!425i zo)Q^4|Jgc{cuSe#nd^?h8KJlrnSWp;>d}En8psf|vs_;(UC1IJ$nf&Ii1@sKi{dtK z8$1?VXI{xGfgec6Mal^r2OXRS*7eMU+{It}Zfi{6w1;`>yz*aknX+)Q~XD0vM z^U=yZtMHut?aI!Y#a6D;oy2h26suY=JXP8M{!BpNq4;-pV{j4+jDh8u2~5 zyAc#1Rq?l5W+JCssJrrCUhvOxL!va7d?BAkWQh>40ES3nC=|ZX(T*;Md(xlBd|$Z+ zzA*J-MWT2@6;hGGbV_9r_H`94okiq7q~zpow(SnXGx)~92U2C0Tf6#^Rb6UP6A5=R zyZEX#PgGc`mS{+;*dt1}z5An>W*wgyC50xH#Ss$^kTfC?1doj3Ol4Z&P+Vljd)c~u zXs%2OWBg_N?Z8G?4>jMu*9dST?niPbIC6Moq^rpMITYLp*r4wKC#L?KL*C2wS^{|i z%MLdn8%tEV9exZ6@c>~G;yeT`-~|va(+_@^^i@jFEC>vJ!D;gAp2(;CRw)^ZP-5dZ^D&`v*yU0uXhwTTZ7>=MfpA}R|GGT6 zkiOLC=sd2|R3oDz$SmCa_?M;_lk=T|zhytx;UU(1Vg@D~_;WnZJC=T((@NXtA!r+E zBbIS8L@CfPX7~1WJ}=c+4p!d>MVO?ppioI3LuExa-U+o)#vijYyCQnkWrO{@hMS5R z4iIEylVGW%0+LFwP|I=JG-3f#>i+f$aqqQcT~Smz31iN@2};bG(c1_v-HEksxBtHf zD3jb->1=k)mt1?3C8W>xZ_&H&{p2D_e;Ww?GIJKQ0+=*PDl@^RLYL!L_pm_UV5l7+i+sti8>+ zHHKt*6<#J_jDK>&26%X*(d>N03lfM=*yj3Cv~KEsRXIEHy?IM(Fo_jGZmd4H<3szd zy)Q3=My^$}3e&N?IDf z2zDU_g)0bk8>qY$9f!$F@J)!}D)fYPxYl1G{8v=YjVBq%Ml9gH=2aXiZ&c$_f=ijP ze}*TGiHOq=mb=%nD4S1>td;@= zFvl0;$dl8_>D-NR-*TFc@3HDEq{e)320l)keDZqvA5t)E^C7zEvUQ6mE%LK0@t-f4 z$u~6|#d+orD@W|k#AnEeDk+o{cLK4ZUtR7+OJN8}KMnaduE~3znDrf~V?4=)Z2vpc zbf$f{eF+aQ+j=Z|DUHiIKxSa(Nr!!+ypJZy-ZFcz|PjE3y0*xps76c)_&kNgEMM$qJava74*dq!~6espKKF=uxAT7)F0 z743b4^7D~TQw3}<>pO2fa{q)S5S=OGc_JNJ3J#~0Lzeg(M{@OPXWCAWONZQ&DU2tS zsp@l!{K0-%+3q5~=^3E7YGWYUGI?!%-D=pXjTLyg5}Es8{M(SJ_1r$?5iG+k)zN4h z)%{=AoJ^Pgy`>^;ot;f^Z0F16rBksb+4Lnop|+Y4&s@IIpO)gKyBGDQ#F$`hxjlSq z07_QGTjXpOG8_X73T*PaJR&<*;xH1^n?MdL#?oZV0Z3?N!=$YIuJR~aC4RR_J2WFN zf3a%-ya>v|=dpQ-w*afa%geC_;72MQ2T3MI zfGm+ZtX{VVcpAN}L`W{r@2T&+w=_wCH3vcPwX%ZL^)-#zTwi|wNPDvDmZsKQmL51> zSA`F_()A!0&{o83s8Jg!oJikOH=m^WDu8Y1=sqH%hm1cwup?F}v7&>Vk4{IwvaMPw>3$SnyW%V@WpZXvTr*2*Pk!cBiA`}0Glp*_v zh~eEAg>(z3y9Yv!x7{34sX$r&9;^dZv5BpI;C}O4c!O)abb#!e&Hkm(0ouy|)PtYm zme=vd0wRIW@0i)!nGEExoks&tS{pP2UjF`>80=L@KRGi=5Xk|A6ojm|3EuXe? z06(6v14s+86|RtpV2daStY~2`qDSo-s-E<^UM!7vcp{NS=_=U|qt^Ob_b{O_V__jf z0!vJ6fXPhnhCYRRj<&mut2gz0(WOW6|A(&kjB2XuqDF%aQ4~cHMG)x{1RjtgU8Q#j z3B4%Nq<5sFfCy3p0qMPk5_&HpA`p5np(qf6(3C3u?%?}#G{4yxow4EIu2%w6#aFn#x#fjtu!?F@b^#qL5pu5 zfEA%oW&NgPkrlo2a)CctvSi_%kM{nzm!MHbc~CA69Jx$Cm+wB6>DCPzC>Q(zj_?jB z@1r|Uz!Yp5;9mipUnR*)ug;lWCY;v2#T%$%a8hQLm6Z_*I_{o8qJmX9Apo?Klu|_R z0j}<*_dgG$JncBB5h^C8V-Mi3SBV8p$m^Y(FYSw=Obrt0*-<>i4 zJl@jx01h6Bs7r}pd#^Gdnz5-++AD>2lD}2vrbQp!uH{1hBF^6K3_{Jw(TIv78HWc6 zXYZZgRBWqT(Xl~t2bI^0Oa+n45Pc`~%N%Y^kzTVbS!5GVt!jDj*+J4=skV+z^FjAF zP$7vK4C_1os;M$mr%fg(!hqQhJ4hc=DW7%k(A>(_;WdQUotbHE-bUb|NS zaJ-2nLuFMD;A4P-r4@`1_yLgbD$r8-0?v4#0TlomEb~A@S7Onfn6L0#jlKlp)E_^6 zYE66YADNI40;cR4A3lTtf*8wcybZ|hI3ub7SL$XIN&z3BRlMPM+Kqeso%G3-KgZ$G zHI;5zMDBYO-A{FEv>@)5pQ%iAMk$}H#D;tvF8-+yiE4y(!C5nRI3Scgf3r@Q`9h$8 z#`*`PEacmW`=oIt_HTi3D42BTUvj!-@G+?O;=D(|CzkU5hYG(=D?|)vvkDmuwrj;1 zWFHI19fKa89|w-~mGnx-fiPM@b?U$;?VpU z>j$FG8Fu;gD)+Mju|2o^|F^LcdCp@l0Ivn)DIw^Ki;F)C*d%DvDyJ~08t#qq{EJ1sJHDBd zuSQyw9Q4w&T`LQLhE7+?GF7!naXY!YT0DXH7A0jXLdg0Ge7`t3|m2))U zDb?N3%*yZ>nyA`+&4?%>=z~%&vR!a5^cm2*`PVWZdXl57clahFQ{5HhA;Y{Np@w~ zs)n`7M7b|8o5h&1RpV=K^?1Su2Jb57i|3iImi#PHT1}*_MbQCm^bvp(D<(Ly!aAE~ zkRe20%VPC{|J>jHD=wqvijFg0$?*-xN6y^k^v6=r$ChcOfx)i9QHg!~8^^cVOn-!lRrn|uGm zk?!mNm3kkSj$~Y}$fRmEjVB~=d>`_a2p}mvnS0MoTw`JfUQ-&)n<+n_o$!fUkeR9a z+oRDP8gr$(uaj=^;AGbg4~b?Y(bzfan7Eho;|2r_aVO_ETp!G<(5=mW`KQh$56bnS zyxudj;f}iG)h*Yy0G|P4c0Tya=>h9P)sFAq=gp;%D9-46L|^(?ci?VvK2?m-Tj~X3 zPmbRC)AomI!_4t90K9~8G*2Y}p(EG8QJsQ+UHRc7?q446&a(P~o{dj)B4Z5R2PSIR z&!&x@jqdi`B_3$jn7;z>GDbvxDW#FEQf&JzQmNM2WYSIIrDr6Vum-b=(q3{}`;s5D6fDH6c~@=A+cr5M|;Ll0GRoqz!y4behknF5`r_LB1UBgw81xi>=1Xpyz|Wl zaHPSxZ+z5}aAB&SGjtJL%~Aug{K#=CO}X*-32zj^S7a#CNS9%Bny>fm%Nu9y@=D^o z{AL}i@IjJ5N7%^w?#{N0513>bPY*ZRYJTtG0sa_NW66i@Q+_;uDD~BZcfMnpkth)X z^s~oflRhDZPw3^lxo&pti41)km&}zJyKE;^i`K|vJ9=9Z^Ra^qk6P?pG1TRjehpr1 zM#jTG!zi2ID>LM`X`t$G!(gHuargcDGfphO21v9$fMmoO|Q%?9{_Kq*7$O$rhEuFN;@?YtaVF@Fwl0q ztr(=n?Qnm69*V36sr^1ya2%dBSV|O!1{y-N%kR-3B(?zk!r(v1H8uy&)JM)&UONKP zaWavsqXXQov~(=qjMDZ>VdG(?TrO8@&7T4F1mevH%5(1O*J~GBa42$HY+J(lw;MfL zCT8goLPF1^FNOdr(_W23*g=An0+RBUr_5o9G;HXKngV*#g^{8jw&k$k%%JqkX-~|+ z;zn1hY}tYhutQq9F&C`z3iciqCpR9l>*Oyp{gx~ejL|L^9XC`e-32^6AUot{-u%d` ze(gfIIaRCmB>?_cA^RVJa7l>SBA_}w-HD-q!#(PmT zB2n1RiF+wp27wKBO*iMi;(ft?(zWLJa=$+4lT8~QHSWf1#H=Y#rU~+qNDVLB5vcrg zor?l&$u||RRbt5i+3Vv~XKF?3$e;L@b3$OW8OaOjk6%Q zqQcE&(jjQ;1Bt$o#5MV6{-t=$tOILrWWx*-aNJ@7onIiZ|1p6cC(xlBC5H!u=HZr> zJW+3&|Kioatl01RjV5rdjGhcEPcOO?zVQ|sL^d?0Rh%PLrTU>JlP*_?q%5K=S5!l2 z89jSeaaF08{bm`9%rLMW<`h-`Jd7j;p9f;?g*STo(Q`10o9&r@9et=~E5 zig)vN(R}iPb>&sh*hQ|KJ6Bd4#>kcTaD9F~FLUm_@RRVBkDraK$w3xH+=6GXd^D?C1*c7P#p&#p{<@f8tsq;iU^RDV;$DT_Yt-%^ z%{SEMC;@d=WD%r7w=Fz@8S!EFJOuJga@s?|ZGU4^f5M1x!!uCvWOuf5bF@;j?d6YC z90Wp{Z(k7%_d(pz{p7!U#obq@3gf-jQEbdyat;Dv_^%?Ba-z5>tN937V=WMTPq;$o z!7U4A#;{ABN?{|>ay=B#DckDXv06(|O_8g^CBAr%rpzx-xS^Q8)7}LXcGZh<%gb`` zGL+lf7pRvb#bIq@D#4Xy!}4 z6t`Nz0mtIb?6&9 zn|VT!vzwVSOZg@=a^dbL=5`Otg6-SpvBLzs)X11cIrDcZf_Q<6DkTOIz-x}uz;aj> zdo#mGAdnvxNxu4Q`(8FFUgbU*+taPBFy1;@w6k(Bnd=w?@?3_IiqO-X@V*g+$bz-L z?biN`w=xna{Pa!x?R^r6-{d}pzcoL<5hKC|UYX<%hCywqAwxD`CU#9u zHShTNO+idj#-`te0r?upa9 zy-Ws)pJq7ujp0OJ{=iXzidpLBU!Cj5ebhHuB?I)u-czQ6`|Vgo`2@pz01T^Uo2#)a z`aA8m??L|fSRu-F?}XFBn?&s}sQS-96eT?!_p$KzF%SBU{&(&porswW9DQYyeS2}j zS!u)UC@#0g!ykptBOHIQUDkYZ_mknKgTaW>QMusq1m_(?e)x+d=^o{#ofI4+%c$|7 zgo3&gvJ^7xrrrEVBSu?!G;Nm@+|5sJ{$56nJ_(tVqxCD(n(_$DagBkkF#D5ngY%FJ zH-q6%(IIdIS9S5=?KgaKin&cSZ;#v8ns#f?3o9JH6|Vpqpp=fompVCT`+^tE4PHVz9JJHj1$J=9x_!vW*Y^}qZmu}Y} zH<*e0e;5;ZoR5YVF+^Fu870w))>ySN5)@|2I|d(+-q-A5{@<;9B&J?dp(&L+eCNKi z=Z3^7dsMQp!W4V%g(6i*!iSa{=|PJc8hceP1yTqTjbH?oa4EW^o1GMUJc(BkvMA`c zL8al7X;CipX219H=$G;WiZhi7!Z9?#Ei}vWWrI%T3HQBhHcWW#ZF}%vD9H9G9y=^5 zf4eB-pOP4sQl&78j=#SnI(YZ_IqLoka#!(DZ5GvDz~?>yCENuA;? zLwKE%L7vhE1EIX!XR%^-+cX%&uT`@UKKndhq4xXnB$0ejpckEcP@)B2zQA$1fCe$m=tYb! zSCJvtA3Y;NUQ>@c`}#q|Gi`3mfL2FyR7yfLHXTNW@ZfdNk=xizck49?uG-yB@E+5*tjSA#9?`RF9uq$d7AJ!CT>1GLtJfM+2R2R!uE^#agpk#Ltwu#DRr(V$}UccW-fnl5BCX&k%GnOhgIPLqQ&UNU?TuL;S z76JS9n^|Y18*vWaBa9-?F3uO`L1!pn8&`(QhRO?B5!2A|;Zqc%jmx02;3v}q0S;7F zr8^yry?KMnoT+q7}Ztoce^dp6RiOOi<_)>*ONS zMV&@84>ctj&^R%tKJUc3GEJxd&<;1=-DvOk0XJx22@WDzpUDL72~z8WAmQlKa@}7l zG*sL4a_1lkVC>N120x#&6`;>PcEYaWg_?GEGI_s!3F?MG_-cs)2W*Ph=*9J$P7bVs z!@@>Z0C4)IcBQy}VbThN0${M=i+JB8lQ2HHM<40htfN*XrXSL! zdceBhylGi~0HH-V#Ov7akl>kMW)U^%L zfVvm5>2^W`NU&kRou`?GRDNFQ3_iawICf`+zQ=l63p&&^!wl9#kQiF|m?-o6AD&QA z9R(G>37*ku7!ve)*xGu|P4 z16@{Rfg?O}p;|jroCwbJ!aKU6|FRpWr|EiS`XaoNC&wDGw#_FRu}P!3$>b}sw5!D= zkOVT~bNXZ?ebLjjOF0YH%$l;lrDAj#^3&kaEE(k46VRSd2g;+0W@b5HRPA>Ojt$T- zz-&kq5cmmjW<)%bhB{fa-N<*FVW6rNa{Rr;?JVLtywtj`aWM44!FrmpG*c#6=!pg! zIp1#F0eJn;3W__TWv^v}Kav+RY8~X5?|!ZUosVE*5j1?-jJV2c<3XU^k` z)Xy?*a1N4&&~EgoOrfCR%NxZtt{}Rput)P*YG}D^sk`KZKmWA1P4P>HNF;LbWXgbo zI%A?dd!dWlG*y%#=Q`bXiQlMC`x0)d7#anQ`&ghbh2SdPUScTCl)Y$eRP{EP_GtA9 zSP_B=yVjfd8U=+cbfHIWMa8dAzBfCM2#P#zWTDlY50ar&^{yk!LT#b6?r#}Kdg+i7 zYs&rA}{pbIj5zdly+5aQM*XX-<-XLA~X6~0X)|bIH zjov<|ck?zv-Ok;>_&iLH9XVkXU152VaX`;8Ao=0KYU}QbaA}eLa&1JDUI1HdvB&+M zL~CjWN(tMfyBQhai$wRHIEc`?cySIfn=SQ!8RHev!^Ux4Yk2MQUQXH3K-o08M&jtX zQs=woyJEmn5C*J8%fnKRcJa6w&YWTR8>e6KBsi_rX%5mwpI@DGTfWxSr@Q>#!6L`b zrOq1<#)G(<`dck8iJmY-6h*(9vgP^qO^(nbNNR0_fHCg(iMz?APRov`>dk!*jT#?# z*NG&RIMq)b3)(ZdK9QO`y`0ZRq3GNl@0=)J-GC!(Crj>WHDbo2Rim;vFqg-hOlCxd ze*fwwtXVGm+oh(sPL`kM-r_noopmgLFZ_D2`pEeR(1T{_jvG%g?x?YUG@x^wvG9cs0@c z3+w5Tm%kF7uQjo*u(-qw<9tI@k6I-+X$E@dCVi zCTI?SZvy#W=z^7cg=| zqtVXRV?btixj)^1#ugB*g-;Zflwy!`FG?3pf@=+O!c!fcoJ@f|D=(PMNcPKk&|FfY z7A|q}ws8Xt*DT*%`21S5Q^9iSD-c*(HcSpkHJ$8^WF}K;F&_Nsp z@B*X={2UjTyC(s7RzRESzyZwn7T~aej?(tizQYg4Do?P9c5coTuo*nm@Hts#EA2_; zri0IY#Bp*ATcMIU*;%7wYSAXq(+pyd?*u=g!9O$2UZv;J|9+j0@y;h{z<$~pHeXaZ zZacZ2tARD(TDGe6Fnk?kKu*ap&6zT8S=7TyCX?yxB;1B~T%J!ioCV~bkZFs^5j=jd z$9Q0m9gRk=T@TtW)n&oHjPyoPA?FJt=h3%ZtL$creZT#eB}`^9UuA<_DN$7Yv{*WZ zTcfo3Pi~Cx)GND4e=?CVThb)!sR5TxgM&Y<+bYk&-2$`)=w1~tPg)BrTlj*Dbri;r z=RFDz{TMkFa-|bfxGm-yJH&u6H65JhW>9N{>+0C%l*oM4-;q5^A| zLVUyCd&2r-YJa(%KAW&lon>H>HEzIlES%@VlAz>Rf>x*!rXB3EMBVj&E|Kg1x-aqx z={3sCfk(i1y$mqiI`u1cH&fkZnom@!^*ppBb}!W%6n?ne`JZR_A~aE1A~#x941rc+ zJx){(1qMuDq`3D9muK0T%i)@~O6 zF~uV#C08|+-^cCZMYWS4wY$pxy5Lqmkbz(Tlg)$qS@2z;k??A<7vowljzKE1vge}* z+q2?E7{%(HqcUoq0>91z(CddAsgC+S)pPYaEF%9c;IG358}&Eyt9DYk*4( z*CtolCWA_~+0RLKf5bLg;8LcuK;q7hMRpHsG9y#=eDo=0eNT>25D>IdbaX6R0AFv- z!8x+{!g;4Q(@=J&A@t@=3Um>@jA7&$hd0R@KTQl zhy7#D$lF?1;Pg%^n%!C>b+}~pGGuwTV zvk?UM{FN%<4+P!fnI{I$=dzdfVh`@;p8U#N2|Vb2aeNK#8-0Yq6Wj@lm8Obc4jq6; z;sH(i$uD+r(L;-Y2Z@*YTo@4<{5*NyAe(sBCHPS6xvh|pM#`aWfo*fWNnGgin4tn=c8NWiQ}C`42Cm=nK(jWbpz0vf5s;GWj1e`P^fSGN>Kf8%Ec>sz zd?qTYzl}B7!e!JB&qJPB!O&2uV7cv^A0C7R2M;*y1g;Oj$mDX9T@x^3pbuZWiRJKK zlQ`Y)bHo_AqykX^Eux}LV__47p5HQtNakr`elTK~0ge9H@oufabfexExUC#hnhIdS z7!<}%tMdgG(7TI**tY>kxPAeUO7|+cYZoZp=YrY1)&NOWea3x}uCb6Zd$nljl24RU%1sjV-Un*4prR#}l00%C}4SqUsrBw2H7id1-0g@xi-uwdQ@cvrW0{ z(;TXJ&-H?-CJ`;OJ5(WE%fp>-(6iuPeYb5cuHjbNbyH(md4P&fPKuaIz5V!q!6HtD zN0BMTxX~xym#!%EiR~pNfg#N%Z;9>i_jyN_hY|0LPk)ov?!7E+c*U#G{qZKH_g6AU zWtHμAD{lJi*&vK056x;PI=DMA$fQ}dp%tp3Eq@Z1MoI2?h1B?tVTonx#O7s2gm zbFXnnHWwd`j5UGzM|{)a`WAfkW>lh+lZj^3qTm#+IBu~EJP{~ zH^ok1UG&6qEDOXRp;hM%z0|Q?V%5!n6Ybb5wD=eOQ?y>f=)p|lB5rSXYjxrE#*70aqi){G_guj;_O_pR!4?I`5L)cDy?BSPu(Ft zZsAR{llkDfzI$$-8g~Kn%}NZW0CQD{ifS{+s(2vS{h;&?*LFkBNZ(4AKg+?%Oln~F zW6`ueX>ONr@dn30OcC@dkRzLKn$y!-n`B9S@OU>V7lH6FA~dGu?R~H;*b7mRNdO}A zKCAwuV(mYsjmEIog!ucm0*?>z?;6CKcKtoK7HW?V#jaH^p2iWx3iI~1I~T*FHwtDW zps(b_K$f5$JC1en^hGT;Bs%?0OFCSLZt^a$3aP7l8$>u-Y--#z+40n>WfIhCz7tXh z%lGy!?xm~Nb98jv-ueqh8+X>My2K(-8b5{Hn%Ms|P^vtS#M?SStRz_ zS%TLl41g~qV{{vpM~4KfcM4x&eXq08e7Q4+vpxm5Aj7W4;eE^usUff)LVB*(wvVO! z_v*dqR`-(;)$$x(+WxyxG&HO5tCN^op4>$U#P65=O1@Z@QM*9Xu8|ogHp8*+6acrJ zS^wdfnz6#G>~7&K?!NZOzY1?tmxdOo;^s8Xn_o0A>L)N!wXybK0I#%@K98 z-lPf8QlFc@mDQEJp(y=4yjZUCcT=fx3DpNfX*9`sXFaW~kdRG)ANMJWvF*IAny$wO zfr#NcF@e<9&1nvuRf97(Y9OH_%!ZKcVL8*?k z*AW1RUD;cZH%WXdWcCFzZP?_HnN?v2bWr1iz~^_05)^%Q4i2@bVC}Ljllf9i2p)Ml z3I7&*ZE5t*%Gk=hGZ3G;1gC;6hL-^cYI#-Fv1kvm;r=PW)g#Z7%SJEe*$l5N7D2NN zhmv)4kcr8htsuu|Q?!{Xd2a1mPwTtcNkWwmmu74qEh=-yE69gM#Br@nDrOHcD2^4Q zLS5IgZM^O$)0Vq-X)aIaXFA^%f?faq{SrKD^5_p+nM)_-y@Uy6iu@W*yNH+=l)Nt@ zxd$J6tybt@YI&$C`2m6ZFh=8CZ!$dqS283H*6v2G?}}B2rtb7gR6gN}G-jyLU9w1=dk_eAs5mt6-M+ zw$HuZ4pZ?dJoXi)k+pbf$u9&BhDy~(*QQv+Zc%21O+3EnT5m!p`ly|i?NggY%(SU# zS#F`yrQ95aZL-!Zi|)^USDq@)xXUVtCvl2ViW;S1W}1yCIqxh_<%EHU&gh$()!rt5 z`1x)rLKD00)Eh;Tn|#N9-TCjY`#kss?98U)1gOsZan(bMy~X}4Fq7&oKi4&981~nB z(r@FXnMp_8LnMnT=F#$-srwT9rhN~V?^3hT;VN#*nr0Vv&a%Y^2P0|em`IaVCg5rh zy3D=V{{YwS&)C^@iUq~7wUQfF9M>KMAt9D044KnddhdMYQ z`7Pnkq|wq_i;8nc`-1{pDp*<-?^DAyoT8U=#qF55&fO!g<Q)9khbH!eSpCjUo@%bjL8;c-RQ6u4Rza+;I-* zIVjA=7mt{TX;chf9z+-yYIbsPA(Qv_9latR07eR6^!u~$*2q$nnz%+M^YDPj7YoGT zzbDsc)%#dSnI;Oz2gTf$S5+Qa91r2tV=32QlUG)Xq)b$z$U=h*I0t0= zoqciB7ccTEonDKGsS)+ zLj`N4&;?+Mwrfytvh?MhaY$$kosdx3tZm%Q+$2hSPKg{VErT`75%77Yl?s@3v2Ck5 zlYPhK{^DfkZqa~#8EZ+Gu=R8?Zm}z$K80Y#-RPAs6@M-$ z;vyuCz9J*yh8LJ9 zKcN0XZRAyY!eaIGl3uh~QH)T5SaW#uVyK~G7yAr}lI{nCi+IPm=oG?rtmha-^v!9F z_mgapXP^}q`Xf=)y{y>M0cgGv(8@tDGH5%UTha-y7R#-`xzZ}3f~-D&qk~DsjYI#6 zuq|?)i;!oEGLJ`u`4$~!^lNmu{?10-bFm1$jThwO1^6hTxMTfZ228$$w~*MhOc+a* zaf^O(P`OoUbl{Wd)l_Bn)E(r;##dZPb%hZJK0xW+<0B>i?T<9VQrB<}L&}1kV9=QB zClqT$e6gu`nWB$|i4-gqwe_j=S^0^qt=Lu*eyLzd;d!3Ny}=yj_;)&J& zP&zz83N58*tab?cDt@EwFF!`Up{8TQ8RuU$|D(P4WnGil1xvVJT60j7qiE>nS!4#H8 z;OBnfHeIDn0;;{MmivgAWmkdSO|vs~=0N(yZADK0qX22WC&W+YTeXlZ@q}HTu6Jw9 z0RYH@s#GAL6pUMo#bOmEn!M{DjBbEo^BACjYv{T<&hQgE7bjJA_u^gjVd6~t~*+&!^D>Cj0E$U+AmPPBPzsRTYsGm zP*6w#>S03o7!6R;N;5JsWdo3t_tn=6`ygG4s7Xdg9_vL<+hSZq5_?!F#Q&ykuTE$b zY8C(=Ky{+{M`uqD${udPz0Ii+bp_mU3o?q5sn0-It>OH{s<0Xt@s)taiJU>^j8GWe zb4|@y>&c3ILtIQ;9Q_{wP|)AIcaIl{I4no)UdWLOtSw<2GAd7z}1c;`h*h2P?I0|fVXeB&PGWo zR4M7{pO2}6bP6n5$sipsd;74sv z5Wb3z^kZ@DSk84q)}P|e0mm2B3;%YVB0}}Egm07R?)d9XFzUU`eQzW#oTwD}fK`Z- zvm$7ACn74Nn+WjrC*&*qg67nMmV-;^{0=bH=};D&W(Mrr>d<>nWDa)MJjbih?@?%5 zLoG1Wo~IH^14clIklw+QN75`-(=KBMb-W`$n2h@sFarV2E8^31+3Jknw6ZPr`eT)1 zt7=rD9KJj()4M#&bM`>beb~Kv4&hu_h%-gMw86_y)vkEIpmWcA`I_A+W1wj- zv5RdV0|F8w(DGkcw#@P(mRMw_yHR4pA;t=#H+K~FrD9+;T92Q zq4}8lUGV0PVv8c^i=SHlF2FP)f%OyI_p=L_Iqw2GOYFc@DQdzN+g~lm57W8shgOjS+FH7WAEVhGF|! zh~KBk|7dIVZ2=_-#Q8XKd20Xkx=Ue+8 zj|q#9kyj&BDO}^gMO}SqZXp4I=!OD4>WPZZCvaHd1`XqPV?>QupdM+! zflwz{J2iTqJJAh}Z7+rP0s~>z1w=wuaY89vyyjKSwx!YD-}{goUgT>*mr6fdHjz7P z);S|`?fI=bxn_ywsY1QLKkiHg`AO$5IACTxY$(#LRG6{_hc7V4svY%KP8B!MLWV>{ zjJ58%fKkqwV|AeIhSOwI&wq8&AndkYxF{qfRB5|tFbi5GWYpkY{S*5)Z5QMwkiVP; z$kz*PU62sM1b+3uNSF^M2Q==z3a2jA2g@^ zYQI`5Es(5K**tuc7>^0W9o3QEIyBB<)%1v*+VbgFidfUbN$P9z)R}@}w198*XrC?N z36U9Bg!>wJ)f8xtdUP~{Oyt@3(0dxW1Oz~3MXZlOSA9X%_aNF3u)dDsd(3KgM)ka_ z^G!G~|KIfID^C=3;Dt04WM>sXKM@Zec@)4sRXlbGESd!fEeHZ7LGQkBlN|_=xB})E zm$10O1JPUzrV$L26d&!u*sBm1Dvcn1v*zelZ@<>O0H*SRhrLj(aX?>n_k6ed_|aGw z)K}pmAGq-_K-vItdQ1o)p#q7@!0}+4+R0LQI8Qxu0hrlP;Yf{jN({~J>@;O`!T3ZAgAy^T}k^uhWA!kaZCkF6XUf?s4JU#<9@=G_WS3f4z@4*OpF}ckXg}9YH+8C=@eO5ao zL3l=wmofz;J%odgrkh3w82Ow^zpD9M=^SUbzW{hxuV284xHLrfPNFX(D*gpXc`MAM zrBl!fpfTeiOnEAW1}bCPKrqOZ$Z`NO1qR0t+1cfLMYoh{cPFh}0t+Vd&}4*@2(jJI zCyZ(=Hu-BruXJKYo|T;~|CHVJ_F?R;vop#sCmT6t2k@g>5V)KfQDZ4$o(rqxeX%GG zEdmanbaA*XOQT*fxbcPI;lS3ZA711t{(E&?*NPS$_nli3O&9#sKvqGcbWPo<-|${+ zF#KZn^?gf&6pVN04PpZ9_Z@Fk8XW>yffcrva?J-YG7U@1k9m2I#I{2dz8a+a6aOl0 zpJ;gHAq)??PPYKK95FYUWI6!1g&9OGiURT@Ae3==|4_%<*%Ym#SiVySvc5R^jiQ)1 zE_!BGCW$ZLB3e=RuH7*e7}a%Pp!KR?)PkZ#>5Z$x$~*EK*;_znK@;bjQ5z@)wxnNX zu^h@mo}8FZK3frZ`2`~8_LLR|w0Bq6A9f@2ABdf5-xEyBw3{AV*~0?3?q*!Ui9{Ws z+i(It%F7cJ)e#3*Kr7~%IcLGW(T$Axq0U`UODT&~(4UbXufZ&Gi5b%6Yd}h1@7ihZ z2bD{NRuIB-xoIBe?&Pzt{~o~VyD#~!(7;>nK>D5UPEyG7SEPb8h~=j-;Mz^380Ew< zF-mcnX@fxD|BWjDjN4}MnNc>%PN+rU?88&Qfv(vH8nYaCKH=RqW-&(1x?^@A3E+M5 z?W$!2%{|5VshqY$kdH3DxbI}vHPi_iHBh18Fb$t3f1fEGwLEL>#2ji`Ij3Z|` zkc^9J)OOa=8SSy+eF)q~k`ZjfG+U@X@mwWXoyCaDu^_3^KRMp;U9p4%&k8bEw;R6# zp@#K@w?bTvzhuvGdp9fR_*A95I)OZtQj5A0_M{?pmO3!KFmH-I^)z7-a8oOs{%vs9 zo%Eo_j2vUOvk*qWjH}c3hgW|iE#f9t6O%yy_vlZA?{F)IsC>@*@{6Ca4LEIy70Zj2 zn^ruXiDTqZiiQS{?{6t=*NypfBabM9j;!V&Jc)UM=O8WL5O=TrGSX@BT}n{f9SG5P z&;;YIlvc44+i$2>xxnVU)8Wuj(th&4+q|+C*kt-?fKfds@81%L*Vfj0JZk>>^+h~@ z5s1M8#BJS=0T$&A-qZgrh1f`4yE0qLx(P(+U?4=GGb(JB6(&dbBBzk+wwX_3SC*-R`v#yE1Ki@ zCvyAW#p{D_(?nK-XNKym6uUa`u8BY z5{;~HzX`n&9aVClNLZ%Krz|9Uz6R2J3o+X3LBz7I%y}6mG#~oJ1Qg1~d@vhY<#uCU z>{c9kOo6QPP7zLBjq8@7A#?MuJEWC%kbas!Vb}k+KRNU%v8z;w&E}DM@3mn6q4$&E zsOm4vA8+j<#Ccr@uXG(p*&z!|=j=UE#-Py(T#ehX{q_0{6JUI_IUq|PJYjv^wYvYx zUGkfo{WVRSaixu3E5pBi;u`9-@7PY$pDpKWfTZ;ELScai{XSa)t0-f=K+LT9KC1l! zxDBin^z<_sIB#6EmL6?$MiWtzgx0Iv4G)3Hbg7|=ynGZ$fon$(#dd}@4qVfC@_#bf ziPzR=eb-&Eqc1{1wsH*}Q8R^9xfXbpF%Q5DO}~tQY<|t&gHCcQ+GW9rub> zHGb25x|fmp;gc%OG`kxuW%Un@(GB|gH9^YO?ghezs9gx6k_nfnxB<+V z({ZbwV|y`_3SnDoGpaCQG#65Fh$m`^&>H?hv@3pI>xo0R; z8opd8pLj#|6<5}X=^CU*kwva{dVMw-w7@}=H6u_Y+fC1XL$#HeM|iM$J*svE574iV zUHI#kvyilGVybh8iZvKckLjke6OD?!%hJk=d_TK^rz-LP(#*y_9TInX!f3(g? z^3|kA<)@-AgjuBaM;pst*6W{+a>L;3ub3_RKI_2S{og{fBwe! zKdB3qs^FvWTdKU(GL(+sWBD$eg*T{43N5#LF<@(usX}=w^3W(?bW1b3ETD?Y{PVwohtPG7- za{*}NmeGmMjA4TYbu9SW2WO{2>+oM+ox2L+M=b5tUN5Sd2|S9{PA^^u5%2xR6br8m!*ezemiK4$(p z8A|GBkqmMJsl$KTnx| z7yJt_)DYTf5!oER#_2D@hFv%uoA%hw{PMLP#L50)&ZsZnWdeQ2Y8`;H&@gUw-k`ZVvVzd~^z; zU;@fl{|w=Ic1$G_;@$Os>!r&s=*{8PM41kJtMK6{6%M-4OQG3SX~z{kEnStiO9A;x zq_6%5QtS^OCxc4Ja*SN>V8h=#HWqH?vXZM@J*zu8=8f{7gq$T%9vl^ih^(?R#0D@z z38cvy?erXd`l8P=diYc2duL;~U#ILp2aH_}eJ->H$(IeD<8?7VCN)ILFtxww2pRlD za{R*3s*xpHJ*-s-=DFPDb9^uYIv9Xt6(2vb6tPH8EgiqVMtlt3R6-$MU1YHV z|4P8iZ1bc!osq@zjvKO8_;$|2ojRCo_S9)7hSz+@)_v~%$sj)q>-h8qzes6Ds&IuD z6*}0Y6j-FlRcy9uP(rDjfBKXTyVKa}ZY3Km>4UJpT7)cO zw)N~diWnUseEPb^6%8(HYWo`@`tiJOjSqgQ>L6X7_5N+9Unu4g!`G=#e_H|u7LaEk zfXU)(ddZN0Rj@u-c%FCn^X~JtzPRRJ`%sfM9VJJZ<{x0X#BcNw2Ztg+$>10pKsjjL z6hHx9-ADm!WS|VJF^r`WY;QFQY1d{cD92fxdnPGE4U=Y5S&e6;@F`Pi(vQm>6M-6R z_Vo#3&8>Djnpv+$8m`U1pGH6~vxh!;pUL}}sk6GlS@zupT#!%eLGqI{f2sYjQ*bmV zv;!NqZ)>h&R>B~?%4Q+EI|+RGh#4~a|7OS#Kl@REUg!|!?qhBvaXE0tH0=~Ph2WDG zePhgyRSiFc0*SWB~b^ z?vsXDWZ@5HSgroLpA?jT21Bz)4Zh;8LvSnRw!m5J8&)mNe3}NP^TF0gb07lKUSj*q zwT^G&{B{6}-WA_>^3XTAQD*l+1!<8l%;xy5t=a3?=b#Cho~+X!GhWrhY4D<{Z~ z#=@gbPFWg8mdV?6|IQXp^};shh$OdAE;XK=@@q}+?^)i?pz!I8JuRBy6igadR^_=f zcm-1N0G?$j0}}WF1af#A=$gtY@;uJWVI0BKw!wboS?`K>8yaeaS;8aWZ#J|-z|>nn zDkuuENEXHInB?T5G5oJz2c!8oNmNk=s8m?6Ku(PKqTZgo>fIF_D}@dRhciSu&sX<- zgKsO@<|L|Jv3mHkU!PFwvFoR#qv1VXt7WeI!YfB4?2rn;pyiKNP88?_0=;?*q!Ql3 z6;U;W#|sK^dYY@~{gMKq7-1-Wt1=~o3LEI3F9RBNHMr8(K{eCsOe%z3d(gN_xLiwI zJmCtX27d9gIvt{Weur*%*&ElX<(BkFqgO%(`MSFaM+9n!VWV)-CuTo!ce&4;B#mWW zFWD^ZosI#BN;$D%Rew?sB5rWTARlV-Jp)ODP%pCeE_zCwsmiU z6cj{FRwf3J-WN?L@LMeaO=;9Em8Z{zT~{B0BYAFq-t;d>Ux4YiqO^3|&FY@c&M2$m zS{Xa@O5JSpzLd>=Ns8cUTZuUVfL#!EC%ug-!zMuyj~Ss7Z+)K51SV#|GWz|x`G&89 zip@D{V?#*CR)|I8v5y4hbVD_~IKEVg%hx;jsG-O&01Dx}GfUv}2bK;7L_wT*Ahe;9 zV;TIxMRAvtdt#;mWN0UI_ts^F_G-e7!%Y4H^yh}CYjX&ERPL({xTgcBbkJ*6-`w2H z0+=_kNp+9ch1o*UE>BSxD3wkOg72SP<#QRgbV;&ac z*}KHKwIQwp!T|G$KNshu=EJzADs9$=2Dv7~I5sY7G6VocnkL>D6V8{N!J9#Vpw88N z^;s9&1VK5c9~j0ZSsi76SM|}GKH$> z|0N3U^0d=35xX3q@3T{K29``T`!=go>DIdz5qr>OZ-!X!0Sj}W=A{Muxp2Qsiptd$3@f%2#jiK^i7(^u#11aR;ZD*yA$Xsq=m0wfB3Nj|6w@1}9 z*P+P!GlpE|N3#^0vv?txI{u2T1W6iCl-$U-VNwHh(g6^ve#|++lRU>Vxw@axuY(`# zg!jDq{*|r?Px9uE2RIKevz`WQ<`Mn^II{v2#4bZa=>Ym=^F3bqe~NqWu%@=|T@)*B z8-gMh5K)SRwo$1HC`fM-5_(aJBE44uw}LbQ=@zQggeJX%N(U(-krIk15eNc`gwQ!- z#eKg0eZO<=bAI=E&V6qFP!c6;uDRwMbCmaehbd(d3Q{bN#J)^bw_*&Or6kkswFHN{ zc8k80OY#Z|9e`it0aR-}5rfTTG4Z@El)GQAsHmu(h;$eNiRuAm!_EYu1jm!ZLcfy@ zbVv4&?`iPa{L^cy`gRZ(#%@R#h448w{1$z@TyDn?CU$8%?xK6KKLn$01u~0^;DmOJ zZ*O_bd`NHAn#%?K?J^DWz>~C#Ci9;KL%Jku$G6Zy8x90})AbioCz&Qpb-}uv-M1lg zA@~)t+*jC894f*uyLQaVtbe~G#SRLwdi?Q?Os^Fp&j5PVc8sE207ZyZ@;+V3DCVwuMc7)oR>$O-oa6Yho5y?Z#Mb>4vI7j9*&i;92R?N+4Ob1Bi&^{ z_0v{y>M}DOxf8P>U=RXQaGl`klgqjc9qPE`f({cp@%ypv?&bH_=PH6WtzUD%uGF3N z#>SyA2WNG(Tbx$AWu0`Yn{2IL7)g~aRIn^8m2G>AvG~+yT+jX@ULtprl2W{s@pxCQtJPhJbW@-Dk9&?R zDrHDMq*X8OY1ftW3JDGTf`WVpq%a8GQyznqdR}E12k*x_4YQE@8VKuiL1l z6po(%8#FLkd^_poq!U~`!rDsa65<)9K2}M7A>nf8O*PTJMmeXr^<2qlbFgZVC+=U0Wi^k71jJ-?S`yd!=3m=K>Gp|0&G{|(QKxE=KD!0} z`jzf;1`^;;?Xo9A{wg`wlpVNvvxp$zDsg;^Ti~x#)pu@rKjd$hOeLxo?|;D=)iLMZ zdHO)f{W+J&*$ccyil!8B&QgU3QCH;{_>gcs)lz)8gJhDf5Z;$!1I-m?$XZC!VdE?N z_EV_T$|B@Hq_$t`4o;86^wnt_T6%&mATr}bNr8r2W60=aqiHc|l&i*~Q@V#iI`-kk z?ApafcPXvR{nv8NMWK}1^-yUl>H>4SE{fEq2t`@1@2HaaGcr#rZxDL{;v3&N8-?<` zfg|7qh&jwyH#4;(%;?hP4pN!$sqkn}?g8%Ir<6m+|DX#Z7No(dG*qP&7hjZ$3=6x0 zxAW^kD@SM%JNCn7;?sI(nt(!-vIq6`l1hZDlH1+%7n&YMf?w* zhAH(}i4VHNsoW6pGkog=T!7I^BCysecbG{`+fAFiN8aXo9|;_aI}MXVPy#tW9n;)%FHw5y}d@C%ft@F&rRE>B`2nee{O}t)<3eU)~)K&u` z5ZXTbTwym}ZwzdkOmNqr()h&?_=R_8t4_`LOgQegE%5-1E&yA&goU+0({9GFF#anz zL9pAZLqDb6=}FF?`q~m6a}#SJv?#!%cl6;fxYdww($2)|)-(pOn^xs2WxY2RYGmKm z5qz;#QcL*-T=+jW8W`y=?}6}l`nNAL=9j9~5Ss`!lR{s@j{>v20zZh*j-YJ)p!iNp zVV$EnT_z^#fp&$6V`-USoE5aQ7O?!yR(T<8j}NoqkEVmF9PQBboyC&ykiP>c8)~wg zXdlzorZjH%-vIhxqZ*+;FH3XMdQagozHUpKbOBBBq3<(U;TQg<# zzmX3sXP#D`8I{&Rh}#-7ai~YtoGKYnk{9VB&ge2-u6}FG)cjHmYn%I2-A6UTJB7Tf zs7wLF|)O<32a!dVcnDZP4w2axM| zdPTBekhs(G2DQE$a?{{AR!MT(l-UzP{%k};zsjj&t4r}wi`gGeLdfVn=qvUuu5AgB ze_wQ2I#YSa+qxD66Sg;^1=bG_KkDO(xZz+U=pW~89lW&UYWy58{p!`LNyvD8(5~by z{SErMrj8~&SlTT$>T`!(|G6<|O8(IKUt)7Hd7C0~&-noMhRA~?$C^u)MQMAN#l?7M zsce_-7Jj*scl_GuQ>8#|;ZmDuu%UGv3p=0Ke&n+N;Z ze6}8KiY9~_2YwOHFo-MZ;8OKq?fv+x7_0Vp-%QGJA-QUAJeH!V`849^r@buucN`gm ztbUAAu&8RGyHl}nVUt*`sT>ndN=HR4jEj%Hcns9Xy-_agowle3UbZCgwV+>tVYiW97WlR1N@Fjg<@pi)@E4pfm! z*GDE!;TG0_ySr1XqTq?U$W~aG0_19JVez&xxh`_3(Lp7uK znXLxRiMxCwGZ(FrnA{T`>#2I5R@9+8yP>b9aPmb|Mz-=fh*F}JgAm;yfpk*sOd)M4MSz5?vPXRsQb~E;}wNTR%yQj-xg*Fd{eta zyw?CHht?-WQ+y7S7fK+~YbPh3Yv(N`RL>RcB6+_k@vd+%4)V-hvS!bY!FyLke(ZdX zD>4&3`QoSk4l6Z_%v9y0xZ1_T!(8hR)FpWo2fWr7MCW6Y_K>{w!@qfueyr)1!U(K1 zmOI`=_tNlN{l-yHcR8dTplo)|oK|%L(nboTfw%5iuhj1zuYQOQ%4_HS6oR#J?#J-& zvFtXe7}vHP_(*-ZXtR7TxVIfscg2$_*SkDC#vUgeoqR0+Xcb+6C{nTMuPl0w6Gm2L z16vd`s_ z53kL@V{ic|Y>~B_dz9^<%gw{X4D3udajsRCAIF%aR<-}oA+ImOanvUuqSJE;pP+5l zwZXn`A0xOV%7&eUmYY+LyTI!Ot8y3EhV z?|HICWBmi-mptwyJ%R|ALf=t>xjeQfJ=3=~PDSV1rE^jH)vfguqH8NG5=>00)47Go z6**2;+YV!QOlus1M0YgqhM@$=$wR&+wNn-{OR4suY|VdHljs7xow2O}7o@!Sba}f~ z5^Tp!@iBN&{3O2iktB?&)OjG;G&63H&$6t*6Z>oXoKQN4>~KZVUQXRiE`=G!B*`Yj zOuN%zdTLK1;C|upWI#G$J-&ot(8421eOp+9No4GMhCZL=hw?Ypm{?&b2u{2)ff9se zIA_Ry7z}J*5oR1yeh97R36mBuWbt%tjyOHj+Y{lzb|GPIHM7{aAsdB_Pfk#|?bYls zU(!K}A366j`FQ7F1`Fwr_hRC5G4JADcmzlGb)r6~lo88{KrUhFNUES`cB(Qi%sFAw zYDoWP0a3N&aNFvxD+KTHGKpsAk5@V=>~w`YX8v%VcVc^)-vCcZu@TG}v*kCIYw(7m zg=7f-K1Ec`!Y`}zxH|#X;`Z|r$tpaim?2ixJuTncM9_ypl3cpInW-VQ`NHQ%Ff~N< zG#8YgaYe_KL&`kkb9uJiL4AYDVwI0rx6{KS?FmX{oVlscDHBY~;?E|qAzJkTLgOrGS!>_TWJ9h1Ee9NG?J`^z)L?A zcfvgWtu@)%)6xXnuEKooC$}EySiEZYj+Yw6!^Rc~o0)E^rRa_%?3$0yf(NyMtHri| zQzcYb)vR>;D!Nb;dd-toa+IW^?gz#{glc>hl5lM#NqBoH>)gV<4MinfmGr24zu}eb zki;Q7xp0>fHLGCxX9hS``-q{KjclldrTYCA+IjG~ggEC6?bSPgp`Qscw=)nm+krT4 zf%sJ(^G0>`h86a2=eZx~$oh6CK0lS4J&aiF@#{g8Y^9yaW;K({M9xCJGCP7@&37p) zmV_F=NW*V=(HFB-M(`)q@q&$~@B#P$auaHyzr{h~lXbMHPE3>jd9F{dzGac!qRfO( zwlQ2&f&{p_J?sw${cuWkVUwWU*Su(`?i!96bEtasbL!&MKCuAI^n7*pZd?T%=Yyj5 zBNspl28Z-KFNTPe7kDz0lNKk1%>qV1{|3Qv|A8eBTsQe`=rn0%s9Ri!545`#NM!h! z%*PDuf}p!y*3{x1&4M(g{C8OyX{fExsfW+Uf=$alj|17N)c0u(DY`MIcJ|gJnv$Px z5?ly^<=D%WLe@*X3x22HSp@&!(rGrpyo%F$)x{lBbb?EvN2=31X-IEWbcW*qVIQNl zZ|u-nE$afJLNo{{gK=ty-KpH_nfqO^e0CX%D1E#fDq9=f`;$)2x~XN0Ufhg)#3k^v zjl~NNvc)9GJ4U~JnJbeaIT3Kwb!G|3WHe6@dTNGj^^(C=etJ_g!wkvQJXoVaplCJk zhi_+GJD`rAnqYeWr@Sds9DI;!i^Zuft}VJ5uMLJl&>5iP zE_g%N!ZMgbhg)-Js4)c_{}y(A@J&=iY^96fOA+1DzHy8A*-kOXu4$m)xzD1k!G4$%{>OJXSDA31 z79^$(3&#?vAR_{~gEINLBE)+H$&12I6a?CiFte*O)}R~M1!#`(G~kq>y!tEg-&mu9 z4lEZvn@Gw@E!vuIBolHe_n|XT+1R{sVKh!Qs&nE-eQ<*{A7850gssS+FUucRyvB~=YsQ+GP^E9k&rE&M zv@2ZJu3$USG_6~wMtw_S(!F;tY5mCUve37-hrd4>Eimn=zF2Gjzx|NuL}mC zPUu7~E;A3zw7Zvl<{&H9EH|%D##Lt>Eq8Ej&F0VG(PJx;CycO6SbZure$*l!CHL8_ z13hy@o2|!2_eQ`mg}wC^=SBoW3Ubzq98aCke0Rdrl2z%_2i&e(54n0CwUk1FwUBu+c-r0y%tm2nE9IDIfygb4kQq^#KfM#xm0o@ zKFW2lI##cw)^q5raXxZ53+_F5BK0T^Aoc=jZQsO7+2cf!6KOv6S88SEx5ky-w90Dg zDyR^s_I&K?qXGG$`Cmg#;lB#feN`@ZS3!6lspfc(LdHKcFYmqX9#t-DNU5u&Nc+0_ z%=BeOgP;p=ZIDt3A~pt@d1Zh!F1hFG4 z;6Y5gEF)ua=S=LQ0+d9{DK4o5!Km<|A<3wTXXX(fA}Q^!k8-xyCkPUh&+>J2W|~`M zltw-|7Szc#``x|$&zD?UT=*w!eDs2eIWi^Mjc31+NbPuF=4f0RXVShAFsUPM>2Dc# zFh?Wf=B9J?XkU$_{@q8Tf}^J1T5Bn8@%Fh>naSFOIS-|o$A^kP-HGYGQ^6+4g-2Co08K5gSS~8 z-B6;Eo0!TLcBgCgb={d+87Wz`QG4R&3zBOWNF>rCiM{aYVVeQ+`Z364)>HpeZspZ0 z*N1PCn{I^o%%6~b)SyloO)Cr$3=9aV8VuV zMdwqW`S|1*?wL@U=E~B-xM@eSze#zmupE1ddHO3(bVC+%w5dLh-LTzRvT5FVww^Ln zd7WpM0snDhis|Z)!L4XeVR|vdkF~GJD<4{N(V)q$;LL9oDV~+iY!xWZ!aE-&L;mj*e(xhPA)G-I(F}cIWDZrWmXHyxt+k=-bOz zmuZ448o-gA7~hU}`w~AtTB8b?d0;@?s89AxgL2&&$9Y!sve2PhPo$03VPs$7_x-XxH4XzhqsvJM53rbe269qLu$~K)7y=VR>nOZY67w;zeQaV0~PlZY8-K13fp`ef%5@@yc?yIJqlwZTe z@(n?oYsA#=FomA1uQZ6xTs86nMivV>#j$LoJ_Bb3A>ChLo=^cg&DiOT6p zuOM8b6ax!P3hKyp0q3yq2KhSFazJOZ_v!6THAe1`@Pixj;EG*yxsYFQ)-^-uwmF`U z9Vg@#si375g%pwNmt%RgC&7_{s@2AnOef?jXKhjdZodf4Fs3bQMg@_ANF1>)B>`8i z;(XJOS13W|&O&r8*;*wffwkYxVKnx6ApK;7QO|{o9mXD`TovPfKI%T}qN~)ucABnt zd^f(BU5)>+g;%L7ZzfX_Y#J6zX7F z$bR9e&vO692^aEM-6Cd7+H`$+v}&#FCf-PJ^Xk^Glichkf`+mB z^lUWKsLmfd*NR+uv1G&HoR>{MhTn1}CMf3ZDV zm^|Rxb!s#L(dSx@w9H&{kK;_=sHjvb=qd10Hyt)T>)%5>dXum%-OySuA*OhTnR&+Y ze@dzN|1G6Ln#$wf92{dWs)q!z^QV4Xy>Ay-2JVFd36C@56}p-x%Q0E!iZLnzLbX$$;fso&y2$J(kr@S zyo(##@=3d_P05Kj`z2lttBU!I9YtNgL1})Ou}Zq$^J~d3G_U)*o1iz&1(TF(l3?uq z{3;@vvW%^9Z_lQen$oxqdP`#mLcJYw%DoFOjOQ68LfJM&VQcB zr#YYNC4;w#rf%jGoPl2&&YaXSz3aE}Q?bWfK>r-e_EP#J1|!o$rlzN_ZLZjBrHw?t z{7R&5=Nofvdw)K2a!^7`!BsPgg}q+8ext=@6I0OnZ$c?#vNWp5S&W~#UdLdoxQFygP#&o!>s3W`L$+0OG&9)HcPEwc6 z<2IS@860&pd|?DsT5P_T8x)e37mbUlqYZ5&IVf>*%tTjzJ-zBksi<#77iN1K*Ub+n z`3#ngRJ){g6Rm8DXMFX4iA@vxoiLG^5xI-LxT$KF3>&Wxkqb(1S$fuo$yd=+F$0Gr zh8AyA+@^YMs`4Ep{cNYk?5dL*?o~D^>6 z1)E19Q43Nf%F^gclP7Dqe`CszbRoY(J3Ev?`oigC;I%NK(vd^|{z^3$+^ z4%6`J9>22iof(3lhHjQh$RU1~gAnbFe?C5wGHe?9UEVDCPgc?UkiNNA`IGxy|FQ&D zUOo`1D%86%T>bH&fS*KxGT`j~izeIa&sGh!vf|7L67R)_i)@ z+}h98(M6@W05yqFqNnkKCPHp1ADMAWRYxbh)UkV5H?MXj5A+*m+IT8F_Or#tscN+! zXw3;IB!^-KIJw#b{}72Y-~LpdwBl23&NJiF7+zmKoKOJW9ulg*L~d85o4O@0 zT{h&zuhnAPRgdy$EX~a>$M!#PnM#?vGQD5iq%b>s7B8(LYm^9Ah*HriBz+gYI~|JY zXq!HHA-+9Lv1uTD2l^n1I)PEtnAFrrvUQm z#D7Vo-7MK=O?a~>>9$rfHN9Hl!q?%_sJDJ{Yq-gGFYT+_2e#HZrn z@(tV3EWz69&4s1zwG9l0>jGtP45c9@aC~&$(AQ2J$QiDtP|>|1wHC0l`y`3#x8dTf z^2TlIEeOEKXDgWpEGcU~6_S3bZEXBHe=u+P0oB|f1MZj^7b{mt^1rfhdG%L1wIi5u z*V1;vw(E$DWoM~H)WWw};=(T2w( zBC%w!)uDd3BP;B&ibCWV6Z2`FILo?bPYSNDym}jSnzolK8uoAs$!u@@f_;&og3@8e z9V;_2b~z;%K-?BO|1N^6`u6-DfPSQIt*oE&#?0JezM8w8VRZjz^CILZ#jMXUx1ItP zn;eLOI416(v%0_Gd#JuWhG(|pIDp==ug>ZI)uMXnrTd6e>kP+Z$QPY)ZXF^y2G>-R zHH=R{?m+ccA=*@L;OFeu;5H@U6)x2a_SxD-exPSa40?7V0?S+E~yDt9uI9Q$%qK4DJlxIteN`UF&=VB~Fx3q96=%i@3(oN$hr1!nNIQoASo+K6hLZ95Q`+X6;hD6$%_#z4SSghYlr+CR zuUf2Sq5+iCMOD2*Igr}2vF9w&&?{B_4IwXmKsmAZ~~L zgtb?ro^1)6l=(&uWq!*1qjrqVyKBCzFWze}Nar+#q|ZH4(G%yqmsTH9%|O;WL-~2N z`gRR=XV}lDobhXtm!q=AeE&QZxmAYwLq3R;nUKqXsv=vhzY%%&k-mVD!!3NDJT3W| zs%2?u*`B4IEc|&Ezai$giL`(;?yfbiaYs5f&lFev9_=;r(|_K2kX)!&^a>FhCj790 zJFtYx1^jckHHfE%mlq;*1!&4H1icp*L40Mcr3}Hz!0QyTE$TdF>L%bIKBhR~{!QG3 zCF9Iax(8XGTpuxj&rGK{0l%MMXkR7&Dm|T4(j$H4`y)QuR{$jFZ`LiPc+Ch8`3Dao z0O14>8EHh>Z!g0c)+Q6=yKc}R$pMK7Q4gVOfZXaLKr&`ccOh4Rm%2JuF$F|D5UiIs zNFJvvM#d)r^$f7RoFXFFG0(5zw>%&Xewl+-96XzW;2x3A;r11V(kq{LfpWUq>l!#l z;q^f09h{LHtG`Hp(Vyv!>yts0K9t-j1l>+RtYpyAOrSKLeU7i_+FoZ1U&LGC$oo#wbt0@2LlT-pA^#An^ez=xPL&N>^(E}Z! zcXRGFk4FEvY-D7Vh{0f#?%cWax@ISc6BXGJMMIuEi3MYM%VUrF6Fp!Mv?zj!IHCwL zAuGY@2J~Mp0F2>E{Ht@=XJH^J$s? zGBo*?o;?;g&!GhCI4J9Mlnt2h0qgVayg_co{Z(f#pyFG;3-|-Xmbrng>Afc9P`{dm zD9DJ8$3V zbf5rX%Qp-l+YYRRS&?1_V9cCJak?FV;Ek+gYZWLxcI=oE7z#>Ru@7~=!OJv)?bLxH zjMce_{W48j6XANO4Ms(d6T#`SEJc_Vj`;niOH+& z0ccjzUg(?Cb;l(I^my#uR9vTf-}-)#XQ_g;B%X-F;pRb3U{6h0{xB4UvLl<*J~~uw zE)yxffIwMVzMJUum}qgaDqJCb9fXnW%j+w23hS0%EQnKPKDPQ!1hSCLkFc<04^ZYu ziFF{hq}+GSaz@pZDrqtP74!9Z5&Yy z6#9}AKY>3Iny!@b5Taq~OpF~QX6K`$na0=deaxDnK+ui8xjrx~1%cl@{Ta_Un;@^< zQwMT570qU=UAJZd?De4{SLW19F^A=R(^9J_qOEAnQ6{Dlz!!J*uYZ~WH7M6{U<1rU z9f>|~yw0b>2N3UTD@NWknL8?+Mr0e2xM2I2R}O-hXHRjT8?NF*q3+L0*r(kctMyuY zmP7sQu*VSYj{86E&J*tLPq+tBGu#h3e&%|(XZGI@&-Gsqk9q4`pdKqwmqrc;HL`&w zmS!dN17&sh+1}cu*dal|&9$J$O_SGd-=5zk6H~mtJ#PeremQZ;4xtVor7|hL5}YKT z$oZNx=5NAU(VM+xmHYLzH^q8fGQ!Xb0o}IZATuNRU@iYvn0Zi(EI+deGWkvz&-3vx z@ml-#!YYE{jRj}EV{yVvpO1$@s|K>brwXjdpk+xwWePV{x1LX-TdM|%(9Deenv&?xq@O+)XWAfwUe-=wXr%_Sf( z_nIh?{5E^*$OSUPbu>EKX*|X1pG@mk<>f;ni&6lF;1y$cpodjTQwx{t6nlyf?kKX94j) zy6*D5_gQeJ!Qp~KX_4Zn`|$7>zFgRS)Ctr1jsgQy4Wx%@mE{k-^>4F$jg{HNh=WxT z%sax__%%H}eLmSSRC4IZR_jL}wXo38$`$&z0CX1I-l8_RwX8vkj<({b5IG>g;(i*K z@5>CU1!)kZ`v$VuraiY-rz}Cd=c0MbL1hh%t^XKVxHzizKSx#yBV+i_k%_|F>oBrp zh6(XdQ2R)IZVRevTPv+Hp~SgQCqIn+jAeWQHJR+lxDJCYU>~@)_aGiomPQhgm($`8 zE`bcQDSHZxQF3r}2i@fz@P)9p&jhE_rMjJs-!BBu@(~>32FOa2Wo4Fj5A;NKAE&6O zPETiNr%j!4T)lQ%x$VhT?TKfi4I?8)#`D^n6Ul$e0_>=O1)PA|foqw>Hi+LmIg2C{ zx+8SN?b9aW4ec4TKk|Oe)-3!b`r8=9KeCZSthoaiv~cnLZxvVx#g4xF)WO_=eS=5C zG5Gg`3xNj*OQ_yZ_G$!Fu9aZPP_FXuWYfA`%v=fB{)u5>z;+7?3rB_dUy^YA`~XoR ziMHwM1#Y}gx}r(@{l>dybbh_3 zyPYQ`FJT;-0o@y~mufv69;!#%S>CX`51Zk7Z?Fd5Iv-)JUnw1M#taw#mpIDx--a3_ zj<-A;vESH5?rH=$#q~@9d^&kK$mWBd&0@&rgN6W1Va?~DBo0?7uG6aDq43 zuns8mAP4OtFrSof+-SN@{aP6q1nmQ)bX$>~c$oQwfZprWPee>Fsjlnv1E+r$cc3_) z1c#tH*eq^176NDGtT^JW^=ZfBv4HJsYiklZp{4cRT;QfWX#PDsM+xgo?ZiPbHmueuY+mA;LWibotCACt#^Z;8K(1y z?cZkXIzM3%jskA57;x?(7Q^+3V%gOut+aMFvQzA7$C^>s^)_{(gp9h@7KLA9lIS>4Scg9#O^Xsu0*J{YFOH{nQn>>m;I3h>kc!tOP+xp>2XW^uVl`I4+`M32e+M#2;68Ip9K7;prU4n+XdR%iDiz=vlJRI%PnT5C~hF3dl zWf*n9pw2Dm(CKf6)Hk~r+Q2Bz*^E22vMqu}jf$&lX`ba6OzO$e5%f^C!AC%Q_(l9t-RuH=xoZg!VD-Cg4 zU=3;_kG1I73z($ozrD2(*cL;KD5rj{Y_qiH3qnF3G9cqFS7P_L>1*3~@pa;&Pb9dd56ldzviD4TDFGxhgYnFvBG z3#`i~E%2+f(%>Z906Pq4ReYe{{x0w_FT=w_JjokioGb#BSZVOse;jDK{A7+ngtY$8 z>%p!6b4>N$a?JnvO8BYNn--=2^V$EMXym^)kh1|uh>WOgKpT_&?z%9FcZZ9PAutmp z)T6u1T6CO-8j$3U4ALnc@gxFSeX1ov>Kjw%)-iVv=rqaY7Wv_vLQ_OrrZEdYG z!m2o;)`W&HJS4{o)P-KhCiJyVAY?qynS%%+nnUh!FvKKaSG*uLaGmJ_4J6-n2vL?6 zVFh~)mRr|V7Y=kMT_FK~ARaQx%Aown$-~nL`i(i%3C%fGyP8{XclvIF7?p~F!Hcm0 z5C%+Nh>40yTS({Lo*RdT10cHhddweOA0xonM<*q*z1{|?F8v6JZzuHY?^h*yehFb> zV-xU)T=?nDG5}qkjYn?i1QOdmcziy7XK$>UdbJ0Dw~^wquz8D;wKe{*GYC_W(t^%J zgGVf4<&U&<$h?C6k-n3M`D`7??n(YYv}X=`Zs5+UN-aNPkj}aEsRtBkhHAY^5VM-> z_3fmxvhoM2HdPBwa1?Tu8o7RuM_M1K`uWvgg=C)OGlMGUOioVDm1X0bH&dzEYm2Qi zTSaes0MqMvO|0u;Sg9|Ui)gvXu1_3jyjk+1vB=;=YJg$K)riWSS+?3D5sgbPB}cy zT$=f)*bPb8y>r@eUzhMXIlcN{L9TY-`6L{q;@`LY*FL7~e3&=Bi0IjLlKO_oV7jR4 zbHs`S@K@U)1G8)1p-6A?g-yY|-d`{SD^mYuebK!Df&yKs3f?xW>(0mP9cle1+EV$!E z&)2v~g`8%6J@_OBB_rnM=BH~NU`1ug@*^AJ&n7KoS19uD`D_I{D;2Avu1a01eUJ=Kufz diff --git a/code/global/output/risk_channel_irf.png b/code/global/output/risk_channel_irf.png new file mode 100644 index 0000000000000000000000000000000000000000..14fd6e2f0736b2207962b7f5a7a7dbc668f6f1a8 GIT binary patch literal 188194 zcmeFZXH=8f+b@g-tmvpHN>@}mj7UU!QHr2QM{0BsX;K722?RtGRHUf%-aAB)4hf=; zAPAv{mPDik0!hFC0YZ|qGyi9vbd>#b^J@9kky7M5w_pwjFWB0$r z0$u$4-F>{T%B#pL$%?rJ1o--^DkymU_bGB;wt`iyZPbwDsrW;y=mxZ;xy4 zkU7^LF0NZQbu5FkR&d;Dr?5L+YpIYk*uQP3am54XWfmkkSVvP$Q+Q<)1wVyvH7hJm zI?Qb+c{=MCg+f{JW?`tSxrr;6d+n~t5HPgoE|$E-tbCMd024eVowI7WnsTF0MaL{NM1yygyoZfA?13e06fO^ML3*GnwHp z$F)`(3(0!1_^6(N(!053$@yZm&AUokh0ks?^qX9IQ*56U`r%t~h_2n|NZH zrcR&sulbQ1zTxVyR)JptvG528xN;byykn7X;Ax;JDGuB84`DM zR#*{#pSGEE4*?5Z7yDF+i%ard`#1c0)7P&zJqPk49mCfCuIW6e-RXYa+Vy#=6I^Oi zZ_n%EJDJxeoD(!2$=TI^I3nLHR^c*8Z>Tw7-}v~6i|%0F&ooW!XMr-Fx?hb`-XEKr zueY&T4BF$&4%SX(cHpG@W|wHhJ#+JS!mtrDign}T32{83#otx%&-+gi>w^Z@5IgJY zGN-t>v;$8hg8iarhiio~;}lz^&Y}XzcFH@Ip>)b7i$OJLd#R`2mjS7^{rJc_U-yL@ zKlckIF=n@T!XJtHmX{=H{}^V|qli(|l`-dLTBF}qHS zzNU?$YNM$E?TQ*&q)atYU*=-U%nGMNhiYv~ZlA+Ssy-h7aGTy&W&6?L+MeQi>{=H+c51sJFK5tiYtTeZZT(EiB=Phw6f=He>Nd~(VO1#fY_c>a86wuMBN?=u8* z1N)%YydxaZ!~_Ltyg)B@8djhmCqO}X;_nMLr8`bt^PXN@`1+WDMzuATIe=;aBeHxt zm}fRaCRB0~F0N|qGU){G{Ur;%BQWcE^T{Dos!jrywjO3yTI7vuZK3CH;2XZG9pBh8 zt*0W<;M{oFyvAl&?>d)}v6WrcniW@~M>I)crjG@!P1P)ic+%=fri8?4)) zP*M6Iu?kT$tKXH)%7Km9ZZfDHqHAgWl!g&w=5(nd-${a}$FJTbxTEw`Kt{zgC88N1YSA~)0(7lSMNDY?P;)3b%g`YgzZ7A`=YOV7L zTCD3UjWGQ3mDbB}o#v{YwJuTInti3UTo8I`YZPXQwJWYXPV80asL#;|wQKR4pN8BZ z)Xu10t+8+FM}!NMSG4A8-q0{8oB>;X`vZ*EC)YjR3wEppoD1B>r^?D{-!Ht4u;sBt z%*JM6e3X<$$@o%fE(LvReNvSWucQ%&7W8Rdx*Q=8l5B%DaJ;(@6W5+cXa9;-Xl3SO z&CXdFSV_BQ6Z7XbGtRkvdvng)9_|G*;w7fodp}9A(15gI&9W8JzxPv*P*I`SfYIc3 zjD6RSx0K^Wvll*Ofo8GYe}Ie2bic6wk1Lv1FcJ7-ej;7LseK%WoC14Raazl7&Jdce z=rYYLHcs6H=jJ;$PiG(7zyJE|Q9@_bfvf61l<5|o1kdT_+EDM&3M-r+3cm47q$7eI zPAD(UAw{(3RCc9GTlL7L%GmVNmysQhg0p+&+kW6cFQFc&W`iAu+Zi9f2L;DCOjs>& zy}@npa|cQQ;<0r`&Mv7M(Gsv^9=KEFdf@RjNEkGH z+oc=JB(Pb;*w|Q_s1GR5{z$494U{#^s*n%6i2Y`7!ah`PRV!#p2++;~F*3GwMbxsw z_`|Is#1snBu`Mu~NGDHWp?H3$;PZ> z!x02r5J5PwLj-hUG>ge#^cE9I_cqp-u^5~UIxHkeekq*H?=uxN{luW#Z)sc(5R@I|0PmLFIqN=IVK!-TE_b5b_j7jSo0)RL!GrXOL17Q z!QkOI`R1Q{p>}q512p{v5po9)GzbS{Io~3>0;GblZ}R4qZW77Gdm>Q_HPx+9WHp@t ziaz_wdluuH%w2fPgmTn@{4$L_Q1WUZGCF^;oiQ6kwWT04GO5kJ-G)8bzkK`Z=MM=jzh28sJA0$IAF*2j zYSLVUk2iYR2hnPZTQwhby^W7t?@EuKqG7ZMY;{Bq9(Ie-%Q!le>w~9j@2KkMfjW`y zi0NQZ)Plm`j2pQvbY>pr()8LHQbWHAQ3(3}@}1o-@1GDS`gcK}_8AIwxb$ubfgk>( z0qeBfD?N&pM{oVb4j*l}P1VENP-(|@W%nZa>4b<5Mu$!5Q22nKuI{ro`4akKVZsjb z#3Z`rifR*zW=*vrXf1^>R>tgoV1$^~{zi|{9&CGvKiePHcWA?big0I_JE)chOk09p z7D4!k(G>I}92LCw14Kr2{&45}p4Wr&`Ne;@Rw z-20mIw>RoY(PAxycEzyO&I9Lr%jwGqLY%0UW-s+F0SE8T(~0`5dAY4aA)tLZZLMZd z^;$bkpAcoxs>R&+o6(w|j@Aqp*pHrQ+1+m7|I2k^2s`5~an;Fj|Q{ zWzmzL5G#;@%0Ua>)(+;T{lJxxwjk<<&Un#?-9b9Qi#znUuB_c{O7lVNy(Qs~@H>zy zo)p&~X;)Z0Ki21u?}m+}Rq?n}8!XJt9X{yMrSj6zw+}a04BAhH16USC!Fs~?SaP?D zpG!0m<%^)*>UPCMvE=v*Fnk}^h^*|8;gM6Kkx=L+aAJ+ivvkg7Ga#eJvDG8Er(y-yrl+o z;ULFVfcN=p9zd0aDm-CRIJqU#uevNtX!M%erSqOs z{3<$UX%K7LmAYBo{~p=FnvBS;d4R7Kl{YP;O&$r8)NTl+d$SR$m;hnp2st(LKNZ^; zj{uHlQAN3D`3yJ1et(zn4t2UOp|-~zDgJ)1#!Q(k{LN-3DAD`@aUrDg(p~yehKQsx z)*Mrs8%`1#MupO4J4yl2Y9qQqNo^_`WHoNRjkH&%BzCZ&uzj$i-~CgcywCuSq78Y9 z8W0L;+vFy~4tDvvO7`2rpxHHto~C?&x=*AhlCQeB4J7GTZj`|IR1){mev${aQ0Q$k9}NRbIN;% zxVZ8Re+wW#<3_qG^w-yUqkjVo-qv+dLL#P}J!%7m(JQa0{h2q*jUE{&;prD2^zTiP zTh2LE@z$Q#&nc1o) z$WFVsl}Lsp6AcIZ{Dw%VfLYpTznF?fPQusF;{y4D(#Fq%m;>D{pAb}GXnGvqQQ4p! zeT0H(diHM-sk7-gM4|TkG0&ThWwRtSgF@?g6u5T?9Qax`jn_cj8YN~V>6(k!Jdv=J zCVG0nd3M`K)2 z*f)fKQX&haBChQ?6`<5;zqM}ic2pyk749ncU8x8;0D&&DS#&MNHZk|AdRL~uIc>uo zJ-elPJu3sQSABneI=T#zDAW`cHpo==>G$mz>i{Jp zaKa_EB?JM9sNd{pVk=JZ^E>$Jbk7?2zw7<=ckCBU8~>9frGBL5yXhSULHxSfwQsguUg$jOnulM^51HmyLdE| z-w~bQ%J^XC?pdp4J(nYpnBs-T5%tpaLG|K{7~1C}3IViqIB^S5{m`$@x3M3_$H!$w zoimi3SR2R)TL*_M7X$Bj;tvPdsFgQmaJ?OK$3KA7dg^t5?~3U;REPlt>FM;Hf?kW) z>-!=|Y91nHoWi$PXAG+(;o5>2#Ft}qBT3b3HxrJ)ZWGt-1n$*<^4~&js`|Z<$)0(-9<5=E~ z_U+qm7*${TBC5Nvl{N|(5?8@?-mn(^Q{=vDkH`O{vwdk7D=DB5?@&g{E##Cf31h?w zfcu(6hlU-N@Y`o8p~^_~T_ye(WgM5ElEr=38 zT>{9WZ~e)P04t$LFZRp?JPfcChfj|O@^r?^EhL(J_>?S(T--fhAd|126$5Zz?5Tuc zkemF49)G70Pa5X>U-wXVZ1F2T^4PlE{F0~fpK&jEqyl&PMu@&e+0*sK!9vG<9q|&7)yl$GV=&6}1gzaOuTm$9hox;%)Q>V9 zS5MV(tq7O!KLRFX1hRU{bK0ByqY z2@Mdd#4PhFtSVa>{(ih>iDdv&r`XiB`LuI|wgr-PHScz{s9u#!dn?S?B-0|AUKBW6 zc6mG^#NxN#y?b}^cUu4VhLKsA`t{(|ys7XqZIkAoqd*REFVv5#+gQdj=*uZ`_S1B9 zjn|Ue?Bd$teyI26gH)P8L`xnCYPFyVUv) zD{PutP|cB}(%Ler`s=o&4B?tnR!-PG>ndee^(0ku0!&J@?z&=f$g2C~?Hgr>y_T<6 z0YWrF;v%_~P;Vn5qDiv9)<}vK`{ttQs44JAen?OmqI)}q=azl^1cW?iQN;RN4N!kr zR0pK^R{**`=ftEt8k|l%CQ;g<@cUhm#zxN(Aob;*JA;@EzqA{Xn0a z5AJha{tzj{hkP&@S{1Cr73bakIty*`NXh6<=OWmalb~Td>P=M$iZfwrS;a=w#|Uit zU--!7cO8%qv<6^G-br!#VT){mkjlIP4oT3jk1jaT;vs7_Yx{^d&EOplbE%SQ)Ae?L z{P?hENarH0UkwxYb(G!Ev2k6Y?7i*2@Mi4M4R}jP&EAHB!8Zc0L?|W4_TXm=?sV#L z?*|ZnS-=zaI6+hkANLE8ws%_htDgq!3=W4pp}3FKDxXwYE%Cf1d06&Ef!UHkesVa& zR)wPU%p9x0T?pwh5>{^X#*yNs5NQ0t+8_OED?$>Hoqg@RdcGY!b4hu5_c5&~o;eI*Vzxp;SP|9pHaOSEI-b1i zTl8DDMs&l#6|282HssHuI&^(Xkn5S&2@1C)Bza7;XNwH$FdXDBQ zFPyLcf+v2gd#QVIdcds&s5+S9hLJmkX6^NFj6ADPjO3RMZs&wjp2YnUIA8q9+2kSJ z|9c#VS#al#4wPebyV3-h?MhvPJ!Wy?G=ZM0ZQLd8@{)x`U2%fMen*9$RngN6 zeVJ|bovNbkp8|I3i0jkCSNJMA!xw&}pKm%Lf2l-Z^W#Z}qk5f++{%3GrXOxcUXA-n z(+`^8xU!I~wGBJwV)bZAABBNPbWXTIZz#UEmEgH+^6YG+c+8D8AUjR?>PX>;apRau zCS@&t>BruNiCa^`@{Uag*{?;>swB0gI+|0XS5Jmv4c%tp@B1thiSk*A zwswq7_U$_J1KpG)xwkbm{es!3l0nnx7$C=uzmmvFtM&@Pmj2M3efHtFpT7n-7gx93 zKfrhV_rm27s87+$U@0fpzKqkqLj;n`d1TC1duNM(S|rNKv||S)WqtTO;7;fELM4PC zIWm%Y+cUaxlGabXf6skCb4MVq#B4IdeozoqAy$)m^14Q!V0B)-{`K-Fe@f^qt%S?B z%=EF#%|7B^W^1ePRv0{Mf{xr0n|4Uqma6oRIuQSCb8mm7cpj{yKkCq_%UeX$9nr-8 zrIu3ibC0s)x6g}6TRnyw>B$_vpzJZy?dSJKN-sn*1o5WaY}z;|N0ex4{bH(#^vAEh z`E*UkAaBfRm#l%sT{pAW4+q1>^4%;S1rEbPY1d=XSlzhEkt-xv@Ig9ivD${1`D4hR zxbTrOUhCYLTiG%WsW2xX@Irk(Bl+>>ht*y6vtOBLF$0us+WKwp*yar;5hBZsSQ|AMk|du;d}y|WN_L4DGQ`5V?ENbnilM4GPPGjNn7fJ8PS(gP3t|?3n)RxN zrLg=}(%A(Uhl4@=bL(_Hot7M63(lR{;|@f|$5PpK%J9p+IoFHllR$sfcsScGP^C1a zkD=+?_S(8;&$C$c4tAe;RN=Ff!<&+4DRH}pilXl&pZ{<>ea*aFTI_@eU#9+L*Rgxq z1f|k?Ww`xP%NH|KJNHpkUj5#%(aRtN2p@wSC~3l+KD)ZNxuw`kN-nG(^T0>mQTSuN{%61@+EgZM zo`q$Y>rC+MC8it~y0w(;tbkfK@Ag_8UsW}FZoHgrfrqfuoa4W5`oqGdK1czBu$)&w zshivegt|5*_QauU?oPAf2e+dhqp0$H zx!bpB_6|${xeuC`<=|Nd&rYpGq9?V;F?2Mt`Pb{m2M$B@UXla^TB^)?-zH6L*ybQM zZDk}at+NjlY!8Xw*-%CV;-`NuNr_eUziwgT`;KK}K`U#lTwcx+R1f~Jtdu`1E79(m zq$vCR-7tYaMDLtZJ=@CX_18^+zv7?&<2443e5l|tL3Y}R2cia{emEGm#q67mb(L?N zd8Cx->5=t?EHt@s>e;hf9_pjre`?%~Gg&eacMli?s^zJX?g(0+avPff{5)@pYr-?6 zk!_2|s5EwUb4Bw8QN7Z&@&wL(Daja;V`A0)-UTri&vxgcFGmM!5r%gl_x39 z#EBhKt}v#xke(c_D|5ba-_G4fXr0UZi1P>Y;KL4DX62W-7Y8K4Ld7QG9ch$anq1z0MM#kW6;? zd{h;

9Lf6Brj23+g)MF+D)r@}|#!Yf0yW$}DFLIE1b+rN3nKSXpqbd8@kjXa4g zw1&5`;K;(ffcw!W91Q~chFL8@X_r$7p-p*AP29;}UNiTX5v#WU*{ZFYqU2#mII(2F zBEf6?Ue>6*&7&_mdcuSbh}@OVBuS?iXKy}>ZC%)3GGzVKj%XB^z$@%iVdLfmXKMZV zau=ZPR?l>0VLj`RbLW+eXoiFq1Y;9MI6}IZFZHLNfTX3-&Xi9Fj@{yUZ0*xAVUqS1 z5VRDAso7N|JgRL9({nlBizmlox>2&rD4PCS2O zx4hc_Mz)+K|0yqp4^esM_m-!Jm&?Tm-`4TZU815mpiaITMLOz)xQIj+a;SohrM#m> z=;q2fOCL~wx|KBnW4N#gPMn!%Arcr@=)!o_o(NCidm(jS9HwaE;2Wg*saj`)Xkxfm_{mj)E<*s3b4D|Bu+Shz*1}pd*S9M+44C<7kR%9-`2Ow^0?1c1 z7+MMV7 z`gHGMpG=H8?Ts2bKpxm5Pl&jnF5#eu!A{okZAnRdP%0k9B;Fk^xr0+VEh62Tb!Ld5{XVp>=NztIAa zAqx?T4>50_u~5J7mnPt3ap8SS30H-ifQ3}U z4pFT^Ex(JWOXLJ!SYbJ|2iLO0Mx0@CHo`Z1!NO#*Thm$1y({h5pkN>!utRMaUn(u8 z{d(mdXtntgRLaZC(H!H8(UUAgTpp{=3{?QJf}<_$cKR|ZCQB7CHEKbu{BNI1kI@PW zY7wyfj!&2iGT2z(+6Ly;_R($#$-Q2G4s97QA3HjC)5bZRmYzSHv;P1_rH^(5@nH zP69!p)Dk%WF@s5=u%>=Hj9-QXfqiF*X(mn@3oddKw)cE2hrx~TlwMm%0g)vT}*vZFZSCLXsw>I@murG1q0nq8Ur zh%E3{5lqD7?ei+1wWHo*7>lEn zhUB%}pQqM{_N3(@@;tpjW@LKfo8X;vY*L7BVW&ddiXt5i!&d=^y4Y^)<>PGQw3+wdI4@o`n%Wz6u`)K%BUB88}U`Suv z=3>?lsqCxx?%UM%^^#aawnjd77-mr42v$R46^-KysI!) z6&sM{d29}-FV7J*`Y=+KX~}*U5wmC>RFKQrL5R3SA#r1(f>KnEmClS;j=Mg#c19i% z`Y5Sx8=UQcjq^Uw(4bvD$*x+3BkRGe!9mp%m5baeJN+BhuyvBKHoV)#FTl>LtPz#6i(x#k4e z&4Z@8W=m!5ir`Bwu8F*WRgM-AIp~|s`&x#(gYV~n!{@`7T~hbHI6Nk`Rr%DeUg~V6 zSfwgmGB^#_5*2#^R$vh$*k@JA=7)jbNUIdfP{ltd8FI~~n74YWMg!km^G4U;iuuly zJB0UHS&y%}_RIc>l|yNv?%p9MX1m`5Z~NQ4v+UeI$-AdQp+L{uEu8SzExez6T4%~^ zKxwyaU&#w;%)bsAPDOzFoHX$hn!;<(k;QC#E;uzkaAQb`v;w z$1#y#>7*^z1$TIFF|z}Ngh0L~JYWU(T=d?gXKfRrwFT76$Acv*;gFs`{>E@rMg2vYJ8k+%4~m;`s(!2MGBgjICaSTo1Y@YW8dc+ivjzOml_t$9yzPcUg2)G?i z(kSrTIoF$QFSTpl0JTMU3aHif98+ zLk;+S)kewE)^!hW8Jn0ia=ZW*hzqHQ#AzaSZdRWpT8RO8oia~ctc9EWtafPevj&xh z6Ff!jkSc+t-S{#^5FF-@@E%YDd|pf(5Doi@k71p$2HA%v)EPis`pt0TZRIS-YCFjC z#K%(RGL&6+?&_nPRX;$P^Ih8x+XODx6zIF&oa6oc`Toj#wFQ;R>Hy+50>jRYzjnY} zB#@ApZ%TSI}MUEEq{=fkge>pq<^Sggs0^UVEM>dW=E=qYez;S6tL7J(->`vOB zogj&+848ja2aOCqae}HKnw)T&uik9$b`0R~H}hiVU+_CEJ{#O&fZ*o6a`WO{Iwn&Y zW1_xxvkhb=s{n1P+3PS}4=ndDFZm&7(ZD$m1XybdnC&Z2)bjXX}<0Nv*&Se9|U`| zI!rHJzi`>wbU^>moTz!=K5Y>O2mIVQEwE8=r2dB@3O zKB4Qrd41VxSZHn=C!WOKz*ZI(BX4+wfY#!zWXK8GZps6Mrfg>j)>m+Or2KtlR|US5 zr^R$V$1>LoWOijjL3U=PM+R9B_T}^2ugZS+7|@9#%F%78-w!%vzOl4uYeG1x%Xz@y5Nlp*vc| zx60gY+plQ@V|Ui7$(%7`W+3e1(vwsRB;97So`S|sKyp3{TzkBTe~)kC8GjzCC8+Ej z&>42;PFZV~ehMc2s>B!1*G^YIpoS_#UwB4XGRY(_d#;DjHiw}ZaEENxzCgA9y&qVi<9x~UbLqlsw60dggedOP>`?w`uIfcF}p z6nS@)4VCMg0};W^;-?L-x4@B!4%~q(a2SqWkhC}qYqdsU^bQ1`-PZ%qp=14vf%#GBDA|%PHA_&59ANNatsJOqW(6RfA z^pF$9zuJHOi$7esC5azsblkuBzHXZAv%$EH4q#Yrv1gq?OFgvhi+JpDWj(C^$KbzT) zFa1=#a9TvguV}fxRQ0*Jn&+qQAfvQG@>-gj1)-Ur&w47@j)3jf2z#&o>}2KDKl@Uk z`ktho(Qyyf!b$JKQ%JWC8_Jkw3r@xlB~_UrF-O$ulzK?$Lt3^14DRTj?D zcYUWunUVEE6mw3mX=4~ANzO1pK#;jiUzwN%4pO$g7Krq)cR+kqcBN4_xi^#(5J%V+ zcac;8?f$X;1Voww69ge1mMKjDMkWf*kffXt#t}egCPUdEJ>0gDpt(~Pfdg(hu9bu7 zzia}@>mHP&tAMaN4Y@qs@(~%ZG=$~^To-G>o~WDZ=OiJZ*!&v@pg?A!-eRfy#)T05(E8QzW8Cs&u~>=tKQ$AnUpn4jNG2 z#*LU`u!N3<3{S-sdz@1!8N%MFuU8^~>!Ugafl9>M0HO211jJ%~Oc+qrtw9Epmys4k zKwvc%K=inOtPzAp20fbWL3>Wg{U_^6EMpb!#1c6iZ5i-Zf!~mREq0g<=64ysY@@;e z?%hVd)+GOdH9t}BZV|m>R;HR`!ya}X(sF8=`4Ypxf^_RxYFgT&3>4jt1f3BrXWzI* z4FzoiH=7%>ke)~-f^frTaY@Mz_BfErtF>r9u11MaN3(An8f66!>hZV0I05v5oi<8q zZ#9;XH74^VKkj6AM9~V*Q}xDhCY){~?xHx0qiSfHCux=fFPY^6oS?ggqsJk?ip9)d zU*KF?eJl_Re@f~Vn#;kH5c*5IN?myd?#Gh9tpVl8&mR*q27Ffi{w%*9DOP==0B>QY zzEhE?Ss`B}XyX1;x3tV9u3fw4mfiD*^ytG!N}uN<4~VQPcxDQmsNTufFRu5R8jHy7 zDH$9TXdzF0cn~ERQZP7D;mI>ZgLLKSa}}489YA#eS0;^h^FdMS-G^nhOfmbp@`7oT zGwrH6)|{AK4Zgq<8@W`Hd`aIT?snGo!85Y=5@$jL(uM8?wXF{s;|QO23MD?|ESY(% zOq)k%e){ny<1an&*r1j12OyT>Gf)N{C13o2=QmvHfat1z(5#Zx_!7;BYt& z)Cuq>o13}s*;oF+r2KGlAFrT#1Mj1ohqnWR`-Fnsuepm^UtEE*IX_|fbwCq^m z&L(-;T<}qfhoVq1(^}$hZI7&^Pxk|UB8;LI&BPq3uAtkW??t;}M1pb!C`zsc2L6x| zBq1a&Y>Dn%WCj+7%eHv)>Szq#{yTS<8bO05mzcT(au|Wyivm?p<1duKaHk}sfdo5T zCjFEnWV*I}d2oG(6qrw;jmf+SglD9fW*@uJaK%GF00$iDcjVvY=*qnM#`oyiVpX$> zFQp45W4oh~!<}TYCase4uL@2vKL-jYulx}wLh(AA$b};sc=D#XS>}rEF1i~#L2PO zJowFtdmh=lT%za*n(gM-Q+Mxyy6|z=X$aFP;k565Gh?qoSg4CA5SZFtd<2IwZ^xk- zA<0>Zf42yEL>}bxqhvJ<$gUZuRHirEyY}~Zpokzv?nk?*1yTr`U^HTiBgTl7hGcKB zL2th2{jd~Itj4xu^YuLs#K-qnUel+B%uE{Wqx;+!bPXSsDOcMY5|f1EO6=lb8CC_| z=}}VSmDRO^*N*p{f$wC!G7|NtMzQT}Xb4R@Ip0Z3Yadp??5ax-pN+`IGw>Nn-d6I{ zFEofgdhVBgsH|<>*UPul?z?6^(H|&#h}>f;C6;XW=d_%!W%SU={w$SYSlBK|MOOio zy$J;B{l$d-<4zNzp)0kwdqrwyu4R0S7hYxsl14Z~Y(O3O>B74{IbWwscAY7irj5lM zPJCnqkXG8o+YVSylB{9>vhx4Jm}I0#GLG|M^ro zJ&S@IB0oPOFQYdL`XU9cB8@93{p;`7H5XFR61i}1xVYKgkmNzr)4CXF1T*;ouKCIA z?vmGDsG6mnNZjK?kmDxSPHGY(3=3pApI#(woP*)d2wXxT7=aAV{K%r4kE57EasIQ> ziQ<6g75)`7b&n_Nyzyv5?BHIhpEX<|COtW)-@=ar-5$0yoa|@r|7aiA(_h58UU~lr zCoSoq(|kqxR` z;O9dU&4V(Wxi^5J6c)p|gTQ+)>c^?GcWE^`KMLOpRQyDMKrIVqhX&|F7i|p!vp8tn zyF;#9t1~(}OQ0vV*2h-}@$@?{#+@8=6HMp{=9HEm8xtorHNJKmj{?BX22P$jMgA_4 z`{O;CV;L)tZh$mowuv$%ul&4BV%=Q54*`!4qxB`G!<2^fczu@)HhIhk?Bj|fms)A9 z@n_`4!YAEJTeP=;QIjzfJ{o~isq?>ZlB&boAdwqZS9yd0U#?Ec%F1$`0#kD@xyF?(AhJAIIG zw+oA{a@Qw$PdCe1l)PDXD3w(g8Ew%W?Yf|2@ohg(h7~6Js%yNlLXFA0NLWkN*sNv3 z=*v~GmnT39@^nD%qyj{=UU^HR0}z~>JpP7&;8^6@oL02@rpelJR`%1QaaSmo+^;vM zmKAIRt~eP&%;sO8mZ@p}oD;Hej=y_s_X>$w_PSa+i^QAel>Y0YD<~v@&-mkewWg zTLJlHb-7V=Ay1{ZsKUnzF zCa~0=l%E;RE4G}%X9Y&W^y9)gSzX6(LZeu_CU3H;xO86=N#7&P;**_amxZGVZ%##N zxpxUXo0Qyy3$!=Qv*!&J*eE=zRuM&73&ACp^4x%ES43`tnM~hoK?pTZ;FQ%SXVfhI zt;!%6gHVR%kw!fBTWZ|f_yy)Jt{7u%q@R#|kXLL9LzF%nGIkd-eHu%1;L-;3U>oU0 zmE1PVoCYL$X_CNKR0Him8xK~&qj&;?h3DEgRb5bKf8N_rigU8IPf3APs3623YL*IS zT{jv^A&Y%{*;xdbxLV_kNTNY-KL&w+e&|$~fiNry`SANY zG_a#~YFZWq2!LrKKoZ}7>Ttl%PkRV`%7m3l;QT3EzO3)4gy6vYw!0Cic|i5HIUh=6 zIaz&s?mc>BWaKCkb-!~jfpZvS99!#=pwOX#lDPxa6ta^I#+a(b-cGO=6%<@{TF#FV z=?Tb{b!;8ckYBY17Ae%vuMQc(W}$7Mh|wmn?H;eVfPK+EGiqVUK2!uZZO-k_C%NXZ zfrH1|=?B*#vI*nl)Y^bO&Y1xDed*%GEsm4N!PZn@v#<80U+rQygSf+&AOKvOcZx(x zcL*R3v?zGxuba_c;XsG>7OY?_ZS3n!3!gZ3FX*TgPdPD!V#N* zs51^upj+_G$Wr|-j_hbvnQ{r7WRCs9CpB`!EbX7G)egVlqRhqB;uhdg`_~K5_;d9Q z_ztU@z}iX8qiZ0@`kM#5{xPTj| zKVF>J`25Tr9EeT8(Iz=jPkp2fkUdRQmoA0^$c-Zc-^29YTRfrAas{`Y2l zSP+cfI}avy973uUjipz)%_EMl}c*G zMFoO@%o3(N`G9jT10eg@1CcJ<_wym|oMzJ;IH^2LK>~nnd>OyK$cFCiTumI43<_R1 z1GRDW(T{GdAJ}Vi$<1zHa<4!N0_i)pa;KIVYJQA~W3VIWMdO`_@&$00ikr6&VTapq z9%8WBQ~C30Y_|R9buk!SLPMTq~+8!H00G=3mXnm;#fY(^_?~&6} z9mG7jT-3y|m0Pu2U$4h1obx>Ns+Pj0_!`kx5XQ~9hWv^Cm@K0c$Gd}w<9B`f>wR)EJ)%T%?o)BwF-&^& z;ATJBnf(4h*Tm0Ki77hrZWB~9%{k(zliT|UyGQ=4d(M>$UbWfwo9POD{A9?kZj{79gldl#%ng(|YaeVUkY3o9LUV0iQW)2#Ma9|r}NMQ^ z26q6_4^ILE*3#Bhi{_{LGnEw8l}pUhp<2xlwV|V5XfYc4|PbeBA>#6dPq^j zYy%cRyOVNF(|;vPTh(#IR(&l;g&}He{*4&BkB|Bs(ntPi4kSVc~AsZQh zxJ83;h@QGx4oaJE25}k1X*x%3<@pIjB4sxOCy70+9SF7F1nYyZ{m7~r-K%J za^-vpxjPi0ogpQ`-6V9O#CQyyE4x!?2Y&3x6N}CIHcW8Opf&K&N=palp z4;>Slh;%1{VcRUQUb*)8diVGCV;eVn@o3Owz(;1zP_S?O0h>J79v&<{=rURFel3C( z#{La%bHLmaK^FJ&1cT=+CZ*Ua6)b)0Jd;5OZ!5#zSyo^-o{@KO1GQvB0RxyWh;YwB zbHHSx93aI^EjhV9FHdkI6z>pBj)r##nA-EUjcBOaDClyx#hmgon4&Gh*5M+_H|E#M zvZRq^yOSySghi(Ngj)YPtD%Z{eo`y4D}FO~v(SK{yxLmL%w>%a5-;vd*RLQH7X5i) zE5QK{`}^~2W)}>*;v3ITKC+G%PByb#SP@d2ln_!g&rIAe)L;e6gc1d zNN8z%u%WBa^IgrI0#Bh*&zR96Bf|-6y`-c3uRA@cAPQ8!(J=T}yVaGqh7SgP0 ze!z3#9oj`@zt*;96Q;l|a2AHHROrZ}IB`WQYp0O;)12WqGG|`d{+yZ1Hqc@M=m|n9 zLy&D`dwZ+y|!05LyK3<++sL9)wSd@a&eqLj1xA{!K2fbag zZ1^jLll|n|pHo@W+LO)5d2gcnof87zMmkQWfH7v(FTtHTV7%&Y@J@i0?sNm8J}ob4 z2iyQ5KVrKi)7uYw83Ur@E(++a0_J$*nk`W2qQF)7znJ>Uu&DNEZ$iL>f`yk~0I+-D04GN=Zm}NyAX064Ko%lEZ*XNxy4*-}^jwKJevmWM=l>>%Z2o zR{k0I|3p`=WLR_qkY@##{G*?KqC1KBpCu{D<~f&7=dqgYo>aYP+tE|8Ige1=B(hwk zZEL+=eO?<&lK48WGSxEs2m6ZSef1XzN<3aN3IsHdSEGmz?cO7az5LA&9-I2)u6un} z1>J_ur>zadZaipt318*=FjgKfVpT?S`14s}yl!_p<{1g3i}Rbei@#YNa< z!?TGD@C$lkZe^=vnl5(BS2mU=`bC#|t8<(N(wuY`KP@E;Rn#uuckV)up5nvDC0i7A zC8M7gGBO!yI%9t?#igOc<%h2s#XlJ4!YU2(_Le_tpW69U=mP(01dbLy)N?#42c0QD ztYM@ni5psp!}pf!xtzRZnuE=;yG|(wzoBSb7hOk`^|7Tq0qx;y)Lu8oTlsL5D*1DN z>udpjXXN?3IOb~~V}d%i^ z6rk!HqazKwxUNMxN*<^`_1;*e886>b>&97g804fUx>#jlB;}(h1&3@l6ieud zn(_`jE=q^asP7K&tA+1aKk%3P`jX2yH)7?*B20#X+rd+LG3V8z?Ca{eCKdH}`;=}~ zALq$vvspZt?`>rfJV~y@#Gl6e?SrIFQIhY22;Kh%_SKrE^4v*+zmLhz$~RlIWJc9Y zV1Ind&`CjYq6b*N(^nK(4z9$^d7YCEkbzV0v5wlUXV%Z^+=+`fYkS?Nie0Y-q#cti z$Es7D3q3l6nb@hgB_>?Eq2?^%wHPiIxI~{aXSk``4s)t=yBJ;hs)csX?pU3dn_HJV zBP#N7;|63YP`L1F^|1Xy-GL}5;6Oj_HzYx z=D`OFz$E5Pm|&=_p;p-aGe21YX<4h$;K5yshI5e+9n~n)j<6u@lq5OkujGeAKKHHy zCzIRM+`Jca@kB0+$omaLXahhff4M#Ab$m_BGvOG{BpJA-D}SYv^J7HBOf-~*h2$_{ z+zoeP`CX{ZL17F=R&{t3{JWDCQ5WwEgx5v8B)AVh=J85=MRS7k^y!@Y?}P(2rabXH zNM4Gc{;-Zcv(08h#bCBNTV=iv!(S9m2VD(li=Dm*&Y= zNWvMhc_0@CEr8`rG4OdY$=$lcGeX$0zx*^DCuuHWG|zJU8tD}jh97PTx^{%T&sh8+ z8I$;4S8vI79mmP^x$kK#YBR-YGw$$Xoojuh_s&^&%#!^~vD1LMe6LicHP)KK-c zcA(@aWJL=#H1`uE1DnI8&(OQ9hV498hyQ#I&RzArQQR29WzOK~5yl22QbzZSWU!l_|r(x~rez-cBti_)UMy_}{ z31-yxhwlA`BU{lMnUY{So!YkN%!Yzswxp(;!R)oXKBoIxhKfBf1OHa_Q;*dCGOoX&LpSTo)WZe zM!y*dzuH-u#pG|iZcwz7!qt)LbnkBACfiUNv$1Zbpj%!_ZQE@fJ7!ysq%K zWL_k-_h4T^XG4ZB+~!xrr!`A%#c2k8;JRwt zdg@Abs-Ec1FUzfwjIQcq(Mmwv;9Fs7nGbh8 zCx6^`5>!~G{BLOX@6Ve)xA5Z5?PHS_Ki5Mmo{Y%}i;2;=*!D-#;jTx&s=Q&8YtU|7 zo^P9Yzb=oM=tG)u>@NJZa8K4h?=(u4W(=bOYPaaO05ej%1j&XG>)Wd$GYh`PchIE; zv87F9+NBxK^fOj6jOHAJZ8HjLNxF}joq07=(izB%&gL{iM5IgJQxOBh=j3i=Kf(R& zB=V3zGWa68w4`WzU8Ok6sS+12zx)UQ^nnL33GWCSa?OYKwOgxP%-B-j`_REr5Goh@^SOpOXK*KAgY)*+PCrtQ z9FC+{2#teeCf(=zwE~j*DVy^2^T}@|jZwG5<(O@>y1}wR_otfPCVkgKfKJVno+~+d zyw=lAEh`Td%57QwK9)&0Lr32Y-`5bZucBq@xm*@n;riV*rtzAj3(-bk|4Kr}XXiPU z!JDjalcb6MP;A{>3D%dXJ8Nuflp#F$aL1-Uvzq2pK%|BaiRKhwIW#xQnu%It)O)}N z>%E=AQl|0Ls**Ls^YD+3vT$djVNtD8o)JwBaklgB3Aemakt+Fmx{m28vn;Hk14mS~ z9leFmHF}RapRcy@lCI^ambhcr$+FQxh0=50Ol@ltKEb~)4PIeaJeR#|1>SU8fp-fa zJ9c6-93aXObqDg9!PP*K^Dj7WsstheHpZU3SP)UaZ#Z*>H$`J&{$d?-oqecjtIY@5 zv{wR-@;SSG!nbXyW$V~8Ois)1EIZhqB&U2Wcq(Wyd}v2iCFeq%oG!k?Yv0#E%;)_n zwW5!egM;FInnJXb2&f=P(3nJR6%sOQi2NU>8b7SN%?)_Qz^i*s;au3d1#4Z3Q}!R# zE$G!+PIMOMMg9WxC-~gClSJoIoCmlq?ThD2g=(s=3Mz7PN7{{%CE*#VrO^ZY)auDN zV?5W|g8Kb~6qc?i zfh?!Vg(xHca@W!j-YwU%1$W;3fwLx=$_jbYnYn(6a?18<{+^@M29kbx3>Rr_zs06! z>|qG0gf*e%7Z$X-d_ybc`4}qkYx;FU+OJowou8;edt*rYa$y1KP0%6?Aem|y_Vrw8 zg3K3M;TT}I;sw#svv;6XD?{A3YxVnfH9y}eO}Gv|%sIA-(ilYoitL^siCl(Xr@;eg z4WhR)*+BZhX0V6L6blytr#Lmydlqitr-EbRpDks4laHGq1JQ0gdfqMjM8#Zw|c0 zSITSfnNwo_RorU zCgS0cI-1g4*iH|Xmi#A8i< z0um|>*G)3A`zDkS8C>nmmI}yYT|Z*ZoV?B>*x5-c>Gwq}hL?ZKbnIM`X0^`cyy2A| zj%oT(uG{p&%vVNYevot_sG9ahC9bzuKc%X`mLM3e;@th1kN-$j>LOjq-GQoP_6U;+9}x3 z1O|Z3G#whpi_NCq}4o?j7N2>qzD;xgHM zI?i5@LHlW=&7?paBjmU0T}W}B)K59rZHEf6jDqv}hLGQZ7+t~?&QA4tOYYYp1i<`j zPiIi$>oS>b;jjuh)vz82lmEMyC=v0XkReu(I8e66yvAF znR03iHPXA~F4&bgwM)?@7>l~)6~xw!Zm%nR|9dTISJUel@K*-eq3oc*{=s~W&c1XJ zGNNbmnSV~IaWj+lEjJtskf0hZqinj?(U{%=P!86vj^Ut?*l@ibk{>RqXc;Q!yQn0W z0ZMM<6>L=e#@$vW)w=IMA$8ZM*0d}ET5!lO&KiaCvnclzefed@z<_|kpB*{jxQ98_ zK93hzw-s%>0em~rQn1gTfhIi#a z#$)@bx=cZW&Sa+z6;YG6@gu6V>Z?E((Cr*YaqH5)+9et0m5{h6cBAl_cAg1u?z}pq ztc=5?XX5ZTWl27#4zBTfRuS{ofI3d0t;LH%*a1cKSi$r1yM=|?IZ-=KCZL*z7mY;| z9}A;0A`MAPQi6F*!zC;0Tp#7bGYmp+`~^}KsXmn+>zVWW8ZFEH7n~OBG#Ra(?-5Qv zl#JDWt@N^U*zxMRO;-nwhpT41mo$EC&qCZV>qKWwwRwMiME7WkW#!>k=T(Lsf|>BG z?7)68CbPNAm(L`-er>7+ z5<|c3pluuDBC~YE=+ZfZ$f%c`j|J&nqh36^x`xsAVT5FUAF6OY+gd1Es!80fp( z(YdBm;+%jS#c@0NHVWV8t;;CXZvM2ijRl}~tl?hH`{jW4VT zjJ5~aqu17;E&=AK)2IfsCQ9hkR%Gpc)NTA9%oaP$tHcZ02J^&~HTHc;pXb$#I8gF}HSCx1^*QE_-FicE^9e*y#n^C!NQFmo+Q{c>2$Fxoj17{sE4PEsI z`US{~EQgfS5fDW@h6-DnYDP%?x{S-PoTJp~u~~T=H(Xwh#qU(@_0~GzZwg*_|F}%m z?l$()&C(zvu00>0LzS=o;@a?AU=ER2t& zgLyE{CxV{zsAYZ3g~M7Cv~v;T+#reBuW?%Jx)~$GR&u}hB?0LIHW*Vx)Vb6;Pf22b zQW^-_qeohDW~;!no;O_)()8_J;N=%u`8s1_AJw`ig$l-&>G5=r46VG}m?X>;*J&kf zQHQSDMxIZJ{6~Mye#Jrt6Ysp6dR{}pf2SYDh|sL^+~m@^h4taJT@y3230wZqP*8GM zqZ!Cl>O!#F-zDhl$TC)_U`?sUw6Ajm69_M9B9gx9e{%Sf)OSGzGX%<#ec-K@<-VUF zPY(!W*)omei7lH7{i~jQo2EQXXf@&qAzaCIIWC~X`kQx=Q+svU6t^YTwb7>Y>j&R5 zA5+Ekk|;$~UK-jU4+aOV>Sx(ej6|teV5(h_G=x3#y?pWDNqD*|JO*cP(repBmA}j2 zS257+iQ-K%om4?vGpgoWZ8R6SxMTz6K_)4;zL6zth0~g%G|eNW4fy}`J4xR;CvZKE z0{pwaa11%^AL8$-A-&lV7o&L+Qj!Q}pIZ%wwLp-B)_5Sq=<&-kGwT^EwZ_0&yR;sAAU*9OI zd^LTmoWQVc`EdU55Z!O|qTVh`@RL|4w|qhEzbimQMqI7IcJ0QE<|I1@BoJ}y`}gl_ z>mX^lC)I6W0u-w^CwmA*sAA&X$B90Bzufod@`71|dgGXJfMCBq4I#^HCxj4bu+@=V7bOTUsy)Sq*>nq6w(h~# zG;>z&p?nYIO$IFlM!UBqx`i#l%s5iT`ucvDT9l%o zq5)EoY=&28>6qi!Egt+reZo~W)GOfNl;o}M9y zP9KTBJri1kc#tM&n8PLhyv(CRG_!~Za0EgI(7J{WvORNyRftvRL;)JHe;?F7xNuqa4h!McD{adUafM77Z&=>@87}bopFq`_##4FNWVY>y)(K}zs6 zGz@1Ga;BRzE8&M&$s`0Ly8?u$59IaX&LE_ZgCv&8H9l^Qt#n`gS1qWow}H*6dA@*z z1$cUqVd?3N#)of>&zcNzn7N2>g_zBmgE5Y!6a8N%SO(ZKqlCrp_!PzT=2g8;8K4_fyC4Av%bVbtT zdUIA35`1|-BOKOY9UMOxkP#gHi)nq;nLwZqS)Y!91z{*By#R)OYFSY3Ls(a}%!p}7t@)*zZA=c#bjl9XGA*t|u1mtmcs zjJGm=z)X@41ATOCtTnf8VMj!qy8c}OK1hS3U%LbBUm4sm;;XK1YZJ>A5W}JiOtp6| zDuZ^Q^^9PgO=R}}p{WPH=Yc2TpSNls*_MGMb12zoKNkz7?Zs1Q#C<{77uR;4NLS>C^ir$h+b$*fByM%j_?!M5rMR#Rex3l2di&RXkD4Dh18hBD~QtY z`96f3vknvGvs@3U0If^4`35xKWnd>fF$quDB2$`JM)Zp_%yz+@&o7DxiCr1QXqCyy zzevUKW5Wy3ba#iodVZW7Ip1!XkZbn6EOdL6?ZRq!<0O&&uk>`yuItmD}`4nH2mC@R>5M}`>OeF^fG zAANRcV>Ur1DR5R@Ha>=y!MSl2LO`mdD}A3V3;OO8gt@hIBYj}%qNUTld6)iYpxo@E zBbyJMOf_SD8vNv|Z92R*aF#4Bh!51hkd{el_6!}e_cJsy5Ho+@`B%_c%--cwPl4Q5 z%AwuJz37SD67-{cYs=zmz)MylN*clR9GRPvE}0rZ(;yKy19IFg)PBYPVjINauY$$e zarvcu*cBT@(YPz3Y~lc=!5~W|5$Ow@iX?$apnG|$gVzyU8{>%nPVUW;*A7Tn5x?ZF zxcBmJw<(V-G>1{CWvCZ3ossif{LhO^pziymsVX^G|M$l!21(B{^o-_xOH-Zmo$Fa* zUl8XD{V@{aT?1E=fCyc8@cZz{O|6w{EkJ6#?v26A0)5(PFH3&FV^;}Gu8=kAj4CJ- z^K_T)1PnZ+7qL$@=03!eO%-k2pTFOj*{$8;BQn<#z9amGP6*B4!OXaaQln{N|Au-2 zMN@knB?Qg*zSlQOkfr?E&B2Xrszbr3av5@wCfyOqI41YEP6PA?L}o-UpP60Wny7L} zQ+gF?JR>H#@DOoJ4jZ`lr7QefH3GuS0#E@J(iQmr%5Hg@P1)dk%^TXmZNN-wn0b(Z zuKn2*W{thyePBSBaF5~OrCwDkUT(;Drb?vP$+E0!c(N>lcXsqa_h$(?JxG(*yziYx zJvf0(igq8D$yob5iVR97>;15}>(^sSRoI71pA+6)T24}e-k=!t{$E3nPRdWaDAKt@ z7E1NJd6?*ppL3f=+3WI`K~Xs(C*S)p5*{HOhVA$77jox5g1zK2(?1>=`0w?jq{zr8 z;Cy$T9f77^ivOqZ8bLKA7;fwT(U}3$+d^`%N)^& z*s$s7DSqA=s|WsIb@7y84~i`pUtW06iW{N>{`ax`4<@IAb2mmWTcmDk;WCEPi`TxkbAqioKZB(F8mn8y25uRT?=&)o*9r`GFD->JO! zI+l@_A7~n!5Q+{?72S{kOysE_0C$XKjG^y(Tdzblmb(A`k^H>nGvMKr6JZ>Z7BdIl zo^R;a1)84sX&k{#wsy#!_G&mg>#YoZHTW?iZwgB+JmbqZM8f&!H}`eZtl+j_Jm!<( zdGP*Lrxl*o{!H6*^>TsP7`mvsxejp;WGD<|pBD@x4>QyUIb?j=I+&S7 z%+k^@Q?;X^&qU96eqa3EcLz4KbRTMmJnUS|l52uYq7gw7+Xp!dlUqy|Y{jQ4O8&%t z9@eyNMuf7HeVTDC`EZCwkDJUcNkT0tD-u|_aV4XoW7U_YBj<5QE)Uv`A|tzSG@B%J zR!o)q`j|sZWf_1E#dqsb)@V9L>alh>(^p?tUnsCxA1mb5#T}%e&-#A}^<>@p(g9ed zdfKW_n{(#$2GkNJl0IqHchL)_AJTM3|E@>tDZbpC*c#UY0G?!oN$6K#qS z52+k&y^+#0ncyC0_9MTv%aJpLeKnlicCce4hDlvh{YfT}yoj2_Fv@6|d9XDv z=XR~!7m!UEq-%koQ_yw_L?Nuxsydtn=_E0bumyx^kbEc?W4hed1b`_X8kRI&Igcr8 z1WzdpCw_q8c>KfhiuLO(yE2FSv}8V6oV0>otF{Ir4vF!uvX|{C@5yr``cELaEwkxT zvri{ny6aqwHLUNiD3~@!4&J&h@dN_s7XAGN)E;t3{*F~$90joe|Aa2!f8q;)nL2q2 z8QGCkt+#9=><4Qq%9r%}X;r37HGNSFeF@}ab=U?5m&|0Bn4_%%*&-AzL$KBq0-@H- zKcnwC0%f{^Ou8I$zKtO&poM0roBBNu=`*MYPSOcV>*Q*E{~wwFj>mi-IN0n^Z}Nr8 zGAm(c((vEh=f>U0fbceTCvB%d;H4-}skOjcxi#yx#!ad>arf2Nmtk#4ThRHmWL2sQ zVO<4ZFj#4vUCwB%zjHR;28fb;-*GOXxnXUg-7E{&r_;04FDSM%#-NkOZm;JNmNw$6 zcQc4-R0dAv&{;kQeNN!tcf}Q^Up$ZhG)G+?FN9I{x12OR$BCS<8$JyKnTI8Poh_F* zgm|09c8b>Jf>w^D9a5@O<6ZI|E7xdMUhfye$sH(jyOBv+c88H}k6WEuU03HW*telF zO7nYlsjq243r4SrR=QAE1z3QE%=&^NT(ah#inH0x@&MO*Zd!xORI?S!^?%s`XzC6^ zP;0OPBf2f4#YW&esB6*C`Zzo7+BiQ-pMch-uu;uIYc6Hsj3J!<2;~f0I_}|N*-CUp ze}RGU&QTOjpUtET9L+J7n&U^HP#}QRkt(kAEXSirJ68ZR*wW|Do8{XYl~g{vK*#K5 z-S&z}5c^|C3EQ=vH_2fWE~n~uJm!oZVg2rzIR33o+ftF1s8_b`*V&ce&yQb$Xw@+4 z!FaVt(p{DUp_{%z0H~cgcQcvDK+3DsXFKV??VZ+(mjiptYn`$W|afV)l->&|3Mq^{$FrNeNGYnV}ZF#T=Fo*28U3gjJLC34Zzx znY0CuKJc+J7wwX4>2+Qg@jE(*2)BVKG(=x)H_(FAe~>8GBXMm?8x)?PGqgt(QHSuk zT|wzJ4w+U2q(6PA0L+EMT(drOG?VUgp{}q21^0X?Hue&_@0u6qvH-~v94e&H! zL_Z+CEWHX6{rIwuf~9ZZY&9D1dt6f3+1sk6K4M+JBt<7w8nEM5j^p9Bs`C3j&4BG; zyMS4FIUgMtM;paQ;txu&XP&HQZ*o>A93QvsmH5m}Wk*9Y4$aN$-enzh`o`-|4o}{n zO?{iFC|dipZ`N5ppiU)REUQ=$Z_K8&;YHl^s=iO>)`%s52o)|$uFt$55i&0p>|YMO zM_yEVKTW2Vyd23ad(NJGTcod8=`dyK`+B$Y(K8pJl|#{^dEP9cPlZ`Rtue#AiqlpA zvQdCO`fNS=Z;WacE=q9OP_o+ud7oW-q?|PY zBtw5ic8bckhlgKdjz318-r<%l4(!cnyZ#`+pCDI_^UbN^!{^Z*jm`2-%Jl^{`GZ8sJ{u8O--qfjdBfB#pk&dsc*w|vERDlHQqC5c|OjX&t; z8wXAGv9B`f&U#UK#nT_2=*g^#`1eLOzlq^%aT_Kchkn4X+SXC@^(_U)8dRfuOI1Dzd5Ifmr{p7mRZ_P8Hl0W8Ho;*(4wLe~?LE4z8A`PywCg^R* zq#u=?f(?6!aMk`};ycz$MprYRxro=Lfar`puveN1gfEL4NyZ1(XV6|4#@DZ?sKwBm zn{aH6>#C}>-KO9onrR)%tZ^N(HN0bM!I^vh{M>4`Q{2dBKeytz&_1#QA4`5e18kBc zUt`7}a7VPNwuN1=?hi3i=LT$+Lk}zo>bLz4Q2T#=E7)CAN_NyfPskS+!<99@={j*W zUNL{?bEa!A+hl=F<%;F9N+neL(TK%NKzgml3k&wYglTPPglD9u|CBEdkeFpG3TU|< zp*xh$%$g`=8X}>Y;i@mbVZvCjp22go81u{dg5vVw90Nb*n|LYTqyI>eM(@F6S%d=2 zvBP+*$zK|$o1gq^gM*eBs8-+fFyPi&RcS081ZmfObBTjO)Bcjxn>OXiBKEs%D4 zxMeOoe=}y!c_?VIVo(lMy~P@#eLA^@_q5uz1~^N@GSjSk3UfpEU5kMlQ0_TA#cuxz zATdb~JXN1L|F8(aN=)DTZrO74~5RwWDWDCZ9b0*ulBj73Zk zi*s&4X=5~UwjI-2s>y#MZO{o!^F~1R#GObBAi3R^ufG$=8|v0_0k8ka@Rw+{(}ono z(a=o>(A|&M?)6CMAsM<5BX?aX`sWq7l;kS(Q-Ry*zNmOq&5RPS;6*TNkTOtp3I!Ace7mFopL_g(F$nFslZiTnct&h<&3 zusNz%XYK$Zw!*Ti=+MChss1;G!hBh!W!}N?CfS8~!V{Kj<40}$1PCKaoZr?#!FJQ! z`~0mt<;ak5v-nXe;=ueL%hcr!(yQ-=c*khVI!-Px^=zJ(geNsrT)WidZX7yZhiT=( ze1!fBr00FTi7hmYd+XyhbN-?3Ru!E| zpSr@N8gh_=m1{xfZ#`P&7?ZK8N~JigeEY_pnVI>701eeRdv$fj@J$Jc5TmVu|L(_B zb!i{+V&A6xz69S3`@47Ttgi zb37Ap%s+Ja?%M0;S*0}<&D&h!y^!J|niYzgEhGiXAfm9wG*7RtEYZP$0;&GANtYO$ zrx&zqFuye4gXZ;mbekLe&DM-R+DiKOa=w};3l&~N68PZScvw!a1#KVV`7wLhQ8IX? zsMl^({*;zcFCV9$Rg2BJuQ08I8X0wSDd8#)TAX|&8nFN=um_top8lj9u5h<7l|7Qi z&8SP3ZS|HDzP!MJD5FucB|T#o(|4wQ6`z$gvM~5!F###DycXXTa=qLmHw$_8L4i24 zyoxl|_oeAuNezYx6^*K@6;VzVCkl#CmHCNY_H^_X0ar8Sf+$_r;F;UMOvNK4^*2we zaS1s|;GVXoAnh9_kyfAI*u@ZjK;xH=gA?pR$!UI=bJ@1Rp=IYQUot2Ph_D{{ar(q*bh~9Rcn1{pS(XWT4V2&BxDN?RHx86}A zDgIEMJUKQUZ>Pm-Yaz3pl`673CgeJCktb-uGZGrZ5xQ{Yvdd|@xG)_Ufc2vy?Vbtr zqlk$jb|6E@6yn1_LB9S7G_(cDr|evaBCc~%Xks_nH!zLQwq)>gO3h=xbt}rq3GY&M zgot`C+YIG(toDG93Pv*zuWH5RJ8L@GI_#el%|F1ADgT^CClR^+DJ%t5)o31MGQmB&m(v z2gHz9U1~oqg?NDqAhu=_eDda?f_5ckzYZH|xwI*}e(3i=p)Rr6sW{x8i|5CFEn$?w zx9cjGX%^a}f%^+ZjS@Z3%_;d0`R0ti9>vORU|X8M<{a#-D>zUcDjiK(w-`@Mv}xVE zJXk^^ZLH65HX_m#RHXU{~g8&>={pwRA{b8^tWhEYo{vvJa_m^E}!PuQ_L zHx}NK6R|uQBS38RID}|m!18o?ElCiqGh*rtxhDmH{tW?0hvZaTbaqxEEqRzjDPPM! zuAxbt!SJU{v?7erMaBGeXvXi0pC3E?S3Cs!J%-AZT=uu-JY*R41yG(pYn&#mm(6*t zEs?grJfA2+6HWKNu!e)nq&zaw7flg=$1*CEp=3nkOs2(>-r!>D!cdiFd3H4Htfnzk z_}&;K6j@Q@+8>yxNCTohMOeBEVz!!d-AHJdpCIKJLzxytq9HNh$G^36wH$J)&M-+> z!_f*EOgxAM|2UIo^3I)Lc3wy@Z4DIlD=fZ~)KU7m@y(s$>#H!`5hDRti;2!^(*+v?}5+?%UPk}?CDREPTiy&-b1{nsUHqw?n&daInHT*8QUsA zf9K&Izs20;@*$8!T<7p$`=)_Z-t#S~qMYjyzVI2YJbtz*2j)nk5LU8}S<3MXM_iW@ zFw@49@0_28%5=E7@Q7in@yd7u`{oWg5slV3X`hS1 zf2H(P+z(1cUi!mPKB5mp8k3b!No>{3^dq3iOK&kH0lvl@#|$3b&zd=wAChrjn=jU+ zqL_$?t|C6<3cy8AYYK3MA=}f1>BY2eqa0*2Cf6Y6@il*DmJ9uVMk7JJ8aHc75SB*B zd8x@)%DAq2G^RtMSconA1d`M$QwbL z_h`YI!yOyy->lur#VT0?m0ei_R`f93&19>nmAmSXe>&)CUBcpB#~TlGXifNp#$-u} zSafOhhO8GMP^((KbFao@!_sXU7VueKrRBUvxT<(h=GEzjyW``qo%jui8H`-NQVHt;9^rTRCHNGfYA&o4AsU#x zR_`GDS?@_5!^WFpAl81a)UojOY4lHIFL}%jTRf~2pILC#$-MVHJBmtrmX?9e9ML^& zjcb*gNF|uVLHm2U62OZ6wJ4jRy`{0CxqP|k2)&yPnZz`F^IYtx>bA?``l(CmMt>)~ z#K-^I$Bb;6=wcoezlJ@1^!Wh@Rej^4!0icSI ztZqiah zcUj`w!PtmxO6VL~FNxN9y{kt|ME20oJAX^8^3@<(yGhPRq-7g(ccwvgPvx%z7aKuqlbh)7!u?u_rQoc8Z5mp!$SF?m zt1CT^Ow~+)ozPyt2O^U{+Tz^TG|Ehw=NC3e zdL6G1rp)DF$d)5g1*by*8 zY^;2B`k}DFB)B^AI^N)PBMZ?vjXZ_xFrMfN+aInL=bf3JQc{61TeqrJ4@9kXA zMTA8lAnTzdta|(^wE)XIsZcs;bMTZcel$qfXqEq3LFHw5TkNTGY8gN^o*xHae&iv# zs$akioG4#V5#V9eex^kAGhyXBPk63HdAv^9+x1Qdn;J>QMak8u<)Q5n zG2~I?orJgv4ynyalWNP$!fs_4{km~DH}x?RL9kw97Y)0W3hOLDasZ;4y)AqYNuOd% z0jK9%@duJ@GjJ}Hsi(>5gEE8w)#3Y7KL{K}Fw??bbxCe$@vIQn`WNoKkW~c9!{SoM;K+u+yj_xzaH9f!C;N0F%BKnMGB<=k)Fr+UYwl=BE~WXi62zP1 ztvi-htx_}~8LOiZDu@30UBF2qO5&BXvZ?}7{m{0Lt()B3ytPxE{h@rtNPOCunMiGu zvBAVUUb$LvZfmgR;e~({1Z1E9DP)S`&BfJYqaY zbs~MEwJ$@7@a%%hCs^x_3;6++F*1#S?43Lue6aEob9*nV(h`Mgo9$JE+>4>(=N11r zt!OGD6753m=C10R(!;H-tMR+zw#S|>nY)lmQQ^2oi}$i|;?@>o&7D6HQ5kv13M`pN zyBPsrZm{0z+A|tM%2Wk(o2S|ymH}L0(13Z7_*9#lu9loAxe5scL)IJW&}ipq4n6Y8 z_IZ)j1s|B*_wOu)FrBj{lFB4^H|C~3pS2GLldlcw+F!0eumn{oFTu4fScc7_KDKtjUS6%}OH5^r~li;(LLXI$5$7`&((%6@IEA z$Mp!}P6xxvToDH9O>YW02ba|TD!*{$ThF!a)lvSjCSu2Yg~b(}I$sZl-)k6y2Im)k zhh3fftMQAOsLFnNy_O3r)izF0j|#!IXr*i966Lbo*EBgWb2Jll2Bp57u9M?w6SJ%& zR5WIhlVd~6(7q%j3>JYn;ss?N zSK3WZL|QVDC4!jf=NW0d91-pp3(Nn=%W7;?me`ZL`x<y=;AS^k@g03i6rlz#%@njEyzL@az84{s%u zO*Q0~O~PTE?2atJc^$<0H=m_!!E(657(Z0taya zgBG9nkfDlZl5IK>TT`*N11MpyD+2Ai{GF<%o$v*Hu*$)+|sinAC zCTr!J%kqf)Q&sh)1+4iKpfDMLv@#zAaXJvXP&5AYKOF3XbLtsHfc6@`UZDR^=4Yhv0%YOh$21M7ROyX8F}P{xK&7HAUKm zb|y+W-9m-i^wP81c@7ActjlotT>@Mdv+F#eV*A@oHi)_qEHyZ1M-s{ah#;<6Eoonc zoSG64PK|$h{57yogIHD}$ML_RlmS@liA_ybTyS018AmTBd`aI8zG`JaR{SzdH9-safhDihH zf6|Puv7|!N_-VW4x&a|GYIV`gaufmgVdqg# zoAjI1aMjDQL%57_LXB?bXrn)XD}4^GV+Di)TgJd%q7dp(e>*RXk&+^>{oMx1c2|4x zgfCv;d4gz=N!LEXg5{T_G-{x&Yp4SS-WtWfeXI7S$yE;(d94s$-1pc!pp&7YtHIdi z@7wjR;=xlD`s+^ddWQEf|2~5y!s*A0yV+HI+<@e#YPr210^wzJ5ksHfVi~q?hcXe- zm?2`d;8S!sqOyg+A%Q0;OGhBcAkX+&55BOC;&cvl`a~Owinjoge06LsnioZjiFC~#%mjycCifX)JjA@hh}C%n8^x<#6g9zf%Yg&{g2RschXqAy zfj$76$k}?74{w;*Xdal%ly$u(5<*wLn~IoAV8zeEr(E^!J+{ z!&4{m;VLqsCYeW=H(MfUZ)?^L|IAJ#Q;Wr+QTo>e)}2QcU%{-XMuVNG>V@dvh{ryl zmXrg3nWfZpipMmv>DDdG*~Vrx?FZF{3p`Y zpIHC=)($ zU=V98nrm?)Bo2_SiOG_C0hYH|sAfJ@{nf)eJ?77wEK3bNdScuoAc3^o_!zs&3&*{2 z0!i>jOEAV3T+wBdoB=jufvOh0z#M5>%OraB}!&_srs@UPU5GG&dt8XpZheZwfVG%uRwAWq`AZ(MhCAe z@TPczcM!tnZUbCd5)JpPRn>C!b?)FD9lYcx&gsVh(yT+^NLe|07i1<>_F0TLXj@eAc~8t&^8CEb40soxnU zTsD8A$Mve)I=BINap$ZJhgo}&^6eL|wIuYU77hF1d$|Z7)g=9-3+v;KI;!BNJ>Ir8f$i9)YR=!@i1&O4p>#{K`{+%903aS)NgfHcyvxjMZ)9sB8LlU1SF_EJs z0M5Kbl5T5;27peF>?T)YL?v!FX@XJ5n}hY~Nn#rs#S8TYW*bLh0!3k2a|&DegkqOe zog3ht0fIAsxLP?MYz;z|J>JK)eS7)O2$uM>;j)Wks1ef5;tfJYiA>NY)&{M zE@m#fNyyuaU7sfx@CHyQ9OrQoVPIS=NO&bczmc`U3~*rlVD1uJ3B(}2$O`qX`9-vy zP8U+)cR_iIjCvrmIf;bzs;q)5=`-~7p^r@=r`xQPAiy$R@Y7!+d}0~ZY2&z42)T2*cGb5vQKTenfi|B_QrIS5mH~8 zDy{N;!lV?1CXD|6W5EyJetZ@(m$@;nkiLbRP=zd|0e3rN&_mRKm(oODP72j-EmGHk zi=pnddT4mMFNa=u4;M6)7q4avY!p9Ul6|9Cr2il;>~1VJGe$>@V+_egw~*j?30+T> zgbLrmh&{|{U&>PWTpt6!ff&Vo0ppsq?&Cr<46Cd?M}wM?9R~N#oASQJO&l>zY#bD5 zYsKS+G+n@;ACHXW!3KD4*01l)eT`jYm}lUHCjb(p{*f2PK%C|Ob&`3mS~JQ3nZ*0^sbzy^^LEUC zb@%CR!0_x_6G073jB89s1oG0H)<8)(bwLZ_Ha%Fmjd{BVK>~Cd_WoQKf8!+1c4abO zjSUzr=4NBXTsZ7+s_pk5!4Aq86@^znG=Al z$lD#67X?q7S%P9@kQvNDO;}DVB!)W>|p$tK<5^sOFA8a`9=>$FzGl#@S`Urlk{OHo_6Xkpx z-(**7Pis_zdf(B1pcTu+QI-%wXJdqAnq}bf{h8J)w0l2Oml3lu2fD=3U(Lga00abR zlotlrK1xxE^_LsB6CA@}%(~C{aVoO?j1M%rG%^{(RZ#X~Hi>1>;J$%BfzsE)US?&#FIu_Ka@@UC>%+Ee;iOiy^caPlwOvtA|)O~8Kbdj}jqWA7T+!5sXfHQw_I7U)_--!=jBx(@c}r3gAo zb>Bkl9#tz9W2Vyc^{J+5?~^TA8R3@}L&*pmaill(Rr)({_OE^K(=8#z)|Nq{-30$S zx+E3GKQ~4{_sZAi$F#=X+F*qmdf@gl&(^X$;)(Z5f)yB?wt=R%XyP^Y@Ji;< z2{tAMy>B$mi|wJS)h?5?=k@+k!=RM&+=yNq*IxMx6c8OoX3m38=G`A>?I3SgfOoYv z7`vV9$K5rU3KKc9K&)3l?yL3q!Uz}rwMi~T`rB7d2SF5puof<$%htPe|7HZx)rx({ z5O_~rK?R=tWo19lISQ{YBFX0@J6SN9aKn zL@EplG0?2^r<-%cp)+)2zJEe@nc2Cre&#P42M_|j?@$aosd>&+-!Ql$kW_k)#GdZ` ztWdE5?*K$x_FE>eb_voWf(;161o|FqksF5t=SQmoH4g+@v!Tu_*Cp|VC5Ggn!1_sD z!)c+n^+t}nn+Xf!_D9BU`P%Xr86QF5+0W2Ot|&=OHexZDXj^p@^``b$MUs{80pie1;9MS} z9v{o3iyhCTi>n)&oEm!E{OaDw@ZB-P#vWU!6>m`jGEID6T3hAGRUgmTV~&-BZm2vX zZ1rj;!s<%3{@ka=_#ic=gN4-va>hz<(jJAqt&@(hGYna;b}0?bFCromoOKD8|H43E z0=OUmpKg}8bx!S>sU8(PeEgZYpPdYY{vFse*_yJkjQ5@=E_hVPXy^AzHj1_*qHP%d z4|65L#OSE>Qv)l8A`d@YyUN`&;qz$Mi{{)p6ywfDHN@uoxw>v?>2j zz1H5=c!qX5*S4PE->ix%v7E~eyYTwgQy8xRufcJc0o=II1CXbB-zltkF9op%Xo4e8SV%Aqz5Z7b7Vu%#f3+=PJ(wMe(ASZX zk?Ws#dMaS7TU#j(W}g5*a%IdEeu2Z~+KIOPXQKf9`3FxZWSknu*--e!mDXV^;Okvc zIaCQM0!W4WkVnr?^9M^b%NUj)bYHzHE{S8nbwcWe;Lv?( zPk0J@e-w{`6*PLe{~H;Hw#ij$zDlIBMYy>l7{Gw>Wq|@{?E<2&A$vcOg?7*#SU{uT zG|}Ix*s{`}A*BQM)f_cLQBtb0zT!^JC&CN|3?&i$Fr5A#dIb!3dqnaGHpRA+k>5zY zEJ4$Za5kVl1I3u)!75?L0}&ParCsV5VAEs+qmCfve1kZKcyZdgeSh3DZCG6~$W4AxttpWSy5iz9?tJO=yvU%xIQ6TexRH{?zAqPtT|IiK9C z@5;M+MG{MlC|Z+wO1>e>52@-ay;pYS_|{P?c1PH7jJZR@EMTlF`LHAmq$3V|IiSRD zP|{8q1nrObqGv6rPlwOzt3mMc3F~98P1~~C-eCrbEzo%V>)y({pFA zc*T2c0A}i(-h&Np_Mp`lxf9EnIVdA5TL})`=ALgWeaEB^X~@Z!1_WUYYSJgQS_76! z@ZW8L3E4vJs&AMhU?NvP^b1$oy3+L%vY_BnCut^7R~7G7OWM(0uA?Bd*S6}MRV@k? zyagq$g)NiOQ$I`$2{kLLgwvybU7dS|UVTS*Tsp^JCyqmJ@?eW1;Il>_d{|-lu=eX? zFTQwtd&VYy<5oma+7q>+Lj8IIO$^0RrFY;Bxno}Mj^;d`MN?Kgbv5DdBg1ZCFnJTh zPD2+1a?oNplbZgxq`ug+v;q!aQhd(b$#e2xh7s1t{D9Rj$ptMzVlPm^L<9Z&uMQmI zymf>vFZlJR<4pjF|4X+YbUYb8r$7qm-P6vigRq}!m?*u#0Z2tfy2<|D>E57^`Nc~a z1w)+HUU#h@-J^naa7f{}SspuFY(M!4ov5)Q=OKdRNgLTcP&{Au%JSTKO} zOg}_9cnq}5wjfT5iRaS1m^sxel&E*lGTk(={x^yQ!}T!d@p`%YYLWA=PRU~s1dMja z^Z0ElJbmi09dBJ+*LDU574@(0LlCM5M$(t_N?(&9l zu?dTk#OqVuUz$0~Y(6JD>QOJcW4<^?XhyxNnQ1vB*$QIy)rmdHp3WOGeNC=W=2%49GoIE zOO?GgU;CK$26cEgFjSO5YDq+eIk_K6_D>atlnKY6_jxYb_+Zf6+dQ_*>cXexo~uP~ ze5pS@(3^{im z+3)mE88Yl-*N9g41QJ9KJDEgYpat4y`!$HlZtJef zq9WFJh_CRA3m5Gb6v&qV!PmME_*ZK-J|RKz{=*zDOt4F7)85Np;5H2b@w?BRcUZ^D zh+=5tEwujM2%hzGGJG&bc7s6v(FQleUx=#It|#9C89Ddu2hC;~GF%7U+b@sPW`$G{ z*H%)E+Gen6>uz_$q-TppQ^E^b95>o|7>}1aA0BmL?+md zf!j`YOzx^SzsFn+jz`6HrjBb|wVS9}adDFe7ldlQ);@?Rbt)a+jGEc4zx&(4X5@ii zb?CAyVq|gvj!1b{YVIB$XS1hJr+B#>f+vLqnDBhrBT;zx23GLAf_rdv+>D{#g+RWV zVn_8>x?~kff!;poaWETs?w0R%@D(bsdnTWz>WQ+9gHbTIEekmL9Y%;OUqCUMSa^ zpkS<|tm&mM4*#IL+J>nbnXA85#x=Aw{;Vp~xcZ~5Y9mMdWUs}rQEf=vL|0lyMooJ& z!DDwhjT0!XAh<>|pep?adm*%_6FM$((R)#!oSY0Tj8&MgJuDuZhEq9lM5%JUk@*s3 z(1Y;_^3^4R&A|BY&4ujQK%;#dXtMwDQm|L zl~4N0%i8=yMEn#3AKx%W5o5Z}kF%Rtv&AV#3-QWxcMba!`)i#ywGWvY7#OT5icS&? zSGy+roF3B`+U$~lH%aPl@f{(Y={r(YRn=vqkq{4XTj(S$G-%d0B}BU6h?p2PHjbL; za@Z}Cqsq)BB_-uo6%%Mjh&51ilbc&vk5dq!d5dVqJ^P?Fqd zU%!Ih<&8DrJer3W5~vI1;J|&>)#h!TCr_VQoXf27nf^OC+Wh;zN@c6!8pHXHh*duaSQ}FNQpxA}BlU)-9un5p)*-aQ+6|)g^cYZ` zE3#6-lj*H-k>AxX=R$sl#w7;Ij6%Y6@27&>QnGRs)wK;v`G~a={{D5m@xbvS zY@mM7bi65aL)2T>{@366_T8ROS}-siJt63+rZ|@+e~Decydhf8N+aa`;75xm0|PguTw%hey%6Ytn1QpMVR*iWhw_pw5>2r_<-Bbl`}TQEYB* zUO?ru)SFs@)$B2 zwcauq$Wz0b1v&WJ(9o5}`&?_v5#A@k`Z)@#l0pJycK3;hCMi6ns6!G-f_Qi4K167q zCKER^Vl9}GQoORij6QvALXec4j50MeoORh!QB?fInaDfXORGq};y3KivMV0IIF_Cn zyD;AS+kvS3%uTr-8>jYm z`poGf&5=i1?>v2&F)=U34+hVJKZYBJMa(K6zbk_?j`x$L262-kc1QopLfxrmpJvy= zO#i+~mi;=S&CN{#hZ&3%a9AQ8jiq<8v*QGaK4Y&2R$vYHb`+)pB{>TfykAWXKO(#Y zr*xQNmq0yT9;;xH{aKgECQ$mfa;hnX1k1X}krBQ;qbmU)g^*DCF#aBiCr{aS;zfug z$mK*oiwa(c&d7x}E(#5aqPD*N@5SVf8*9=A>|cNTNX_leQVZ?hf?uk`GDbbIzx#di zd^zYt3T;&w-Lw}ik{y1$+wI~e?*ExrSoj@gM654m@^TTvKQT0W3O+|9V5j=kNsj?< zATwBk@@K3WZ#P=KYbY+gAWQS$2=n^9dd{ld#1FZwoG4Wnh!r*I>4n79P4Zt>7>}^f z+@J1J3KZIMIO->l?b5OP`}tu>OG~d7`su5xX8Qhg)CJ|qU6~OmKe(QM!N9*EpqRzq zp!P~GD8w%lJ5)c>7mKZTNO0J;Qq&{l>dYYt5hdx~&2B;im1R@5vArFfc>~To7xq#o zHvC$)i}D=nR|ohQR8-Z~vsa&NO*Ux6@PVmLF|PPp%DHsDosW-C?EbCtzB4Q-$CbS` zNgvp1NWqa@l}6}1^BfmW8X1m2_{&O0--fkbeNm_T)CEiJ>4lYFpW8C#6DsFRn7zgO zk)kpPm zZ9;kWR7yZ_g0fN%0y=GBVbQ(ymV0WwG8#u^U7EsMx24~AT^;62Kl%f{Px)Q2ZiNQ4 z&*P`u1wem_N&zzX=g&7n>QaAk)#(do8XDuM3d^Dx?f2(2er)lmDs*6AV6I`81iG2$ zqKbatV;OsvM&m4rh0#3~2JhpUdybrBRE449ZL1KN@~`JzOlP9k)6_h5`~CYXcZ#?w zHP(Q0S5InZOVhN~P$(@O#?~h?Y-{-W@pNnj%oGoKuFv|tLhqSF-U}uSci^R@B#kGU z0#((9y0!<$`;bkMy6`s89jV^oc@V$#tB!|RJ}D5|_5#(($VJ56U+s}419xxzz!}kz zOO_Mob)#*a{-;4d`7|fs!P!I2KjCGcX9;g!rqVMmeR6{ud`Pz$4|+7<_!-ozvD8rBNq^upsEZNXJBK)gSM@t%D^r4 zm(+;Q*$y3PND99_Er1hOgoK1hbI+R3k3~Uhk)t#&Cm!0{(~q5zdZoREmsOxPzrNVn z@`e8$xqc^IjC9GeHd-PgCF9cSGUY7ZaTm+bB2ikY#g&H}Wx?gr>w2*vDqQ4T2UCib zR)gK2Y?-)W{aEn~5D~>yq9E7-WYyHmg5gL{E?@J?Hye~j+IGS`N5$fxX6ARjflcdG zqjy5*<5+TuJZk)KJZ~HlL<%NeR#zwdg}2Yw^YM=?{3Nd1wuPscxopumpZi>`KA$}bx#9fu}TUkTvJOb0!( zQ;%pFmxOB(WSYT%UDFcMjDBs<9_!J5OJ_dKB@e>Vqa>MnBRB}Ww^)U%ARmD{QQdGE zv?O_`K3EWi54g$q=l6*w?_p=IWV;{mzpE%{+;iP8!~BEug4ZH3FtDTiY0#BtvwA$Y zIe4=uqoJ%*85)?tQ8Cz5x1Kpxmc%4$bh);|!b$yKeUw|5$OuU!$dE7q)!7!~lyywMN@MjbzYq8h?eQ|UHq=LqiGPyKqnaf@N_ z`|JCY_e(yJ-4S-1aNs`(sL!uAgP0mBXI8kMm`xYmBi#@jLr3%I*-gIz`28LxrJmjE zPn~IIb84D1Kiyyx70fSJD_bRl^E79+Vl_QT!gDr7(lV(%BA8Ojh$Y%wPiJmaadki> z{pdq#YB*?{W})T&0%(h(re^M*hPwK1uv7`Kqa}*RAxlr!`8{Lvo=ZXUQ(@)ftTL$f zx!-oA*$#a<*=)_JJm$>SS7Xa+@7=&Z&Mstyx-(m%r^G`6*Eg|*r~1s{!h&hc`1P~~ zGNzksr+2U?Cu#Xy3w54}iw|jLs?PqA61?y%fwGLSE*(BJSA@evk{f#QI zmz+DvyWv+A3V*6R_F(+^%;W~wN?ozVY=5PCeCW?9fZAI8d~^CiDCkI^$(28g^nf1F6QU)t`OaHICcv65XHc$i5FrlM-%k zdSzv$x>!9_->V+&VKm6vHe;w={_Hfwz6lG=y?O5)6A^^@&|=2%e3@sd*%9vAz5`NR zcJjAmkiXJw(lSgE2H!t;KABydv+1jjTzKGf_ORN1y4XrWrccWa zcxfPa-Q0-Dm+TAh8^{D@PM4&|b*&_L`&LD-j3{=05dk6u&zA ztIp{F2ABRoe>MlKR!4WYDr>#V13^J<3W(^zn9y%Ca0@9hp7uL^_EtQ4<;bc2(^xSf zFiHmTxzk>QbWcOqnED%KnOX%q6y!7xef+3>O zI;WhpL@E9sS!yaFsS}h>Hu3zuR8Gs4G`R7~b|;_YUzD;W>Xpg!HZd`=!0KmFNEyNt z2SZ32r^W6^u+i=6xFiDunKIlVh6T>E6lgPL`)R%f{M@PBkt`7LP8mE_$W?|4wA;&)RZ8GG3IG9NGKnls7z56&n%ut)LTqU0BEto2H5I$T`Jz zb#EZ!6sl@!vv5WG! zkiG_C7I{@ENtx&ybb%22clT0*>jG^>Q)Zrt!dd)GMk}YLI4=52&i;JC>5}HI`uzc< zt(*u9TppH?l+5n}i*m|(R_DZZ8ryp>&2BRbk4SjguSI$cOn6Iw|4T?PCvEnoW&CLy zD?dLuI1zk7Dyi#as|xq=uUz?(dbZ1gogUP&{9nK$BZ=M=;=N*cx@(?EH|<0VsU=VK zB}BsP?tkS~h}h86Q}CU=w4iy|uXJNFvS&+(fq}&7XxGwZWo5;3Ia^V9o4~a-xx`tj zrvI$8+$!B|U00catjYTt+(AvFU?eGmDDpP|w_jl~tD>svTi~=u>QezPc{DC1?##3M zUKTDPQM|jN2Lx~>0N^tH$SaSHYf}7+{CGX&EX@1%;Lj(Enbqa`tc`?f4+V|8_S=>8 zU2X5_l)qU&;>C&USu~yr*(_g_RRzs-&1 zwTwlhswl|Br_&&6W_xG-hE1Q#+p~ObxT-4GfBnSb(B^MVI4tXER!)zjoR>DcDJe`8 zmi==BAGy-?O@MMd{PP7X-)tQwQi5`GbE(h9;4Rw(v-)sysKw911wV6Sf~8S>&Yf z_awvs0Ad%&<9|V=CiZK%Lcr;kjH$qOxC6h5N*<$& z9oIzScXQuOu^=x9&VTX)Cv31N;79W`$GPsqmeDUDbf2A_eFNSG84%d`(W3ya@>e+~ zggmK;C7?Jp5r`x-HEjPHb-R-dbv&hiYFQ|~J!$W8FG+X(>bQEokCv8}M=Bmn$h81Z zI=i)HkARmba_r0?2wg_cX580Rh&0by3{7&DtoQ8?XKMwAi>7k#6VaT|H0oJH@6}G&I&fE?`^38^^>WWB&qJM?WZe3-ueRptt99j_MG*kz@n? z5y8g;8}iCOxgr{*O#Ta_M{_vrKIhQL>iT2}Q|5Gc%bq|j^Hf1$WMXT6{;d?P`$aG& z74Y@*0|1<%;@iLEs`aqlY-Avw&&{oGv!F0n*>8uErNCMiX>0|j`(i)tIl5Kux(-bE zEL!lzex~vJRmgx;bRCf(FvbS&Z#o+r8wB0j-Iy5ZlVf0;iD^NemH~6nKWCJ@{d3l0ZPQCnc`XoUYTxxzh@tE#_k^XuYYfUXKlPBVfL8*4g zYvZP_^KZnJ?#hw`A4^8MB zETPl*DCm-pE6)MlFnkq$cwd3gsAVi2h{>K?yS==4e?*A+&GIRm{M?8SjW}F0c&uWD z)yFa%2k?F~Iq+L~Jzjl*>h-~#-XSA8s7j+)(|NjH>c_*c`>D%%tO zDE#i9?UYdlfq>dRd2VfA?$CXDIS}i zGy}>0k;k0i*Cd+dGBHs+&*@hsvUDg9o&?~xGQKH;afqwWhI9M@!2DIMv?m1k>z}0sJIV5dAS3;&m z1PZO3aO$EzRVJPATWci`C&FEg<}I`G)h8Lx!`$SUrjv3Xt8z|Ni&3)@6B1&i!`j4u zQx)3UW6Hu{RjJza8#r032HL#4GO=<4uK$IzhNEKnpr6GenwNOf*1P-J@jv#{V)AmQ zDb9Vs@!HG*6YV9?4GnkFL3U5t8;D^C{48U%yptPE9 zhq<>Lb!q#F>J36VsS7_x@c4RDMGY=xW=(aWeJ5byB{%j}D(A#=YWR-e2R(itDUr4( z-bpFyJKB;toy8Cro-AuCj<@7_T;heWnTNNz_=c<~(dg%iCfY0Kh0W4~c@r|DdE?}n z(e56kcIP(le)@=ChFLn?oBPI(cjcu{v(K(C##n{_a61G#s2>8?ZZi*sJSWODFLxF; zwHtv1+vE7waSM&xFHBKUQNOD9fgOG9b_q_FmaL0E6LX4vz_5M%dF9K!F>?qeZ@^uI zk(qh1It5wksTv1s$+m2(Kf=esZ{g{?ltH_eY4*ul_Xqp;RfVwh!d>kTQ0sK)Rzl-u z>9bkfbncui)7g?r_C6#^fOy=vgiP(Waj8M%$Q0%%kWgXeNz+Pz9BCkj+4!Ex^iu%uQrum?83 z9<(eA9HQ?shk>plTwh-wA}4_7k7sTUv!ihIx!mX#pR-eX*7Xv&BN=Py?B0(@`4C8l zD(h%~`@nmAtV=D9s`bdDkeLc=Sz*+|wx)*UWK8#=+u!65)hmVD%pwz)I36ZIuklw! z$LHajg)O?anC=UHRsBgae$ZvB+o#6FkV=N#2%7!&&KL%Rui)XSL|q1;O)n#Jos*qi zm9;c<)^8uy)vcBz0u|bk_3+Tp#tl@zaa)K|#iwF(*a&dPOG-lG(uE5b?twn%Z9u@( z-yLGelBd>yLP%)HlzDEmZZr+m%8c$!libm?#XqIyPHnnF6p^Mip>5dFnC7tmqHVMF zSs#1u9k#qL0ND3Ay`l%2bHSXMDE{Fd=8sL}C(%s5g(Sv)=mCM#;xL_})V(e^2DO*6{NmAw?H zwZWvKeiw}{+Nm}3f#FNPoV7E^5^6^ddHko)faQ-Vb6e+s{(KUf3fDGp)nLicds*9l1r$PncA(@R{JZ2S4~I6smr2YNxI!^r1F*0aP^ znL|&QKRhbD|I2upnKX_u@P*_oca}L(gFSdV6YY~P;~OMyZdcce_>cGDW~$2dQNCOT0!d>HZ${FKu|E>c@kE- zd~)( zsXBR$#5^EdJ$?491y(`^MMe@s3b>4veNdkMfl2vXO7XTYV`Ex+ot^XbPlK=a7iv}L zP$@=Zey4smqDQWUdgy)f5-k8;X-%`KFlChM1h;lluI3G#;o@H4av5niF7}F@4w>sT ziH23amBF?qkGC^G1Il6L+L#&g8H^;*VJ^#kj~WEEl|6uU3HFQPXzK|RoBzP)ag4Vk zU3Sv=E$O3#xMY{+@oVwWA7d!Yj8uG$rEDLR+DDSNhP^&B99~?4c@W z0?{p8db=cknPDMje&^PmJGkoVh3#_U&Wx+*!GYdhg{;I_%nF!>oJ~DHVJX(F^JPhS z`55&LjAcx`L?$_ElyvW^g`4?+`q}=d%rrY1|8{3cF8Ck)Qa(08RU!lA9H$Qz2W#3(%~x2_UQfkMY(mNipPB@Q7(hG5HMb>9G~C-TygmPf9A&j)+|ZMj zA3VK8rEI05azXX2zyB;KG`~V=ZhxQ~25;K3bVZ0b`V^?(5Fk5e;pk}O#7z8(U3eNCGzuNBLhR?%62h>@ZC9u4pJ0lUsdC2zf@HyC;R`^%0tO|Lj(BKe6lg4HXwQcefJ*o{1N=V&JOK&uwK#c*lEZ^M3gnLQ}V#*67yA~^zu)-n2=gH`g$y7rLBn|eB^JCq)-^|3Wv37 z9d5#ozI>~QNm!`P|L{*gNbm&t;{%@ovzO5D!07Le9?@u4%+wj%5+2n479a_UJw-~R zkSaL376{JzK;+us`8W+N=1+9kGkTBn;K2j5!Q}{dX7a_9*kkyl-|Ah~hSYgX>*C_# z5}wKei<{%|tk*VD)eiiPVt7{gjLT!k&S$d-!z=GFLT254|{ zQmWWAHOkP9qc7hjoc7w+`wZpX9RZG0_2Ju$493Pw&;268UBA9TnO|!ZlxX;m9C_$_ z=H~xt5QEOIIwFugk!v3_D8xT8oQJvj8ZcUvkk2WhDH%6*gw?SBADYoHtvf$I{~YAS z`Kpn24%&Q&>-5leso77{8yXtEgjEk*u=qBn^Xtb40s^^j8o^{s1+|LUQc3$AfUI{0 zyfsuF%_iP_vkH3eFT=xRVDquGHN}7H)G8XdvhRx{~4!+kXES3aHOyi9LW+o=27m)&$c$pRIj)0JXj1OBB zb5?QM#cb1N*^jVchQ(*UjF^~s_19fYJLsYYzUSk3gyFqStl{DFal>CQ9Z~jFS64SY z?hUFmiK8t(bnx%!*xa&p`~KYEqIkVAWMyDqk$&U5t?+vusq%XclAZ@r zwcny+;^K@USKX0`(H;5VRF?Qd5#UPxoM&o3Z(KLwyYCl1LY;)D4~|GHI4>Q?rVdYx zc!dC&&y!4|y+=nCCmV2rw2}bgP#Acj)r#gKA1jxyJ+&m)682&{NFY`+{sFlamD-k0 zFV!p_W1q_=`EVQuL~uxzJNfWGF+e>LG3Qf}X#Zq9h4gzRmBZVga)+1sg`s)Kj%#_Q zE(#;glBVLQ56aJASgQ>rxF2JJ19LHt3DJTQb?NCB5L^MN6`n;g=aGJ4;OEysX*M~` zLX4n+s~VxSBACwc^~il~L#S5pQv1Gc^ZI38TI2Nsq;>}5zS<#o-HP*0(qSVehPjc> z63Xn;I-MfHXi(U+fI{j8Q^gmgf9Ns|@e*S&W~OYqwfqoriFNXHFu*6#m7)&y#S!007VUGshGNYEZ<~n z(gP=?!yO(Ogr_BtUdJpGxNx0 zWov6XcLM-FwQ_B%-xYe-1_qxX+OG&)>6|tLZUAvR{!&=eedxIU)GVrH|84#jnvC|y zWkd$IOD!lLnh41r|Hl+UtW~V0{JgjP&nqnl9kv?xzfMk8s(c_J($W<>0{TR~s|uD+ zlqef4_k;mC)COWeH9>s1=?qS0rnXrFk@biA1(yo*GU_&W1avz)_$?;bozk#NYxk`8*y(z2?y(#v1_Je7q$Jwu-j{KhRjo4*}qBh3>}swQqS z{_pfkzk00uVu@|C-?p+j9~R;uI4`K40OwUy(#J72H8%D`1P9ONVcPjC#B?=W?K)u9 zn8`lqH@$j*NOxkU(u)cI0?3h>nK|^ocgrDknK~qy2FF~(v+>b^+D_PTe!;WQZ<@#X zuv@qlGJHv~OnxowJTyGoU0EI}N!9)dqNLZb)C3<&J?mYleEG#Ub(as2rBq`_H zZ)_&@bvVuAn#j2{j=GoQ^{m!)5?(&z&4Q+#9-(~iSy=rU5g#7}0Oc%v015#IdhfGC z^H*nwOW2m=&{$pxbZCzz&*)@Rcg6Y+sEeClumCp^dyRTOL4Sm-cw-qmv$X3H_ftcN$1v@9)gc1}5CQYN4Z&?Za)E0cc8#PO@ZNVbI zS2HNYJl3M_L-ihQ(8_PYcm0P$smq#rcd96zr6p)oXlZF{&(Dr$=jVr>_rgKuZ^uT9 zuim|N>sGGXjf5d`7%t%lm(k27icF_{kOqzXccSA%N}Hw-@Sxv1e{GOyy+h2&?Xok? z6f82XxV~OMOhlPd6-tUdNfI0@(lgrll-W#g!a!g74e(-VBX7mj-0F)3OQ2rxg^nkH zX?hImHcx=>A<8W>2GeXh8LX~|8yZUW6RWgO^r3QrEMaLX?) z7m+3Yku`sLX=Ma;|N0&sB3WbLRtIWTRj@a~Zx~83uGJt1fx1ydLGoSX?StRe(xGXe z3m=!wz#_THt(*BilvB)oqVLLUJXiF$I}gG@`!$38-#2Inr<;&YtrSGFgA{krEcBj(ZwR@^T^ zp?r;utPSKd5fKdwqyrFT_)l-%-B*y8Z;Hvu(D|Yj4w0sf^=`uzP@9FO7nqKFaTd{_ zK7IC!Dta_w#(8>qPwnRxFB8KKq`RQWX6BoAo&i(d)H-bVp7-!K>Z=j{kUgGt?;K^`U!9oh%^JUB@D(BnKf}AjL0i%!s1Wxj1nUW99!VpM;=%s<<~(e9eVU zFFrnAv(AwTkaLG#;eGnsx1V};kB=v`fYz79sS9&utLa#NYY(PTP+*BB30pW-w?3{X zig*d?du~+e*s~(V3FY=5`!FHe%|N)3c_c4h?0qLiNJcDkUFzEzI_>`+dgXo}IRJq9 z^v=&_tLi`e{?f@)3uQG(yD*?rMylZuYG~uz@{qyislkn1yDAqu*Z=%^6cE2*TYP$1X0sq+&01P~k!{K7 z<}k>C*y8;?j&*8m1C$iRzl@=#poS{Hwb=KrbYA3BT~oHn5wwm&5B`+tEm_eybBeuK zGtuSf?%#BDaQFf_tiR6595|Fj3Md%%=vFsK9!#qQ!%E*B`pd{flq| zLANFjt%(Q1_4qUsyNQkgUCqvrSj+zSSDF97#U`)j+-Oye-D8cOGjsl5c-DsS%q@aU zVj$=$P}-)FL~6NW-W7u#j=}wSk}~m=z_@G;%hyQuiK9l(=?s#>C7)JjR?A; zSJhta{N%rak6&1l1IRK1H=gpG-JfnnP3wI41XwIf%dB%3Nw&b1z)CpiSg*sqw)Aa zm_!lg)UACDVrA6-lEqbP9X8N5=oTGyHtVME#gN4qqgl#69y#*!`X$*B{(E!n{{WRs z$Q7wRPYUb|wkB=A(|M04Q(k4*nkLW~8XEoVK@MMrt@ozD>c; zLOeVytKcg1?+xo2dNrz1ZB?bV=G8}0^%tl(EWE-D5V?l^e>a5W_;qNGY1b+@UfkV^ zlWUU{baZqVFJCTr#sDtLQnfzzNn=XE<@wf2-+xP+rFDi84^LBjF<1F{smPB4W+5S$ zpPyeW$5kv98Oy}WtELBJ3y1ml_?Kxf;QL!WG!HW~{AvC|SzM-I4ucLqi70)*lmEWV zvZ$uE0g6~(g`phZeN000)RLVub?~lbKeQQ=qLc*Y8kd2A?6?uHL-%#y=ym960+d0< zQ=i>e>p}5~R3aWe1l(T4!MX1GiHV8tfqyWDoa^A=AO@t(y5@&K2uEv49g|S2Rj!Ta zEA@m21ztf9`{dS;4%BBpF%L>c3Yqi?(raV5jV#sQrc8f~d*KLi_rbUXksqjl_?%jh zAY*B@xf#d`JBz;Y1EJfHIhh*cN{Y-+EVD4o5cT~+IG?#zk>}s6`qKrI|HiOs;E$7W z%L73U6RbR&jr;odt%qeBrdwMtWk`h&Pr15QwJ)wcG$6AGxF2)$0#%=#0 zbo=(2a-+YHi6sNjWZXq>M1sW4ji^w)?w+=k>ouo{kC&~sxWT`cF6wWIwTTKU8OF6M2{+kkmOZ4mMZ6jT3ceJ55o!u6xl2i#+0R4EYkYAo#svvIpF zbqFBvQGNqg_8nD#YWWSo>dX(8)VEPl-VTT15SX4H88l>!d>7N4SbldO#Gh=FW#&Iv zz4rkg=K3c=2n77FV}Mss5d1 zXZPaAKGX;X5j(t^xz*MD#vamvST-PCA&EnO&@8w;{H0W4rY>nj;L0iB z{=}sQUPQN^lbcjb=OCh3@^SSLA#0=z?dGXNz=Q}U!%lb&>goXyj%Hz}_7i#eFjpUt zmi+lnqVT1+w?scB$6rcC_GLCtfJh%Ef&5X+MnPO~+3H-z=pVI*xQ;Tb3x}Yz@E-m2 z20B(6aM~t;)IPJa5+t@+zI0GQDAh|XhHWf8)xHVw1(eyTXX~?QHu*DY0e3gF{ zhI<56R7Oh3mnPF6R{qhFh9KZgXe0_HddNNNj|*9!OLlX@|BiXpvGe06i)U^Zk+=U=37 zFE6+96;biMH{V2n+=G|Jw@bdam4q7tE_o-5g-DBe50jdOeiFt7U?z+TNO>-hMMZ${ z(&*%1d({Jo(|fS21RFJGGSw7&D*KBfl)976+zRw0a+*T z^3TlGsz|Kt6&yN2M|FFnF)&pB)LJeE~j(ri0b`s@DFKt)PQ&hGsnFwBD*h`8oTb zGH$wpm6iJ<`2s%%>QO>M2Bc5gKx-TP)JfOPMuDEkNnI{J%n><0C$55szv7%6+9veh zd*{5Bv!_qDLQ^X!1>Dvf35p7$*4pOg#deEO0-m$9w0w2#4w0^|?mBeHcLDe6c-YfB zT@O9z^5oBN-*laB+uCvf#}?6WWCtPXHi zNi@NJNhE6Gv@*yet_U`+6UiKG>QzIk=uETYAT5%{_^)5U_pO;yyJb=?ol zy9WIx&3rfRf6g-GI%D#DbJDX9OQQ;#tOjg55`*FLry0*<`eXnmIH1erl9G}d{s)Ye z!bAh1d%gO+MUPyW5p_ab2f38VBBIrwVt-p)m!M%N){QQgJy_JLCK|l4*CcS<(E=31 zOZ#u^%HIf34VS8LzE+v10nu-@%41W{rg`|6zf%btFq{@^pJ4l(ZsnAE)xi} zMTR9-e3K8;ukY?&WnYna(bOHMW2zr0lbL4ljOI}M$+oogfUjeW>Mk;0i@}=-{n_i4 z{P|_(@L#RzBCHyJwvLWo1i+W>VtHSNWC2krm3`uDNWKa-1qv3}oMHWG>oFk_nE8mP zC`En!VTyv4=9Jsk#>THsi*+aOz|prA)#>PL62?)KR{$QLiFd+Mutwv<=szcp&J*yE zmk%EIDqEgjn`wDeqRxVHD7K57t5?o499&zB`?)V`I>#sb1(&*k8x-g1#Bv1forSg1=9W;NmXA{#>)#cL;H5HBnoTs}X!I?nFS ziprEB-hUkONW)gQOr1WdzSU;WW@R6A6ULQxk@UAmPmz)7IIJ(L#=DZkby2=N%AWqR z&UpD%Q#{3(a8!qiPt%iFMHk%F=3k!v)y8)9sk5Ks*Lu!v}!Ci=+$ zh)9ZPB4`IG6CZB^o!#{+b6_P%AakD#4AK=9z31%{d^*4Jx0WG>6Y9%|U?!6~zBJ+l zk>tUX{UvPP8{bvHA+;XDq{o!Ysq+2+Fr_A)d^06@mAz|IKU{|LDL@c{jZ*11sZ_b5 zD8Lb8UOAsw3e>4-?S@XV6w{*zCNt51TI9>h(%6NGN1FN$lD9yeCJLHDSVi>*-amFS zH|*0X_&yK?u1F2{Y#Ge<2t{KW`6`^rYAM~;?NJUpDzs&QD8&Bqw*L3e%cG(_R7o;B zuqY%hKMLhPv8O{Icn9!v;I9Kiy2fZcQ2y_USv-APX+su7xAi$$ezUy1 z3sa<}fNjs7_%3KaiZ(}6k5%1A%{B=H?rjbveK@bFc_Zkqk&tuE{JxIn6OL;o4Jt7v z0?T`vgqWY!)~Z(&L+QIu6N2tX{NRGozYhM1v95JDb8xXll7?NGQ)xFX zR1rnRXjRphT^sIfHi=a(a;Ag6D%!?GL-^YQPL`why$MRCDXnQ8=199YT;BN9;yUbN zp))Bqr7K~svS!(B|KM^0Z^H;+(x$y?hspWf`jYy#nHg=UnY!oIf6jae@NpUoC+X;@VArzq=D!TyEE^B{~Gg^k7@dy!eHLys<%g-0NC~l_X{CEn*CZ zK|9#$zJf*%;6O*Vf1y4p5GEm))Y9jovSvh;hfR5bWUt_6?kl6@1Z<$)66SrgqEIuDvCH;1`Utf1nZ|A=2PQ-UXk!l$J|2P#?n z>zzH1){_P2>|Ja=>7kTWA8?89B$y70if$!g;_1VPGrcSGJJBahXabvV8RlaWZwskE zz%qQR$@ut>ra;HgTv{d$Ie4!y$t#~bN7Lvs-njkj0sPh?wFHKkdS2b%e_-3DUCTkz z8@Mx7V0cnZm60wYsdpN3+4Hwaprefn6^|WD5PA9@wmGkH8f=HIyO$S_D*Pn{9i7-z zXMr?~e7qVNtCOL$nzkva5!`=e2XtgEoq@Lbze_MZN#ok60TpVur1t%$@osd8ooXNf z7V8n0t`kA=rhA~WDBCrIA9mP>oz5jVo^bbkqC|Pt8=;+GoJud9x*cQo()K44Qp}vc z@bMT;EH^4oCQCeyIbU5F*&_dVz0r_JA_@MtyTyFXam<@A?;!xdKo_(#T ztbEF_%rX)@0ImJPiwYdJS{Cfjv4wT`WxLLZZeX*KKn=|E5nJryQ1}BlId8CuLvYnd zI8-|YU15}YXq?xB`9e)e1R2b7cvY}}52aOUF z2LJ)1Pptu=?KcvGm69>Z?69Pzi&(qCe~G^-qx_%%t^cp=wao()j*&-n(dlQ7htkiL z)!vKEuIPcN|owY$q`s)se;L*M_n2^q+%fBX8)xnDr0thjaN| zKX5dvGKSy?sdb*%u6eCw7}3dt2iEd85VMaz_Og~nd?2JLS?s*5w%xtGBbLeWK6HQo zcHjP&bK{hwlhgi-clLGB4+XnzHMFFD5i>F}ij}LGyTKnyH;bMZr&d=NKq{)33?}9q8yh5nSGzFd1rN8%hKEfNLp$3>K9tQUK zAr8;SYsOE+Pv&~>!a};BiT7-bAkA)T!j6-Ut71pDvOO`00kXN}|7d#{LB8ZH9gP`C@|qeP zOY|S4hw(oMTTet?ku4o0g8Z)v`Pay1BvV~DF8G9aSiA<_1~HuVXUY5T#9NPcCuFBt znrVLHFwSF$NotReJ0B+z9;)6_#`81^Iy3{`2R5yt+Fj?}eVrSZM(1Lwn4F-8zRj+pO9# zua^N-ijnY&-vRaT=khyGJBtnHPCLwUaj(n;a#&xYhc+hk<;y2|o?weexR?`DzRZX? zK^=7Jwm%cpK00ogHk6k1yPAd`Rj)VlEfp(m+g{(#my>YOlHN*C9Tf`LzE4OQDz*v& zAvGc515`*$fxEqFH?*9g+-L;>A9kw6u7`f>kO^fII_=qEJqb;XBkep}w3Kd9sP#C$`J9#1#C}g>>t$iLC{{3%T$l&nK*z3J0V6J`qp2oU~Xv!{s=0QEi z(zEArmCFHN3Oh%wNLoWfLw&-5?!Ro#F{9BSR#Z>(5-5d+^EOY~=Hv{=jBq&YBMAxs zBzZ5#&(4bHI~plV5;a`Ryty*6MD}dgNVR-3W+d{b?ySQ`QiC)^jY8*T@ zTie|aet>7c9FQH=PfXi{HIAPcY?FS?63=Ke7y{~#>yC7@^WS%zQjJCA3y#a23iUDy zBr)+iFLYH#*#HQtKt-(s7uKc9u3+M*VHnsT5AM;AJ>zP|oe zO|S{E>KWzfio)4#vm5u@0;|GjU1GzCVSg2&)V&)VQdwDP`t0NH=k+ReQD4vbqU~L^ z?jO#vZ{Ts=rHP69R--xW>7RX>PW*H^*LUpV(YyQE#AaB}PD%AZ(_|IFKUjlh5*NSu zU-8k?va+8P(ow@ha0s-&dwSB~bjIYQIbk3!AslHgy^q;iRdAY&aaKFY{9RX|`D&9Do=bWu+Dt$`@T6;o-0vx^??Q zE@u7FqC`>FpsywD24He}$GMwE2dYZp>Rb&6RAoe9Z;DWRe-HCz5TY$L-o(Fg=(x$> zImj*VuooC~$K}Gz=+ff#NTx+WsXf9L~*yZxBocj)OQ*h>&a#S8Gl8wBz_HoHTr?n{K z4EX8Jw%g@Xv$NLIttec2onFxOA*d8Z}a`wEHA@Qwg?;ZLv6OA+ya4s%J5;U;mw=iXB@p);c&DZq5QNbkZ#FYk9u#&C%G9 zREB&yowPyGqNK^h{xrm{R7oMIQVQFRyq*aHyhZduUM&|s;K z*Hhm^Xfyp6a4BuG;vobmq3;;EziD>hU;lQ_HO@Lxbv zVBDUmDtXuLa!;uZPQ~$LrD6GQ{IkM13oOtMs_`x0N_8B1J5&-+LcSW{pX1DRW94vF z_?xd?A)bkzj>{7*v|u`YWDZLy294fVP|@=oF_@|-Ldln+85T;Pk(rrEe?h{3^D3CvqhMgVd^tI=J{X=n6ykiW9>*dZ z&oJAEOfQ22;kgJvx}m8zC4Y| z%ew=|9HKikpi2f0dlm5q<B;ID8~aUJ>(1T) z1Q_@ai%;KFdpQS3hA9cX>xV5zDNENmFNGM>k8vL6*QC8>#eCNop#j9SX?=hN`E?-Y z+L<{LBf5&!IhW9hq~HTyt=yGI2Pa~aEg}e+A6S_OFN4lIw=OMLenJ&(bM@%y9Eh!| z#(xtBVJ=CAElJf%d{*SJ31NL}i?A;)-nsGXmrfdS_8g|RbgvII4QNAwj+Ylz`YzGZ zB6m>Xg=y4duFOCHWnG0qr`>`b_}HUrE>B&X`w0E;qK-&^u{(hNz^M(+r>~9#fMt@V zD1VMADw{L+ovQb~%Fi#7g4YEFwIfw@@t-7vL61&yDhh?5T0Ff)FSoF5=Xc+!rT|}q z*AetD5~~xt5(dE*wPKp^3>4^FQJLbYkw9=5aE_3bIQRQ$H3e50WvuOyXFGy5F`4rZ zU)pd_95pVL{n}+l#^NS^-h7U{6ynz$r%J@X!ctiTakuSluZH)@9x}o#QZLgMQL!OP zNSHg%^!Ko_=13?ntziXy-y;C6BO@-XQ)mw*>8zcO{Z>K&irfn2zs0*nmtsoydj~RC&2MxZ|RXy9>ZylT${1U z1{mN!7={IQYq7cC<|O@Oe{N>Gl+>9Q&hER&YK`0xbgtv{*p>p=&fubw1bBIQL8xAQ z+)N2Q>~|s(Ph|>wB-G`oUY}RjYQu315AM3=`#f6v$V!vI1ajgkGG_;*qH&Of+9CBV z|6JkZlm-mpi3d5D`PpCfKK43@+xdu~o!B2m0%xNd_qxRixELs4Il0*f(w{9+yLD@~ zny`FR^&tWxB=+5ro59Mvc~so6H-i@*g1+IR%wo6Dx3lAfp?FMUBH|$&J}o?I>-T~U? zgUr7_)&@BrhlccATZ;V??~BLDuYk`|uR2eIpPB!-H;Y4qQ_@t9*_C^-8!}+}7_m!^ zCo4txYIBclHe~2dGMG7SUSfC*bJ%igc`B&I7B6c__#MZ(k1bpmI1e5b{c%?qseMHD zh$6m4!FQ4Y#h>*bmfm ziw4C%-WYT$%EIv!l^^^-gR4$%hFhM|o-;=RIA;NV^C|bv)6roK->Amu^yXRLy*n(v z4b9g&Y+LB-Q*QJSswFO05M!|anmyT{H*j9!#}07Zs*S+LxO+GdE`WFHZ3^4i=iOzM z@3*l`T)h0H+9CzfKc%DPeQRIb%L+%{=wWSSl#nyQ$(tD;7q>(%H_F})r!!SV&|2LO zdTbuD7erHV0Sf`~u7f{?z;Him%7a$?;>&A)W24G_ziX3IE(SpNTMp$q8eHLU=3eFD z(Jc!935*`7n@_-BQl?VspLqB*1hbOAcScz&U5paQ@^p=jjryGRa0bxVn|PflGqXxI zw5J}S8gT0U($VInZ}?BJ1NbeSo+ z3dW%S9G&28%&h@b40Jx%s~v~P0X!4}{5!Yn99j*x2fe!Fet#aLv|n4ZN1kc-6bO$T z{}kyWm&%KA%_xo&OQ64m1j{ml-hMtGKZiF@nMl&(bdGfgUc=xZZBvO#z?$0GfWj{ip zJW4JnDdF+<=mYXWw<^skJwcN8NGwWk;9~WDS-Rj`@J%8DH@}kx_bA}KR8k)c+%L6k zdI?L7w{2~tphm2nR^0^^C_|OASAeO%_?=cE3;oNBQ{h_K*UI#fQH6QpU$L5HMrcx7 zg{RRrv`0U$sm;8zzkmH{xlGtc0(5XxQb68;Ov35U-d+y$kK}SkZ`MI{WQItH>?CC= zlZLyS_e-Bg?m=`x`+axF>$vnwqTmA=0bs|?$44I=Phyw zupY1Q{b_k*xPkw9M({k-EYa5`h)-2BN-)43 zB+eOp-kfrW*9P79E7-o(D;;fDq{FLZpZrC>2&^=$=BGMBmu>%HaJZ@~FnY{`vC5LCWXK?-yQ{CN1Xu zny&c59<`bzJiYpr5arMq)=`C!PHf~6hZhFJp1AD*NPEYS5=A$+a#3b}kuY|OTFzsu ztE(lxw~?=lwO;)7aRKn&-zfekObhf3ryZ^Qd8Vi@U~qn&jYY%-Q+QEbQEid`tAm zw}i3=atcpV4RN+Dh%W}2;t%jkChy^1gOxxqRCVKmMLDoFfEV+hu+!q>&OYCdZLU!) z9Jkx`d5%FKoC;cAU(~Ej@jJW$2HN#7okhmEq4dd^IfNPve7M3vZztN*$S@Ru$yPYe zGq!+4I4F3LrEtX+5#;3R_R^z3m$BvU28Nt*1n)mXwrl-|i-k zt1QW@ov#ORdhdSc^{K~I>0f~R(nx8hiX3z{4-iK^ynzTnj*VfcYX1zNXf~&yy|;q) z-T`UvDGAAd`mnLH`vw&h&*yPa%t2+p0K6Z>?J_a9(;x; z(l=XkF?uk^15p{a`)0O3Zg}$b@`_`B4He}96EY?K)B!X03c&AFqv{1TH8oG2KCQc0 z$3X-5Irt|c1H#A!Nsd#cuQe);Mx25)_Z~QKj07>gfMXG1T9v+JV~sZgO+s1oo+^XA ztz5k*nbgkm=sEC^-Gm7(0t@`lS!;vF$o4lE6o^Lpu8dUrJ2VQ~ezzVActJT?f{eYM zRPyRIQpf&6N}>+5E$GAJ#bFn^Dx;;EyimWw=nMm+YZ9BkkkZCofLrOOpYGX~ybm8f9PYHVw=a|!X?y^C0mOuiSU6$Incw`G z0_i#76>S=isT;e+(^Q*VnSpGYyd-%gZHtisEzZyb027l_Q+KeK;&FJ{2LQ3KqYi$v{XqyX1 zuSYPm7o^6I!=HGeWxf8600sL4G=`H>$tmdxSnzy+XDw1OlSZkSsQhkvo=d_lbQ zpsdZwOO8MKctxf)_pwBSHyu^X9Od@lxcvkALjuuLP?;dv@a3>Q_p0nbtHg+78o2(x zpQ3~oMM<1%073F6Q*;Ei99l{VfIfzmJxxvtpQghC)oxrnGA1mb$!B}?B{ zR*D^Ojv|~tD6CPK!CGryD zUHDW0?qpbO9E``Ky!Qcu2^L31SU6{=DRTDqz9Vhos@8L8^ySyQ348LUSqHLYP$3B- zU}7(Z)v`4$w9To%n=GJ8+uhwo^oV5vRHNAnC#zMQ?MX9_j*m|-s;g;8r>B^pDgWFg zMrLqmwxDmSAC>q!>h9Z&cMHP(qy0Vbk+|dVW(tPNW>naeraSLR8O7Y>8pzL~=KEQX0meHNf)QOGxS zqV!pk*CceU>zkXyYpx^3bdVYn4ke%Sc~iSjJ}aMfrlK#yi3O9>~Gk-3kS$4jUZ1 zy4cja*4DA{@wrcWz@a|cpao&t{eb@<%|80_`26G^be+*L-s;o=0_?zAG}Li*7sIas zP_o?fd-+*PI$1TSIN*K)5;5_uTZAJ0fX#(q=);Silc6a($45>h+_vU|i`#tC&p{Bl z8I`Hk`w=;^Q-1=NBiq4K5&>}YooqRPvzwd37q$(jZ)QpwS)9{MmwG^zmzP&wS+w@| z`HL4tM^#WLUotoe5$K!KeEYZ76sgsJ!++F_H#EuHK1IoS-cUkrfrV&9z0X{XDNB0n z1N%JD;{BklcRXMIfIW)F zQwP}YjFXBacxX9JBP|C7od3jJn(5l|3y78f!jwq0{lLwVCrRO{(3B3OfoGxMUVY$A#Z3*|u0MSt$Pk#2~_lYU!U zTQ^@{Ba>f{Y2mzJ7fbT{tV{6Xqs1ujKHmZA{8jF&qfGIG%mK-qv!8oC7bN*F{rl?j zX&96?2#qR-6#pLz8)5t>=qtb$h3G5t=IpqDNVKqL(~klnY@Vc-r>9;XAHsEos%_2h z4V$T-@JCpnk00DJ>Ms9#@cxqjqm$5ZU9)Eh3k`zsfI$5gO`;)~=R6mOG+G*%l0OD` zR#D;rj9@^=Nm*Q+3S%~~qPSZT=svji$r;#geMD%~!+y@puoZB8Q1$^~9e<7Q z%bAi^O+Wmt!Dn2447@bBq)ynO`~uqbJDBo?Fex~;lmgxdsH-0U4igg_n=Nrk_uMv= zXKgUy9KQc170C*I2;=|Jz)1*fnT1&UHzkKxC`C%)jd=5Bp-eTVk4Qsqe0-c9lv76X z3(#MVoPpPC?{Zw5&Xa!KwQu0G8XpgUWrQEZ00A@IM45dnm)xa`bEbVhohS~TE_IvL z>jK&&d9{Dth``+8*kbPTN8-4Vz=a$@uWI^nu;vcusa<@<)*0fvy<$@RMJ|~S9TVaA zSDn*(_KQvUao>qo9DmI==`XZO6PhN3On07=jMFzZ}>G==sK7c1@@EgdDdfIu5S z2K3LHkXGBYnX0OuMnvldGYbC&;2>lGKV(7Jo&y_Gz*QHhN>>K#xFE+;NtAg@(1G{8 zgUCzQb9!l*sH#AlY6My((ynt?WQzZ712+9Z^`1#Cbf!yh(jP0kneHGgFap!3c(T_U zQz-4tLA#y#j14EK>_2C1D% zPe0f-lmkyh_8}u6Hjp9ME9n4HWKX(pd+R&0A%S#_E%U>@(UzsZyC90BgXDKy`ym~r zNZZR27`Ych7i}5mrALqQW1^wSYvMn?06T$EIix#4W&8wiNi9yUs@SVEoH&u=jk)Js z*qevD9wqJZf%pswZ`FpG-d@d;(xj>x@G11dv^l?d7%2q|sxcx+R0Top)}<}{MWNBXZhW|oFoumeCioB3w|4rC2;930-U`J7~Tt8VB$nV~3=gyvOGjnaq8}vEc z=oTEMP^JdM%<&Caa~En1dXBQEoQGZuL39I$jnq(i~p1YYS|n14WTZHV3(>t z>l)r~Y594_3>EcL{G5JrvSIv;;ol#p{<2LD_^QV6T)Bz%s^k-3=3lJhcr^?tDG8@! z^$ZCbz+&M9Hm{Ii9%&t|jD6Q8ovV{W$z^8^ba%UP3-xxCs?zqme4x+0V*z%a9J32d zOr?X~Y6thomM>Y`J} z>ZEF;qoc>!m62S~{C!T=Tu`(>g_(W1(*XAeXUU?eM_V4(lK^=|8SMk2-%sT7=nyWG z`!ahAY+ze2{3L?!Zy(k*?Iu?0Twyc0?p-;n@mk6x@(g8)L}w|)c${4jetQzihSpu1 zgInU@dQ=W?d8YaFVSga`vKsY3PsN*su>59o@M$7R8iMkR6D&niXbX`X5g;8P?R;Uc zx}4mxwn&B-{r&wD{+6t`+i|I~Fi~~Q{PZo6xk_E?>xvN~d}ial%dQr?b__$D#j(!I zUFYg+r5n@?34D(6GBs<=_8RrU_EY;R=gn#s7VN9Fo{S+BrIZ*@bw>_+F}fLrnwC^8 zkH#17&AjAPsjKs!)&W2iLRg`gQfI}xq3X1T`r^rxp!ak9^ijX#nG#()!J?dbws}xV zc#bfQ_I8-65$BYip((g4aJMf`4}dfo6j{1UYOdnp9|BVrWJHf;knX)r-O3AFAT>6Z zli=squuwH^0qsLUe*W~x?}vZBzF6{re1_4cK9I>5`|jr@Teik)$zX$$c=^Mi#hXY< z?e{=kxOA!T#*hdfpZewH&lS0+(P}+@dTAfkperM1|GW=$6QwP04vlR9Zw*S}!9`8JGGC+>5%Eo-pGox)?)?Kmxhw;O!=L;(}sDJSJGF^DMg= z2#wyI0S~o zcBntl1du&W_eusmUu@-aG8{fh-DdGtShmu0>cxT${QNOa#fSZB90ju`)AeLrP@;aP} zR6VAs+!N4wPYsVy#fZ}A+UejIJN)7>3rYVj$L6^^-hqe_Z@CZ5w~^I?r;50Ld*ACf zPk^i?_abQe2o9Oc$%0xUpIf!LuQo0**0 z3zb+q^mG*ue%m1w(Honda0reDO0nWND@|-Us)|FC5;a9S-+Dqx{{;paF@UC{4Eymq z;OyAg*fk~8V)y40gWlMpbWLdI2v*<0Nk`y3zVz+e5+U?v?-@+56}S(#wv7x7urgs7 z{+;!8ZFx5UOt3$F`#)9gQs1;1-4TzrO^WV#^fApw4%YV&Y=Ldi2+qxAgG6C0%EeM} znKL%eer}YUI&<|2P%SC~7P-IM(l|t`U}LN$`&dAHh>pA;-ofH*W!>(1nHAp;UqKKJ z%3l9sPmsz)fRIg=!=Lr9IH9fRM}JR%6m^roTC39Irri{1{Kf!9T62Fa$F6dO(JHBv z0j16Nv`f7fW-}q|@l?pJ5@~j!dJwd7Q5RtjYzkIcY;fB9IoKXUSWW#>Wp4S#O`ar^ zBd``JBa6-JbS;wWpT-%e;MqD4z7Ogzal_;{{`eR0exAA*ogZqMku!BW!3czNS6YFN zaQ=erc;-i-A#gWD4!}T$5+Ea3y@Dm%4tHN)+kh3c?STp=#rFQIiS*PhKrp` z`sdEd!o250FqDktzZqd$aYp^eZGm_BuF+p1wh9*R#F^WK%;PXVgG+y^*7bH`)D3ZQ zIvC#Syt`@@Llml2b*GqcJvOY7ZEwvo4K0K#{}5P6TGn;8t5BZgKKK4E_A0x zG@1lO#B4c|z;|a#!CyTgzHk!?lkXs8`3SiXgfGd(sUkOYUzix4KeKxXZ5z>i@APzY z? zt_86H+2>(Wg_E}h9ih#=dj)Nd7))w{UV;bL*|Q;K4uY#DRrZvFgM(GbroX@C(V>J| z?fF=lb~~R-V!6^VXS}-;$ByAx&=NzSD|1&ELmoYo+*5ZsX{064#)e0WCVl?(0Q}$U z5EW~}p@oH4SNk9WNy}N6davik%!$(J*Q*Hv~ZPXi6RfIPJt#9nw4-np)!Cu!S}i)lZ<-!nP>UttX|F)C-x|65{?w z{z5;2!#j3UekS0jw8%Rh(q>&aSq}a zW7JZt3Uq{~YX6>{YlPThleBMUqH2d3R0CIjpLGldGyHoXKO$yLpF_a;bu{QlKq}Px zcVmA!(Uh^jO5&1^mB*0zK~qNR9KG4Vaj9hDFz)$H0Azo=L^Yt{xVZYvUCUQ-yh81B zB|BX*YGW&^;Da3-a2_PKcNBN7AGaT$?1iaqPTRfM860vg!WIytTdv%5gKjha0+RM? zbV7XQ;qS=l0}%bhzItU`RQmh&;hxdTRKrEEap0bAAfSfbhaf-7*sd>r{00o+`i`zD z7y0_QPH5wE^dziNg_{rs;xWfLKoN7?B&C8U*d9Au$}1s34fUDs#2D zY6gfFECfmzug`tF3d#{CpxW@#arSb|S^a9sy}pfAu3EQJVFL;LpjK>!e&;3Yj$*mN zALC5{wW>oUorY|)iugaudO^=aT_SJ4VtA-v#_B%vATctJxmG_wUDqEf4mp@o8Hlob zC^LNiY{F30gdP^-LrJht~K?9z#pH~%;P{t!QpU8* z4$3oXC{urbe}rH--`a&Eg9)*scEU*j*SycBaJVXn?Pn6?Eq&WOS76{)4{(iVo9k$5 zUV%Y2)IANHBP}0WWj0-cy0Y>$FTK#E$WNA2)lNnw^2R9!yh6r#Ot~Nc1ll{mvp+ww zH`guD4EmufUc6x5-h-5 zDB{q}@#XgKVHdd8V4xH|-Hdp%$?8)H!)@BNU_Mlk5sGrrY=@+^V;Rxsc$Ya53;fLh z2GK|XI;Q>S^DoM{wZTFOW00ce1VwxMt1ztZ2m*sFPY3@yICkA0j*1V{#Co{SM##iJ z?axvukHF%hT1(eY&C0Zvwi@%HSo|1o(Jzko@sJQai20MG!MR;Q zD-t{RFv*K){>A)S)aO4ML|tmUgnvjf#lL@IAcV^K@kXu`d?l5vXXsnszejS(w#3Em zf4(LX96d)JMxO*rPSQT|TL8pbu}Cd;R9WuQ>}!W`qbv<$H3;E23rt)gW3#ZrJIHcN zQO!`hGple-MZV0kNxv%qG*hrkH0rrKmj%#<6d3p!Vml2$5B+TVACy4eDY3D^@=r;z z%Rr*CIxSx>zf`l+1Oj|zkS*O-WwbA z4YkcDo<}rL58wmEyJQ9m>}*_>z`49U2jyKti@U_dKh#gtB3a z4NT4|v4KmpH4YtIMqHADC;W+eB4d*vj0OMR`hT^2e5&YfT zq7*99WmpA%5iAGH(DK`1iGkRRfw&twdR1IyolJcCmKZ2 z>dzc^=sZ>l6nMnm5D);g1z0_(Hp!f$B%C8d`CcBNy>rkAD;koDiC&ylRR}S5hhG+! z?d0}4F=PlGIze3M{a zGf0ifPE%-6a8!0$(jHxD0N^-wbRC+CYQvrGCC;&Lv$F=;d$CGP1CjzK-r@1797f=Y zAYb3kWO{-?r+>7@_y4U+rZuvfYs|eQ#zuU3-*b?Ra!LXWI;-9)6Bd~^cc8nTnS*xNoq;{)$VDgqc>wzaqZ`8Y6orX_tL&N?`X41bCb`;-Vbj zFN+x|OyU1&sNy5dz0bLYovT-bG5EEk)DE{x=^gsQzMdb-PrmQpjS~IbAWY}bzA4Kk zkjeXYNz3{x&G#HI#>(@Pj~KGnyZ_~ckG?QjR|&gzF4hyIHGY?>1j)XSh7Y(fPtm8hMNRCJ7%O$dA+{8TszMtSwmqjxPGh9 zmW28w^b<-Gnl7(4ZeBpJV!O#|Rr}HRRh1x2bj`1j?f&Y6vr6a?3S~7}8<@rc+!m&B zGCx4+xb*4oO?3XdsaiS&n7&D!Y+xk;Xrk#~zY9DNaPWGxn-auIxc&ZG#~FKlHbsU~ zMA4EeLBx^$?5SSs5g{c6T`aa0PvM)AeaQHH<$TC&@gBallaf^zg0AUj0{^~Dp zNB1m=UqC=}%E!Z_6=aKj!vSbTG^uX^3{VkZ?Z@Gz;4i?#s90$NZGj{aq`4rvhZGmN zJs&-vOvrOUVqe_dD||8|ZbV*@wcO$b(P}lBGuceCg*(qY>-IrXMB~a*i!b83NqBP~ zZg9EiieRvN*_YQ)4QwGfCKNYGnE=990sYB^3l|E#RN}yX+0}Jg$nMk8f+MuheaKsg z*}0Av_X9#B4VBe!t|qrYyC+hV@BP5fZ}_z0&+%%tBi{5#{+^G!`YkeBFJF- zr2msQ=NzM0*3vd){W|P)JBzbeIm5K`Xp`rl+_jAe=^S(ee|-PGKvKwI5op1i$xc$> zLa+INPs7cWh=9L-SxVlC^g0Z1Xw;ntYzYCf)vd6`tBOKMo zz)RWT{}?)TTBex!V=M%bOZ;#q`DN_HW_jD$*_9l46LMXq8^sa$(aDo9NS$xQ=9OtK zK?qo0>m&@&;flf@pw2hKP51u``B)tqL=Mm<9D!`d9aOqOPvQWaJ9$?UFZd$>z0>94 zhNk@h=kioLan_LBl@B1m`CCJ-pJ3z|ot&)CZ0C$K)wnFudId(FFpuav#j0@E3vL|{ zCX&Fmw8D8pR3z6RxmZ6|&I?o$dPq>0$<@-B{0Wx84rq!HK>*KGtsTeK){@2uxnd^l zF`=;?fYJKK#*&!n(;Xvm@B<_~btjwa*6CnHpioUD7f5`!B} z#UPw0%KN>sF}(B8A}xQo9bRHY-M8ZhxFZuzrF)VPo>pwAam754lxsU(iBP@kp{gP; zj_g`<%?1Ym0D5!Z@7?&`h$`{Y!@9ailtM$=Q6t7wfa8m~QTh7m0X)bybzUp2awhvV&#FnOgXC` zhUVJ0hLzz4%V9`LO+5{yUa-D8>~wmk48WA_37EZvZox%H>7a7WA8PNbf`X&@ciA1U z0&%^18(AJS#FI0 zmuvbnGk}neU1WNZ)09p&ARwR$mejV2v00uy8c~ET3oW$%#dC-A2%!dZrKOl?sGj=A z2P9Bx=YbSN0V#ke4tS<Y0j2G2ZHgL> zY>xL)4!SjGFH00o#1UXgr(_my3hLGIA(3URf`S4h&dqTsW~YKUU*x!BU4p#FSxfkp zh9}vstUd#JfxDk|s?QOVF_4RtjcJ*-s^ zB4IRsKw5WktCBMVnSNXHR>s0Z^ARA3O$kmg(akgDBK8J$X9Nuh2fIpP~h!*c6K-oqG68* zyBoG!GNaz0Z*@LzmOR{2OovGdG^bRYR~+=z0-bP?j5zK_e*k1=Z{WkPtpr~?8c{$p zt}0P~wDk6?y`Tj`(M80<3;&1Ej$&gIGnV4A8{<3c=+;utP?NMLt&O@(`h~tDCY(f-(rEvtOg7iwN7)h^%n0kRC^?SYP z4hx)=Q=;%FS2>MpI@7~NUS3^Yvh}aUQiXBMYgrd&;tVjA>~F}l;l|h4l}D+kA$pYm zSJ)JdA3(@T%%iz$fI@q0EhGtgl#WY*;39#^$H}0-zmy==ZlJ#hwyS0%HHM*=R>IE) zHX(rpNYn;M;id!Q&RL*0zJ{NQa*T8Vh}&Ryr`0M4${YD5ScP0|pI3zx+tABA6#IK{ za1^7wirr`igE2^9)3>#A6^q{33sdu%YmX!r*yRnH0)?m0kZF9_4 zHWmwt`gS-y?m|Qcyj^AuhyS<9lA$3p(mK0B<`q2EB_i1lGl@F)he&9s>pjJ;Z+MNrPz z@F8*F%?RAsbs-Jbg42VKMRK8iP@j3-`QaO!i*=vE|U1z`C_!vxJ3!>v!_)Ueb zX?W$rgM+{3ahDC73GUbuN2*2AJkY07($X6K;6V`TIce`63&+Y}srkLJw(-K-qw#5J zXHK3xdEw&4Q3*qkRf6>$r=h&Gju;Vt%Fxl#jVF#$z2!ua8bW3rC>PC5#gP`O5LaK* z0*-NBh+<-a{SZu?LtHpxsvTS07q5Lw`1D=3x7R2_(a_#13mZ-O!jcdMiU(Q&qv&7`eV0RI+Qr&+-DljoCH zW9RAVX&vZ@dbKX!Y(Fvxr;cWSsTi)fuodR}EP@TnCBkt(DOlCm=!Btidk#~>t;=o= zz-co6@$dV08<7;aLjJvicLR~k^}he>=BL2(t>#`2$*gG+Y9zP-EwOL-j9%!DaM#CmJ+7^ zKQ6G`PD|e}Z;vDJdK~e4kRmPRNyGjSNy$Tg9GJrGX`E9`{u&f&f&}W&74nt!TC$tm zj#`y^LEX@o+f@9DsZ)O%T@ODDN&)YI#D3uny-Z9}d|F`f2MCUrFd#vRAYx>ZOJqI{ zFs>W+!$ix%Q{Zi(8_-Q`K&H9uva!Sc;|!HsDK`kzNH9(^`Nf4J#8?iJmU&&G@90`p zU0P6{i>^OzA4AQ)*223x-oMae)V8(?bV9j_Syca#Nj zc;()^^;YQZN6^KhLb7TgQ{eZ`j_a@}+|pa(4H8z6V2L1U?vCVt0hO?Su!Dg$d|r6t zS>L=kSs%Fp0cfE1Rku;5Z9wA-3r{`TpP12?77#aqN3%`gob?L#RkZKlG-pwB)vEbNM8YQK^mJtF8;H6@Lnts{Rl-ITesUT`!5@B zI$S*kM(`yWB$tS=3I$JZe-F^dB=F7TsdO&`ESDoSPdv1;^>jA3%*CA+t2-nL3i6f} zeVFmM`v`_C_5N{mHECOc)5mE7eReD)ep%K_GJHutM`tl z`hWj`HB^-9qmWrD3ZZ0V7nM!cF%OlMEi=1CB`X{vBb#$@vJMUhEi-!@9HT+zaWW1W zhx=u}x}Mi_+}2B1-~?2rzmqQmBK_!6IXdT}(q23STnL3v zi8}THPowbZ3wnGU9VwuX{t=qxd>k~sWX?daftFgjF|H+1szL-k+W$(gGMC;Mlu}7v zhStqILYDc7g2W>>#B<5b=jG!C??QUkd@!_&a(!X{OhkUY)kdS0{G3!?_U($VKUf@JP9%f# zKdo`K@a=1-!wbEdxyx=-#BZ@Wf=(xO(C5*1H-OA&>?D!=BN!krx*qk-0X&$;~Ot4pnM zXFfWP>2%uIeyz0Ck2NsCPj|W3Z`h~BfG=k;i*CE%v;wc3_k%CHESxHxUsZ}W|6cSg zk62whcoY9C`i1`}5Lhn)6QVa!a6Qs9 z%NqK>VzasNQBH@k@7bfqm9m=VlubujN9pl-cfA1+cVf&AE4q{T&-rZk@)-i)(>@Yo zFw!>1DBIevS3msK@AQC)NAsY8dYc5(ZLNT-8Usk;iq>`W^j+V5y|y8?0`!1+P~9CR z7&A&-{v6-}cZSZ;nhY`#bZ^hYfVU$IA@ninJ{s>g1125L(;8e)wk?yt|i>pgzq$!r`m^alc#2JiIL58s516EaTPJ>rH-t3lFJKtHVpAzsA#X)krj zlr?rR_)CA1aDZ11hJFpR#QVZm0?Im4!9k=;`8b=%>7|7Q9wm+v=qu-||;h zjs$|dw9?tyU+PNUt}0Kf-^RTVa96WgFnr@d^_HMBu>pEY(9WB`DticgE`GG+c>^8s zb=iGdqjys)k8P^L-k8U~g)h_9BVJAt-0U15t0wva3H4) z=Eg?lzx=<79GoHjE*o0VVS7g%{Jdf*FMjk#J_OoV*8 zZh;pJEMg_$WN8u8x7*u6KK_JNr022_s9{FV#29CEVc#_nN<@J%0u9!ebSW-b4;pQ9?3jXnPI6>FSBEIp4(1x{A@SMy< zMv0w&`|j=A0s=8t4{27IP`D=!ull7Jh88{pW~jX`!$U zlF82w9{UN-Y9k*{`d=y(5d&0Wz_o)R!m`bESbTDQsZ9j?!20UOSi4~1SgvpEHs9oi zWDajHAB$stg^BvsRO!_2xyjuNRZ{K($5q znT4ZM4=0x-C;hpW3Fks>WLC!CG~DA4?UPv^f75lSW1{6W!Oz>-qQxODNSbM*2t7|Y zm*J|iGdPJ0T4UO{RI~wKrp>Z(X`sW6KeTE&8M8Isqpc|{Dry{)tD%^jl$7t*o*PT^ zv{<|_IsMnn4WBU;z7tqC+~wKP#X82k@vUdOLu-v4=1254Upv*-GPWxY(CyW^fx|FZ ztz5k7IjIivJ4@6UU0De>5ZJ(a(Wy#rjSNRHV-c<$218+tlNsGMl%6)UNu3|?!>#%_ zmGtK|_5Bav`SiF}<-NSgduX{Df*+mIIEbb0!XkUA{R+bO@G(q{!S1U!D6Qo0rfRZr zbTKhFs?*a)Q-=b9Mt+H;Az0M*iLTO>_+&*THtbY({?gFUn0w^m@#nPCAef9~E^a{} z&!B(MucoNE>QO!5@sHoxf&O{bXom&?G34 zO7L{A9=EO(DGVBp?vZA`EB9{>KyhDD_3Y_WG~IoV>?$IHLDRFdkjh@aBLj*csMdIF z9PFs@yu7@{ZlOzGIp)^Dpt_%&WED_|22VD2-W=I7*ITU~8L zn-UMzCjzHtz=n=+%&mbbQ$LwLSSHcY7JM4`Xt{wI(!4RKphlVlfY6Wrr@2$~_}++y z+R52{YjYE!NbNVA3vhHS3@AFL^!NIh67>6c!u_wzK7nv$=6qpw;%VUBQV5T2P9QDwsH7XZ zX>mWV(17=06=owP{JR%68XQ}8&2}hU%kpm~qGBgd3W?i`&$q>T}F|3Y# zIpsl%vj8?Zye@G$#pzLCph-s>sG`I{Cm$m~VdAI!S0hT{+j~=1W{J3$JUQB8W{A#A5_P53B4*PvW+V4{ea%kd~{?Pr7UEG)Ydn>+Kwn znshhZxef14cEZE9b=qxOC#qu`{A)xr_NXYGfTex=(8jmeuA?j)+HbnpSvJ1C>EeUW zA`hV0gpsf$q1#hn#qVz>7B0#7x6~phtApc!E%eL0GGm2k$108JAl04Ftc^F5d0F zx$}h49|Dn=Am;G9Ug+kDap+#mo9%M%KxmY)sKc1CuTQdqzXX6_;m4G>k9+<8R_L=0 z59Mcop9g3VMwYeq{~92P3F@Rg&(%sA+~hQy?a#V(7$vM-u1P_(3Okxjtpb@n8^+)x z#y(|oCE%0;pBS{`LFxma0AgfN-e$K}Rit$I$PptS+~l;au`$RgQ!>TK@y5o>wQ-x4 zWoTVQm4>2?#zR5OoGqgjeO+VbCdq=-^04g?=$QJo{rE-j^x;o$Vmn$+cDz0~(AY*B zG0@tjcKbHk{5kvtz6Ls6O^s5(7BR<@R7M0aEt5McBciSe%_o+3<96c}GPa{9cSXry z#&rktZF2oAIbp3;SaO5Vx~pp6*DfqIJh>}m%l2+NGH<7<$TR+=$8sR6M7EWIaT`S+ z(cG#jwLuFA48#n0m9^=I>@KIYqt*Sz{4bf^635I*HmD_&#bGHC;7TBC(YjXofBtxo z(RhHH_^(|vh5s=aZxvJ)!SK)@80SkADYGe%KxKy94hfZ5X`q8Riu~}RH>4=MX2usp zE>2NYSE|l7#6Y+t87)`HR=73(rs8LufgVD&jW(qyL~%Eh6^JoeA@l)Pm1^31)G=c_ zBg|gvH-+G5oBJH3BTm`dd^^nw^7Zg_+dGp|38*s9t(bB7<#7m&xFhm#Y9oXKr>olXiptLE zY%0i-BrZHXEUbH)!pFDk0)3R;M>)HwcdK z!xTb7LY$_`>*|-c5*xV9I7P7yb$n%6YUA8(3lK8I4p{A6kIKJSp z0Zt;A5QI!EgH$T4F=k+$7vRMx{Qg~S4o~YAm(vr7_Q3a@EB}dbRNBY=OA58Vf^FDA zm$YvgY<>jht@h@@-ea*8cb(hld6sy(wK94llRr4EYCJ$I(4yWjH&lRzsOcw&B`wo5 zt0?!K_z9mg9m)Tp3|DRYm?iJZ*75#p5Ks6YX9J|xH)7+H!$$*J1VDY2{u0k2BD-J05KJF18|Y!5%2Uj}t}14M>eB}G|Z znw8#Xqz@WdG$G7bO8Q@#B2U8i@>LP3c+Kb9PdTsJN2V-XwrM)D4Gc=br4%<3PRI2= zkai)M&ia}11qgGC)3`73LyJB&W&My2E+Q!md0S?*jp>Yu?q6-TWl*$OxMyhd#4{zc z79}!yOWcTY(;m+~TLiNwk!dW7*aKrZR(hJS{J|Zu`F;N*0D{=Mps3(Wp(+Pmxt-KC zyY4T!z*E%Y`2N`0@QNq#uR|QU&f3f?n_3=20(21Y(W)b)gSl!m1X`OGCqc6gW3TYl z4h7}pgJxtLh)rA-VRk@7vpq@)_(^Zk>K7Y!Ln;T(V|ML4vx|NvX~e~u-H;GGCExnz z{X=;S{?*GPw@|{xYo0v^7G5c>ctr^RFYq_ty$sLlOm* z#gh>;DZkyKfw*E)L{m5lSl$Q?1l|8gAX`S~586Si(~_exm_#2;MK3od54n6P()swc z9}}=HBwEs#Np-x2-?#*>T3KLTP+j5V^g;RrjSud*mbpo?|0N+60cyUWU2YQ$76zuE zGu`=9N_SJ!Jp`(;!UGfD2glC0R0FE#BOwo36mp0LMF?Yk9+h9zl$bDHwYhD(d0r~D zPVc-_et4aO?d4}My#R_uUUEx;&@lfECqriVMPTsjC79uLLQuP)5^vr)Eh&PLBIf3( z>v(D>B{;?Ev_{y&)b&2 z&6sNuuu?~d*0e3?w2*~S<~?r~uf2P8D|cBb@TX?6qRuR{?|Q6=X{pT?@> z%8$Ld@&TIGqDyZiN8Ia{4%m04M?rP+U|(n)!eUP6C`9F2gc|*$hsQZ>{f{1g}7tpl2uZo!J&5y*x5@n_TM{OQ)c-mI(YW$7?>a$1?6 z2`_l#N@rh%B(`*eo}f6{?2=Giu9dZi$K}m+3f4)6Jkem?;jsfQNUMt(5O@ybP~+3+ zl9YaW)G`p%jzb_5JuFvYE-uc3&%#jNoCwfq4w#0ETI7Y;gYoK|EEvokRZ~;jhTQrh zUc07A8b3Dl#tombgBlIeASkbVnht-fI$gUqQltRbYlg4U)1DNg%HDi?C$qCz6Bpv& z_^@$sIGdQxyZHnkY@s>DWh9ZI^A6a8p%|JYHnGczGbZ+WmD_i`|H^MC?N9v&F69yw zn;EkpF3bgpYQ`vH{cBC;_EN}kKII#F>S-;}Dw7UE*rFA--`Pz5`T>q_(0TV7;oi0} z(|WJK>gxZ^ImClpe1JiiwmUhWRV{5P$p6MQj`zWs|9=*iP6t*1D0)n^3S@!sXPIqN zsDYt>X4qMZ2`y-T6ECOnRm!20haR{g?1RvexK;)JFH$WRKT_!YTH_JJJZ*jDbLBa$ z+v=Or8m(fpBR+bmfz-J$Fu{Cx6R8fX$cab7pA1MN0+XE^8gTC4EGR_CxUU2$FDW?~ zBX|~1D`g&@)1)8`@7{fn@8ADn%AhxxX#5%fd+tH&knvTEq;T!%q{a%t>tkR~Ahk4p z@;Q|nZg@DbX!k51qzOA&+xfEr`uiTCt)+9z@Gy>!)zkPOJf3d+1=8!ro{m}dq2wpa zy_$qA+w@LwTKm=gDO*VHnDetWg2Fu;XV(wX@l#F)#>8RfWX6v7IYli3zs{Usj|LRV z#C^eD@H&0_ms~r2gaRcSy6AO356VRW)Y-*JkG)kJ^8Ee%h38}U=LH7^83~?&mprP` zy=-=g&U-jh=_0Xb1e6hh4e<3pVBu9~HYYHx&@C58Rj?zWBrNw1mbICSNxl1x)5gmf z!7-K` z0NU}7vk<(h&6qEh<86#UB+3a|sM<0+gWk)~D&iA5Cgz{^BXoQ1$fUK<$3Jw4G9B}D zCxfu{I3{a!^IC}dN#5B(sC%fT7Ut@);B(*192Ns2MbuNDX&! zSzhx+l9ur4C|vgReC6I^%bH%Ljmh};!Ao&{i}jFBq5SDWZpd7IKeW>$4bKIBI@j zw(;IO;Fl4z@m8c|w@2}1#;A23Ye4u8_9EIy4?Ub^yU|$kf*7{=iY0AVXx=8Jn@L zfB035d#T@OJ~UWMCh;r-!*we*Zf+YEIireAvd9qH-vW6*fV%B^WBzYkFz!zbiyL~d zx}s7p7jNup8X?VOE6dt@o!#KvG7MM7V!ZT^<%81G45DVU3C?gkUdREm$8zd&8yr%`^RS=ZLYlzP?mYn-6)VJHT(3E@u|aV z8o#fby93>-WZlO+;-t{e2`qpX<}R71lIt_}(LEvl^AB#I|+CImt9> z;z?8Cy6f6|<|RLrh>l70u@~>i+hvM3-9O4vRyDEHCyWrWnZhKjT^N=E!gL=^2bM~U zbDOcaeg08bC*H0hhpQpxb}ND2*{y2hm^S8-42!f^GK z+>_`yh9^@7dl=MX#3m6~M;*0n*6%Q!^+)gb{a$BMtgSJ1XDRoa3sw0lW1MKs_dQy; zeK;qYu0>^tSvMX60qpLmm}@KpGBXOXqGLE8?#24JKSs>p1Xgonsq9t}NtRk1JYl}J z{&!$)T^!>&QKEWdeqrokcgU`1p$^TrMrzJ-*gVx}Kr_Ln<%I3g;#=1R3%7?rG`9dh zbXC0Xm>%`e+;I7Ys8R-o?*%wlc?sY6q_;<>pB&_CVPkTnd{SUwC`$>kF~K0n1t>Lm zgihd&JeuvzEe8A!XHy$+m*T52Tk%}50u^gj<7szuYY0v^nf+=H1pZ@j{ULw_YIGVY zdD)%%d!l!$4fT4io`GRmT;NgPoNMvn zW~tV-sK5mbNB;0yvAn44@Am@Dd=-;#RS{*H{2~_e;{uWU3ORhw16J}$`kU_C%O9aO zjCZPu>wTi(q?4cKYknuooa3{zrnEuAst3NyjQHfQ9XV4xe+vfhVFu+h3h z+d_6-S>o#3!_d;InW$IsvC5~y-749RJCQ88l`l8-@fMdCK0$zY)y$>wcrSD&IAFqz zQ0oVC_WOKy{Z8|k`c$bvxPp~&A77x{qXVW!1WW@6ec$iL!InEQ3NA9e@=Od2w#j?1 zFc#UbltBQNXOhn8@?Lg5(u4ZR@63`BnIVsCaBLzcC{4+3-YRylMO51UbNscyAfb`_ zNmq92q)B2K6ox(HybKJF3+S%>yGm2|Jq$Is-rNs!ZPY1VXqK5CS~e46x@T5?_gxgv zP7<}YB#ccI<=Ioy6kc*C^OJs6;o-sfc*CT2kiAc+hbP7IsJ49| zlZzfeoB(BtKcnz`c-cASkxO}!dUGPlGIpOW)^p(XaAA^6sqVh{p^0AzQYw@4!uOre z@NkcmO$dh65AB?){Kn-|V%rgbsEnK9bl@t4vmjb!&tC|NUub-?&-WA4h5d?oOIdIW zm#;R(9AKD4gfTapQ$nRGt(C1FcnKC!#V5+_t6-}`Q)}zPP=%@ZW-s%EQ%o{q-k=!n z;40UI}PqnRYrb<|mcCv&Up?klyu|_N}%6$+lE84!q$KKY>r&uB{8^N>h_vGl~ z1;ZlmIv=I-l1j2g-)|jDhpVCHcD1<0W9HM{Pd-aKk2<}hwmV^li;60*6{Z*$TE`t$ z`>v+KrT!x1I9MqVad$uNUH@9RlsRyf3mzCDb@hD=o>!y%hiKae5Eb5aIvY-fp&N^? z2QBqx$4#Wlg{fM0?am_p$Rg7#GP$=17%6`y7lGUXMCN`eCRJM>i2zt3iwgTZ_XQtB z?|ai}Q8^r!aG=ck$KR)7J)=cxY!(v1@mkuoKlJu)d^67IG0Jn&cYb(Z3PUWZW4n~F znWZ=>4x67O#0K<3Tj>Z;IP;v4jtcy7XM$AlmNlOGV;l?&5~>{UtH{W{h{VY9ho`?) zO@=n9D}0b?tq-{uo)+Y2gCVZX2-KFpV;$nkmK(h`&{%5F^*|sS)sHdAjo|L%X=#P^ z=mP4Wopc&J^U+XiY|K4X`g%KMP<-s()le%tt(}nsr_@DqQ&A7;M z9i4cm&ZB>gyXThep65*VKreCtPnK2j*RNicp3qZx!#W1nk$;Atg`q`+%NK3SB*08s z$9DPJT@a9Moo-4VFQ+}ORHzB4@cXZYeeQXt#8{SXMcbKuvT2=$X;`hqOG`<~`I47C zwvCIBpLB$OC-#!H_`z z-~vX>J9dC;;sA*0V}zrW;hKsK5`c#$fm~y`1POnJ$D<_yza*sp{2r9v%uUAmIcPrB zr9a`QC8ZzV@Q5?v&kruM3oO^M{#lK@?e;SbJ1(oO!u4|TT3_0M!Ae}P(=hC&NLdP* zd+k7R_F>xZ-THAI)Lh~RdquQ488LN9T3XO)E9%#aL;L<-c#y0Vj0e|lKVKO4?=Ax{ zYYq=N$X3MATL&2!Vo&%R+rkN{)o(NXjWjY;z~OK}DhGYfrz?7&@3KIYDDF5*KD>rZ z=C5Dz%cDAYBOGc4H&T>=7;dYvFf6WeTRJKu01rXlnBkzfK_oM;KPQu8<2PU=+Q7`E zGSrG`2DU&8nEPbot>Fj3sqBw6l_8s zM*4ZT<0afi@V+q}JXLM%+x6o|Pir1E*ZZ8M4NF3YW?wVlY%-p*o|Ecl^>r%sak7+C ze+x(sSe(=5am$e&3|c(yhb8k3*{};JjfJwJyI~8x{0hoM^#yy>lHx4NPJB za?WZO4)sbgB;IybiS9~OlIjgngw<%-F(^`gt_1=YMh&?R$U-F$+AVsrZWTXT`sv%N zabXw;uZG1Y<@P#N9h8P$r9Ld<89yPUY)^nycehyZ_I@Z-qS-{#@Mp2zm!600C;as)KVF0dJt8|oikj^s}qf(_Oi^W!zE^zuem#E@q5Q03oj^dh5WyE9{y0_U+z5^5|xw>DkV_ zAVPmb5OI(OuQAkY!ug%W*Aam%d<+8+I%i_Pe)&>8-orV|9~GG_VHcse+O6t_hjHL` z`_}5L4WuFYf*^aO^+Lu1R#(XB8Lx6kU7#CSnPgQ=Gh_(}N;f%G2~X#Bz@{+KP=pIJ z9JDZ~_8hksuT*Y8+WU|as_e%w=5RtIr5GobR%371m$?i}K)gB8Wu(G!4M3u|UDL1s z@4E8nvShe!^;et_cbjRtxwSDGmP#zBG!FrX$c4QkC8wv zkzi0O^J*ADDN|)hY6ale!9fbNxfwXQ)wuyD{fLqTo#U<->n^ zI^|t_8BQyL#ZVo;$S!MWQDE(D;egg}?Am#Fy6flpMPZ~$0#|)C@jAe=_|hJSOBqzH8=kp7O%TY zpN&Dk9W3U^)d{Pr7}o9L(iNMg9NBLZFsMXgCxBi*Omc5=>Y-0vsMPL14!bwd>^UqwZlH;Q3#HAYE6}2zs8SRN zfsqJrMRe1Bh-iI*>|KZF!JF5+q;^qm_ZL_h`@Xs-@V>H(E_k3XoIx0Z5EHoyVLojN z<}pb3nL99ttAdz-Y9E!cht0fe;Oa(#N%h$fQ3ijLp!@vJ!A{p@DF=QuxXToe+9@|E zPmB}1vUGS#hkRW|{6^Q=l2u#XCjB+9$DF$I3Ar>agv3{*1+NVwz}ba$0w<;L8)JL?b;XO^vhSe}B$8;sQxe3J4)R1`nlY2BkRzI-z?< zvYHT{_ANuOBVM8x*cZMlKyNx$J08s|Zz*9;=j4m%pC$Kh&t+N`8VGCwDqnmjQ^m~; zj)TvF_oN2P9z;&zTuJz{0nH=rUR{dQ*6R?2R^m{#<_^2JTd*GBla}I+`dAl%Y?ym% z%|rfWw?pRo9I6fR(+rEXE!Rxmo!F&i)M9Ve2HbhQrl#10x`=R}RR;l@z3%G8b(|{iwU0zPN~I(|oT|gf zi18Y=9*{lEirm;umE;#lVys1UP{@(2?d2|IV8b_5L!5ot{A@=BKbX9R9`DI(; z!Dv#E551rPDnyCp2J|qK$jRab}t4#Mh&Zx{nF&=gESZcx%;t=T#+Ono>tFAZ8)iKQj z;#d{dhoJsBu{6J@CG=;+Nx*mzML)jqvvnjaqt;LkA~`F991oP7g_%YmcLt`mfvs^av9j)tJ&ZyjsY zm8(TvQXR9I&n&eBzy)kx%|OihmAODJgq+=a2epqP4CbUCbf2*(y&tBWS$w{-$*mr^ z8pvUnC~9iDgk9SZ-e-QnwBS;8o3{uPSs(^T5;ntMoyCpCr#vVZ41{G$u3(|NbB4Y9 zMogi0HS4I~n2*WOH2NUjlg7nw+&{m;MV2Aw)>Jh{N{pPBX0bM$YE2v&edX-ByO_Xl zq@eL4&UGip$4%IT@K%b9_&QgKxf}NLem0JBhCfOH4Z_o%=aK@@Z_yuSUm?q!_w??H zwmPn0u{wQVciBhFxBAMXEx2?Ms63s$HF~$3-8)?q>Y!z-R0KzUw{Me>ZF7+Np&qbn zWS4_Lo)${j4mi#CQ%KP3=hOSqHK=rWDqZ}{LEkjZT7h0ZH8d+&J42fZro-HQTbGEI zUQzQs#a9YdPjqovSWq0>aKBvBnlDURGB!a@X@)sRI#5$O z#Xt$=^~#o<9oNQX5&(Q6X<+BmePGBr@wyQYal)`wgQ(O}*-2F|3@q1&Yd z>Y&998?zS%Sf^n7kpm32Mjus52jANW$hcaT_U-UBG$GGC`g_N-JK*5Dz%VqewS}I9 zxWS4*Hq2OA>gyjKL6aoCfS+Gp?Oc&Pc?Kk?<54oH`dnH!P3nCXM~6{?`m$`aCxkzr z4kKmbJiIH@SaTe8Hh;xLA{9@$S)SmXfZaGQ@_!{(b8e(3dlX3f{EYXhw3(v>UQx6aXHZ!D^SI{-cAQ%Q^zgzAk`57f_O zy;^unDU)M=OGIn_1{`9#G5)H&ZVAEFs}k67+i z5)^C#qI*)NYhmy<9PDK<7OtM8egF8(6&!-ZGL>vwVzZU3u~_Kvq*8P0DKn~CL(nE#yt$BpUG^WUtng)QPPF6Wt$z`6LN0;DSJ zj_U=h(QYo^$U5SA*k2@cYqHQRTD6YQ;7PAnRk|5aQ_d)rvz1p>3zgcYN-1T=Ny%lF zv5D}WE=G36FC$ZSVVA2&llQ}yU9gfc@3+_}%un}RM{E20=Hp zP|^k_;z_~4l^_123lQCVwJ0t-(5B=na~8*m35%#o&_NEs^Y+gz^OvSIqx8pQpyAm*UwSjOa^9j{EFdSIT8~aPb~hNIMjown*xEyHz}G&G#V@=| zau``Qn_Vrs9X?sh*K6f@ts)Xf&)5J3wClo9DGtOKe#+$ur(DPDYD758e90p=BMv;7 zWplzkynmCOQsd!7BS9U{J`fB~1H2tFDn&5C(876ZGmI04DR0KF#n+p<1p4uP=zKPZ zFU^PjE0$&yUF+NK={8d8n9-5^q3O4tmBW@VFSEJcGhXrCz}N305+ll+kir((ov5*3 zTitps^{!OezA9!7aMAWrb`JzwQUZt^Xy8yAwI!Z1GWtB(qQ{+fZD&5kEM*qsULl zix0-R{I-{26Xl%_(3Y9Ad{vrvE&(gnFbd{iyCEtnDnmYW(7d=QL@Dvhx?K|T_u_%rzjju0t1!^!gRYJ0Lo2?TFOi53IEdZXQ>bNIeBZNi(>AYa`B<1>b4lU8OX-%rUt z#zz> zU)kBBoUY<)hPo7YSgg7cT8v4ns}%<;F5aL4&0@bO-T0Mz!n}(H4sPVqX1;PFfGgDt zUb-eZ>i=Li?;QXmGaYL))7?qD&0}x1kEH zRN;@QJxlK?_<6CU*c}iZoehDYWUo(RT^))qgOxJaE_&qbgrJ0t7vPmvK*4%Y6mThc zYtgxX-==8#77H?as)7<@)B=zPmOiIS3sKb)LCWSnJq=%#g@Hvd@9C>J|LC@>#WGRw z-4@B*qV^)yv=7#8MMJ&yk33-(O|y706d?AVVl`2gre%R3a1t|1ep3kj;9`yD^`wjX zpT?{S0X{?Iz;B-#XiLk6sd$UpU>XIa6KK4c_rA%w~D}gm;Ll;w{SkLnhrzTmxuy6F~%UE9VNpf!6wZq2MMyoLj_HZk&nPzaRHzdZ^cKb zNQ8mhRj4P}B@=x@verw=M&+cTSt*Cz=XPKc{N}PTfa%CzPUuyDux|)7wB+y$`jg0AD_)rO*?c6b+5jD;j3Y=&OOtx*}#eOTug-4HP(dg&WfmX8hA-;Snbs+ zh9b@ke~ix7u01XwBvjq+th)|vPc;pmlO=s34GR2XUjL>VwC~P(h~8T|tc?o_(jIN^!k!46rj$D~)L;UYgZ( z#aQ~;gTeeK`u04s91j8!{FQP-`j5HcbcDDstdP1%For4xvvyDf%}w6V0?_75o_6y6 zE`^mykUzFk6X34;n-F~2iYS#G#dm+4>+se%c$6d~-c@$XQ0Au3ghx<(h@%d#yf5H+ zzny@>S_w(mw*qCBgL-$N4y%M3nFP~BGH|SM^qLEf*Ez8YmfV~bsJ@=%-HzhgI8F3b zIoEY;=+P#mnUyGiy0j$eUwZwAO`-b2J$<-BdVe^_kBsORW)0{l=nhU(;L;X9k?@Y3N|sU zBom|$s&;uEE>8emhPZH$c1P&A$UU2EM6jf^~MHkU))K#&Hi-@9A9QPSod?J!W@AK+nUCEn+6(}#GFs&t|AK~mc*3J3_2>qI>P{wa1)jXcd2pQ5yl z1$0nF>B^xY-Et{J94jlUw(jTJue^6|n$#hH z5-1~m69MzZM_f-Zc$U8%I)xdoE27rmYmm#P4)kqY#HvJn$fv4!!a2Ud^gd6>T zPa?pw!*RzhSXd=c0@Po#Hh|&+)1Z(QkSFOjIz-3tYA55g&W?A2gZP-&6jV~ahS#__ zR71Zhnpi#z6ETwj>gpEMtMIym-K+?ebYi{VS5qrGGYNzzT~GH43(OcB5Ila=1+yK3 zrSAZ}(Cks-Ptgo1=fQ_#;tUMOd2>ZBjJ&t7AQf=fkF2o-(%SQb2;bJoa<<6M5(mcn zpN`x))TsaBtZ}nZEta)NGa=mI#d(3J){lNLVgL!!YRhADr;OJ0>B^HO>CHeC=hY_7 zgp2?A62O-l%=gADjnDanL-ZldAKIxK#uC8eRjK`*;%uq+^Wf>A?n*-lT^OC*#sOb-R)Ma4 z&3%K;Vf9wue`$Vl-eovb)e{!(s*7=?PZ26#<4AR{=3QE?xkYF1c=XE34m-5lyZIWb zy!tfw!BD`tzc7v)BZqBCVse!I@%+RBoR6;`(L78tg?mzyF@|h6C#SE714K8FHfJWr zEC@nn@?L314mg!Igv8=1G!F%|n-rNow4^fuZt5*b+;aKeQS7>^b=9@6?HtE)7gyED z8b#Ljf>m$JeVY|zkLsDTa4jn#kF{UA8@vrdcX z)x7;W;GzlIs5N9~!$HKTAl4#pt@937iN1~KYq^V+uB1U!9pb(z>@u>uc+IEt>yQ8c zXy^h6d}@W$o-jEEvjQ8>I_iPRF{hrLg63-0?@xG(db7Xy z2h7h=RRbeLGD}*s$0vcf&4NBZ6&!7~_3-XfE(Xtcdl{KcQJcA={nXJ-+)NjXwEItU zQuWzt=5RXGV`noY?49s{=#RKQu6(>l(4y`vE8%CgA!jm|lvDK<^CZ2~xg0C(a|K8TJmq%bx)^_b2YNgbrk$`dvRJ7KoS|)4RYlA{u^iK4kbT z)@0lr5N|iD+@r9RMcu57Lv3MJ>o#8NVTi>phXJ?ycQxO6zNnbuac(|EqR5GAXtrdq zWJ5J#|50aR|K-$7LlWSebA3#YmjVR1*RsPHU{ z4(FN&iXDL*-cIr(>1x;$ro|dtnY5(`%9RaSG$L*_U38S`J09$IrOhOKrk6iz3%C zU#qu@JQc7wBj1-z>20*~-7FdKbNgQN3mKUc;XINUzHcSqhoeO*6aQUo%P||b; zWqISZgGKz#tzu1DoX|Wr!-nMRUJ(}R@&Rl8*8W%M2DLgU>DS+0*+;;p!v9!UgnD{w zjpR<7)|M>H#|H4KNigp4{ulh>)}|#3*{>);TSuHzRQQ!*&|iEX zPnFsHOzq%S?Fx)QYn})G!?tziu9WbNJqcASe>U=r$~&VYyv3_dK<_(!Y`YftN=VB$ zPX{Y3*yOi5eSJF>$;H60Voy4yG+YNWWA62jL4nh2uh#kky?`F*TH|{PrsyrwoBWu& z8aFWOs(rOtI#Ul$Zaeav6eytg-NF5TPY*rF?F|NIUTLV+=ca zgY6pe`oEt#Fg>~}R@t#_6vuHbg&@Eu+{T?1;OKUs+1zUfBx9ke7M@65n<@YPnYD@C z9b!gKH_j;hPXFaRfARk^ODm-)UsJGzwG?jduIwdt7(LEqz_g$Uofqy_zFX}<{v>eeShl?!A#fPQcf3eNga0GP3UQ|yXd>CN*?Szlst)Y<%u z&3NsUbyg=$t_R~<2MUU^eiL#M+O!U=sr0;kAxxX80btHDyJQ+_aBsGVn@s$!S}Yi= zy;>_)WxB>Wk`#d*da4w0Uhu3n4^Ug|L6lThHEv$;ZXqM|Qz9;*D zNk?ESPtR$#8LyPMdmM6I{n@Hl&78)yH0R0(>yje-O1rNt&6v)<_4mHCuer3pbnpsC z+P;xQ6DPyzm%j-&<2(5~0-5yEY*NGZS^c2~7`!FTe#e zotIag;N=U>e?;iIIU&2bxHKcLF`<)15q8y@KxR=SIs$WVQIYxn;`cHsD)9O}f=%Y= z>(3xKKVTJ!p;*S$zcywc&@y$hpktlyOe73etPBj=r6>Z|s0#J%D@4DIzAgnvT8aC; zO7nzDj@uJ14dUvuCck$o`vX=N+_Kez-;pP#uGM=#0XrXHmwrZ>A!cnWGr|P@V`S1O z_(uLXA;UiSDusGbTiWX3Ad>WGD&Tq=+!aSFx<_f2K#i%PMgB8DF&~D z9Lk2tOLl3BqE)gR#XX{4e3fPbrDswqkx_561V*{j{-k1&G>D2mDzjxP}SXpYkf}!MQ^W(l#!7xi-a^FgJNM~{? zFzP+`q4|}V{VICa2o~| zWq8n;`XsbPRmnS(uHzk;C%q!)3jDEe3^mR~p{DgWJ~yT>z}9fT`eKQA77KtX{pmWG_~E3$lf7NMu3DdkU*R|(m4FqS*v#L#Ds`}?+#X;` z*w3tu6Vl*$v{+Co{by7^4iNre#1qos)?o!d`cG8~)Mr(nj#q=f!s~zUTD>TvJ+j9i zxy`^g^mH4g5M`$X$kcHHSt#@L+r}W+EuVGr3aswyf_DTjwPq>!vTLZ6^`@^z^nE6q z5U|de$TILY0S-%6TX+|+2W^r#GvWQcCi~Z^M~gEP>A$JGKcbpvQF2t)z)ht;^z4lr zY&A-|+g`hGvo=eZ75r9_?0@U!yl0ND@)kVLiBsKlI~n!0&^D}@JHIdcxx@FjDZ*-V z$b{nO&u~brm7)yR%U}i1Mm6uN8(ay9_s_KnqbzSPA+0{#DN6ITGYs@4*Wzr<+LqKK z#qY_mJQ_bLT68zim!}0s@uby$S;`Dm#{a!B{c1h!z16}dcVs$O*w#NzGW{|-@X1gu6x|?*7x-wMBhXb9W{2VCF6`@{3m?gbWg;BZAlX? zkEwV&=WtrWcSKAm!}XDqEB`P|`KpC-#s+NFMm%x2v!gJ+Q(lH#E@UlUE|i^UR(U&j z%(So4Jkf{L(#rhT(|N+}CQne_z%~D(ghzv@r}%+^ud`{IcnO(`gd@-ghA!=U>NqMb z@Us8-B=rMxPB^cCfB$6)P(Ln43&DY#F=8ngV|l+X^vGhEX`M@5T8NF7h`!kNpVQ4x zPo4FQ7}%@zTTO+dhz?PW`6Q(F-#UT5|9;7LALa~?mWtf!TQt{GlgjHHuOD$_PfFuw zv#|PU1hP8lUsl-dnZOaxSJ?gX6(|#5Jbe!~T*LPdcA@ zFg-$!<{b|7?uko!WD?_1yan)4HL>6Q85pxPqkgQ5h4u{?=X2SJl>=e$#AGS3H*BK9SMxnTPvaxy)TmQDg0z*#@qU4 zpAa_N;8Mdb{1^2$7C;0^R39^sg-}AtOGE4)BrOfmxEBhI3`ZA94UxR%M9dSo>XX)* z*M0bmh(BMmm$h2+aB3k4_O}&iNpuj>Wck0I%>CBihkn0N$EfI@5fCt@-e7V>EycjB z-)f2{$2(O&<^lC7Ld6gz*?(1TbUJ%~Y1=~#J|gauBPtp(wZ~XM{{@fn^*aLxd1X2R zUrlEB1jvaeJV#JjSgw9&DUahdqNxm=s)#|eXGgH30}8)X3KRDuyftFbuV!c%&lhn& z@z+&beO)csB#nm8dR%g5xNgh*<>UuIJZlPpEA9%ceQEbzIg18wHMGyM1)Z|thLTcR z;88QIhyi-v6!iCR9G}=I&kM_mZ!Cp0I!^IjiX! z2sS3Ii}i}{ExiIM`);QU_6|~8dk<0MKu`8HvD!xhbupDZlF@hE$AJ??LaM*_q-qvK zb6ypE@G|T=eHh7|a%buf%62WB=THv+amoHLu`}JL{*08w5%H&mXzcVX4$H24EZLdQ z>1Kisz@#i+cz=&qa-0w#<$Q>l#q7N_lDdDrvsS&31Fy7?x;&_a$uDDF)hsZSI>8W) z{Bf4C_+e7pKQvopT5)%sit}s!_@2t;tIOFYcp=NykIz>@Q=~8_s%fM5(SwAWdl=5F zXG{KC%D3X+!Xj*@(I1?p(A2CLvNPC7laFda=NuQA&Cd(y<7M1tRs$+r9TW?|H}X z`+nmc??2xfV;>LwtmnD!dCz&x>$>Ka9MPsP_bXtycW^%oS{Cls=1Kzx=np8TfQJUR zdi+;lQPZf>>uZf9>1hDi@^lM6@rh^mJiyq)SNCT_E16VX6;w+b+8Awi%_;QQIoUD- z(0y)t=>S{c2=#FWQ@%G*uHUK#$!#}-ee@EZx5yjv01QVatp0(+(*;9A1%T7A}JJVV1?Hs?wJyOYe>tTx~{BOnS;a19P91t}$d zaPZ$}S1)=8`jGcyQWzj9laZ-uL-WB4+%5*-ulaAqgjd1oX2>-S?%90+CJ$cqq#x5| zeT={l0cLKvSUVX+BmVtL9tV|T33S=pL%s^SFBl?jpca(joCxngJ$)mbTlFuW=3eaT-p; zUJ#7l25n{mn^v|i^urm>O~gL0=dkW&sLrVskP zv;dN{n2<@f$Tr&j{f!Ov&}c;6aauaMn<^UPhlEvJt7a>^HV%_D^db83U$2e02n^sf z{E4ko7g#Qu4p;!g3z5A4dNEQ%w}vH0t?)1M*Y8h;bNu`3@QFfD*Zlq6@Q-B4rSB^n z-hJ;arGe2>I|phYZ4g#kEFOZV$qJWyH|P*eA>2I~FP(6i#}v?$xObFpQmiHj{*FHP z8L`xrcc2S(k1(5(%cSr;T`#=*U3cmb_HO{*Y}D5G>RAEIVojTVC;A?8iN@sViua5bWL{uNU?k z@i-^q=sUsn?_Wd}%2&uRrMvU-uWveBFS2Vv9b~FR^&fAKc_(Q#pE%?3fHIo$H3N)v#(z44l*rr}y#PhPPIQ zQg)YFP1vLY9zSlIz%>tgmpe^(17rTl8Yde4aPoVo(R5@2gMDRto(9B*^)1lvY#gG! zGW{$hgcQOkv|KhXa&{fYefP(6WBQ z?iyTQ0cFRaX0@-6yXavWws8hNE6waFhf3#a1em-iDho>Uedc#UTq?z-DWWYg;np>UbY511GO%Wy+)y6%Q^qe(i8zy~7vfIy=P zn9ducqoW;^JoV2{jWPF}eg|EmUok2Wv31HE?wEk;#|P#ji70!8-NHkyR|RNyhX9E2 zlP0Wtuq0@SH8Fi#X%XR?<_>jT0LZ4gTsA-zXyt_>SQ<9XA3#59sE!+p-wa_A>1MjI zZJ@&SVO;lI&e7v&*KX`{yt5;v7%Op%Zq(x(=NAKbQdFa#w$Ov4fAe`%in`-*Z@@ls zZ&vw8hl`Vd1V_y-A=*`ax^g^y)b{5B5&iH!oV)${PYCESvjq%xWgo@(y?_3&8B#D~ z-Btq(te~gbk*Z#JQT_YdF#+dE#a*6I_k|L$&v!Qj&iCAdy4Agkuy^;ujg&Nu-Gzn0 zRTJoW-0@7P00f4x*sFeL(Gohml8zU;%4znerL@i8jf@?>GgtQk9sGLE5V(C2RkHf_;@S?Zp9ex{Ng_sLGFCp@Rk()u{YpAfwP4y zqPHGD0Y#NCb~9+q^y7mAoPS)_!NzIj>GO4sG42k?`9(_mMasu;&M!FUu3*8O=>yX+ zY^HKK!J+auq0K;KG)0te@XkxeasJ08z+OR@U;$fb_$Oy04(!xE}|9*G0f9+5E3jRmv-O1JfabUp?=!sahp0nC#mok}-dWuvx>H7Mr7nOIfgJCf zO=qSW2+%}A-8uQd)MI^YBs&LtNWThD_g$(+;8~PyBF3UtuI>xdkSjM7|EN+~J_uLw zEcUAX8BXbIa4+n=W8h>D+Mt7{!6OV8c0TnOcfNEzVhzA$!Rq=NM)V!|Y0;JxP+MEu z2NN^~=yZUW@ql^i=ae@+RWdYB_#ste00Uw?xa?~uNd{;V9dlPoz@4qafXM{zlJTK~ z^H8YXeXeT^B1Ab3axg!L7=3xSUYvCU^-9Gi$oTcL>xFiN2oUvbKKSoh1D%U5kQ3(+ z9kI8!_Z`tGcZHv8Iul|KZUUYh{o=)eB9MXXE|H=U!GL4zcXWNQ_;Mhm>?p)m_oM%M zc1{wbQh&f58fl(!d`s2fk4SkL-_n7pPzB3pGe!ai1KN249XO@JPmtfy(Q!dA-TX@| z_rel7vS>LCr%lA6jj=ue`}-H{Z@0KL+86Tj^2?Nrf^AGV2qC{fQtR9~rD)Eg(@=(HVPuq&t3mj9oqTu{~%IcIpL(JnP{G4;18rd2et1UmM)>G+d*! zVXWWL;}6P?*24xzI}B~^(=|c5(Kkjv?fo@ekWu`?B$J`q>Wj)No?1wI#}8KZ^isd( zg5b9eyI1>n6>#C~DwI3Dh`pEC)8+6>9&A7!`t?D#-Wuc=q~B_Y5)_%BDLfacprtO&T(Lvydz^QZR`SYXN$m;=8=)g*k=z`{Xo-r5p*8fRi0*QhFO)m zAo#I?Rkj9TIRQA=S0G1%{UrwUC5N^Zc$=Sr{F4W`+``lP+Gli+j2@YWo)BW?6rGIZUycVV(ys;ly0JT-%*dx89~yS;RU-%v zRbbvBeX(*S6t~QZmm>yx8AIIi20r(~Y%H{5zX~{x)?`+aGY&$w`OgfOyYKx!wFhLOlD;4g{a1?mzfDU2{fqv4 zvi!fyg#WPaNR>mJ@?PMRr{LM)4?R9RDh2->J-!#o=l76V9()ByDa-W>@TSCX8bU{% z?5-49JxbwO8o(^K&~?*mec@~OJk&xg)<@|8d-0DwDyfYUZJ`>Z-~qzI`=38OP!fTM zjtcfq<$%2h2F768nfp2<+Jl;{T~2vKFs=$Ra{kJP=x=D-Ac(g`eAA{hM6y3RK?F=| zN`1jJ6Y1CO43I|1AOjYe9g|W(D`E~?1B=Izz=f1JfKnlqSJM3Ybo*yW*R4P+@g5P8 zrKBF9b)dJ?2gS%ZE2^c)ST5`jk2&#t?Fbp!mz|wWdlB&10yhjN&JPOm@`Xr$#Rl!< zta)UAg?%2nSK>g(p(6++r&|S&!=3|P0Vi(H7BAu!#ofD1A>4HZ_lr zxwXsC9S}>;q4oPUa}nk3T-G2nILy;3IQC}_cvQ5}L>=-!?K%D;iTxxwCA}G7ABCVi zW3|}$MIr)%WBlH4n`rV119SVEhKB{3XPBKpaNS*pJcQ{t~%#-Kv??G@ec)h58IamPXAO>(`atdNPEAUMm%slA{; z*=k-}98R1qe??&hHP09p1A0)9(}#HgZHiEj;!)5)@T#JMM;*l>#EL{Sk?6>*MNb}xsGwgC6Oar>7zGd~E%=*fB(YE$8_mmautxp^{BIH?}KN?K7CN*+b(o z84>LW#;3{r@GD z4ZWHH!zV^ZaTs(jXG5Z`4?_hJ)*1a~ynFj#_zECz;W>P^{hg#Z%4bMB$^?;1}AUY?52;|^A$RH58@!7Ly z&SQ21KyHJayX4)NQ|6Tk#AgQmb1^Xd4FCwoB|C%j@v(UY;H*+I33u8~0R|N6!DwY9 zlsz@mq#AWq{gl&zg%JxAWr=N<^@L(S`E+P&u1v0MJQ!+*pf$Y!%D`_Xp~84aVx&I@ zRFtHJK@p1$Fnt(sCVB4Hhswpkx0$1KKgTXi1IYrmXQLY4-dGvheGM$t54<@ahRId1j z|Esd-k~Cei8nUd&2>2!yfT`IZDR!Zu!$(IwZ<{stkN2bb6Oc3QJ(3s$D+2>3@uSnXkny=4gJo@B_Q{hcAEgNduLAw@HmIQ7J`Y|# zsD5P?>LuOON?jegbC&Y5q5qdvcBsg&aQNooUn<9sZE?o>-411lHEfstLIW7DRS%5(kVCnh1$ydcLCtO zqc;MHOzBN#&l;Z=tJtplkc~gc+H>oPF<&Sp5I*@jyrID6MhaH#8hnrsCmd4t1c z^b~l3S~*}fbJ5EYkecoipP$g;j_6Msy>#BVY^(So|`fD@|A}Wdsq;?Z5K<{Gq$eGoUvoS z0MSuQj9D|Hd<&I#m5hddqK~Y^X(lSxKGz*0qZ(swt0A4l^jVeoivg5xhZgsUsU*HK zHVOGw;h_ZIn##{wd*5no1EPR! zQ{-io8LgLUaR>B&jg0NAbq_c}kh`55{;Ilkr2qG(XfXfdhpRq!3o$@>+n>_<;)#Sr>qB9)Z?U`Ew-}OG;I|U%X`+MXQ&{%Va)P>PU&z-o&n{Fqs&YWegww<$aDx#c=9LzaNbT`=q)}eymQs7P2tb zjK5dOuT#5RaqD_Pe!p#3bmkb1zMfp-Bj#`R^gec0he;9bzn-reNG@Jq^aMo%G(!^0 zOH`Rud^i){*jM$%=VK6(0`VzUsb01ESY?S;yJzltYRy*C^@jroj%0Ua`cVy4pJVA+ z-XqD0fFjpj66$KXqc<}Hr^K2OY=SCCJvX2_oOr`+D%<9a6f%S|N7KKys+|EO4t8y* zgMgc;7=&Bxw*dj;do)nZz{3g*7e6Z%0-$aJ6Q{hPbohaXw(7BJE-@2=IGo--Fj-+| z0>i=4W9Tj^S48rw#4piVl zyXkUQRch((&_>b>OvYR0Qc+f3i+5_eydVhG$#Ujm4*`Nk+{*>b&`ubW$XX_0Kk&dF zuP%;%dBtm_=m7DVyjJyT82I?jvFpA|vWMf~I(J|R>gNnYFJim-qGIg#Xpr7b?3h30 zLTD%)*eg&53tVc^P)sEi<0YToJke^--=Yy06(0Vb-K44#G_$Td6HnQ5G_yv#b>f_^ zp|iz6Cs8Hy!#dN+9#J5>Und%QPDI&lz-hZ^lHS{3unX)|rQ&(W0V=lPPGj1fY;UxB z0tu9thU@;ME+fZ5=Bu~;7a_08VqOo8TGyKqn{loFHOp>tEp5H;{_08}*g%RTLIO7p z%~hFR=PAGx<7>!gNb{p`gG;B9P(j#9S57O98XbR3j5&VOD8&BDz9o1)hpu`$AH}05T z;9BcQ?Y-*4fC7h=HB?P+^1*?u3gXqzvv2toY|p(DQ$t%@b3SZj%?pOy+x6AbnarWi zxnFtz{$eUf0ggN^psDw?O=sTbY~%NZ95U&r+`LCXfeGfs;o!Qukx}9YkN1G%?yQ{o z@t#@G;da}vp(#J8+AG#-C50c6di{1*5|bt%L35o?!edAPF~f)`%}oKn9q*oCA6Oj+ zIV2z{8Z1Eqj-(0H-!x$UCA7Dk0&1>u_1ia$rZu7SHyrOi^vdow!>#Kw(2-O>;Aebe z0^&P;k>SS|^y*Mtp5Q-&cb-ySH_TgsBu3!IV)Nlpsh1gtY>K_XQGCZ7EHsJ?)}U}p z>u&)s7>&HdSAj)CxCCCHGyD1H9GryU-`)fJZ#am~c-o{CjzH`Zdr<(V15+z-n+Hvy zXf)0y6ZmmcM)r6IBo#_D=d0j!Stx+Z5N@XLCzW+?LV^SYOh~Mr-r7~pZkHv3a}rmV zsqt78Jx=O)2NL6ElU!fifsUz0EghuCa>ZEsEuPomq32t?Hs?zRE2zX)RkM50ctPH{ zaN{BiHR;6n^vjWke-?owPpKb33yP#yh;P+Z;-dDJ83!s&4YBlKLDs%~62s4HnqLXg z-h39Y?|5lIrEDWoa~xK8RlBELcJ(U$cU}Yy(#hx`T&z|#SLE#@qFnf1y^!p;$jxTG z7ksK6pD%(tL+;9SN>~NINdC6+yaFW2F3qG;&TIUt%NXds|7@L}O$#1UL`?UcJ!FTOfJIR%vo)h|OAP9k4aCzays zXPnbrLC2t;cmw1JNo>0M71ZIt(yF!p{M z5&Tx_k@XYl^NtDYujyO4G|S8_)hKMD8oNC{u^C}w)yz*RATiK36oF~<3ToyxT0Cup zjFehcwzt-cXwxfj4-|Av9bXt$Dx3F8R4QB94-rgFZU#9`7PDIRQWl#-&*ifc z+v&WB#;4D&r7wdZePH-~o1k1;w3d{f>6}$BO$bfyh_q(E?nEW2wD9`FgV!f2_9@)F zzqV+LanzPArMcyoqm`|1dic>SSfaf){WF6`;Mu5d6pxAu$dHU?EPU><;W^mCup=bR zCjHMPsW%sm>L=l>RwBjH+0WpKuAk(%l+_K)ZX4KPPAFK5P9ji=GB2Z}kIj|2A3J*) z;8YOg@OL@IThmXwufl&!L;F!m-#+Ouq|3dZ&b><dt*;o;uH2)rPpz-)mbRo zUx_k(n`FHBSwWh}+bP%vY;RY&8R!~*4Y5tB@tgkH{%g+}t4_+EhKzX+;(m3OS9&je z-RhgHpTm~5i3Tm4f29=n(}-Al5w3IG^5?l@=tMI2aIoKfe9Ks4$w<30JtAB_HJe+0 zY~Iq_1RqU5>tYh381M-XYY7?^uA7;$YP=v);m=H&vK_wBY=KDS`ialM{ecT<}oTKK8O*5HTzruTM-?oA9YNIvELMxP^u z_n6fsR7b|*0Z$y9%9Ad-%kJar)Mp-JZcuqzE-rO#@D_o9q_>=QWq^zh_Vx_P{2@Fk zA59z$lC*mEr_yRVok*Jd0N>jC^c1{b$br1`I8JQmfh>Ar1u$gnB(8bDo$tb z*Dq@ly&~hEGq(c z5v;r2`OUm>yVS1xJk%Fw?(Quase- zBRVVB0TU~d>iCc=C3|taR%i1BRMJI|^Hi`8m)H$j^?k&B()g%wzVbHv58fw%Jw#8) zbg(XA0{QBazpl`XTdO~*{V%FkMuQR-rQpb&plMYbh$SJ7TUlMsRkl2A}=%2kDnKg#&Ak7PB#( zKij=OQ|E(0B_C@%rHT1m?@<)mqF1|k`G(SIu_{_HNJ$mcp3e=?v@@n#v+|Q7NVx8? zhG5^Igs@BJ6AqOq1M`?zNb$E=xKg8?#W&f)Kblm&c*PPM^9*nS+HhDc~i|X&HJoXbZF^Z zK4?6j@qH0samRJ8is|IX>*wBYCTc}W>Ue&IRoxnrgNz7onoGb*IkSUA#fKZQkO9BL zGhE|0xR<}6lSUizHdh*3GN!g~5{l=0eb+;7J}pN?kEHExFAeV#m-((72&J2cOH9TD`wex9MT-8iVVfvHD^^=eL4vOIazO2^WUBmu7pv>6bedMNe{}Nv3yn z_06Iyx{j_yeKpRheLdWc0+?`rF zBu`hMX79_E6^;7(+axkZwWRx6xn*JBNZtnBhwSTOi1*2v)A8coBI)=V27a za{8=zRGQ`|)q<>Gnf9p}Bk!s9smz)j@f;w+RP-uqS%UY?*l7Q)X}-v#TBn-fuD5G> zw3oO9Bw?&CRjFOc3Urkno3LO!+zN^L2qr3XN!z@VPc~J+?ymBS_STzpV$)K}wJr3t z=`dB*PAr6@9PH)Y=UlH#S+vF@5O^$mo@m&m8)%iO^Vvx@``8|EN2r2I-Mu||C-m~M zEXO3RK~=xuPT5pl?st_XJKZ@w3sCRQ2UE`gW5b%*F)UPjo{f)5-p`)KG>bPN;z7S` z=9itn)*=55R_3m%jOyWvBqg!1o1u&|!eyBp+&PavP^1dV(=lhqlnmXx?MX~w9iw3& zjw;q}K9xDam3{mPW_vc)>dg#ge)2Ohn#F8eRoh^Ut&zpW7YmQX6OoUGk6k|%3EL9{ zV0M-mbdd+>Z$#} zm&VZPTuSeeIq|EK6dSytb_H9a9Dng`^{EqKCXTfc-8Dkyt6Fw~iHoK2jp zAqepQ^yXG|B_MEAL8iQs;Jqw23_S{Uu|)#x-!%S&G7d9Wd zEmh#{{>vDQ42OYb5mvzjrK-jjjo^VDJYp3X&8e2y=&hIH0F|nGsVv;M4hQ=t0;Wt< z{HjTYwi1z}c@72b@MOLNI1&Ghl?i-GcD|K45G@ z0wNoqO>WAzTr=={yy0j`$%iz;ZnroO`rkhhPBIKgKQ9y4{Z(DF3kPX!*$1p4hnZjC zuKffMSk)NEU+n-uRlK2dU7in80w`L5WjwG#qG#96{ewXSIb3kobz4t6BX#fJeSxiw zmBHZ*AeB@I$}c@0eS;2t{tPJRGcYZH26-a~KtDp|8urYM;GeK&q9%z0Ke|ix7;VM)1OT0e z#q5DFap$y(b@c>%=)U9wT;OfT$D@8?7q3<+v8SUx(4f%Ub)$5}Q^PWJM&tcF-=2!x zxE!iwleTheGwCD_vabcv-a>PQUJqx$E;%pgT)~oHsL;e4F)z28rHuPfJ5?bTXz>ke z5#dB)PyXVl2vc=iiJEN=*eor$k^}2{N6Dl5p%jNmi2#ojtO6`N9XmhH705l&#eI*;GHK2igR~gvq~U>%uoW zagh?VRK~u9eq+3>rKy&Ghh)cTSoUs0%lJU!hY`v>j3$4#q9ui|85cDflB-iVg;;0u ze4%GUE$N0#peAJKufaOSy)`a0jjfQR|E%}9-a;2&8Tb5PIPZAw^=NcKWDR$&`4XUy zKU(+tTUpTT^<^ka$_l9XcH--vBbfxxZ1OWsr?I`IV4)sTz6EO(SMZ`a^0F!XKfjX7 zN$AX50IlpjPLf`$PIK8kN6G5fHEenY00)1y_7p*TYnlX)i56U-J3d z0_sA>2!jdgZ?7lt$mj)|ICDh5k())rHz3l#;K<)Vy{Zd}vA5wpxv%HIsmGDOk1XKi zjsIY3{kISQPa=+SJ!n`Kqq!%X)mu*1h-z71ZveZ!*rNRtGiLe>0q^CWJ!O%L1rLZ+ zA=LV_tQvwrOnGokq-%_b#6gk{S`XMOT1p44=LScc(bf_g5q0r68^cC0H|dIn#0(+klj|5H=lh zd(VT2gKX|%uJkzb0iV>o@jl#Z^pf63F>`6QJ>`)#nNl+_Gw#C-j(*3%i?B8;Q^Aq= zH?IR8qkT;H8u(YXAeIvHCJDRj$Ycev#|=Pw=b*GSbv&wPb%)M7zL%!$UCuE$#Hw)gw?>2+cJ>b|}Pxzqu6S+6TaWlB*v^ z)gJGFpH63=IXR02N&qX$m=D86!;tBh7Y#teYeZ#6dW|-M0e1fnIBqO}SEyV=F_V#i zX@Y6FZVUi0vmax=^~9VtGb)@JAQ~_u_S?FHOh)nVVFdiC&g1$|<{)I3p9JIA5bbv? zJuiUz1}_%(WOH_M)r~qJCdqmUpub8Ja*hMsG2Q`0pqmEsv+#LHY&w|W+O3zxtf68P zLMaAbrUINr3*_P%e!DwaO-?$Qsvn~k0R~D03b(I9*ZaTMXalH=WsHzNt9>}2D7eC>O2#+x&aKU%Uu8;aNf!j1#;jB)ceit~h#83Wg=e^(JQVMgA zl}WJ}V{31V5K1u1k+L4D+)zs#3_M4A-@lB=_<{^l5KEZ#fTYEKWo45S%W6Rb6pLtMgn1@Y>#DI>mQ;;f#xpIx$8y=Yf z6ks41y7yc31MssQ96zEhUeMG;uW|A7sVSMT?)hD&Rfz-T?~z~*4l{GalvYq3Ml2_w zcj17W&Fka(31$BFC*kK(b>H6$VQ4ZEocZ`HoP9E$7WVx#MhDS7PBCL)g@Q-V;?SY^ z(H8OKl;_{?F`UqLLzrCsh9&erY~i-@n>U>x0*j@B_>BRM6&4@*d_7aCRv=J9?q!Dh ztstXf`Akr2o&B&BFUGc+=a|0xi^ydSsZ=(19E|U^1@Hcay)_@EyrK2D095{F;3OoB zZ0{$N4hs%!+dpph^dZmj!+Xg58;%}7D{#qy(G)Z_LXl7;oMGzSL}%G4)H*{Odm8}7 zZm|3+`$^a6K)(r9(4<>tSl50dy`O zj)LUT!R9f=i#I0(L%Gke;+!*!FeoaqI3dSKkX*DP`ywK6GJJq5$=Rmd41pUFYD$2d zmo|F`wdZlOe6aal@VWaC`z2~YJM2C_`2K%5z_mGphv#C%SN$naDXnv<0FM=6u%z7I zF3S*_>$ik}pjJKN2v@bR_Z&U*3x;XYu8r&!?J1yp3c7)C(AK-2`$Fm?mm{gcs-(vh zJ-=_@3onS-Tlm-DB}Amgk64IWidEfjWyXPt%^c9^-ocrx2jR`oCLM-mme_y^n?i+g z8#3H(Ma&pvZpjatGZA9$`+;zF^QOoGpbhpzPHYR!qC1c7z;Fw9M2}wsIsz`Wyvb)5 z!e5EYj5^vSgYeb9)ke7R2+YUnvzJJ0nJ|Nf=E^PS!%qB0WY9XI_v6AsbM7WGLTrGG zenB|TR397eN2$gGT>c)0bcn3iStiTc|JqJY7} zKVOJp-vuPO1R^8d`^(xWSwK8=w{J=U<)@=Y;=l|JYqNeXOms9Mf7wv}D*HztXxXL; zDK@Xpe47X4?>%h%p?Oo-AHS);aFf_Gp9_$&Os%oCxmQiwOJkx0{o|yjvRZmzNXSox zr#$H(ihOlb@$o?B569;ea<^_ip%;W9exvf@*>pu10?3O#!Frg)N?QQY3`sZI(Vq4< zRC;nx=>=k|Az9)pU$Lm~JJ9V0TTgU4C3HZpC>O$SEGLWk$Re=RqO-_&0mz4vlgEyq zEoJkFyDPP$B`pUAIa^~#v#+FJ7A-2Lk()F?`t!^{#J*L8q`LpSctQeQBCo-S7AU%+ zezVX1`HI<_*g3-AvQ8&nN*}fVlZhldQ*30US$X6Cd>RV`Bej}9xIMA|5|4hO2Ydg& zsCzG_7(80V)(znhD+hsMhxpD3x_B0b zqsQ-6lP`NH0{sJAKex}sxm@Zya*>rVa$|RZDsFeGdskPzkoEN1HgDR1MB$abBSik0 z22(rvwO6fEyotM2uT)AKcfV}fbC`=I&V&-G{p@ziPrv&zNa@jbD0OQpeeY!jbvuwh zeD~hNe$DTS8ki{0(9z?k%T3F+zcA6+|XU$df@mm%(FStklbNwz8~|Un`3As zakzR)Rdb!3EZmbVC$q!%x66HefmtK9K-RTUpodtZJ^&F&gI|H7at~Am@j%xcMA}^> zP8EaEmUl{Vz{*2BVI=?ZMRPRtc)TO5;B<29F>*&9=^Q`_1$f95i8_&qW_1e`r1K!r zDg=|lX8LOuxAQBPpSsR=Htv?KjX<*Pw3q^OGO>H?0S5s2ANQ_sg|2$*w-QL}eH3op zdWR_VZjcWB;!0f=e}-Hm!EuD&0OywHI^W+lgduVOEh56t00OErG4pw{{LSnaKY+T? zq-vVCCbNlwV3UTKgD~X2nc36Dy<}msia{B#5769>RnvwWO)BZ%9}|yVOiWy@y^H2_ zaw;G6H+&o$9(xssRMlp$F^5n0zKP_SS15eAJHXuQb;(S;@yL!dF?vyBuW%!4#6GV> z=ctxn5d*L@V6ek5EF)(dlN8Ej5j3_dr(M78yqmc3Y3aajc}VdmnN{77k%Y6C!_uxi zbH#t!BYi=f`{&et#L^xF()(>Rdj0ntg8^Z_r$OqUQ|SNr$1%~`A1Vy(-ue0eH`7EM zGqaTgO6LZx;oQ$J0o&cAx_Xpf@r7phG@uL}CG@{pz;v8*%ss!X{?r^xr2ms7&RO<- z?M0zhu?L2YIhk2{hc zY1CD8@%Ev(-Ha>50I^4g4^G{TQD-C2bgi*DWNfvvf81`}Rkza`{ng-%=NaCy8pd+| ze9<&il@keP`nSUDkE7N=RX9x#!+|69MUtC`graov-|qt@=eD*>s^N#8@NV~ zA3pGvXupq)rIt=b<^3Sxi1a+^S@ct9u@k&^feP_k!JKfx647pq8b3g~g4+Pz(<38$ zU<07+$lU~gyTi)kMAQXMu9uLT_8p>;-ykG2UmKwxxQ0!z5XB>c4t0>#{z!y{Rb%Nz zyRK$#gE8z&gW+!=An-zLLZ6UE0gyWOiGWkJj*c&{b`f6 z%@_q_;!WFppqgx1=&sb=Pq%Wvg18JV$CSu8%`i>YSL^e_7GCbNYvH&=p0oiK&#iD` zaU6r?^Se4FeI`R2x8yABy-jyx>vt8gfi7=5!!iW!IZd0|9io5P$56V_AlNcAzcgN# zr@fcJfD``W(S0bQvi=Bp=#%-n#-00HbDu}*JH_M12X}ZE+#&=Hu1hrI9V_-N;LHjb z@I_Z?$>WB6cbU8 zu#`VIdA_MQE%Me{6Pp)nR9K|;kt;cjy1soOOj|0WeeA7q5z>=ZL`ix7`s{qy+zAi) z@Y6dNh4Rk7TflLDpxWZq{uvPIY5)Akxzd|;M|*hew;b*NbXxQ+!aO7TOb;ieKW_`7 zf+2C`S&!Xw%v{^NN0MAu7xz5AAn&$Ma!9>utG$_MB`V7I0|`m6n#mY5Q7GK5<8`#L zhBI{(-Uu-5|8WU2R|@(^TpR(Wlo-}i>x(Lu-?d)d3Zcpju0@)1zWrGJ;-(Pu>Ad51 zB10u-^)CJ(X(p)QRX>rW)w1euSysyU7+bb`wF!^J0XNSs?lT0g3j_v{fD|>A>Ck;v zg!SE5^CoNc)8^XMJ_puCPLqXu9X+02t^A#M?6_>G1r*l0g?b?yxKieKgM_us{G@Mr z0;lJt-(wEqhT`R)9#~y)nRxbWe>AVSdQ)$dSUUM7sZ4Uu=L|$4;>+yWZJ$e8pM)Ea zEU3uopB`}U)jDv?N5VXQ-|ll~EMctcrNJ(C-KM9O^+k;1ER6fg=^AZ_iifJD`al4* zmB>C$97EH0sB?1ElqtiJ?ty_$#dI8OD;nU08?mp1;Q?6;8H*5b-C3#muqGJbo|-~D zd2jlbI-ITXC*+MA42OPC-$?_~8TO8bnEEGt;;pQrap2&XBH%1AFfgfJlUiNO3n~Zf zyH+jx6vXgETp+(E@+_}mINW_2E1FWuW_3)8rQ*yhiM&ELA8XKmlCfAlrg%beuAT36 zO(0MPz6+|)n6gH>+YQ|ZkFHW9gEey2gmoK{?@gyp3mo`DD!ihe`|b_(Q<}n`cZ0T{ zO#V#1oE9ARh3;(DbHi^M10j3z*q6RBzrJvG_G8{sM^r9&YOFCiMIDNKCq5m`pA%OPQYnk3v0#ZoX68tBKcuVDdBr!zWvJNPq-opFF3Ll@_3K=!q2r6uAPyMG!5V#O&Ue*P zoOL9ZaE`))HMUEEMzVh(cAWL@V0G3P!@yDMF0Tkf(mo}5b)3Oh;*K4M$hh75ymGlK zYyG*T!}9)doi|=b43dSfyUujfl6=*|V#deKknYMTC8gwq_Cch2^6z$&ytexq zDA;<~E&1NW6i>Y8$9Zm-y!!Q(hv;Vf%M^e*C%U_zPSgaxfT6!+r&0q8>nw+=924hz z%LJy~dM17PdOnx?mj^oCZ4?w>jTDSPwPcHnd}Y)Sg`3{stfU3pk%!c1x3Rqus+Q|0$ak6$OU#Td;=3^->*LI3>td9nd~IIwO}C1S~z9tAsirhDC^cL~7Xpy`|CE@V^|j zRI!FaFo3X-_|XY9HI|MchApNMSt8#csB^LS9GpFt;uHGhiDO8Cm8jO(WcxHJ5$JUQ zO|NrTo?{`p2&aVR{q%aju~W|M*AiIVy;Wlc<%9!;I+^5x4Afb%SDg-c&$c`gZNyQV z!?1_;AsUvRcAc>*qenKI2U%aQ_lJg$JG-5aAEQ33->$x-ovRs#yVVXpgS1ifGRBwQg508-m0) z_{Wy;OH5CEEl1v*j$NaMubXanszmn`YuuR8Cp}Z%ePrQ;nv1l{7gN_4@BE3Jieifw zO$f9*1t0yc??zPf_k-(xo>bmw`w+}6J$eAqkq$RDiWAB$6BVvyoKMtCtU4HjZbZH;gj6`E{v z%-o(V%NUN9n{DiOHz}njmm3pUNjXFoK3v1Pe>YV%=vbWqOxTdjG3Fci*>08b0=ebE zw2$0-?+bsw35PhCtQ>BgMIK$}qW#Hp<@LG7`%&M;wP)K6v+pKHWyjuTjq=~QxM7&9 zSxNKw%*>x;{uisJUodwhAH4b7U+FQsdRzZmmSwe1NkcU%3bpZar=GpFJpdN>L4nu& z4M%>Am=P-Oh1u$tH}hkAXY5LI2TdP^z3q8V-8?&ESfMUITCSNU4T)s@sn%0~%+*{x zA>Z}8+Y+8WnBAM}m}4_Fxou}mGl$0Sgs5b~{CMaU)gOZ-PN;Bd1s?16lc^APxAKJ6 zM=yC|ytQd;SzjsWa9WvXU)qSXNMmEwZFHibHF(SoGgbv@9iM!ZxV*Nge7O-&T(Zo; zv@w6-wJ`(dt!6ZK`uDuHL*GsTX0in!f`8`nhBu7qGbX&V$xskwr^qWv1+IjPep7#pkyD$B_w`(FCwI!(l$<92V z%Ddu@T05RG!HPuD%upYaA1_?+uRCdcGwQXY&koi{07Phi_6#M9ftSSMKqVLJM&)_d zjoc1`xXD2Q%@4VnLHpTtQeva>jRywVHVg$5@~;A3&%s&rXqEZ5#~X`oS8!S$=4D5T zQ;W8MpMyz+(zabWjPf!@6$%+6`JV}gSQP4ghu(Q-DD66?`E1T!@tP_bo~vHpu`?Zh zWsRm|z0r}0ey-hmKZMoglb)+Bc(>0zovP^DpmHhF2)-A{SiW)KVYKp?LLq0uMm*P~}M$xu0tl_#qDC zZGX43P#FqHSs}D6hmq5Z7xXD?fbVskm=mxBqhEE}3elX8MHuovU<@t#q9h>`!JtfaEkzhN-KN$IrI;l@w|w=(Vb<4D;Wfqfu)^0 z$X@_RA(WQFe;MToO^37BAJMlUK5El(U^o7M4e1rsG>ykRpC;{_5ts(vv=w4EApD+h zJZq$t?rK%!x*qBtOgA(szzzQMU zfJ67)2WFpDH7{Vh8JfPPuayE1{x5vL(ELF(GOveB07}1i1VXtpYRFo;{rquFG^Qf|SQNJ?=zwjZldKI& z#`Uu9YT#-ana%*fKDb#1%0Vgj8T0n%zrr6C0z0J3 zig;h@>*nmU%bBz(Mn;~|e7JMIb`PlA?YNtkYFhn4*`Ab4&WkKoCzdqpaaTdx6hQ7M7r@UV)jQ3%7^{cz%4H)iGgm*QS7d? zn1gH>vn`0L%vHxy#1G?j@~KB?ET>qi&7R$vyH#$mGl-~T@xVu{j)q7!b z+L+-6fZZ;~j#D4MM-DUZ#Zz+bmDu+Z}6=1x*U2FUKNY%X+ky<+$bk``L zC}nY^xas?yaRA_6@I>v^l_lRxrj{StHuR^Z+Cw0|-NgLC48UpvR5gziauSZCB$nwg zUtC(xk4tQRf*|-onlfv{PUlag2SO8(8Uwc>qzCk;_m%!XY`t|{Ra@9Uh%F)p1{Z0NEKikbEH(mPh$6iW zw@~EVQ0Wb~6=PG#Gg*pW{z})!i;G6Aox$&~qS~@e(tZ+;O5g^p&HOG>P=#E-54UR{;P|^( zD}KBb-B1YT#aUxmlo0xED+pQ9HKyc3^s(n|2mY3LE)(!z1)ESxMI| zEV|@N#WUQ7q~ck`N~ypb7>29pbfSS0F~T(O|LHwoUH_ln!<)L7K@u|?&b48m4tCcZ zE0}L(a~}17-TQutvhuxXpraFf>%kB{V(Ji@cWcjuxqA-@s7w(uLJ~P(Jhi$h6EN|1 z5I?P3M##GN%SIGd=-TZ0dw*7DsRJ$7x=6~cT7x2dg-eRoI_$}5n1=|Dc`xHtk#kaW zzVrx&sna><{_jW91;kr$zZQ*?I)^hGWb(si;`uH!Tdyb`PFq$jFInL7Obxenw8?0n*5wU7w6~n7bRP>PlV59=ZOLNrWc| zWJa`#N!HC3Sn~1&DZ}i{`G8)-?pqR)E z!MCrgwl0t^>I3KxpB0UQ!3=%J8q)rxiXJUCsbEViYK`g~vxrw;yF^~3&W z^fgzJ&~(p?a=>eX+&nQP`^P?~+ zx%0@1I)wNRhtAJ^y`g_4gayXKu8}#kJ6MiePu2>ypH@-LeeXbHijec=l~@0^tPhd3 z_^LLU_Vg2$n=VR>mai_%(OhUY(e?3qZ#h}q$K6DOYqWu3<<6|~*7&%SDhC`0I+O0?Y zpTo9mzf#{h#+x+-lt6x1u!@}2brh9fFn--^VZ{9{sG@_bJ-|Dg7elE`~@Rd5% zdr_!wj6|Rf3-27uBdm5~nVx6F2`qkg=5_e7Hq#iT;lvsj)zMbtyr&OlZHU=qB` z4D8ftep~wg)lF#CxSD2PLQCjomzz!SoNO3?OE!`0?eazWHZl7XxONj1*x<5GPdU2jlj#;N*_K;9|*K*$W#Dk53#qDL)?7!1)Smu=$Fh={HJ)r-UO3*Iu4C zu@XhcaIk!E(oycsTj6l$9H>37e$QI`_QnB9waz2UeDKT6ao?J{9WQKR9M`(=%J@&d zec>X{-l~Jc@j3FmXD1`4wv|E1`3FyXHnY*Y4zhJ9Dw|<)g95F;a-hZN0Erw?9T_Y$ z*Kh@_MF&P!Z*CRzS9Sow4|t`{JdkIEW;oyOA?Qe^AMgXhuoz}SIv`jv-k&OY{Q$sp ztPRYFvw1^-?;W-~7z2leZ!bxYHsnUDoDKeagja>qyk|s36b9KxR%L9>-4&& zR>!b|oAhhWpH-k37{Bh+4Pz$@5D9rXwC(ZRbr-T-6m+LAyR7)%VnDzoD)~$JwIVOR zd8J?q^y@}ICM-C`c>3P0G(c@<>mL9ttpuQRWZRE#17aFPVzyF@LHoD}9I(?41lJ}# zp_e`Gdi@a$tv~@z27EF)T`Lf6`M_E=Q>Cx+z2L}M%x+i)( zeH5rcmL2n?h%$t9!R^kU{w>4+6Hx`()sbrGjQB8U9RRuXZ$yC)Id%b1>-nVU1lseK zw30z~{!0K#9|3^%3Icgbj6rb6B2teAdG+0VvTRV*6U;crO+1q#MDo zBWNF15gAsJ*NDZA0eC1Z%~HEMl9d)FCxgAAr6GPJuIoa`!_#IRKQ!xLN0XxR;+<~| z4I^d_n2gfGvHpg2v%q#u@K(-KhZ!E}uOFBB!aY7LN}8DVO8Y;jGufsquzzZO?zEDo zTNZ>&r~-hm-y4#6vGz|fP=8su?{7&*rW^v#=V)n(hYgyf`87{RQ;oKus=L?YYCT+> zjsORbo_iAvU=I~l3&09 zHe25n)P=21f+dLY-qEKa%6vf@_W?{3$xJN-y2_5!-+Ln1ba>RBx_|&*Hqkn&r|ND@ z$e{B!ltdDTW53>&21UCNi@gGr>l0vLhYx(X(#WaNPtoKwYH}jCv(VHOT&t7Cy>T0Rc%I1m>r0LNs6Vl&eduvBSPX;KK(i zy(O;|D-Mwguyp~!^re+~$h!EifcttoGEUze`?iLER9*{s$FViMBjHuh=GG}7uoVDh z2oKr?UQx({*hH9BsIwL;=sl zh<)7j?$8{s`1`)GfW=^UuAcS6?2lbdd~j4t>3k#rfw2X zcLI%4FCix&{9Jg?mz{SARsnP!E;#n&M~8Sol0LoyTF!Hd678VWh-Ftryu?=E-L&0I zLS$IW@Uw_Zqd(o%d*=euHXyO_V~l^1DbYX;rPQWC()-iH4PEXmlKH57i?^_&_?Wk} z^r>&Ks`oiBQ)+rKt2?e_uK?aCdY3OD!UBN=Y0apQHq8n2=@!12{wH^|x)$8u!_;zg z!e%w^Nk5IerEV=n7Zzzm$crt4=1L8Clqu8ss&TOpEuJBPSJ#74^TU0u*~sF{5uv#g zPm?J%pB|WQJ>%9@{>?(1cMx}W%}>GGZads%$|V>jfEv#kGG{z`JoK8RQh#BV?K+!A zDh4aFCM83CX=iaK0rK9}v+r!?GK8k1{3ikm6t3@TE}+l3wz~k@^Y-&s55|fHUW2?e ziKECE?rtUPU$Sk}Lr#t=$^x+}N74d8CBSazbz^_o{}_KC^?Jy4-38;v5>DZiyRrJY zUskj(sn5k~%5A}y*o6Z${bVchYIw0ep-S2ro&qQ#2AeBK6?x7VjIlz^@n>D_UAOc= zi{wVnCav`Q&5Cn;Wk!xE=Ul@WKVPPr{59#)7?m@YI?2>*dxpwJ(5a#G_RQj;xRZBe zK<~^^xr#mQwugEoYvkg!bc$XXDf7IAZ|_QZ6-p_t?~>7_Z6_QighWBnIO<(T2v#a= ze>rq2ocxZEtj%5RA^ZFDH|!BC&f<^>4LhmwjN=IH2$+SkA>PMiA7-O{@Mhb=xUR>&^olD-fRtPrNvp%w^ zsE(@!#L_W_`FVy|?QZS2R#|q}U9}DE)_l>e;|m+~g;zbNelPD|YbhvJ{CVBEd!0XH z22!5+V+W(lzMHFSFVb1k>qXB2kp7eUvooOk#d+4BK*b|->D4Q}$|Utxt0UK;8|u{` zw4SY81*v2`DN?JL`|eTD=|HS?he4%!E(-MzW1*$!3e_~bjoQK6rqAkgX*R^i63m5U zeW*!}ipd7ud!>0E2Ui?6(gX8FuddMG*yBO`#V<6vcb9&QUr3THAu8zZ(Gv=a?hfc?FB%ZB78IS4&2A_R*a( z)rx!BzTUGn9vnE|kVyG-Epatz$jiSt@ zDHpd(=UQ%l!1sP}Kh67U)SNzK?WB}0KZ;A+ZHYNZtvg-SOWr3kYwJF>!L^wxck|OZ z*WEAS1?`UJqe>Krp#Itik?q)g{W|_Hmbvd7UP${%wF@jX>s504Z=aKGwW7=Di>p14 zX51DWjL<@Qpj+rOpnLJuFzE=oV0fYwpykc*8?l(|e!XqB^hGC1*>iijcieBj6fmYt6Qj@s?2^CZFoX1N2beU=Ph`%?I|AX7euz)LlfB=Ktz*=Y)oz3H#U@7^4ek0l$G zG#3_;7;22v>rK1jZnKgZ5(=mnn+O$GuvRp&DJyG9n*?&C=~(yHy6~MW8Jt{M9M7F{ zTOF&qX(^)XY?0{aqNnGdE3L8Q5+2qz;Z7Izti3g-UlnB0i>DG{hUt7y{||hCQox`O zLt;1IMwY`b3Bh;;k!OS!Zo2cCs5Xvz&@5WD&UX2hb#nt4+(li90+~1$XbYA~Qo8qh zoE$ki7Dcx`)(6PH0|3o4ab#+=Rqzt8K>z(KbTqUP&@$-wl?bfWKK%2M7M2lOAm?09 z6|Ze8z_?_XE?V~$cq1aplm{~skt-fxv7z@$yB(1-K)i;If>|ySN?P(xC>bWD3Nhqk z(i!qODf$-q5_KAXHvMLb<}L5x=q%*^k1u zdmuu`W%MU8;W4VnlNY9*cp){}w^|Vqze`~@#GKcF=2SL=sQdyZoq&KYf4aMNj@zvN zFUMw5>a|NUf_|iu{9~ITUtWCHTmm%D!D1D*{7qwM*t|#sZ#USbX?L0!gT_5(!qbxZu=?!ZDm<%7@CW=D*C_k=V=!p`1d)%?ZnvvjCT z#9#5q6y8_0M~{9Y)Oex}87Wuj@4bfPt_<6kS<@U*x$_AKnRKZL1rQX39fV$p?Q*BR zli^t`hZ6M|ll8jf5AV^q`YV@TDNn3$0PD>IUQkRj8NYeYs~maeh%QGZH)mk61kR_vhM&27AbLP{Z5QW2Q#-GDJtv?19rccPd{aUk|xmBbILR049yVs8{TuLlwc8Ft9T zKYSTTM}B3Jpi1Vg@*wo(bCny?SbP(2R_}=tPU`=`ee?c1qm(Nc?xJ=fszDuV{ybZ0 zd?<~QWx`#4|4dr*51{I+zu&n7lOZu;6V#RTk2D{DsCpcL=%LMe>~4iB@#T+gXxCQL z9^ZEerUsb`?SOh}RlsE-Xq#p%Ip^o_>T_v>ruhqm1g(>iX7}RhKA>l8=;{Xqbe7KR zEA1@i7FJL2xUjAGMa|i*`vXx0sW{;Tw7SER5#gqOA>IZVia3JysJtiXCw89%VB)xM zcJgNDEN1lvZLLj;m{zezs2*lEKfBe761w#L<83yJBWqKXfuK-l*=FnXv(}x?_7cSY zJI`XEr43FtujQM93oJnd+=4lWws4;3!F^^^h#*Xn{3d>}oe^ko9c?t=cD~nzTg-Ro z{!5=~{)o*GsW)Gb^|$5lFy7%IL0B97xn!A58CSWXPUA%^n>vk3vo4kJvWG|->LLIk zD>CWrBt#HIFFOmm4w|3Uhd>%sW{z1eU#bEY$M_8^#He`GYU-as1VUZ&`1ul* zd!tNq4G1iLB(GB1n5w^ju=PL|8R%8~5ME`|u)Q^GI9c#^ z2gBQL*;Qz+i=S)*&9c{)E<>fZ18)mcR9oqeQZ#`axU`+86FIZhW}N#*z)p^mF0ibC z@pC>!mv%-G-MLHv=BXZhHhPHv8oHzth+TKS3{w=l-pIJY*B9QhN=s{Ypb1DD)YluZ zw&kch^DOsuC$O82=^hH?i`c}f6xc2lRAa7E$a6Eeb?4M=9Ir!~Mt%9J6|E-W`H)9k}Ns}+Pr znzP6YU7oK9hnlX${`nuo1N)T};btZQy&AWqzXh3I_DBE0ll>^3jp2sFAM4PD`&GZ+ zfaO_;;$cU@rw9@u>V2NV`SQLl?dz!x8zIX#7T3rHmzzEuFZtbh+5ABrgK=45)?Df{ z(~NBse=CDpGSeg%pi9|)OUtJJhc85?aH-ihtjp~0HS%YGE+O-ri0RA`{Ijhjtep@Q z6SKh1s?t3NLrw58Xw=QNQfrW2bJi|+SNcbQgF2De@M+}Ag!_Z^dmY|Fr-W-;5xS4u z9RV&J=1xL--?IMs3bRFr#yc9nf#yV9TL1p#0gOO&^KdYQta+(<`&kkYj&^`vS^du8 zF(RQT%fGY@ROYED(9#;*E?pfeoP6V4=L0KtnCQt;jz zqPJoF0bJp#14IILuCeZXu8g#~KH=9dy-{&Pk#S#a`{QM*qz&40 z491A=li6R$n>YbB*Wu_OCgtcksuO1R)e?(UXv{w45PDl{F(#`bQrOV4S=MgJ9bq+g zE~#&7vui7E>YvpWs;XW07c1NZT*sx_?gm{xhHfBKA0FgP=#NBA1TP$+CIq_s+jTbe z6|K2FE+q-7Zq&0@a0yw;&VJJ`$^muOnbkAgD?2mYiy`bl1K_TZjI0MIEK~7i%qIccaaxb6dvcB zPq3sGuKoK9EqRh8w9ihs%$dEW?T)z!SKE*_95B<-$MMjWJSx=bvF$V$>e<|5S3FvK zjv+arC^@bfbG6dX)MvSONt@VT=2elx4!rL4r#um)4LYX^IP4;`CY%JRIJ8<{fiJ?$avv@6y?q$*o#T5K{qziYS7Zs4Cu*X|&P(Q3%i-4%s#X`>9C!GN2vTtfIn zn}}fM8W4VV5!vMW-@i2{7kX0NA(7ZbY)ufQl0AbOI4bIa(6xsF8OTMXA3Jsg9sX)d zL_yxlW1`mC>KHna4x3;R55vcm`BUr829U4h^_&Uh-ABYrCmkS-as!4qK0h%c0B};< zb+|npN4ba z#c{NXNFS8Sw})c{F$vG+cOch)NqGHwg=b!FL3JCzFU${CppMj%gtWSWoRriY5z_A5 z-rBO;EJDnN(5_Iw&+mL9dDyW6sM+{Y0PS;DAcJP~=sd#srU%fI_0q2Hb<#RTO2gFd zf#A^hw#e z{7IekNptw(nl3_c6#ILjTgKCbXTPLBIXmz;Z~Jw@7IzAztidfA16mgygNHH(&)|!S zjDaD1LCYAt-PbPd8Oo|xx7^}(gOwk#J~%kgfq{%O+8w5$^M=?ppWaErx;l^$Lc1>i zE}GwgB(`G(DKYQexnq2yY1s6sh@OFA)Ou+x<2Yz!VXff!8p~1QU~qcFjEpB{yZo3? zX`lCr_0r_!bkB!d)_#G<$BUD9Nsr=FJzXuftiz5imG>)TM52yY;Hw;bA5XgH(pLHL zipqY468u#n-IHb8IwSCyZJ;5WXTL)IWPW5}^3@UP7ceQY79K>zxvdbz>#;%EbohP3 z3fRqBoOM-TebCmnQEK5s_c|u`BW!c;P-^ecYm{gE_IBRZc77q6)ct6);&66y17@6= z4f2tp#~p~Rrj}ZDdEVCp*d>gdlZ2nFk#GM8m2A}6)N@skjl-xtr!P8wM@e%Ea3dm4EpvCYP1jsKQP|+%p`V{$d>;SN9^}AW?v7iVX6i8$>ih*q0>Lu- z?{5s3aLVtH4bz-CenLd7C&^mRaG!2lF+1!Aqj!&bYiB2?;gIskei8S9TtgBf)AmWR zbtTPmjTvFcto4-{mYL#Rr}Vg!DFE{ zsgG6i=y(Y;L|z^@mnA~k8P(gJsui@q*%zh=A9r#WGG|zuElfW$09Q|p3N-uu^|MfJ z0qoiNUxl(IWFTs4RDZWKr!TQun@2S{byB3$7GCgFXL(cgL=TTqb66ldjly~!%R&`4 z>ztzy3oUza19Eg(xlczY4JWhQ#Ta3IgYdW~1p;rkXYP2e*a2D9jg{OlNKrPCMTch| zt1kdCA{?ZJVmTLgr{xM0q0JGW9PZ)CO**h%mO9DeUfVlV8}wWxsW&<-+idTVBfv>&C*)Ehy1>~o!iPn zw`PM?`lP43{BN1{#cs*u%t>CjWf4ViplWp+OfK|j*xr?Rn7WNA6bWu=k=l&wAM!h{ zEwzp@ky={w=RIZ<8DC5F(A(!;KhP}qY(LqlXo$t|s}pB|pm+gnbk|fg;?~rNJqA&d z=YX6t^Y94L+6>xqlr-qxuQ6vC&M$nJE^_Ngh@CCh1iD-EGv@OFy+iT!4)lsitf2R- z<&QCqZH694L;ss3g3-xuANOJv4qgOW=)K{3YcT%zm7@&JS-h#w%(mCOKThY1^x30# z+b9k0sEsO@-4kz5cdWR3A}SIc2=jOAIFTD64&I2U)cI7&U7X`p+#o+ku1-x&a zs`I$FR(B!v`3;|kQ5kPc*m(8&zoAxczh0DHJhU|4CdX!68&~@L1f>(}kREUH=%4(_ zie4Gc#f#_1Q)U*AHPPqy#9l04TNiBkB7OuPS?_MAyyGGzGQ zNKK6k!GxkiHKI31t=dU%#BEemSNgid1qe~{o^ME3$d3WN7qc;j6mETJr03Qrp#3n3T&KWv*gDZQ3(35& zd?QObZ#(y6PJ6WVDJkC~{tkASu?^%4)d`6!#h}k*2y!7V+O<9n4Gmx>+T@`#I#5a4 z`FoYW*!$7i%{4oZnSal5IqH7=HB1EI?pdo9u`_sW8bJ%P_{I0LC^(|EsKx;od-K4UCo_HRiINTYjSv4W3;z+*Ievnk>s9Kpo>~s`(LrltBhxV;o&(&g9woh z;)hYt+|(2`lB zmI|FKmC}f2GBI+DfUOPGu5oa>2G z%lBS{wd_6rQT8F!8hw)54|4(HVdV4s-iyJu#{dA+%kJ(8^atU)5YU&Bf!?V}Q%p$6 z3*l}Ma&-``HhN#WgBe^?fqqoy7dp6O=1b!t+2~q8xW}7`0LUd2V(y2U_4V!0 z`XO%aC)kzTpx@mb@#QLKeU8$`zq$N(&!NGQ+V>;XK}fB04&;#1xPC=1zQ1rVmuWyR ztMH=dn#3I&8f9&6D~@x83(RMpESKQnO}VL+!mm~p zFd%0I9xunRC4odP*pRBWRKnktIO86nXg+`b46p#eto+o&UD#_&8P$>QOk>R4MAwhA zk`k{v4#T%*eflu*-7-BhvmG7}^P4AuMx9%<${~)$ilv@UD&Lv(ho|_MVn-Uv?qAPh z@i<=2hpXF^P5pWpz3ebsFw6yieJpZ(eB29#oGS+wnjX^m+TyKrtMpVYiWeuxwdotm zpI%#Q`LSW2jN1|KDxf88X^|X}mdm#x z4km(oXuROJpKSl$PFY#`!Y61|;oV`PrfU;HS0+|Fg-35Bsi(cUw(5C^ z)YyJ{beR>keJWZqYF?yeCntr?bB3;F+M&1?Z4Bikji;K;Rgno%xBN3ilMl4_5A*%S zG(?8|0AGf;%J5iFl&fz;W`#Om&BlU;hDJ_W68>FP7?dO+& z&RH7&VQHtM-8NFMNdLvGBBD$CcFmLyztd1xNDL28@GeQO4a?mCwfd76)wlN&61H%6 z&da__kJ_rvDZ1*K!uH?$K|K3QQhT)E_Pow=#pcGwM+)NGs?&xLxkVd41O@G6y(pCz z8+cK2kwib;w7`t%Yg@$bP)kyvq#i#H{0@>csLDtY&k^RDVC%1w*E(fFyerDX?h2Xv z2d^10^}$EC(fR(eORKx;ntK{I)yfRoRzXq}QD0kS{ zoryu`DwH4SXQKD43=bA*i>G$!UagD7lCS98xG%)Mc;BAM4?f!z=U?{GV?4YGM`^Jq zsy;sw9=;?ykOEaUHH6Jk1zscRAT}Bn7q{qh6LjQKXteUxhe4lMrHssxn4B06Q|g=7 z20RrsXE7BTt=^W+|J}_^xEo)?OC`qh4CZtKZqY}&iOllpKj{3~$%)}3cgZV3u+%RS z?qy#rG-RRTF zo&D8i7aar*R7JQ$c1M~D(F~8T5?r$>xA!qI=;Of`RfmJ#5mrTs?w(yi7*}Kj5+h_BUKj>7q~?J_4Y0%7-&l#?&;cs^0U&12H3W ziZjE%$u*nALv&;LwMcZd9#y!30h8peah06~eE*fRy*Alu4XaUXve*5C_oRNVOAy77 z(o#S9+?%RMtZ^M~4-Z4lxYbWr!!2L}r{~V-c_h{{^Ll!E!pJW0sGPw>I=6B^lxuc% z@DHWx?TD_VL0(pUz|-u0!?FYgO8OKH@>PzgHUvh%tKT9Y|s3=KL9QhN-mK>q;W`F5PC!ew~>d zB3+mtGUJCYa}Ljj|9sjsQ7o#8%Y9k9NIP!_U3O1WHu|MMm<|3xjt9J{!wbaiAzhv9 zSEQh5zfT3Xq=aj1YLWrW@$#k!p^R&ec3^G`ontAH$jKMu?L0$gdd5VL`_1XFb-pFu z43Y3<>8NPwzdk6VAkp*3$SINv>#0JpzCA;S4_9yX`M4d@1>)xP$d#uM7aYvGagF8T zqg>{5)_wMJlm}HEsm@$jHJ@HQyo4(~BC3^Dlh#qs1bRESZ+WA_NFr&*3vyUKhk6%K zAiIfO$>w*qtivtD`SPy<0ua#<9|PZ><tZyt$WFpcqY+}PcX zhAEtC94jjFcShV+Lsiw@jfjhQQ=cwNrT$Ac63dRRAGT;nq;%4~a#qnq=F<7|Qc$N6 zNCJSsYlCsrJtJ&fJ-6&>GjiMwMXv}$j8BibL{Fy?eaw3kxsY4}Y3-T0`wW}!WY0 zl=vf4V10PN*xvVd!**rg%C|Jyoh1;se8CA(Bqi)(nwtJjt8j178Ei^Q$`wlFJW)z| zf+d=(Ik_UEQcsFEwE`>n^l_BgmVZa6(1Byz5)wRtM?eg0Oo;kyS(>8!ybmXtsK{^J z5RJ8he~Z~+)4vs=K*vak_g5eJ)nD13=H&ef|86<}feHWmb;q78QMI2nLPxoFsJH#&}^t$jj^HkuCqUIiAC- zy-w20=*Yj`D=wurmCU4N+oC&^7X!BK)lPPBc}DZ^(~VqdC0BknH%sz6EHeUw;Q>i6 z0+(@vMq@Ghb&rWSe4mpalS)PlnB~w33F!?M=&?k0GCfQ;pi{XAI{@x|Y;NxMhwWJt z(bj9Dh9b!c39O-^p#`MC8o_H5S2A7qvU^@e78w`EEa0#l0;6wfb#?U%!2l+~D}bvB z9BczzLY|9@E3KFs7DV*s+1{*Qw&T-S!ZiKJD59G;sS&;j_B06+_W5zBNE&kO>eW&M zkvD(De38lZRPCuBI@^=OwOmD!P5`+deoLUb2hDTz~J7X z6&s8<8pI?eYwVDtnUNBr97(V9={Yy`ZF158Agadf12CdN$o|SC+!mA?W=bv)u;=!s zEszl4_3^3by}1G>iBBdSrYA_QrmFDdC7r8OW?EVcjG!5j5i=_g6g?X!rUeDFGh5HdSfLwjsCE(++V?CgM&E|zERb*b+wnv1g@rpdzgl{wY@JOpc6y^ zs`I|SI|r9f&wED>EI^gxmq3RPnLD(0bVRh>i(sEn;a}2Bl5?~7O0Fxk#Ry#LiWAeRJ1dTVx<* z>vJOZH2lr^B2UZYr>6VIHm-|)7i;VU;hI^$i^rXZjgVEC>i;t{lMdWUIP+Hd9s zdKqK&LgT!v^|_}(qZ?yP+%h|)-tvFv2W{tIeVstsS@382333B8Qwu&&c*un$guGE!g3 z^f@Kg$1PxXb~fr>2sHZf)JJuHFWWC9oCckLlVSB!RsR zV&&9Gn1`}gf;l%%`nm1!k>y+_uSQ()csXHWLv6{(R$Rd-P1C@$^LSG}q11sN@ud## zUP!Z_z6n1csbiCc7UdtJex~huZt=+jB>toqY3CQa;cO9IjXCJJM*t~6%J{AZhw^*j zUxV2z@-Mx`Gin>O%ku@7Pe1luWWzHAS7}`UeC~hQK|{&|%qzL7D)BSesi_ylr^$Ab z5ujHy>fn42g(mD+-z& zR>G&NJJHBl^X$U@rT8STGiT0pT!MAAY4Yd=(bsTj6`~KI>f&r ziwS^Nw$8JZ<}@eJ^Z9ro&64zvCCsI%5D7lHgvZaX5CqJBge!Z=Lrg_XaXeg`K9Ll| zFe-cbOO9{bU%xOquEEa(foeuOL-9tqt;7N{csv+nGelZWb3SHJ@F_8uaT^djT^#)w zYCS=!mB$Q%|9b`|r2}&$3}ht>mL!^>*w-&EE>0^B_=L~-1k23*G_i{#p;GOx!iY>H zHJ+CwHRKSjA=lb-SZ-no)6wnqe=r!#;!9ZMl!Oa@IDzplTI6QDHm8=BSU_kX`tM&G z^a1c{5p05tNB!nuG1rwsA`741B6|go6`uCYm58cItGl_r5KX^5yb&T%#hiqHi)b~0 z3AU_P8xv!x>BQ^x*6938LoG1bG=f>)JlW|ZNnj(C2nIozq=jPFU8`6=bcWO0kPo6H zJEA(-vA5^!JUW-_HkL4?Kb1}248xo9SO*^I=QG$p>7?*+k;9VBgTY8~-X)gDGL98w z;}ib-=;-H}3gjeUm|g|6Tzg<_=Q{P7bW`t^hw{tkK2Dt6!kmfSnbv0NV1S@wW8G!H zq}Y%{%j zs(4u2#I05Vc8P^ZgZscu+}yYt*TRnaJm{NL(sHFeUqw#isxWnXU$Wd{?Yo5um_=%2 zwS9i`&}zF~ftc3S-j4ph0Po0D_7jvffyC$@6!=~`ak4XA+zDN46Cv~>^+n1X#Vno% zmUv#TX{Z8iuS@B;5u{N)c((C2s5LazPgm$Z{Jg3SB`TML1UcLoMZm!!8oJRRD^zK! z7e+FAiybX9H!qcwMl9STJt@S5+1^v(q_B14=As#^+K})gkO*=y)vGdgNT#n@2;&kVmIHI-jOD1#qex zl$6OT6rju=)fPc*xn1;sq1VBt{t#Eivun{e8@{!EVkHoP6mC{5=y%PGS0Fu zU-p=^v~}dsUY8GJ{}G(43-@C{gH+Tct=HKiAH^mn#=)A!vf|#QALT{W1GYYh| z#ThV;xK#2_hVeNd2Qxw5ya7|{I6^<51Vtfz#-C_d1{xS_a}JY%xpxQ%IJZDbItRQb z?(TsfcfV~fU&V%0p5 z?}3(|G*EizAQPQm0JYQBF`+~ddX23z0nOJAj4AMF|^*>-XO$Jz^4mFdle!QxPyas(63^Eu(knALYXH| zo`^%X#TZUD~e}dLDo?_c4ux(9&^eHCn7qHMG0n9y^H-lK*oKGZ2m63L) z4dTWV_|Qh6d4n{qwW@5>nP~IRm{^YfVH6QDK<49{d@#ONL-af4=g9N~`prZj^9V;! z%03OY-Kp+rWxYxOXzj2Z|BVAPjurq)G{&1cufDuaCkvSff8Ofry_sD3V!O6$Jh1$_ zK3=5+*mv6^C%9k)DW5@>%C24xa|Ssc7W4OykdQUSgp4?@)9>4iw zFw35#n@BHP?66Jg_ZzM4jSbJE!%))d=0q9*YpP)3p9Wy?m-oR z0TVaTg7QiWtzqMI^M3r|TkD(wHTy^HEiE>u0Z&2kVblrCC8aerV-QLpu`XY;gTjGq z@(S86{7V$fe=Is%nqtMp#9Gue^o$3o?E_WAy=tGUmMM|ob{&XxFthKK;<+2vLSvKj z9;Vlxft}gKBKDX?d*2xHMa)(L2sr**Z&(Z)eV%b*My#E42iqq(-<8M$G86?&n3Yxw zf}oq&!25!A-nwq+Thel$(A!_(1y#reYtRk2)pj^~)8O3CuqcnxaRdDLERX~qkZ{B` zzMAEI}z2-*P*Ps!_N*| zyrc-^+#eDzP1Mx@*feHi6wD|dMOr_a!NKo}hTwBB1J=|glXjYJD~bczDw3}Z4LjdI z8;>9+A#omzL<9HNgc$=OcZCV{BW>b3Q)9O6Y#DOZQ5!qz&|2bJ{%#bzKVddpgNr$G zYwLX$7OzKj&f1dN`};d6B<6xWExX=E{p#^}fT0l(IQSI2zPTBW4BXDAWhy3n44%Ql zQJL8H#YjAQSzY)K%X2kaxnVL`4@ZYi?xBdwSIyu1T9H}68HzoV6=_vkO{l=ZI6FH^ z7@+9Ar+Jj{KbeDL1stbnEy%u*Gz?3^r#x(t{9`FeD?x-DtEVa_t!TcaZ@ zbzY6_4BlLT*Os$pm924=`!Xc*aqUM3y8)SKT^rT!FOhkJ7bZumB!g)nYq$@=qF+k0 z6biMBzhN269t279^4vK~i*A8|rpDf+8C3jj_aiqts-KEG;+>oo!LQ%7*zm>TW-6bD zB5gDqNo^)c#PW|mbQWysLV@=YkBi@N7<8Tn?F|NK-$g=?EurXa5U)npn@b}NI=nD= z@(y6Hma6sl^h&;3neN+xGYQX&uK|;TUtL#Y|8Cfl*jk(We|odZ3%w7mjR_YzE8!lY z0d^ThXfr@I$CGYJ(T``Rsv1!RHUmk@$*%

AqP)_1lHxOJe->Qrq0#R3$V0mgN)F zY1TlF;>l+V!2xi#4L&2P*vj5x2Y~spQ+6I6^;Oq)Gej~G?U1cMgNqRa#O$}NXO@;| z>k72d<$46o@huqIqcVEiR%&6Na6Pq=b#xJz?=LT2(H>Q`kAiZ z${o=DVo|X(HweYSx5W9dph%liWra88Xi#Qya5=+m!Bd1v#5u9dDFx4~$2M`5}-OE)_Dhok$ zBjv6lg4NlqB)zerOkost3iXE@1er*#>!s3D?*g{4pZLany9{BptGPJ;9aNy>+aXRw zJ54|wVXdmF>Mb-7xfQ{FoPgvN8yg!5N$9^*Pr|D{&z-NaU!qUyC|r=eOBs-SdzRQz z>FSj$#Rd$r@bD@}b$ivbC1g?2gTa;hb>XoYpqpl!Pt<{^!?C}ReLZs-uu!5IIF)KH;;0n)L9 z@p~xHWa7v_!eqLpmC6J72kCI)Dr=qXRZk8dW*;$^1So{p!yfS}z@z9v;HtQCR0|kp zp1LyuyO_RukLs6y;U*)H0p^FBv+;1W1zT!sbfT@FG`5l)w9PegNAu?VCuBu*wr{0isV;9j&cQMczu} zLN5JGKlu3hM<{gl!IR#<+1%&j#dhwmd1y)N+=Lmk5iSWJ1O%S1p5B7c6%BXd&ujKu zSb@DRw`}jyo&2G@cQuXnf0q^8Mmc&Y!cpc2(R$(4EF8}r=eHp94TUj9tmqc^kS;AK zm4^Y^K}RrAVZj!foH-{)cS0OGydZP?C2ENxuxGx1Ukq|Y;~;|c&3u4Ojb7K8>RV3` zmO#-5bTuMfCa^%U1sOD1h=rYBBGpTJ#3ed?23biYk;(yJt3O$7p=;D8sNs4A`mmZv z&JE(oDO&(5|M?pcwgEHEP|#NU!TLZS%&O-3?)9%iA&TTxf3SyV1Gv7p=N<3J2Yfs| zTrgE>7o)i~^OkhWi^qa2Fc#iR57oMg5)pr!K3t!WWJ41I41oJu_Et?08DGvuuY$YM z2=7~jcb4%qlsBg@FXyV~*Zg;4k1c%4K6X$xh|jwx)2I^+K;dNk7S;+RE`%~3r7F;K zk4iXduKq5123tWE@my5B2$LuU1x+{B9gq7#1VOO3t?Ra;89M&e5uPggFu=Mas6!a@X?6}5PsIoI*; zG$7p5kZ=k$NGpO9SwwG{4>cnKfhJeAiFDtN>>uqxdCAiFUN{-g?|ve^l6Ubh8Ij)p=iwQHxqd5IU*s+o3xmroW5djr>8EP@0z-3Pis6& zyF$m`<=nTDGwIO26+9^O-9Nll0@A|Elq~Y@Ecr0P+8##Om&w}W^Dv(_}x=Wn6?^}n)$8t2()@Wa|5LmUVH&KqzwUuPSzqY%q{uG8i zU6d`*&uQrtjK#fT8Om=#Hg*t6Tn6xLsqx*)|0(trfOX$;(4eUF7z zevGE}18r1_D>xBmEC!Xk^%s0h`oz=PQmlWk39c! zq^{#BY#g__Q6r49g#5!pw^)AroL0P^<(qn*q^wo1C2P48xb7iz-z0zE8q1 zr}VRwya&-gaWYa;4=yQeY;T9IM)OAe&3VFg;>SkgnO-snMY=x>fzI&qM!T>X>Iuo= zdHH=d;%yUOH6RE?a`$go)4~R1po3&A(DMw3Mpi7>2gFyd0}#$e%->4NEW=rQBz0sX z`yGfPh?i80b=u9%P;E%tWOclYx)YnM>x|TC^@m@{lboavr9nr*54DKSBa`$Kdz%eK7Mi7uCaNShpf>mMy{%#H6H6tbXD;W7=bT~ zU{pn?$;QV@)ei-as%u40R*tb|U8p1n!~z~TBLEB_qE~|9d^1JCGBM`2GWWPBh@lL$ zT7ccY9fS7lbx0X&6AUOZ)etm+T$`&1+y#K?Pk*nG-`9Kef7d)0mrS!+$L{6sY!kVp zSucqiZklyn-zMj*J%*8x1=xVD__5O77wy08Ct@sQKdBnIhi5;9G^)X8vG%KLccrGO zWPgK_aE|_LvAQBE{E`GFBRK`2a}|J zEA)+Sh9CF+n8PZPH|-v<7ds}jTdmGECm-AV?GVN#5D;2^w_WbO9P)?ToCEOq)IOTz zEP`)K4bG+sa8`=-qVUW4u9yG91>6WhXA{%6N6EpYZ$k9>`+xy*;$01bL+gk$=kowfY17W12~3 zwfiV?8y!yd=D&ans)6}@w#4v*(FMRkTzx9N832ADdFbCEiM@aDO!M#FyBzNTzdLvb zqmS(|Unwx9Tu_`)PqA(%$_*Y?VlE5vlm zhh}a-q){&xq?8zaSR9~Zu=!P+V^wtDxq}&_4K;Ce*~8P1b;ARNX6iBKL}E& zAYl6wAJ^&`7dynWva;U%{YqPNqI|C@yf}peQ4Rbj;Yqt|m%~^N_FwF9G$3ik^o6nz zq-dG$TwQGbls(=sud%g}!*fFYTjxajl)F(ht_C=0f4H7D4yE}ck~D~jwX6n)*AiOf z$-<6QxEU`XX!RwtZ2#j+*Nx$yF_9C2y_ys{f4(Ll6ct_)ba1wGF0Y8*s=++$#2?9{ zVZFrc#Ia&pFO@7;D6H^p?`{@jpWc~FIKM$f%ZZZ=TnRHUNO>nTboJ`&ztF=d?gHYh z2Kd^IcDcE|3`!XkGld5;htR!_^IdEEHmVgt9RqqIK6-W)nr)c%sR4sKxd5KPHU0x7 z!(A)>qh1lQvFxZL%kKW<%YOL4Mq|x@iCSWCGS6x2^ohoQ<6;{_MU~CO#4tfKAXJHO z_Bf`J(h{JvPm3z+=;#pcbzVf~c585eA#MV?saOcTYJ~m5hns4P>FCflq&_{NLy-P- zuj&=>^pL9`4IQ(=`Z~OzDfAUwmVfI+g~<7Dx+5m0_b}ey-#=}j;Sh0a`rGK3FTv_T z6DUo&K=RdHy|B8ua0+vu$44c#rO$_)wmsaF7e$W0xpwW^I1GLeJj|UkIB2%z!IFLF ziF;={XbFOr>da_z(<__8Smc~?1%Mr>({!0Az=CQ^`iC+YuY`K|FU5hRo zY^nCFBn1W@OylZ>6$v=?H(WsIDmfxPnr&n_1K|ZDr-kZfK5zWyWYLv8I_ zVwZh<#f}1%!Gu#Sy_EdPPN^{0A0n7Xl1#x!Ha<1g49rI5m8%(ct%CF`_k80wchB`z zuUNB}^;b`nzVyAFW#)EQ{do@4FXCl#4EGbSz2IkVx7+V`x(x*u9lL<`2Z9_6IDd13 zDgd^xL#`oE^=9dKe0(z`KOy9dQt1WZT_xj6ra>!rKr9p9RwWRcJ%aa=ZN3q&8{~$n z!q+z!F@fWx?Uni$bJ7d}_L}VuvU`e8i8=h(Co9ch%#Fp!H$m2m+DPMP8(5$!#{0ur zF2z;EFaThI81DvE0=XMLe7|+QZfC0Uz)1FWZqQy$zYe#!)wWu zCHx!SpEUOF<%~~t`Z8d+dBTpXz{WF$DuM^a{$@XYs4EeB>-*#S(TQkSo8N)=WR;ZM zh+ZoJv$D*g*E67ra2szp)!5JwUZX9SvGLAP=f4AqrG@QRE{v}ffD%K{{#`&o05Qi@ zyhoYoJJz6kIVM;4(s$B!&)KIFPvc@$ee6QfjmIg++^1l&^%drbvurEOr!xv$Jp5Yu zN%mEi3Fgm9tZPj}4-BPX05iwO@SD zO-8!hMa2i(gvU-fv~9V}p##rDDPtnSisRy0@}iRM_2#DZ)~uV>_jqGKB@(^=_>H zfZS)2pQcikMu7igfYtW-$NM@@++i6`H1Xs`14O3|@mS`vj7eF7c*oPpw(9y#s(m#} z8zoNlz8y~!!beb8%J;27Q?! zOE$D1*W^vM>}4QFe-YVm&>ZjG2sBmssKXcGO=Y?Rwhg&2TM08$qC_D1&@X@UFN9}n zf+6@yR5twAKVFgZ_qd>vbFmykR=|><#qZAp z!0a1Z;)Ts+d6Uwu#rIVV3o$7v&JT(3nfK+hmcGGFn`Kd1j$MGQHW!%>6)@&3g0IuL2$UP~C^2Myc5a(uCv9Yex6x3SpbpYF^+J(gbxH$FE<%+`9D*%Y2MD_kT0y#afsc8A*F8$bHyyNh#-rJ7k;g z-|QHS;j@=7qZynlbgFh%P5U6@Q8L57qpPdyhN8BmWmw|{p5DdD9D(E5k|fA5ciVIJJiufeFSFQiNjJ|zmIR}s5Fjz>26Qe`I3ohX zT=x2zJElfAQSlC@5zzl>zuMyx7eY^Am{mf;2*qDuVMgiBAczWE%S#N-eNinsrytSG zvT$otU7E<;HD+OS(N_##=HP${QUjeC=vq9VVb9|r1ZQz%Z`bh2ae6DY&Chy1DyILd zVT(hh#!8fIT5#Mfzsu)L_%O`c&xHj6ghD-gc0`ZqCW^^}7IMx%{jER#kK#3@h+EMn!@Q;q--SGPGuZ=4Pm@|s zug>fT*-p*Cg|!IvNTT^Xcpme!pFUbTdHVDvurkJXmA%mpY}~S^NX;RHg6;}IESgCY zw`s@0QMeHK7v%X(kIGnOZh*504-gg&E$!s=+tMQ2p;0zCxirA`Xq|qx=tw_dIh=7Q zj_r|?GgoJK*2iO*`Gy={6*^klCIFdvN`^cWkF#7c13%TBHWs-6Gt>$nre=0uIjQqf zkEFOCa2&Lhx>Sk0cZ``bjLn`?wkb*C)7So1crR(he?c)CF79x+5pMAx)w*dS=mQb5 z={amKYP*A@JHtG}O+r$W+wA3i1}W8Tz)Qf-z<{Zt5KcpsSW^tIMTOCquV2}Cc+}CQ zj!PLv`11r{Q8D1xAIW9rW*oOY8)NZ-{YKi-vjb6c*A|=sdEk$XjI;);T`u#XrVHo& z&D>g6Dq8$N{lj!d`vtDGglVs1h#TIi0rDasBTKZpKdr^M~7Mi*AEuZ~f^9XxP)WQurYx zL6}lc3~q5DyYHOwLN+hd>yO;s6ed1aOnm=KG!4LH6qC495>lLELk0)1AIng^ zzBf5J%JQ3W(&+M?I1L}i$JR_d+%q6_;wzz2(Mk|$w7w2G0d-I%!jyq$Zfs8MawQ$S zys-w6NB}pi$isxDt0FN@Ga%!wWj|f^bhBq9oGiZc0*-^JNyHi~kL<3Bqj4lVg8-gr zd=p`iNCvemM=GBonVS&n-&4Udoc@i&j*G~mL9aUs-G3!v~Jc+42!Dj{)gQs@V$7bk4aKC6vB zt`gTH2js>ZxLq<^+%-S&@qownJfsU1u0>z*SP4=z)E%!0wo%9#DE)Cw^`9hkIh@L5ap--+b zv^s&nnjqVD0Qq$xLWvKY9Kl@b8Nts&pT2%{x2c(jJB;qk8EgJWUy~__s3qI)b%y1q z8qfL71JS#aWIzS|{|5k@e&Fxm#2;MP1#SN%@eXu0E4U*vri!$Ch2{Z1zA@w0e=I2A z`#1`x0a0h`4Nw6d;*s4TalOw=boxsVy88e!h(c-WP%m)_rR7v@EDk(zwpqmm(6F!w z>__7urT$<$D4#9?YY+pW9cm5^AK<_^c0@&bHQeBTp_QY~+#^F!Q7lRI!t2p9DM%CL z%=v70WA@>XvBNo=-(yRCB7}BQi;%c0U&chjf8u`i`Nq4kCHUW(kIPsqbU2B;QbL<+Ee z*5Qgtr>Gc+d$O4+!OQ#Pl=i(3!F{aAc3Z2-Cp#7Ts89RxpEknnB(qt3L9j1DK1 zP0W`;1QG+I3}KuSzy93ZD1$-7<-J}V+W>;*s)I&Bik4g?k6cBeM)?Wn^EsP6>;sIL ziVNq>Axj0f`zSAL1m?z+su>u>YP=G>ij+^GK-46>422DDf&!enu6V&KSsahjYJw^8 z6VL_O5u5H+p^pfei0y-jdT+&WR2Yg`9lUo4>gW$rDfPMu1(M7ak7@Ee55CJJ1E zK}@{043ispkjoZyF>MF>fhKnH%tz^Yqa=EmCeJ~49^AyRPZo4d_U_>sw3*P=e)i@% z;QUZrumgyQT;0A|PD6~F@I@Ly>v2yk_>1wirGLsspNHyZyB2nN&s~5tD>!aHnk~3BZgl$_S#># z35Swlh47=%tUDE81+Rc2U=c)M&0ws@R*A_@Wsza!o*r=4YFUMI@GQrrOA3&o&xKls z?>JXbxw|*QL?=qCy|uu-yX0MuA1lV-9Be+}gfyWLpg+3#3d+f@T_s;RB_*YD>p^%$ zOM-C+G>MSBfUgf>;6Ru9RH5G*7~wUJI?t7bUcB9T)veFp(PpDCnXnG=0^FJ=O}?aP zCIm^F`RU=FXHLh@iIUX}mEL&bj-B}EC|$0Wx>iOAT;AB6h_h^&2*049cEE0IbW~%* zDOn!kX0&8N?mP*0zSYTqLB)YQ7EH*fsTEl&hZIS502}aHDOXm%dTw3AvL&KKCLycZ zBj`$QB+4cRJT&>70q`Ga&G@0jh*9S+yAE?EUHYefe^c;Jx?+)%(cbIkosp{lz^U}t zq32;?aUkEv!l*Vh5SU$lCTn?%;pE9%Rfp^7s=%c=WiyrdI0Y$NL3GIK4Ct7#<05Rm z%uLvNstEIghAI~N)4q22M_O0be^U5{pKD{Q7_ijUdLsA3;7hhtUiZR?k6D#(Kt3dF zG}w$$1U1z<>bZz+8L4~Ot2&rBa91t;1^sA5ZSD2HA`t8(6bB3;emHLmz$|f)5*5Wu z_`dk!7kp;?tWEX52Og{Cyf4y-4YqB76?n$0nXlxPkX!$^>*5poS-LPv>;DR9Wv3uc zO8m2qc?W5vy87q|dmehKSeUm|^}XkEzlD-8b)n2_+tJ!@-3Bm;8;&-LtRtf~0y)43_ocv$xzw#O^B zn-CpuQv9V}#zD7L6IJFYzX>4)aiBv6AoGSs`vfFk{SeNO+uOz={}-3sDAseX--cgQ z8Y0q{Ozax;nmw|m^ck9iOifB1z&KO0gLPSZaG2S}?v5_A$j^cI`aFp1mjp0=>ibw} zDadT&Zxz}OfNowJWY{3}uoN@~!o?g9G$oJ77 zfYWA(IvWSwtEW#J*dCDc4du&FD-D}9=@etOqm`l{g3#SF&t=iuOpwWB9d&>(Z7~Sd5;LPdKOPWiHed@jpfNavDBt(pe zRlXHn>c4#H^C}}%G@U?m&I1QN*{lac(C9qR=&SH9@BzZU-tvV@EMj$l_XTuNTFL6<^GO>srAKd) zZ2LgRtpaI!+@PuEJN04Jlk7nNaVmjb_mk?`jj=$63GjjKR5|icH-n>W0;)YVxX|K4 zPh(^rD<4Q}ZiZMso^2dEE96{&HoJ5_raseMD5KIXld{Q)b>i%?u@@Yhe{|gXPeh#s%PESS2w@DX18|w{ zH#0YxJM1W2Xz3X)er3^&wbAoz04}{Y|M7By+$zd0m>6Y9R4@9sAkXI*X81{v`@t^e zhp6NtNoP`egwkIE7$O?7E}@{-PXoimXd@Zv%a?Zpb=c%FWJtwpO>ee)ONmOu6|!Dc%!PA#LE2H#qDbaZBBjF?XzBt2>wb1c2@%Q=TD z?!Uv4k#BXW{I@4ya%v#ML~yF64W;Z=i+pC{1~5(@qhG8pf_3{0z!HL#K(A)5lCp^(Oh*O*Wnt<*Vm7cn~b6BvxuEaonwrNRj*OUq5$8yOTV%xKl=!qOtAt0pF1tGC;;P(`1s5LXd z&!?5fRt|;%&n{axIBHjm<-qk;^%AE4v~>OA1#JKXbYy*Zr01y)EbGkj`3;TQ#HT4b z^>!Wx_0&+;iV`x67NrU_A_KE6{hz$@z{#Khp1ex{o_no)Y`HiJxJPmu>7NyeZgMN& zXLSdibHw)?J>`3JP!LoYzLxjitpfRF5qQc#{m!{g|0(WLC5oxR^sx%b~ zP=ART_)Khei!fpkG~)(2yvX%!15ahrOGP*hmA%M3t(CAc=}iV>!O;jMj#^gfd zwzC|ihcRT5xvvc#0DGbQq9JvgV=6rL2jgX)db7gv`qa*b+cHlYW6MNYUqC$~)+i7GDABg^`I) z<&6&WA1fCw{NwL)Tj#19X($IjbvC3{phj|#U_-jFv07F~@b%aEcn(t$V|p-6SV%}s zb`Jtdn!!4C$MY;|>&58nq)2`s7vc7MDNzz6a+(%tsrt*u+oTsnf^k(<92xlX@XV#d z$us8eor<;>^4`g=|9mvL2-TxFWsn%KIU)>fq7=$>SjHi^+|%7lY-O}w34R6<`dbCS z^22`62{4g*b3WVpx1iEU&JtMdDuD!Je=uXI$;$^RA5K#rc=P**a(_M%oclr2?Q963 z+kkPe2rH4C8!J4VS3C_9m?NOqh{qHjiZ6N>nv#-o^rk&~$)y`LDC7Hv?RzJ;Z78Z* zCSZ~x9#}>8%gXI!6MPW%fM6o92^eL~J=69PthQO`ciR|aN;CP=*rO?CT)zkzDTa`| z=Nx&u>>xkGB}{1oJSz`3k79rG<}Q zxTRjjDkoh11EVWn)pfv5jOU#}s(+fFCf{@(4Z@IH{>x%JM&;UBl(7j1Q*9QPLF3z_ z8@hiiz5~14tA)4#s^WuI%Aap5CO+hMr(j>f-LSIxg$3tL zQTs+khPfg`+BuEF>Jc2jU*}Nzom(!$MCSWJHUC)<{W^bp6B8s7v;~+@!f{5e9I@2koO96&Bh`Uf zYjg{5)4M$p-9QMc+TFD8gIubEA!k7M@%}}EdjB>kO+|3ga4_u^o^=p$$>|$V>lF+x zNbV};3w2;%Rrd`o_UX)16wWI;d;I4yllK;uC7Z)4;$wCOa%zi9kX-yV(i*5B8v2=S zaZb?fu~!yfn+zy1hVkT-HO*)6?4@KwI~k`xK+Ds(tkO9HYlkrCTF+#gKM#a;41{+l zCO5pQPXX6c`g7-| z39e9NudRj!ZYw~Zx)trL`-yc#TRo;~jqD!wF}YB0Fn=;eJzRkrYs!MH4|!Qkn1(%( z`&!-31S@=^+v1wwlL4Q%K6Rw~qw{5yr6r z_Z{~eFm~@bH0RK^D!1C#@WZ8IMd{sTqaMW_aREW3>&`)@{#R1Ka7Y3S-!C`((! zQLwXucr~rdW~EN9rX7FhASRDeW#C?XG6+x904%xXvyP=d$=Br%Q_jnM?r|y!n=n3B zev<6AcpOwEMz%WC415Zil}pP(B{nK_CsB*%BpK%io=J_Lrab+%wY5oq|AG;3a?$;> zB$b*y3)WP=AF4Q6t(4Sd$-6`##B|%8hsqJY)wb^U^QMTN+?6ZC4%;16=ZhED%TFno zGGLowUqiNT*=UxgP@vfhkQi3~K9Swf6#vP3D&dk_r$;NgiFuv^DBPuBLXxv!@n4Pr zc!nW;K^>l-4Qf=k+rSHRujxEL%#!T>dOiOot@7tOAq#lqKV{voU^OUTpza z-wi}=mX1{@@kp*tch6Lq)L>$CpoW=9PjnKhrUOvEJZluFwiMFiHnS@Cs~2{OYyIg$ z>$wIrv=f7qLUZ!P+kZxNjYY885Kd+$riA&E5VQ;)69+Z-Js9(c(9JK=WT0)@q)yzu zlpzbFjY(E*BlpKb(%Tsa1r*BH$pI3@R44-^-njlB&|#b~%J2_Y9~75^oenG=@i(ZQM5ksWvB$G=_6k~4;aE(-dynbRq3daUGz6dP#o-3UoF&3J*BSN`}f{+38 zy|D`OmQ!31E`;Lk3TnDxD3&2LJ^nyiPQZrkDP4{ZNNeF|Kp8>YuU8`rIs-!QCSG7V zsqMb6>v4%2r{{Z%&;^9u&DDc`V34d?=&n3zw$W2(`JN0uH6A9DVK*;Oy>h8T)dp zUDsH`HTz7`Ek`E#iC+e?X&zP{aF;_IxSWMScK32>$7`Y(fFD8=a_O4M)9)fi-N{Iz ziLs4M(0S=2WTDV$s9*6Cs2hVa4x~{~1aGqa2Vq=$R1d8=|4J1i-2HvbMV$tJv^+qf zH1eKM`vf~6b0-N<1ZYlq>^STfv(QCI4#lOU2!O{|)SuvCe#V8vgme#v$xjw#F-S1- zuyC1=l*Ft)DS)an&Gr#z{L-9lAE7Yw+=p(fn29d0CeS*iEsP|n_UmKhfWUT5WyEdX;F z|3>6zXkh~OFV#N>4<1~Elf4OP7#5DI9Xm=LO(tFvPF7OLneV!`{+2O*z3JG-*vt*L ziy8M?nJ_XDP_f8u{=Sq2Hk#$xK0T9S2dt0g@7fz)2A^@{L|V#yY~9%dR2+~z@ppx< zDH8G`p>fq<_WN@chiWaD#rk4r8=T$~u${wy|GwAd+uI4QW(PGdt=BTfK*isZ3j`Sk zQh~OtK%i>om#RZ=c<&v2fW$oDz!^syH!UOMlaGp1_SH|*Zr#GkX}1qg4HNB#k}4Nl z3j&)$yOqs^#4wq#9>9qjFKQo4!n=ViRQUD`Y$Pxy;5hr3{uveESPZ5;xu3b9hkV%x zGt!&_zS~KR!*lw(>hS$Bs<;p;;GC}L9}0_|r98$n&3qkkJDD0d)mG~&sNz|nVSyR- zmr$w;z|*xaTVpT3hl0z5Z4p4=Q3OLc~kR6diN{U?vC^kjM2C@8cp=S{#o9DTUwT74fwv)s9Sy_c|} z|GtBvOk~BRu1TK?qJnF42%efc+Lm};NzPBbEHTLZp~N)yyPc}$PO1JA5t*=gTigki z!x9J6v`UVEAhiMz-|RFHBCier-TxTii$)l~Hx7rfNc;uVFRRx-b33jgy&Tcj%nxZD zmd`^%MDg5xNdWTGSu@qwXoW_3fMP zaE!LuHA*DR5H6F~ltHQ;Wn_vWXDoSk*O!YZz2v6@qg=qC^~WFgEc1a5`f-3Q#zL;^ zB8!Z)LVp{#+r>|BDp2+&_o>Oqps8oU!NUGsD{I7&J?qXM@Y57*i~>zAJSVMFzWzji z`~`=U8su<*yef&R5=t9CIMQueS$u2l-X532tSQN#qhkeqt#oFHm_?hhJipIw;=a?ckNg!2X2VgB%%e3s|IpwciBVr}K} z{L_n)#5G&xI<(4m6+H`=c2Cw_GY$NTjaOxYn`9v#G{NZ|2nn!W8G3WJsb z>|^ENsCRGy%}K7TFYpjZ6%llo*mS7aZ532Zk+6kS0Am0-Y?wCRg=1GyruM%BJV(XH<6sxRo@7n+SWOM0y>tX z;H7a=0s2=k!s9;sL+6~h6YsBIZS>V@*(@RsKfJkk-eo zgrz5j&RH-H)W9P>QxC7n50vt3ZH`cMLCYr{C7Kg+(ylibRC;BfewS*a+@ZdYF4erR zJ}NE}j?J#wfb{XKla45eHx6PHYfqx}@|#8K>^XYYOIxAtefQy_ybH2J z_OFVhFOTM52{^iv#~emSy0*b9b@CVmfc=n?e6oC36O>E_QtrRcBV_Tw;nO$y+Uk3i z6Fh?BHgl|OK2w`okG-SNTywVcxbEq!A8G|!aGO0p5*?iD4AwePT))m$i?>G&0{6b?N`9OxZT@xy zW34jwaKGA}vWKya$mIYc^Yk;OP%*V^fDJz0{I6byW(*ixR6#ImCVMjp4;309HRWZ@ zEG*8ORmp1F@M7NXf0;he9^3>g%M~bxaaoqtRY2Y=804B^+wKDQkJxLmDi{H11n?P1 zS4(bYyPidCGQnz6(^$$T10_wa;pZEpSu9N0TYbMnVh{}*XS9bO<}3tr@jtUANE|yc z#Uccdi6`^@pJYgw*#e3@$P^iK9|1xO59TvuPD)_<`zPr*HW6|jaRPlz`*m;<(`2Ol z1b%1aDHQ`d($PS(+h-9O2>sI{B*vilL00H{iz;QP{lq?KXI=ooA&XgbAZ*1B`(iz{9ad@;80v zL#5{h!yeFq6#><}Z8SL5H;V`v9RvUt>3@Cylzsfz;c&eVv=-SgNFmvM&#_^ve_I%e#zq7>bHMqUJYQg<*wZr1V<}sy($hY`VG!JHGj>3`tW)6} z9YpH_mSKsU;iaV#*xuY^!1cRl-3(!a5MVYE%!_?o_Wi389TTcv=t9rgFon0Nw0ay- zQU0;8ZwS+T3S@UOL~-kmn)&#Y!+0VN-6A{v(;qZdR9qT=44Q}3a?d;P%p%1@O{Fg_WPf4XhH}VHn?wE|!80!9FM;@5!6y`qt zy>Zx73aoc%e5>A#+puTYT|y$?_lAg6;CWZ=d6tk(k9HpgvCUa|-$#Z6qf*c@Dk{wqMkO25zf5ImbBBz}Zvv>;BWj z8mRsUS2w%4vPgKtkCTQc=z^X?Y#V{E&;RBALfrZ}0rL>qEdw_2eL3?pGxh@2>p$*< zHFg_X@_}%}M)TLz!aW?E@U88|;FFyCu25ch3|M_k29z7}*k%6)B*8?4Es4(`Ds)@` z42WRYgKj(P;NwFokdl#xEx8}RHDuct=hi~n&1t4?UhP&+YgP0>S@|^oT)Tg?rY{vv zqmR~JL3#$qPY1x;l|rdjC=bo8p2@2L>|v<>XklC41k#=#6t;WNTQIw>2)dqtYm*Nt z_3(>8bhMXQd-<3=@q?M~i-3V6t*~k6B@j)%4lQGam<_@Z%IShxx_bOo>sAGtOdv~v zz*(1-4w-8Xb1w9&w@k~{(`^AmsQBg#`m9CLr}027zG#@Z@}lkH<*+L5H+ zK(w|sREzkn--mz{){hIN3);GLK4d92QD58Y>R$-i68xkiDrk;ZI}1Gzz$evEf&U}B=1CghX5I7iQZ^KNdH z(Ybh@iH63iN!TtgGzc@buXI!fFO{Gykd3f}iPeF7$Cys@Yuvv5>!6KFN<;Cpl$4Ri znf+TB3-TtH3+UQ?*jcV0Bx*G9{=4aNS zcQO+(NeTmj=w~y#z+zCM?Y#^WxDxn1RXM*e6A9E-}lwD!+~3W;GEz$Iefmf}?3ypjsV z1QAk=<6*)suX*`*hiBb)0DxnER1SA-x+rErHECmgQ)Nt3lr`T`)L0T1$D2~62Zpw! z(L?B*%nls6Wd4Lrbw`YCFM#6DmFp*6huuGSWbLJdHGrqJMXuxO)vLNWCZcHM0;V($ zMrphde;Z?DVbR%kKbi71mA@=R$*F_H+5gwYk4l|ZVYb{K*8#;j1J}dE)q*zyz-^## zh;7I&V^{BAwYcsqU`)6U{7VmwY~}@;lfOBb4)w;Rn~&Laj_0ro_jl$$>JwUdB+8y$ zKJ$z7l?|1Fk43DQiT^0JT!wqMVOU+`n{eeXPQW&!CZqHr&%pkTnXUaOeRf!huUd52bspi<<#h`xb zD_nn5IA^HbZTO}w z3b=bk7xYjpSSU)ss&NNXl=`>dxo`^M74!OunR--cseO1=WO{7z*xcI83S0GTcoA+@ ztzLaby=(sOq$Gx00E)DqyPYp0hv3f+=X@Y14oNRZT{aA*wX~!~h%78OFg6`MawL3v zJ8EVOil)hXV9JBI8`IIzQ5arP@~*F)=_#w)((57(jL%MmKP)h%f{eR?1;z$D#mZ7A zvIM9ivxCE&8$)0Q1*Bd_gMTaq-GxDPxZ^5^4FPH!Wj zUm6e2jI4o+>EDlgU|yr3US0($0fB*l#Qjh8;>tcw8qK+leq2rRzfYewLsm^1uqEmL zt`TgSYe7-7g%Zz!myZG$YXhW)j>4Uv%q{u_O~10)i@ig*^~C_SMO`~tzCsgmUIywR zwJIbT5mh_U+)+54IZe};e^%^gcVI$6C8;q|h_EGj+%Or*ih%`A4onS`qLNY*9Q_l} zQXt{AuToyOoovQ?o#9N|M<`vw`g9VQJ_~Di4j1E_(;(V)2>bz7K&t< z6uoc$MsgC-jD&Fl-|guyCDO4(gC#fa<#)4;`q{o$>|^5mF`GGe@Le{~Y4m1h#pNhm z1P-JL1nquB$p^1{ae$&(gX{u73^H{N=C}(?wC6caBunH8!Rmlu{Hu^0Lye!0k{sa^ z>IIfZ18o8bFYcf!LfNi^gM&0j?}8f*3H40?{6*zNIMnrVAzef3n;U9T{Gxc6O=B$Q zoWh$-P#|;#TT$7;-9_+UIfaE#DRdva8&k8@r*LtG?9g82UhN~y zhKtPUD)dw53Y^j(`b6Xnx`@7*u5Kin62G4rJ<6xX(`PRCzUsf|St$I2rVgi3YMm)6 z+4qS97zwJTR|krKnof9A*k9_JDtS)i-YxPj@B`#Brw<%CvK%$O>w)(UGa=aZ@EdQ; zI5oEO6^=NA4-G7T1lLY7Y(JkxKh+2ITj%~@4y3tSxCsY)vL8IK6+fiD|6q7W)?KuGc_~W@ z;#H3J{Q(;=JqQx+_f6fW?_0nuZ*)OhYh{Bi=aqu#BfuDbbSrr9z(gRTqcsH5=#|Os%>e1 zlIG+2FTc+dUWAx*mzIY{qFB!NDSxPs*%ge$&w*%BAL$GVez-`Lt6Q)j9F??$uF}7( zkVdjPlfdTB&o~vuK?wG&_@A%ecOA|NXnQ9>%p3>Eh5D^qZ+5C6sT}6J%=7X}srZ<8 z8|4NR7l_Ol==u343pd`l(L_N>~Oq+3vU7w3xU{3jGIjD_2d*rr&3$fg6wJ z>{&KA%ojnw-2@3$SVG!VYvNtcYso0?D$XYWiTK(*jCmw(yK;w|VhnI2bA@4wZtH(; zFH!#n0SPqc%{G5n|L^=H+suE}Wa2Lcgpx+NivucGL z>vkY>UdXoPAApW*Gb&bTw?GKc`lJqGw3^kp#6*S68h2zB3VA ze?5q#@4~G$!Xz-X0n?+!>AQkvyglT7*8luMdEPjmP}}$SzBN5w2dX96sRUAA{RV-R zZ$Kd}QIfKdis5P)3M?Z%A2i;P@Hu@2nectMDX5Ovvn2{6uH>jveQj+6%HoATI9XxQ z>oCLMRQlOW%Bf!(JZXc7haG zxjQQbmixgdV3X{UFQ}U85?vYHNfp(Wz1zkZFYT2Jc&|FNdpAhVXDBta2DljLW(7{Y zvXJvik#r{SqL@yY!njc7olt=y*&O^-?wak5ZEgG|f01=}-$M&jId#GMq|y5^#Flz# z#&^**^|seX*EY0r3w|cOGP%u0d%WFq7M^#^V}lBv^0E%vmj*}(24ms?Tb4Y3{~#xBhK-c1Qh|BG#W11!L*hY5S;2$2D4p6J<^y!y zDVz>HWRndtcd+m9|GivTm_Yo0GUuV6y-)xdgAKu9xWX9jBeoc^^K%^vCPfZEEUad( zzgi&Mzu+;$DkF!nAC0MRSasDD9(w0ECj=7*Z=djI=}%RrE(vJ=O{iI3PKFBqvLK&9 z1b_pHm)?56ghY44QHC=`J>=R~o}DX4fr>D&B^C{&1!d`dn)=8WsTskK=$tW@Ty@WPE|`29n}hlxl$)NLl?~y@vZt-D7}3_;lrR z;|qW5Zmz+TfK?Ggdr2PBxc^|}WE1pXX{=d(LI276>-LVW(Jfb> zmRygl$;`gvjIXn{d*^0@cPme5a)^H91^kBcR_46Ls!5|m6=783*2eG`OtehD`PxFeE6fBn6Pk{*^?(vUN4UI zasRw0-~U%K!`I^PR8r{bDW18GzlPTGn|^A+bkr(Swi9uUrRXQT5$X(S12RAQF!OzR zaIM`HleWhc0a?#R)8IZ<$d}lksQB$)l{_AX=8@fQ#r{>T@SyY6G+})Z1GBx{!%e^T zMHhKgjhKYrlSLmDN)zY+ndptO<3%tk0w)Q(ENaeSpiBh91GdIiwjp}jsY!O4{O#(4 z4w{$60X>Fhu8#*n$T^Dr&4PYrBIrMRNX2a<%pkwTLc4tYA3=HW;L)SWq->CXSz&G* zHwzdqXA~0bVB~Gm78JlH1|fXz5pZ$EW|qE}&G2~SVP=*D;F~IHqdk0qZL(#`baMhT&oUJbwb@M9RD4-q0!dmox4vzui#e{J*=UJ0iJ=s@ruQn z5p9rqPxQ8nHKat9sAgArqF>GwJ*kBKN~}T^>Yp3( z{RUk1#h8yMx|b#I!35xXsvu9FB{QXxpSc#a8qqT)3XZ$lB?p$gb#`U1RJM98!`y8% z3L@9xxW@N}=?RkQPQSkg!R-prN=u=J7qoV&2?dUBZD14xGz3*wc-W-ug63d&%$tv6 z{AX6cag$A@*`mm1?A;EkF;(RvXO>^J6v`O?`AQ3%$y3No6Uf=>iTL;%mv`(?5VS?l zZ2f1PK79FdZ9jH|sIIP#oBj(3&>g+^o3I2YZv;XbOcHZ7%!81%Vt}x`23=@mTnhBur z1n2agTPv_nT2Gxmedm23=#%@hDY_7qQlZR;g+G|aD{D>OrM#AWe0(4Y{Pt&sJsaO! zdLacjadPy)^XCHZeGdI>;S2~9dK;XJLP}-8#Utdie9w>?KJm@qy$)|+s3v_Nr=c>? z5J`krU?5G*{WdhV6It$(+Nh!KBLygftEympa#9U!tQy+oLS1!X=bv67#`>k`@*vv= zP#~e;GweIy50q>ItvV2oCoM|cwUiPETP`@2NJS42R)tzRVh?F)_YdyrK zo`{NB*I#5Ct9^BgO9R=~3+W=K#}B*M0)R5P*RXGQkr{e;kkT4zs`O3uzn*-cKM=QR z=l?V-v~RDkLbR#b6}TlqC1xd84MfET&{eDuqsvP!QU3qs^-@FFQkcx*v$EX|1?uOY zAZ1+;UoRTulyAge>hND!35Jv5oJ%gGOrmkL^VNF+BoKCZv%D8kC@%$$%i9Cb4WM?` zNO{7``=!bIdeY(?c3riWfT{kQ@yq?k==W0ak%Z+pM#tJ0Dg}S?aUzlmbEW{orph2+ zB@HT4c1n{+jnK43Lwy2B9TomqbL%-ifU2eU6b(I7Z^wyBn2^GDBo#9(x`uDayz`_9&Uh$c|(DUzfh$ z@9*#NxO;qV?oZBj&UL-tulIBIAa%LS-4L#vwIz(y<#H!^uDnG2`;Q%@Te7VNN)V%3 zVmAY{ROan#E^v*5+hF7C;`l^3l?DR&%5g+-iBB`lmH|Kg=B3DPj zrw^N#$Vd|Cfk9$X`aR7KlFLu%_vIRBRqG&pjT2f~#Q7##cMp#i zh+8W{;vDWoiQFge9UM%9fg#;H4~Y6W$*wp9Tulu>S4*$8-y zMF0Iyq6wMwzzRgg0pSoMTnBQqYg}A6`>^-QSO)X7gsh2;^Ub5GbJ{5rGU=N-A?Xw8 z>lSVE5=9$fHE-jimeyj|*eh=@k z;NS>uo$LyCAoj_oaj=lA05}VWbGQa~$An-w`oFtwrltc}Ul7Z`!Y8s1zhI$m)2<$@ z^{Uz(9f2XEc)tBm~qCtoe2k(;Ob7)KSd8GcxK5fT!GSkSmyhs22zE3qrpmY)FiB<>#M3{GB zoI%gm2{O>9MZ*C~8C+87Km+|!DPeMf)?Y_+rnV?xo8GLiZYV-Wq4%&LP!&kCd6CT^ zja84NDrz0a-h1YS$MZ4W$knytnr#m6;(@8ST?jzB zRbj7cNwuVXNF|PJ&^t;tt$HT~5?t94*T@>ExxeiC>}_Qrs)SLmO(Udp%mTo?j9oDU z137K#=sb3YZ`d175ypCgZ)Ykx&V&`7(}yjLd}X&T<{Rw|KBU$w9L&GUja1s_pj7GV zzgj%bCb#jeXd)j=_{NRnF_LXaBd8+1xFlT5>N=b$VFVn0lqUq`m&lR|VtOIJNhFH^ z2|@*!-udMNX==j%oKzqA3l92VA(HI2S>wrzQr^`k83l8{Om73D^*~B@WWEo894)?0 zBp?B?lbJI^e0B@n29_LRbuF%se3(%1P+c0Sf_^X9YNldA@Bnc_Cov=a6Dozzec7b6 z5~~p41+%MRus&orP_t+GvdJoIiF}CAva@`xE7zmit@>!GUo%8HH6Qs0Gi#sUO@%F1 z$4+;T0;xOva9s%$)I{9XPq~Mz7&A9SMCz zWQaRBfLe20h=(SqW@NSkmFm@-65thg+ym6H3!+usK;ZlvS^@;x$;n*O;w|l}76eya zG=w^5?lW1GqBuA>zA`ZgS%UNH+N=>+(3a0j)=FxY%e@u}U2J&^S&-2x!88hY3twCE zPS@DXsTxSyX7tz_si4;KI7%7yM3%VCeXAgcc$oVRf?6jshV#`b?|FJC#D~Qn)M`~k zKj!rLQ-4gg-cqOdv5FfgAzIe~(Gpz}CMQ2H5LgO&U zXtuRy>{>1U<+bS%3)#1_Um`teZA)L-pxyMnJ-tBB2#qpiB-v2M8`huBBI^Si5{mmt z4ElSNVRmWt$H2tXPMSCpAVP0{8Tnboodw8RNr*upsA+I^0Q&zpFN{GT`-`K^wD*)Kw`8aF z_$R-O4eWt`HY1ziENGeB@Fxe+9SEbifJoEL7pbsHpQ)-#3j_#Nbs|(=VQ1dL>&L zE=bu$#dApBj+N|)W8Q@%{(<%xoZWt4psO~BBOF0xu59M> zR0**}tpgIcp(Fq0k%%L~N%I*t0!v-N@DDhdUO=QpS_-py8&nIIY@r_yKDh{3Je8mb zLWOl2*3-MjFr1zVQ-n;y7^H%2-OTc#K}(l4s}B090eJ_5d8cI8wm9{db5BUFkhR7; zkxxlB9AW2b*P9(Bf+%J@RB;b9s*Pqqj%!};PXV@JjFJzg&We|4i@~Z7`~{9!ruc%R zIdgkN2-RlNEsXnOc|1aAAmBXcT|P{58~)4v^IiQY)@RFC5))(g{ebfPWMr8%jU1!l zU}XVGjVqnB~^!|>1{&7y9)^_|(`4VG}inX0}nYy5){dHJ9_d08>A?NfYj{$ZOg85dlN@5IEQj9!g^R;$#l?#0b)|I5%4w* zI@R`q7!G;nUPv4t0a(kbzaaU0xbsQvC9vOu#OKkY^RQD081!uaZB7u7_)`}BY=O!s zeYHULZ8Ko@katrNy-lVq6?s!s*ra5 zXp{n%l4*D#(`x4CuiiYr9tecB<>7p_*1c9U{SQuQekk( zyM&F?K5bIhoMaTb0*fsDf%MCFlN++g)Qw;F!)8u54X$I{K5BV06wdsPbmby9{oS$z8Wy3k%Epa0wG&uMyYtmbb zdlI`KAr?`V|3(4nwaR%| z43({pJD{z&+KobUYhT?vU}XyVXa?_b1e_UO+MREGwMU_^j9zUVyQLxWs1$u((+ zhUh7Zq$buVe!wzSA|ic@PwRiy=4W4I*LRsdJqY!iAA0sckXMuq9nJc7XZZ7(FcnC- zrSk5-OvP#QMzk$EZro{?(0hfgAanBkQ*K~6>;LC4vCw33mWj!;o>gt^8)m?6#1D7y z1E`lmTUur!rYI)3FG#8wUlc9x(1OBJ5;Q8hNil&8ez7kV=uQ@9)@2BBLxp6=rZ&-q zn3J*_imW>C4%A5MFuiS}uBpL{XgJO^K3fpNS=7u5g>*w1FBKRNklAu}cnK+6GUy5j z;BZk(vL9GX@m)Kyde!7k;f)diAds&ozrcb^ds{4exon`)F~iGo1EOlo0fm_AnC=DJ z8-PTu8o-N$wIvc>Pv2*@j%6phCeCuy0JEz&5@GPG5OM>GX4}c9j=ww}e)M~9x@i-G zCi|@}vpVlcTF`(zEV~_Y&`9LF*&!5zk1z}?O1ap@Jtn0^BYz#q3~*U}yl3x2Lcj9@ zsSkYpNGtKRhG`G!3KFy*y$*e@z$U@yA`rBIL~TU^H2R9O+qe^8$=Hc+i4V@Yj_yn! zBHe!`kDOQ^Zx}nuemSDtK;zJrc`spBg8Ec=BL&ro6K^szxgxHqOd?FAPCuS zq(HhYMd0`QQqeIvukgMK>uvwn8UP>HNKr=V^>P-F5eh|_Biu^+TU8Ymf!qqB}me* z7UNyrmLSC|3oLCXR59VAf`SCI?HkY~@UHcV1oU8A5LfPfoL(D^G`q}U*8S`u8ZR;x zqWlIK!ao=#K(hOt^m>iT1Nw|?JdmU60ILA4jusGhU1vrC-uYNve)Oa5Ge<`Yh_RR| zSWjB)%Rgu&7hl-*TxBV3ee*7w6KcKr-*pGMSVVdj=$Id@h@{=eFYQmkrlk{;_IvIO z!^N3bKT{L_=Qj!ZH|$d?V92ey^ik-B2v-yBf z?&_t}M>2S*kb4}_Fziy7{BZ*ks?m5 zvg=}3(9tZBfX8vLp9GZjVi;O1A4}((>Pj%3qoC&??9#6q@5B*F^K%$DaS0>DnNO3x zn}N_qi>*g8&R%Zqbkl#!H2BWpwxj#N-S6UYE`y2PtBvUj>-f`e`^)04lx01>UGQ@c zq~`v@8>|2Nx{Ky68KDjoK2SUs85?U!0}AtI7tBk0YE@$L#qQ9O_L5W>xwhc+pAO;) zO{n*FtP`{SOBi}5z=wf`0U=eiX4lZ)M{2DscaXId>FcC&)~PT97`)Qn(?AJgzVzM*K=XlN>l z8gxW4kHlDnrfWGXnA;Rwu{KyD6vAq&*CCmd^o8TQDnk{>50D#&UQ!xDv!D)UrT>UIOBXJLGT*fER=Molg zUslxIe2n;JEnqafN@m1Bjml4`Y+<@|`jBS1MQ)Eh9S@MEQNEv2xNS0B|KH0HZ+6B= zqb46uR=Eow{9@PZ)VBc?Pyu0>g@3pE?`g5RK1o<|X7A$e&i*8jhos5=>E z82Q-L?r>Q}v2czd-NcisnAydy-{!wt;LzXplXaY@G+tNWUH3YwQOWYka_%d{b$4LN zAZtd!dAa6X%+WOLrrEUL><4M6}2W2`kXba_h+lC#kWAM(n?x^ zo8GZ*ArmaM0G?L-#$;=J;mnxR)B5 z`PkemWqDUS9g_3@o~ee~2g8HTZ8EMf$Z$>n#0`7oM>zc=UlliD8HZS1`%sW@zI#~w zSOMdU2Ow58Y^2;isnR4pBclc3u#%yjiGz>thbPkl8o|gQwISh8vgnt>|fP%yXmWX%nt^v$Tr@Q_@*+GbS-kxS;IPdQ4LJmz!s6f`+cmsFkf)et9 zx6ThiDO#xvbyOV520Gmyc)QgL-I^z%KoI1UY&Z*~yV;k@!lOZNJn5QqZdE|=f{br)aTQ4E-boK8&7_7s7_dj5FXg?1Zt;n62^VGi zrUu@LJ3(I`?e1?ZlK;r!_SygILV0IRa!vBwAvuaehfd3->+9AB=1~~x8d2rdJR;x6 zZ^bt>@^C{O^iz*nDQs z_hiwrU8Kx;A}+Pn#rdY_%sxc|DWEL+HK zN`(Ktz>^O3B}Yq8 zVzb^}b-l{iYol@eQF{jk=0114!%$pwVj(GkVk1=AT?+9LY=9W49SPTrzhxFkGR^YY zfjMUoEF-o6_42Kh;@!=Ee|xh5pywb|D2>prCpi8ngizE0ZrxnV<&;c2WUupmdl|{9 z05EwcOm5`EU2-94J4x?YrAz~@7Y1C+=;UcAeYS>>D3=KHCr^gJn_yzeGHJn(gXhnR zDn^N6=5BuoV03Z+-YQ@>Kg&@}K>gUL&deVwajt6n>scA!#gm3gEi294sxQSEG>PgK z8&7H)HNm@D!x}2J=jt*k4-&BjNFFMIS6Nj*j07h#LOiwvv^#D^% zY0#C|^HuBtDl`xOltHX)zlxI&aE`v_I)&EtN!N}X$co6ma8mRl5(wL|+L4euIB}J7 zYn2*cunOFIl|;KBZQ2y-BMRYJJ@(vQ1+syPU%!5>$?UTIfrWY`GA7=h$J^LG_(^V| zTzBx|pqLQnSU`VV>y*KIu998r>|QeY9?J;Jj@Da#y%iG`S&}~Cv z=!_t4#CZhUX8;s(E{Hx%^AS*kDpmgV1ydvt$m7B8d=i#mz}HH+y#%Lj@;x`?vsmes zao^PG_}^<_oqPgos#65C#0a8Gt)r+i1xg{KYhJ)l`jbRrM8sL9V$s|H$Av-DOQnG^ zydlf)ySub-vv*5JoO<5b+U(vy8@0^J-68ZhyAen9dpd0lYuX-rmbI#&?x&UsJU@hO z+m5>EG5CI>Q&%pF2x+KZ5HQZCUkhb9{$WToJTw?C=8bnmF(>mT`)p0pBgHEihh1WR z9m;)s&*(-61Od8;fy+IZ?)?RAmlLF_Jh+iOg-@*?W71!)Q1LOU?%Z=+a*5V5O~vJ} zKKIB4N9YC^hiY^6sL6cOPKRNS&tDumM|SGQIsLGUcN#4$Jc4gwdNO|oc2cL`Pj1c! zyf0TKz(06&voF-PvR=1S`EefB$ctXSRFuxBe6(#aXWdDqCD%}tc-DUXnC6!YBG9Q~ z#D>c|CWD;m7dIlcRIl~$mgAY#H%AaESp~ALLPe%7@GSLMe1<}KLoNxM{+slf0LhZ2 zcgct*@f|RJWD0_S4i`!7&Z^}FfU_jW2xF~c-JdlC3eh9eI#ja?)=b&o%RlPweGE+5 zD}yP7Brb@3obH5il=Tv#SRvmSDrze;CorIQCK%AA4^us^$Eje-hFWh#S1D4b6U%e$ zREGMTBNj70MlD_#n56S_dJVb#Y;&}xfW9x5!BDDUlF>w`0I|Lje21-k9^I6Zs_V(S zeaP1}jc0wv>B!`J?w?Ml|8rF@%w8&r|2HoAnx_wEfqKkt#fiol++y|Nc(~^Sj5DHp zy+=V@QUQT|2Bs}KL@SCOuyiF5D_u>}(mv|z7F*jPPccTK#%2T6=kC7TT3TkYyZA|5 zo9!WAHGzL?L@rw6R$GY3!r?iZQrvyr^#3GWR{>YLy{YC z#C^iod}_=VDC}a=?!tT@Pd$#;yRuVr$`!vALh}` z-{m`fN+G5QvP+O>bEqDkTN@&zc=xYDww!Z0B$bD5ZzGfZ5Zts2j{6Ip{>z{b3+6a% zMfRQLK2e5U$IjRF17+bH*Pwm80OmfI(T~Rac!i=9%jFA##1=Zc zqI1Q$AA55Zt5AgBA5eq>tcuGapK7H!X?N&N5d@gp%e@Vf6p$&BXJreVSryAku)zF{o_rU8d?>pc@g_-}_ZqPq(z(=Og)kiOm7i`~1Bc{48f z*(utRBx@g?8{D<=Te8eCR}&8k1b=|TpaATVT>2(R>&*frc$I=&@tY)R{*JO>Ejs&k zLfrXpw{mofbq(?lq@!dCWY+p=*KfoDG*R^v3!mn~XR8s2=@Oi)elwxRL()>6dHz&l zDFbB26+_vj+4@1oA;XbLtOaAyc15tQ4t5z}YFH&;i1P}eZ*!oO#tRr#0x04#SF=1?k-U!%opXskkUBMW zp4M}WBwF8g!j|m2?A=7xJ8{vSFF3t@)=4+_y<>QdF8ui&p@t_R36alDOAH;`1U4YP ztRGPvLIdVC{&Npff|)q2V1UZe4hi=8_aV=72@}qVOqJlbbcJ!>$HGpgtyjz>48aQ+ zM(8Y=5g*J}5Yw|4m<08FH>?oztpd5bo1?3Qp`rnz%|JqF*<-i6>;%ha&c6qKvG!Fc za_i(g3}Wf)!Jo7=ojm=dL)ou!y9x;Rk%|r^)RNh`-&F2L&PNjBN~K(vTH0z7!_Jvc zzQ^;cvyx<5q~Uq<+Jw=lvhX$1`vdRKjkTSNPZCVQCS4A&z1Ruz**{V`W%l1M{TqkK zjwQGsC)~uuZOFcRXK%E?=yZP&>XI0h@?ne)NFGXvb&_guYu@)kXT{aC} z;%E{32u3qJ*O1_3Br%ZlNEc}q;-HJE_$oe?3c5ou4h}wekA(fS2-PB}Bp-UO-J?n` zs*m4}+6Ry12-}W z;NA9pmIXkzpy%JGeKPK#ugd#c@)1`1Blb`Zzmyne6TpiMJFtokK=B-;Ag43; zqfS9{bR9>-VV=3lQ=*ahMZWxBPfCc-222bJjc(m4#J-y8 zVb@dh3y_VS*)l_)*_4Sm5aKIa{wV-~n*%(U6at6NMs}Mx)*gANGltjoZEM zurjoJyx%PCGcJZ96>k>@%b6Xr1%N zs24NBi1|@eY7nuo?y_(2J67f3ipeGee)J*Iqf&pQMxFY?%})@3DP9FOhIsH}PEVb_1`{%btE^8j8iY29EedQePZs3l%L6DV@>o%kIsx#K!9uue^e> zIst6_aC#O*2AvQv+Pr@qmaqo_dAiOQHW~H}BWD4zQAkiSanam{j@_SNq1ksccGBC=8D>&Q@cp3iV* zae8vHlIoUTp5bisoT!pT$nz_Byq-mGumunldAY%Ya!dOoE@Koi5-^;RAQbDlMMxO4 zyGDRQz2xZ^h#v#<*!xmkHQQy*}V6e&gkwA_`8qBtOL(Oflh8g zVmBP8T2DmR0b%v9KYvM~MKfP+Pi)gDJE*pN=@|yj3xW>6zKj>~U)7!|5C>mJ0i1m( zTEI11MnQG{NY$UNq8_=JnboX$a+i_pd8zU#tpVDY)JaRX#8LUBX)6htrpzvPYcDxs zzQye73(2HrZ`ST5Z6KY{!;M zOf2{Rl#I>Wj&Jf%rzgZ?=YyX<_O-Ztac4vR>1OuHEZ5i99IB81qnnf5K$ zIgKqy%PW%FTMlyuAKuWdCZ>rWjM;@Uzui)i^Umu$)roAo;h6|NqWN^#qG?vvWO<9- zu7ls1uD*~Ari2CICO9iKfryo|JiV2Kj26knc(kJbJ# zxPJU#R-toWObOlG~Q$hBeM6qZpG-qdD4R1ZcF9FQUrs6Qe zyKV78RajRD%`kEU2}<4$_?W6z;BY$I45Kf05)rya?H2w9&!Ho!pbzZo(jcRHZqfT3 zsz_L_XMFs4bP*QTPdiIK)Wg`ty5XW9!V|AQF}n<(b_Las(H1POj;nhM_CAi2z`9qi zfY|9mCMX@A8n};~mS;MB-DPoD zfK*0aB2qNR;{(@`w*#uRAZ8CFVOOtqp={_dEPB*@M39V7Gr7+s*FOWMgB*hVX`mYky zwp&?Cm|9*#@!l(W5Vg*dvZ-xAWJ7UAm4>EZ33!j~2irD@lEso$8B4NbAv0B7MQbB3 zYn7wcM_$!p;LqT)fU=hvwsh{py=6Uu>m|6-HaU`Sbh?*JNmypC*ZmUOtXwapk}&vF zwS?B%WUVC3+GM?i7Gtte5{5CMUm&5Py{VG-kIt4~-d@Pe9eX=do<0_vF_!54JkdLT zJH&l^VNU4!whG2zJ0#e9l^fFVN25$U76kN+E1o9v5Mz_gf-=lrh4LnbiimEwie8=V zzapw@WgzPOGSq^b*nd@&kdpF*IHDqq`_!E|xf}pF8m`*RZmS zSGh^>9dLL&MBSZRYO61e>Ft}Ibvkzym5j&^y)kq1WNOKxzsgc}jf`v=1CO2`B)%Sl zUg^`hG|ptgyqQ{@Z|jNV>@2Al!cO%1=;OGM&62&O4ozkw^)l=a#L3Lx2DR8u<7tVd z+bXv5wLjKKp_71BhXC_o~y~z{D{Sqs8l83PH9e<~o@hgB{DVW62peSK#GS zMq__@gKo6`;oTOvm!o)y!YL~N>KGYtuIr3~CYgt)^)H~q{w-as&tX_S5ly$znjmsV zUzZDUlDnAHN$@KBC^SR&ts(S23gqRYqt+`dC8Kujbz7D%eVFm`)W3=f>yT0{O_cQP z+{PG4rgcnf4X_HWZ|Q6t`dFLP;UzzoM2nRh%a^hXuHFCU)f8kkXvaJ?A%~xEwg&RX))I6^0~Hn6aG(TUq@U#Kjn@&Il#~6XJZS? z;hX*`W^e0G$uwnKl0s<-*j%lF{dk<2>2KJzK4zF@g=Qs&mWoaVE{biJ8$$r_&**hn zttwW)8E<&7I}ton`o0bo8kW3&wd(O(-*_){vWMy{E#NI=kvhAV#ZDNU2 zOK>kbH}GdtNnxoF74NZ_R67PJ-y6rj>#5+HV^{&He-Y4kvR?Ms)qbchh7)#h2t@PO zY(n;eDH5@7)Ma_ns%8n?gVsoSB@C{uu#EBbO@6>XYyI}^X-g$cq6;tBL!XZHW%7K1 z%xyG8wZQI!uHV$87j!pnuszAZ|4njtO{;IWbV_70Qp-Q-t{Fe37HwDg73+|>-qqg^ zB}IYp@~l{b29}@!aF#<4Z)K(w`U7>O0UBI+Z5qC=Qe(V@}xWD8jE6`THTnnYs}jh zeYSY#$@3mtdG@xBrCVh9$&mLch0$^oIb6BD6^Q-VIqaJF=DCwR*9qHJ4(zBL07l*~0#{$Agsbf6pN7Vp^wAjSYv1Bb;?tQ4)3 zzZ6=>CHBNEcTj42BgvmwRMza4cCg7bm+S7SI__uq$^>nb=JHY^jkmj*I}1LlWAklgYHruw4|mQreVc6qxnTN423?t5gARHvD)wCJD_y!@O-^c$$eH0P!9 zX+2$N-s1f~K~nvh8mW)AjfHRau>0{1ckFG0ySrix#WnUya_#d=bB#hJ_he}leGM5| z_-iMPw5z>$v8O+Zifhx}gG-ebXA8go61-B)?>%Fn80M@3xpac2%ZSG+a-Qzk1oqNp zs}mgaJAPbE!rMo`#d7n}y*VW+=vYFtmjPH=)-Rmv&DpFKYE`6;uTXrX7 zoD>eZ1(kVw!H%yFk$p%z_jh|-uRMA49;=pgE)~PQS(!#?S_y13>G7ienL@>Q&#IrP zH;dgQb(1pPhbq5vDvo@`3;F0t!O{#IWw0B`aLylM8^~&zclGco#g8yjlv1WAK^cXR zZc(EO3&<=O+Xs#f!slPG3umu#B#adskEB>EIlP0w(L#Xc2KUrmGtN5gZ?*gQ@1M6F zDEj&+9e7E;d0n1K%cP8oo{@^iqoWXsSCz>Vi9extJj)?BRfB2`g4PMpOgHtgeDZ}o zzTXTABH-*I+eds|mg9|{i!;XUBzY`~DC=5t)J&A4bWP={TfPsEIY#08mA5-oJ;R@o)PUyt!_# z<4|JRnbKi4M=k5OHac?$c{`T<;q5pt8p!I$Pj;@qZ#jM<Y|oqe;zk1DiDvn*T&H848vSv$J)K}zU=R+FI$r9Jx+NnLNV zw@a@f&XqxF67lNuo0RzKH>82Plx@BSTb~?Q!bMXf2{Fqb#N1YXKdNyCd?J8_%1GA% z->G|ZA%LK)e1==TcC-)6&N-Hf=zC7l9#7MF1RO}Xc&O+cSKnm~+y`veLSYk&!qoW%Ilp$ypY4euTP4IJv0^=qquR%7Y4}FAjg_4(8q4fvHO@1E_N9V=Sj1l6hc?ABaHdv}U~epj(`?gkFR~BfnmndHC$;;Pr2SP!k|PZ{Y)J*2IACX&nG%giQLvk)a3Cp0MYJ0Yk2kF541K_ zFPOHj%@=~tv3LEgCR1m04q2J7pOZ%Zx@Ro9C(TqsCX22BA4WqRb}yr&DP`9vpD_4_ zw=7t><@Z74k~DHtCHrvY#6u|-8Jn(=bIS%Vh2I*e{Eg^K815O56ajNl@S1SyQ!R8f z1=tQ(bOtnfQ&LCWvxhs3fk3wk=<+;JA=d~bt`{LE(}aNN2s}41rtWILlyjc&2C^K9 zsFS2|A^syY)GzKeJ>2M7(Qj~%OrtAoWQ97NKFOM+g=fFf>f5Q-PEP(t+o_H&lEhYd zZad4U%Z%c-CDTYJl) zY^Ad?@K&c&k)6qQ~SV_WS#Ne8Ff>7BmZSJ>$Xt<1nh)I zX*)DLov-qw_(JLIa{kB{NhTVv>+^&$y_-zW#sa=w+I-%!lDv}lM_5WoFm-BSxNMWK z2`^dN;4;h$rxAAO!}Y_nFx8AcZckh|%o#$=URGgm!ce@eL}OTw=XhW!(txt2ihlxq zOl3d_Z+gyDCz}?z3|Pbj)ELu0_XHaED~g`?PFL;DLS~&vj&|0W>pANMjryS{-=sz{ z@>{A~x*-z*)w^|1jd}+Se!*umf4{xh{X3ec^Hf1Np_tu%bN7AcQqbtsk>|fZw_m$L zAU>iy#cH{YWKK%E-De9qBL-l?^N}ovuY&L`nw|??)`f1Zx0?Fla5&Qpa=2ocs9u++-c+ZIYMQ4I8( zE+37l*4!>;bRugE%|9x3kSo@W0t~Z|9`R+g@5|((cvo zf^o78u&5z~&H%=g=XvRo&vI0ebgjrNW>X8`NGBk$9-BKbK>GU?p@iibz<5wK^I3~% zTvMBdi`+cN&7+W~U-;-ITj)HEiRm*0r)tNntOgxz!CQ);^tQJbetl}zdRi?Z&a^Qp z6G0oqes79v^@en!FP1y zU8DANQs2BcBC_RbKYkzq{h|{yA{ovo{PaT&i1YJyAp2h*#CVmKXVM|!i}S)62>yYz zj@~RSPMcal?i=7~J(ZvrVa%w=>xE2Z1x!ny4@~iB2eJukCWZup1e_BA96`x8lsMW0<*H9 z>F57sema)uHX|nkcpiA>Z-toFf)D&msiv;-go3px(?u>!Hh^u(ZtW7AhV(Q+KjIS; zjp162Oyn8OE^^E~l6a!$TkWMs#^f$lQq993dNR%hPpV>VX{Wkkub=Q{DwIU&P1Ri0 zV76aT#kyTlmp{#ucD|LKfu+hwe>&vWw}3AL%rtqbuC&;1^=ZXna} zC}0A~GSnx-_0h*P1~z(^NW*)uaEUjx!QX6uc#&+No$=%wml3sShtO{4fEvnIJlboQ zmh;A4`zH2u!4 z!ocVP)Tu$?6f1D&y9lfpWngs?EZ5)wY^9*IUZ%T&P@!TQ{boke2=6HIg9IGu034I8 zfpT*h$%SdX7wL!=gY=<>DGX>?0d%V9AabfD+Q>6G{72d|as3$}(=PSr6FDNKiTa#z zxLFlIiyCG|)2+U8YZr9{%6cdRL*E=Y* zR*{#NQwI~Xj9*dGx!>-9brou^T4CutEQ;kM(YFxqRhCF8{%znQgIcR9yCvTX{zJk34Un-SyRrCF%!LR zeT6?@4=LTlFuOqEaOJp>+0PAPUpsIHJ{315BW0$c664DJEv(Kw9DlwiLULHaG>8;f zCe#jnFaS8$Emy&~l&cz*Tj$g`?4KjvE~sR7ctq6~vQlEzZ5Z4M2u|ot(LkqM zP0ZRiHa*u{g> zHQ3>{Ie9s=quuYtDYqxyH@QMjhOt0BfTb=Qp8!BT z1;dLV996nqFd|b+_8~(v*rI|B9|QQX@mKs*{v3a{^}YMf#V7XPJt^kQJ1LpxJ6GjG zKk(}!X8vz7v*EYz0dK!fG|AH}u%N=Ay_3VHda^@$AcpQf@lnjT;Kx16jR4szo#S4z z`pYUM9!~ojVxRRaSxUq8>`y1dJHkyr{rL#5iG*Y_n=DNzX1-zEw)60>l+TlYn$8sR zU`i!**$ht|$)F$J_SFvy`mc#kePi<;nn!Qgh_C_|O2&5altQYkC8$|m*1ME?!KPe! zzG`koI|AQAvryy#k_%_(8WrO#kae%^ut zOg;Vy9F6&6HJr7C2x7TQYXwe%(B(Loji88_OX z>%%#Wy4wBwRgZ;zl`?iUj789ft@Sm<4qPtAD=r?L=`jNt*I%(F3*_dkm*q$2`aKB- zPYhsQ^v~}t`~!>*&$52A2W6C348glAKrQ*)WyoX^zV5nF{`I03Y^HxtZ9p>yIT_SsoNp0ZX+6U$t6FGusgO3zWhC%OW0ifwy{U&(li_ZbUMJZIYfDI_ zJtn_;M^oHT^Kf23A#~lRWC?9p_ncv+T+b8)r10^;D-4)hI1PYZm zkgtCV3Ki%Sh~{CchqFrm!Z~*yLZ%Cl9bIA{fcDC8)KWTDS3LzQI-3?T54Y8gb4BNn zv$)g519?U?YQJ956oTS)9 zlwfAX=5`g3fA5dN-VOf!;*+bMP_N{bA1?`C*Yi%HT9o)1aw=rf{dK=wjB*b6vL|?j zY2&WLP0P^ZVc7V@H%jz-WWNIUs27LI^SwY-vFNW)ot)+~1f2|S9d;rUiJCE(kP)o{Owe2sc-`-qWTFkL<(l9N_ ztg?P@31@XRf80(_wrH1%(1C zlE=#VxHP;B=*wmV*Su%+n?+}HsT^fg5`;TJ7+Bo5bP4q|x4_lj^AA%9;cNNhP$rK7 zm*+<3N(YrnqS2IL*uv*ajqWbK^Gi#VVeCmmbmxyfw;1zx>vKHOM`=LPK`H4gPkhFO zxr_x$)m8OAE1-KOdg-?B?=&h5ooJ2_*Dh~5Rn2b@B$pvL93Ot>G6@1zo81NA0Yn}? z4jCwz@c0;A`-ayrI3>Is_5jnSen?RJpr5aU83fQ`Y$ND%k3>M{vwqW{O1pwyQB^<3 z5}wuGlqG~UTLpFSu{@4niJt!h4@(?nO5rQ40WD$G5Ri32yz;n=-bZNScouQF6yWr8=*tbH8w@HEd$*7;v#-N^o0fpW^xF>@fFecN$@E94DP&btnJKd_Ygt06SHJs?Cwf6-f@Y7pBJJbsjg&pH zqIwSBbh4RB`7cyzVZUwpQYfCLFSe61-o$;DgPw-Ryz4LF&0V$M!UWX+LDkEp0& z-+y&o7~ZaZmD2vfO=h-A6EmMOlh&J$MhhNLE3@1ek&Z}xP4o=9m_pc!qyB74+#MYe`HtNfY~ z7yZK0t2;l08;pEOfbaw8Pv%wzi+%VU9Up01xbiOLTp$I0BMa52nQztnL1&K>6wH5Z&F zFNfTI<;2q4cNoghhAfInQM!24s})-zwO0nlpAMs2%WMUQ9uM)J>R-frI-j;-D{nuj(C-Ox|`=s)hdY5S>E^hbNhimP4y1fMuf;E$@MX*^`(vW>>C zQNnCa(a3j&#wvtKbgFQXG<{EIqQefiUfl>0L{}0a(yDR@G*$9mqf&WwXW^Frb^3pH z{f!4kAMb}!p$tXykVG9_mTOF%-%o|uf4mhz&x<-o_>3}A%9$+op9Pw^a#2b_a}wqe-*6dGSwxWb_h@o+4e{-Nj?cZVix1{UJlpVn}Rmelt0?efJP_H()#?p zcP;l;xVSYT-{yHf(MC(k@KMB=fxdZ;K|pU-tmk0x1Fb6;g7n+b8?jKg5EcU;B_I~6vQO2yFyV>A(x{u%GwDe}-Fll~<#mzip)H*EbTcR9INM*jd;Ps^*Jdw>zQ z2yxIohuz1W@0znCfoi4zMv#u04X9A#*>GoeY`v8iwVWPfYz;A<<%pRCXO?TZBZy7U z!0N{4ISfM@h6Ozf#&h=99MrIqcA)Ui>E{{<=rR37Mv%qw3;WR$0zp zUh>ZGc)0gF(3h1T42(=eUy5kki;m)~^+Dv%%llr+@0X6X`>X%2xhoH+YVF%n9Y-pi zGXzU#ZLZ~c+0Rn~gev!3;Pe#3p=ziwl5c2dL3#KUglCQn9ICrwf?cEE-C zaKW2dXuCb@weqlwGuywR#yhgQDfFA4vhpcYfrXx((ka2i&l)_^AG>rP`52;9LlA)G zqoz`MJqgym*BB#>JBFoffW5LOVMVRWr?w9b-IV#v>XEywH{P?Ioy_$d?9WTIRw!|u z9i4KS9{n`8_=f!4dI$X%iOjOh3Z2fg)qDYmSm?fz7t*8hYOvVX?!v>is#g2zN8biN z=DmLa*S-5)J^?lrSb8}Gfe77W=>>rt=lEp?{f1o@&BLy-EeVDeS>lXiHG%CiU%tDa0WW8ZDf4LknHy4CyKH8~EJGaOVKc!dwZ`zV zG~-;)5jci`2@!|{UGpZ&fzKe_AR_M%SLxZ)gRNI5kpk0-JX&eF$?#pmL0SH7(Fyyu zl8eu5kPWLuBT#Yq%3EuT(Mn8I9t|bmLfY$HAJp0NY>0SgIA@qv2I(9(8+KwcW5-}Z za>K=lm<}&*se=1)oT?E|sva%XZtRJ?pJX-g68*L#qq02d7eixb0ah3vQt=)+#ZmDY z8DBaq;UrV-zR!$hkZB&%t9*|eTr~*92p0gp1<^LY+qPCKU1EA2>36Na1u8{|dDy)wc<(q;%IQm?7kY*2)GIR+nTd7+96LMnUoz8TDxHr`RoGGz6fD+UTdUv|9X9s< zLCeNJxj^b}b++4fjGf>jB6ft{x3}_+)B%S0lLULS*NHh14Kvj1oldOP+Pri2s_E7X zjY{mbCEd5H;b-G;=(nON+TF&^sqU;nOx_d%oT*8eMK` zoN7HW`caMTH%5DCL!z{2Pq*7l3_mJuFn)Vpd_);KqIxqGc8g6FmQvkn%G~k7qxraY z+E&Y_d^}d`_=i3@zM6~{ua6QY%!^*(evL7~SB<#OstLaW7!5&%ZgdwE8fVLkGrNF1 z5RP!8a3Jj<_{(-`M1SXQ>FZ^x`n9e1WqFHtiJeU9*`7cG%`ufuHUz@^qHXp3kG8^W zS{0lM0i>F3viPCRx8Ph+%Y>d{CG+*P4B>It0awt>o_i0AE4kXG`2yH?mcyNlBlqd4 zbo9xctp~s8o8tL)U zxHfvcY_3~%VXDnHp+OEy|Lu8GAOL%c4aew13kQc-LcOAtqCI0;Dr4@Pg};wIdLlno zx}ylw{4&}4beU=zbM4YhhK9sGSH}U@it2Pm?b=<871ilFVs>T;k^FZfTsysr;H`A}FFX_pvlbS2c${hNnClhiVg_FuX;@Vp_7iN^| zzX{G?Jzwu_NM8QYQJbv0qrj)wjATfj2;&VzZOALV!@QhVde{AQnCRsl1>Gq=OP1Hq zC@rAWj?JspYi9pazACSL)$}ZJsr|IDdhh0N0HIF)!qU^c zh_0;!l_A&txE_pmYL;_vnbOr(^lEId$>E=;l3HX%)SM~F-5haB0;#S2S@orUVYq@f zlb2|8t1OzcS8m;FMXC!eCPHSs&92~n;Zi?mnl|b#=5l}dDPq&OPkqqoQ~U8{MTIM^ zZhhy;OVsqv4X1isrt63w(P_OdtO+-!y-<71?@^RSs|)nJ4y;T}(`@xeW!wDMDC5v3 zRflb<$BY8c(eHQp&bT&I;L+Q(p)rxd>b$ajwjezbns(IB{0>hN_qiOnGvpT^ZVenK zd!sR)@q}UOE$@&*wIDiDbSHUdsr8*>V}&tAa`mV8@*cJ7as1-7_ZVcy-mBLbjsy|x zXjN38(7)Cfad&YF6V!K<2RD15NYxP$i~SEA+85z|M=Ml%m7xxNY*s>M=!hgXwMA&h z?5y0$Gg3Ed_S0tRu|oZ9D|w;R_AX_GzQUM(J(m7z8>1HB(*0r%y||>HS=79 zIk7OV@pyF~h4Do~;GJoT`hiq- ztCDn}D}Or_lP^f{+n31kcm7$t5n@Ekw_&+?p{{5?-|xe1+h6vZ1bKtTU@o+rqtUTH zGkn?)P+{QXk0b|*Lto2xcWPC-?DlUAKRJ}=13b5r?vay+jiz~dB2V9KUt*v3(gHX~ z-+7v6o}l*G=>LQMN-nWp@)NMt@B5H6>~P+yr)RBmw4z+qZ)6%n9fP{Csne!_2-lJ0 zN(G@hErc`}NoRmMV-36;{Y9`C3-q3&W@ctwfTqJaviSes3&Se|Z`_!-iBQ7LQfKQs zp7Ni?K94*E-whhlp-pg|9N@cV0=UIOCdYiS3VQH@IDrLJ<}FI{6_8)-_ z05#3tU+9}d8lczIezO(rP`k_=mu>PFB&E85FEK!wxnsKW1d$%TdhEj@zJR&VfVoOC zk*Ib#lOkMq{gg|qAz{^9H2FAi-H*CLO!?_4*97Ed{wS}gco@5??KF2^Z)%gvlZhXX zt)5)1A&zoWl)j#|*L40Vo2GOuXxEw~7(zj+ij)dhPMmKmP;4s>ix-QC^IpkBv!L4?pg0W)lTwC7)C-bVU5 z@ghJhT#K@}9!)Y1ai*a-xF7*LrWmZlabW{Dee#mH&U4&AqTF(SE#~RxF-PR*BEz}=VIIEM zTYvoS+N*PaoyMPT_U|)wo4kXc$N%Em+kSo_xY`9l@GyTK>bi4wTHi+s^|doHFhHNv z2!e|UzbOIt= zLAmRx&RoEA1}J+Yx(V=#rN%8`MNby7z*yK%F z*^G+&YDLK=zwro*#EqqJqUm~4N^aTfeF=M)509WHfB=A^Q_+t+a1%JG0W{@H{S)K; z6Q@~pVCDh|DAy?q+ffcKwY{p9A@{naRr5~+@MMC<`C5Nn(rFsZD2qOyj+ z=0g=P$fB4V>W{XBR*RwE=fvpg=~?p8dVkD%eHq=Y8241p^FKB47zTtWZu{0Mzrwb=pJfPATTcR`* zSW!`-V!V!lqQbofH6lzjOH~!jDs?R_ttdodftXf_s3+RT;R&ud26~793D3A+VXycB zyU1oe0;q%1coL6>>i9H|y^CuOPK=mjnG@R0%U7=iRo~!|5V(7=^ouwuMr+Gq!CX>7U`-pUZjK~+KMZS+{t<&oB9F5W| zTIncAnb56@e<^?ShK>cAUo~iZ_xQOyKh!bHEqjkGE}c4pEdF!4+e*5>Z%y`^3>fN7 z_s*u#MD_>G5N&qS(mI!C4``UPd;nPY92RKwOTj@TG`06kzBOcjO^uz(hWq3)jA#d4eCz|t8VqfOICEw^ptpZ?{R;pz0hT#{NpR5?fC|jr>9F*I_6ArkD?%#-TBil{_{aY?k^$Hg$A6CG5ay|o zj6|mFc)=A2J1iCiD@xBDv6|W9Imb*&zolDUF)a;~>ZhltSn_bE0}aXu+0vnG7)Q+M zE)&HTcb1d04}$y5WD^0tISgD*wd?G~5N~O|s7&0_7mNzlwG$STPijP?*0Z6wu%T

%+F}B`daM6{QK6oJZ*)Pr<`*fKcCh5|dJ9_}p`f^sJ*@Dix|WO37_I4xN@h9q8vU z;!wA121vl3ti<$#e=o!{KNn)OJXVDBqTO?esloTT*4*MvcaZDbkVEJmay!o)u#CEo zW9?lZro74;bJO3OH4CNSHmT?njxg_-cePEYYSXNWdmu(Czg`$CF(EG&**zA~NJVy^ z?$+BB-YXGD0@`6lF;acv+^;C0oblf&iR@qBIN;cakOQ&YA5*6MU3_fCf~GW)yGNG@vr`9YrHQ55Scx?>N*2b#AaW{hhT;UNNQ&i*-)2X z#NEm&8m;x5gRVKG%LCcNdWjjAU%ouH%Ga^G#28Xa9!b4&j{RR_%iduPNOHCqtsgsC zSo7+y4|X8TTRL83#qV$;TyEW1=(D@O+kdY><-l=6fZZx`O6l*_`o~E}M(jR+_$S z+(7Xx*3Y8AFBW9`dk2iqEW4~uP$ew^k^XOcg+FrXYkqtBbFEOf#TjCRMwFh!Ok}MftTWXtO@v z??yaFFgas!A*kb_#jWUxUjO8f!w-&(c3F=j@Aw4#qujv$4fL~R#Oo%wv`MFVgMluo zOMsOiYs#!YrnJnpzeVM1BWSF>V8hRdASoD@b(qQ#!29VUl+Dk`X8<7K$<*5VbwF7* zO_^BK_=l6C)20OgS)vxL(&Z~IRq9c_1pD23bdTHA5=OUysYr%67fLThZ^1!##I|aj zerSgeK{F>melx>If~CAxo|^d*4U9TdJVzmC6!V6BW>FO4ysi*{5j&$KP3-*xEMUxl zFgfEb{d@RL@As#)n|3O}p3Wi)vgUiBLG~Nr zdfMGOp}!ve@aG`q;+i(M+5%`vy`Sr9?l9I z+T7X>7g*#+<$wjNcSczd#C+FTKAouuc5aA>tk6w^1_u>h;A&-M7NAvT4YMdZI+uJ1 zudr_%eW_*?7>6CTaDkkSJFq-}`Sm8_#TY|$P#I`>_BsuFGw)s)qrMNR(AwN!UsJNW z8y3Z5Fw^mE&Fh#|LmaZQX=o+u{Ec|uNGLZ#W~Mm8pTK2dg&o06Oeg3OS(6k;vI-LX zJut|NXR*gX-dKcx=d$l{?qfLerbzY4%gL1+eTML6FP2dn_>MR2ys#^}vp>bR#p%Sz zp4U?w%S;*qp|RYEtC4UzVYmWd?z21W%|R2(pXfyySK`D=>E+axT{4k};h1SPTF2czaf$P(y12!&eHK9w7vHNj3I@nF z9iGN?sqW^S$4Q6pxi{IvdbFSBIY72oJp&*ct_^jF7pc62Y2UVluyx44T z5k5wUIr+tE1kJGO0E}qAmR6}MO$}>wUG!gTbS|osd?goe+_i@d3M% zZ3Bk_Guqs}VPkNLX>+a7HJ=!W-vBLps!nb>NpZE=V2qaiBpBxlP?8StB)|%r$#qlW zy20upI1$tOAw*ZsPB~bl>L?jfJGT=|^DH67{3=2bduPqca#0*=x@PDAtj4e>#j1;6udPVWHVb2Od4>JFug%`#xS= zeEG~?Z|!@YZ5QKBcK#kq_{$qJ4x#y_W!5M>@T4lA_~f-j$1v^hk!xy#0D-k-MGE2s zq&E2xg2k6K50uwx5Go|^x)3BKqTU?V($LM%jWz=Yz2DAD=Du$Rak5i*k#OG{p$6_m zYL<}KndcJ;yWHkX;;sh2uu>nw8ErNSgRVcFlr+(E@Gv%0LpwkPU(^b literal 0 HcmV?d00001 diff --git a/code/global/output/steady_state.png b/code/global/output/steady_state.png index edfd9a70f225dc7ba7185cb4d818c02207e49c6d..2fa1e86055c2617eab677e2901c6c1cc842f8b96 100644 GIT binary patch literal 134733 zcmdpeWmJ@1+qQrLN=Zm}cS=f0Hw+>TgOqfKG)i}alprCX2uL>q(g+d~(v74v%rNln z@qV87z1R2q`}NIQz!F*4Tzg+<9LI4Ud!n>76mcGqKe%(}4vw;toc5hN7?^kN+^xby z2fsO7rA7lkL_Os7J)SvRdw9Qav$~`H!o$VE*~7usoYu?A&E3}7Nq}3F`!Of2jfaPe zyBH6T@>@B3omqGwqteIy(3=J;SEB?C|KG1-%=8ZjcmDkw`~Xo(T1NVRy;Pox zNGr+x>y`S(B{(tiUoX~sD|hJreG$lmE)S30!{C;o#!(%GhZhr2n|0Ab?$E< zSPo@-ej-KamdY zO-P7E+epXlO^n^ok8IO5wY55n781Uv(%N*0v^;Q`?woSdHpAa~*+PzL3ySsDan-e2>R>2?PgCh#)nMNIcF;)rlRhiakO!;y;<~2r7VE0I&aCszAO-_JRXgq}T{Sf6 z4GSAl5Jb6?Ns3IsS|_@#FM(!ld#v!Sz)ReA_`ws)?^%ydHowEy;Ysw)bM=mcVK|5}$w%xaHyjFumRvo|U9p^+aM!8K} zOLK0nPe!eUzosYZsdbW7(eo$MK@Z2F%N$0fP;l|IrNBvYO6?MMR70sx@>3LuV z139ziyV&ci6N>c4n)-T$u>$!tE8LOXClSP4hHo4DtS5Vy?oe z`(N(k;5^r`FbYhS%)N$A-11~g1+|y8$XW`4@|KFK^t6O!H!~=5UE`Zjh~4IJrV6|( z46D1RN507v?9Pu0lP8yOc%Ka+KK^@7VS2@(;kSHiZ5K_ij?6cFtLvL@WXB@jjJj_O z78}=l{MKe-QA@YD-1$_wyuDp|s`)0@s&^M28XC&#V|M&@{ivTm#BIIrorItHyrM@X zw4=A`MYmS#E7#(s`uh6*9MKwWr6Gt6Z0>SK`lZiEi`Rbie3N_k;-YEOK26Z|QHVgb z4*R1I>e(OGOO>nYYSYB=GwCQjjg%rU@BV-`xyc)_%T9TEdwJ83lH;ZGwR*9Mgp7{*RlldwHlmEHUb#YJ|AEAXS|Ig7u2r4 zIi0hTY3Tbl-yr|9>V@n^^YMoLB%Fbso}Z)~f326CFHoN$ka8+gxkcfc^pjqtE_FsL zo@EeGnt1*8Xa^_ax_!7iVU~EkDQzx!%dj8f0oDPjm&jz~P~=jenM{T0)vNkI2feZw z6md7&IFcw`qxR3@c6wq+wr6Wtj@JhYPfZS>J9+`!X2$!4&LzUmi{C&>emqr%rAzpE z)jBUxCs0dt!E5b)s)(A@*`*z4JsfM1wxd_soT@P4h~QzB0zm{TowJ#%|GXkp{`s?K zMMcG6*^T&1?JC~`qGS#4aebVu~rq=0s? zQJpF9a;N=jlVZc0f`TG9B_-uOl$e;fSw4oi$2V{Eoy0^_OAAE>hvP(%vW1PX_knSD z0H;Cqx6=I(3_K4fC#UfG^z`)RTHA@8^73-*E#)Obl_jsE)t+WhyE!^aRMG~w`DW+l zcK!YR%^h$5tjOk;l$4BFxVgIW?QF%8379*=+qYl=8Bs@TeH;x`bqUpRauSDUyVHZ7 zXlQ6S30rwVH&G`|iG8foNn58k2zYbR=K21dLS>l4v+6hz5fRc6_*#OLd2KeoRkWK4 z!gKvBWg2qp4Jwak0*zGsn1*RKo})MXd|zOG1pZ4iM+EXaR9p`Lc)TFUL zAIG5-`O{YE3idUwUf7n#Gf(HUq2LCBWEwHJk4qsmTvZv_e3+Q$bF%rn*6{Pn?d#L; zv$L}c+1Qj;fd5ozV5xMTbo`{2$=|G3Y1(50AL8jCtxG{DPF^Ghwp_j4 z=i|F@9D81AYSPpS?Fe*jT?GyKLpOPnAie!Jo&-Vj<1&200^5lXgZp0FKj?a`s;PGK z2oZ=OA^fu@l~~88x7P#i5{|zfcROy+4c!-4QE;=AG?x)zYTVV`jWf3a9^P6j zHADqK$^s&3Nwwd%S0tbxT$SW#6)Gl7flN~NK3bJGk&_E`GqtoVIsk2=8B|F~lcS=d z;sW@a*k4pqf%AV7V1sEKUlz~X{4TH&DM6?Axo+0GpVd3edSnYb>)y=9fW&2o3hj2N z1)glG$DVucO!5)wY14kxVeiIxe5J)kqjgF%Sx6GpfjG~03#>41@@Q{w56A0v^SwAQ zDR$Xg>Ig#3+qjif8;E$!#$BwR!LxogTHG24!|TxwR$#jL%=4xg0ot>tU7cam-qdHK zW^^2KQpw?-^QYSuF&(1ro20OXQV7FFchNh4myABz(XF`#XPhnXy_qrI?W6a2W8;TK z3e@&D*H<`mTW{`R#nwOdk!WpgO~t!_I4(5*{9`QSG_TJ_8`c^>l+GpK&&s1$kpn?V zf7_Es^g_FK$ZYatYZNCzQObo;>Px`;g4;K&b}#TL5OCeajN^NKprK$N8qI#N%1-KJ z^jSQGhJI6kF&{#jG#sGfOx8yvL`0UC=36Csfhs=&2HZ=WTV9U76RB*l8X}g>Ln5PY z66#~);dc^qrPA^Hg6TY+r~5&-2=9dy7R}U?kdP2h0K5c=)Q?SLcdrr=;iqS#Qn!yC zDz%M&R+!9b`44!}!qvz)CrLJ^E1`Pr%PG74fvPvca?{P8_8Z6IKF8a-<@zsXXhOrn z*b+=eGKR7r+nP@v000xtQ^BnEHs9~+xOk-+K;Uh!gZVNC_p#fHZ{B`tdjUo|A?P^T zzXk&{BP09gs}wBb`pJ)m%;Vm++I$GAu&iGSiy1F02V-Iij^tN=drCN>&RC5%%3J?K0FYbCK=$n&>KgA6Oej~#-I88 z78Ym|tX07Re}uTj^SQR4D(4fw5pw+1df}74rC4#n2e&9#j|7~EO%7C=IBPhv@il{r z!9k-{ls`d2O7kfY0ULBwO(Hevd~+{tXuaFg!E!U{CT3$B5vh5^QWI~^!HI^7iY*XQ z^e}o~b*s^uRpkXo&GPAVlU=LhA{XuJ=vy{rg=X8 z{G>1IC5P5_LHSY10dX(4*i^bPq*J2 zYqo!jiW}xm8?sU;Za2Hi>B(z<%L8rqe{-OUe(&sp3C+-TY*Z&>nx`H`h%9kpN#k@{ zKiUcrxDr7V7p!V;`(_-u>n0Ff2=t`PDYd=Whx@L~uAW!^2|6VOmBN zIshj_-pfX?&te@*8BK9!oCL!6g{>UD2e`L?ej))d@Ep>>v-=|kApM@ka`kMX!Lm^P z4tQ%Yse!IcHKDl?6d{`>>>Q7LFA?ZePsZ7JPZ$?Rz@#`kQ!0g7>{ccp+X6y*%I>+;i^GaPyj{CfDr z`ylp9Wx&m~PYzjhUU%QYT*3I3y4_>);#<(#Hp4+Jdd4s)$y1ScY2B?@ ziE#$cOGtY=*Y))^nuP?kn(?g8{PqU1=D>3mu?l#~(amlCQisZoe^2xyi_h{Pc_}LA z$RFEil?&Dc8ewB$J*!@p(kYP-aE#RE2Z~1V!{bgUY9&*ZUNqO07CX&XrpG3C@7^`r znJoKrdipJjkj1bSCoF#(K#X3)>jBS$r4BqPDXGFCjXX)m z%f!Ol#3ZdH{}c6`46E;1f>S(aXDI#0D~uIyih&?7ctV^FS_*3k8anpZ!;agEy9*fT z==5K7KI!g#mg2PUS6IJf&zsPC6^3i}&&&6oz*J ziomlszs}aNP!Sk@S8nw6c@4T-Pje_ z8X;_=L6SIE#3c+yz9{Pps+nH2{8g9sJBp^bQh3|w#9?j zP`g;AN32^wGAB28tyK-$DxbGed`tWA3{B3VX8lwkrXVF#eJSX+&8_9w7yt-8vO9^H z^=`9u_S1jZR>ibgVOH9;inoXW$Bh^-ghlkPa|?+8n2zXofZE^84W6*j3|nT;HM$nA zTmuxe3a+3s#sbx53>{7kSi1AA zz-V&ShzEA715`x!F7hL)e`rk~CK>wMLjg5`4^#hAMDMUSzKlSO$C`b`%J=kePms_Qun~IR^ z58TuA&k(*&-C|^l8ZBRP?m*~XxhbCu+D}(JA4yrVlA^b?KF^?qdM&gE%<-C~J%pk3 zY%BgOW`teD`}oSQk1`BfF$zojuqeycEP zNi|T{Rzm#g#=A^DweRR~{_a|5_<7japT^+L%k}=GnVOe)i>*F|oJlOIJpk$wf!%!j(iRk+r;@6;Q%tg z@6!nkSmn{)hFG%k&qDi;O{#AbH$qnjO&?q372-Y&R9VEt$4_GWqVTwv{HR->q4yg( z-__5^qepePm#J3zQl3VaIdit=`_QF^1XVTI^lL%#u_s71JEYWpP|>7Xy2BxyNX(T> zK7TaccZuMRTan@0v z*{kE50H_}uI#9K!cRkm!*bwauU|VTYn5rG;>g8J0MU&lu{%7;^W7=^8XGr?nxwVpm zxlqYl#cFR$A3l2ef}%*l0=}t88X&76Rz=bQCo$<%S^O z=jS*DP(9qBfQCZ+md&#MW24^%H_%JT*4%t@U{7Dqv^w{u69vMoC|23ir7VFO{>56S z-{H&E$reY)khUMB&S6$tG%bhQC%!NRFBu57+JP6Rr#gX>X`C#Kv-ECr0MT8S;@!Zo zQ4DmoI%0=`R+F{~)FySWw&vw%?d*awtbA{_)@0*hDP(=HX_w`%ukTZDv*}mm zSKp0KEN)o!-9PIY01`+UHo8K?tD{wsmw~Dy;ekG>jqyb(i15Jr&5U<(l~Tvj>C?$p z3QLOo(FN4XTHPnI<#_sC`w~!0cwr66x=+R|6!vCkeIP}JIFcxse@-`jI_4u#;4e3` zov~LAv?q5$GB>d9G#NjT<#Sz?V?@6)mku&YZlLqIlic*4Wi(_s-4O(E4%rKSd;NMW zHjScX#M@;b_lXis8mkZa4ix+r`q34o3Af1ZN=J=AtMbPrsoo!et4P(ToA!a5CY4+t zDxcq0tRC=zef1lNP9kfm-rr6TPbucJpm?Ri3F9FwIs`I}`sCbH&{Nu;Z(F$UGPab^ zt1W|AMfo5CE$UxZpK7 z6hfrAJWYwmt^K!;>28+@-2K#I11PXGf#CVF9N)qmNuS*Tef9utr8uGNaKTo5p+Y+H}ylwH3QcsX)$UJN!)pNWl*o+byN zZo1ZkkSu_$TW@${U|=9c&=r29kc!mY{?RNWA|l3i%Y)Ql z?Etq+bdH|Exd|=$;-5Pi1xb}@7i-s7mTt8LBE&}eBrN+McAo(Tk*|vDw9s7a?(Hq? zd$1vzH2o}S5y)8hg$sa}#R7o_Hn3ZI+oF_2|D|2=?9g*I{%kr8KJJ_W%938a1;T1< zz@B4k$et<#Oxt>9ZvZH6#wOz@<<`uTY&)h-%L7Un^L7A*3DC&i=U$gXJ4`RwP7|I7 z@>>mBUM6jc?FfAeqC^h1ir{m#wkd8#qHgOyZ0mlBR0_ZPV^#=HM?}}v)gAi*=HrQm zf$M*0KUwoqvoJtw^8EN&o!xU2GqYvIw)4ZejNX@0?{zi!>yoi_JBtQbwu)N0Jv)VIRc0XX>;&;H9pA!z)vrwdQag z2-EBhbOKQubqVBR?`GC-EYCl1S}@MNZ}n!JF%Dd3ZPcVYRL`_xC3y16G`=>4CNq~) zV0zF>j1Y{5D(@Bt?Fg||MzmTdBaYfzF%8%i++d9E@{2tDE&AuvYkdiNZWgCL z4_%Wk+V)y3nHxFn*%fC8)Q&*{va6=5XyjcxJ{b4TWC~dC$G4i*9e9|!{|v?o7xGEK zrVzp=;Wp-7ufd#^8p2QHm#x!!FEc1=FRv_%u=zQfr zJ#9~)fth+LlJARx8o8jkIfrifTQ{QmJwnTtTOy$Te9O3&N^Dk(N3VSTDLom3L$PJ> zGP^Znqm51V7T{f>rwtruIr9~J&Y->&&%dK2SMqTG8*$Ib_MW;cy=&zCOfk z0DIPM^9p(%)0DjN29&M}e^JH|!&LtPMf<$r%h9 zr2C`|Wb@AknhC z?&%L`!5`H!OY}7)HC?juV#AV()`)+Y(xqt~1pw5cQnY1#6u-hbXsZh$Tg5srPDC*s;QxxhE1oDPeXH-NBw!N3TcZ=X-VW_5Q- z+?E0WxPVAirkZKnIK4HJyqR-r2qLMP&I!4Rr#{t)QbZlwC}qFmH%Z5w#E2vOJ#d@{ zD}KuuX_8K_9Bp~UI6Gx!z~r8{ppsXwnNldf5fiPP!kpRs1U%AI>2iGa1VMVHK5__p z6N^Sp>9wT!H_ReAei8P{Y|f;Q&^HF?dzNK$;W!lRU&!6Qc>q@d0ivDhKvwtI)!iLl zRQxU~nZf>Cp)no@@1MW>wSi`&g0I?a~ zp+_4iOWeyU(ad9?4d(CGtiDX<9}rD6rXdqg=8;@aP0m2-T$z-~_>8L5;98LJF7h!C zeFkl}N_-|39XC{_7>L`?wd{#3poASYQFF2gQ`4 ztg5Yp1Rvy1X#hEcM5hTmn{ISRR95nUcnk;50a>$#yP-q;kHF0ddkUv8UGZ|#{IHzG zy#MjwR~-iuQ0+J+ktG#qrmv>*Sb_0pF((Fn2)A-(U7a909<{@c(~p;_0Jai6VR#!E z$tr3VXCqdQ&&D(v$#%6}2U>*L>Gs%Q(i>zM1{E~zgo&gID8Pe$?Un<{c^mBMe;Bzm~tCNmp|NfIrlR=;i zqdZA1j#`4;sNUgf`y7a*JwTpK?Shm{^!T&^T$B$&{2-3yF;~Rj7Ny$tl>Irgsj0hHFs7KLJ< zb&dx2W41p3{pYbhhP}b`L>Gb3IWBYMQSv^1C~y+pZr3A?MK(69NU0m#O`lI&#|=!C zjgmsx$$^zp9>{pS08DR)^YBz^a#Nov;=t0v-~$HiR^ zl}9#0E|SAftH0A;>jMn=6=VJu|Z3gbpsO#|)8@h((> ztd^DJ3sh`Fca)%|s;6;mftcx@3cm}h{(Cp8^)$Sd;vVe`F4r3QZt2-EOzAhecl9)B z6!zcS^Yw30XN`!WdWI5j2BU>Skd4#(aIps|9<3e*Sk7jGv}W&69R2)bB9G^a_@C^E z#6&xfL)%Q1&%fn9oq4ZqovI;)e_v-Wj(h@fBo2KxneB?X=3hboSbTg^LStNi?eFs$ zd->>;NdK0isvO_Rq$3)}(XC|-VU3scM$H`pM;YoM8BfRCLY75~*NaolpXO0$S*?n1Vuq zNsE_K3NsGUtW=~?GQlP#2aOI33;U;Xe0k+gGsCAfkpFNZA);_gLy&z#Ic&ky0#fx< zm{Tf{##9yYu;|8F6ms?IOx9M|0d}#FPt-$WFJ6$)2a5-(xQ%u|gVySYHpIM6&nEFT?kWaj0*b1<&tCLCG;NQPi@9a6q>A z6v)RUN(R7Q)w#O4@n2JulSc!gzc0(14k;yjX5ZZ0+|TkP&$WCZ9(YZcKT<9c~{k@A||0r<6$ zD2AJxTScL#I|2_F1F6@%HvEB=ab)y{)Y9b-D90-J%BRHx!N4$~TmMv8SQwW8nRv?5 zh;zxWF}s%+hIgCcrDpb4uQ>?qXdS!RN_SIf(CSTkvYz#l5tZ$K9&7M=S_@-lDd!aB zeX9-DoSzCe4^aF-A2JS9FWqWUCO?4+D{r8@g68MQ=;F&(^^%BQ#?<217nj)6XN6%A zIK*)_3EF}RtG-oKhBuxBeTS|5c3Dr_7!-t#rHfI?%$!7|Nv(p{z!1vSG;nW*Z{rmB zT7+^ge(FKl92w{6eUC`-@n3`X#D>On?*Y-*EoMbrl_S=R45SyTo@OkAlbS}t*HR=} zQ1hMmhXdsLl5FovqIlt;b31;`oi_AXdf!#QOP)ID6wyQ&-c`^eT`qq1kkI>}FZ%~( z;fL?hFZ;gwh9xczj94ycY9zbGU99$C6}P9L3~-Ogqu+xh1NNv~;i=`&^;g zT_zAcDoND2D6M`d38P(y73jGQpOD2_DqSUkuO@@O$i`7^$sH{QZ{aobpl^ZKRQk3q z7tJrdL9{3KC)b>#5XL+3i^GJIYS8gk0BZuJ`!&Bk_eW5%IeON>_c7oDC>#PL`vZ3R zW1An#H8ko>VU?U5?3ONC>AsAxWXtRn$3q~ygfls18X$W-n_xcm%2Q0tu`YKf-o0{8 z-bz)RT8fSb8A~(Eb3Tf$H?Li#1Ktmhkp7$@EOu|nDu)lKYgZht*VG2GJ-dqK3JH;e zCKneoIXF0^<^eAT#~g#bIC7n?U8Wq0*sGHSSE;y%yZAO$?AT!f`WA=fPNn0n1oiNa zuQohV;y5~Ff}bHGgyF&-inw|wreU4qlT=EBjHjmQ?`Ny6uz*{`Mg(d-3ns9dS8bpo zLmyYYQ^ZX!+iqwpont`Ox#;_)P%W|<(T7`y9Te9?+cr@?pRv|{-G|oZ)KW2Ah|3qw zM#Q3HFy#j-A-ZOMeMZH=_#P@c`3fSXI&=posZ4I+=5Jw!M8E%u`cm(XfGYj*H+p0L zb8fs=T~qsEM=0)A#k|bOVc&8|@yj1p8)Su@oqM*=Rx0yURx9h7z6ZK!G0u%KIy*bZ zQcL{$({O{?0?;7o2!)tODZlKgMN@OLIk0bC0=h8P>f^3crhN~LghK!_1)6^d@C&nT zfpvhD9OIS-Q5MNp=CYR`O`B=9#FkFI5H}>by-zVJ*(p+qc!hsmK|N-#^%yIB(p%A* z#9FwI>|gJm5ZLFF01uxZ@lrGC{0+{r4Ycg8Y$ z_mQr9ZA0lcn;JIkAs)G9`08(7s{?{Y|K}tVUeLZ|25)>aru@Z41=O9BLX)2P`?#gI zS`yOAqj1w_ffdZ|osLQ13ds_=xQ(?aGoqxJfzgplR1ZcT^cK=m!GzDheKnvs>f)wQ za5{@POuJL~A@kXsg}$u!9&$oCv z80IMPI_&44>t0)NoWO>hVVaEzv#nYu{eac`Bfm1FJu4BHyFaKvd>I~OR<*pe@+iyV z=>#b@fd9{P=5N}Xer**OjMK3)n5(FQ^U}iVb(H$Niz}P}+teL5$OPR^?MQ%}vbMff zIzq*#s>IuBk|UW3Tw;@PmYWaQlff&M73FM8kKG{TS0c&fuG(Gn%a2+td4js}HZjR{ zz02u?_Z`!7I^SpW#Aa=}sD~xRiEDkeG!E{Jqy^8 z%dqWs=#n3<%*nBY%d*BpF|{X=Pk3Zvv(iWQqxsauA+#w1$!LI<^-KUVrnYbo*+=;y z(uho!L)0Bj@$rK82&|hN37QvHSr$|66#6d9vDHNClfkg~=f?%^X{}WaEBGODo6&y} zF64oc#_31lDvLWF&Ic9?Rye)~Ix7ZMxDq>?eRu*>Ysy*Iq&4UKtvr%^c3F!9xFNeF zGAZV>$*1@sW3Iu8tnVBg9o7H22Ur)%*$;X1TN9j*JXrGQlj$?!=X#HK1Tfl#7xT}E zq{MwKD7BzX(&!tdoyN+%`R`bZ{{c#=GOf)xm!}v`t=Y6fo6;Z$^a}l*F!nBAqCWU} zShU`+X|x{kr&$oKg#zzGs~Mu*JJy&FIN?@p-=8k4D$=x|Y`i9VFCPfP zB!brq{v4!5<-+*#QpF?Qf+WRiN=hQ}H;KuNZ21cDlu_h@cKLupGh2H}w8#2)arO@Yr?%6$lK^9?{5x@<)IJho;&9*| zC;;{n5%*1Opm9zP?Vqj6;)^N}7Xg-zj6FyyZdz3vQ$#0iF)rpR_L)_)f>`(s`iq(7>yE z(15cv{%-9D@@(_#SBY=R!1QPam@2e+#Y9EP9;mR5ONOfycQv_hVGm98??Kowc}$w` zYHDg)o!dp=QUNugf~7{`tVq)rDD}^yJAg3HUQz(mtV`H#Wm;xFK(`HJh;(ifLpGu? zqSxBS=LZW$K2E0sRza657*?D6U>9+E)&RXtlleJ1iuZ_Jhj;$nX&o3ah`6m|g5O_2 zMv>ALL>sphPdCh>Hjn&Sd>BJp2-D_;V^l1HxcktFH(c{ca0`!YTvqzPTJ%_)M3PkI z-N}?Ifj;uXXS+{2$q%Xi5dfl=zoVILNiPrT0zS|~^F(LHO+AEQcAwX871O5UFk++c z(BfD_>TcOB+<}Y6)+l}ObzmJ9-DitI{pfdSp#yJ;;-PVmJARy?iMod5E>MLI+*|9Rk?iimOtY%ud4!6PfIa5&&XLaP2^jA+EWH z_EI?w2!Jf~4OlL$MW6?j$i50a1#q!sm^IBD4$Su9Csqm{XBwPoVib`e4}x;`3VtWa zkZSPy(Hjn)+HFGt8inuBgv7`9e)#NLT^jq^O+F5(p$ebmiB*7Fsq3PgS>6nP>4i_i zG^%?2BK}r@F_s^pXnodbfrPIkwdQ?3hxO%{%@`V(d1V-G^S%%q>9|Y&T@w4Y@3n%u zMD>T>x6JP@e{peX6IeggPIfKL7=^@oJ2xEaJ&dl%Xu)+rv!X@ zu_tE%_`*)}!5tkP$+q}sCDN}^2RFbB=E#2?*+rE_@&h+6zGl(5ZXHP2)QagF7@ECW zC>N=7B4_r4_U!m+l=AhA+PG-xW?U8fbMO5&A&ZN#T3e=t(7%Wyfg`H_>Jrfc`m>z*#)wJ1&&*Teo2A9BC?|FL7Rq86l+N+m&XPiGz*5QI8uU$z}K8ao)97Rgi zujF`fj=cE+q5PXakC&H;N+;RXM2O;DBPQSK51}mhoppPMz?Qes9_@S=38g^kR8Ws; zDel~6e)p$;$GrN05Z}Ia2G&KM0BXN>d49>)3c9a7p05as< zEn$$XknWSD1urGT<=@qLAb*vFjY(&KgGIvq4M>dGK|$QWsQ+ApsgmAqY1LP4O!+m3 zfC3d272#*8tT7X>u3;6CE^dB}rP==zxefK;rjCqf1GY7lxM}VE2 z-Rt{OdZAm}XN^Rb5p*Zb*t0iJX}3@T4V+_Tw;x*o-Y!A&N zr9!5wV3^?N*N?uZb$0&ln=EaiC(<~t7daj!9bRz9FoIgkoDBpjgoGR?)%_H9nwe|i zZ|6w^#&b(zkd~^kC~Q-&^1}b7WijhT0vguG)}a(VjMWy;7nc?hFg$77wMdkRJx6$;)EAPtw&d%F45S!~t=}31dpWC_;kXSH~2ZXGxMluBy*@(cX z(a|G-jR>gzmFtiDt2yX_ze9yMv=)A~l_zB%#KpzM2ad!%c9_v(d&u<1G+I7WkX;lX zcvW>Oguzk3$D@Lg;&uJ0M!wnjI8Wb zaU|mg6gB>_17yVZPokXRe|#v8Ges8>!<6MW(@pO!@A0k{eG6)sKeorvc>PTzzgQ3+h;|MXN_4OQ7rFoRP(tg2VnM}0Kmw0?gf1eMm|0>&#> z9E=0oAG1dH>{XYCw|DxdTpk!cA^B>S_etM<=V@#)0ep%rRJVA+tcL+Ts-1P}70o{= zjmiF(((>?9@S&1Tx2eU^EGG7!=WBiRLT@FjsO(OuqU@h~*&IzP=QN%SmfF5MjVGO* zL3?!ak{7H#Gwi^<#9@L37+|$DteDLmiXM(_k@v?Al-?s4%jo(`*@o%!1GmhU$9=`I zrV8>Z$%jT1^R>_^hN%}Yp8tyL-FcS4>$>%m6y@xHRTu7opJ=J4t%rgeTXy^;^TdFa z_ODat-K#%CrOxYYDoNU2|J?N@BB^vY30+`|TkgZSI`6`TSUP1pR@`~Vdu~4pRgX{U zH2dtE;kk8>hT|eKCtm?P3Bm;MKSgQ8qSWg@wBq;2y8<+LC&|s`H8;lWd(n-ts{0y1 z-uu@E!(=jZ`96MOQQ7hmG`RRniX&O0tERs$@_ikUPd<)yoM!m(%9FJELw#4TYe%7L zUgn>sYXT|Xs}sK}*9Ig_v_mY#EKGPCF@H2-_!k!l*z^(UW#O=RC= z`NU(q7He$MFEDhnk9B}hO0-VVeq|Q;brV30;oQn=mnGrI9_z6FdM8XE6WjpKe{Vn; zW3mHx;>8c@N5t=MO-arT2T1o?jN%&XsL+LYUKcP3uNqn!Vw=1oU^!60Dji*p1UL*L z2?ct>f9a?e1`50vrEiPwv2h3=#^hUnMoQaf23?=F@LUeQkOCrS_Dx4h+@H#NvR3;w z_8=srY2pcxv6RXFyWoL)lQGcx(mxkhn3JsQZ)V*|Xzgb|ysd_VU}*~NXC%_n{JYFl z`V7K0pb7}y#q>q3Q;0A>k#X6rBhIxa{0KYp{ibQBp{C`gO zr&^-!giIRB?F2tQC%!BT#o7^cj@?AtG=p!D*sRsjjG_Wj*S_gx*AH~;QWlW8fGAV& zIJE-G8&5-xZkP0Z7S(Htc>5Uxhe|EwozZZrFJBLZtoKhs3{RHa3Z~MDFF(sTc~rim ziT9#hgz5OB|BXym<-3!W9pz6Ga@imEJI7t2&E*?b@--HF^=hh)uKrM83x-iv051ON zk-7BW@d1940VdwPzdS!wOq?<$K6Gs~#E#QxHI}9s``clN-}2X7+R03>hu)^4q=E?M z1`Y+`Be~aVBh0&jZrvy{;J(3~?*_wmc^Jk#TOPDHD;D@uvv^PmpuPAR)KwlJT($hw zK(3!I?DnNS7s{E>Sd)q=`Xo3!3|^T->!Y6*zs93{ox_+3Pz~Xpd)|1Y5(DMfkcpF; zUR7q+j5Xa&*eA&&7gRq4jra_!(Dh5XvBu*h!0-Pi!Z3e6b+gi5OX1VpNAHy!U%qBS zGb;EV*)!w5#wDh4)Lkgti*lBBOQV$+^FL9%@{{UoiIj^)$??`qh#mIgCxs+VFOgb* zMwN!`ke-M`bC{b{Xt*MImVx0NskHB5rgaZ!>)PV((?}oppvWNCOV8gD3}bm}Xz5xn zH%qCzJ@Esrt)yJWWvA7q0_Yv-5!neB40~drVd-&$Z@Ov0e7G9hvXQ0MCz1X>0HX9lb|GPW=ob!L~`NL2v zo!0&Y;D|Zv(Y1x#^N;xX+K+^%s9`j!-Kf%hVpdV%`?%PQne>9pWrZBB{vciGsewV@ zVm1rRgFzuy%lZw{aTYTDJ&l7T@aAeOp_#^Z4(U4N{9SmOhI}7_hgyeE2;M#;UJ<^q zxTX_)FpAr|*pfs#RzYX%OBInX+u$;AHc{W`*^`=mszN7@R-zdxg|1+<=ZLpIBS~D4HhIV zsVN4uL|%(iksRop#jAJdm^h{@OhSr_ZE<69u&_ev1edgtHRK4VG>E$3d3t72vCI?Q z{!kg5miK#jh^otelVN=_f&6yICgW{`6cCaCcpznTdI9gP<(B?PlIe#J3*;3PRoB(+ zUyuBBUu$?#v$6`I>h?D7*-62R%@Un6DsQN6qpmWnmufbNrzKd@W34F9CAejGmZlT>cFZca{%uK5#Ue(@^d_p zJ_E1)UsfJqNV@s8yIYnm8EDth+$PPeqpCXK%*tzUI(o%*Xn$iU0}l+5^|&uiPP&Dc z{0qftz(*m3R#rO4wu}3oVuEQr2hi<{<}>EEfPAoaCXU3@J>*(a4V+a$obd}d3cthX zw%cXH$lql`Jhyf4`U~8V5Hjt{rvcR*eYr_Reqm`ho?9ac$IzyjRX!Yfz5gy7l#APAH~Sh|c}ls9B$ zQ1mxELSsss{+5d!blJ%u_m(#eORYI%vcl8*s8kis57Hywi7u`Sprm(xM&9j zT#^Yiz#u9KEKhNNEIh1%e|T}APRMB&647Q3(QPLPVOgIdk;V+gNq4}XM->VxLSZ+ejb2q zKH0|zV|JavyK;#2?<*xrn`%i$8t$A9#RdAj(}Q1+v~X2Vz_*kTRmUsb6{~E)FWJ4B zWbttK@OCJXQjMeMohVD#0<{E)j$4w@Q)+EPx6g(;-v1dIp0X1+K?oz~clgIGam+mh z3~N&A!v!H)#sQTpUx}g@0SokqN?IE8U?HIf1qD4wyXRiw0DS8~VCW!2lV1Q3CnU!O zMDi;Ypi{4DO5f@+rL^u*`xd1pfZ?yqC5SdwN38Cq?$n`1>?d&)K;Z*19 zJ%CsBWJ(6lZ*4hfi2$6Cx$42e5_j*OARq#H38JO08ZUPrR|9kKj1gzque;I@WL^juH5@BV&8 zsy0{R<$+P6i?WuMBq8SlJeMY5{wkW^)+|NL`&0>IJTRQIrd(nJ2vu;z%n`8Wm9t)tA6i$7sGBon&JZ%cpF{dZM^0Ei!qr#7)%w8 zKMz}dbbm-|@7I@N0tc}51q|*oo-PirSodBdPk=PIUjQsn+555y)6O6lW{(+fLjd$> zOv%td2A6K~X@iUME7*4e*67vDiIn088Lu`iTe@BtPn4e@%QDpSlR~9D*zsX06@m0NPN@?q|F2kPGWA8~} zil*JU%4NDRyX(QsHiQQZW^qm>-EVpI{bed?2DVn{;Wx!YEm$Z-eF#4UcmEHFe9u@ivR%wF%mtSyIAg4IpLy!aoZca@dDnh zOn_*X#xpeN_J_@gvbs8Pq5gy*O~CSF1LLGT@a>M28VU;CyZ00Lf;4#ObHiVyFPNdd zi3e&mGr+PNpw>A~RWomHZaVEPJ%r+>02`rc<%dNfghMYMEdwgkFo=n;e7}||?5b9z z#ccU8=@XCtxfgJTse->d*V6pv9qmv&!o=C>DN8GuI@Hk!88S^0(k6Y_;bxv`)nDm^ zskn>0u9>)NRMYpu99{M2rx_dA15yfZ(1Cwmc?_l`KX5>y_FQ8_KXb9*4r7p`zsSCj z3tsE#YtaO|jG6u3j|L0Xj|>Y(qw;Rh zmIjLZ_ADjje|T9AwJx%`sn(8;*se6ZiXe6;3eZcS?g5zY4gAP^W%5pUxmP4v`vD*$ z1WLa#GB(~l+iqoz=?7ra$mttq&AX{W&Qfcmz{3>}#}90$J3BjVz=VXiep>2d9A{_dk= zTxH*j2t!-z^QKJ^tQkJ60#8@>ws5)M1DQ10L(i`(*@!$eOsgPa4N2tQrTC*NZwBBM zoq+A<_geRR=G#T*JsC-A@~PTp60_u#Tl=m_^H}k>RecSr5m4RTjAu6_0==vxpcHpE zCwUq6MY*_eKx4q~J^}t=;RAwT?cz5FAjAY$uhwXW@L!U7Sv?Fvnqj0VQMVqL@`Ojn zN|lva3P6AJ-M}zgbGd0%i-B{~;g|s|W2)=o6ppvL?g8fCokKAdv6nK8+gz#s*|Kl( z^wsMpSVa1a(Q+nCx(_2&qf+ubD?4AGhK$tf>2NTnV1SYP4NwrTn}hnIS8f(miO*`% zRIeeSQ>4{5W(-L|d8Pw>R_Q9PJ|IQugh(Nyd$~V(@!_qG+0~ZWSU5P2_padAU7@i{ zjQ3{l+udKs0`$Ic2Ad4azM#DW2h@)eA*miDKZ^K}24@4& z%h=Q%yJ>%*pa|F+{9wlfu*VB|9b$k-wO{~+1N5nR7V2>8rY~$q0_wcBZ?_I(D_mIAlPDNN=J};IrGV6xCTO zNz(<3K$@S$7+YyDCek?jD?nXz0(LgnA_ioefgg1R%mQ&PR3RSxsa~gZ_q%ZLM+LZK zS)G{z{AM%Wl!xMIJ>gHd;n(|@oV!-L|HGCXsp0M;!RcOV>x7V2m_~u(727w71{|Hi z?+@-=Ycm_qadO8=Qu?I0*cCr!nQlFQuwi5OHB7>k2%NG(1&YG5a&mxE|C)P|75A`K zRFs2LPDaM!7nr->2A;wyNchR*VrgGub;Tmb7MQZ}TCo`*o{7w5_WbJK`Dj6*meGD< zEa8_^Mxv&=|53)z5m?Q~JuP@DFV>th0Ptqf&9~52Asjd-Lqmr!VJersK9}W1RMJU3 zm(UUJ{mzMg0QUbrhi52-0d6($nOa?KZKzyMkU?o^6sENFTK`)I)!y3PpI}G#zJg!( zJB1hYpc696w@~}8r=R&DjP>-uF6Lfq%%?XwM_i8;1_DoOmtC@~l>s>A_jFHxYJ8js zh(kdzK~N-mm1!@Z#TkC3=rx-Mf&{VPW}AbEC&1~RUY$^&%f{LelNxQGNB-#5kamf% z`(QzGRdbEYCot={biC}3c(2#z5x4_X8sD3J1yxdovd+OI`c%|7Z6GPyQs)Q2`YYTw zbija&4CVd2tN?$?vHFs-I@c=hvNA}y;`eY`{OTMZObVJ)AXO{}NJEa-@-uewfM#S7 zoTex!C~8P3!Veyp0{G!Y<n&HE0mANSs6Y`f&e;q~Fyi-9PL=z$5# z@j)7h-uGh&!=s5I$}$GyC(gNA8Oer2}`S3N6yuyP)GS zcxFk3qTSpNjR99;S}flf=ZHu0%j@dG-n}EgJ}F&sZwW)hv7nt6s-UN0EI<5mq{A9y zxjYuEM8@(uU$ZAVUAB|wyhr2}W_>HJcugGiHQ6><_#&8tAxFL1Mm>&2Tj8WHo;A@N z_0y_FAx5(7J+n3#kZJa<+%43o6?6!C4Q|Z-=ezQGy{~*MWMsSP+Y& zJ(#HPpAAL5r!g-5K3{8f6HX?n(wYo;*C*2)^j_#Mm8fs`l&$(*#9W^lS?k|LfgwO)d6U3DsZqLPl*_oW< zose^OC7&`BZ|eQWD#rsv9ca9K$V_Fa|Bh2Qg)N3Vq^ZQ>?h-n&l?G^MlQktS>_In2 zO!b@#bKdN6(5R~0@!1pDS0?4Cg4!x14JR6}0#E5Cjkw-NFt$9BXx8UWoXi7(H&i1l z?U3BOci=@6%(F0f>qL4HjeLtwhK2@a?TB_ZdlOyTImG7uT`MFju85q^qZkwTnNkp? z*Bj}pBAcNFZMeuVO^<@F2pR}A5OkL+;*A$WP0rc41acHp1srEZGmL+FU^}p1|A_EC`b}ENntdbPq$GxRt zKi$+ZUzWxEQI#td;h~9RR7d&#%mg~iaSJaT^HBenj^j^6%|BX%SwJo^s6hk6FQiNZ z%fMPloQq2hhy)%Sn%XY`sxp#wh>!{*+V z7kJfD$zTa5e(nnNQP96JVZ=_N`C@L8Vx}6fp?`VYql{@#f-Rdn2=}(ZKl1n6gvS#H zf(5@JHYVy%y*2(-i39u%HNt_J77DGfbbd?%I-QBvmImlK6xQn4A4l(ks-b5 zCcl^ir+rGgldENa~5C&pxF<}>Sld2f`_TN(p7(Ahm+ny<8SPb8}_sXblsi& zgpeY>Mk-lp+)p6|h~mRuzs6w*yaZw$o3FX3_dGUjK&&kE(~Mp)CrTkyM}ZrAPf46A z`k{P>bG9PU()m-q1Pn-IEGnxDM8>3#DAeY5ic3f|TDf@gtRzJ9L~a_yUN+I@{ik~? z!H>O2i@IP;`Bg+CKJ~$N#g>*56Irpw=$}0`Yu|$heTcQ4WyF8VO_%IACkmQZz~P1f zMc`SkB>{_;8HlXn{;;^TWN|FY$43CA8q~lL4h|0PHkbwkTp@h92dU+=35e7JAag(* z026l7k{ff-V*V7yo2^vn^k; zM>stz!llUSY|F5fp7s~@n8R!jXZH8nzdKOyWEqY$E(ggqFQBdiainJ+#+)ta00eP_ z#Kd5~BxHrmzjUDW=KjFgl^q!w`9^&gL~hnMHulsI$@>ScWc>-+@1 z+?5Ll=pAwIjp-4+%)zEtUnyows83;-)1*Q9jj zQqUZp%qSR^8LC@=Lh1WQSH`co5Aj#rTTj-rQ5@cj*QH# zzVq%m6UfJV4x#j4zF;l_fEf_j(_fcY5bNnl*a)F@QTk;BXj1P~I~FlrJP zlgn)bcx{EULMIP`9WT+^<8l@xVz-B%P7*#7w?&*P-IuS^wuV3;oAqDofnDN>pPpwZ z(D@o5zZJuv)4b6>%2?5gAnZ`W4zMH7IEa@tkOYbFK6l=)AXKx&d4DD*(O_V#?s+Py zH0eia8#O*$twg1Je6EfQzd;n5LrENx`>7MQ8qQ=K= zl}vkr?6mp|U7A-|VJBw&q@viGL5nr64`6P+6u0iRDm|6`&pKv@f^Aua^<P?x$hypt2Oiw56 zz^b_<+9RW6hzCRiBYcmL_`0_XC2dH$irgM7K*6+5BBBVMt4GR@l<4zP2syz3HBmc| z)x^<5$}zoBk{XIS8&j|p^>t0< zWx5_t!w0(FowY>=wS99?vvY$K7LT!tVn27|EuPhe{py)KUODztnp_T}sp%e8B6@z3 zRZV{SjU${7tM;xRO5EP$#oYoZoVgQJJcFFw9M;(DIOO@>z%a&jAQM9oa$G72D#2rXXHiZrz)?0O;l&RLuX z6X-*q_p8E1r4aS~%4TCUxaMIqGz4zvaX(Z}M_93o_&}d1oqRTYTi{!Ju0NXDRaUwy zz5P(nXH(1yNc*q9Y47>WA$;u-tcj7}n3u4IVYXBBzFxH#&yV7|Hm?j;J>T_8^{TPM z@m0fo9D{g|V8TRv27JKxLeF-@8oMCllp|M6BWg>KZg@sMq)RzfeXVCUNvr@nS`0-x zcv9Yp7k~~CsZRjjJyZ2w;6L3^8)Mst_O?9C&u#MqiwjBaJQR#qy{Hr0-H#Rvi1hr{ zX&ZdD{x8Ne#;=RFA5M%?(0Zj4Deh5Feo};Fy5bfz8l#A-)_~Rh^c8((%TroD&Ord} zUH??!fKg=t8F*Jaf}M_Au5D-w$wYIyY%pJjrkzGu=tBqYzx;zeOTy0{H>t0$Qmw_I z3lI*bdw-uOAWtgxjf5KPb=$i?<5eNmNH>~SRqTODFKyu8r+98N&Cigd?C4%@Zc>JR z%%^)T{+MOcfFCiB{37(Wff~Qw|W(C6o0HuDx_3X)KV2>{kg7=4mjbj!jIf~ zidK0og_}5ArTC%l2Jh)u^@6TGcdoy=g@S`4(6lyV-zq8aIzfr@q@G{K1QNmQAr5}~ z(!j2nq8tN8P6F4@M^}NTc5+_;OaX0184IiG*tWv1w%L)ub#@i;XqlBQ^Hrm-L=FKb znDA%OpT5zMpYI=er`DfAia3mDg@k7(&(@U3{j!e%t$V#*@==~)Y*JtxJ8HqLucW_f zd+o;bV1mZ_W`!z=h+ujFLL#-3JRVE>uqbX> zrYgTiy8Hbij!{1A`>mE}2KnDC#oeou(|Xy4HRm?E+=81WrAntb;Xi|KRY=tIJaE8% z^_>&;pdCQnk#~!woev9%l>3)^#u&(V#@jKKSEwP38~Ta;i0+5DNs5|!Ngjv=3fHmo zV-k~Mx7906N{o~Ww6otYvKuD+MbNd#`Ab31&`H)n51qbXxn$jHyW-T2qJe^mug$bC ztGv2g;;uZp#lsAu02Q(z7v=j+f|-{qK*jdu@4^`!wGDD*g_&EBSXGpcTKV-T=qF;O z`Fe+Txif2MwB3a>I4yJDKmCA3GSW-=4b74VY$GuMMqMjv#CS6O*FK^s2~k8#r&ZE* z*5y<4g)*lS63r_4As81Av1Dy~v{omJctQsa;9Gsofv7#^pJIw*o~67Q z+vzBi)_QmZ$;rc|qeDDEws>~2VrSlXAbB0RM$XR0%Hc_n2jQPg--BmP<;$49hK;P z6eY&7GEDRr*lM!P`a(&_ENPdFL^X%mww^UpaW!rTneIJJL&^Br-!W1GbXKC}6%eI~ zjjVLQN^=Qgw+*lZ`b`D*aP1y z1fU{!GobLxY{w?=a1vJwQ&DCy+dn@LBF)si=Id`ZS+cJ;`uADl`WpEY*M&7FOQ+$l zU-3qf-6a~cc*{4B;Rllbe;OnlKmVJzmK`UWv)?i0341^bJG|!hd2?7vCBdox4eW*> zr(`#s_VC%9;&;!jkN>kWlf$`y31EJQkA~-m+M@8}bf$XO`Hn#oswW?Z4c`jWQzWRv ziRERN6Fhw3J3a24JnD-7vuGCQNNdYE@8QTvVbnrrUBonu7iDuUL8e`eA?PhN-Wm1h$l4 z8PRQ-qAV}$*tv?%REN*YQTn52dg5mMQFQDuzF5#E0jcva@T%uJm-DYl)9@hP>^Jq# z`YQW-L}n{A07c3hSx96z*YkS_KSaSHRBlhZ7iSkZdu7s%6#!I|uLeCx{+)P#vee<_ ztzeLwV3bLoHh<0TI#q*6l*LL!E{8t3rs|>Rp;a;SpWjO4yqB$NWa^LbkEf3UU89BV z?HeS(Q=3DsubFr)X>N^ZQ%cG8aqehU=n>JS*d?+Gp2&(=U6Rz>t`c2?MflZF32 z*vXEwyqR-n?|oLc;Y$hdtKrGo!ACQTeEpkGYlV(In?t)$cU60Y!29 ziA$^^1VUh)j)J+59-D9VS58Ov-9q&eZ|V}#ujul>Lx5SdE6(Y$_NC`r{NT5WoO{TSN}poiZ5 zQ{{=s$N@bS9B>Tc($*id;XxMqRsTD-1pN_h)J@K#cX>kxMW3T%&w&CEH%a<9#Vgz5 zx_RlPxD|)KNu2P6&46ulr(PcVmPiLJs4U#Jf)C=EEA=6Ieutz}zFimvSG(kb`=#0_ z99TvwaW^L~+YeC;FQCmfRgWg4m@f{U7;&<2spw4ceym<~K%G>*aZe{rs6=LHLwHg@ zNB0ZdaiP76o}qrq9xhHY!|lrAK`+R_u+a*oKqI$fjbna8%$xZ1Q@_^a@Ojy2{V88> z!I~~u1E26^t9bt&_-qKsFpZ&b#5&~b_fJsk%6q6wi_E{5Xn*(SMfsLb(N_H0Dd#=# z7In@0gv4^!Kscnu@7oI0$C7y&r4W|ot6g4K+m?ICKU_U}N6V!*cvozqd_f728~*NS z*_O=z&MHKZ#^8cm?un755obx&qu4=3R|)upbBP1dm+t>M zDFLcf1Q3a!%1Of}6$V+6oP;O}HTCJU*PRf?HzNug%NDokJ+q=JQ*BttSCftauPEek7Oy^Pv_=6;uE<3M1J`|k zMJf#HKZ!Xv)6~&;k7hq*uOVkeRHhfC)i5+@eF>Xm zT@GrFihoEAr>@a`Q}KBmW69>@#4^Qj>P+oMzQ~7c3GyAkm#}I&e~SLQajAP4vB&w> zCEzzsz^J7f_vIZ(#<-ENXESxo!kk?qs9YkhR)6=D4;}=H?o+8q0e1z-i$6~{n+ad2 zvtrNp8G^6A9>Vj;OB%aA&lR{wBHp0iY@Api!#P4oH_^JJ@KlRZM;>!3uj+n|8oX;f zk(Knz7r#H6iU+O0v%IE*`rDty3AB&qpj*3cGs2Zrd1wa9{Ofp>dR-9;-63Dd%Sc?O zp>(9P=Bww1Yxitgr>K!Xhzr`Luq(3w?@!8a2u#gh3ccx;E&QcSTF?u`&tgU<%joQP zA2(}|)R|FYiH>J>#|3-g+5?MtSubFd*f{>ts^~);@Y827M_A;8F6FRPA(Q5hu1trW zzq_%CxM6SQQ%QK`jyq8TGlV6xy1oWqJUf^p6_jq?GF=r=36m7a4yCbF@03gap$5Xz z?bv$U6f|Q&(~jNyK)DVq439vYsKxTS{l7;%CfRnm+g9;0kOQxW%68Z~k&LxPH8M2{ z*=&250}{Tk4~jewE9-ofqBD?TQn+TjmWRl8M*u?$d3`mJPROe%s()~-e?93gbd8i%4ZscEu4+@rcc2&2NcCb z8+YeKC<~mg`4P?%tCH|50ux000eaTM3VTo!SJ7`Vfn>JK%dy4lG?2m`13tEu=<e?)2WY*^&QNkjV_J2L{ZC?Ld|M&UMA>5)LGLNbWX4;R&qe$2-PK zbFMJ|tGlrN0_XJ5@-Ywt#A)1~t}R{?etIudn8{YsUP=sNL?3%DV-OV2;LQk}J|6m( z1bYDH!<-L{|0)yYf8c=^T1B+csy~y9oV&#AoV_1gp!Er}F|kx$ZC#{5a1rMkX}g$$ z)E0*?tOX#oMfh#uHwB_$Y$xB=1-8C-t$W=>!T0^b(ieZ3pA_b=;wwsAID!_#)8g+a z>s`NwZoiG*)Zif0KTGCCVijXq6`G~I@!NdMvl zVzXih9vBE8&Wq1awrfxZ#3_Tmv@T2ivRZUJA7zj|em#L#(z=h9mVXR1@^i&Hu(`F? ze;>1cy(`YnZ_K88B#d{Hv6_+B&(pDa4U= z$hF{-En^jNQ*z^kU=PCb;ltj8?$ZZ0E1@@;{(dc|Vc2WFli|H>^mA33zjB0jO$v1C`e*^%q%CE4+;W={85uopRz`B=67?k0&|+3QLv4BTYSo60wm z|8W8RxCy-gJ95|@_*7Eu*@x@gmCvW1(2;##{!~vnQO*D|W&ZOF|7*=CWNZW9q6hgs zhIOZ*LVhx0myc$wFq!qJ>QHaW7l)&@rfYkH^XGYI9WV)7dg$B@Ncg;L3kG}j^V{7b zMw{sm<78R9w{l313pL+qg7@4UwXsvE1s_jVoFz4!nF0QiTA>xaKy|V0Z!M~Fx;k!L zmdP-FAy?c9;6joXQ_QpwLNX7Mz`AdzkH3B>)^k2@!4oZ8v9SwD=SPPS_40~JzGZrv zY=j53+vC6^|M>iP*9K@PE3JnQtX-D z$8;mpsfTC41;Fq#hZZafo~po8Y0V)0ytwvu;CSLQ#)pFJ=6|N(${%e@z7xNa?3|S6 zvYoaBsU3i6FaPN3{pz9?1z01PByT8Kzq_=Yf$YYCT{YlW;K$IZKaxc8+0w{)n)jyu z*%py~f2}M4?DYHxyqwAbU{_J6`d4)NP^nV%IV2kYUTLZLd5_5v*y za1jeR0RKAxwKS6Z@GXf0P}m28%QGa3zhwi*39$Ez9&Szs18xBFp(KfTyU>ZTKuaCJ z(G_OrcMihXhQ1-(iph7g@$de}AO4srO}WEw)KW*0s+nHnzSr_kf*YnbLWvwT^@Mzf z618BKWDpB$Leesxtntz~e3QX0Yvq@skoz`p1UCWYC@_nDUQu)e2ZQ>f^5M1Y=p#mI z{JTmL4<4WZekJdG>2o}~Z4H^TsFA6u=~#(AK8T6x9LbT76%X)Vesb#lcmxS}z|Y_ptzzd?=t+=K)~@A{sKjUTS)Ic`@aKV7m-AC3SSQ#ZJD zw=+eR%vF+wT$%UK655?V7jRlP@jE-pL2}MABuMJ1Hwu^qIT%qOKK(GdN*R!R=8leK z0|xPy_R!hz1|$wVX90%A&-3%CZx@*OSV_psgHricQNdhO`u zt@Zy3(5!8N43>UiDEV@QIJS}Y%qqmzGP3jPGv?B1E%y3=$VdM1_LL=zwOvqCD z@Ulg1mwE=N;?-)* zPW4zh7h_2-Y!qvn(~DN|Cn(HX_?@~Zo!h6bIqxf9td@uVE&#AQxMK9KFN)0m$<4{a z#1KiOwoQ!_F;`dUv?O`QHefNDRB>YA16Sq8qAL$ zKjKEA`@(!a5(HK5D}k^G%#h6M5X^0dd~tDL{mLj@fgVBz0ACOrUK8v2F~f;^76k4i zsyioPbNoPUi`riXuQI3Zid>|+s#c~f1+hFwo&oH^Sp6G~0kbWDiw%P&=U~F#OA%5< ztNYus36oCaO3f!s3(;!j=ma`GKLhA7FkV!4G&3fZL*vlh5NMokH zxDhE=FMft$nhTYFjvlUUoX1b4KCq(fde%BUEQ3m3qQgY*aa2;Wbj>0SP+pgI1zeLE zB%No-B~LJ$`~oQj`FB$BUoD({t50pA8k;No^t?ZzfPag|!_?o(#>x`H@Le9O0)OqX zwvH7hUY;7LJg-@_S-{O{$|rt6FU-vWu0?HjnF6=}=KKLW8!A2^EGt+cPYMKp#KFA* z+p#y7SAp{bvQ<1>^p_wOsbE@PZ6+Q;1Y#3|#qE!p6>dk~ZbqJzX*#LxZ|hj3HT#H- zhTkI))v%-1xnbS)BLN258$rRF8Wh5EM+&Y+3@2AX(0J|6G^mXkEtb(N~sS0hp>%}jYtro zLt35j*RFHBq$Qfe!^g5dWus`77~o64=jbkS<>VgP z7U-Y{#WtWU4%xW)mpqFh754)Rg%adP*GkH8jogh!n2eUTjmFTbr>*1Jn_rzUe$Npr z-^XaHPn%fqTZmAv+E=M1(W~Q1AsXric^ygvol5IL7WZY_rl-k4ew4sDB^A|&)C6YD zZl~GdENM($8B3y7V9^T#mm}l=!>9r+vjezEu#_Mb2rju1tR5i zU&=6x<>ADOM1qh?KbHkaYqviFdjNu(a;m^)XmqTG(i@o%j9z~<5eFCY-p3NxT0F}m z-kH}1T-M8z(Z>*%)z^lBM#n>aeS|Q{`np~jTYUQF@Hqn{k(4hPf!F&vO5{t=&tDnt z<);I@W9j}Vv^Uazi47j%kwzU!5S+)o08c~b`W7fb80>z6ur}_&7@u?_#v*Vj6LZL7 ziGEdF_6WEPg_$L`xLq=(ciFXbhg38I6rvu@~%$5;jC z)^@?Bt;sLL>ArBeELt9$oNxw9S*SR~WMTeLFMD7$$izTbb(;or=RZjT!V8=0wgiNh zn0e@mJUQf~q7w~e7m2bPdj2Yfs6Q47;Eo-uA!%C8^d$tQp6XK6C3-ZIJ9Je)&F>z-w8BCyufjTAN1*g*AZX?v)GTbiRfQp6*%vDJ5PqFQGWB7vXN#Ct zP8mp*foUL}DQR*Nf$|p8BZ13+X+(lg1Q+2nA0#laBr?O6wT}NB=2aX^_C}Coi5hV? zfRfJK(b~U_9HM)sguel9CrHr2@D@^Hgy{qK;?y8qt3Yu`I%RiOp0bD1cW?aJMwL$} z1=Lz)TrC$+?{WQOMXLdynuNVcj{E|7!958p_^4<&D_69|cIUpp`3Sl0^Q{rTKMcp> zSbVnYvHbi{6oqqLHDIeia{LXbrho){AWsFwXms$hPYD(W36u&v>Wggz*dam|5dQ?Q zyh8_ZDnR9YL&trr_ap!lEW$Bsf5fGq7~F+N@vZ;`)3+kQA4M3SUQp^*oG!uVSPcRx zh-NX3L1JIwCwUy)F5(*&`i;?F^}-d8Rxv?T3H?`kM7(Wjk+=*(9r2Zzg&G3 z<2={$GGLJNM_Ie_JE=&R*z3$-I+Uzp|ND>j#@@YrgltnW= z`)hLMqD-984cO1bj4-xMfdCW?mm+6(@LW|wADcu{^qy>G)bm$T2^jlCoK{8CKe#ZJ zXqG{*Wm^sfxp#Ez|FECVoYA&gleV|6pIx#yIPIMN#1p}{vPQgb^~oxcmK?Z$jstH+ z9jbF=$_tF$`vvMS;)z3ugu7ORut;4KdB{Yrx$5;YAwpL5yXyfiU8; z>56=YsmJ1+A}9CY${mwl7jE@2P9XE%_$%|4=|jJH&jTaP^xFye7r>KS1n{z{!j68f zOw&ZuVebNBE2{dKn*3<6m=o!d4E;L)4ey!X;8t{CS~`P<6V;x$kpMV*H>@*uYJ^Jh zmrs^kPVKRCpYW}01OzF8+-1VOd=MiQ!Q_2|2E_t01AwULCCz9A99lTMi6vbjYQ%i0 zo;(1dt@=61U2Z~~>-k@fi6fMg)zWiuz?O+M5DVJN&pBJM58+F2bAC^C*aXQl?*tEP!llljiXZt*WN-x{} za^8pVC`vT{zhjWl{wf|M^1DEeQC|1EsW!sF1LOsbIqC&Z)2QtPqqZsuFnEESTdwM$ z+Yls~xLa7HcZ@0>sp$&nmH$&ql<+sw21Ge0HEob3P`4r&R|lS6BOAj1@E=6xDUJ{D zz=`ragH}W%j9I>u`?D&hp&|J>&r>QM;)0+B^7H?jbcAX7MvxQx5Pn@AF8@jgZru@_ zx9?)mrX6i|qU4|2n=~NRu~%lMO!zlp08+;hv_KezrXGNwp3Ye@+hYbm~qQlOl1 z2F{Ap;Ps^InYX{%Wt!L*eD&RjlyN&95EP8T^Z){tIeGTq_FXq?bA)rU$IARhGC1p@ ziL989@xAVpx&TC*Y!D{y{-3fE2z^kveYf~XW7`dR?a7R>Bs&h=eCLzsEBS<g>=rYgl7E~OU(*bMhL%OBI=96u;r_?7-rB6^I-=0^dpTpe|^ijMwH{*e3T zwBJ<06CGk-3_HkMmuwDxD#IjXm~X3Ni`I3#)?0MYLxcqxK-;FFpX`}2&3&KqCi2Fo zNah1CT~SbY0OY?F3-m-jG=_k81I7IMM|Kxn{?N?nWEZoyQV~YxcBr$ksmb0@Z5uc> zSK0>GYM4}Y#nizMvZm|+QC^&c3~>M8VD{E@M!L}jD$s<6ueyTN+VVp{COimy!^^$s zB^0_12#+Zw&7Nq9wAz6AiTFz)myJC2(bTm&nX~ZP=vI7S|3QBhBz*lRnV{g5t|6(H zj|#D-d(Eh5U_V!ha){WUb65>uf5bCZ@Z@9D8dp&T@}8yJwa{<>reN)VL@<`}511`0&_)7zf z?P<(X-{`hGbJO2HF6F9MK{|x5ZIqaiRto?}wGg=dtYr9gc&Ta}QbpTtY~uOfDA_(HKuHCvxER=`aI-||pi*}Wk*btjK8%waL> zTo$wuT0!Wbqr?1C!p6Si3esu-8=Jc2pmYoq9{%X2KB+(AVD#}uFL!+nz|HnG6pX3j z=LU8mNEc0NZOzPb_6I$ec0*Aq3N4ukV-qezpGa?oOTQ#?kfH}*u)JVXxc(>LfFX_d z(xwH|lkW-FeuiTGfyk$VCw-|;xEmrE(X1aj0K5kGuYwN`z{1#0c4d1jg*_nujWSd- zrp89A6yxroA=Y+dEQpv0RF?pAJ>rz^K=6le{}Ir+wbQf2Uf%fk(y;8#`5_1Gin+x0 zU~BebCMwCs5scxGDX(q9;0O`O#gzgj*8E9dUxtiD%P~6vSd%09kh>S$j^GC5B95Kf z>?Ssvi*r%v3tIJfQ;vJkTnc-z{3oxoS1!Fbais6R7HU?^r;cS{`AcZk9d7?_X39Nc zHiQh>VrQOV)Tmz>a^(h(>!aBaMo-_RLS2RtMSUt0ZnF4I7v)!SBcCW6V7ge5LP&LB z-*E_@A~~J5Ik!zFeS7$$>q8#+++>Yu^9O#OeE&Kd@9U2^#z#s~;uFm&r zDEG#HhdZ=-c6`|xTW#&4*3A$Dx()4UK9bSz__CFaP#B^N>P^m7_6r|B;vG^Lqa$^w zPq_xxny4eF$+hGz5;iK9hA9Vr_~I3zl!AnBPCPZ!Dwx#GWA0bi`v>@3|B5Is&yTL} zkpP+~6#l%jpc9M(5$b(`av?M| zSo8oC@erXP9ThS^5U+Y&<0d+m?2J>F1B^a{%P-ZHMbwyh|1E1NCzx_OCMWZny_NsD z*^2q;k3_BPh(;}PGM{gC zwuSv3W_tJ&WaIj4y8Sy@V5`~=DnL~(OFAU zae&4lwkK%2p3ZRHsscT*@~|^qHgFk8(E;oT0by+JSzgyy*9DRp8ss1MUv-tz#?SlF z6DSj$ITxm?GJ$b9KwuXTaR1KS|4~r%NE$$z55OD7xeSG51k6T75vQ9s4KXxf zMS-QXH8IHr8WRh*T0!W+;(>?`sG?>!`l9UoevDFZ^bz;kGU-z)eMbE*>nMpd$(as@ ze29YIHlcgLSfbfN{U0xi0Rg6#f!ioFoBVtRlvWuiP&(@>n*-O$5N^TcW8!K(H#F6fBnSIH(2Hfv<3UZ*GCRU05PC%T^-{hglQW1MPBWcgmESc}$R;OW#di5fQeJ1|tV zivfn$4s4PL!hwMyu>Tc!YMsYB{UyIYTJIYpw`Swu^s@04s%%Y4!Nkh%5kd0tMmBWx z{co8b6%BQg-Y3H<1kfk{7+QE;>;HC?6mSijSYcvQai!*wm2poGbn?}=nTlNOqE4N| z#wk@?^bIO(!n-}9bgPmcJ)IVVSzpad{%VOJ* zHwOC$EBDBWN)z5DtnamA6tsQ;dJOHZ4!9rH-p~gJMpiZEevlGv&X^ z)E3`#UyXOp89SWK=NE9!sHGVCSiDn|^fQZWj$QJA^yU$#)3VAMoK{vJ8uyvIhUP!5 zUaRI;;f5 zyrmP5dZh6OYleTVJ?mZ}Q*-5NxX!cYNy{h69g&JwHmzq&Z1&6t^eGw=|JX> z!un|COSlUgMzl<`90?Q{7__m#(>23Nehc%2H|!>4{7d>XhsSC&_x;|DQ#(_tnHGXo z;I63lb~PJX1<u?QD{=q%(Ed_Z`bVuF%+8(buX{eYx-BR1R-e35E-URD*NhR%oRv2HO5U$x zGqTd!z{X07Td;CpS2OGd-rnAxOJ_H18?QPI0u0QFkA$B~g0$m@8XKoSQY>Rn!Ue>} zWewj39~`STx{nu-nB+Lr{IvSn8|VXl)(+0Yu)L4bd48^6^20bL4;ItyF>Orsm64ir zjXUxAR>GC*RHw7q?4I6NH+AF`{aT%!{)DjfHH;V%a$rDLD=B!TEtG0kTPnrrX^Dld zYZ@0VWkm`Yoz4DM?hi0T_~KdiK38C@U#q)pUZ&!d>t zF4^EY#kQq@K9ZDt-+k#(L;qK#csl+T!{^zNVIQIOv&Mu22NU=VNbO&{*%Y3*Z6m%r z9qb$HkQU%C#4pPbKi7yHLMqsP*B|fD@4&deZHG}%RwBmE!kbA2eNmCFpb(9pX3T4^ zPuylSrh&;SgdNC#=sMWlp|^p2vS zAOcG7EtEir^cp~sPAH*C3B3mCgkH|GnVI+f&Udb}fA|mg&fe=;<-YH=)@jW&C1za3 ziUeb+tmVjWtcbmBXd7N+=3@NPoHPyw$x$J>lI*DIpdOU;<|6*g#ki^}fHFqOv@ibT_eyjafHR`wrmLaBbM zy*>th@Gr=oV4Y>Ysc<$|DXj6ZPSq2h7W^T$a&d6Yj+j_7M_M<}UwWMW;}rbuKfX`c zS_@Ghi2p*1%zI_Nvw_y~E*nmzZTh4UT$P0X_0xOE(JZ@d{{A3N7O2KvJw8(O7si7!S}7Tf1Vxa&{_)+g{;w9{8xui-y@Ki#_!{-*NKpETq$ z8PtX^zib@WCH_!I`vVjeZb+W^b+Ok{*zZr7_B%>AaJ4aJUL`xTMT7d6D}`h-A6O^U2+vqxnkG=H|`)?5-u7x zMJSm_d2gTTy9?mAlizlpE;>VI3K_zDy-6bS%S=~bJQs1G_=7x&At&ULP+w`Wz4%d^ zAuCcxKguOnQ=UacOH;!wWw`Vb?Ce?Ddq;I(bYX9#Y!QW$i)Jk z*uDF!4-G8VzGPZ|T|RtOw3Q&|K&N{A1O3Nds)|XFKg`cJ_<#!LC-$;TA-*;ubA-z# z;Z9(E{)&qk=kk&UrH)1S(B6urc7-SWwR>UGzHya;!#8=2>>8qhksZPPO2b1~Gt0=s z9p0MbBTs@u6QkL)=T$B?{0?GZ)&sPJCX!Rz5)73BV{&;DF=fL`#KEF0uqz&MCl1U| z!@s{s3pZ(`z$J<6L2HPCCTL_Y30O!o8R_B^1-;jRdu(yC=>5Wl#ePr2tA+3RG?L46 z>*%2k^-$C8BQ9wHhCbL*ADrr_zR0S-(l`3 zdr1FPA~KJx@N|}7Z8Kfq{(}{caukTu`wGD@h%tK?v8XFrcZ-)ke_20x%&y~LEmajM( z0qrZz14h0CFEIHnjo%$U;_BI^5-8R48nLth7cVpFuH>O3b%=w zw7!1AEfyAPK(cab=f`|RhJQjE)sBk2+3VM{L$pS)SAw;ha4>Z+l3jha^7tp9hcmz& z6RVX`>>e=}<1_HK(Ty1#v3_*fsAXfREx}Ua&h6VV^~=YRd{aNZ*~Blh(}8ynnl#Wx zau<`L@w{=&H;#)3P#`04z9mE4n6Iy_I^L+Fo3+Oju~>dL zWcf1Ywai-rNSvjZXwI9weAzB*65!umg9O^F<1G@HhvDL5L-7fW#_PmMj(Kks5JxJCmMi9%z^bv91aaeHB;TwGBoRpV*~^LDq5sS%Y9?`{^V(Cu2gvpo zYg|Dzc9+6VybO@uK8!(HIymed05`DX^0x8x zV`Q)kchmLil63GR#`wsoj@2*qf|LdkNKa3ffu3|WMC;H-+@?~;oA_LL+6(xY{54BG z?lNZ~SPJawc|dmAF~Lo>?fPBLF< zneExB1VeaG6^HSbs{7ADG*eYwxl!Hyd#u*zlGv(WT3Q<1s-Nq9KtP;BIwcN_u*85B zCb4Uwl1I-$M9tXn@bK7U_Ngr{hHvn3asB2!JOq1S4lv4zr(olMb*w~QCKo2}O@3Nx zRadR(UDk%8FNiFD(b%M${1;Nf4!uM0+so3^R9i8NygIIcKMyQ_MsfUL zkk(JH%B^>r-e%^O8+28+i@nPJMhSUyv&r2*2y^;Tk>@YLtA1P~7*|(ULtuhm6Ycr0 zU%qStr6LjNMmdaXs=?a1+phbw@pci3iD)WDkq2Mvt-wg)HpLsjhuhu>gIvWFn{vWV z{feuAZM7tT6lVPPOs^mX?BY)oG4wURP!+OXn|oHgsMl?x0xKiwSc*Z(T zCnFfqUEuVS`CV^tw&B~81yVpv)k@8H=}cf!ulwaa!C1)WyfS84zf zCt2+y;In{d-6#MP=4PkBNp1hRVPueK2ZB$?VyQVWbiA_DKqtP}1CJ8IO^$>5;?Se@ zj!fCWPfRpobZBWvBRe)}*CoE=wz)`j6gxvk=yI(Nv$$&XAyZ*`5 zFquO@`3pUp@RiG#d$UxSg+E=Q!#E7vl*LMqfgMY!)h^CSjbP;I@#-GP(%PGV6gDQ5 zno~;y3`&m-4^JH|GCy7>X^%-q^BCk$<&(jhrD-q88*N3@U{&NLQXLU4;yWe*ao%zH z(V>0Mo`hmH8w$>guj0tizkI)&N@2T|oxT_0`Z9GL%4*KS>LmT4#LR2F>;3|^5uaA@ zML=4w^H=Zml#}?5dS=aeU-_)-pL%-}M~R1hE{ru{J+{NDaJZD+h?@HG_Vd#XSC1E$ z%E(}6i=}pcT;!b}|L*SrwkY6wqSZ4McL`RXdXSAD+reRG$AC?PI4`(vd-eWu70*C_ zzCo_gs9XI(CD;(xvExT}y1nV9bin*b#YoMtsrfWTwr^Id zqts#5b&#<6R>^u8k6aVqS=YAQpM9-lu)a@5+DNj=e6s{)X}fS`WeG&H%`a-_UK zIO3?FiAf!dc1y5g^4T#acr2oaz{1PP$;sX#bCnY7LA>I9Fm-OTf!3&bAbNO_3?{VW zx93X#w9PQeTF1gQ8XGauHF8>u(3hPMN9m>3#>}0Hi*4BK`ZRVa+cyItBi_TId$T9tF_R>)T2I@1i(N{RVI5y@v}~`V_28EZRQz{}n9iIO|IM`P zo9b0y4tpY4k1ko$VWF-LDwm32hnKxO8`>bS$Dl3pOVi0<;{nX7wPr!Zzcn4tz2{RQ z2HOnM7vE6O<{Gs;8tNQMHh7+{z0wsPyssbCkGYof2jR+997w{Nx1r3x{9#5?G>sUq z&0(uoT+&ikFlQm$p+rWLGqSG{v9Yiq zzMl5{Vm~?;Of_2sdp&1iJCcQ9GN#R7VS_0Zs05o?0;A-M$mM9a<5gUf+7PUj2iZ#K zc)_j>3^sQy_Zi$=wu59v-Wv)4C4yxnm^2Q%`X}U(+#XaZ4eb;ZyH!WHH&7fF3h8yW z<9fR}sl<2Z>xtd28I{-~tt#cJ(WPWn5&bKb+S=&C_fRm9s)>8J0}^XaPR=$lYy}vh zH)bGdKkm0&wIZ>h{JzF}e|DxH2D|&~6B%ht{I+*LPKTjbl!3U0mD%s9-`t%uB5pY( zFMiZ9hnqL8koWR3_39JsIFuazgkAU(%DKgUwHP5U>B)WcPiXjVqW!yaX;_h(n>Oog zn-*sVf!||3*MO(t`>GUBQ_x_NlPn*Bzg({n!}@;%L!IdFzxr@TIl+SJL+Izvf|5@Y zgzS>QMu;4sfNL=;Lj3&j*X+0(c&&+pg-GWW1qGuoTsgCW<^0>v`_kSf--;K{v=bde z75N}Z>yzms-%K!V_@S8#2(Rrk4begN5Cps|4UUf=xxC63nvkzPhZ=9C+=VN3rNbFJGEF&Y4fB8*}k4beWxAAayvlH)idMg2- z&E(Q){aTLuKgYn#x^x%1bG@gf$e^6pFYwzilG{lBqO8IKEw1Z_w3c{eOpZ70Qqc1L zn@93O{hX4v1N~R>U5}a~&+}jM?MMH)05B8S$oDqm5svXYV4vVXIE(D7vSCdyCo7W2 zV8}pk&ElVmF`v2X6gZz0*`38Ao9nGy*y`4_t}s>XCUJ2+$Oc(P!w97wRf~~5<68wB zS3#FPteI0tUP8(z`q(EEWMDrRY@l=ck#vGUQ3CO?oy+scKlS9UWO-$1B4kK2l_r&2 zU&p&(MkKo{HlIUQNi98c8i0ss@O&x}Hs|`MVJ?tw&N2fZlte}f$XVp3uWC;ly)>F7 zx+jhqh)V2V{wdC*kQ@EE>)rAns1|St>ymGP)a^znojU|vT^(kA|3!`Nf``hc)Ms4%}XPPU7Zdjqr|LS2^4N6wWWiXx7lvXk=Y zbiGGVFTWcG*?Lrs?#=Qs7ReBoNYTY>vI8z@k-_gR^vli&kkHzf17*NKc%{{lgb@ zhDCC4d&=nUe|}Zgx@i?3H`9?_heu(!iFRPaU{bSE zH4MU+R>dHIyLs+3kGP?ZAfH4?9Zu3_P%_7zCWg4&A6qF!nd8n=*E+|{y2^L8)UDHFSHr#>nM!$==_282 zCYmz;K|mY)0d~JIersbPQZZ1Au?%HGXeElwQsaEU)@vKn%^j6M0Q-v{B8va?M#)f% zk5>Pul1}<`a&J?q!|K1D4GHrXFF#fm?N-WK;&?sA#-X!DqjZD_^bsd|q!j}YtI;`F zJH7Rv7aJOucoXerpi9bpRn+`9*F~uxmIIGZ{*2|*UhHtU`jKfM;ye@9hL0*}Xs1&GG>lDc`-S4R4;-9@J0YU$ooIC*2 z5VB!{*|R_(vy{X&fFoTg;@(3f8?Xdk(tb1-2zTiID^W#wvT!r%-DUd03i^LOqq@Eb z2ab_rNy#Yyl;=ZU;jHmSF%q#7IT%X&x~MMLGZ{;oX=0raIa51O)N_+VeiroLiEb;`i{zC z`K3u~TU_2-Q-;wV-TS3C-w-+hJ0V(zG$gLMX7wrqXTr|;6B>YKv?ymQZjzq zMvthks)H?^e)oAvWW2E0A@Uaeu)uc)MvzMRwoQ2i(bb;erU{nVn`deG`7>K3Sz>wg zG7Of)pK=sDoUy`mFWA3Dhl(;LI%kMEV_{d?Il^UlZm*VKhBVEaRWjQpGgk;RvZJ)P zo({@IHHI^=X?7?I9)?O+0W8sPsxqU6@c=d`vKobGfbG=~b)J z^aY>T78FQs(P|*H5G+enhyH_P(eq%NF;^8KA7B=iYm*JSl}oxhqz?JaEhcWp1DHx$iGCyxfl=%mAg_k6HhJ^3ecFBdM6|oKFR0462Y5rskB`%r zS|bRacziUg0*YVEr$UA|zZuVRX(Ve|<#X2}r}ARg+e03OMa>Vr<;!b9oPR9?z!A32 z63Ph?EhbWje<3^G_3*t+eF4 z^`3AxkDc)0;8=`1y^4f;ra1YO?QT9UG%$b0SXp6sn$l~&vo@s^RJ3298_Jolxo+W>kTcib+b!2QZ1n@zJ5YoE)Dy`yuDl z<94v{WuyQc_7R~J35Rb^d~-kN0i#2TkIzZo(sg!3pHXrrF@`+lGOC z6a*YPNF3p*o^LP9%a$WH0G4^9^Wr-Q7EAKp(qUy|6E%5q%6*AEPCNOl)G647cbseh zGg{f}rYjs|v?RG_eknW+cW=G^`e$i(ifLeEY~ucY58x)pG5`zA2xh0j{Pe;AGPX+o z*)GiNQdozzV*r&wVeamxQ-cNvhK?DVtGU41x1E*F({|zPKEfjK0jaVguR25zM4j@_ z>zh440Tf9Lmt|4|qv8WDG215HHvk*IMtzUH8O7XGK|2y$B@uMFXoHlkr40?L0g!rF zjp682lM~GXqL>O$wg}1haQ8>aB^Jnw4^21wQ5zz~%_WHz*r4#)kNB8LW5^6rQXmLv z+$e)h4kTys5{^s<3BFw@y~{x;JeXgl9)B5#InwzQLhrI}WVGR{h>?9^TMirylPgeu zb|FrT`%h(nEs65bLPZ&MlGy}rQb3F{=^73gdQ9YE$8B-r%(ASak3W+1b!g3Jvr!h zzg-X;=J+3c&l*|zizhOaK>ROWi!itzrS#SHV?1eeh;hiB!#v}s<{I*cK#k{H%V^Hu zC(p>SQhm?{dW2HI0=1ZyDdsg2sm}_7&CJfe8^RHpgdu4*nqrO@>lj-8cOSYJv$C*kmHZLKxTI>OT6r9&yalW zCV#Mh^nC^(Ymp{pAs61dI(Y9cZO1v(l-qimm$)Rg+O#mz%;y#uOpzSN?p!4&gY6zu z{pju00L)LvuGozmbzbmugMsg7il1(5ZE1nd))I^EwCWLhfO|*dIBX7{>xvn+wklm< z%P+L}ImtyTFUeiL{@|hd02^ZL6iC^GA~`eG2oQF#$_1Xx?-cicB}U5>V{860AZ`l$ zu1F24nR%$B@1YUh6I#WEUCPAM_ZOgo-p?E?p8*64?RkZWyQzS|ncMinst^|nR)#so z${APRVPRo`-dG*U(d*C-M6-Am-ppa==|X8>hg!)kQyQkesQy=x-Tbu-jyhVuK-g7C z@aQ)NLA-7QIuIAYYOSHYkm45+5iX+?OApM|C!}~IZGTB)d_2lN4_>eu0?{yben=T! z>J|f~9Eb8UC(@yj-j{G5o(_g*zM7yQnYMobcbI&<(f!SXWC^iM1qB7}6#^haxg%(i z$w{pUAxE>S6J^sYwf0^o0>7k26glmDQ`?MyPf@a&Z3mE_I9c+suLZ-Po^JZ6$0#A=5i_MUa5cFJWTlQzMM&VGObyhePNu%LiIL+9skj zOT!>Gr_Knf`R?8;e3pUaRj?Y1WDwlA(f-NcB!HUZGeFIY>ag(!_~0{xrzPcFOZw>< zPh3!$mw~bmlJMB_{t3LvVbI5qY9JUVZrnBNOhU^txjzOQ-h07{BcP>Cg^(;jYT^EV z&<7zQ^?`b^1RPJk3EeAS@W6+Vx_0DoO+1CCYIa;)hgu^34=U1rrV50RuW1@58%e` zQFqWdtydF|_FfEKk4x5(F{;7he=9mJ0m1yl6LcAb8N|`(l{=lviE?`{?BWj+zwj&?>LYlF2 zGxa_K@|nlr5ZX)M1R-f!?{75tXvNUn;a(e2^$-!O>vdo1>!_PL*6=`*(ja`|8N=o_ zNvpQMJMP$;A3A>5JDd|AKF@gim*etdu<;c~w#6vw5>Z=w?5l5H@O-n4w|2g$Q-VKm zgqBDDIRF*3AI`8JT9BpG@%xzCCf~#{-$W(}NCUCg%nD0n&X~b)>j>sB2dp#sh7n|_ z07O6`UkcD&(Q8BgY`n4kKNeK|n*uR&9p5U%suxA4>o|g{G(9yloR>VCp*=9O+8S3m z`&QYo!(M0TR%uM5(?{+1X*Z$oKK_Jy<>qc3URYB?Z>4knI#T9Y_Gulj(?9N0ir@y z>w(HwWqs4`;9M?ZcR}Q+`>@6JojOHM7UoYRSMrk}wNm{v#*;;Fu0T&cu z$0q;+;yl|0q}(ngGfy~cgI$ z5z(^WS&-P?c z1a@Q%QtM^K?kUq{p!RvJOtpk5<9!=Vb=FoPy`NH|RYV5R``s9u@Bs*5eqn|Jr%|*P zmUWQJ%wmQw7eM?Ar4dNQpYbXf$QsRGG0bkS-n&pN#y zB&F~60*%mKlr^Q!E43N<$l^^>^5#@4OI#(3LhPV?Ivok->Hrc^JcIffO))k%CT|xagaJ>_HgPi<>2!vT_~R zDPen6FhfIr2n4pzi{v5M!r9#(Ah!-XhB$KD^Rik16PKUcejvTiGo-Qa?-Y60P?1^D zS2`YRBH$HnCEA^qdPF}8GIwkaJWvvlQE#K`C+zhxcFRjkhx@f%Z3=?%OCE=jyI-;> z^3M2qv5NAWOC4SSnyO5?q1TO>JCjH2R(S0qJc^S&yLxmi0rfT7k^I;7xF@uIzw*~m z8eAtZ-qqVlU;aHu)12()$XyoX(pa2INT`@lQem#GQ$(*>UZzclmSv8Jhc6C+)O+pg z>h3AGEpQh@w)B$vuj#o)A%Ladg$`1AzU!YnI)&37KAs@?5+bLW1#wN}l>|0nq3eT8 ztjG3%wUW<-jx$tbp#ixl*_&F%iMRbEiq zllK=CI%${-5N&Y_^1<1e7|idkH-~IMk*AR#*66CB`e@-i1n~HI>E5s&t==UaY=Ma5 zdLl55bUB_BQ`>7OwWidKK`X@K$8SnUs$sK+Y6=u}w5l9qF=f$A2wk<#;vlW_P>pf+ zDu};zQw)010N(!Xa#DvwCH&96K}b|~r***xF*|0Qgs$bM!e02IB`GQ7Wr zn>+rlX_ja`Z(ehMHfyXUp|E24`oUX?QRD9J$R>j*!R)ID5dL|XW+4awTs~|-Q23&_ zc;;fX{lIaN#agPh_|eW3YKY{3E!)a_b}8A#P>BBfh$bc>pg2@B^@XpHH3Lv*J_Op- zuv$h#DC8Ub0wKTTffGkVevEDisrh;*`|GJcY}Rs7-EI2AAD_HGYwMbs_23Pc;= zo^@A#+2AqB^1+RAvC+7$@HUtCihk|D1GxF1Tr^Uk^>Iv~q*WC6 zH00@3>n^@~B3i~5|DMCb5`-VGv1ZTe6^1ImTE*z-Qp# z+c4^AgPY@7b#cE^9Fkd!u`hx}r?tIx<$;{m5q z8(yk`?7P@)ZBkN^9&_ijuL~v2uNI!-bGVQH5`9}Ch)yKz)j*HqeSvL1P0Gaq%@t$J zd=xvFp6(UbhOeZ)UVkn=*QYmLD0_h$IXlc(Q_f9DqNJ~(|2fKMAlw77;#E4j9Kg-Sd+xK;9;^q) z3EAZXMsPy~EcAtYZv7S;6M|i}kR5osBJ_2Z(v^lh)S!TKg5YFRehoE{Rh^~lJ=Xn8 zU;!{vw{9MJpv{%M_uZsHh6Fp1fr`S#)*V8@f+K>616J@L*ne!fT`Rt`XNFc&hS^(4 zk9KjZKbNzk@+x^q_3}R3b<3BbaZstcbH(tpm{^An80(b?Hkty$Ti{AVAd|<_Wbdt5 zYr*&p0Zy$vRUn)reJ5w02QfcN^Z=+}EWv(P9>ePFogFvU+qdru3+sSsVE4D}N-C3{ zcx>Q=NWY6ZTL%WT!6IulDJj3%&ScD|PZUbA_hrBoqC>rXzO5Hvk_6LdFEohmET45N8`aRQ7inbY;Oc_1tMSG!S+a;v zxA|_}+V=mI`!OTD*dP~Z1kGN~Qi16u{Q!>I1TwiJ9(Op9+;-0F>e*pU@6@S?t-S*R z$DcocT0k@=LSsq*9OPdgL-2vzj>?f{hH9m_RBdh39AediuG=jsUd4nAUKN`DMCQD{ znxdQz)Aw%=H`i_YNcCSsfTRhwDg>bfM|_PoetO3$aj%Ptm&J=7xZNb=%ldYxPKEYF z(&#_AGJ2%A?OmZz@AE6aZDFtj$=4uF!kJKRHZqn#|H*jH*+(pVaNTl!XHxGBI7bGC zWj4ctK-vVLw=DVEoym*lo(Z*+M1~+R-REF=s4s@E7To(1Rrc{N-9ow`egWOF$|(69 zYKN&DbqQ?rDR^#_)wq~tW1cvGtJ&(~F>5Y5wSbzR6-LtZ{84vu(9 zm7mOS{+WjBc}jb`;>J%4j3HJ7ELomg1dAf`z+ksnqcNu*rP_TTAYak!PLsjKGBkd= zXi^SJ7(0sWv4e_Y`%e+!uL3$UhGyotsS);N=itP=E!w5M1^tXs{UwjNM~@cl!IXfM z_*QC{o>`DX`!=rtTN}4)k&leRa-qGo3+LMX3Ercb<(F?>uoQTTX{6~ve&E=9r5C_s z``U(WsN#tnlufeUDmVEP)M;r_Zu?7R@p7?zCbZ4p$5=YVTs4e<5 zt*=oF@=um2E=Y%pn8G)s9#B(K+Mj&LvF4Q{{?}r#9a`Eg1yKJ8Jb^NJ(njsjDyi0rltY9 zaJ$UmWTx~5Piv85t7R^C>HVEaj~az)AT~W=Set9b(xm3uA4y9m8Xb54>B(YtI}F4q zM|Z5lDN<(!;D|!OM1atV|MS_c41h?lO?Dv_16zA-vjRoLQnPVQ#iLbmhQnd}R!69HnYRL&R@J(VPb#2tY1;1Ud5l_( zm%F(_?8}Cr!)1^67pU8t!0PeXP@vp$EY0V;+#ngJ?f_)a(JPc7E(4sUP#eg}M6Jy% zhIN4OO1F6N3ucQs2Kio=d++A3LUif$f#(j&wnQHBxCs?>0nCO4GM$G2K(vi=dJbCZ zihYxc66aMs(L|v!RBN}x-b{ntX8q*pI2SQJEl*pAd!QZDTUVod3dhOb73z`(gX!~v zr*!C15{eC_qoZqWZG9%?w>W|Vm~r%1BzWNaU!R)990I+3AJ~8wJ-{sKGs5t~?sh0x z*XzC|0rE9bwaO_^#OQf;(kD9kme&@WcrGQwWb|d$Agk$I%zXC>mBq%|i0C=WZnacU z5u7stLuURKQRe~? z^#UZKKJsGXg28soyZo2vpWJ_5WTv$7xTjB$f1r}SD$ar)%*J~q(GkNp<%+4LU&JHZ zoJRy;BXu1sUnLefypKN=J&3V!oX-p`guDA|sz?xwww3qP)I+a2uv4Ae-?#Y1?I8bq zAY$?cxP>%Gvc}Kt(>`5+heg#)b5Sje*r^|Y0O%<3oL4a}k0VsMKSODdr{SSV&3NGU zSY9Y|o&tcr{KO__?iI2=@Fw+XSI#AQYG6H_!Q?L$NK!lnR`RL=Fd3(wIp(vE_SpK3 zXxv^VSapQ10zw0HVDy2=!C&Kh>?2_POBDd7-OGe3O{9F}vAc@)och7w!3B`PJjZ9u zVB~YNM96!uv&nNlKG#~cE9LETTYk>Jm6&^of3FeThF9)WuzWR#;-CZLwn1O2ltftV zy69<1b=Ypl%oKhk6`!Qj(e?dy6-P8Dt8CRD9>Qzi3ONM(w1;`xUKRmvd}Yd}^s<0% z(7BWHSM}acAd`^_?)G&6-62*-2fJ|+-jx7b$%OJAZiE)b(54i*K&UE%@`%PpVxP8A zAJ9?u79-S4Y=*VKz|`68d;LmDV!L*1p&$jkX8z3O_!f@w4(nX|s$8ebd;_&kT)Bwe zN-du}t*0I&}gYaZio$dBxCpe}lO=SyQ=P_B(7Sgx1`x>@GpE$_n)xB-^w81R&w zMnt-3%XD$UU5gR>s*eTBtgg{UzKJVUC>(V36*#-+zPO;(?NN-7IlJ>Z?J7)lHSE$f z=Afgl^Qu&hy+sb(Gt9}M)=~fGv@=bJp>1TDQOY@r>u8pSc#8#}fgdkgK1hj=Rm6$h zj<|Q6NCA_22w_LCvp=r;ogi38Y=d+FqRfc26ERo4W~d*F{ww&tlVjpg<;LLJ-d1*P z$DKJtq2m8w%Vw&e-GOKQ%|T!?yWe%)b#`!85p$P#CR~h6t-c>okSeE?8(e+mjh3IP zMG2H8X8(_>`YB8a2bdJ?>cHvK2ZHBd`jY?Pi$K0X6E97RXXMZ%G{8UJ5*SGwL zBw)I4Dp%3){dM?v`ksa zui4x6`U^e$8Zf&4^5IbDcXvbm31PH~f!Y|4-2Vj%>2$q*p|WOmsdZ3kv$d;orlR>{ZQ$EyO=r&~Jj2l$ z;9=|73PI-WEp<5Lnt{YT9Tb+EXmF|u;3>4&6U_B7;5RTom>@qT+5BO139OYFuTl9uT*j)$$(`oE6|Q`Z2) zD3=3*WD(s|vV#+K8Z6Y_vItU&tZSR!#Z9UncL;YB6NUQriaJFx6I% zBjaT6;%wo%pzTc{a*dwryALZ)yYiAJkaFf;)I+4hi+BvuWx`2>W?4&80 zm!Ei!d+w})|kE?}Rk zv^~OfBnHK>EjOW*IlX3Ak&Ve!rhf+qoHuuwW;hb7>8Fz)Nk(VQ z4Lk3=EM4|oHO47%V{}&X9du}z=5v84)5#V4+Kd(;6k(E($0#ox&M=aU1Pb2!yj8b9 z6KZkKUcLHD{CMaA+Wsf8NPGcEh$49N)(Dq1&>H2}1*xE4xs%xb3bCMouiOTU1}8eg z6>L0=bpmWrnhKq@V!DzHm!7THXhb8$i4omhdXtde7xs7Kb+ai&rjT+3y+6^{ip_%u zmpU0~g+gEoJPLn`nQAQMEs#IE4dCqHcRTTNQ%faalpa8}p!~M}=F*N~oX{hAfhUii z0%x8>!(Qkj^cT`AdzOtjGdcu#3QDJ7osapf8%@GpxWf!t6^UZu6d zq^sO<6{v^7gQ$2`)K+pGZGaY~8^mOCqP{v)X)OpPH=9EUP6FLWBfZgW=ep5EJ&3djI# zP3j9tN(6J3=wcM|zt&5TRB#NGYhg>AV4SnIyiZ88-bEdM;}g+OtB?@2oOT6Xd_G$> zWHBlwATk(|F~RB>LC}+W_|;uT_uwr!u`R|z(sMn(Iq3gF`IXjh+TRTR@~Cxju4qD@ zFWA50Q`&eKl|vB4n(M#*kcTPaGPwddt+4cy&)Rt!XEm30)0ocdb6_kPH+voi|IT`S z$roh1*}m}>;=Sc*2au##>WwrwrWf!Z@it@f8?y3H z8Ka>XPX!o$EY^jN63eO#jlKay9=PgO^hIvr=s1?*rznIOsGJWw@&~K<`%p^P`{sqE zB$*wWU6dT|{of__i2)|PgKi4MJ)-9F8&96HXSIjnmQ`OzX_RZ3msmMtKKDIGvmkXB z7QfQ*oF-1mzXNmT7BxRPO3wxkb1Zrj^HAb#qk^j{Q`?cv6&e}WPeYswA4Qz73 z{G67vHwWsImr&FLAW~@v-v9OYT|Yo~6{6jmqsW#l{z%Cl2RFWWG*|e)vOT{nD3^f5&?gPDw>>);-$e5>(pTKpTd{6_R#9l$m$o=%dARqQ z6;)xRqHTlnJ1FyIt@(R?2x}2Pt3VLpcqGS2*_xXFX)l3pOx)sZ+OCABe z;)cLc%_y-atuV>LQ0I-$i9Ps2Qt0VGmozYy)G<(A`?F|0DElqN#Py=@{|3F-MUkS! z;hsiDe{kel8y>+S6@Vr?5#kBJrT?58v~OmBM#A4C!f*Lqbey-_6{$HGa>%BP@RloX zhi+VLPcI~55OjKqCY6o3Ug3Y9M8}c4;I!6-a4mcTmRRgt1HO*H^Sh`#4w9Ez*)VNu zm98i&({>bri)!}#`i5GqV7EuPgUp5#J9N{!(+vNRKZ|iwGD50TdV$2GA7h;26I$NB z_`4MJzON&Au4Z%_pmM-i8)y^`bja&MAY&T&P{?psw6(CDF{&Zep{e;{Ylx(G)nAf73kT@O|BfM}NOyMj+xZ@BSUni=*Q*5SawlQg55P$NPG|*ED$9 zd)k3A&h<%rCnOpJ1E&TpGu;*WglquO1!WLsk^R0mxZTwXGh!ge{k8u&+!(1u_Wfq% z@j?ypo_nOSp+h3;Scbe>$Px_(d>;ZdY|5Smnt&k`)Yb%$k(Jn)%QY*s3vD<$ly>c# zN&^2=M-}oGk2^vR6sshyu_(>W=Ns}DR>4wP=$>xxOstTDrvVU@kQ%`iNc6VsN#fTb zI?Rc$+c_osh#(@lDufL`u1XbgDgpuONoU28IdJylPrw+blcubeIDXlCEWYsFSncF&|V~eM8b@!+hgPR z&P?gcLzv?1Gq|ef&vl+StP1^Kshw8_a7jl@M^-=tG?}FT} zC1S@=J$z+H8Qos}g5r)-O{Vp$Fi3y0h-x4dpExDIi~qCZN@s}*iycjy(|!QoZ*qNg z5tAhlF6uOGU0G09QS8(36!P1Ua?}f${S8S-S)X)wHv(=~UhJ63?{pAka7Zh%S^t@$ zFHch;D~&#<{$%D5)}(6;4D*7Y3?2F(<<-CaE0Yv}lGjY$H5~G|rI1)PmbvZXj+jX! z@w5d2<`T&3{$(!fB!htQx?R>Q1*`yKq3sK6A6=4XT_)3*-t-i6Hd}ylSAUmBoOgfB z!*D$YYbJF?sD^(8S_~mZ4p`DHaXDSM)}C{v!jYgY67Hc^$)TN}mt{zoD#Oki=`b`~ zy}ZOK-j)3}$>w_c>OVErH1u8T)ERVGS*gBsEWHkR#!dBJD=$~1EL4>i(`lMZPulWYi+9l5>{(il+ko7J{c__z8$`5UY@H+|i<$n)m)J?vc={)R< zpR-a~7+A-?5{?)3|Iyi`fQ{FBHm0R{^>=4&#|g*Fk}wL|gYG8&8mF+4u<7}_fwXjv zUqq(bEi?^QmE61gv@$_dq2}p_+JMo^SwI^XUgmJ|CU7gxKiOJg<2)eYcZdU+aztdp zl|s?YQ2?Pox79AK*LIa$`yisEgTB&IA(x)|cjLiB&9uMI97ywmKed4|K%kdJ?sGzY zqV|18`jV0SvH>JSCZP<9igdB18i1t(oa&Q%+x@(lhmfQ|5o+@Z69VqgPn2Qo2F^!I zq=-P*+U$}&7)@m(7MHWdSe321II3&kp;wvt;JMhyHC3a3#VO!DyPf(UvQoNHKn=Q# z{c^r3ptrs0qszwWq%molt03APXL=Fw2vEGdS0*6W?e`Y4r)gwyF#bokaM9iT04}Qc z?`WT?HmO1InK-QB4o-QC^YUEf?A{hjmO zJMI{F|HByPxc1y@#yj75p64B?R(L8gHkYG717rgK2fJ1@81+VE1k#EzC;s3zPs@V% zcs5Ehr$`uLg&ly?{>6F zdt;CdIJWz$zR$^izGS)egt@Y9d@3Jt-o8EqghB9@ZbGl>q(t#a2{acU;5Pi!;b4fzW^#7ruxhzc(Vi1$HQ zt79=DmUVgtut0PRAXpcB7h;2A?KZ|B1_jB;YGwiz@!M+coy%X z8Z1PI(6!-STIBH^U4A1ot9vtXwZ1p{cv8BJzN!@Szuw&NEAW)77dV{fd7T?AxYDjV zUzl{-zyI?!Gkcd?OFq`JZy;R{Cr6Z?6zq^G{@W)8!d}V~DrNY8NnGpE6dkxEz|n}B zYbcgKknBoNS-3S~TC3KUdn_6W=9L;zRM7ON#6XBnT=~y46Hr!mBLg;gx0ADadr|27T)&wyFk?RTb zM&9u@(uBrQp$(R4wt@i~pe0X0paeyQf){^GkWe{{F^#4@mnfE4TgSuIE{voyQ0e?c zz^9@tVo;T)B`PF(?#{6TNSrJ5SE-QQIX*?i!AMv5rXX1g!A=0r!`|LL1GHpgOJ!YAkUZ*gvd6Jh(6EY;cm*IXgXysW=e3nS=g0e(>;HTFI+~ zV8uIZz2Vv8zh9=>rC)9p+;zAJP=zotNL>H06H2=Q;x-7eGOPj68Kf|~N>vqhG`vbn zzU8WDc9dl8#;O`*d;|s5Oo@S6)mI4>r=>oruR!v17j^U5Ion+{{B!`iKW`d3*qYKZ z1N}Py0;oS%gHu;e&+zBxJ5fBYwR%8O0~Bav?38yyD zRTYtNa!g^5*v@?DO2j?JDu~XU`HTlKmv;Ctt^_v(|3;dlt_gS4qBAw^@rl7nEf$O6 zC~wl^n8CsMHu|7@S-A3A%7>TUp!Jaf5EOD)FKF5a=$`YHwa4`8 zAISp*e6V^zvmPu00+UAnJhkGF-vJ<~*@;#@U7B!{@W&PDzZgZP>gjww+skPqrwbJ{}2#i0a}869dyl|3(ecTxe*3ajN*|Uz047_n{^;UjgQa#ATv|x zxJ;R|=QGe^q}OtG=CJ0y7(S3DpnK=kqUjkZXU3N|ETHS?=q!VByHbha0Q1N01lO}s zgb)`#J1m*Wf|(iA#vsTFJ%C04z>dZd_pRe-G}^F96G}S!pDOt6rR>S~B(V3|#Rp7# zRp~rkQ?0+Kt;kB2$;d5yAU@sgIgN6#HGz;+NQ6H4=#=k(?BRxEZ+|pc_@vm6r4q8S zUh*q(Ljw!BLh_phMlHEVxfj zPA2(R34-{~)cN5Bpli zW2F+IP^lh~f!bCIp1W~}fAo&Q&yNOeIGxgf z)CnJQSXsc1yoY*Un%}M(KsePSj7)L&_jY8rQp5Yp6tM4{?Y#s!RLWP!4Dm+88#dgh z%Sns0-rkrLmrX-NoHp%F=SjbN9s;!}mpp)-p&}y_fC~P{aW~G<(HP)*=ppp$)}Fnz zXx6bSX$_7c@_Fwu2MmuqSalzVcFc=!dI2m{6p3)|DZt6oI+q z_O{OLPD@tn=#?aQ{g8o-rJ73V1J<05-Jy~T{-;V*i3J*%@?_Kq=q-{t(LIl$Z;ZM= z5FrhyD*eV6zKTyyO^pf!3=-EV8?{5W3K>+rLmGe6yyNDy zIZtkIh_uqLH-w+3V;#Q6BDsKGoq0TI`!S15w6#@3k&Elib4~@hB|I_+(;rs@jD0!o zonJ5dnKh<3-P4SY(^$;?$bJIX6Cdz0uBzH=Y>2W z5fMPeEPRmZuN>9!{eawHJ@l~7h08tb;}DH5TGZ}r6C8!2!w$Tn?Jygi@=HtgRC7cm z9BtSzk#7Kg;kgDdJFj)h#Qx@pIqD%vZT;Xno7nQ~QjO8fvwj7R;F;54CBWK&3XKJ1 zA5kA5A-zj@a~uuI0+B_eadxvPa63Ic$@rh46jGVMW-bm*Iqmt20xlhFp*6BgD(2Nr zns-upCKR>Fh3oIiv z(-Z;N3z;Au1bo+29{Q!04$)z+ggI(EaTz)4xfprg#Aiz=S{buj6I>RSjVx$0M*Zm)6Gv#%py|_7o=yX{Q2Pel(h-8)49hxE`J6mG(f*XN$7>>T%Kpr&Icq^$KsQeI2oG(8YE)j$WU^Ct zQnU90m1cRQ^rg=~yOxNA<+gam)q?yF+_cWi<*u1}ua31wq?4=yNI$9|A>kRY>#gUf zKtJnj;wa{&w&%$(pw)3XGraj^{UK@peOruNN*y+`6#1C-60Iw@&V9sY#LnzYhX9(hO#3uptp4L}~B#z!+4D}!{{DvyA|%K?Q+=FyjUy$GVT zfrhP~##w@r3Ufl(=>G!3!b^@ox!$*2g(#G??83 zGyu#>i|yxwg~rSI65JmAi!9V@Hnd&#N1gO>CVdk` zIenij0h}_J_%doZ$y1N3M|dZN1Rs1Mc&~&3+eAu7sdSRzvXewos{nJZ zT6HG>OHSY+AXPN)AhEUn4-2RkJM)`Q(_vaaI)q8Kf8|pxhjd~|>98eFFJq?z6q5kh zHUh{FWo4!tc|UCvZ3EG^DDOE?;_gW9nVOmsrxx(`S` zK+!aI?sHFm<>|NdDDaGSrAbxMmy@Ffi$uO&zozy4OC%s{r;6Q~Be@8$V# zq9D7vhW#7`PzmvO2t&}fso33~x?4oNUt{b2EF-JBiT_heYNTIZBxsPRJi(K0HRG%w znUQ}`S7WpO@!5OlwyKa}bSbNJB zS$35^1^Uk*UjX!IEX#???U+cU}5 z78(gq*MNjbtg{q+AT$_QNGdO$BXWRq0oil*dO(7%eEw4SzD! zbyH*I!7g^03j2ac(z>0ygD9^n93(a_Oy1Ny*AGe8sM7d+7VurUKM9$Os}JMo#?F16=kvC4LoweY>}>Ia4#7iyTG?975@} zESs4~nx(+&t>!u`v^#+)`dR@xu8H#ICa7cb$cnOWJlNvW2LVq{sJmc>{0?U^l!JjK zSJ39Dea|0}ECxce2+{?Z9nO)+-psP(@I0|Was6ZXg$;KZqcl&)gd#B+uMAh21G;sQ#vNhfU&9 zKZ6wFPFaJhL`P+q6Bm|}v)`O{qw@JQUv4NAZ`4y)YFCR+p5>#Rz^znN|72V;{`ucT z4$>MNU>Tek0dx$mO|zONYFwCB0Y;{_O0MsKgJGRiI#}@kC0zUvdu-?_n8r=Zop(T{ z#@#peyErJ~8b9c5%-%gsxU#BY(+{LC!JC2f)ThMp9Q)~?oBhFD=>Mg>b#|{{Ro%_K zC+E~gxQfNYUv#+Dy|Wsw5z+8Rcxrl74=Oefp@Wg^yFk$TjWj2Yc7(Qbdz?>pO2CD~ zZZ#&#$}~eUk6zxByKKPkrRw(N=>ki-M)rSXv|B|CCe00o0y~2SHoM2vE?5j@D%V8^ zPdwf^?{`ucdABRPzfp_V4axdaGH4N*9jz09rW$S_ZT4*IN#yh-KhOhNX?>~xYGAh8 z{eJ!ag{PcPuBwK$quTq1WTC$oRrfO?cr$wC9mEM=b7CxA-Hj9NF$EgkFE$l%iz^yviN*I9wtzC=x`IYgW z!Bt|ev4d6zQMr;=65d*j?WTtN7x#;Ob%aURxj{$sl^%)jhA< z6xQqFCmVwshc&yE^q1TWb!S0BDg$Zs>C+K5sVF9XATf%|0jl@YERRl|4n!2v)p^lf z3*V+)h*i;M$&mlzl2I)(CT24RY75X&{}1qtsrsZ?LyG?XKxK`-mE0Vj;a)3Oirtqd zO|N6~R{oYD5r%jB3CJUz@6i*4gGL93oi->^Q<&K<-Ytd0^cgK0UCYWV5gZ$HOVxyr z=F^A?K3D_H%kJMb^n9KL5z#9&=xtHalyP&Z#H464`H2VCEDRUrMxUg^&Q98rESiyAlxHwpsyJ zUj#8VKwBQL|L$~&jn!JjGCjek+G5qVS{a71EN`ZSVsor!Yb7u>-9J2%0TgS8bPIu# zvt9^^@dFd!W460qP&8da2Gu1XcN7^M$wySA-jk@WK^;l zEah%PtR(K z%D&b{o#l`-aHh6*GElX(wMD-2ymM^$uIkCeSVFav^PiM8;mq$m2&lJ0*C`FCdERRG z*X)KR@V0-qIV?5BwywUCj4(oTg+#|+{x@ERP+UJ;_1Yo^YbZeVqA;1G{*0cZ-&P^{ zvGME7Z+Hv+iz)mmX9DHpEOuiODJ;d>F^Ru!BD@1&Hhcm9&L%PXHB*2yNBv&lN!Avx z*#?h`c*)pgc7PdusK#U`J_7(vIIAf_+QNuRthqaM=%+Xf83F9(WCEO~fDw6qhAy6r zpl-4ON;$>na<46%>TW;3cj-4+mC96hyF9sp>h8 zHh;i^L7LXk5L=0B>j(s#Zfl<4&QuL4?q9H@!&RzG*Tx9$EUofR*P(>dbXY+V*wEz#7i?>K>F*zKt>>t zEid`ab@FhY4Oq2#j#Abo{*@7e&Y*_H?ln%IkK^Ngy9ad%m^>T(UuJXZbQ6BIGffRNUl^zt9vuu~cF<%=`L-gi15(8a)z-cI0ILx7l+FpN>2gxyEBls!gXz0pHp zPXcKP`=NJ`vIJB;K^p&ZlUTYtStIPRe=w`_B8H|TtF#n7T6cuAmqN*VY&s`PZ~RG@ z-P4>`Pv)1BTuc%V%;$U-R}fWeH{@4UauB2nbZ3IS!atprPQKewkto*Ug+&BYhj*I7 zR1|?uV%(Oj1V9bJ;X?zCD8P_OO9R*Qo-B|M>`VEgodObL<^Wl8(rU?ktH0;Km+n;D$9CSoHBFwmxsY~!E(A;t?+49pG651XzU#ME6>7_#iJcfHi!HofFIXW`Kh`vAE&=d=Jqz@<5Q>>Vt?QLjdSd$|Ht;9w9Zer-(SaZ2e^x~gg* z@^|Z^`oJpdBll==;!P)Zz;Op3!kKYG$o<#(0vubS0HM`3%#?%G&@Dr!S;CWvlZe7< z=NNX)e=+>m+gcnNv)L40>O@TUgie=~y&mL$x$A0TQrkyzel<8*uNir_QmU)SZi@c| znj9=pLklW=gz^;_Q(Y%Tw2cCU0TJtp`o}+XwYY)q)ne{c+OER=tPQauUGxWVxNYZK zDX4?hXmLoKse%CU4X4{6f_76sA0iH#ikXZ8nt{I^;M+$OJ}WC)^lMJNngV4NVkpRQ(7AM|1aIC;^(I={N(w;Y;~5 z#blaitB&Ezr}>P&VV09jPo~#$(o_{+di5ip_WT8ke!0~f^RxG7L+;L3*lxuFHlWK8 zgquA~+K;LUBf#1;rcmYL`muhm58w$A7vr=PG+e!RZ_bqPeL)d}rPvuzSS4T2Opx_= z%uxS`-cf7{uhIVOlq>QO;C6B$P?a(1A2R)Oc{g^>;TF}NO4Z>v``->ef$q5{Ye~1c* z9sOSJrz%>PKjNpw&8^DHD6C!iJIi`t)^r3&?m)AO=DHb42UIFG*f$^OkKsF5y0ZJn zS}X5zq!Mh=D+)$d3VJR%NkEJU0M&olfH3}Ub>QAGf8P4v-YA#`mku!NmmR7#YPugX zv-WS*hiO*(>{Zs~mBkrrsz{ZlBaj(j^J9VL2<%-VXG>KcN?LyMTkNbG9OZK1Pi>_2 zHmbrO;jL5?{@*MJP{0Ky$Q)dA6R-KAc)&4RgN_;gKzvUngPBr;>4mPZ0Tsj6ILW)V z;SYFyESd|x9`hGK3>om-8T=joLmQiwY`NdV5q&R~497 zG$Ovj`*tzrC(v=U1XLUF;cA$y=#3%nT}bK#@XBsSueLS$9&$saO)80rYQD=F@|HG= zqq*q+3WY$~Zv<%iy68)Me1r+EXg^%wW2)_q2{|Og`~*9!K?Y5k9d%jCa!i zJC}>}<2eKhB_IcKqbPZG^1@Q|+XJZ+obnl_VYS;@Gz<)8`T@ zn*nQ1Dnd~_Bs;Rxys$xtuT-%c*|L0x3$8uh)AO zs^W6>e7OMaw$SOU{c)%%^3=!;FB6Uwl2v6?)+T9$lx6g1m8nUjyKFO34|fpC9^kje z&Hmt}vd9m!YG%=_^>$MA>swc>k*QhS^(v2BPW2#p%Fj?CFe!ZeEtq0 zr*oB1njbw}uhz^mCWi3$9=(sQjqyFadme68z*vFgRn}yP zam09eS$zL7E`dbA&}CP=OiNr>Ta_%geiklt8}uTSEI~iR%STX(7w|_Gl9^pS0MO5f zt318mv^v72QYPRlna}ERb+wOzd1nX%{(9Q?vflt4QiyPQ-o$;cL@KYJWM{WU)qK4O zVV<4sQ+;fZhm_WO1C4s~s<3RP#&&l*OlbP@Wgq#kJ^kffL(a-HY?>66GDv73e!sgm*gA5f(XPBolN};(^ZvXZ^PX!>g zw&}%Yxng$us*ku!n3daDO%*fjc${ad6g6MhcFw+&S9TM$$xWqa5k*9`kO7#%hWG{N zgz)WbU+n#TDIBZ)-y90!KEXLQ7D*a@Dp<_@l$hTk{sl-A9IZp}>N}GIxnEvL9;KwM zH4(Y?KM&+egeod`?@|M84@eOcs0f8cMV+P@0mT%#fh;Ad@87@s`1#2<1rU@t9ISFp z@Ng;Hq!#aEuf*O3dL33-fpKHld8%fFLyLS%@`-1byBo}{v}kycvHKY612uN29HbQh zY*7#x`!;WxYox>k*Sg9_O^&3zvZJL@pMWdMz%$0eW|V{*wxUVZHZ(ieAd!XHoB@_1 zL>Du#VQCn=dWIgRg4QF#kXUD3u4+jp=%*7F6Jt#NnTm?a$Je*7P`3>W8+)NJ7w8W_ zRRb(6tYjdWMa9G<2V_VX)dT@qmJd>WQ#%Ebap~^vp+)U%u7fTB^T&|zA)I*WAV`HB&`XVuPfk`q z&k80?-shjHR{{TiwLc@2)ojvO^9%@A#0((-3xWTifQ7H2pY8;_)H2_EVbDbj-~QAt z%w3mL>^X6k3j$-ByfLltKrOdDhyT%x5SRlHypz<`^K35T8Gt^IF>A=|zM(YBRrh-w zrfY-o_S)#N9gc6=YlY5{g`u&by8`W^d_*=3>HH-?e)#%%HDki6T$3rUUh%LCqG)**C2%u>3U)hPp97s2;CP6yu6nyFEry%7-D8817c{X!xt-JCr{6SN}xa^ z{yos~fuNn0*%Q*<2y_kO!sF%TZ6Cn`jc#OtWXG>xzjn==nlVA|okIeH5q`+Nb(T7= zIyh zCrNgVWP2%5TGz@vYY(Uep^PNRpxbsq-kaM^+C0D)XP-nJl(g!zGMG`O+Jl)6&vZ8} z8Ld^Al~S>ronM`F}bdBylH&6KYp*jUy}_jhUH&#vGf-rs<-9hC#r*UkiJ zD6_Tg^;b{|z8#T$&jA)EVZ_ED*OQ5gHL(BaNvC5P8u~Z{sOp_n;66uI!&0}+ScUuey42$9Re<-_u5N>x!pzdS#<@=-tJ+AvSi-xlD`(8pCXnBM{If7jW=BeC5=Gi%>h=_>cP;P7h zA$KOE#FSTaIJ%`Nm#facxAWT@(>00O^vr z`Wn=zX{+2{zbX>?vK3Elb_Ei2g3dsAkE5LXdIsl`-W((QjbN6I!+ar?#WB+=2_l!{ z8KP1TKYxD~f<_DHVaOt9)@{ z0m-$dAp2cZnpJQWZtx4)WM<~FT&J|kF;588V@5+PkN1Axg>Rdx*>jgPD~S!#D(Hwu z@iGjsUb(_bUMVN}^++u0DNh6FeiF2Pb&||X|I7cK@ow0 zT|~94l$64tQ5$33B@i!hv2||ri@~+lsI;o+tZL-x)awA|;y@2Rn_!W+-zRo}btdWg z`rJPJ9$O_Jn3E5Wkx>O5BZ0Y8mi7Fs#zkJ&JTBL8KqDAMA$lD%x?-HXxoq8fa4x{RdCyeFeCrctLk;%9 zmV-t@xp^W<`q$5tjtnD?TQaJmL(V))R45YB5fK5T9a~w%EtX~>6csHdlAV@adjs(# z$Q%=7!Jmi^AEoR(ddnCBgHD&58gZ@rx@NGPq}7_S*P?{1oTjRp5dG8LY_6-myWW+D z8at>Q;ar2TL=L!zR#Ir|QXv2iEX5o9gaiEl8OPMV#=A%1x7(|B8S=Bem|1xR*qnrsJ2=;1HU6vB2Y zipq~Mg28E{?j!=81XOi&J8)cIq9Dfg7Pg~cb7VyI z0&@cU+`JX&L%Ic1-p{j+7(eZ>Q7Kr(@z%hneYB)bQTvHlFY@u{ibQ7#`vjcpDFy0T zzc^dHFAw~hKNp}a@b@{_>+I*+?DnV0k%^s?9-ga_Rx4BQCD77rd$=3LL;T8G&l{ieEFxw`!ExWn?Q@@X*;nf}e^huBpy z%OPC?mv+4K$8Vc-JLrRNnQVqGjm6U35Q$BO9j!Gm&^BsHS{;vA&r7ym6G+ZpS%}YX z!TA@f*Q6!o&p-2b)(YvRl>~Ri*C;1eX+96$K=uxFbyLB&ER0!xc>R!#4v+N94Rsj! zFE_mwHExtFe6c=AUUF-iPcdT^>t`|Z)@sh^{T>@i-6Dr0d!6wc&x=_W4xW!i^L>0; z5=s=v8qPn}Ht$+ABr*`)GzyNuZOfDh%#8`@L_R7+6Qp@T@Iv`9%S9yj8()QCcI=~u z+oIcqG&PEc&ruAvvf*x!l?yUCPNy&M6Rl10-XZ*i#yS}BR(h?lli{# zD;Dm@LXDBSe^#H3j)G$jJ9uR4tB(F~G~E=^$+?|v#nuEaY^)v*tRX*5u3rPo)9)W@L3SAenM&tcZO4VX9}b)a;vCoIRH`h!e`}p4dytk`Qc|4m z|45Z(h(l~pq-oX#{;RW>X)((^1OaWUu?$U)Tr`1JHOjK}nIZ$9q{QB&;OPZ}eU`~5 zf!pJYc<7Bx&@+!E4i`%B)OjF5tGC|Sm)7Ah(^;hzjL1XVblRLzsoC7BH%_-cx9?^K z|J)sRYwWFK|GVA520e9aZd^g@X!?w~aN^%FpS?#IT2?zs_xgee-bx&NXqOvKMKLNK zr4$I0X@bsR7vJZHaVnBlD{vB5zg8tlE0JY(Seq@UF6cN)Rygp;d&Sx7`0kfDYKqa4 zfboH(S`V2&OH>=Pj6Sl|7HifKG*MLxAC6@qtyV$bx!)mjyV3JbE1xso^`?1sOGji{ z#fA<$GI@c2iJh4UHHRiJFO$YB@J#0XIkm{RK%l6wyX4`5{!kXJlqT2-^%~s}QWVK; z50=Q|eSZ0|7M8g7oTGIkG_WnKu2D?*pi$O(xgJ9~C08XC8BKHII}oGHx`CJlHzxki z4+-TZ_t}H*5cw*Pq>bL<6{zHRg|`)JZkW3hSsNZk6Vh_^F)l}t4ON_q(Cn;7GJd?U|2_KgRhrN z1T2|X7^X8XsdZg_;(*LjE8H=Zlq4n3>6Lb@t1~gx;I>2GoG(k!t?_j{u#E;4-6vSyk01n)JlZZ}JF`pYK+9 z^sAFtz=s|Z@wkTK_p4725E!*wEU|^9cO${AiNK7fe*|{!0-uMLaf3bhglK_=;5_XpNGo z3K@IPT- z{h73}FiZ^=nfWpy@Y^QjDvGKLzL%0J^Uc?ov5B4MAN}S@3{e4A08CWEIau|lVAVBs zF1=2`xqAfrK7QUAUuZD*#9QN$v1rG#x_jnm3oOz3+EO8HYF{}7YY{yh*@p93D%vHU zPtD}+b|=j(*o|6w^Yk~%e<>hC9vk!thAykrf$OKjkTE>WHQ0M}=PX;jQ`aq(i<3m9 zD$b6qPA=2XNnhla{=JjIlnK&Nj3&i-+}%Ay=q{8|QJi)ekuY5?DNFXVy;*F~q^;-q zMnMzd`ivNrL*$n|n~$qbbxq82z4Ox+vOiOYjr%yPZlXaThjHd&Lncc0{;b{j^298u9={=%CY{p z_6}2e6t?sDntt(vZx3-<61-!klJEVoavJ!<&lpv}<*?EnLl8$9pBzikbn~>8Eq*i1 zJtsHZqv&%3+p}g`Gc8WVIJ{wbJPQ8Gbf|!U1Pv(|Mn3>c3|yk2sASl2s@P&6I&nJZ z#84&^$wYJ3aB{j3hrQ^YQZn6$AT6_z3YkXYxjq<3)5$T**=78Ny;3D-cbf|{-F1hB ze065d%g7Qp6umd>Lyx~Kw$nTzdPMsCj4k56nxghL1sHSs1L;39hYS4H{40YaXdNRx z)(aO&6Z4-Q#S0s*T6nKIl<=3-@q>OF9WZXy?VU@BWW5w=9BTw0Kgeu*X;ll>>P`ZxWr7nXU!TKoT6_&V2Ui;7v1JpV(~Rnx))ACZ%nH-rPC{YHIwdq*HyHp5JACUOnopLDF!`fP61x> z^tmY9nT}DQ&3n^Y*{WQS!Cu$ex@CBRuU4a4AXEK_XUEr%Ek&32=J|X8;lVsO;H1s* ze{Rahy>e0!m={{W&KntN5^EXfksIkL2D6XdB;83xZ{W%FQBkL(Rwjfj4s(_$;spLc z4SohN5;eR!yI-9a_;bE-?5a4JRqTCw8R9gb|65B_;jr>_CM}1T!bk3X9 zKHG>AybF9QnBs==&;fvOg1D?MX4wqgH#>TEm{3L{wNagzFP@NsvrIfh^rDEgdc^*) zV*ZRZiyAtOt1rLjuCRfJ+%SmWeoYLd)c7}~3R5i0^Has3NQ!sSpuKwMy9yLeoOcb7V_f)KLyXTLfuqtM{$Cd<%>kF-LV_?a|cGG)w&Gc zo2>HZyd(`xLQsGs4$GId3(9gp>{W9`N%=rLu61Qe`PG=`UX?ry@YmY`?Ux(2-t4yD z_={_#ozBn6XuG90Ir3`Nr&0oLpEALLIxf=m3K;dBfe>`A>bMs8-E|%7XLcJ~g;n;& z@{Pxd=mt#Bc~HJeF#D=028N{FWertiN&LH6liZ&QxHvb9u=m`KRMAa$Dr3@24Va)3 zGK^suFkPm+3}zQXK{Hm6AY2(!&J=u!7*HCxuKWeSV;Eb+>ay$3UG)cIoJP3W4=F&pPXni)}e!tX}e zg-kfG<-;*}3jQw^_&NJ?=35cFobnb3IzM)~l7tGqqM}e$yY#CN%_TetUY|TK@nGE~ zf646`J)f|6GjBy%Y?HI@JO7uv<(W%hR*>AP2hXW@*WAHo(;Hf)Hrq6y3Dmse5#fv@ zkHvE)n9tO$*RfM8vw~%R&xJ+uyq}0sSv5KUYGtt%GHO3 zg5VmmavQOX!M(>QUqxQ{BA4aRqaT=%hF{y%^lssY4xDrpmFrJ~c?i3dB$=~Zi0lpI zv{g#8OWH?H;(x1iiJm{F@dd?_-vYN=k}mLC1adGs{*gnx=2Ky*4k`+cy$*0F1DT1) zlNLRjcX_B=8ZR%8x3D)X@zs0eM>5ScP|0?UMMO0BMW>T?M4fv$uh{KFz9{n>ui^BW- z@N4sOYIEhb+4M zd7c=UkC!v!AbV;42*YENlv7OyihsC>Ln@~%RO&AaGZ*S%D@j`|u60h&Wn$?=n*-dz zMijW#Ms;+z{_LzQxT(L$|)az6s{A5y>BzTzQ?NZQGWT*A0#wub$z9Mk|i^rVRQA6ef-d-Vm(xXabb*; z3|KRf@=Mp*Iiimt?iW!?;X-FD2qTKvB}mW8YO;WNc%#$z0JsUOg^8CNu$?}ww$nRD zV*JrJFdQ%GW{GeqKDpGw=Gd+0OE(WlX}`uJ5-;Ig!L`#Uo{bJ!#6LldIjiq6#Ye<0`dK_sU zoW*msV5&f0{IQMl?8%eD@|AknMRcg{O4?Ou{vtP7>e<084j!5H+%KJeH}c+^_tn=% z{D*(=vut76Ltk-cUY(t#fQ?Txu=(sN-D9aHot0eLmSzl*@>A>(A5Tzc7#6XlQg$tsk{)>87(3)5CYTPf!t=PrrKHr=VqWl+X$vssv-S35H0-9 zNy9*iMlh~AKZ9A!52iANST1`g61=z*Y)|B)tdcf*;l5yZ#yP)A6r%f!&lUtwb)=KK z1t2>RH9wtVlGWz!j3?^EpHJfm-IYIX1wGrhQd{~ejz?UmU!m>jgc+%_$BMf*(9uZ4dxGxsW6ZcSLVk^Jda9CfQ`4ouz}oFEv-+2G_+sQhA{mh{*-{pe-9)5j3u% zp}J~J4NX_yexuu~-u#dy_U2sz@CHi9?LjX40UG7?H*DjP{v-BQRW)jVxrYzbHlCxP zpfH+GzZt2vFY+VcR0KSDoIcGm(}@tC?o?M8dHPgY>-Uk&QFZc$36lD_3;9Dj7p-4W z2Vt!jTaT`q?J}O?kO!iFBw1qhV>p?E4yYRrW!V#{(9f=~>0svxAkIy?zfj>kvCy!l z6+Fi|B%I=MBx<*}NBW{RAg1q`q#)`?l7rcLvCCuV1*;#U{F2=UO-iDW3P6JbM%`8{ z6!b2Xe!)OOx}z!Ax(82L#Z#r?H4Bm1E9K_z~-ADsT6++yRa%!p$kq^j#WzIE#bHpU}@Uc<0+ ztk_`Asv<|@Rx`%VPAD#_P%F!vZOb9gCI$_Y28Njly$kW9-fU}5Im1*n_wo>K1lq~2 z0_MAO{&{|4HLta~%mrQ#E-qk-Q&w5>e6-BVW2Q25xkjasT^YNcfp>K)d<#Y^HJvc* z1icS8!D!B43CJ8`{Ba951b|F3`22K~#f zyXb!4OCzN1>^fr}W@KbY!qbwIzwLef`n7Lzs5PACK7qArH>{kfG;-;EwEg2!IP{0G zG*bV)BlPEtq|u$pn5pa6;Vu{WQ_s3bYs>)s;gaAskA7*PfijS-(idYkRXec)Q*#8h z+tv~-=ylv?pEp8AU>X$~9V|@82`EU$(3h2zf!@06XNa^ELICIH3%FU`h0U20 zmLafWg~lU{E1;@eY%(_Zr3N}jNLY9NJTtwuiY9+Mko)kPcr1s4UPlz!!P*c!SEH&m zf=*2bv^B-R!J*J@^s}(WL0onM{C?2(Pe|M=plyR$Q&4~YYs-N^ND8FN@j+WV1(a8J z+nKtALCaZ<3CL+idh_z(9P|b0&B9QZ@jo9;4|X6B{`bSc`x%0VTz|GnNdJHTuWo;U zAyEhXY%)|l=Ah~6cTjsm$Pn7B56`UT+NXe)#wS3AMfZ4nrZ@jRUy%fWy(EJMG@!G! zR9k^|)4l2qig+iIjj=K~Xl&)}?;q}RaoUotQq)=kygu`3H*n+nf{+LF8p`Xo9?{DM zBG<2RUw-g7$^PQu@y!IPA=?Al{g_-H&<7ZrYkon&C^^s(LAN(qWMR_GjK%fR371jl z>j@|J!-vm$dU|?GjFfOWZL*ak!p$zSvuW)w4u(?f_ZIce8XFoS$MV32A-l-?3SeTC z47JCJ5Ac|UGnMj84;>viVwpycpsqaGX=`g6`-gLl{xl8xa|Av`9fd_j3-PX(!9XEH zdbHGp=Fy`^7+6@O0G0u3qbq9$c$<69w55@2ueB=<)<^nWs=yoLloNteL)>V|@Y^mf z@JW~D!#VrMAGnVA{FYR{ZgF+-n)&b0mZyMaRs;P@`|HXTd3isx=rljf0u8Yi9n{rh zO5WGsRkH0_b2^^!T09t%13xGS^n+0YSiVvc5)yGW&>}e-^pu&engDy(B*M$XL-`gm z8gtqI;qJYInhcw_QEb@7hA0ZCRHZ2>NR8N#-c+PZ@4c4*ii#kDNbexM_fVuMh(PEl z0YVe$p$9? zF4;*BebvRq#f{uqSXpV|vRX@Fj?k;;SeNy#x}Ne;zVYOQ%L z)b8%^xjE6Q*kbxC9SL(-xDqohm%r~O?ZYA?O$JuKHKTU7|E*#y_;Kh)yUXx=)ka9w z1~Ai89;fgAoU6fQ!S`YZ@ytJ0YCTxmfT{Qo%c94Z1Hzh`SS*t;5O)>)GF|sw`S|{aYLA}U61G~6QW=j{?LtD;MCXGZ!4?9V^I3Q$e7aoBizgkO-^%(y)szcw8QFrra_8nO+}#0#Il^?MC_G|Hr4L{80=mY?A}ioJNW;*dmsOI_kPr-{}+7K;UW0= z0x+Vjm-S%g)Mhx?+8#VOgk!rb-&u5bSWMOqTm6|oV#g88=f z?@z|@l%b8Z-DX(bV*=Z80>;U-j-jzUJ@?P15*&0aIt)hA@z3#&p<3Fg?Ij;Ifme0j z%5K!d>G!@r9j%Pw6yOy3*{>};D}L`@-}Y%mxBybtOV~0rx8vQJ8;Muo zrr-Vd$c~A0oEvAo&1iqR@>;HhJ3ufM<8s*Bo8dZcGpQuEIU5>b$#=rGW6+TKyFhJ| zju&O3pv_KSBs?KK-c4G-YLH`h-D}s_Y>Mu!<)r%7me=B0W!ZyuCm@XB93~YQ7c*V4aK=*$Bc_BP7Mld%|0;VqQ-I(^z`vI!9r$;Jk zM?^&%nI)#*?*S*rA|qpqdGMEIJV}v#_(rFtx27@64~=xd}sG5(b^{mMbveYn*)DVPi=bPI7j| z+?+3l{;t_mI2Pa+xb47g+PO0hr@*|dNoFm}1XNt#3}2QELqC?BzI0JWS2Xs^^0RN=->=$}Jnd`HBAS z7qHJuC4}PYn_y@=z|hvfogcm(KXt7!`{IY?o2SM^*29(e>4~0wYt=+DZ0bKH>io@e ziVEir0}9(*6U5PT$+_}n42}o292`on2s<=+=sxM+K2!bo{pD7|_6n9a!3hmtNr8hl zDJm|m%~LsjeFm4GP_?r~Sb+{-&4`#0oO4Ki&DPBkQupYivQPYTyx8!BganVRne<6} z+dt1$rNC@Wz=uVEruHd_tXm1YQwd%JUeEq5#<&ZIpc8hkCQJS}?<9grH=p*jL#7G- z@`gu^$AgCyH2ckggiJyMk9}%?6ja}2+@W?u9`qyRWFkBGe?90jCEBZd5Bh%t$}+Un z7&hBI66D4n+snU5FW&wU+H_IaA%jb;s3GSU$&LmDbHQN2@d00yyrYEckgY+ZV!7}SX(H5Px`a_kDBJ*1o`ToAO zuaP5&29u6FOt`yEc!JdM1Th(J8eCY8ygrK$J zY)wU_2*;SotFWjj)0z#F{!0wko^N-dKDbNv;lC`waYtL1k57(KV{#U{K zpPd%&{8~GK*VVall6;fe3G)-eFb#^IIn{yjGXTM#2ZMzN zG66%afVaZJ_@vekSukNVa&4158Q(r$97-m(kQ~G)x^iBxdR#r*#mRFX#Gf31Os22M ze5B(QNL_O9Qak_O<$ZmzqZ*bMHV>Q?RREH%0G$psd&{$^(K$O5F@5*>lu!zg2E-M_}dZ44s;oriNsn!{YFeXZOG+W^|+% zx@u)-+-gtt56hoZYQ82{(cPOECmLfMl(FnK6zQ-PH7U%@ig8a*3j_C zcf#UVQM!(k1@jrAw2VwKZx7ZVj5~rHri5wUg>yD*X$+B)KNTIi;dZgsbLNloLt!&X z$>$TffAUfAmRXoz%CsBUuIAgM1@lYH=hRlI`h^__)@{n_aT@ykN!G2SVLYAvG@Fb zh^{=*d04Kt^VF4liZI%Z9Vs3Wu|T0v>4e$oQ@Pl@8VU*Tf$a}8tPOE;8YnRhQHu`^ zHLx!Jq;3#X5l;PP%U8L~uQ+j}*XTIk^W7Z^14houBEJaQH9oh`Gvtkpf?k(GW#~q5 zXw0833ksl4%SPY1F|q~E@m>#FM>he*@HZdTiePDvipTw(u93s&b**B6fI%o0aq*hY zHL!%Y(OawgYa{xeyq~3scN25|uMZ!jgFC(2h&{93L~2TnI~d)`2+kWoaG{}rK`4Q^M7?$C#+>;GplWzxui(5y z<7Y_4UH>;Am@y_YByd^Gtwc>5o`J9B3oz~kjH-m$pDaTwn3{_5!1ig=0{S8U$5jqq zz8!CT?Y}^1qn&urRVqO8O{W~hgM+i3Nj|PBaPD3{e1D=)C+OqHV(4=?I?Ll0Cphd< zzr!l!(*zNRuo&2mj=xTDFJ%>SCMFqof$YnMzvkW<}8;SzBpFe9plu-yyEMZ=C{t?2N>; zbgXf}P&`28b8HIqoO$6s2)O(R zi0u%z!QWAYiHQl|Cg#~AWT{;}6F}u1CD!y>?uMfPyJpkePaX z5ekcb=j-SI6GCu^15^+l91a&50t6Af-LGnMGDc7BMbf&M^Md9jKwDrcB0)l!tr~IJ zs$2pHRP||1NG~(kUV?tY@gtj43B2I5ZY)!PBH`rrs!msW0@6=0?ucK3X|(72*i$ErR<}mHb}jR8nJT|D%i=7$dwM2-5D6=8 z7nZsCJc|WtF^x_I?K?mni5uv@LVNAnwH5H{7q;9G!Ym{5!}1m63ARLgMWNt-4DVwb zt^n-?@Q00Cz2q~qWMEU3E74%QDR>27I&{EQY3S(4f4ZLf`ZM)s;i)(L;0O3#hllHC zLP&Dr%Dv8mAE%A`q1Q-CDcYsO?LP zb&VP@yX-*v+zq-7d_okoyOfQ+rvzoA69qd+HV5iAGd9R%8OV*ae&~$iH*S&Pt!6pL zCVQ)_=W5rJsSy{LB+3evEbT&5SBiI_T`!Usm@EWjN!p*F0tVs)gYYv?^1NVwv2l>5 zT{CMMcYbzWWlJd0@56^%9U2El!520Gk}%gM!2T2+pviHnK#Vz~+^N=eiDTIWNWD7**QEcu8L z;K+=S;3_9H^t?M6sv>fM3@pw81b3<|O3WQIF*Rk(%EqP+T`GZb>5^oM!aGcNeovzK zvB%$k3Y;l+|Ho(5-|C z=RKaQ(@HAw>^E*ShXyvwNv6*}?ff!OW}P5pJycnGLo6gFymAJZt{(i1=tZ;UFRZK! zd-eu+u?Qcq6N=i1!4XA3N9&a{^!H{oX>nT4aiYXlev;wiQs+f&`#gJ*@lu<=4^$?f z$RwN8h2dybHG}ize1y1PC%;Z>zx_9W`)SV?Ay-o=x|8F_+_<>A^z^^wOlYqloK~t? z2}(V%u{X`$z{ER^sA`fK(VZrd*iQG={zKJD3uh6`IKDo_BOuUwZDTL{r1fDLu|N~P z2y4wK$^Hsgam|6nRN`C{J3Bkw=8l8aas2e}DvQ@afg27_*bO_(0aF^CqD~FzrF(*F zBRB+wiCiq!a_S8loEH{+`zbK|&*n9UonvA%|7NN#45SftLqppiC!Om;Mx-0@oDjQf z(s{pKY+bWrGTJ-^xcus@RoiWFH?H8EMTj z1w^i?ycwJ*K9{9e{Odo*n?q8eI9XYlzH`K;{^VrJ_wB{nT^@ukKDu41sW@D>*_rf^ zghxF55@Ul4I5Y2+Usu492k9T@bBKReRfG6~5uLqs3>efE?w;l;$FnYR=|9w7343mD zcvuRTJpwwv&0twr;jt}csh;LbSlIJX{WvtKe{-^8102>$Zaz`eq%Q5dxdf|@g|O3{ ziFADiE$53^Oc16>I4UYi`TT&dIC`tkhXNVQNeyH0MUzz!>_4tVQj}DHRv3}B+YbKE z+7a#}Sn8CakprJ|+^y*yZn^GEDpm3qXc= z#0-ZEn)kLsjQP335-d>;bYkxDobJ3v-a^{xn|@nvyqxRpO_hy6Z?FiuagM!eXe9jiShAuSFU!;0Q71S=!(i+A&54-k0SJg6v z2?`_r;JW7NG;Ln@Sf0Le#p15n?~EoqGHLKW5S>#W5Ie`XXXmXm`+^^NuT3r(UZOJPcfK< zAEu^Ohr|XO#}xsK$TcK}z>6IJ4S7Ao{k$j3?-cyO@*B5v73{j{R0dL8Y~74kI+L$8S2YQ`GGnQD}+wPL8_wcL+^ITcZf%WF8DGG%4uE4{oNF3&1!s24H!`vewD z!2b6ZcZi#>O?TC2X;)XyC_wm`gM>M-@d^Rm5QlKkaH4g-)Ee2}RS>o@LtgW~8nMeu z({}ti71e?PSpDuwNAn5*kmJDX*-0@Wm5M4(PuQz#Ap~iNSzQ>?*rBZJs4%80r%LB7 z>vnxLqgJM7)AS9FBO7MP<8&5&81%QG)*?@Is~Ao*Ua$U-pUdI zQ^LqQF8v1pBX|V{#jik^^$oNH6Eo=v;Tsz+u5jYChG>z?r9E;x-W(jNxABs*Bj8sX zI!L2^iH~2HwQ`Ary2-9TTF9>{?mP3Zf*Llx3-u(N+y8l09YMVC|GfT>Gn@Zs`OyDA zy~`za0vmz-eC;92wQCaF6w1$Eza+sqJU3fMp|>3D6WmsuknknHK14x$W;TzCF$o~- zBtJj&|Iiz8B2KoE&_UWyn$gfxd~z)ZsQJtxW59T==pESAsZI&tM_bP@g)@ykOne2k z{iT~w-mr#~j{u1BJg;`g4QA}79i*m?c1%uA{;8trJmi?x;kFjxg+9$&y(3NdN7hyAJGH`J$EsB~q0RGuKBv$+>gG)f$kxN`bSeTwyn1H-Gri zUK#I29^keAXh{flq64g~6*g`smaO|eUIRpi1&GWEhdAUEgK+2o{Zn0t^B5@r=Y4TJRG_uX*W`XE;9RblmE*MOmYc@;Y+f^9>lgD zYWb~=!otEsJiHcaYA*1VtU^La0Al>aj*X2iobb>6GDCkaB=gf3&z+; zRY;aJB~0mb47D*h4_-sU-70`Pc8F)LJ?bqudgMXk{-}GlUoTl^e%DEr4jTtJeJi4; zr$=rneTq2M5#h_^+2@h831J?~-MfEg(5A1_liAl&UMCDYfp?vp4zEFzqZ1V+1xhXY zS4s@$5I(S$*O+}?(lsJT349aF2IpbOw}(K~8pEsM&3>~DZh3VFh_0*i1EnDlT4p~> zIM6-_$lija3n@M0%;81uvO&Fepy?-+t3i`u@qy;!`;7vRGfY2vCWe`WMP^?4@n32M zzWxLfDf*#~;Z+e6&m7_-y?bf~x!q-sA4ptwV(`!RW8P&@uJ-ISLjRYxz(@&Q>1q?H zQzutvarxFdzdAZPwwH3eK9mOdQ{VrY#O&elYzE+38;26ZW~PKL;A2+k;3N8ud-@M* zhsuVnMbB%)3-XYYn(Gr88zlibjMob7CZz4hK?s%Vw^19(5j<#*VlKhHK}L=1=ko6 z=RDVwC2!xgGbeT^>GbH6>nY}@9aJmpE5|=DQH6_FJ^03yG2!4d7#m4b|(`Z7$|cosj0LZnhu_V zc;b!hqPwYusNY@NUb~dCne>|X@4AFMt!g%nifcPNsy%nsD8+!Pu8V~SnxTQKN&zT# z_QC}tc{eo~AKH@LmT&WG#qR$;2}Yv>t;i`UEJSAxR@R@Mm*cQc4{*(==Or(87$=~6 z1Dd!3hIhr*2lcm7zUO_$xkpYgsv>MvU@LN~PraQ8A-SWA&V?yT zM#2Hh9{l-731#iIKnn<}@W`wyRcftPhG*K6Drbd}@VjCg?9KVDXmH`04Zg^q_>UW% z#sBKZx+x*;=d$x41l*6}$KZ6<^WU|(x}+sc4U%gRY7BB>#zDEUVWM$&OuxWv<|gx@ zk!kVKSo`+dtio#?_Qnq1CWmS&O#S|Vv?vR4!HE)--@G4);v)rD5`HTrY-T$ezg! z;OCqv4&}LB{=CA@u-Fq&7?FDsBu8p#Q>pV1>|%Eva02nFDG$KAG2oKE&SdM@&yTraaq+li zB9Xu2yVDRLcQ;^Ljt@NnHBi~e6DAZ3OG6D81jy>;Hc36dvn~i{7egOiM`JkjS zgkZ~$%FX2()}OeBWIPCY-{v~%Nj1Nx()07RgZ%w(mRd%VDajGXzv8?4J+H3Mdx18W z6Q*Too;rEbaYJ;Pq*-JjRnw6m&WjgzEi0=zC9tJkXo&+Jy<^9-*wR@mZ&4CGLMZAG zC+Bo_WXbz`LjcL=;)y{5@ONQ{{3gf)kL!wA}qZSmc0d*Agh z+U?cWJ?oobgW?yRRMKQ^%i|WN>`@(6aWRN6Z?!q%)ItFy!hh|``Q;<{k${o68*iK5UsXR zCabIE%*?WoY=dx>g5%|>z)d7ZWe|0_HKGs1+ixx$v1+x#JSWmU;VZrmqfpyH%uD=vd-U9~ z!6)<=$33?}GDNJe;C|U{Px%rXqn)pk9;gOkDwJ|P#?`_gE@is;uENK0XKO{u-N9&4ezz_|-z0ea-XkLq_e?Mca< zz=|KE=(1Q-nUJsN2pB(2*H zyH2SAxl(fYL-c3~g@f3q9~4Cisd2W%ORxU~zPMz>w?=iPdn);R!ou!qaZ*rbhk^n` z+!$&aMhhBogrBj|7=rTwoSr_QUJ0Iyog7(Q-!a&bvy^QlE zUUaX&DH21Wpwn_tDvYm( z81B`g58Ipd#S!a)wH-(Ax7@lel?RjZk~?%zycl$5V+dv{ze53~aAvLLAdjs$ob!i5?#?Of7HQsmAnubU zx2)8GI@pmhjy2mWGTGO=c>6HHHuOg>Jpa%B0)WBim-OZdYF5gN>xLAS$?+4%zDj(u z6r=oDv{(IzO8t|lQkqe2zIJTp9J=|CL1W&rfF|;MVHik%|tq5>(Z5?j($8p{}ghB9t zB!MBC0Y+7?SDabhPfriL(H~->Gd^f%g<%q2ZG!ZK^nv9h=COxk4;AivObbDX!K`V? zE;mgV!TqEXf_>@Gt6a_Q5)+=Du4T^`UZ++Do-*U$*y+L#Wc{A*>U!*Y3R+SY|N44} zVKbhLR($zJ@myj%wT_u`ztedmi39h-*eWc8if#Wj?#CUtc?=@&o8Ewi`*_W(*H%Kw z%cmGcryUsLtfkGn=p1ea!WqSzFA%P%s~;E32W|xmkG6FJ#81-FA)dxXf3D#wPL@of zk<%3dIg8&j^Q9Md;8~gSpjJB{9ujYdER}gXw9HciW4id z__F+{1CO|MYN{RuB@w00lZ3i94?0-Ac{6F#Q*qp3H`%^mm4H(i)b)DV>G_ki(<`I_ zNKO6W&o|fP@%dimcJObGORFN<%8)+7T1Ymo|c^!zaM91 zb#?FDcAi{Ve!iL5)Gh~h`dDlk3DPW}Dj6}8 zSPmNQ6{t0kus52#3-u{vb3v#G**~J+PFi-IA>xGQh}}3GX{YIO&}7=}%#*k2tM(mz zKxLQ*+#{Qfom01W-n|Rl>IPQnZlpQ2Q8&QrHI$$l&wX6n(4DR5aHa5fTZv=p7~uWM zZ~KKaF*{Ee=YE%%s*o)$&Xv-w-L~V!Qnnqx7~vxlzg9WX$4(P0`Crvh+3%3>)>z;J zuYplco*SvvX^EbECCIXA)tFvh;~ z>{64m5KP4GGzG_SC*1{t+}E$u5w(oufv~W_=s>Qcx;5Vq z#CDMm&4r%ypaA;2(jfgJEO~MqhZwzJQHQsD^bLOv6JB5h^go$vz$Q9B@o+h|th;=M zDgJ@Sbo7Sexlf1Y>wzcuq&JeleTt*mJFwe>7lxjv*5UDE0&?dsl+(HmWo3^okC--(GjR`ou?Yj{*ygou58{XA;6QRjqs`2bnE+ zhK2zi9^hqXW4lluZ&?zuGo6QSrPMpRihF8TNHWnnI#>YY=pp@J9Kmp{6LG|xoE*J1 zv2|iKq*n}wt6WzAT0{k>W4h9}ShalmA!MPQ!K&NNJblG*BU=)MMik{Kl5D1*bDF8d z1~TC1KPwfCB^AQnpJ9FysiFkj2tF1E#A(yIPPgUD=vAz%#GND0SSiaB4&M{#L2xhh zRn98_K86Y5Sp?_3C~FZGeFfhSdcI=_o;*cT9(f3BZ>o(ihbhs zKPgwH3@@9;ks(EMU+Of|q@>4FkCzw6>c23Hi`rxEB__Tss2Vx`uDTtA4riPA_$89p z#49X<`;2pCt?E0mvTirtN%6Dc7f%j*Gaq?$^p4@=-_`EUwsgYuR`C2*z1{NwTN3pdt&oq(pUseIvF zYI=buy1Vk#U+95|YXy=P(d)YiRh2{L%1Kd@k_V&9X~z^Brt>!WYryR9u2M+2Y1Q!Y zZ9RP5y>2RjYAVO>+*%JZb`8oQV$jKfB(4_EE~galPl0#-D5VR(+0Og6z=wJ=lkt+_ zZh0*dJ7g!}h zcQyP2Jxp9)vgG(|RWA7O@X~ozptN)*gASL|AAOMc#)Z^x%ql867|MDHMmw8I{+aGN zPk$$V=IO26_1)>@plB?DCXh0M7N2mgF6KwSA=n)&iLVGezYVI`Hp4is>N_(WcXCbh zcNTYdI%Zhr02U-1{SbQ;s_9TKv!OUqYEOjnk5=GKPU0`zSzgSq+PNa^mGJ)AMS>mo z_p!O-I7snW@T>z!0E&1HD-VK9_lzG}BJm4!zzn z`HIFVVIWcVDA-!{i)F@QEyQ8={iI9H1VF(dMSA$*tP&qtyuqsakA@f0Po~6fG>;P} zDuC>1m!7h{@nN30Jzq8w-QlV&?E7|s?%05Hztt|>l1OWUadPrqM>VxMk&kvMK7rz@ z!p2QAfvebRYf(@xPJM2wPT-g!E6J$!iwD6j*L#i1I{zdjN&VP!9|etqlT`Ojfq;Dn z8@%(}X{Hyk8wFf?--|qX&;OAV95QHM+bL}|(S<~mzvlPHQ{1K1OeF?|{GjT03!b{e z1WjIbUkX)c*D1&Q0{f)|q7Lez^;;L=f}4XuhxA^n_AK`Oh{Bs*k!e!M%5BpB^S zfTO|yzroiN9~Neagqm4UdZrc*DpOXF1oY*z1J4n_=mm-87eI$s1OfS>hQx;RpqS`| z@ZSolZQ0op#qLSr`W=vZOL*~kfCgUb++lD1%KzCIAiZAFkFHHTc=sWM9# z4(|vAwK^2x79)at>ybizU&mhz&VA%Hymc+@=2{JOWoYF?4??D17+szHEK^SmBu>4+ zcIJYB#r<1N88l_Kuv#P#%t52qy>e%($|4`X^tV1225t{T%wR5q@XD1bv{&F6Qdp># zmyiSr(O=SXa(Cwe;@w4e;@`c0FFZsu?zuZN@uqLs$JILF%NM;7$!e%wm=q%f)TsJ@ zTWo4-DuC5ATZE#71Q-x2K#&b1L0sb?GK+ru?p+kRlz%I}(=w0nCr=9>E$o;@xf=`O z$v;)D_P%I^Vat?!UrSfG%Un<*4MZT)!56@kw-rH0d~-5s{^u~M(wf7h);Kvol?S)} zRC~(<(o1bCkTZ!S%*@P?CN2wD6d>c!fedi!1=8T(Qu)>(NQf+vx(ZTN zV`Wqfjp&m1^GxMbk9=3%j=xCsx-MbuHCnfiOzf`J*{TTi^SiM`I#k`^Po&Qpu6B=c z1m5EN1D(T(UlF?g))uohY^gb~XKN`s<*SEvDEemRJ*aV2P1|?%LHQN6OGp7D@30%R z-Y@nt#Zo3_qs{XQcfa#=P$|yP{ToDg??AV1e7^ZovQE)40xpW$dlmww+d5emLFxN+ zpFaIi5epiRPnY=9I+n>R4mI1)SaVw;tS_VN#cB(_EELB_*Px$)U)70`|D$4(kX?MflyQP#@ zNfLj7aDP5ZNBBbREe+-lJ?;L&>sHqq^4Vt(c~|&oh@HH6tL~;>_b`xwpj&N46DXj8 zNv&I3wT~koW<#G`6Vu1jn7+v>u)KrNf$>GT$q9c46ZESWLBw@_%PsWz8&JBZS=Ji4 zE112Ca`(zkM>y@7f0H$zxcg1;@7v>5m4<`T3B@}cQDkDq;$igmAUX35NeSqAKI{mf z_x(Ej5KF)L&u+1Y-SWia<-pMg8b7oa$v!mV97=ZCEQ{~HvK_hWgjD~;)+pHTYIUg$ zzfONAc35r}3eZwF#YZZ6I|*BYlaSlL8(|Gv7e1-eX4i)?DBJKyhi+c)5FTc%Gd1-q z0<(dIMPXIdatyoCpB7S25S0`b{Lycmzzu9sD9|TSn;gfYz5W8VVN}B=W*T)#?6=O8 zWcL@AWXT|bRE3g2cT@Q#?JEO8pm2#knpVp4KKxSGSxcmK^|>(f3ZJ6bj!YmsicGq$ ztNf5)cj_%VDvSf$Gk6WEKE&q-wFb(d{3^=MQ(YRFQ9gMWw`fq=hR{(huS_!I-ynLs z(DZMl0dqOi53Q}T&@CeLvUOEgYjG-3UnrT?YqhlJXwTYOH%?9mg=Q!{;mh+4^cln< ztL=^D$_hdboMR_YGW+-n_5CbuAE zjG?a!=M~nh6FkKc>##pT)fC9*niJJt-27Sg3)6$XTY{?WB|sy5a}DZ~_MJ$x512NV zmr0!UyaH_o?n7wv6v&+qG-Ax+T*omxv=@$Fcq}!1{HLFmqt!2D3VnCE+X&PmrM=Bu z*u6sy1$LqR=`-RP8729ZL1X|)vRoOqUlx8jjHFs_2{yj)f3Z$uD1p)uKAf?jaChfm z)$1Umqgq2psqS}88?<3YmD%M%_>IZ_cCh>th|k(sDpq~>Q#{K5ZJ&MD^;XG;`ef^% z6GT0FH9p4{AU!SMr%1VG4jRcl-N@4Y2H*QXKhQXKJ^|@@TLtO)xp|wE8z|f@Mewfy31K)Xp*GGAk|qn=gGHFi3s$ zhx&`h!)^p_dxI1HO4*+2lpLq9d{yE#2VCSO%&g3*v@Pu=NL4}Xq0SE~d? zYUvQK%wd=ob6=>dPC};fil|HH6~}Z3#`AIHHkBF8yTW{SfE3aULIbrQ{Y*wZGohNz zB>sbMrCMl&nT~NxFJz466U2vFe`Yj4uqGm$*QQ*Ue0360cebibKzMx`{!4o{D4WPW z>#+jGFTs!q4BMg{SO@^!WS!5gUk7bkpxj<>(z+*6g8fM?qWuA8#@Vq9zQfy2!sAFR za2T6+B(kKC;1>(-OKyxuSXdp-EGr@-i*i;h}tdDVSH^pyYesyg!j*^gWd-`GHV{}m|mZUGK81;pP-Ih(vO6x%$sLlVni>RC8A znqo(wJO)w4E6?w(%2m{VI$CfrGGU9jmid6f58eh%^5#Itcxqp*SFOYBZz&jizzdXr z7!=<%Lp?xSCKNXELH!XlWZ+iv0`AYOeNBAtgwc(??)h&YjT_OGQlg__lBdlI)&bFD zLCnDgCV-FmK}Trd^*?n=l396b#ZPK>w#?NaKRE~mX+=;aUjg|^>C(l$HI)Qm7`zlk zwOtSDYJdO!-EOSGPcMk)Pn`-D2a0wqJbqAb+$0&gc>&h`48XVr>ReeN)A+Br8F_09 z)AIdgM4b;hF;%%C7RIF(<|8s#&}Fg#&_{Yb_|_@SeE0)xJMM!-$Y5o>nTLP~)2I?n z{<8yIj-P$YTHr9FY(EUmB+{{#|E+c26Os7`flLt2Mj67xiAIITDyhF|yre;f9v%N_ zE5cDTXCc#FT*O)*%hlZ49-d<%!&jUWOSF(F${oZh@N~474tk1_pSkDZ&D|HsEtm$) zGI?!G=?tZhXNs!_Kgs2+smjEUo42s+&2(JbQ5aH?cW>99)O_J)Gy`Ic%UALVmiFxm zJSf*>6-#zpTdC&)SrLz1C`LaLb&r?dYdid0g49kQk811G#RI~_y-s55 zH=Ma9xrVWPSc}^_3m17*2gA9Is$p)Fr$OtV$Q zb#5cB(z8e|-&j~1*W;&YNngHi;X|3xCQ@`j#c}CVzQuBk-_S~fgPYYy!X4^6wj<4k zr)&qCU-CL@rr#dQB(&qYTsocN7Q1Xz!(*xhr4S!8S|U~TJAg|{DA;u+l=a38J8wFa zR;L-a6rPLv;#M@oxY57=hvxUcegUy{h=7zz(UY&Z=y)JORwMkOyL@c=0TIjF+VS7x z*#C)YHP*@0*?+#u^?GS3_ThaM6_q$11u3c9Q_vFRab+#QvkT_GZ-5g}GU;@>!~O6} z58uS}@(Cd`RL4;GHOV03N%J0T^c>NtM*R9tP0iR8ff;(}j!ikQ3~H{^>WqkD8QSd! zD0Zdffp)YPGEtTdYbjaO>5qg)rst3fKK@&BgGc}HIy%H^F{ zy4XH&Sk}h#&ShyP&{|U2IIA}*>X&?Nu*V2|+81JJX&N>8RMa{`L|nvi0fa2>Q5AC$ z#uY)i^Uo~OBZ!T9TNH72qaH)dORjWoXU%Zbj(q^Zg0QtYupsDFm0sw}-H2symO7(X zHE63QkDt-68Vm|4e)0EqeEr(l2A6idwC)e{9@5v+IA_u2GEibzaL}wb$Fv<%tw@8V zY6$Bm26*9#6o#OERrtqWNmpKxmMcqOl5Vys&zr)mIh;>VKe9v&e&vNadTa^l^LkVq zDw^9VEKR%5+&|amAKciKP83?d9nt1!zref?Gr^9xv^<4_WN3wz&Sb*5js7<%4doLy zhtT=S9*F<(UXdrwD7pErG&mnt;TyNvw;ZR9Q;v>*@Gtk3*r5%nr-=!E>vz(yew(XX zndKc9V&eS!<$t=Tk72e8W9l94V^o%t=6CKy8OsaT5esS;T?!1S8b(HCb?-N#q*&i$c?|0p2yb-cii->hx^#<{jM&7sh%2O6;7apEl<4 zJ+vuhGMb5rCB{0PJ2?57w@dRqig=N-D-p<#NGm3#dhDsP?pCDiEnmwquWVBbRvDe+J9@{JA>n0p-~&3sV4_TIZ==8@f&iaD{V|H zjM8l!W#ih4QCPL~<2VT^2C4u##W;{qpONuHjX_MjJ3B(WGe`^vWJCy3h7R!DY=7zs3D_Kb_hCRd>d2$e;5%F?vw(tajAZ zSWqzS)zcu1$7B^043aR=N=6gfdRX%;LWZn2M~xZ0X^XQCG6fEq23NDY<8JypBe=vI z*Dm|0R`2JrOepvT)Lf|<4`!4jhM8)eLFAgosg#y9%8jCl`~DWl@>)?T%tUCyyN|)jUphb+3^X$Ltwzx8Ea1pXNzZ4bJpamLicM z2W5GFG6S2L#zq=D2JIDm4@;#k#cuEj-|=SNqzV(!66Rjd1=k(#8Wx zFO7#Nv_LZ&4k$wV3>nxaE$1Ot1+lfzN0O2eW+VHk?$5NeV`PtBJbC)=t-%r$`~8wb z{;9X8bY3PVur$7XS~`6?^XwU}<=~2QUf)Sr77B=k_ZB-QAjhKLc6y`v;^OaD3e{Ef zidhB*VcSRUGnA`w3%@-3^+Af+cxI%$es%d$?4v}s-KSgoY>dUjPkr*$FbDaD5Kbu@ zQ6Bp|I(ZDYAjD?w#Ez}3ZZ6Wb_O}pDtm_JnW%*d@B%z{#Z-D1#fezxA52`{kQmU?v=~7SS+lcB;PseBHKGBbZKf{$S9CnwCABt*5gAmxzf7I z--;TYb(J{OOBhU8x=0F->Xxd{plNXG2b)nh<%0p zgkt8I%Qq!TmTedLy{?|ag4dz)CLBiR4cWtu7vH&2zMROCz_EQq;^YL( zKZu5n;Odsec<#ru(^R9A$X4>!lalKQ6}n8*G-6I_MiJJrjp+0Qod@jU);jDjgnwrm zvi|s{eY+YL!Z+!9gmEOKbLdxIFZQ_5e!_uXP&^R;{$$esjGluigdT5{rx%)RyYp-3=~r zFukDI?AKG1t0?9J_aE2CGf+|WVOvc2Iu8aANZ93oH|+62M^++=398}9l#Bkgn=+sJ zI_>5;k7Bl-iV#O^Uo|{WtvK7faeI&j-&69IP+0YY~1$F(1;;*nfbE7FwP` zVNf5&Cv%11ueE~rA1ZRe>lPk6dNGFmiBuW2ZFjD2Y)RG`?tPv$6aCMT&#p85@Gtj6&@gv{o}D*SA8A&JcPAdm1>mmA!^!;^#3+1(3TflIk7|jJu+E0f$2w!JIi0aB zafdqYo}r&+pZXmXLZtsCv;KbhES*HZBJ#O5>}vhtTzwv$4bHXseOlA2r(ymYTa8G* z677s;{#nC^`|7aEs_=`v1)htyH(h7sqpCb2Dl|WvDSd|#0XMsgrD$hf3ch%=9p$pu zk8x1S3ByVhWD51eI<;7HlA3n(*^gh9Uj@Tiq$k1`)3{PL9n<5OWu9;JNXcuwV39pTlA6N-h~Rq2l;V(Bto57`iIB@ z7vD=86>G1udK|aW=oA%Y))_q4|Haf*KvmT(|BJFk+jw~`?bB){cg(UT%+jjvJPSwOxU=xqAkOC>N~cI9D5%Z{=AhUyy9J zF4F-0S(>BA8E_W+@Gi1Z3+d^wKK;>T`oqbYeN2?ft;DWH*_V>)A5S}C z^(g&5<#)Ebr#^^hjnIE|qCj`58f5&BgDTEBq{x(Ra zVum(j9d&)&$&L%rE0=g;0h|pLGY*GX&7!V)^wWyPr}^QAAAn_jfOI(EDx5S7_l_x+ ztWwUAw7noOZyhCN!_-J;nl2urFAsA_?#v*29nIPqaF7#-DHnFt$di2^)o@4hJ_H2I zW-8rZ?A86Rc~!?`i*Fo-%84z1pkKr{W^RM=yX(%ar`KI@X2}nZD|nsozcgnYD(llL z6S9m-wuK`DTRyg!qFirqmiaI>bBe5xT6@)NwccxwKHfXfROt(l(|vS}FzEXd+3AH3 zZTjYZHcq}c)hs%pwx{H*A;sC3KumOKAxH#J>t^PGrLU39N#0XL3|7ZQ4y{L&j}qaU5eowR4OJGy0M3hx>`;rgWt6CcFeTP@Qdud3lsG|+EO!0}3Y7eA?glx%feqc<;Htur;aAccAk z`QC>!h>mHxX&70;!C<&$uDBC>ldV_tNOAflMY{|-n`@q>2|x}Iu8%x{{w$iU1J`0& z`>#6%NMlts(jTY5x`NgEi^{&dJY0tSA^A&Wr??x{ql!yK;a^6hrFKZ-4fnC z+?P7^Tn=GkQ&0ucRCM8?-*x_m?OwuJStCREUI?@gE(0H8f5kqMv$>DXCbs=smF2sStCg!d#K#y7hAYrQia$J zmO?s#%f0gIs3IYMuBXiQ^fQseQkxB%d1tM2^V2V{8zj|d!X2<42e;HZpkzht1wt;m z0$b_4itQf9uo9DGC5KZUw_nIjMwjFXNkZdo3;Dg?vJxkDDs5x77TRp`3}=2iH%HLk zZ(<$_NYKnr8(Wy1K~7WV9JkJ$>xgEY{IeXQr1l_~^FcYX zXLmbEKCzfyy|Nx`mqNx=9HlDVnyRVn>>MgYjTv1>-y<=zMjgQJ>;9`-O zpM50FV))3XJpm#9D3MpK8#ot=$r(Y31Ne(K?QJWY=<8-=iv1CC2`VGKVFdq!80DI| zggOJ+Y4bL&dadn07NtuS8y(dSFaE5vg`=MH;Lz4LVqDNj4|kMKj?-uE()y$!3k|ZMl1jY+(ugZZ z3^In`{Cq_gE!P}Y67_rxLzYhhB!H-G_X8JrqAx8-j?m~BnE8{1^^r9HQXmO%YBi@h z7|!tFEP8aLIYw@8EAX4fV8n_$AAek1F{Uv!wuFz=@FGqMjW0B!$M?-L<5vq-EV(&9 zYb!soe#7&`LT2Xv#QrN-*Iy!sh3~G&Tu=LCc(nb$%5!4Lw8-!FXAoy3GBC=tu}$1A zoaZW{|8lMiTyBt%aD(BWbl+q4HdG!+7KU%X6M^-hJWor<->j{i|K(w6wW84UFpZS6 zbIP%U(7gt+<^THiohNC?57-F^;`O~<;0e9_nTLKg<>b$Rm*RGd2&hvtq^GBfpL=17 z@4xu;Tf0YT_nVV`#Ex7~%0O}UjE6{J+!Y5LY2>40D#Tn>r@4t=RcHIE4dn*&7$f6k zn&C*{-__yKHUrmn>74-__aAn84ng1v&8%NavM;u_)qdQ8mehwqrMN4KNj2ZFI`Mwn zEQJh-O2o*H{i=%fXwGZrjxy5PQS^+k34CQY0kqY)A7QbBq!*V(Oafp;C=460ecsAd!_l}C^rV7|im zyQPjO{A_}J9;-A@d$dn_1Iz{SJC_^BGIJ4wiGBlGCH5YV?PXbwZ5JQ!h)QM4lUIX& z)3}wcZ5^>RO;$msJvFfq%#Gp9b2*3EWC}K&IRVer@Z2~&_LXG9gZOJKGmr<1_D0_I z=?@rFhq2_KyiLP!p-qJHepNoS0&Kl{gf2+r>LLn7&VSlO9ISrb=J5-glaX3pwVG*^ zoa_o3#USHvERqnN!Z)0Cr5+Sdlbh3p{+YU0jaFwkL!?CaMxjXOS~OpBSoXx_=}YGc zIe~|ZpB4Uw%&`+>L{#r@B?!%&ZY`rml0gtDmSa<+xpDb=Q>p)vRvFlD7-8j_Ie;kDs5e~0 zw(`xYg*L&wW0Bct)>j{#M0nUeSt(~dc5rv@ai1kk_web_$t!EiCNx|zvWioaoJfdl|s2lF>*CSMef8;I# z>*i-nLKqmb0=t%Fml}K0$c^8hGXZOe{skHisnN5;a>I)5?yU9PZfue_PqKjtnd5m1 zkJBHAifiCoT}fmAe}{8?YrEk7k?N}ftIb+)Wf)oBZQr&u2z~b z7r@GxO|(+4;NU-@322PQ*H9rz46ftM!|+OG;AxGsUz`CVDY*jH%iWS|rJA2@tUM91 zf5Qs=^j|J;_GZ~;h;O2Sz}4HCN0#ObR`45Lh5qR`ysgtzMMX4!JO=Pjtq&Bj3rRSR{%n^p=r z!Ms=8tGmoC6&~<{ZS)0BqV4Ioy8`QpIsA~BkI20P*d`y65*Qjk)0}ZKS?ukRjZH+7 zBqAT^orS6fV#HpA_1a)d26w(*MgqS|=F1J58Wjnzz}X+|*cbbq2@m?Zfe7bM!-x|$ zT$<;@A@#-@S-jxl`r8ob!Bc`cK28XI=R>gOBw`V?F=yHEH7G)4{0em367m!nJdab97|LN^x zxlU|#!QbPc3T;E1U8JNXhy*E4XIj&NH6S>c9x8sa5a|2VsZ$9^gEXiG4TF9xM=^z! z(2-NPEF1bs3e8upX~^D-gL~kQ;nr;*G)-uU<{xl=?zr6k00%`wOnVt@C<0LaxJl%m z7Yx1;Z|9HZsnn-*YnmaK=F*xw4@NwCOpbA97v!^Wge0lVEy&AZpPPp?0(>zr08?6E zvX&$kLZGXR$1v{1^#=jNcb?6ymk>;@9fpo?v`~;-0L`3Kpi{joQmrZxBd}etmJj0! z$gb-f7)1P+|{*4<2CKI!lIE##0=r~D$8QE?qI4F>FYA znLiwB?2F%Jhkn9g;o!<3^g5+QN`8yC;07G$BqWFU=3EXRK1?xv)28YeCMCsHHCOyd z6~FAQ`8yQc(LGm(TZG#SR02@}za{_`3s?C86sowT=QFs~hj<&E!>&CJE4YA}R(}}P z7qSY@Tp)F}_`lHfrYo}8sIv}+eQf6GHA^{ZLY-E~)Rf^m6vMQ<=^8B9YJti^7O2bO zTG9l&dg6lSx=Z08e*LnjXj@+KNN0YPv%M*=*Ri7(M!TT!?Vx_dz2g}zr_gJ++nU20 zPb({#`5H#P*sVp42s*ojnS;`Q`$e;&1!@z%IY2!VG@LIq@5{p#tWi)<&>>YpYH$zH z5{gE}kP6^ANL8{Rjr?G#4Nyi;d*eg9Jm%W4vxl8qp%P$#2IlVo&ub4U^k0gQL)C`6 z9i+3}JHl>_y_P=HFmW(-wJ4$FhQn^S?PJy*xoLekif8ufFMdO>x(PlGj;?7ua<4o( zGEyyJ^E~us+JgBEY%nN7w!e-Z?srRQ#e{|`&3Z8|^+O+DM(A2MuhqFjUi!0hJ?~PF z8u_^!Rc?~1qHnKQfOO*Cfpm)L_y4N2>`{D2$D=6Pt|6J$@Ki`e?EZ})ZRmHGQjzt= z(8vh}Bp^)|^*=c>E63NSM$aUyLQf~QS~`pzIl#hPWZjceB*vqE#^=-@k^rQ8?D<(z z_u`!E;sT+$cVD~Q9=XJ050k{OyIE%9AolLZT4&zVoFHvmC*tK?k!yT8`1rA<>+<_I zZ@A}NBx3~BMZC{yPZazC-u~Ld#W2zj)yC^^*CqjKgxQe04jTnfOqN{vH^UxiF#2aY z;~TyuL0*6UZbZ3bSAdfv+Zh=1VnKy4SQWsv4%VM6*R|V8oKy{CV=A73Thm>ThB9H3 zXAjj~rF?x!=ydmxTjweC%=%%pFauqe*e8+fBELleT+Yw;z24n8OH?C^U?K5j3Jn2d zyZH>n9tWLqb4;quE|upH(ZH|>!|ax?`CL4bS${0v3t8qkO1#;!P&M!e4R8NnHt&lZ zsJ)zUh5-puAp`xY1%}W1H}ejM-}b9yFIt5f`KV`ZL^VP06Z9LHV^{s@ILx^U3eh`?OjVsWk@{k|QtSVK+TqL3Dx_1s)ec;vZ-7oXV>z?QF|Pjb zzd8aF+wmNjQBg>OTs>3G3mSvB(6=H*o^u^v_a1RzQA>suf$b+JTQ!mH>Q-Ig^)t$v zk@yc>lKau+DPe~;oQB#EV|rfPJFTADNg_!q>KY=EF!@3pE*v0p2iTw$sn@%A0nmJf zJEPid*AcSNMCLjknaSz7Khu|!o-66I?O`q zQmh7*Ky#*Rt0pvdQHs(4c!09Nlw1UMvtR+QDmtn_OdzbVZ}{}-DF$=(J{3ofv@vx3 zJ@}?e_rSg)ZSfu8hCLkzhny>1=^^9OfW}Aqu5Y32^z^jrvL_x&+*JhN);kYeTi1pA zW^CG@4k7LSk?X3^W=+untX}ihI^5_0Q4`tCRhZv)Oy%b}3nPQgHHO9J=aplyG#~@2 zVC6n&HhD>uJ{vsXiH&V#+?`{N5(P=cEaons(Jm>3{&V3=P_Kg2;$2HwZZUDddgAcg zALmw!Kn1Ulh8K10`DE_JSEy6$dy_Ct{oqnjt~NR==Z>dTWFg3s(8q5@&I+8x`f+1vj5=H;<^oDMb?$B*LItPuAH z{(WVCHZ4`U8zF1eG{b@ST`|85fWr8%EQpx6?t|N3@jcRq7+WWO<{{oo!fD_efFcmW zd9lWeAu&}=yz|xqy~9Mv?rhJ&Z5Q0PW);zqiK8rYXMp^g0IbwBZ!L~cmu@!;`$vbd zr^zinm)7W&%bJrL=vD`D1%KTGoTOiYocPv)ldDBAmvnuq-*%tqn8EYl;{Con!qQ7m zx|^d0k)joU!-(Q=*~h|VW0mf|@2Io!-7dLT6<*wpxYG9psD5bpk9|__hcI+>2c^Rw zt-2!)u6X0kl{Z>$>-JO19aSWIdkBbZXz_QdWA)QioGmKm1i)bYb#cfwzgpCF{e1%2 z#&NOMia!I!4!a9Jt3^#e@3NUGIT<}>?m(3e?4k|Is4P264kL)Alj?xVO*Z{0>r z2u^4lw6MTj%Yi&5s*Uw)ZJktM0Q9o8!;a+mZVxx895e{vjKncgo3 zwKnK^^Bo6Tq{Kq)7&a^WbaUYDYVQD~_HH~Nsgt!tg9g5)_n+%=;l2}1b=!=qMekJP z0~C)G2V(AMhOB}+ZBGp`gA5T}vR;GF_~`~X*Ev&2@c8)_U?u*cGeAzGCLm)V;ChcU zpS0~o$^2sVp3))&-GMeeYA7wkfR=)X^LKlXQr^~&&KPv)8n)Glw|CfVx)m6SFa(A) z-+ty&=}y2p8rRq&*6%&_%9naiL?XG_ftIlc`fP8=XRX_3LG6aI`S~vTM;c`bhxZon zxkag5+HBo9E+NSvY$lx4*-?wVG)ae9(GJ-GH?o7fCcSnNSsNBvLXBZR4SIm*&+QYS z#hl5uIZBh+-}`#A8Yz@S^{_srJE$UOWzz%B{$WAn7~El!E3S!jG93T$7U{Ct)nXgz zkJuB)+U-NV#O<2+HCg>KW+Sm$h`j`-VOr%*E6QjThY)78*q!6Mrs&3}2{#ASv4t_dkwWCbmJG~rE=-R)q{LxwjpF&9d{z6+= zh6a;Ym9vp}X@RK?x<&ANB*rIAgC5XdKQp6ic>@qaf%C(6!|6my%br3Q^#Z}TsEv_G zcqr7t-}!Q<+S<~JU%QYB!1i8Q^XHId#D>;=8@?Xzs$x{F0dq2|O5)-*9H2nGMXF1Oz~kLwdnpY0jH@&em@o>#2$ zk44DpaSTRI4H1aXEo#-8i1!Hm_-#e-j;6y}+ASna`F9r!5~{4(TUB+gmEgyVlKjZJ zFhN)VlFhOIVDxmg=^Lo7>3nz~L4)FCX3TR!qR*sMgPU8tZvF478PX(8YM`flI?#BAq zYm&#zx4)nQH~bt~hdUCts%tU_x0nFOkm5sfdt&H7Ds1)%AwlxX*#q)Kjl)S+CtV$c zh`oGI*dodkT8_g}n3;`rL|L}=h|?mcb-7ow?&MR}%%^oF|64-LmP4ZRZKrpYnUA{v zS>J*s@a%aj!>?coCF_13r2!u8aYXpO9FmR-$3T=>04mnE{2ivLz(Y$MK_?P^mKE?L z!ood~r&1Z2>>~0$7Gbqalw!ktKR&yc@c*9ss;Sr<8kXGjwkYF2)3nd`UmZdtts36- zva*91_pKe3$_Hk&`D~vcgCFH>N7VK`> zLkz{vMGx8XEd4!zvtjIO!Z{>l0>qHRe5X`uIJ#)qRh`%sT7IF{ml}2}ejhdZWzDHc5j#XdiBulo`U6l$Eb(u0;1qg6co2rbxt#DMWO3L}>n^`-xra zD$viOB(D!|G%|c{^+$%J4{;(-3*M~UT|44{TjN}v0nicm?mTbkIu7=shHzNP^YsYQ zCWOURM5HWl(AG%TX^dz<-NxUOU0$q?z`oK{9LVm;yg;ux{QWM}ew7wz!mE)!@HJmg zoAT54_=GW}PAgA1TDWIBJFD{=X`-48S^Kkmu4eY?ErV(#ljrQ9^8n?HYIxid$sV&5 zm~3iOZY=VN@`PPDvAh;Unr+Gu74Sdi+_0Qipj_z`+g$l|^AbIe66af#Zmh_+g%xJG zFDtrnDEdq5hR*;%h~MEfO?yvVr+SFT}L8kwvN9;)#NO&v_EFm@$5>LkO zFYtx>n`LdTZ=iTiv}D>}#ttvi&mP=dIhC6cIrHqlyAutNEc>K}91WTWaJMW#f&ozT zvjX8TF5=G{Z|aW68yw<)RSbuD=iSnkrC0ubK><|WBqu+xnoZHb>}K?M-R}V^2PfeKcw+ob{g5W_BZ*l~}KzU0eE>&MC=%COhcHW!1obFfpd<*1sVUrH4NY)@}t1_ zR{_q-$4<1JZu*$%IcHjIE$%;1)?@Ye4dcI`l}^N$TGI4X;1@LdL~hf`TcVodEdu-p z#(~K%-At#$x0P+L7BTU51$u69SpR(*a$IWL?P`4v*scnE_DM5Feu$0p=`Ym8BB!s3!Af-XYcM|5Qh52ATSVMNmbLB_B`*XqIDZ>Kg_ zzD)g#=+{%{Pfww{fogz-D5Du%K9A~bPdS%&0GP>}*)aVy_PAg4~%a#y} zc^>-H7z)TuLW#v+5p~Pzzv3%_h-k58sMq4XgWKvz9k+W2;#iA~*5s0*=LcL+nw6+O zT$)x`kVQ%GyP?u-98S;*ni8uMS1mw`e6r)qGfZ9V^QDRzUj2)9sY+64DsIVYaRO|x|=#hdH)Oo-Jaw7bB0x6nl?XwX_sGGZ9ZzK zFTZqNzb(3a?EprLZhbwRZ)fG4a^7}I`GjPx^U;Q{_YnpxcujtbR{oB`r<$?F_;U>)1Ro=^cJP!UGzTRa9CQMFrk7UOgs); z!l>1>CUBIlF_$H#^Hi^bFfp=^$?wDp0v5SpDY(gC>TzDI{1wQ#o$L{<;#5=uV_Z8P$h|cJ%%^h zRTx_sH?lQ^Md9AU*CU1WrJ@Jdz8>rI3)Ek(9Lg^(NLB3XDnV9_c|YJntDZ>`U}I?$ zN~0+qW+}~d^!V~kUJj50%LmW@#k}>4$!?To|Ivhxs`su+&N8PRQkCQ4M3s&E`1@po zW_QMB>_e3zV<(4_M$Zfq2pdhj;j`r9QFl)1{s`X|*?b?&iHl4B6>$!&PpWyQT=I5_Fy|2L>%7>21hbq5)+ajA>`A=#-#Rgbn4oK!?VWdHAQ z=ic(Ot!6Wel&e=Z85=!P@PxCCh3_@hVkIcK^-2@;acM;{#hwz>-^J2Ag6V*klj9>9Rubxmt#YPzTz+d#^$+uZ)#`hq z!J@_Y2_Z&&%^4=THSDh&aYVz$2MlvArhkTK$v+DIQ}bgPcmHBomDmoi#iuE!#fht> zNum_8lB zVA)Rfh|v9m4Nq!hAIS~~Dt}Mu(l+@0e%8A*WAtoVn~Dy9^7cOAum@Qo7dVh{JkayM z3ECWsSyw@)$EJg%d^B(ekOC$f9X)vxy$T2jo%*lbgC*ADMHl`4yOY7yFM0U|wvt!% zjb|0nB_PIi&kLkE#fwWtjce&r{38p7%ChrwGCf_P@LOb9M3QuFUKm^!M`n${EXeyX zlQ;q3%xmka&=CzfNu=(;HAeO2bUjEaBxMJWttCjY*s=nK(&t5e;h=T>fGp+J0zy8-9pzw5dzmJHxRRUBdZ$uaq$Cj zl{Z(v=`RI;)+2@gnM)6}zJv6o)b%D&Jdt#6oT7$}S-dQ<9;lA$AZmIj-pxI|EHu{EC z1@HSY8aU%ue&a-|g|3=;e5)(x;?>LZVw`(f(VuD#_s*Yx2^SMjQB$+P*!F9=@1R|T zB_CX|h2e2bObQtvN=izOM`uH_%h+}r@XwDsb4}g4HNPyE@VIEkX1&^A+IVSAg#xE{ zzfjd`A%HzN`QSZqZ%-UqScl8tWgh#>vV{j-_;H?|{o)?KUx*@%)p`}#@_=MA(tbtk z0*tb9Qy5F>4DoA+Ryi{-Ci`LR57HaQI5j*^>;h3>bcpLt_QPkte?Gm;h#-PW7kH?( zq$&Fx*#0b0h&+=Sk#=<+QF`kEuRhagEAE}8ESod?vdZ`Vvtyeplu2hkvObDuy&2ES z6A%%>1b6i-#iW+-_yoXIEurVYLui2|zWyKPs#n|Y5M#`4d9&e2FW)`tO8O3zb1bGC z7BQQRZ9UjV^PYzO%0`iq<3r(Mo((rp5z9F>K=BWB@yuj-+LiKXcJb4Wdbt$Esa72H zkB|-7*f|XyQ)QeqBF*4lJ{z>=1n!e%yiXFFu+tZLm9_PnebujzPY*SxM7NL4&+DTS zQeus@ci*nyHOSw}n|E)YAIT~j?9O_hdJ`0llF!_+moUWWR|gDr>K%t+e81RW@ZXC` zXyO`Lwgnem6)m$#iH-K4K5h^Ui?&C$& zQht{@$LLtAwaUBG?pCoa&Igap;IvVjarH1Pq;jjoF~)n_DZPFEU&?E3!(Ck{Suz@I zo&F7`cbTS)s#_`;Lh= zw#d&N)1bzS?gpQt#y&+qV{s2YW2IU-S;EIT-ep<4@_9>7Ek9ekk^#rCUDK) zjnZ{{{AAl!ZrT-m(C_w;^nsWOllTrK(o+aZXR6v1AKIGTy;S`%CPd=gcd-{t_5{U9 z*Jt)t9_}!A;o#Q!JARE`=$kcnJUadN?G2ux!PepC45_FWD+{C-1)N219<&&IT5ab4 zN;SZHRknxApXFOxII0H4F7i;1Zm?P5u{*2^O+N##cduI81svTTOR&YgpnD0&Im!*( z10w~x^ZvF9181Ug*YxVWZ7xKR3F$w50!_0P^LU8?$?N{os0z=RCs#J%UeAX0B z6|j$z%a(jKg+}4@S6r2{gU>K8Z+&~zQKX?q;LF?a=7C~!R>1L%W&c1=j~E+g=;-__ z4|LcWz8;vg#{zJAFkE65yLa!Ca{cj;C`Ji3qQFW}*{RrE8M#6lhw=y3x$HHtPAwNW zqNkhIKS|bEcDlG;ZWg4{*%em)=6V4?TDTT5{p;xD)C`xuyQ`rW@RQjGame&WX#J`H ze0dcnB#7)nvM~V~b>iW+_Zgm$+ z*RmrHV8=aYRTQ(;sEsBEvxi?CVorQf?1}nHvECoXiLz#5zIoyM5qD|kHni0@oBav5 z{bbJZeSHn1rBnUOp>KdV%o>M&A$Q?6IUUSS3WDTh7>ChgzZnKPy41dS7~8--qlR1t zYl-4jjLL>23X%}A*@leNLk*2+qysICCYiBxwsv<}ODI`*N8xX8DSeF9_XR)WU-KS# z6OLj9F$HlTOqxuFGo?d+Tyw|fjLJ*r;i&XJ1uvulHY@*WZ8pE{Ep#%1?whi7zR=@W zW>dB+U2)-K8xDt~gMsSl$UwSN=gwUN1yY`&S}gTVCJLr_BG>cuHk2^rv!aX3YcslY z!@BVciSyqQDm&7#8~s$-*p0y_o~6{|D!@h*1d{b}IHU`CHM5UY{u9E((Stx8hx49j24 zX+>Tn1sEw-mb0BixJl(L~gD&Cmz`5MMiJDpGD0a1H;b z@T8;JvhfMl z4gSYGKW~N-Wi$=(Qn1nFzHXH&MGQ%lF7Zyn;sI}H_T8g-LzSYz-LGPeEsctEqsKH* z#WXdehbktPci_A>Lna|pcs0TPPx!;>4sU+6%&o>4OrT!xvVET&pHSfn>xuU>WLFBA zHX~5CN*vtJn#Vi+_7Ce?`b-M4jV`eGYLFQjO8_Te-2ELk*A77m%$xR|k&`}&Lb{=- zX743Bd=C?x%YRdzdfT|LJ?qRbR-Jhh;C$pEt-JX@W<#Ld&8)!OnEH|jZx=i2v6h45 zS##=fz9p`TRY6QlAUN>2;UE1nE2cH}t2#YOK9 zRy2_K*h@^LhHDkQv~#`+*a6NBk(IZ>4cDHdlmoFI1f^ZN`!2}eFCe+UmZGBTkjx!( z>3kt@>vK*b4-dawAHc4i^z>f+E_E_NJpZ4+jw7pPfM>M|bGdI)deP3S+T2Q=1Y12f z!lM+(qy2*~KjT|39u?V46Vb-^?wvNsE?fN0!w+t^7|ucSe{))@^RL4YRq9Uli005} zfh~PvWZ7^roJ6^Q#1d$yD3e>||$aMe)V8MGn;GA3&V*Y3DpG zPt=ZARqODVraEx`CDI+u0EQ}MW?tU}lt^pyCYNl4w$WIw<_GYt#Wkq0^W$9iHdCdX%+Bt9SzY|lL zFMe6Q*!BSqm5yQ3O)OK)l`T#2_%nk8SCKFReY|34hF&>$CbZr4=fqX7EXpSYe#g}7 z;U*eAWXEwJYQ~%PWz8>Lb5E(&HqU;CQ?ob)T%WY*Fz1SzxB34Rg&d2s{wFHyle@a# zqQ^xxyH!~OQjBYp>Oh&ee+25>=nfWQWOJ@li67#){7|DbF|4FCcNWbKDSca8biy^$ zmHXJn+$!v%m4Od&p8-CQ2nY{iB6Ti(+>yT|$J@{&D=>S8p-O}LuO2}{zp#rxDCefm z)#9CtlL((112)h)CfTX`$D1>c`R2E&^HvXzr^@%W?MKeytqqUJ1lg_d^5x)3crTn` z5Qvt*0M=k30%|XGtn(Eww#SCpXEi7Nd;_Os+TSP%0Vx*0o$IrqRWDqjx-wkOF7nr= zL>>gv;LPCW0)AswQlwl+H-)diG!9i3P#3QMY0H$&VEj`2w*BO5O8^>BzN%S=waR3X z#54rPbt;N#R3-Iqct3X0PxUO1*vA+O44?XYZ(ON0)a>L|du?s3g&tJN4IogHq?AV0 ziLleGUX02pz%ag9Cr0OH{#R0V7f&DxJTh{ZvGCe@;S#EMyyB#d2Z@LG|cm8CWxmt46t| zfO^Si!x3s#p^btpy}$YrH|dk-*tRG;ry4FQSExnU=vPp(u`QVK@wzo13Qbm2)heBYlho7i&l|#b0tn*tFnOb+)QDnF5d@kXNZI*TOC@?3o+t2t zvD`8{Dt3Vw- z2ToEXnf2am)H7JVdq}*V1p&PG0ek^`8^jo`h9K9u7>m&^r3SL zbH)yR|2c9x3Uhn4^(=aFC1+v1ERFq@^|)?LX=dKeOKfs~B?e5nG-liMbL!DpUmLTl zbH5aCq1rT10NLbqo_MXkut+}1!p{d?bX%yHm26Qo=eprHYV_T`hv97LG;Q_k&h=Sw zjCbX+SL)Dmxa$$p`^NsA@1!u9r{r!BbrB?t#S3`%R9J;!@>bQ)5ACjFWg;C#)Xju! zHU)#b0H==6EWd64q3^t5=vJE$8`ArWzaF>JmmC~bnx`Gg*PC0le>VCZAEWT~Zo&-i zMcSwuN8Of7a5G)6p-Z;8RqiA{=w{_d<^5%M&+Fhfj5bMZb zAPgV+zVFnzTY@pG5%wB!kpOtA(a_GAoPJGHxeet~;NGRi0|X*c0uj$xUuRZ*E#<=% z9Od=TYS5#~JyK^F;<9w`6XpcgV~3BoGNDFS{mNzlyN{kh-M+!FJusDH1eAEMkM3;_ z_GY0$2F2;sTvWIEKWCfMJ}cbYR)zpk*An*7_qT+%QXy|O`q~;SUi%z=m?{{VjaF$_37@RTAY>EdyYD^77f1 z@sW(+gUQUiORh0zk1p!`k5S_~-%4Rzk_`$49pEtB9w;sg6EpSN}1);VjdgxDmJA@eeNKo-wR$#yIznA4LExRdG zl~Z3-eBHAPqPu3im*aQ&3Kz6OQqtH)Ezz&UUOo2G-3J$?W;Y?w=#)k<1Pvn`y_iA2~BSA+}f2j z%V0j(y0-!0cg6ILn!l%u7pA^JYrh0Lt@4x;lu!#if)nO2Om*e+J)n9jZMAds$ z^4paJP1WEiIB^Vy$#Ef@8NaaFN$+m^v=h;ON`z>}eR;yyW{`Q z#PARmNHU|^!QG}ePcSqrj1d}>7FIwDL;vvbYcLus1lsd8!6Z2t$Ss0&e;>0Nfga@k zLGn2T#G}^{obqS3g6Lyo$VoVrH50q&(0w3$;{beP?Px4G+^Dj|c*6VtG%;m2_|u6a zRlG=b|2kAI>j9F+X54iZT8zr&ONfslv*Ms@XX9wChd96f=;M$mG{fXtr7G){M({o? zPGaqI1`d7KMl?8855sGe=PljR#Ic_%Ol<)3N6 zISvzH1_{ukFOi&4=2&wp#2ybijs-_8@{}%Ux%(y)+|I<;7f5ZN-B2E}`#?|uQ~(;y z!%R!{A1ROGd&ggG1Hx98vXq#Z*eFEGPm9#z!uauBrmqj5i?i#u>MIv5fvB+#U!Vh5 zkKh1ZH3dR9ANuZ@ob4ZvZeEZvcZj#Kt4q#idU8^6_L-ZTn@yO%zvZLJ^Dq@zi~~yG znTCglL&#N;DcIXE;zG6=hdJ|9WtBQ4U41R6Z98?7nh_Y zF_oN2w^3DK(P*-E@{_we#wIK}+O-v@YyJHB^GSYbGqbD{)OUH(?XPhc>szDac*i-?Em=-fO zC5na7)Z!PIM&E$l^e3H+QL&wX^l3-F-bsRuZ#oCd?&fu)ZD{IbaJMBW8ZqCwRT`6# zs>(ye#OabhSNh@%!f)!lE}Q+4IpEL+H)*KaIWl`My0AM9arUU`EEDM&#Z=9~u58RDG zavW5=wt*!j-0^OMCC@rap6NJjOeafvQI`C!@>sOo!sfA4RPX^zr36W(p)&|zt0jcB zw*Vz6?W}5jV5+$|$D9tnAr`{S^bgV-na66PVeIQWIySb)x2>mWfTjHt#Y4oDltLxK zIZ_)|0{s1v0k(iSYN@o;lvZpzz4)@g(n}`1hG+gpn+MW^Y(@Y!a}9L^-0@G!&Hw!c z$YJ-Xo5*;&KkMs_aIHli&#hI1DZC>L-Md>z{wuj^wZlt$ao5VYFD&{QXTpOEn@FOh zMgw`#@P?_Z-_h8O9|UV5sBF>M%U3%sEKG5oE>HXOk$j=&WPMK8)y*!H%lNY&(+`G= zxHNZ2O7C5>cS5sR7_tA>-Dk8V9!1{S<)sA}?vd_+sR90Yf@_f#LE4Ky|I)o%Z*rpf zgHgKoSZs_P*idppotoq71<4SAIZO-A%O4F4DdQplS0%T=CLXT5=@gLBhpf8CtWx6K zO9F3Wl%5@!`fjuc7-kPdDMSP#S_kgU=_N>yvl+FM#1EC*NS4wimF+vT6NQ>wP2rH7 zf$sIoM`MZ5z#Y?rn6M8+4?1wEluvzZ0rzvLQmu9T3Pi?Wo`zM;U?jL0`!3cu8Gz)ost^cxj z@*e$|<1L(RIbX~b;;4O{7hp0BMj;@~%JP7rLhYgNn2XYsBV54*ZNdc}#PL4>d#rDb zsEd?q4ybf6mxb@|Mu5nAyNXO?(`SG+pe$~Ouo)+C*|uTl;AuNRrOZ6+7J*&unrf>}C`EO76H!&vz`mWa?LxqNgEn zLkOlN%&s_1Cb)KX&Z)o0?{6w)*5|;bQC+sYZLP9%P@q8B!=O;X?@$Jej&Vog zpQX=6WP(p$H*|8{KpbMs#K$oVci5zx3hPl%De$1B+6+^bz4qcOI(qlj_h{UMk*Mc= zT;t_jY?Ar8+q(itu9oHJ$dOvGIr#^Je%E4CoVpAl{KL^bGE8RdiK(Xi)PB zBWkwB_!LP}L;juUaLhTy@{fErE>{Q|Q|iGA(T%VqBm8X3it+fyJsrow55|`$B+yJ2 zMyBIvEE63&gJvPqhC}0Kr4Fh!Fv*|FWovXtGJ3C!WcM2sT%1zUyv<3%g+`|s!A5%b zU#bsK1Vdz`7IogZFT$aF-J<5c)V5txYgn8n`zX31>o3+cF@tl$dV;ejU@z}7-Svjx z&}g*RC8t}04Z{)w#%^pf8OWP9dK3q%cSTW##B(}xrXPF7e)dML)b!3 z<7xs>VsP+&-C+B@v?H702z(9>;3)=oh1IoSfQ|R|^OaY==8z`K5c8ePKJx-kTVtAi zWEfCdz+HA1$_okJPa=eFO?}}I^x~9vDd&n-t~JI-?T2nt`CoZ)A%(==t2t69v0{Yv zG=lfs)Sg>l;$Uc8p*u-{8REfzv9Ke6Fi^WOW_K|_(QU}EfHhlFnVP^h*CFnak-{%= zI&4LB=PlU3Hl7i#MJhM!_k*NvsOVJKB$>b(rK!^qh?YZg?4=-2*bTRc7RiSk{nF|q z5BqM}kz5JaVHY6kNDa{X*e!gb2gC zPVcVw`s)_NRK?{MWXLuWLgfbtJ$j)ug)^U{T8j`3dHcw`&U!ZKeo*j|`a}Xch4%)`f zK@NL@5MwAEKYTCtMCHl=y>itOAi_|TVx+*|yBEE`Z*Kh-0$FFAlH+?65&l#4>=d>c zQwyNjucURLrp-Eyrs9p#ISK&i34#2J_R0*}()(v4L|JQ-*>lK5kBp$9b0WyfyW* zb!+b^_LCe)W#5zC_c4E5}CiQT6 zPE^=q3*h+0YQ0)6pv;Y%NVf$UU5}N{uy?|fqZTS%xtL2?4CjTkC+Kp?k6S3>lLKwew3v zCHK^ptB#&>L9!nxT!jsPh#*jVChkHA)RK{nWCim-V3!*hl7u=QodmIcoG}rO)8c5b z$(_sQTSWl=yg3o1t@vOco%ny*56uCYey)G^_Ie%J>y(yd5(0Z7>5m78Hq#P?`Ii}! z5Uqj{M+(9!mL9xlpYOUVL0y*ioUj-@M6eF@m9FDQyMiG5k%?wM;xr@WNPeT`Z(v_M z5H|+NeyG%LNh64YcWL~q5_Z(iuprELfOU5(CZpeF(6HLVD)4`B2BbeGuwV}AJFVmf z>O0kujKd|@z074GC8%AGD%DcODy6(;!!={&|Cjy1)I(~&tyPV6W-aQ*hDP-qn^)Rt z6SP7^P#M5)ZmrKdx(*YbHX@sOziRQ1*YrX9_KYZJXUH6kb(-hwN8EwpuRjQRo>)F0 z5erg`;V=sP83ZMW6x$WL_o|f^SMYxe)f5(ZSXj3TY7OhVv#wq-huM7+Ks~hdQSIfH zvn2JlW8o}MIFy75tmuHg^LgvKpFoj$Y#hw@2Pf9p$`pz@2h~uJKX_Zra4`U0P(y7$ zd0g$K-ERNorBkPTR>^ue{Ym_^3F-!8p=Jh|hlnCEh^EqtAY0-5ok1k+&^o~+$e4lD zocY~TCxn*XK_#a=_J3uBp!|0D@9qq_elfh$SqR;8pr#Qi`f|sMJpY&5H%ipV*r@&9 z%DrtpxopG#S3o$fpUq2iaXWv=(C%Fp{lS5ro2by#oFM<_xm80$YXEVT8Y5#^Y1-W6 z+@*^{6b#R+H1>D;{l{lUJhm8OkoarQ@e^|)C9CiCC zWxDTwQ$^pJf;Ov_7>)WI(x|4!87~e~Ft`p!6J8to0yxRL?es!mSo+%exmv1UP5;vk zG63O8yOfI*(IIK<1w$lRNGNEzL3a4w=!s@K>L5keKEr~6u1wn9p2XaJZ&GB$%3^iH zL&V828G7P$UL#OE6g(=6a0GxsY8@l8Q*FCxG!dVQkKaBGh=+0SzCIjIYrYKvsM&Wz zg1WqD&mU-YPb0Q?Y-?6^jf6f)H?=6lGXhdxOxn){{t{+PS`#L4rD12*SvoKG4_h|e=AtVg{mi$=IlmDab z#AHhy1n!`Dfoj(YzYudHdNZ?q|C#++t?l}=71wzF92a#ME%=)7&Q<^uP3AzQ@cq`S zz+x#(#NrmXQdoJRl9Es&g~1SS=kq*5F=|bX>O8*A!acgTzJW)X&^Nr{-??`-H}v>2%s^t({~!NY;*Te0iHpd=aKbLN)4`I-5y3!`Fte89gKgIwC$4 zA*wb|1-~>iv3F|@UK-jAh=-D!D++~6-zQt55e4f zq=pRQ)muc%P|fymFQwgnN}jowz@P>oF-1!YMOW}0ZQwoy7cw<14SoFU%fo%QfW8^h zb90-#KLR4+(+S?k=klK$@$ZU8gM;{(9O zdh<7%iKHD(@)^ED9k9Z!**n_Hm6qb{MvvIP?8_UkUsyc7dy+1pCfC{7F7o9c2eeEB zqjeORB7&dMRV9DKkhG-Y{j#z0zN7KJv)KFWEz=-^m%QQ~Tqo~A@VRqN9eZiv;AX-TFJrr8`Xu@2IqyKZ384Z`(X>C+>x_9>$35NRlU25ZBi$n_KAf8Wz zDJEgV;OmExY zquwr?iY6!9sPhW(G~Mli5>kxXDV}J4=kh1MsLP~zQC^sOW-pBQfPYXB9WoOPsmdIioU{{N6cH9qZH5XWwGM~biHF9)*1KAMv;4tv{X_!M_|HNi z;<`bCgrqUcx%C>TLGAn8r2ZgrtzMjtmsPV6sYO|!u29=@;Um zlBjSKijCclwyhi~@W6DI`CWu`N~@vvWE}B<#9la$ZZ#~bX*$|w4GR3!?6V(zi5ABf z)S4zj;TAz(D&4x6GsN}JN}~4imt1JYVnFx445kmIG(&}|ddK#ZXbQ|#+zzp(=AMW6 zHFbxdxR;1*DUtF-KW&EkR4?-%XTRPZN-tIudG;^-P;wt2R%GajdR<-J zX#ndqW3(L^0VU63Nms~p6rFsobJ|2S5UOk24C}j&`SqBUOUzwD)?KsmVZ5{mvD9di z)FUf}sZynzkV3Da=*=0H$Knv<`Umd6G|5cRD+;iT^2L{ZOB4VKH#|OF#EtC=^OzodSVMGe_iy2os>8T`%T#&B>k0WPN++PAiD%xYi!|_4_v7NCoG5$+pVyi!- zV0xd=)2C0XH`W)$Jv%>$w&q#%w%G^?=8t&&3-E?5?K2wc=VBN6+DU73`yaOv>C8dk z=h-QoQ;muvnZo}9k7B;@{acY&Z*n## z=kedFw{J&$Cg_y`#n$qqu&{O|OxVpIa88!W$Ab!rRJOwLDAV^Mi^JPA3og`*xd7Je z7uXJSigm;>jh%~A|7pjtXiV6-1A zf$6ueiD|_OFyHYcPsxwRyO}Pg9E_@X=34Z?Q(j*^gYY~}`{4zr6x$e@UpOTW7diph zt!p?fE~bTWh&l^|WG`I-3>MQT5U)lbRpq=gFx{-fMHT(}$O5OKiYZ&5Hq;wnV;>jnM$__q z4%s8Gq{IB55UYw7j(YcgL00X=uouT+2x5Ap1DMh3cf1C&qQ(cy?v#pU-vk=Yg){1} zeP_&0|3dRbFuxft{Kr(zq3+98VXuV?-k*)Wc;BFTvIj>0b;K&Js=`}f;1sbV)enqi z&6gJkmKU^yEN_J)bR5d^OfUc9%K+WRi@AwHZz=QE#9hZKC*;Z>HC+R|W*ZXloEW3e zs`u}WRKMXfX=K(cam)mcfh;tA3_<)O3nu@-+*dytzmtaw-BeWGvazuxj2bBj!1%~z z81q)~`LmRWsHoA`2#JaTJSaWYl4iSPoEN&8I(&&toO_c0_*p{ndp)e#i9%FJY2dQD zjuF1DJ!P>=D_wKL0D}N%Ti4vtd}z@j)T^HRnqBZki}whV3gZ#j0G0Y025f1XwIx_L z%QcP7&CRtrH9i~3yf2V4)Af1}5xp+q6II!9ClWRJ9@J7{-P8~y$DK2gUgZ<*@C6a# zOrI4OeA1Nu=`5aqC|w|4U0w>GlD?SYo~Et3*%Y9bm!+ZT8Z+}HHtVj2gtEGRjh!T3 zaJ(fYiPk^LtKNP;fBW&2;yY{O6%rRykl(|Y*EV$mC@U980Ehx2g-Ck*h?Yz12lVCGcMZbM&U-SGcD-s0(g#KuAPgY5tMv|3iV5G z?l{wxA?87Q&W|>6-2Lnj`0)2kG2INLhK-aX(dhEE=nqpxueBO|GpS=26;{k8M|~Ul zEwwuXxSha9DY{gj&ELs1bye+mBgryBh=!sOZYQE-mYZ4Ow2EJA#{i<0vwe=Mq zLYKi*i>vx4JSR5eQvJxk6OT}0p}@f@pt#LFQ<93-)GH(NQ8Q|U$OT`McgaiaH~;oK z=o^9r{5CtnI+W>h*4Z;(ps?UhsyOE+nNssdK(3_j<-a}0CnIZQ2l{Lv_^Wj6sjAb8 z?;pXr#;`u3dH;xX2+;@-fVR*-YOHM1X;Fj_xloi$M9pK-ek|+KLbyLQ%&g zb?^KsDyIpf@ML0_eE!3geF$kbV!m|>@e-E9)c244#N|TJjXm!kLXCE^X_s(?OWb6l z3w`YZC}9WyAh#kWzI?Lp)1hC*Dn`!5T4J^U<5b)}iJy;gd?51kc^98Jxeb{*Hg-Tv zV@3K$Th~)#WUjp4M9Yy-p z{(o3pmC~+~_RaoMdKwFgp%+9B$7=YphrKliH^93ese^hdft-yst0-~?b8N}Q& zBR>DlFQ}&Db0RHq=|mj?#J>1g5qbH!&Fpup9ej4 zO$3mO8cemXiMZZ~>1)r+qiw7LM|kA#V(*I8#6rs&s4Wv2-S)J!5Y3!c~n?ktS>ds$^+Q6FmC6%}#uB)7u0}yXr3kAI%S{jOQpt zSJVOotjfGnPoQPk6?OSJ-z?&EB6NDORz|w3lNA%9Rc_KID?UNaeoCJUZ$PqxGS{MW z{$Gl8V(RFRQ=U7_owD1wui$?Npqx1MuY33Wc$4^~R~C>%zcOtXcZ)ZQ4AbX&QYUWS zT&Z1gbf%G5J>EuNey);hxU#PhQ5O6(l#cG6Bl`pOOugc&{A0jm1K=u;j5ukkRN}^`we8TR~ENYC?+te!+xBEU9wBCR1ZW(}aQO^_Q&iY)p zXexVk5Ox%qqGWXCOy?$R#&b@E2pOH19#rhwQWpEABk3MBk4FbM(=e!nG!K|Ydn55+ zXDjVQ=8v?Cb4FZ#E|&)}TOWB`MW&%vjaqqjbddYk{sW3Fd9EL|M6MPC(Us5Qfp+4N zNpqTQlZ6Vh&fATJ@YinP+yVaekB1_lLgCuRqktkAT8H7I1U|Kob7RHrL$ePc#>Q!j zN_(FX58>%wScv&Yy?jEg+O_uI5F0zd`QeBsEpG%s6SN_C6dP`xcn>dyj-*`i>3zT| zZDB5-hNs`(?JOuwk=HqMtu;%n7jSWv3qEg%QAt0Y^GAuY6wzY{jy0!9zg=oeNqbh5 zog@wUjk(MSXlhS(X8AY5;(C9}Bmxc>(wHKRoQI`n%QwNBOW(hJ)=zy~;@AG^rJEif zc_6Rln8?SUG4H;ntV_H)t$<~Ync;}X2_-OW$4~F$LS)^|oZ9VHu146BvG;7lr@zE` zmgfd8wj`KE&<^)0se^x7H+Rg)ac2=CibJXs^qOtV<@antfX8|dxfm`vn0t$E=UjHF z3wqLXz12*DwjU$Bl#hLp)+}BuAgvfCf*8n!mro3)?|l-W^@1PcwDPpW*6(~!W=@&Z z&mbJbQyuo?qu-Bn-vfA|&0>tvTbnGwvBBw+h-=Cel1(@U8wzu)AxO@?2Hd%~J+wpQ zS^KX3olK>bU?!nr)@TlN>=ySH8<~bPr}jD*71tDq@wUV=R!E?~&3KQFJ;A-_b`An! zQm_`WZnr#W2IZ7KJgNdJoc-n(D5GT`MorGPWABa@B_#biVhFETC%$2lle}#?Y3O#p zk~_`9Dna8j>K0<7K2i;oqtpyV(n&uRr4iff|JA zg6bU=ZGak?lO=Gt5Y-vDbEu8A`OvSP%&fjS##-NROlsdn)~NV&MgkXCiQumvg&@#y zEp^Qong8qzHuq*Po@QCVeDVFZ8A9{IjHKI}^RfgleATs7MBW?G zoIGr!F5lx9a$267WL#0c^3V1Jc9Sk&nmK0YWtW>?bA1MYh`zC6;Q65zgQHVH$Pub6 zFG2dz*Q*MK->aqcE9=Tr;{!uY_wQrV-)qJd&`tYa2mSX#5FI1*MEML_(bo4;( z5`dppv|X9v{d?rD<%felz^Fr=RzC<)B^-YmF$}XGkSi2ji{pSJJyAJ0H}HXr?Q3Tp z?apqoZk1$s9TEfrUs=j=X9rA47oe314)6N&@O;{#z%bjM=h9|@F!F4#Q`;DkQA28= zptR6&%;h?|w~y>mm&blzQ5F%w2CSBjXEZbHn@kQ2jij0bOh*}N|({3+({x)7-enD>$gUz%jSMGjJM?`>2Mr`<7(B=i1jVmlmHb!rTB5-~6!lKce$NQv!zMot8L9V^Huc?V7Q`I_J?Ao;j ze=O(umjl6Bx8>eaQ#XL@2)Q5t7{+7klyKm2&6=$h64*w9dA{DsQf*q6tR%LyEmE4z zWaP*O4rrYvH>getbCy$B0Kc>d=U$m+ZUI%UCI7JDFKUebGejTV1I6;b{xqL%YO0n!9g4QKjGUGi@xs#FLD2Ef#jl!yN>ms?EAD+ z74Y#T}%0Zc#nW%!;Y4R?RP>6nn@j3Bx zvAWYPC-5AIZVmE2t#D@}O}Ds~{L@H+k_G&t$FN9+u?nq7te;u6wzk%8Br7Xh7)B)7 znKb2C^j^%>p*}{$sb5#cfy&$FS6A>9PU7%ie?@S&wzj?!4GRp6B;i8uTN*V>&M_{c3xg(*k~Ms zgM*`URA=_la`Pf#ccuPosMZG^g#%bXzGWG{a0RXRGoS8vXT|nBho%O3qR&~9TJAX| zOxs%D4)&TWpmVL<>Zn(6?m4}V&+jzp42-$Fg3Nt` z!vrZSD?w5>Pw|X6c6=tL{D+^D&lz`QH#`;s?CGw1FjQCd{;eHE3%-}Hs@`et8a5L3 z3P!(C?ub6l3jMupht^>p;+^^wPu^_$$~x!6{KOYo-`-_zKmO}MZl3^n-mmYN06hBZ zo31~BG`0El@hU(oHow7K90IFiZ2zzIz|?NXAETCZsK0anDNy1!zy2>TA^GU*3nN3r zYTG7J4mfcK_4V}=)l=W^W)@UhnCdmLznAA97RJqQ(&!JQ0vTvBwY^(fS&@7AFsFpn zA_v3|=f+-zU_NPNihuj{((+kUmqeR5K6fXjMKh+ zx#Bce!_jV=D*0@u+3vdr2BV4bVP_U7NO&9CBeF_LuPF%6KLT~{(~AXDX3KM@&1Zq;EXdipraGV9^1szVOaK;iI5V<0Sx zr!FQcNB`s8-|EgC!7u%51fp?&~h-2RV`^C(+8f+}FcG3^pP}D8lcx5ivhr zWh(~zLekAdlymC+u;~L?Mn*<0pf`}q3bf$hcfg~hf}T>+QMg%)-n@p%zbs6KzPy-N zOy4+f+H!^1ogHt#M(#8RIzm!9$aM68Id3fLM{O}5jRYkL;y`I}kvFJzd>J5v zE>48LcmiKm_Wk?!xXNg_VL}NDEc78x^?M*IqHgdP9KU|+s-r!71o|S{({#1HKLtZC zCps7Vx9#1yLHuIr#`;Q}$Ts0%fcqa|SX(^G#FV_QXE)I%5g|8{3uQwT0eXSd=6}97t&7L9?WltzRR4hm78Qg-yo1oP=i?sYT@exhMyj9tr!U8<)0C< zFRJn5NG3Aic)GJq6f-*wN_2J!h0PzY$`*qqK30`YZzDpu;$y-!;?U9BABRpW#|3E2 zkj13|=$`|YA=(`rZIb>*`1VorTL05M*v0<%HE7#N!ys*ZI;grNSR+Qy8v`WYG|`Fw z6FO+!WBSko`aU(48n#-6x`I`=O>Do{D;q@m4lcjFR#~s(NypkDDJ7-0e|FecJj`yj z{>$CgxEm9`Nxr_mN1`Qn?AT$pn=F8%Fmd^NhlVn9!R8k=0abaek$3A_ns7SoCSm4; z81YF#b@lZrATCJ{92;NTbL6}dp(564*jpe68Q+!h`0$)9+xI3As+R}40!MOZ;3R3m zxhU!fMwJPPIGp4gJs|Ed-N)zrbb%!sCTf^<>bayYKnLCw0)VUT5JFwN{SjkV4A2Hs zU^+=#R=o>eWEP&c`mj!M?6>Poq`TS_Ai}4Y=E>Z&3=JwKp)jB@X`)5`8&-EtnCSGG zwygdTplk&;$i{IPWYu&DW_a7$$%jAMN{HC7Zm+uHKKQjN{k7N$g_%y1vJd0n6V$5) zoCqImd^+4(T3gk!x^hfa$#G+@H78(CZfA8>TJsTK`nJV0ExqZba4QIFwP(Tx50r@c z3Xh?%=EHQs>?UY85$Z#%aHgd3?0GV7$e42?dxT`*{P&9%yGVgNcBiScYI0t;u}PzvGL-y^_guk>0hBxlIVVK3_{QQjJe6VdNvfE z*N*nQ)PI&gke9mHsto8PIbzZ!LPA4RIv9OkybvNf!sr$+a@Qa;N%YJ^O-)TRx~Ju9 z(l9e+=5GW0OCb4GvNZH63%g>!3m#I96qQbi|=?M&K0zYV;qz z0xPdA7N=?3`-63=mx@;$2P>C-;~YWbpd7dkd4@b4$a%b%+x-gIk%_w0i2pD$ZT(Fr&V~J^dsPt|WW! zUIGk9kW&a1RJQc}tNr+^u+NLrl%5UtWQrHT$l3^&pHa ze~VlAFtheDKR^G7$=Z#n?+qb!XYCJ`eJWiqicf|;{Kmj5xUi+AW#rR#R{u(GraFk0 zcpR}*_V`!9%-sq1Kh|+qwtJ> zaym}yy?|Ak+lKoZIcPTWh&w)1RYq@xL}y5{Di58p_lg%Hb=ClyCYf#vciE~#bnaVH;kXLDp`KGR;6k)aY<3{#pg69iffN=9IL1oaQ(LuN}dv&Ti`x*Hmyy=1J)g0B^de6%kW%7x@=Vduhn^cBjln?ncEtvUsmn0N`d0m zP=mZSu!-%vNFKj!0pcHn5Yw%okdWkorLnrkG5#2{&a~7peNmSMf3U)2E5XkDc`%hH zdDJbCTQ_Oo$C%vlu_j^cI0rT^5tp;WBTK($B(K-fFD3?*lQ$l1XE`U#7U~}qWa8R8 z3M4>YVP*`&WgkLG^=t~ffcFrS&%eP7<%=-X&Yt?~Ql!TwiICw>-P^Zs7gj!tf^~`y z3~4s`GHyO54%zTt?`WUQv%gX@1%Hp?=KwzRS6C>7p8u)8sl(7?q<8dKvUP*vPNF>fLHV6fb6h5TP2h5%iU;p%e z{kITYQ_~^9g>|^`UaG7Yp|HpSu1H00a6CPT0r+#Gt9x=_vkVsDD~ zYU6S#QIhIC0c*o$R|x8P2u4KYPrL0Cea*Mlu2VO@V@n7AE4_f~H_71y@j>#Q0&*_d zDUusYVbWY9_E2_*B&QYCE;V;H^2o36TG`7Yl0Z4kZ+i$AH$EKKbVgJa5L#e>*c!%% zW8FPem$sASOY{O$({U8pQtNi{>%wc^3XzarB-Vi^zNR}zeYRYlQ>U&&de-EEc9HRS zBI^*$?$QR8!s94R+IRj&Iy3HkZsSv; z%pf3&M!GlhI@k1*GJZVC8A5uRx?wm&mf=yY|KR9;8Vus!W!ZZhq-{iS5@RtTzY*OZ zpfVC0+B(RBSXT(gFt`5Q1LVSi4zpCa>~<{synd+WBcH>cw9E0s4rccG!P8!|=g$_j8?vwkG87^hQ#6Pg4*u zoFLCPiEsG-`0-<24{T&riPL=JGA`zUal-ne_yI9dCqr7G_{$xjm*08W$K(bCio8uw z+uWmI3f}X*m}qaXnmi>SpixZj&~Be8gCs*sORHzA)jS-< zBqACT*9+rxn#&w#?>1CbUE8%|M>E8~>Yyn85YSyp1}b2n?)vf@e}8|;E{U?HZ{Kay z1D@M^T`gJNG~kSyN)>MTJ5#hf1dQ)JJw1lLIj)$BpCnDmQoiKKF=lH(`idWKRBn>V)lkVA-n6)QKS5CSf=AW-5>L62TnGXZI zvVC|r!$IU&kgebXTOmP-+eD?`{sUrgf5+%v^rym%pPxd@29gkE6FnnJwYX<0D1jr@Rf)Ow`2;2D^3k_4ljcA;{Mlp*T4?Rc^P2^ry|4G#06A*RJ*+05M1MK}PW1!Y!Q| z*g+`+0teT}%JkP9+?>PRKFDFGLQvjgzP3uSRby3LD@+b2%vQ1T#9uQn<82jJ;)EHp zy2Z9Q4#*HcX)b<7!L;ppr#4tI2q+WVeKU~bZ?o=G8?gnUO4*ez%0q6Lr| zOcE#rzPs7BPvEi|m&F-sJ76zcl}KE|`@)1a)47ovEghXu%99lF!QzMGQCwO%)&5m? z92opWCg4qt5D4aIgxu^(r^%@atJiVz!8VP(t03WM*5PpZvInJkLlnw+Hr~CBxQ%s} zsUl(;zcFb-CUX6x3A`^tXllB#Q^AEEbfGd!J=J_H>=4{rHoUkOsQ zUBcfTClnU49zLi2m0G%^mM1)d`)-svY*s3e{IMr=hF z`)qBNe%x__SXh>w+F|+nW>{AQ>ZBRK0L5UQUk6-u{X1T%ZF`Qe0WjRybNKu}dyk%B z60r2wwkm}Sp162XJ*{lrnel>|KQm}5)K8-ds0a4-@FCw{x9~Tb17_z06~K_AFIAC% zGlb7_4<0-S9#8~>#U?7ME4DJ3%~#5b7HZ+Lj|RGh%g50z$WcI=)bQKKM=dcj2X^e- zxgRtfUO-J7V!az#Sy{}Nf^awPNV+ye7N%n(^kq~e(LjNJhtX=kuM%r13kIm?OLoADA*3w?}Mn^#ziqCrJ z%lx_+s?imkv?+-~MOUMS?D`oP{lgc*bHEP>10aa(2YCr9@1NDqlZUB;$5Av7Guc>& z1_!N`2`vYXi#}>w)H&4%R_bO{EYD|^Tr;(CsMg4y40oNA$sJ!S9II(XuDguPmIVIg z?d#Xa!rdr|=oC-5kw+HgUor3mLPo2Prm70`J$O>&i)0MMH&#!eum=FdY1L$XfQTZU zk1wsQsvzl_VEmL@E9d%v>->#n$iCaANNky}aT}GwGbJa)#Kdey12mF|tRjv|AhM7= zZC?HQIKfpOYIQAT?(WM|`S=l#inW16AQ{I`hMa|qaNzLa1eW|T%uH{-B0aZGf?KBp zB=njD3#cEXnmAO>xuIesY=QuV7#%vHfzwd%^{@fO{o4{yDr|$a{}t(jptWQ%#H^(8 zupX#s*f$d>Wg9Evb|FY*rE8c@Y7dvVI9~9g+p1$b>%M);Bi20dUThs4 zG^`;8`ZCs3U*8528;O>-@N`UTP=$LBRT_GDItviEs9qzCjNIE;omb4{y%8o-c-U3zQZ5n|O%VMA^l@K9-rYFXX;9nL zq>+7#Jp7Cl98KX}bBJj=*AG(N6XMoGPqo70p$2F+{;qXATa_Ff5p)LH3c8h0@rpH3 zi083ug*Cow6+iPN5`;*|>{aM-K0>It6I6ULwZIm}co@OEmY&)d$)P~eJM-1(Pb4?ZM9)?h*&uL;Hd;S+Xm!bSdI{5hG{pn4%cT+FMm z>JLO5iCt^wp>=Kj=}~#;ZI~q<5oN&&y@#~NcekiS>tg!XAm`H-&ifN)7@}s5&%q?m zItZM2ChWnso_XtvO5zz%VMTUn`3@c2-{=@q2GK=oj9;cH_mQitY^`*-BEC23f&E1A z&I7F2N)v0+jqWCYfB%?;+h4~+I>LS3`uTOoa0-f*J2!N7nBy1WuMf7E)J?2Njjd>m ziBs0bSG2~k6t}}cviMh$_~+|T-%xeAGbRpIaepxUHM$P{>=0*&6Q&+k`LdRc97fA_ zYr7B&v_&IUfrN~?)d=M`F36e!p%9Yvhl+k9+u5`7B_$cymuWkPuSRT9*%{2)%zSsR<3U~1AogFSg4 z1asQ?F1xSJdV;*RKFSHn^QasHnmvudUp${a)tRFL`!n|1l)OUSuv1*9?GP9}lOGWR zHH$vSVQ*=l07IceSQ+~M=ICopm*~M5b307+s7+eDBGT>IqqcHx%tcEcjziZU)2`eP z^+O&T& zb?{EIv4z^n<6ZUef9OFkv)~H}3MrS874h0bNy%v{Ar-`xn}QQyE`wl^Z3jB3`L7rQ zFoVR26p9#~b9o#`IzekLRtc)_?JehuVAQYvxyo`QI~!Zu(Ys^3564?7t}h7O?|RT- zchRyhfg4R&RVJX20SR9rFE9$EHn=%b>E4GBeObRTGA1?jk@vJ7ReR^brH@-pF`(?{ zb5@Dvu!Yo2HKdz?s!GHpkk)1EbC_4Mfx8q&RkzJ?o3t#Zze8Hr^B=@ckYJkW*_L?m z4#)sBlAsN2C=+sA>Ys!aMq&Gum+`N0yA|)0~(RM$l; zfwkfvMFdsB&$$IpUEmd+`K`l1@Pn9(0|;ahN_=5#X`&o9oob#JBz(|TIQM890E2+R za1UhaojVdPSoGX1o^KS+-Fx)XJxfc=9zR8~L<$lpY9`3Z$TWi9h2tw^>=kR!6VKxQZmZGUj&2zTXv7ApU zHfulAK0_eH>%!z@t#C1H4$x}MsWdrwN-bmK@0wKWU5#~v2q#G#ss=<5*t&S{>oIcl z3UY^;-@e_RdT*ZAOj(#p?uR3w1@xbZ8K?$j8hj%XlP_5IEk#vP0bx)&<$Q^3I+{v@ zIcqvx>3wF$4mzN?Dap%wEr6p>Ht;#=dr9<8t5rU9Q}6nMZn-JQhefD^qYqdbmw{qm zN%Saz5WcuhGE8;fSf40>k|sR_HgC&esD>Fh^6Vn4^@|?xDQZKp96HW-lQ&$Tolo%6 z7Y}kf3ZFc4P4Ovo8Pbjdf}xrWb<{{GLC6x}RH=jdQb2|p5~(1( z4Ax)K&W#OUM!e1+z(0V~t#$YAT|-c5nuF6Dvj6igo|1^b56uFltk4bassZW`&n^b; zn{~rC({#EgAE46=lOVzh;lzAgrglC#1a8bgBddph2r9M4o(7x%1(}1X@YM$tMI?q$ z|1;v|>)~J+6M{(r%qfJ*rRx;pTR*=OR;A>??#QlMIi6BBa29;7O8~({kr@*5ss~|p zOvg7hZ!m>Tw?-$LXNJt7wH_|PeZt3ol?oEFSeV+Gw z&;N7&o%_1D}Kd_ilAiZY;SGnZ2jJd+SSz2>AjsT4~Gy3FB`Rmv$MUEFej(Y z|NH`noufG?<)?WI@FHmTvRY0E2t-frzaE&U|9*||00BWx@`ajv%FY~$GyYpLA@j@l{nlu^Z{6aPVItNlLxaMr}ODLX(qa3V4pn{M~NhZZ_FZ zfJFAwlXB*Lr-8Kofq~i7snpcI>V?!REs{uSI_Uqta-Wx0ouK33{;x}Qz%cOB|KoQM z{{J8Rf3qHrMO8L4zj}Jm@fekVp5)_io_|e896o-GjvknrN?|#c|0t;SV6-=UKOIgP zwohjoy(3l?91_yj+WO#afqJlXR(CvqC=@CiS|&GO7$FEVRF-_n?dzEm(e!Q$#Fl7@zcTHfnN>^fDj>IL6$rDT2@8k*MM1@e*}ed2f5bbjPG z=e4QK!|{g<8!#!iiklarb}r~b#}hsxAH|p zueHhN>QiDO)Tlk|n@RU)Zsi{v$KyJ7tur%ZRujeTQx)dyjlz<(SbpDVSrA}yUA>&m zYpMFUvy#q36auy>JNP`#o39i~ zuvCkUt+oiLGl}-wu*G2kh$sfe#&jW!@STc5lag15DzA|Oz%^NBj51!N6EbUZ`e!U& z@6qzc^NU2(8t&iQr$_EJRudd-dJV)ACHkEk>3&fXAy|g}U-<@Sjh)x~l8$^ATLK2h zw&NV{%9O4(!K(;dU+p);FE-Li{GZ^e7Pk|}q@jqTurNW4ToVQC(nN~Sc4q{7SEqg` zB&^=ZQ2E4dKe1MMqfS@-gu(>{o09F>ewvsMX(TCM`3Vc|8fin|C#Ljz&r_Bv>nYkh zLJi)&Bp#Hp{I@*V4(3)?;leI|x(u-f(#4~}X`A_Fr6_4avW!wbU1`<3^piusDZ*{u zLq?fMJfc4lvXV57FV_LKr>CID-J6|Q48Dly(d>QUIHmCAe%(P#NXGHN?7taV^hP+m zG<*H}^{S6qTlDW^yt@g-yWV(q9kZ=5nx40b)bu?YlF!X)J9zFmGxsFTL~{w*wdoVi zkB$QC>+4fWKrF&nV^wf)a1Oe-wTslT1Za!nwQB5_jP;$?dcHV{L8|JuzHc_8r)v~y zs*0V1lh-9|Xe9Wt&Sm>od%IL|I@1oDXM>osqhom?3PY8gbu_WrE8UAS^T9M3y%j?T z2M1ztUlCGX3+joP`FYA8@?Sm&{H&4h8ttecbSB)tNYM8UmH)!hIy|geb#r^Qe`g-e z_C-E;zxhsYToo_4sJ%Qh4Z2B_;-K$Ns`A?Zw&x40V=e}yxIQ^KStO4@LIH{tPOPwC zz}0mDD<#cj$HvC4E!M(4PM*JO(1z>nBrKz6a=feS`X&prR{}dJU18S0%0)x#5SQe% zG0;7z;BC8njF+AT7ir(fS1+K_;d$Y$u7!K`R^0`UQcrKEd9L0qK_Ua{g0p}N5$S77 zPs;1p_q%y3MtF`rVbP_TA+q8ok8eh_Koj!`?} z%6)g5&qQF@>W#I^&E@HuaSU}tWTej@SC|;}XGw}@uGfP!iPzWHtKPwniQ*S%92ArJ ztl~w|#Ly1)!oR74?PNU{IP~0|sqV~wTd+D7ljH%fSZMI5(9O0O&ODlAIBN_Ed33p3 zy)cw1^|;n9?9_U$?i7#W7-V6FkUmIJx~3jmz4O-Z&T+5nm1yyyN-K>E(I=tmhqq8F zh}Y?#r&c#fXT3RdR29dQ@v4JqqH7Jyof8oyGLn*UI$Y9yq{cADHsZr6@a+s_xk9tq zw{e!^P!y*#D*{#}dVL%IEuBi^_(1FI(cyep70inrTCnMPzQ=wpIxMHITM4&+{RY-- z)QrENP?jFgsjuhjCM6eDXmWO8hEG8eNh9$w!*Q+0ajtzk96qK-#8t3HHLoHeA#r%v zN!9D}WLv#hR|tcc3jq{!_muN_FO~T_a8izVXfp43o~SmRfnX2oC0e!F7)Tu)m0oD_ z>9?7jo+cF)5m{|~|1~Hm$f4~cW;C0C#Sry{cb0S{<&5u4mCc$rmCs3xk!Lc$&FTpn z9)n1^BB%_jPT_>?+)XFH6^`tlJ$sf_P*C8}SXfvX!Kz-^b;7qlS8slDcE&%*8qcm< zQ|eI*j?`CuV-V(Hrgn36rY9#Syqs1xHa2Xpze9&RIyyv@3a>`E+3OwGX!jT2WEqF%2r+O_$^G zdVlQk*LB?VbU3+E^!8|;aRWH=biJX2>&<8NK38W+Gaj=?ARaz%Q`;^-i*fML^Sv<5 zQqJMwbX)DhHSYfWbj{rMa;JSILI4sSy7i3De1O~-B;K`!v$M1H{rS`FiM4a3>vy@Y zXp9{l9ph(?!L9XP2)pjU)6-?lW-h2su297N$CUVfl zU4W(lxQ>sPE30hhzHZh1`2L-E!F>f&%4_+!zs8QwW_pyEjO?nb97Nh1HpVC_l4hCg zn{}7N@LhH9-UYuD4$AIS5xNcqDLu{ z(MtQFTc_<}F^7#kQ9i9Fx1r~kpkxSC8*1y34=N3$J~L<2C{Cdr=p{Z6HO)~wPuW78n#1Y1$K~dL50Ep0jm4-{9dC-)lara=tfbC8h^G?ScEr{N#-C&>FVjUB7X> zI@{G6kWm}rdLbJ(7#hnwB7S#Y7yNoutd3T@J3##><9t_VGHO}r_*Jh&K(EPr3JxNm zx9Xj)ZUl&mur=irLBm@aACR&YXPP{YvpsHL;+Hv?x3KOvQ{|?Sch|?!*XEo^j~;bX zB`b+Dh`S#w#;k#qV1!jH>7V;LuaD7U`TOgXj6~&8Dy-Yf^L@@7kno3Vyv}WpyGv`D z9acVKkjq36AI9xFUUtU`%Hg&CKpk(#KFU6S?d_W#YlQ1>Mz1%6j}*Q(QK)6CRxox{ z3t*6OG^J=Cqf=5Us;JdLwMqArW6$$HV+v`CU!izzWthu15z)~CDpZ2@8A4l!`i#Ry zjmM;AMfXK>KfO1BQ)4G+`8e^JTd_#(pan>S>*q%REyX)kscIXf7)Pw?>i#do~nOCc(!+RKeR?De3Iv29!doYud6vYu@B z)$agjza3lrIs^3+&s9Wwv_bZo_|3_i6;LMDPQHJqU%9?G?tc*r=P*0Gf-yV}Qx z)O>tzZR-;`(m=wgHmU_sCdsentB_MRgn^;sTG!e6=kUUU{+<)|-M+`~8%TY$4aq$^ z<6w>I1(>rI^FPO$5FBd)aIBZx!_6-!Mc+H_dYnIc6Zne0Q?idySTE5?8DeK)k$bH} zPoxmXM6*G#P(6~Pk}UpsIKXJO#$kQ(wYRcL9cCuxNLjvSnNde1nZVZx)oUu8R4t|S zeZQxu?cU2ZMwfRqRd>0&;VS7PDkGJWT4oyM8hVQ4Qi9~!jyKM0!_1L;D* z_pqJl2uTXbIU8X`%VmH5T@`yEEMzQKRYHtWnSqDSlQPLy)7M=zjUJBI7{&dVEh{s# zPc%r9I&La@Ku(e@3E1}I3EqLn4O*mZmu{RH}~(^$I2(P zy;dDj6jt*BpaNdOdXvry)Qhx59ba;q?5HvpTYBNP>Z@Np@9=K`+c8LS9h$M~u-`AR z;7zxIC%$YVeZoO77*6>m+kZ_8*)Q9mNh+KWoFZCg3s3f#p`Nh3oI3b5w?b*?hm^e3 z5OHH%w&bYC`%PE2*}LI-OWA8upAyEdARueV#1phO!yN{B2irQ|yg-V}-e^DM%L+ol z?%ARp>dVimM!i-li8(}}kHNn5L#nc zN*gg`9|pzN-%UJZ5PmL#83XTU!Sb3$S~}i&oQ?F2`7q%N*gMUE<9V-rQ=w>Cqi~Nu z<3(4n8S1FK>^nYMG4I4pL2Xbb80B%oR_x6n70l}d6e5bL>fFY4VK)w`xKMP`acX)N zgm^ZsK!=r%?&-(NepSR3x{BKi&CTR9??yS`pfEV>&o`P|>PJX$l8br6V>T{Nw&?by zRM&qVKN5M}E00r&$4#bx)7$_kN3K@II^9Tn4cw@~c?q1|u#MZxZB7r)4t=7MD=vuM z4d4M4-b$)>@*qKR=pjLn>xl>N3^Q+obKe=}cYlf5Dw|waP8BxQ{-ibf8|NNfDHEmUBHF;mq4TgKZ&lC$KmeCu%>avn-#D6iW2N^55 z;xwWn;1fnF+Yc2+NE+j_`u#oBqwkvNMZ|)*^X8CZI?q^+2mI=UO|n0-vr!=X<{Eyu zlC)QlX&XZQ2Mc<$FNNnq^dYC{h5N<;g$JjY${R9lynS;6gN%y`qR#Y?P*3zsTYWRv z-Dy+3l0wTes5V7rQ&6exT%F4T& zrA;CdorvkyQ;Ai{kIU;s-`l`LR$h!8ZfKcqG;vvu<|^idYk$txbbvu|wKzEcP@w}v z;=X)`oxM_!E4~2Wr=sz&VcSPhRh=xE=mB0c{@w7ryu5P4YrppQB;sV5fl>Dn|2zdA z9ypO{P=Zru5=ulE%?Ik zb@rgdj+4#w&u+)2C<;(B25rT}#Oz&MeDe24DsUD`I`aVE7BV~;Q4wcOd*-}}6Brn{ z>ZUV%+Y&v|5f9jTm-9}S<-+0FS-d!o zSvC*>j+#`nq_u2A-lx^n2)3Wl$wVdM1uutm6x!Ci?eqJEC{9<~aRx~p2K{iilO!XF z_Tub0CZAE{X84dqO;4LS3kAV1830)h?<9uUwL|ZyY&b`B8a+?b@{1xIo~u(xJ2~px zJ377$LdN9wQ6~42`uqxq0=+zJ26k)k8RF_iK5}^Ku$}g{;p%&*+ei~!|0-+C$sj^H z?(YV}=H@7@paG@Ekv z!=TcdP=i;p%F4d0eC-6_*Ad>Ryuuq<0ae9(y6sr5rnc7fB8A!At0o~~OOkp8P13A! zRZ%pYlEIU|@pN3L{N14*2$Z)B2$Cu~hr0ImTxl}u8t9_eDX7y_w^t6mVN}yfGjR@6 z<^&`p%=@frxjOE-{}Q^~ zNqrauD>Lp~g?zi`Ja-;fyVZw)?V5Rx1v~{+JjgCK;1{Y)pq($*eb&_!;&ZS_-z9u7|23}p_`&Cw=rP_lCaR?n$T|&9}0bQ z`A)C#+ajU8H%g43vJJ5nCHhu>nv)bugh6(YbS>y(+9`q?kYOn2!xymGiu5y8Kgc z)cMq*@Zxw-e2w?8S}+PV)7(`VN8A;JZGTFsCu#PF@_*!D^(oA@)r5DyeYw$S?$xY0^{ zs2YKLtRDgY&fW2#mWIK5)+@!qk8mNGF?25NB)@s~Cgd&Bhu8=G+AMeO)26|9nys-@f&)saPoveVo&5Tq;D`*B%v%Mb!)%x;{gQs zMLn_W`jTn-mrcG4-Zps=pm~YKGD5G;_hkS$^KKTfnKtp=dmn0WB&TeYzQ$k~Bhv46 z^m9La>lkN0VDh}&=+c|&_FA!VjdB9O800|(SlJ5ZxUmXauKwLbmuVh6q_9=Rc^jGD<tKrKF^ZTmnR8^P(HB`1oFx$I>=CNX%8T2`0!8cUJ$}CC&B>$S={+D&UD)wR=5P-czb%fDybjYVHb$*~?{r zg8+^02p~K$@xu!d+h*R~H`so@QepTp=h0;A>#$HfcU8sCk2{oms@F_UrvY3k-N~~> ze|UAeeXbk4ksdcm1=t?L9ifC-Qk1cZ{OBnUAm5o}12dBYb~@72}_xqe01`@8Mh9h0yT zNfaVl`8FVTi!t6_n4V@iNH^bJ7F_H{NQJjYF9+tbf{K=!p1y#0LnnYqL+2T+*XTJm zt8k+Acu*XBSm$kx!wNFcMbEpAjh-pV1@ws&cgCY$E4J~Q5U|}PfY)LjAtlJHo-8-* zU0pEBZHxdw8i1NV&Y|aU!(~aw-{0kq_O&Z5yJwD{w4sGg(`N1hZAWAzv^npiKe74{ zZ$w`wm0!vR`k=YVpJ67;9H4OSg~kJh6eX0z3y`m3NWoN-PrZ=8SB*?1+@WKXj!YpI!k&;tO``g)D)cqi1$0BCx@C`N5RnB+6`mxUoTzo$FfVSTo_8C*>cxMRD(oUZ{t)*;#-3#{qJUu|W>-lzVya?4AD!jG-4EJizv^KvG0~z^ zJRv2#3Px3yY`J0!-f&9(CK1w;7HG79Bl=Nl=!?Tu+pk|H)VBv{rGjWguqMVPjqEg= zWhdQw*P;u0{gWOaxI90kU+q9|Gz?eNwY|x>?nn_MGD&NhxjxZX5oencA3v_hCfS!7 zrxg~?A;LLN7t{5mQ$m%I!5A|INIK$glCxV@92%XZjD|+9bjYccx}QVTqH-J0*An#2 zl2Y*HxZfDPDlBntRt#Qgce7vm6WV}zH0@Wkw>fCbcRH?n(NhlA#*1=*aFZCrWFVke!)YPX zef5y#W9~0`?(+qF>E`*Ekg6g})Amfb?o85TqO|wV&(CizEgAkS zFMq9QHi)EdxvmGTtLS$LcFi~H!vp5&K4+5` zGe6(cZ6tFU{-PTUop>@>q+Q9B;xe=Tuj?*l6&0~FPj9QLs`vqe zn%&&mVy3zmUc*dzY=aW3Wn*ZiWn^TIc@LWalU{)TRO~I$t^KW;uf}{)4P?mSv9Yo1 zlza6R$b3m0ephEUS<%ta%TD|@vG3mzv1uNR?w_ut;bntNI>X->6&o8n+sv&T9vuwdncr6pEe#=zZ2u)P8312O}Oe23QBTxtE#H1 zR+Bj>2=V`=^Y?Hb2A$fSt#w-8f9~z=9e~?kIp1Y-c(`=>kX%bW>yciN!oqC z@77>hQO6UA`T+Ho-RgPh(gW`CZ14 zH)S=dR(iF>3aTDkrd#f&DT^fyG_Ck0PykI;bcTz!!cxs?JexxG>N5q3nNrw3&f-aB zOJTD^Zk7s66(!#x4zs>GIRTt3uc2J_WC(wosn}oGX&nQgOI8zsLp|LZ`xi~-%c3|i z;cy>?!ec-+`rE=yq<0b+24B023FPb6mR}bF-GbH&BP?wwb0c*!K~kjw@Y7z_JW7V_ z(7}UyVJqmJ5NMg+e@>TS)L8Y zUW3FIFA*0e?6X|<6zW3M_#No2EFm@od`RuL%;T!$GcY$3ph(oXA7<8yg;PfjE~Cp1 zOH_jJJ|ec!mOgnxIP=s~JbcMs4|WSL?BqWbe}l8l9<}sOLxlB#WF{7YOV$4G%oPE= zaKvKmponvIbL^f5P)O(<3M~hoMv&W|QMvIT0*(5x>tr}b_9JGCfvT;m_SYw{{RgFWTM`*ZQ;SaY0i36Xf zcsE)Y&3cb;1!WsDp$CF`)kC8J^J4qh-PK$9bh8~toImuNcsn*`bivJMozSH~2E{~b zKk_GX>^;q}_m%J^1q{5WPkt4?kOe&~KhU@mioj(SZ-*K&nM3*tHXE3Q$%e8dL)2qF z;mb+HeEL~Y(K~+D{%V?5j#Icl%rJ|6%p;yjV42{9q+}9~sQx2WRAI)4&4KbK2D)|5 zdDl889Hxsh*mxSy&=!@GCpUof#}I|iFK6adZ-Kqe?En+e3U&&7xDQ0yC9bJnbucJwlG|m@@GO!(6jb!=m&1_C-RCoPgDcw z!%V_oZWwiiM@S{KJ3D+Cyzl?5ggkD1bB6n9xc%+h_E;)pQw#x?QFj2#JiK%p)wc#&FE}rDqfF*mX_92J_46L8bZ6)Nvuj=Yu^9$ z>0O$j11&mj3^_r1GBNEKFH(i8WWE z2REJzi{$Rtt}aG zl$uj;Sj!+@ev4r!|DV-V`yo%@aa0C+8b~N8bSx}Ax}`e!s6=gQKkXLge-|WihQ=B? z1TuV##0k!CzCKdGAm#1)T_9BpMZJW*N?Ddk0_mf7>AB?Zr<@g)mFOtB)l9o)W;11jN0DSfTUb)W~;p*Vv{NL*lD&VI7`}%+72hgG)8W|bPZ}ag# z$Hp#gZN1-mO9NJ&Hq)CyM1-7!g9927ac|)vjyU{#b4;8a?ut0;T)2XkmD&m=$bSu1 zYo=f6shQD--s2=&N~(q7O4AwiLyR2Bjqf)QoHpA$hv!E=o2STby7Vg%ur6fUNj;?~8Ll_CRtDc`>3?6Y&qH!mr$@9}v$075A;VC3n@y=D2_NTm6RdVLbza!dKes z6ReXPMuPLt7^M}J?PX8(joP|ra)*p_e@uM-xA1AOAf1+Up8$QY<;|pUxU&KahBRG% z97lwXj=AyI1*Mwtttg^U4ICiPfNM?NXm%*v?_qt1#OWf38P%}Cr6J#rwg36EQJI_o z)+N2)oxiOBIK019UM8cHYoU^-Mb|rY6F2EZ@)1?$zJ{T@iReq!R-3oYRU$b){O|tO z4yQW3m*}4r4zQuo#yESkbcskOW~oa&Vm>I5H%`U;M^%zB&w=wr@aJH1_PTqM5xC;y zHU?RB5bhvcA~m5NoxBI486R6w6?fl}EDYRYM!ijDB}9u-gzeU-#j!4bAPPWx`FC$J zXX%AVCwb5W>$N3g9DlcaA>+H0wbn7GXKeY``Zrd1xE9U(FGS73uIFfEWyijm&^#`F z_V@8|nWY{*cqWZ7an>7>nY^)QCG33e*uFzD@~UZsx<$B=*1$VVy~C}UqB&G8l9Cf0 z??Y|Mutl6eW{QES=~KJ=dp3FCNn!8#sML<-$qJQ0&kMeMPfg?J5?&ZLaK4C5Zh~C#0AQ><>_D6HLm+vKc$!QI4el4%TCN9|ZD5v=q&c+hy7qGe3U8`LS}}Oh33A zxWmGtkU@;vTc0{brn?pKd0cT5gqvLGu~g8BWXSP-V%y>F)`y;f;bqQcMfj4buLurmP$U9x?6W6RG_=Y~wV;fXa4k2Pn$^qhNwwLtZ z$1E#nQA9;Wlj!O1gLl|A_2Ko$?ypC_8kNK~O2ImZ(%!%O5Z`RDNgtZ+Gg)?r3m@@Z z2bON>e#j&-_agJ2DrOYrnj@?&2&aOsU&Nr=|SP@-=^7 zix|)AGQ;-b*CcnE1ll_Pa?cBgH1;PvAenxlbH!ORK^cNVQIFpig3Me6yjkE{QwfbZ z3VWfenII;I7{_c9-fZp@C4FP|&~+=da#UDUow6xF%BzHS?VE5=$tY`(>bu5QJ#)C? z4J~iYOg@K0GsK02v~JlS%EXY<_sETpkCy^+R1M5|-8ZT`358lO?jukMcJNt)AMw`s zTXcPB`hex3T`A$rypwdhHYd&IwXB4%?dl@woMzfQq+xGls5U037_!WrV@9 zLpWOU9XHVr=)tn6@`u*ZZeuK8J|IV%Ff2_+-=M|Z+**tHfOY(;roFSgEndz$jPTek zWU+chz_iR0?-?1Z_iW~8a@&sn7zYVkhJhbS4wNP2^a_w&uk4$cLyEsIT3bgp33>Q& zvq015MR-j87*vjLxgN_jAo|w-Z98F?`?|}n?-K?)Ojy(6RhU_r;EbWg2B~{a8Kbph z=`kZ6{=dvb#C-ao)DPL-QHWPKwUDP~J(F-OOxyS>&TZ(l-qhci=6Hve1irDQlekM2 zF}eD)U{G;djWg;{v{Cg(O~E7n5C4)n4jwW*`oYU^bN=jQxQ(c=x)UsrIcw?k zlBtD3^d|oMxZ&aat3p9}4V9S3J0m|IesnI3`ZXXVKK(y+moxAMc8U(-&S5Z( z5b3zOdTDi8;D7-vqT2hQzd?NQ)woPqS5k6WVyO^90^Jk*5mtPc4mjn!g+>KZ$?e~& zN?VTP^2FYq(%u;tL%1^HFic3Ny8uZbYAeH{MO@K?MmCgyY@Pj0@yeJVIPt!W_fZ(+~{vv6y!1xlUBoeyy}9p&k^ zyQ#MKA6EZicQ_eU%uuswr%kZ!G+vHJNU0^sN!06@!!@q+%F@|=O}KyEjYPAS3*`7m zpv~R}I{T_+MpEHLGWWi!8{olUufqC!pV&{O@2fA-#EN#x1BvUmljMaGsjsWJcrTIQ z7RUx07HRK_MvT{%$TWYRv%Kc@lzMX?eKGH>GJn>`f-T%^hWtJIncn+*QZ06ZIKCKH zcHhT7H5P(>Lp!9?=b;w|ngCJ$szx^h8sUoSlt;*t2JMC3qCf9o_>S7s2 z)@-i?4x|MQ@1dGZ87w=b`%Hq1E5}^cGT+^jq;J5xIcy$_ZpHatwIUJIb>JF@qFL+V zWc~M$51xp=&wTNYU4yAre@IBD`p6quyV=E^+e_R2LZTs%1-j?Y6P9~>x~i{(3sI>t zeZ;WFH@@fNBqz?*FBi<7LR^;Q@e3A|Aoa4S%JZbq#uut#TT&^zTtA&K8%8Ga_%pRd zaYb=E-2Sez&57`0AB(|0zQyMi19Nr`oS!e-f2k)P`-)2@Zbhn(h0zHZ1%5)O_ww>mD0z7QY-=tML;pJqY7>-)nI~||5cJBh8G}}< zABR0|D+lsz(Fofobpdjg6omVj?O|;b8Dlxod~}WhK3~;aK1GeGP9|;SNveeZO#`^5 z;oBhy&A89i5UM^}9QI=on7KK_3fLPVrfV1(mMHBz)kbf)Y_164@1oQc-JD9>1tNgf8pyp7so7)<>ZlWEpgU-Kprq}BfSmq_^jv@urQKsL+o^ikcEI=Aomw%5 z+&k|xN|nzTdQ-`$k>jT$%nrAR{rh8l6bGMa(WiRQeAY6?N;K^A4&C!M4c(1VCvF%1 z=-@(C%JJ2Yv6ZVinoX{Ke_A0XdEBPCT#++OtMFTP*0n#Y87-`+-OG|VYunr5SQNsG zpamxb+Ntpz@50{XC@*!z(A}4}fW%gV7NDyRXUSmK&H^#l0GRFf?`>}(!07bTcJ4B1 z+_WQtq`V*eAFY@eCGd#+0{%EoMCn-2!|QfodRNEWbbDz4gi+ak~V)!mg&6JMe#fHtn;ZK)RX59{v`puKd z6-WAP>-s<_N?us@pD&yk3wmO?I!#C184CXacMgi0sH9sY=Y%xtMY);If4+Dm75iT6o@^PLw&UNR#l^)zy6s zlE=*zeGH7!;F*&FR~D6(dgXp`WICGvR^iM5bfO1M4w%;QL}ipJ|&7P#_FN|~U~Um+zf_?)MD+bK4hf^XhUUAcmoSx0aLx52F` zYQKn}U1j~=R;6&eB>>UkWK)&bZvHxH{_%aO?pfRm{`t-9S2WEF8%Y`z)=@UI$N|2` zh9_{j+$kq}ZMWgO@W1I9r%;@L<2a1rtZJNivW^?Ms_3D)*GJ<=TD}p7#0x(Ry@BCa zGOjn&sWHQ*+~fmf5BUk-Cx~7fH$U-EJ_ZB3E@t%fE+)9N&AjH5ya(jDaN0RHZGpUOFiPpV`cv2MeP@8011DaO!3+T)y^_eK! zpx~Ke4}|Eq;ma;GdTAgh7!eb{IbebsTEc_@P``Q$vS$=o7 z>s!mGz+yu)(Y^{a?9YwzQY{n|6gFubojP>T(IbPqtd4aC$G~$oM4u`u+--HqtPpCTEYn*>@Ir_vh>V$!0q6>aHUPm zt<%s$vaCvz7ySFt$V)|*Y*IsfZPkfs0*{=smvkjFm^?lB zEry<-i4jCg*)w>iHUXc;V1rm%T3T3VW=I|mQ35}`8n9e65Vfw3*v3)^iu(Ea(K9mk zP7`P~7U#aDu{6PFRo{u(qP6F*^*qhT$d-B+-UlW|M9r-3^$;cnz!1#{O5MZ5dbwfz$YmC)U9| ziHN3R!soLKE*}ff!6QA6*G+{>m@HK8i?qtt<+q=dKU5zKkwmy}ZJ4Bl^Dds&1w-6g zv2%ykML{)Od3|3Et()WYQuORWIh>%#|7Ld_R@)>fs;X@4eZE=8^%o0yKEOa;{3Q%( z3H628OQREuCR(ZZDB}4d(hfFl>5a}oh7wbRlL8BUMIilvcP zMZE}8>=#~WEPW@Be|0$FhMA@8Sg*dyywOn)%zC#@(R@UEpM-JAMeG`zXJwHLX_>$+ z)70HtpaazlH3Q&qcjx{2Igt|L&p$w?0)BJ8fJ;ePylKb+*2MF{3)obm>S^)0yr2=UHPlvS`k*0YH>^#n!;76bmqin%fp=^w)%! zGs6Y`$nWs)i@=Bdcm#4Zr4lj*d?%MrFIKGn5^0UwSFJGk-o<}{MOiqq5W-}MY55wj zbg>3G7AxP7>*310h5zrial{|_%!E@!_`z~Ihz_E{T9CPVXBx$wwIGHsBA@gD*&<|o z$bu`&0fx#XK4JufgoNy{3)xKbPEJjkl0N6Ro7Y8lBFPCyIP&=OMkEgaqh13gq((4g z@ug8vYH<7kA~KWbz}{pTA|8Wc3uwWwx&C}Vg3Mz+5GSfu5$=u+IzUwSp2z}?k`B-Y zUEJAW3pu{H+%8cCMm=jUb`VIJ-fO=0+NrHpSF3FfSEGrZvO&^o9)&THnn>_T)+!?%lddEf!!ps z_dH<5mHqJWCts;6hf&%)dc-?nAje(b%WCJv7yeN*0I{%Rj`?qEHvuW+6{a0T2Hp4_V;;9cNzw5Im6pkAX{|{^TR9t;1ZlV2P z!S$ZCZ&hv_J4JMYE+&XIDAOGeF*uZ}A-@vwgMpR@^w(hFr$tKw#VD4TUqiLqt(XXf zoOrPC1W*=VOEwtQ<%IBboE}J{lss7{wYpE<;L(D3yUU|QqBvfL$p`<_?t(&X$RW$g z0q#DNnl1O#-JxiG{;T7vw&%DneKtC*SropEN-JJV_t$OQv#arq_Y`?)a zFgIpgnl6vUHJ`G2%M?H_Fq;+%Oabr=6-(Zbv~0P~-Qz_JL- zNKCjxc8v^y-17754KK~4vdo4FEXR(o9UPt)1ZZ=z64sLAMx!MonlKJU$k#tJQgq{V zE_qk?-1XMvKSR(*IWRAvh9?cD#py=nKpt{ zapTXUi4{CZ+bY4m4Vj^hMSRFr#SsBE1uauei`m`id+}fd=YCPXk93g+WsYa)eevgU z!?bukjvU#mY3y*94Iet!sENxf)-=(#$7irZgH>*tv^zs!5q6wZym_lJZDjYt8@OJ} zvjG*^-t+15n^UMPZ%cg*=H;l{i!#r5!=2|En$LbN zCZ3riTN6!D9r~}Xav`&6+hRwL<3teB=He(g+;WAin>>nOx}f7f$Xcb==SwF{5?hUI zr1C1x$kWjL8OE*};y(Z3hsa#@zeQeBQm8irWuWlknQ{QlQ^EpH3y5PiY5TiEdxv>4 zreRakOTwZ|%~cBvUhiroTDV@Ok>}Wx;>4j5vZ8!~!KKKqMgX?%>t|*I=YP-tvruG= zg&!T$3fgMymf=bjDBpH8juDbi4j8qH+LnF3X*z5Q^Lhq()O9^-x_kY|dm>L5{n?C; zh4H^VLa!l{`TY^E(G4w!76ei{bSO&#Q-Cc+oJ#%O6^M9>guwSRB?ebNWZRtuP;|WK zzL8$?m!Li)+a{lf&PM*HP0&-%+uicbqh(miUk|gf6*^w<88^uUc7m;A&~@(D`c3xz z@s4yBZZhX=Ou$MbQe`GHBw`Z=bSsWnF<#93T?Sb_1CDAi;l6l;d~pEOASDg@tcH5b z+q7xy5-c5b)@LZkJO3LJj6CxTd;P(VpDn~N)he?_=kogAdVIe)v^`#u!56)LIgg^y za3_6~Le|%87Ol;L)5YI^u-sRCF@>Cp|CiU@lLVi8Uib5!*B!r%7XD%QE%7$g zQfLCoGvVZ?hM@o6jSem6gP9P-B^gt@-!!)@)>z|)hWk&5MYd3W7M*(jr9VpJ20)#< z_T|?7r%oMeZTG+%YzPErczFLl5^awIt?c77#A}%c4g#fj(%S47y{gE%#M{Bb=^X7= zYspN6XtfEbpb;YYgcNAUfcDVUkHhXu-q*_2JG?A|e=9@w+9<&1O~43iOy;n0hasOC z#c<2iJbE~aTbudF#6ylPQArn1#PKxL&e_yf*;iZmN-jqnp=EQ8OIo8-56OVB^Zu%# zdbmT3_F=bm7YXs>p4w700)-`Eb=v?{lSFC&&tDPfFi+dN`596{^jzH9Q$_2dqJ#e_ zJG*Q@As*QO>1M^%lbp09y=|Fier`E^kP(ZW3ps`&BTm2V7^S*P^iolpwu)?_jqI&L%{ts{)nzMBBa@uVVOdgZ;;FFkrT9jXW*M#6 zZ$>{(g>MSt&4B{QFPY^&%hJ6czdxe)bk#j|xK} zjP2KmPiM*5OKJYC-3KVo`^r^1>G;Q>cAn#{@TX+{9Q%+jOQIN?@8*;jK;9vAIlcV4f^xQ0spr$#l}l%1+wFt z1WKp*I>=ddW0LRP7#~~9-nQ|W^i%?cknL$bhRuD7<(T8i z01Yu=PlArXcJW^Yb2DL5FSDLN9Gs}JLRdls*w!thm)=<}MR$4TY8|~Qk{VP zRqd#2SgC7YGw2iBa%&Ju`K=^y8ZPbaMfu)b(mZ{-2INWI26tTGS_+7b#RCKFAwcIc z0G6lnY*-}OdvSVGXZV33@PSGqe*i*R2N+99&yx<$tCF^`*z_x|E;Z{Xy{H=)7+`w+ z9aX#9HWD-)S=P16P0)ZF%p^nipHeE@#7Ri zGmqJ99>AkJf|DI4-KAI={l+V?2JCb~+n)vEcej!acW>VHGN0(U$j6Yg?O2B}H(D8P zFUBikH6egcR$yB$s-AxdK0ScT=h8Vv)Qb?Pd_3}TOaWj%$gDO3^~3$>GD#OO|DprW zf@5u=XJ(keex$(sb1)5cKlebw8@gBm2A8;@o~qls+uN*&1HeIO3@po~)zzJ$c#QXU zG-qIA8lX!fI(FF{dXVmSTT5@Ab3p8Ihf#dexZrK2)vmC_^ z&w;t)5X=D@fDf2(-)}tU@;bAwZ3GUtK=2s_x@Yr^UKYTh<~?{X?%jWaiLbBkk*J)k z?5Ey)_@foU@<34Vv0xLva4MmA%BaCFU%pY#j2RuOZ_7F(!!3EG9hpQ}x~D*{j$*8idFEgY(9w*O%oq`M>(P)fRykXAvuJ0zsLk(LGt z1?f;yy1PM;6bYq48tE>7vjOk(-21-Y^9R5_XV0uz>r=D%_+~T35My+CqhnX7ybmF+ zRtuyYG@DmxBW!H`fPDTiExqc6yC%cGdWN$D&ArUx733F;bCVEu&)0Y)V9Wxhqn1M0|bV0vUq|51uD|TBC*jZy_ggN z!N4#1!5@OXzf$A@DURFy4JJQT98SpWQAT9eMS3?=DAE>C?<78<`ick+*>2`zpXvPt zh99d~#`l0_42*C54d+|+dv*Y=Txy^)6L3Gs-85iub#u!AW7P>jm-_`By2DA}8+D_A zr7r{IAJ`|N@JeV=1z&@n+sdGhXObDS0?G*<5Klb{6D~SIt*;pnrg-KKB|ov5uG!>H z zgD)3A=DR$nfZfR0$;t!_{vO#*uVW6cK-PgiD={k|2qbps5?+_!ws)9ulK!aYSQLOq zLT>>}v(##mTLNZneVxH0E-}~&TWl_lNo$Nu05!Y$#Uw3e8C3$lZ~4h%etxeLCLFUO zy=GmcMJCM(*X3h>u;W+rB$8pPu#l-_zXmM`EY+tl5P$w%vavF}y0gnG;YY=w5cuJB z>&f}1l>~>ZGdJ;pJ%Q)9e0FeP*n4sWr)mj>0wni(%V)wpcT7Z`K-eThruo=uc^nf# zyinZsRkxG%Pf(gd;Syu01hZ7f*MYPgn=bn7cxRW6RcqZUNp&K>A-%K7Egy-Uif6eDkj&>YC^{h~d^8uVB4)1&VK1SnUd-z*A zCKslT2<9XL#+s~cTHOEh1L4v__}(~X|F3`0B|jDU4Pn(5#QDdu+7JgnHEF6gT zD_YyOI=abISoYn{cm>QJe{dZA#i&D~C)u zLljX1e!<|wi}E;tk$q`lVr7+QQyyraG|11m?+%ut!7H%Xn9(Lpk=cGl(wVTBE5B4r zg8CG>2bOx8QQube13FfB4>1Xx1J;L!Mi8aE69Y|(y)zOnO@OV@rqOR&ZHqp;{_zh3 zvz&F;>JVB>tGMa9(Vd{({GsP780-68eG`3To>7><&j{VM{*_yw zZWZCu#z(9>EMypAZ?>18Ak0cD%M>e4A>z58r8^xi@HODX;ot?h1=ipnlWbfr5oH#& z0js|c#Ig97PfVx-_KC1o0#GPyh8-vxUxdo3jtuEYaQZ^#O$eE_gF((rLQuR$sUadT zL=HE499wD-4Z|NoZVJFP+TPMLyA8ydk_aE5FaCph3kd=_ewwHLa#0ByafwhD+JpDp zJm;|7B<~x1P~fCqCX6pk-hde)^qYaC^(U(9_5(T3@lvGFnu(PY6YNI$|N8C{(*j5* z81PgqA845b3w=`>1&<1kBqoL03Cg7eo_1DPsg?Zxrt{x(p7T`&??V*&L1H0K-m%Xb z{=nL5xHH#kYiHLA6ugf%B!i!@^WZZR9I^wd&mU4o!yOkl9+85M%Xa-ceH^_K5{L|l z7n#ge$eM%B)h`e$HROT{A7fXR{#oMqW~o5{5hi-bwcX$vECacY52ucopKf4+?70*NB1@RWflLiwO2 zKzC+`KDInd6}NniP>MG2nY!5tmUNj z{w@Q;;wmS4jWQb9Q~~<&SKudMe+tSi9q82nq1k8Y0qJoW=ENgDw|K++DSgsrl^pdQ zAVa7@?T4!x)x>8k(|0Dz4i#wHsbF0eK+_5%I~}aVsgnQpnyMV+|KuRy=MxODGZ(q2 z>mzH4IE56NYk7$zU*JOU5GdjS-FN3TGlUX7Ywt*)zEgF)I=~8!dqCYW>Q6Gtq(|J; z=qX?9*-9=*U0ufB^kseW0L5;B836%7U@tk>>Yh6(Dwp|pd2~HdjdMVvm012@r+M(e zSYsk374ujEDoTdOaNpL^XLti@l&QNmRSbTRF!kb>btVt{?~Dz(mGs&&El0|U|M*K!-Aok@YBQ3lci0i($Y+&@ZYJQJO*CtwI+shVs z0A?1DxU<|1$h{LNAgRn)8Yv1|b$s*>2q%Qec@H`CW$yAze&SdtenKwUG=iH&^aqhB zN^$uIn3G=%lSJ}87Q;@L5IfP#I9sw5Jdu!pABC7gRLyxVtyAX)mJkotD-gExVss zSGgcz7Ld(8tacHJ5`wq0n%A$vXEaTn)@NNP=BQ@1kYY8A;R?;xRAGE`N6<^YG62$3 zr4oX9FvoFY48-4Awtz0r_(dW}!~1ft%xPony)GA62$>fw7rX%A#CF4R!Nnjhy%D-< z_EA*-d?fnm$SZo6wB<3DA{#_>HM*(|hZp!WHGyBlD@iS&ktwK-nj!aw($IL@3Zs1i zKOqSFv7=>v7UHem=(M`Fct0(7y#`C@?x`Ih~#p2&GJ{fGZr%_ z1_YP_68LUJJm8V2dPVNI_~_vINPG7#?3YKGv!14tN~#;2nNG94+~Vi0!DokN>D`f5 z3s7y*^E4YX^LR+#x9>1>$+z_nLzqbIo$CXW3N3^;Nmcsl=$U{#37caMD&yd*d5sQC zZ*O7zV$9U++y|8J(%ZtxMJ8OiMTf& z@Nj?es#ojpqe$KCu+ni`rU9RA=X}TvH4cK3N?(qC#npn!MV?Upd z5ntqcaRK6A17M(!7#C|Rbc;>Hr3mjI3F2MPNr*$0m#^AGX0Y0RBlwfa9zf{Udq;8c zNkqD-Pia&Nj{l&%nIGB)oKK3BFdD`?@VGZGKCrNgB!mUyIRVKO=(_=v-99}%{jES8 zF>c}yLa8vj4}fnO?tKq|c3SBJ9}%=a^5jC(iJ?F>i@pjP(RiyTrF$y<<#*8~EqOie zmOCTd=YN)EmTx?22*JRk&+9Y*I%1Dy$82R`ftDhRT&aoi`0>N9^eZ7=a@glo&_c|F zquF{`;(%^z9OlV;oJ+cRTwrW(GcY&K=DJ*5;$qJ>nEH8aKFBNM|l-PZLs#- zIg3h5HF-f+a9E=Rcc3o%Llg%RpqJ|C=$Hc&>RxGB7@Efi#YkN~x)}fLOSw&`NP;k` zXn5w!JK#j6nT~Zr=*0T6!1JrDbE zd46nMRtTaYPEHo1cA@xzy{tfDXCnZ+sMp#*5ET=fUtbReNrNQ7lfSdK*BkA`tXYvO zGnA{$0I{`Fy0LT|k&%)1-q+02<^Z4mx$WH1vyOuBgc`jFl^t=jrY-r}VnBIGeRI~&hAYXi z{N4&4o^BY*l6~cT)z`ge<+lUI#I8x3W+wPHP=M2c`H;S>k-p&>0!r z-vc0Lf)YSNbd=8{k&a~G28!OanzagP zM-yHIA?7KWd0S4;zqXWG8qkJWq7sm~@pS=nOtPodi2N1!^EBVW2pGE{Nfn)NJrT+Y z2C_AoXiQgd9`a~{i#9wDvPHw}Hs0JKV(36xX%A`N50da%xi`1mk&wkryI^|mnIx3? z2eQma?UN|XW?23xvnR&Dxz_}u0hd>RV9yWaUs7>%6N0#pca2hk0LUQ$v+)yvguV0u zFxfK)?6a=BHt7Hnv)5oV`Ln;XyBmEB3kxg9*?i)3{J$vA*+U!pqn&%Y>c>Xe<3qRCpJj zSnpNrO6o@I83GkU+{m?aWyvAWuBB-WR7}_`iu0n4AyXxWt%oGLZiA8u2a8CqaYg#6 zIypT0ze8;Mrb)sP-j(g=&=Igz)$<61@oVXeeZghZZIXdS20?BurGPLGZBf8(|KbUB ziS`L3YyuJVuE`*}v>^@TNAlU?>v~^09juR{ft0ka=08`Alq)o;eGurRP*vj$1MD+X z&2TBLC|VYb_%5Y>Nd;)!Oyr?zLU#*zx6z}Xwf?g5oghe1O`7PK?!laO%8xuwbZr}Z zQ&~oD`YWFJrrE?XMgz^svDVKotn_Tl#Kmz11m^C9?NyzO^3_&yomAazhA&qJ)4zy! z9`ttVyi92w8|5Q&6NzP^c1K1IqD>rB*82@IbIVq8f&Z7xdD$S-C9ebs`7L#optC!Z z=+V~09VtqECl)hm_|mx=QZ7Q;`;yzJ-4q<9jQ21nG`ws`*yJ;gHDkFq`rG_ z&f;b`J5+U2q9O4RnO=&m3sTQhoi*>^waBr+Yc)w)%5WlCNlT1Z(z6m4p%fQ$>mPse z0AOZ^zcRX}3*q*aqjofzqf<%e#Fp;Fu9L+>WVBpQH$(%Vh*JO_GTCeUhAix_pdY*+Yim3tek{4)Qq>84O*_QUYmIb+m{0822TQ)PDjR8+Avw4p8cm1T5{Iw#PfP-H z(#G?XOe>Wd-d~e%efNmFT%ot3JQuKu;2e%%*bYY5wjTqi5-FeMa}3!|WJ%soP-3YJ zyk>zsAOGR)ZpW0Li7CSLFPfOh_P|@ndKUYTPOnJ-1UcS^?|Azb+w)*ps;v4I(7mRs z0?~9L^pC^`S-3F4wgo`?VkJD#(gzO4&|KKJ!f^gUZRZX!_>w%^*CUcL<1^ZAo&G!d zQr$uO1KH-B3T}OqRRmyB+4-K%dhYs+6b zR(Cpyq~aq^%YPTrT7-rPn6IiaRiMDcJ;Jo|=ehgN)c!60o^glw;kwGf?@1yMCfx#J zL*~~Lxwndl{Y*gMWR1g0WO5V8JoN+;m*o{DMMZWi7Ct_WnflsV!Cii6uv96&+2A=Q zL+~qOFdm-1am4?^e@YY$PCT#hhSo4{YRFtdDQ}~EZ4Fx2@s8vq=>vOL>fo!px8DUD z>bLknPYvTw__*Eiq*pF*ju?K%S_j@b%|`xv3{fS@|Ef+%7FC#{b>rXC&FYENyuNfd ztJDMmxt(Mn2m~Z=_nbppY*-1psp3{jvBE6M2 z9!7W?{u4)hXEPB-Tbm_pusaV1ikVMp)saIH2$${vAuVJ?0XU zn5kp7*MEOi3T?g;0rLf2HEML1Cr92 zMtt-NX*1O)0r}2}p%w!vvs_n%_+4}gAqv+^(|h3UuQ!SxXxQPxk0&e8AcxidG#b0> zRI(OED1)h(+D04&{h2+CMoivPboK8ry!&Wy+Z#i>)%Xp&Y7Ebbj-PDbX@+NsQl&vY zMwH0-dK|>s&hj*x?0{JZ#ryRbI@(O$;IDgvT#Q+(n#ow;heii*6dO^*G030`+=wE7 zjO5?y11T2?%Xx4{#93>hUJ{&jK;;SblA!RyfcTHi$!aW?#)EGT#qoJQUaE*&*NUq? z!#0>d!%x0op6)Ee!;?OelLTE}hF6`zLEG&7g6bCm56^r;G-tie;%@6NhKCP9eL2Z> zlkgnPAVuY42-5s`@j$ry?v3S`Zm`}NW`Yej7&Q*-&z)}z{mqK=^M+v`jDqIcGdKWa z`&wuGaP_6kwM%A=d?tWuz?t}GEz-=Z%{zB33dm;Bd-+({XTDV55fwr36TUj39!Tc% z2Xar4HI9*X{qJ$jYIVkeOM4T9Ri4jnwpw9kgxEjxVRWys2tv33R-_> za_cm6Lu7LkSU$Scc>V;aTJ0!|GU&UaX$oQTmCn@rn@IjR-YK<#8jt;OVl#Lfhw?Sq zAhS*WgDJqM$BpcHG_>BnxsQJxo2fRb*`%Q&&`#ren%3xSCweD(lk%nQOiBywe*`5!un{FH)uBZ9@c_OPKUB4lE z|0MOgJA7Y}*AgArGJopBOpvn;>{d}S>~p<)a`O{T>~oq^0`*VFFC2v6MyiqTGbj|A z<-an<0^bq7_}g067x}A|eYi!8NlTk(Xnh8!&P=1V9L={BE)K0msM~7$KSLhf%Wv)R zpOqTT5QdM8T>g=TtDI0ujdFKTk}lD%th_ZnzF;m^_1*k`Y2L(d?afa-dADfqvbsl+ zG7h{_m}!UIH<9&gzuWGEp2Ej69SOV~@pjx7zL|OKTam;g(|4!M;|*8jdR1=~cp8fK z=p`;?k7ThvVr>S)m?jk1tL;SF&y7V9=@6*10)X%0{V3S?dp{7K8%!PZKRiolnN{hqK$87?e48w0o=WQgm@ zMisJALXQ*!)J+$F7!U&`~Fe(B?oh0&8KTxePRf*438N+S(unRpJ3W4TL z9{-^gMqJ=J2d-G($~jhT(YyZ8d=i2=8ibK$Mhc%NRfPedwELCg-eRGeKM7$?q(GlKm z-p8NW>hfFKU>(b1=##m}(&lhaLIW4Jhkqvih(tPH1pq@P0T==~(5^pAkH7>_jRZ{O zPeZy2UE$ySR)hpY&X_j7kz#5KBMWO;Z74y-l3z9V)UsmmM+)BAc6R6rnJHAN15X`+ z_}|BfL$3DGS@iLFQFe0i<_USzWM12EacZOkbKm=4ui&1)=N?S`w^8#*^l{Hg&3j^77IuP}fDkeId|a84iO?$;@z={qT-?_TQp%Do+gSQTxr~IN2>QSK7Lf=t|$Y1f!qE;oU-X?g~Kb zmR<<7&~qZZ3o4QUog_o)6&N$yQ^bLFj&1e+lI=hF8-rFqP3|VtJb(dPoIa6Z5dsVv zhZ>z)uvJ0safFokzgIR4HGdTo0LPZx(JQ(c){?IUcf_DoUg*oaWSV!(S9!+T@nl3(dHJ zqzzHW95hHbvQOd-xoKz%0J;<1QilY7Qq{LyP;U16dFs$af=K6iwp5}~vKR~ot-ZqfO4X&;;RRgXHik1r*fjZ$q1pWBaUBy|Gj!YYmVec> zR2>1IB2e+l#B_0LuF5dmo%3vYdV*PI%z~at|A7f5b12XSy;vZEDyo&;tFtws-- zK`_CWMj8nvAWt(il%uTp{GQ_FO`3!PrXnqD&@x;%A-sD;^1(3y`OIWO=vZa1;rU>5 z)_`tqNc11 zv-ik#_sb+c?7iFB{x&{*y^F*k7BVBhcWMI#Bj6nxlG4qd z(;##MexyF}0i-{R>b~7a<WgCALy&+JVoX)6wV|HK=XblWR`(KtFR1%$p!_4L&yQV= z+P=D=a5sL6J`*9=dGK3g!gD?{lZ43QE<~|hYAOf#7e?IJh;N+J2NjNq71vthhdA@Q zNa)jfZF;`^uJ4?>2VO`IRUCVOU7dnzS2_3HN)3DWp31PY*Wk79f`17Wz%MsXgVMux zV*nOM2r{a6JozRUSnh(cdZg&&*}NcEbMl?*TAa7A;{+Yv%7wsg0d$&%;=pd9LDyJL zr(pfJ#*Cm$W1K`F7#1bc9?m9*#=7{p88vsrFRru#dDn%u{7X1b9^{3IN@59)&S^5% ze2W(cMQVg&V{3x)H~WY#Xw|7@?OjU~y$^$~>~rP2!&W+|p>_3!U+Yd+ceiK(kXBzT zZm>7@B%nK+E-_zc?7AB^2&Cd`A_3Nb+a*Jb7guic%QAS3tslmRHd|>cx>-3WI4xy3gkB*(erBp5g8}Sr5BBFcD}zKmpzoyX|G3Erj}L zhpG;L|H>6=q>W{x%)Dj(pN%b0gSbhGj9?jq zPX1M??Xk**{Q;6A3BUJGb*&7W^3anyF+d)CilP#WQv(+=K*c3)=j{Ffh?UrnQ0yT!WTxz`X^U;^*i2_SF>420ihQspx>04LPG%k zR`@XqEMhv3+s8R5`63NkswXE3`yV6JDiR!I{8?t!{GNr4X9i21srX0lGRPd)`*{z} zDsV0u+W5ErF38Xe3$-nIAxHCfZ#!S1|3p2BLZS#rqCKaOH`;!o@4M}CVt9Z)@iRL| zdLXddVB!4}?V7_U!^*W(vXD|9xY*lzL)$|XXS!~-cJwo#x_3bZ{ycroInR$2@mp)d zAo%7Coz(APli-|6b+nuFJJl%Ay)FVW;^{EZZZ@hmKazAT42cQ+W}$lXvnXKJQ6{UJ z?)((ekbb>LKn_fK8E*aG68KC}OwJl2aX&r(U>fyY*J){R_) zbz*S|#HT;%i3L|^Cu(aFOtI?I0-kBX+1>lqlu3d`X%7w~a zC-5yoH48Nx7{-fsTb7#pV<~dOi5t}-2oD?CygSo!HAf~dv@@TFNqC5r?(I0OqD$^O zMx$SpXdrryH1SWR{DyR0*B_LYzy7=BXQtch#`dlDrlPmQ`d-G}rw^_YicyhcVUw`h!k>Dk{;1#tKHH&UgMWva`cR$6Vx+RZQXW z!dIB%v`wM*@A4B(F`@5mEq5Q13TvGb%M*eF-^I|{>XlQ3|?k}%xAxvf-Vuak`|Hxn5HR~;3_}?D&Vv9 zDCJ>cS9&g2?z4`|m++F?3PzoLObqxgokRsk8S-u{4&k)sJvL3Y$k4pRF7nAN->#Di zm`gN`M>cSAmt1a_%aw~T0vK~qCaEdUg4TC{4hqsORXZs4X1O_w9RKYn} zfrr+ZEqQnCLQS3%*mvYwa}5_*lYpHB7j$zT^fm*jfbWNv5K3780RcBJA56@^(1n)m zf}uJimwiW&2&ES_(*pEZ(Ydl|daJrQoM5boQNy|^F42e`y2uw&@U4^mvEm^oojFcs z=Zb=T=cjVY`I<~#M=vp!x6r$`Y1@}PA0Ba!om4m(d+yppR)`m(h^|@JbL?Hr2_%BIz~1C+jaE;H0^?`MNAKFY(jVFL{ps zeEiD)R9w!US5Cj8nBfl0r({|6+l07G9BsvSxi9TJ=k)REwSEZH%i6Tm#m*s= zraVpzN`9RM_;(>hB-td zJ=&avDLfnKIQ;ZtZZ&}d1!*cI2#EfY8*mEDvX$>kg|DmPHLkz+Re3Wn^|zTKJ^;R=@+rnnPEuxwg>2y@35t1xypRvpz>kc?R(5I3;IZd&x<^@`ZOiVuGy?1t7fw42A$Ecd@)@~d zf!sGbk6fRyW(-JKccmrNK$_LHghng3jwmnW<@(+z)UMqG=h& zIgsPKGB+4%KHg@f0L=ki@$G%9_QF{>C^y33DQ-FE3dDhKK7%%wOE%9v0bV}74{47H z38_8ekR%=g{PtgBrn)=O1^^KW-kJoqmN>R%6zMP6(k#tIXBUCuB#U9Rx| zV+Z=Ti~rgWguP^Z`-Zj%-T16>KO_Y$wR?>_5!cH_Gz;hZDB8$&@7^hH?c%Rhi#FDc#Kanm>uOY8JS}b@@l}4CDrX zLWUWk%Bj>hNzj>Y|UVqq{oyR2}RBy>17zsopR8+$%ZiX7H=T`6j zK3TJMl%ZTRzCqkN0GuqL^iO)40dy3k-QqD{|0+zSh0sHW7dOZm9E;aNS)^n@&M~}! zaXbZQSB`n9J{T+9H{X2LFfg>R3VhAPU}$@8PWLOpIq(`T09`}Lr0F+)Z^`6nXgXo- z&zKwspns~oLBOBuh)i;M2aqX;j~O>@*71`rc82Jxxl=WH6sTr|GjPHKd4Jxc;>48RVpcM|^INV|@lj)R2#^0a@b*6mX-9-VK}U6K6zz4Bq{d!IQ%x7z zs!MiU&BolmND2KyL)~<;gCUdHOePWfaU4?sc&(eX;B0rD8#3vE11@}HJ_1wsK_Ax* z!yHV93`7?_OD$I`R`sI-IlOjHtFw%C+>}-Zy$7W9LBQGHl(e^% zzbxJ}yo`QvpMmc7)J`TKPkc{|KK+2w{6G@0w;7^&nYl{!tQW6O2%OeOc?K!orN;n~ z@@-nge-l+VrxVJP30=q#O#WyC-5djC(X830p3EJZCsdgcfV4>K)XRn6$dHf3w4IVJ zkzQ-!4v0w0uqadnB{&ZL?{j1T2jc)zBnu+CX2(ZjR_ipu2{O=L5V$#%f!R7U8RU*Z-D;yc61?r29Pr`Z zTcbEYqJGU^(3#L^PV=g8XPW3TV}low8xwO`;s{X!n$k6Lg{X_a1Vnm-5OE&bP%)*;L;?MI9O2RvfOhs%s$osF*P-WmT4JkmB`Va7|(0V&RHuB<_ z!Zzcy{Epo-@cS8D)l9Rf{SkIri>KpQ_)ld1X#NLNqC2g(c?82U|Nnk#Y3(DT5)g5L z`Lu?KHl;R8>ez`sSQfEq{g2SYzAMd!hpA;*fC2}bPZO0a(#A-GtdIlLlWn*J1V ze>2nP^4}4Gy7|BEd&GqrNZgzRU*I4M@CaZRV6tG3V=;&ur6R};+!3z0$*fbPjHZcs z_BZoL1>aMkw3opf$Zw!8uc@lGJdXJKHQFGwE;~N{9@W>sKTCJzrO)isxhsOxAz|g9 zVwYTOGNYEqfd2!)z*b&1NzVKLF)q{7Xww$)Z{7t{-JVOPko{K= zN;LY;3x>-_f}c81E*UQ?i*)-so(@>) zbfxC;1K$J|lxeZr$OL0)!829fur2-!P{@tX6YDR<*2B3^%7W%}RT~E~O!TT?7e!%0F z0lStZjJ^3r8u;fm>a7Dk7~9P=m?QNVPjZR?)&O9)FVHf1gPtP%8$>fbE9eEDE0r34 zRz#6Ft=&l|GD=)d3T%QC8_hXlO6H)L)0+V$!_>b_{bI6tRkO<1Fq%4JcxM{d=dH)H z(6!O$HHM-P_^kRUi0393F_;E;;jiFrZJE0g9|1%2|FI5!h808MWVL=i5d#J zHJ^imZco!?YumsPdp!|=8S)(rSu!G-9xAcp({IGl>D-wz_a8SL4xH2dF|qR8>r^W# zEq#`xQ_v048B1Ml=oBj<(Y3CHJRyjh6%P@Og#MH}#ci**+!h57n6hZ1rFJD8cPMJw zy@8+&^Wx6=5!?t^t{LK=pcrEX98bBsq$+bu>D~$;zP}% z!jb;Xu8PCm`iK}2e-($76?0v*-mO&T%723wF49DeKWiN6LPOlSC*gluFS;|io5V^y zlJwso^efuy5*#ftB?J$dpa#AK5mAkp_fR%~G3Cun!qL8FdO*C}(NHdp%umu_|NF<_ zD*;72(E0C}wC_O&Hr7r&DfBx(&r*U1JPh&*+|QSM^b!{0zd!Xn9nv&v`?AgIIrS3E zC_O9S>>Nu&?Ir_RVV}$Zu&JHJ3u&L17K;8gW7DHf{60gJ=N4kEZdL^lJfaI^%>7D_qBvJ8O!Z_1!zzjju z8HD%1+KuYM_g2h+lFhH8Ft^UJw*|#Z=2PH^{a>Fr5T|D8@^3^(}^w50^EHr~;uieHrMq`op z#A#oF6trATd?;^O^BE6x%r(;8tQ~@v!)m_d)V@R0d-D+KyQMfk2t$6A%7s#lq3(11 zc_efM?f(X%|4EQpO|2ki;TIPYaF(XE=+SGk>8KR*&XO5X+ckdkWAI-vw9G#+bYd|p zP3FZ7-<9e2^q(yuzZRb0q3r)EXtboXvU`doc6dJtr{zzqKY$S!G4;B!&P72k28iY< ze610_s)_dijZgERpp=OXpFhO+T$4PBKfG)So;Ls7lrHhkzO&SzU5;+#fM9=9A%vmt zCr&u-pv9WXZ!0FMSUn$O zOemd`0@ldqvOJ^_6AlkhW2+GtOk_ec^L`pngXxKfRCVXeZvQp4Nks(%TC5>~xmBts z#qVP~-exkXX5VA)BvqmfZ>^^8ZX0GR6w8q`Z7G(r(QgI02#Oo{FAj5+NVDqPGA!pS z>h{Isnr}jFl;y?$W9S83O0chCzDiNBf>LvfslQ# z`{VJJq=??HhKEq?0x|8rntM>*P$WF=$nz(lo{N>+bKB;BBm3&@i7}ewBms=I{b9iq z13+6+G%csfsZt(y7eZ`DdL+^9Y-*|HdWFSxJ*+e=k~VxRBfLuV zfa8KBSG4&R>J>crj97X>1NxkDAlL&SPAxp-`R%vc0eT$ex#>m}Kr^*P^#<`!5bqQ= zqbB(|steQ!BORXK`OWbiqk$wA#8?&UAHVYmH4)e5sZer)BntqHgIcYZ8aFF=Ne#4G z_Iw~>8f=I5z^m69K?e28!7^}7Cm&fJ_9tUq2=ke-5|{33Bbb~Z-sh*cG9!&8R0)I5 zPGy!glZ=SJc}$?;YE<&!C;$!DjqT8A87G2F)N#IH?Zk422U>tV<^Yq!>Xbt+7R%yE zNWy_{Z8(mhZQ4+Wm(bp1su2PA>D@k+lz|6qbMzC(61Q(4+j(Xdkiak2+qQE?ftD}w zv=*(JLceVpCb=0fGq+m;4KOpU3}RT2_Z@tC@4M@0D$3I!tE{n2c3hhG31y4?!J28S zs0~wSaJA#pam1YVoX5 zJQB=1GLrZk6pM-L&?)@Mx8tR9igfyWn$APa!F~v69+*<)Tr@L{VEL)qnN!HvK0MF` z)vlYDr6U~2oouG;gtKy$LRaPj-t6)!Euy;4s72C>S@?UQg?qG;gnIsK3y)(lFq`~bNL!e0!QxtA`bn- z(!1)wYKJ?@FQGKO5!VIW0O)kt;tCOq8c;5bz=9rK3IY7SnGG`HudnmBka4MgeFTO)=-`AVm&xk3 zW8tE>9+6mZx)?`&E4ig)Vx5qZROl1`F>e8V)vyG}Qv&)bNJXJDWY}TNm8s-gEc6E* zFM9yNa>N{Y{eQgIt2&DB!*k`ocZ%K>0~e0nG!||uza9Xy%DszRw-{hzzkBz)Et4Xg zR`o@|mY{n=8rokKzpLDEczrB+(}ptnHrH-IVT4np^>yGg4h}emfW=^5nA8>9I@NPG z6_)a{K=k~5M5*l$mYt&`ZebVBN2EaXLYT zT9RS2j`v|M#&g6U(iJ&JYYcIIWL2nYxB!vpbsK?m{}2`&l(vQ>`rpM&Sq<0dnYy;s z#EeTs;%k%SGbF7zum1dRTovkdSe1@>dO%VZz&nHYD8QwcpoiS^yBh?}xs6}<<|}z2 zd}V%CpWJ*rH{jB{SQK1W(^rl>6%sGj|G(HY+p*AfFcCikQbzrv~{T9R-{&&sbb5z-_l z+b1L`{4U(WMD{E=5UfMPDLE{|riL88sfkxS%7SKL0U)c)oNm4Ae%hWIO1?w^sar#z zdMUCpkt9H3u%l-q%frJzNQ-1)>8`sKnfw0>NFQ8hRQ)#~Egg;kx=!Yzc&t%=>L%f$ zR%8sc49nyBiK!gg=N_tljb55wt~ldf&DSYa0YYNia=_{0IaP*>R9%3|BlS4U3KM6^ z>$dDZ1i)1V5J6xF)X>u+&WtaD;DBpwpc;Rc(e{HcysmwvCB#3nIO*7Lx50ou*apwU zp^^)Vt^q%nttY*$EqD5}nAoa7v)zbh(u`6h{H3SE*q z_Bsc<)|garBC^btl3{Tk!n(=I4tiJdJPRGLBPGQ5YJf-A#m?T%qjsG)i96oe`lU7H_QjO z92a7exSvyCJeo>`MORTwH3UzV zGCMoVCUDkaOhwdbyCakTD$!h|DSQn_w!6eWaysW`TP%pb_nhFu8f(O;4dk1wQbO#) z^H0O{Na#Iol31Ad7D+Z}Ub(y9#L>_wkoxgJdqlb491-USqu&rJtV9_;PHkhYV&8)Y z57=CH^hHHQ!?Wr(G4!_^_GRColk6oe60zvY01L$(6cpAb5Kh$%QhHNf&w~G~h)zQp zSWM|(Ou-%M&xgo8GnF8{x+(LTZ@tU+EGt7&T57?SjVZ|2d$wE5p(-)dUx>mQeGRuQ ze|&O?zM&!1pUPfKDH4)GODs>gpOmP~BMnU{516XEovm1EdM0*`F&guYUc8`FX^&nc z=k*E<5}xX9**;1N^?xUQw$rM&{VRzmDUX+qN}{XNq})ZGDK#@I3q)+8_4M?BH!NSQ zEBNi(x8R6~C6JAekB^_w1+q0#KvX@si;D}t^JZ{-JQ29+mg@22BygE32=s;`-WpKpU+^a!#}v|@n*X2Buqvf_v^0o zhwNRji^H=_62GI{|NI>mJk=Ms2(=$0OUAA5*Gi0$@PF~PF*6rgz9<&0rZ{wS`6&b0 zQ9AaSRGTK8{az^qJsV`R(XOl3MU-?#%QAmkJQ9B&yl`?he(Nb-5N^`}E;>J#3(3*} zmj=iboAxF6b{ftDiv+iq84(#-9|(X6Psu$3*U?f@HLq<7om8M*oMbAH^?fO46JQ8> z<8AIwr{XAVQV&LL$ZS6UhhywN+2b52j z4WX8NR8-Z*zC$zVGXqyqhahs@<{+nL2HZb#3a(FZ1-I@-jH}^w16*DqQ#@ogx%~M% zhAunqBZM*#O2^}g0ev03$5k1&Qm+(~vrbkP7DPi(ot_>w^zqC#v>QbUnsjVprpi9B zw?iMMWlqY@!(Z*s-I{kS&}(G_mn&X<_w}5o12#9odca8{1=g>Vv4*6YsxS!{J-{aeVJr3M+?Q7L}o@*_80g&uh>*W!R z?eXR%-WsRRB8SNr;}e>j6xj}Ra#~M~*;$P@@6}av#+Bxbp4^|9R?B}1cOS{{ifwwL z77}!;(%oklxzvI;3&sKYfemt{Ca~owamvRG453G}-sxbl<1SbY`hbW?3~Uze?&@Og zI6b+zApNZp^X{}@sCzu#zg-gJEDX+0H0=d3T)b!Er^u%K3of4#O|+-WQrpYnOhytC-7kE;veVvW ztv5AV6wT!w(#4daN_1<>lYVnkdS%vQPg>h`iLmp9`)l`8+(EEq`l0v5dTwFJt_%pw zi*cXhF*S^v;?~3*+-mc)?*JhY3ie9u~DF=aBt^VVm6)=pI%ItV)TsN&5BLlvnKN zRh2^Dq`&K4=+Csv3*xm& z>X@BO9p%Rws&#b~n9>Y2qzCrLhxSj%KyGhJbzBDVrX^8@2$c$_*LAtZd*}Q30$`Ov z1ht4WrL5l6canvH%*3n`;v1cZx3WD=t05ccO3ws01?)35^neWv&~kH+BATa_=;`UR zv@&TV<2dh2xKp2kVQ7CKS9=UxEouM8R~{a!TKp<<{PKA8(@Zm-D0t zgv<4irAEU;(cK9C?Iz<=-iUKuqT3iRUAbFl0lWsiyfXIY z1&;B|ZT^*q?alD`{HaSTBCW=rmJCe_0gc0Jg)IwT&yT_~Zw?IVt-Bc)yk#Z4xs#_H zt)p$#&5!ruz1@4!McJ=tOTDn*H7l|w=rh}XhHEXsI5$wOwy%-)T6B+j(NE}~0{`~r z6c6muQHuFAda{cgp5wXHST*h{3YPNx{DrSU1Z2!y?g2_To?t`J*+HRqCUA~ zuJ46IA$|IRtUxnZhJ1Xs2Mf7G=&?1 zaSeFfwoFQ?b57wdN5Lm`e;XSS{65oBp`=iv*3tSl%j{kJuA%AZJ<96bU%=N&6f0S# zKPoF_v6`uK?Mvk!9V0cjVV(T?>bkbu8ciD5zcPE42z&t9+aL9^?IYCY0k{n1%v%h?NYAvi!w@#JLyu@6LlApT69pJ8efW( zR+xUE9>FHq5-xTC%rC#{KOcwNNYn2pqG(4_+_Zb%11qo3U{ewgF?@`gn}=7$#l^t- ze%@HNk>+%}uf4Rq-on9TPEwC`-BTc?0jM8LmdV;{Ub=9|BSGmX@3+}_NUnX2L9A&B zF81t2r`Y0&rtwx!hSb_4TG_-71zEwR<0!3Q`mj@)fH4QNtej2EX7EN;bY*o=(us8c zV#AYukqkT>!M9HWV<>#fgd3nz(hw=}zsBI87We*T2G+r`?sjKHMn}Ff+3XH){~{qm zv!{zgdgPZ3X~zLhHkdcvu=nH%_jL-k=inA2#kJmjBy63R_(J_*?vuZaj&V%%mdNGB zbrOE&=%%KZ@Cno~`HVJYC(CJH|C*#r&0yhctu!mO>nukt#!%t}o&`hkzXZ!J@wogV zI-4-KKTS1Q-L*F#7~?wcPi*VJ&^LLQRwuQ%pn8~Iqo0)Zj-Cpxm@qKE6h3rYRMeUn zT|7!4;$pDK=vohXb(vU2;!CZ*ARXxzXNXs$9DSNw#qo)Vlz9YZTj-J_Iozl5OOT9N zIh2-)iF!u?@aMsP=GCL$sues(_zb`SQPZB_2uqg9&`UTtEBOox; zY)nXNT_g-V7CI?2duW5FqHU!TNIW^*GCA`T_#UndjBilFA*;+leoUks>Dw17_Kbe0 zC+J1>S;{{M{!s}BL~8Rd<`QiMeP6*bO#`-r;IwsciV@S&oiXeI0O z_`1LG;6@{cA80TSUzrwQsd&6 zCElGoKVoy7_yCr7>1j&Bx=sF0P9+Oz?fdGj+%wqc=@XG=zuS(-RYbh$NS{^-RuRH(o|RxyTJ4KLhte-?;$+D7dw4q6om)TI%SuPHcX*# zktEkWlbDB~{2-JO&20Lv8VnLP*{e5skx3pHn=$uW{y+X|#q`}ZS>cgnN*K-uP04pK zBV>pRkytvHG<<8mhhb}dx00)T_t4g1{XRHd@GMeA*gO}I+1C$4u+Qon{2dT@!o6r< zcVIYmY&UE<+`)D>%8?_M-mDcIFFmAqYuDKDh}Dhj=gaQ;>W&pv-UCjwSD(Fdx-`KfJ1mZ zNw3ZWLz5RXxASG6Y}Ri5>|*hp#g|wP|D65QIlC!d?Q7x+ zMzPpAFnHXzh0i}V5MLYoAvL7Adlp5vOi z(I4D};+!Umi9KaDvolr7y2Z$mh!`m?$DlIuy=541air6NDdw4;S1IKaG+wv3?0oTv zk?am#CEtmZ$DYNt*WZyjTG`BG+4)~9Lb1!SsU9vG+7ms9?zkvzaUmk8kRUvR(Rt-}9f^nTT~U)+EvP(y~V9h*~33TmS64Dz26U zx3p5NoQ!QP520ux6w z71qr_l;(b@b`s&y%XZcmYdNF-u=9`aiqz1td-YJGfmWKe+;u#w3j%yp`W33mO(pqC zPwuxid0+LyEvhQr`GLripQ_);XFWJlJ1O1!O!;hNpA%E@m^_VG8M2pBcu^qNGbX2A zXs?UUcy(<>yBaXaXqzLdbxV0#9KJKv)o>8Z{nFwRTbmIx+__oTiKQ^c|>F` z%KOuuB4@PxA8U!wZ|p2V^$GzUIudpD)7Wt^QehE$z0g+AXPPADGSvmLS`T6>+ZxC8Dkh~v5S~hb46%X z{3$XrNprD(>J{061XcXP`ntL0g(j&(TCn6oSoY1r(IWG=Pu$;-%)Sv_j8wRCbhtU$ zdLtx3s6qS*`;w1d49IcsZy){Ub>ta%zO49S7itak8LY3r_Na$HRh}$26PVUSX(X%f zo2ew<*FI6Y!kK?aq)goI4QQ(N?2ydu?;t+iBdw_Tr%~RBcmKlq%LnI`96o2VQ9K<9 z6A%C@$lr2FD|Em@=Z^xAx*0uHHRgNSppiNPM2PkyQHkm9pvQ;*%4r+a?? z)NBy=ESswSLaod+PL8~cp-2_ww(>TsfKSTYsln`*4B7<3)-xpuy5+AvlePQWirxVa z&u%_7f#+Y5SwdG!;aJ%~_X!S%zo%8z#9o|Mu#GHn3ghOA2OYQBRjO-ib~SU8_G`0o zFJ>qqm^W%c3hn9WA99hkvG@RA*Au+a!o}xhK!XS&v#EcP=5zh#!jjUMhW_NBp@F#B z8S2B3VtG#->j_T9UFHqwW1}BWZWCt9^y_4^{&hIw$5KhL#_7g#Ir=mWmtI(SLdRbR zv0AioG^)UDj z8;2=fovG&U^TqD|Qq0lUEImqdu}~wI#7p6jhOaCQe2j2({pETWpcJW4mIyt?>3B7F zlyL&TWOVdm;z9l?|`C$*M)Y3Q@Z?-P6q4P zmroCbv~PEc0jcM zQ`==rL>+gBF>)ol-UOJmOV!{}8lS-IFWMPO>j7>5pf0O!Gt8E{I)Mu7>7IbQWMIrS z)!VmkHLLCY!7>f`3QM(zT$U05i#G$a%D`64ff^=o0(MxrgomED<=~GuqVaorDiH(A znzS|yO`W5Wi5%I`io8y?3&IsZGl`m1 zwdS)-!I(7mEid9eTjOMSt-ONVDn#8F&F>FEm6}b|*`AtM`}oUp)3IAvVW_$KC%-AI zDb%A0m?eAH{xTh1zi+Jv5Z{8s;i%nKJQT8*rP>2kg7BHOK>=tttg~aQdl97T?Xwq$ z++cc|_5K!wvWGjF(=k%rF*!9^0PdaYF)xVXdreDxZIDE(5?KjKMO?l;BWaM1Ziore%e4d zR>wPGJh`Z%OEU3iT-_}wOWAE#;~Q%SY4g8}A54J}K^^78n;yoZG`pw@(G0b7rR@S{ zfyolAbG~7krb%@2XP;XYg$l5*o6Uj>duy^o>Ga-!mbKJ~-+x+BexrF({dBYrT}(Ka zAO8VE@(nfln!WS?-bvp-}0A`lWJQLOU$CjW9%K=)^rW6HE718RFu=2EAn zp`urwPFv$EraU&~6RIShA>9nPQ7MTgNx!6R)iPdr0)hJdvOzHRvfL9Sv9^A`uVvszLFf;w-2HCqsSela*Eu zBARcUK#o*GZiS3{GiWeg+btL@U6BL(u`f?dG|J75Jl=?jvAZG{$?UzM?CJW`ya2-1 z$EYC0AM;WKs=*_mu=I1ZRp#Z#QBr=VybK-^&nNy}Z;HtUV$74W)IS%>k!;>m9)Qr> zkhm)o2&18L>i2cDd2OvX5Eo?v&zG*2n%4f9?)?vy7f%eawo7v{Y7dyNi^&^-w``>V9){giJ#Lnl2C(jR@9&6>^xKbw(;%0HHRe)o>Tv26g={19xYLCf8EAZck; z(D5--pmSU&SvUN^LttvvU6vxvM(KeK9yfF&fAG9-nD;ob0a#S~pW=fe50aD@}_i88M566Lwo1wPV-TiK1?AyN2jqN3TK%}_4tFsce^Pm<+OIr5zo5} z5a#k6>x2N%4(P|)AzrrWm)yO|F?>GTW|2N47D}pJTG?3?jq(tdKHBR(=401`n{VIe!braX502-w#OCDdubE6XZCoeu)8^jYhd1R~?m97gTj__4OO7O+PEH}+(JBt-NosGIqlM;GKWXC zXVkZn@+a(jL%t$8;9K9&cY=<5daPt&1Fugj#q56Tmm}mpP&QL7vO1^7K7Jig zVzT3v!DCv9u)VL9ZL=7jX_vL2E8*(h;8jddb#}lEFuy-kHl<-6GXBA@sCHwgts%hl(auxf6@qHN zg*9GOpSruBEH@Q*8gpGw|A;@De(Kc4@DvPzuw7&)`n9XuwSwq3ITEV62^uz)Bff3G z2ERMHXUV3^*xDB13rD4Nx6#;%wrjD5A z!k{yPhMoOS(D~)!U_)uyD_<51*mEN*f&AI!iymtu9Rv!WHw7-jMWd9ebsUqt1S90evf9uuC0ngZGoY4Y#2=y^xq zx8vlE!Ig{8m#$|tafm@1K`xqz+sd=MX#{bW`_ZsIra9jO@naXb9g^{iSOSFu$uO_q z*ZXO2svdztH3!=dY>6r@o?J9vICcjMnft+@*;Gdj@VdxCat*xRz}NCAh(9xuw%zj> z+L0yp`|`{*ZjL{^a~>+5b?$m*7qD8(;hf59u}Ldb|G+bF^Bbi3{wYcqK;HSXylEf` zGIf~ytfGnz?{>+siJ*wUH1B2n>cLO#Bfd93x&7|zO*bDo3qRY5_cVcWBH}-N$->FQ zxAZj&ZJ#{Hga|@FozUM)<8=rCJCAl|BTBtZ6XN2QzqkWET-2-`>PFtfyLO@E-&(tg zu<}ZZ&TMg=ryc1ui0c8t)*o&`Jmip2w{UM({Qs|TPuQVwc6FTCyv2lZc>a4gDZY(} zW4lr_*5{%VB&=HkN|yIkh2tQU_ZCnb%{X&@i7Ea@SSY<2hhX z*$WE!KSTwG8t^*N4{<7)VtQx5boVAQ(G~3i`PJ*{qj-z9JjXZ*k)>hpof^WsHISx5 z4G*?Xz1mJaCBgcW(Arvl6cK}twQzIUNZ{LEHk*r0a_zLi>DC*fyMQc+RK;7M?yk6BsSSr~^oC5HI=2vJJav3#OASBI!?d$RXv9~)zR z5vGGh#pz%EARG`9^~c#&9xW@&Y+eSG8q>+IUcP~I{^M?E+Z!eMA-y^n5o#da-psdj zcD#V1_1GVtevTUwMDSY1{ZkQ`V~#_?_XTW#Q2;89^}qQRDGK#q#Orny99Q4KpjeeL z?BR}?;^qnH;_R06A4%eWIuO0a?Bu-bZ7jKT69(i z?ZW6_`i@fYbqsGS^;DnKPxMMHrMHN|JobSfP$soV8}oq#)C?IGQuT|c1!X0CkCVa1 zWaM<7S_l0ihM*~&tIYO>{U6=dr(=l6s)hWN z!|mY?O76CgUWCAePCsU+x%Kdn(n6Hhfp|hKf}|RAl*_xOs>#4v#mF}P9>|sn`_)B7 z+srgA4`t6oUdqeg4-O7i%~uBHl?WI>T}=V52Fa{}zJACF$buG~KG^d!l3}c13%8X| zy8Zfi^j+{68OpL-FdGA=eRz0ygjw8_(RmDLUUwO7;K}HJCr3@Uu4*@aN0b38o+1xn zpM4*Z4IOeypSl0}>`TR`hT4ocY0}hy;^&V{mW`qZEH(fsre=#u(20z3 zNFG|tCiDI?_T)@pxWZ%MA$%_z4d$riJXOu9aD#a)T=JmGN^W4a*cGX|Zo_(i3N2)8 zMqhl#$fyaFdm+)k2BIK%LPR^>1UJ1Bk;Co`KBegshrc zH+leOz5hSxT>r;@G$7pV;FoO!GZ;R>&=jl-bHaz#W&sRfaZw|ocAmt6i~KR&<;1dV zZQ#zYmfr=Bzw8|`fAR-uML!z#iP9KPG*#sy7}#|3+r)esA3cKDK6@G)dDeo*VPX%M zO|nt_fh=|}IY#~m3INIl@U&>i=gMtZ^?^MD%}wh31N4wu>i>fqQyo3 z-S^eKQc0Y_XnJ_*CJfQ>hX=mayfH0T{xnLk)l4y85e+n2W1?Y}sStK%kztpyqfF{A zP*IuTAKK9DQSQOOBv~e~b zw!C&$ZXQue76v%s(OS$)2;mL}S!SRA-Izz*{kPPtxP)foVY_!fN2@hi6i)k&^kci5 z+zuV-P~n#$X>Tq|X=(6NalFF4y7!|<|1!3u_Q_t)95s|wC?CZ9lAvakD6OGFpZ0t^$G5Rk9G@BJ;jIa|m4I?d zYP7Z07dZ8h!3=SQeD(704Kz@>(HZrkNS{ZOZ}V2FWF@jGvWS{35NuXscUYDJAWE2p zMx>_x*}@fKtpE_~j)SnTpdQZ#Q!hlVSs3Q#vSc75r5=YsWW}AgcAWgf!&XGQ+;c_YLL+w z-GredYqoc1jllGGEK2Lc6>O+FU_=Wq8t0E`RfTYvI}#k~N}3g|xIgZ4V5_pbvIFf6 zXTjDxDJcH1nP5ZWUNJMBj{RFJX}|5)X}~?Lbn@9fM!-RUJKULt0kx`HqnD@LEErlU zG5B90*I*EU#veQ zpBbIkqs3+Rr&lR(DnqvJ0l=5L@oQ473^v6W21%F3$m?}R0&3onz~o;qRqNdx;qLuOFRCvuPD4+epqLQQkJWhb zPj08k1gW?Ss4LIEYoZRMaPb6eS(JC8FG;L9(um50U$`v4t1$Zoz&n89ed7N`6hR0; zKw9+Cz4UUL?~pyk`u(XTrQgeNHQRlUv>IQ&lnICY_!)|ecvFMV0m1yr>uQr-WX2!4-t7a`PW(#IN0jo!b1ZA*IH3V8Hof-zg? zqqh133(h-8lOAeiP=N(zXVa;aAYE(@^ctqTFmkshI3e>I{gq`m6_y@9v;1$kZELS; z5%TTi<`GGB<$Cw!x$K;06sPoLOO^Xhrg$W!*kuzn@epYvPn`7ac&VW;%=6u!nI8+- zJ1b3i=_5!vI>sx(f~f_MxKAzLzt;04 zIJDzy+w-JfvzT5TZNxk04_k`G1s|^*(SG;qOe;cp!3u9=^Bfg@GKtfi#(C`OA2~6?sPO4uA%%hsNH#>#g{rVBQ_+#6DhFjr zG{rn90#_UVhF6u2Lq*`1T=iPps*#SCRRt++yP(BBfg#2G^)GswFx&OGBWH9Wc{pE& zp{J-w02V?D`1tsQk@Nl;)2jh{W|s$2dx7IafmMfL6xpC?TPmpCw*PAdAYz``ScM`t zoVGK=JC}kp;WNU9_wT9xaq%u|w5h4CwmW>s1?XLaZXbs%1dYJ7OFmj`@j%vDg^mR} zTxay*Ud%7zstOSdY;pLX7VhaTcW{Db*qswBCVv_{-Ki-(7uey%owjoyewlfUJfaeF z#>;kaaUC)y11^}Xx&2UMrE%(?!+_dcl|ouRz9jzbDqoXy&p1qMBHdyFA#$mQ($m&rgiW(zg~L((k?a_>E{sp&*^qdU+>Y)}frLD_5jHy?a~Db>Sz9)zUs zl0=pLAN(2bnHCF+>9>1~ux)2!x%U3>wIMRnH?4$?X~3q7QyMf?CF*{?ihT*v8C#Qb z`8P#iZc>y5lz?|x=fcb>)h-e4km#o+ccM`MO){_bD>6l48G^0(=xb1Vq#d3im~nxiDYk>OddNuB~HuUT3>$$ z)*%Bqe|V`ih)RncH6(Zp0nNn+5%1qsgcm1kC6o$69qw#KEiQ1u-`l(XAK*U|nZyeY z@`@PXE8V{fz6p4k5Nc{U4u=6`K@f7Q_8NS3R~%Es`!kx;Wm!Ds&P=i$_j;XGcvS`0 z1;zuS&gg^`qa@lq{eR%@Z+`zU{*1Plwl&O_JA}_aM@KKXXcg~t|xl>K(Xcqkgk%-3LbnZezj~WkM$tF zt>of;w3!FJ4jz{C?Rk*;&Syaa1bcphTdx6diFKe-ZaUcFvhRgl?Dja_suY7y1HoGf z=r@=Z{4NF#G|QyCrGe$KxqzXTyV6W%oecybqMf0PTNyp z@s0HA?{C-dKIEJOi*T~R^7#(yA!tES?nuwaYu8iIh@E$u0>2*WJx}fERWT{%+%OT- z9I-`RU2DnO8i>bTuq6Ys3_G00w6`463QS$2&v2Y>F_?H%0r8hT2V{Lu+8g4}e0Bid z)Wx%NOFX^&w@v;{fGWLpF-Wm{(&u;kL6N$Z2bE_MfcYKhsYf}L_S7>;M;TI}rW>?1 zJYl{!K~;!{i<{+vLUvf3W*mYY3>zAfqQ)nwGDUpzt&IHtCRW;!bo+yB#!gko$*xqh(VlyohqtFgpMvpTi0k@IB33VRwBafSn-=GD>^rOn~Rr z4QFu)rL~`W0@aiCv`4F%?7a(yLvF=WuqmaB3BLPWl@}vAl1HSIMdS9*`!Z|d*=$u> zl&>{4O(!ez2P5g9A7TI%iTxAaUQX%y9zYd_&22w?^`;24@yH`+e2k=&F9%Sku>IABXDI2P(e;ZKX?$iLh1 z1y{(Smi{Wq3R}!5(!cRE?}J|n(n@M#zGtz&x^7>dBTL2RRqeX}XoO~;ZcDT(7o`ll z^aI6~7$CfG+28&pR_X=}2|V-|;-k$W^PdTJt_sx*x>>Po#85q+gLlq)$KZt12A&qD zeVxJE#4IxrhGPgI2l^bR6G*dkL0mBP=#YA3VPWCoH~FDBEtTi~pzu~2^iL+$YejD4 zeb(#Ga3|7n&K?(hEQ-`ynBYv?Jm<_1i{vWETw2^){-atWGi&-BcOujWR*~0S^J>eb z?(GYRnfSMDASAYzZf-}mh4_d+#J3u>h|MgEtOlYOdB1cncE_y!%}=&tLdesycr=8X z2u*_49jp@<*7=58ar+jTK#UAd+IsAP-(l9i@Y(M5dbb01r?IhFDM3NO%PrW_(o(St zLZ66HRhP4hMd{aYc+DH|iM(MmzmA%mi`*CuQ@FKG&ZMN%&bR*oi8egYsI&?|0N%XU7~D)!jJN&t|( z%OF$w26{*gmx)-d-Uy^y^2wyNuaj8KZlFYm%yBje^i%EuW3bPy*28Q8BnJX42iyfL z%w8``Ug-mbFRkz*2fqzao1%($7@-O<=!_Sjh|aZSt7oV^4`er8hl;+w{=Ci! z8C8UKeR$Ff3uFs6%kGo|Os>JhH6%GG=vL<-A2^Dbp8qgU&d(qnq^j+`fWpf8WjGAL zp%K}%%vnpUX6=uG@{p;!Z12!^Db~sH7$|aF+^f*?gvG;!Fb`mOP=igBMw5R+OMiNB zGWoYY0WzEIUETF8&h6UEYYnH{{GH{#*$Tm$;sk?2N1^rfhfd2;8!p0g_CJN zs?)Z#WCU&U3r#-P!AiZT;bBFL>(@bl5xMh}-AxedXc-y97Noi}fHbf#8n0H!$I<51 zc6s(c&XA9<$D4e>JXmZXZak1;R;RN>sEV@ewF7$mM;tKyq@Fa^h*cu7rD!3vi?7z= z<^LBBUCfG$l~0)6y0~EpDwzr4JLm3s;msRbJn_-p;AZr-1wXib=gyP=>Bi}}9*71p z<=D_*R;4 zQgJ$k6ofW*0Yw0L?T38Ocm-$(kdOV&i%S{`4U#~hVOr~V%Gjc?KmZIlg{=KSZUzWk zoYSh8o3}@+m70zfJqAc-y0y?i+0WJ=E1-eWIrtesK;{+iA{`<850vayU}SjI>Fm9?u@$GByBku|Lu5^MxFpa0A`x4$L$$t z!fvULFe%&!1?goq0zv_!CKa@afZb|jy?zxzT_-f?mGYCgF-_uIi=R+JotabxdbPS@&bU}MVo04=yXV%a>&9Ie5eNar5^PSbcl;jEN*!k>c&ZE} zuo+kn>p&Fh&N$iIwhQ^=Vcm6l@ngkbvCYYnyVKj)egVoxBfo6Sz(i{0-0<|nwE0v* z1c+@(S?0*z)i_<(xbDqfZ=VX8QYxt`q|csl4vN19Cnn%$@LsNcjwmEPm;Sc zK=Y+j7Q__4Srx$nI=#+mysl-2wdjIogRtX; z>nkqqYUC^Og{e;fTX7%{;_O$i*q3NY&W~rnR1a^=gFaU4;bHJaE>rUeR4=(UPi|O_ z3bkoQ>t-0&(gnG!YZ(iqgn=$zLI!2dGy)IjK`%(5Yd?@%H-&CwgZFk0<(vKme?eXp zopef5ud(Kl;EIp(h#V^4+w(eDNPKJqAKK(qE}N27RVA63nQ2RrHUj!jv`UelYOX!V zy|oI6oFLU@)a1ZjhI7qz+2t6}* zy9ufM*4nrreuL_RA3M^ru6QW$h%4(JP+NlK#E@z?+=2{`j?K#s2S^m8hk&x1BO=L6 zE<0=pjA}$bhyCfwW0x;P--sATQ>VDJ_k3l#IIB7(KUg2p-CDs8*G6IjPI8N<+dUUA zCMgy4of)XQf@(QKEb;zTW}6%cB{%YQKmKjOgXma7>r@x#X=2cb9eDczE$Ap{3;rL~ zy35B(eqX-8-O((d);(QGle~J9g&WzFD`-1dKR*hqsTa|9HuCzoZ)9n@$V#uO-OW#ai=?f%TVb~R!YM@Bq)`qZy)3S&l# z6xK-=8Dmn+*D*OMBQW#hp)Y^nJ;jz%Gzx5QfhI?}rA{ybD01kzbZdOpz)QxKW^P-2 zZFe1q)_9TCNnHrgZSegi#u;s=kC``Npuu_%P>TQn9jU`g+nzWB?qOjcLr+_q613%- zY)zCmCbDWvKlI*70%@yFNu$kwI$$DfK_Sb=e|Bs;g!xN}|ILnB=B|_7V;-IaokMh& zZHjC!oAgMP7(IjoYcDlDQ3Y|MEB_5T3%G~rTI9f-mwv+){NgallHG}{f4<|U!R^>r z=jLDM$O}P)0;OXD??z;_hOO0J_H>4`ik?LL`<@ebLa6u_wYHX-%_YjrbwO#Zgp5A=VEPqQZ68=pCGBwm6>-9wH#|UX5K@F>#%E3LHjve{{-QylM&hKrj&Y&nRW#(}c1z>s z`Ht9=!-G*BvbW!JE%dDZw6?bLtXpl46^lL4JB>d-aDWLOWnY|wN_F8+rsSp56sRx5 z9C|%HJXWWwIe2+_p@wR}=^;QXvcu*1(PhpNP`6zArf`n&kXFF1Su~edg_5Z`3rH`D z&;HI+;s6}97+3}TcA#sip9&8NqzYGAKs6Gy8n|S7T6qYL#HoPG-H7hVz2Kzp)LnYU zQ`Sd*#QfIdppo5To#5nf%fwRXHV)3y3y-8h6WhsR!=YWkfrM3Yf4TWksbgfMz1jkb zmhC)Sw_8A(Ny5*y6`X+}zNB_7Q)P@`gWB7`%4BE@wDS6{02hT0e;s9fRwQ+J8EldYk||vQcw*p$<}j&PsuWtE;M%ZqOS(KV!)o@CM;89 zt+AXlQ8vHh@|)S_cx^oB<$PbNY_)8|D|1S}filjn<{6`}6Ue<;p$B+t^hW@DDxgq@ z*W3T`#RFX@1qD0SlayGyz?Ql4dQR6vCa~OA&+?yg*JIZ0c%3yNhteFL)%oQXNh|YW zYg-MKnJjR`mH633BOTBx?9-gaN%^rwLaWZr<14wPCE3T!rl~llG zw~_o5Ak?I@01&nku(MYJx)@iWTMchRfFc|g!EGvGNA3|x;s#>V)RV#~#|T)gdYYf>5xDHHbnk4wX- zgwvo}1mH6d#i!45pE>~l(veQ(bv*A~FXxn0k#`l zFjd@?{5W)AC|{gq0S-4CEuetZ5vWkp3qG5hfh>kR9jETq99iN#3|M8~q;1Ear~}kY z0&#Ue=~fO~O^#Y3Szex~Q5&)x_Y2J#>_=aExJMTkx6Z)+UfehyfYa}%lqU`fU-m5J zd318Vcz23i<8bD<9H$+Ae!5Z*gD^7Y6sIh8upRptLWTnlw6(Rx6pq0dkLlH$^IKb1 zPvb%18iiP%1^x2EOFLzB-$8rki-_=IDyx`;gyUL+Eq?W_BW0j90;&h3>?kzaq%#GQ z6#VN87foe$?s*A^!m1bWN&0IXXG#5=HwCbFK(pr5U2*CDH7(7#!P;N!KP-XmaOqRhrv;9Lvr?jF$YHXdVfSF39*3UW~b zEy7@$O>Zfa#oW^N@|h#}X#@{s814z}|9;J=(=cUv!$uZ&t5y=28iN`O6A2t4XcTQw!%Jms&qV17Pb&Xs; z;~Nptsowd8`P06DP1?L-lhDB;ggBe<*4FI>uohg3?Q{Uv5l%)HzOeOH&DNF`FxW{s z+-;ZUAdqZMl!pREOCL|f+-!H(Gl77C(a|I4&anp)?vWvvPhG*SI_^IP>_CoI?O|g* z6-dS8k#efMcW_yp{Whr>FIkjAl$&sKtG}bct{8Jj1*LRXQM= zsyO9cH|X(bwEqh9zXj`=A-ed$x3tFqL0jVri#IA1abQxiLr|WsG*e-vk>Dg*tGL(KhSx=c?JueQs!!A_Q4+6 zU5{M#{rYTNFYPxMM3Asm10B__rpKgI!!~*zr0s{>&v&hH2!P2J5c>`$Swz`BP6hgi zagRlS?ylWW1DtnHtFzJ|RQW~;s}-N^?~kbH(6F$;Ow}*bk$Vi5#woF}2ek(5930_0 zm)(OL0KK~$DNq0>8{Llur1U@SK3jWX_^dN^n<6-ih3l*Z=y!(|WJ%kbSSo1P9S`&h zG)KYgh5rs6xIdr;ZDE^v%YS!LLLKO?>kl*pDLD~kjd`u59xq=45o zIYUj_G-iT9$BMbi{Vtw>Rh*S3-x7yGGg0g5fr;V(&x6#nH4nUk9J7Vlp&{)y0(qgdFmPRzaCeYOSRdDi1`$}0 zzAbXX*hICYz2R%Ah1LFX&JgBbkO9s2B#CNjVme=cFk;(yHU?(i?2{##h`i^S|C%Dr zOb#$!qv&ieh$jXDOOl6CI@sLaX=9e_G?zyiG$eI=kq+@P>{VWRQ-B>>eLxRzqouh- z#Uaj-PadG_E(~-Y+5OvF$*o>|COUE<*=6HNA?!6(Wo`4w`-L(z9cvj5Zv~w-24}AG zE&i9BD_}T@jmOS-X|}>BNc$~Sk$RIiSvQ!+N}GVr8#V|flVfB+CgFF~f-CH;&6GY~X5?6xUGfHX zHuA~#&)I?@PvP3W0HSg^-#K+L9pF;1``y_Z4xmxh12-~0aYbkvf@s^@{hW12pvN-x z@P>zTOGrrAwpCh9c7QS{oZ2eQ`vUc(?lr)Zh6l@8vuX0kuT}d8e?7l0&=WAfHw7bS z{@FKHY!D1)NBP{O)NLagS|N;IO$b5O z)z=s?i}nl>9O*WbZgOpule1JY2*Ft9JqoyWJP!!TxUHY&X?qH(dixzJ{M{Rx;mFF_ zwovUJJ42bD@KQO5$smQ$Ht+S5_24xye*Em(x)Yz|j@WC4=n*U~WXCaKv z>PY^+@C)3|`;?pO6a!5x@fL%QJDt(aVczaLv6mwn@u2;(FxmDz#y3Mnh9;#s1FwU5M7|v8tzqW)bnND z1+;+@b(YNAUh!N3A8s!dcPQCG!{6|uo-FBeq+Jr5fEMdu7rV$I) zp|vtJMdiV2F2ut<;eOrljSdW`TZGwdznqujpsP%3`Fo$xZi^lNsQ<0NV#Bnlj1Hg@ zooKS|f#{Y{YLBgU=eJ=0aQ{G{;; zrHMR$GslTRl(zqY@rrm$yyv4;5YT#|e`loz1s}1yQT}UIy!ACp>3%8d@JQhE6yHS)_{kq9_ls%kO4rrMem}8u zX)$_i(+C>2dSVTxzlBO^lJ^#aDR2;JMCOT=Jj$$v4vbNk{^*0)fyTg*0*VA00|zzR zT&Vlr4MvILxkn+>(iqiB{D4XY9^4G8q?Ok#=j{MJU9#MTuP!4%e5V(P14J_Pq%a=f z*gm`U;8%b-SJA48QIZ2uX|6$_qgB6H6)>@4m%-f(@aiv7 zpjAyN6&mI!GmX4J!&T7bcziW$DNE#{oai3tJf>~?YuHhu`stIV%R(%d*p!#w>ZwKK z?%FaKXpk3yIP`_OTKK?FHHBY(=wXf^;UGbqOQlwj&r<6nyBPYEEv2}>I_kj@;o0s- z+fvC~;Q-FOk5-w8JD^o2vGPLDD94puWL4EF&;O(9Dg&bGwkV2mOC@4rb2uL?W$57JU-CaX>!#j7z_}=_yICJmWwf5R;%RmPK4r=h&PM0X1 z^h(GL$q;-cWXEj4jfV*}9f0J2|G@*G!Z?vsmSktI%nb%MB&Q>KaG$~&-4(G>bX?#y z;vqh6=!b+~h{88})Ne`(1Xx(xy?*VUtm`p{O6i$g<4mFCfo)H`Q^Ciq7_RrRZ$mRf zf~@oHvL*jALm<0-(RB|L5#E`sU*yzXE-!ONO?0>~nj>!kSx8U>U4V$-n`s%#oOZP= z<+4B7AC$OvmN~tIa8fYagu1OSKyShMlEL`LAy{Z2GK7&0dMBjvTk#L>R6q~D(e#83a_Nv z_-f$p&WF?E1EACLVPFqypW!fY;*;b5wnATBZWtf2e1&TzCbT@ePGa%2_KHy5sNzqAW6eb^mg{e?z_Z+5I8Q zDa`HZ2BIQ0$GYD3hfFL~fi@Ky;s9G$E;K#|it=eC*-PzC1p!v>{u0&V2fy1SDQ*#E zs%0qq1yrb%$Sj9PKSm9(RWVaLky{Sdq#O-kp0pl-|H6PYIuPg!#KlU0BdBKKEh?p$ zh6#1#ND-lZ(2tj32HbC0#)-`^@*>b#aWNF;0OTR5h3Y`W89ic+Q?OTiuN%w^=inHE z!31yVLQ?8+^ObZWd-^P!Tx97OO{jl))XTg(=*v2+HpKbW<1^-Xc-6dl>7#M0CzoYF31NHlf5cX6dNc@oJ-X?}n)b+JI}qplebbIRLsMU?Tm& zWK$!UNOwZK^7eOeZw&{%`$KgCK1%_w<790*Vg=xsuN= zY6GX|!1ywRYqrp5F|F7>F*&|oe-txon;2P`eZWiy|42heu+f>QHFHaeY#P+`{~>-u z6JtQ479uH+Ffi2J+9l#Hue|Bqe0e6n^_<|!*BHI-#L`1#nvlbP*b&ooA=^x_S0ez_ zXV`uGdr<$!+PWAp8(HtLs%8dx?O!>#*S&?efObJwv4xJ7+(PV$4rINM8pHHjriMp@ z#1zF%%AMPJ%v04v=FYgya|~HF1UwNq3FJ z7A$i0h?HC#j!gm1=qY{0nRnE`;ZJh|a2z^CNYes0=~3-s@Ov%;B{H+8hG$pP54=sa z%jp`eDB^~+Otuyl#RH8y!U#1T{CY@n9yoI~us@R~9;M3M{6D&P96#`f!$D3sSFh1{ z|B(%ov$cWQ9cGw4IIS`EE?089D=jVLMO=F*Kp2+wo)Jj@N=^$j9f6po+QTr7da97! zRQ`vGUqC@}xaVng=eSuJd7(b{G)%T9L)9ZZUw;b_G)oH)pXhPUwMIu)H8UgZ@AQg? zJVO1PhE_qybfzAz7{2YYU4JIWLDL@Lhh?zY*cJIYLnX*=YfdKhHI27s(~K9G!Twee z{C?;nU^66JJdtKWsqvt=Ezrwx>+Z~QxcuWA+ZTfO!PEJ88ioV;Az;>t$??$gLHTS0 zWgVmUhCTj*9#B7A!W}AmC@2JBMhvxiTZ8h}?5H|meGeYDe=yUJn#oLmvj+)`qt7D|e z`y4jq+q?irnuj_j8}$_P+p~rmhws>dazLmX0QpezBl;Sc(l#g$?`MEA-5}7=B!!gz zs%EDX#fNhZy7p%WZF>zHaeXL?mNH9{6*9Q^R(P~-Vg+=e!__;$1qB0(Ty_wE0zU5^ zHMmAvyghl?Aa3&+!2Zv8R69%8+HdFAm+@ZelX`KfCtnJ66ynxA9Cm9n?oYyR>jzs-))H#I2ZZj@TnFlTSqosg zKzwhb3@KCLfl5>x)*$hb8HIoVY!a$!Z-Po##-xMPCptOl-uaBRI5VrV7NB?A3{*tx zYPU(L;2br3ok^12(4@5N@X+ruC3M~mZmX}uO$Be4>SoEwt%FnCaZRuG5aT#-L5}yQ zeR`5*Ts7O{i5)N5P-?INZQ%dwPy+soICM=_zw}}smAkM`-(zlc$k0~3l*w56RF?nGLJh;b873z4u^GIpVL_E{VHQOyBxJN#Z06J_aP8%E#-ml~K6 zDjM4G3b?;*=8P)x0gSV!Kq~qvj@q4rZvOI7tEL4uvmh{_#F&Q~PZM zK@N{Cwc;`>Fu6=F$8oQM+fMT6M(5Tow*sEZ0LI<8m!7xNA(8@|&3suF(B;8|bKMSh z(#w^nQW_s2%l-QG&T?-V<~V;3hX|hzB9DssGZkZNO@$OQ@8g5JK;AOx-7Kv`Hi*2S zIFHgAJ04_8=LX}oGG&%mQ0!mO$N+IYw04TYx1XRt zIo$J%C};iZe4EM7-(>`#JdQzAjDI4pBH_M4Hxe+5BvQaI^1sOYhZ(11?QL4n8_-Bi zvfegcI5lha?RSgR^eLz3H*qaxGW$xj1~Av9t=C&fTV0^30BYWIsHb9x4|f5Y3A4$Z z8s)qZF68syDz;gz{Tb8?Tn=|g)~lnsB~-HA#fgd&CnXKQd?KAomdClaGG~+vxkmH8 z(LIu)`q1Vsd#?k1I`!Hro$${v}J+t?m0sH+n{d>ecEg+=^snvE4tu0Xi-6V1`cNp<)c1l!+bY#V{y;V3=LJE-H3I$mxcpLf`qJ zvL~MamsF<0O#Bif_Zx0bTOLLm#lyU`R?u&no?cWyF~mw^7jBEepV2}J9pIHLLOzv-7Shmr zCg|mgYoYng=;F>o$-+~6{LDnqER_KI<-dM*ITTGk%I$mT8>YkLwFZVS{Wuh8W|wAO zLYEYL?lIl~G;>v*PqRaE9~DR(!ukT=jxSR$c5#7;iTWb5X^=i>H8lwwJMPgK_77d3 zT3l@!SLp|$*TL&u!ZzS(nrkf-;sO-Gl!`bQ?+bHD>&CCtnL?kLjU%jS(0HG!^<3D$KZUwklDW0bq*Q&a@UdNRkuAjDh?^6FC(e-!j+ez+4-P_fT0 zD)QaWGJF|KkCOyE;tx^AiLiESG^trslOT@sWZEljYIP3z_i0RyoCDKDQpT)b7>k2v z(^~pUcbBO@7a^{bk+qW9j8rCm7PpKn_lpA*$qh(~H-Zn6;zeZ+4J%^6Xs_u&A&R2X zP1H9+8<9s_!E{m~{qS`C`uR1DS|Fw(*{fm|hM)oz$cM6T_)$My z!|oi)NbbMOg5Bi8}h1* zbfp2bA=DN38;*p^7qeJVwv}VVq_oci>hY${*=9(;pKtA1_3(K5{5x2tp6xVo z0$_P8$>UqDoNrI?&Z9~hb9*asek?`ppnyaXp8{@mLf z9m99rO4DTg(PV}=Z+Kd7^jbSu_r3O0J3R(G_$&g zg~>02H0-ZKd7rT;wr9%gVbhZ<)r2LC>!(AyP=UMgTdZ<=5oET|hvxim*#rEGoQdMi zk28qYKagG#CY-!6^r`V=nPP?{hA|8ziU$D5!vbe|pTL;=83IvgbgKp;|X zSaOoZXY)HBX;lkc@fGOP&$UOlLsCDlmoJVf5sO3WDGo}t7?7f=az@wxMc9F8Aett^ z^8sO5!|c-EU!40;p9T!vy$4Nj7UQ8Gb`jo84`@GakDTw`&%AaE(wa%-FGq*QhKfXx zl{QfLfp)0GM;-ui&EywF5au~}btjZOO|m2T<93>?rU%>O25mVSStOwe1S|YW2+y(T zm0?ETPXH83OwP5}NX`{$+AVNISv~6-Ku%}E9ZpwfR`y5hOE5aknRgZmfz48923 zGeK1??t)SXRMq~qRJx0OWfx2M&PDVBrIezry^(^4_IWeCZa!OTjyME7o0s(z7*BU= z$@^+FStM^vtU&ZZ?!j~}kSw6T#P94oWF==AaZ7K&Y7Y-9jY4(Xa{57+b+ZEavvy4L5nQgD0TsQNK?+@vi+o^jrbBfAAMN z`GJ&kWnyC8pIAx=q*L@}tT^_sIG#0g8nJK_rJE`ajWQq}j~BW+4f}FKhNc6R_*^>} zhaCCl+RTTlIcRZ&Ql|6;^@+BzqCAS~DsrC=Fnk4uMm$UwJ-(}uwoCsRRr&`=p~e>= zYspr`kOnZ{)vY+r)kQ2LcYn>JYvl&E)PNhL4bo!Ll6RAnprp0m^w6_Scy1zOi4!O6 z>VYb0?-!Cn_Uh!*su22Q=tez@MfO^r38tITp3d^L^YbIfwn|UvXf8s+Y>r1FPn?cd2#?4Dja7w%+IKC>}cG zeTl0tPq0}VgWAdx?0iv&medi~8Nw1leo^Vm5X0S9L~ESMrH=-1!A_I!bR)-h4z8RU zv5&hMS+qv-YG>x>na!pIr$suAsJm?K4u!ncn^^D&Xx7?6Uo7SUE74%d3gY(F%+-$V4{`kynW#H~;%4ZG?ASLM(W>z_+W(%-DnjGl2X)3GyZ zg84IV0ZZ+rsUC=$1l&oR&GRS|5v+NW|7(o#P3wgQF*bEET-~~#`5j3w?YUrBPff+Y_O+`!yqec{E( zJ5PB$Eavgo50~I18$8r-h!q}6JmH9)6Z5-QiEWkFD?Kxe@9=|1ix@B(-%PKLqc z%H-VNoa^4o<8*Q?tP19O93gu= z_=251)UsEoSC8M?_JYVEUT{TDfEr&LQGd{Yc6`8N)z7sHG`|HilMW-6G#T$N+<0eT zN|XKZfIu-;GD6I7An(h8>J2;XCfL(0Am_9qjRVF9fS~g4xGK^K;cy;>|G3YTE}7(p zI18fV7wrdg_iliOf{?yGZIbL3G`id>4D9p@EqZhr21b{cN0EO#xIcci$E2yH7meW# z{#5gbH*Q4U=lJ-@?WKMKFc^1OEh?dQy?#S{jv80sPs3*g9fn3op$9S5_)nZlGqu-m zKBW5CWL~bs+UPb|54D7^4sk{=4okhs9wOCRsRdiF`hSAyeqe9^5ZWKF2c8gFJ6!H_ z0e}K~Td$pqyY|ZaVQ)JyGQtB2f*Wh*y>_cxqv?E*eqlUV+r@Qp%Qis2sHB(+jcT%A z0Zu%A|BnQf@BD6avD%*q!XYyo7X=vM`;1w8LvQ%#WJl7a^| zIr(FC#P_wmA9GPDB%o~t;%d5ruh|b>kiDUe6%7TB9D+MO{CHrSd`2gXfl^_i?N@^0 zzFwm{=xh1_A^Nri?XBG$+uJLA2EcYwhfQO)u zNDM&ld9m2aaqNiZ40gz;5)$kjSk44oTwRsR9XM?YbnBndO&yrEJ9WPT((_TWrf_M<+seou3sak9v1hbKJ zSl0*Xew3tQasIgyQ>M1JKs`buEIB#y^YRFy%*#HqpywI8XXB*J`evHrf%&}hB}rvz z?I<81zIW*s(I9<`Eew)X2i-@I=>g&HKPt*44A(5Js57esN_Y1zh^=ZgRs}P5q$@MK z=nJhM#{Sj{Mtr6gC~;d~SC==-wfU_$e{H_R@&cHb_$)tHBux`+H(LL3FQj{XMNuQ8 zI~eGA`urN%OI*K ze9TPkB=qXlRWKMk1;oF*`yZ|Ym|H2GtzHSDSQyK=_5;OHAUBT=L*Q=DBodNSB33Z7 z<}^J^ip=vi1#tH_Sj2+W_cKLwpGt?kh4$9zA;j#8G2e^{yeU&%ww2fqdL!E67Z zgFhI^cbqu`RuzzfTXXLq9~~bbhcfHn`|LQm4L-hYDTr|n14C4h|MikLlk=evcINQ0 z9^)n6!8~I0p1q{4e#lzQJ)u0O)0T%o)1RbNVllHid3vhNm4N(-cX;D^ z%C7(%{Ok4%z*u2GRm74YNkEBHO)W3(ud#k}`yepcVk|ds`_-4g#Og<*vFxovE+*fHtvS6BupCDsk}F6cjCcD-t|zl% zj$}2->nkkY;b2V!&HPve+GVI|wXXmzcSbU{`sObdtC=w@3mfb}q8ISEp9r}5Oq862 z6zL!@L3#Y-fAV;A?kU>>S<6d0>I_r%MF7^Tn9{FK)rF}z$Ba1o*^~9Dzgn@ZwjG#}4M}uvOj;*j>2- z)jLm3AA%ip?|{5%k;3K4FJG8yqNTymkIWDl%pbdoaARDX^3*_-=XlbMlG7u8@ zU3gag1kgU#Vc7l9vszia9+GUYNJ0*`aQi5muz1*ULqf$aG{Lh)R2y)*ryt22`Ne7g ziBm|m5Bl7B%2*kGB}?bi$+ap2Gj>L)9y^bZ_Lp9zI^4R1kNa-o*QX0T`XUvzOvf;) zRu)N!2C_;n74mfxU8+vKhdiiPP!=(u8qGXN%*s%^Fn-%oyWy){ARVDFP|@Z8K<6u; zMi!Z5a-Vu!pZE-{a3UIG4KzPD_7BqeFAkDpVD-V=@a34DF`MI|lHFR_-sOt4i$IBd zdR~55Ld%hR#5Uj2NJCfsuZ_}Rqt4(58d2Xrehz{n68>vPHz$hB57UVodB&sRVaz%? z%M*5ED!1!_7Fyz@&Fd@g)xdJ4bcH7L^#-@HpHyA3=zy691mz(7Ii0;IT+%%vAVA2y zH)4T%hvhTijo;B!21_&d|0#U@#wyKuK!P>X9u$F3lJu)!@J(yQZ5y*MCD9LJkea$Z z$waqkUr&HeW$fFMRcH~?u9iKBMH^f1fL01QA{RUJzcs4V=b*T|PTx3J0To^N316M- zyoRkGV4`oW6powM!nvfKZq+viKJH%sD=d8Xx1TwtLY?AmepMxqdf;f_FhRUe8S?j{ zq)2q!n@`4RiRO#8jx5>gEXe&Hl~1%}MNbD-p@F@%l82*mSk5K{=n@m833mY|FYQjx z{L9}S7--iEis@Ii?z$uC%s#9QRioXWRAlxWbvd?dl!6iem&2pc-V0|V5!Z4 zDMwj1DM4~ofw^>-&CuR5SYXG*)Os2J$``JTMNUxG=49GeUDc(K4F=LZDe`^oe_zKJ zL$>|jD?LIPl<8RKnwJZ3?N2aAMuN?c_surdxj7UzrS9t(Rho~kO4~E7r_A*db=(Vm zu8Gd}iU%RbeOB__6C}^eCrwc=!PJAj1@AJa+lhn!MNRK`fXF{q$@@wdHM_uMz`QZv zE!o}kPeW*{|2Km1rEP@~zy{`NoU4kTc;urK*1o?q9A*m!Lo3Wihq$*_IqL)E-NDQw zGoFcj67Dq2O|G!C0yWqNfIcngYC6Ng`rbA$HF=M{6?C2m3#4u=b{@P118BB@?Q6SS z%v)Nx%h5cy@&s>Z%beZ&dOigWa&Z;;FXe1`uk`F%?0f{fGs5o0mZB)OWB6^Wsz+cD zeB5!5taq*4-Op3&8>f92i{w8!AT>41AIZSP(Xx66YmnmjgS(uSq>Lm&uYWyYSzc%P z;L@}X25qh@rQ&+}%yv5N;+x~7Cr%QK%YTVc7kD(k_MZj`Sjn?baPbM7Q*aQj=s7FC{p!@dileY<+tLpwW zz9Dyt-VpMgaEE4Vb%SBNnNl4}jUxu{+!Rj8Na;o2x0@2n)6M=(wG6y7T|ozsW{i~a zMoZn#dOM@d^Sf)B4G5JTy}lgQE9Ciip6IX-9^CF;KO#{8V-#;+Uq%hogD&$o5H&X8 zvx(JpHh}YuHd4wG4HK%D_RNdlzS-K_GXs&jp^?1{K8|^jk*HCOojqWrufQ5ABD8`( z@DulCAO2~Zk?vtnNYpgr9?abxcX;-7V&Y9Oy{eE@*j29b!cW~HxsuhO6L2?2=YmcD zWplLGZitRmVmoPSYMPplcAkR-R31L8d88KjNX7*cX#(S>zJ7iiC4A3-Bvv?!o(mAw z;pgYKT{>1nh(w`l_D!u0zE29;wjOA%J=50WI0trBp(R`I7Y!;Yv(oD!vzuGa+v-bk#k)HoFZcz7X4f}1Vl3v{W@cuxv|EXn&%TwP zo!YNfoSpJHn1E1Nw-*{3%EukjfM>x)3@D#a8}B1+O|RWI!9Ac=o)NKe)^Li61&EQ= z+hDwZ7WCl8(YxGIloLOrt!;))9=&wd`QwRJwCi{^Xy!TfLCw5i`<>W2b+Yegg+Pi> z$h$YlMC<83*vXmR@Az&8h!n~F6(vLCw}5n3gV)0DF?%hJoSgi4#l)G}pZ|!lND-`4 z@CL(9xi)JW(?ejYYo+M+ty>=+dMAj7XoFoBfSc3*b@*%~Q~g<#(N4#hj`llsAr7whTZrB&al(NkGEWlx~wX-97CIy5^yoydj!vKwj*=UI6L3XJI zkdO8n)0az>c-!%l-+5uJe^s`a1}%>>soo&YuAbwzJLTEzb%)LADOg(#02v>hLwxPI z#9Wky`p%v?yLA9BN+hv%^MV$5t$q(^DUs95lc3>39pjB$#{@&(bGg!KM-{o#s3Cm*HUa)WZ zIO`4TK|ImRzJ^0*^Z8L9wA)rWOLU}n2X)$7=+3eQ)_AvCnDsjjWO+`Oh%*MKi3k+D zuH?s6g?7Gma0q2MkAd-Pc=PGlBkPKZ2u|AXH8p|}%TK_ZW(<%M>mH+E(e2P%;%Ig{ zJ9>{9UP} zN>k-2CGC>~j2B-;&fLYoz$kUQLtq*Z{M{bQ&^FW`iaxoOQZ@Mw~-$Mz{=b^x#e9 zyBp&`pnU%1@{b`;#u;45m__l1LuAI9qUJqlRoTwp|h8cT?&_q z#t9q!z)D&K7%pC)&`*^~iaH%3`^irMe_5! zFxRSu-n0-hm%0J4pHf}>HV5(Bw{NXAJb;GGA{bcl`1+>B@#ecL!U;s};uhnl^Z7-6T7^hfiV4O-Y9ECEk-m07ib`;XUy^9V}>^e%mB`X=##orw1;>2@3#k@$UH5 z>6n3MMKXtud3ke+?VCSE$n>4yb*H7GCSx}4C){dQz4C{SJN#ZpMn1cwJIlv|>E?ke z$Ad-Jx6Hxc-^8k=<=&u3#6Qnba`BkB~f;#c0NO8fg(Td!D6K>kdsyhVCq3YF)6Nn)u0^kK|78e()Z! z^Pj0d9gfrUt$-V^DGAtEr^om{=UZFF1dn+FT#H(jDYM13ka$kGc#5d;EnYDLSS0U= zh9rUJR=PJS=ezkKQq$>I>RX-f=*M6K;O7Tk?n)lBV*M$4k;@r6{)1X7{vz0pJ|59i zL40=s#?o~cgDC8uN&^MY4~;(OS!Cjx$up9aNgLd9=zbd<>=((`jaP=8q+K~C@ibZqS|!{|wE2E8pZ@GDePZ>J z0&s(3>Z6F@6l@%pJJij~sZyU#%MtkktfRdtiB&Eht?dHfTR2jb956?+wcN>!>u@Qy zeqjpYJECW}&; z6qH9r74b*xCQ(;K>W5q@+``pR$_?Dx1Ea(*vC&>9nkqT%(jeF91=~9k>@|<1;vgBDroQWzrWdcxSn5yw?Z)r{6 zc61uCSR#37u)?b-$9(ns;xXq*r4%C5NZ#KS9Nu)pER<-}GZct34O^@N5z1F!V4p>= z>){tSZNALYKpMH8GJBiani}1-3B6q86M zUs2T@Mb>H$;gCQ#K~u(y9lVfq(g9yy+V4bUb?T<^QhAOdj1td&&e5x;nv9oo!Iq0o z3mtqk!Vof8Q~uoHg&E{=*@XRyqdAnNB`4rp8d&X!Ol~&b69Qs{RwNw6U`s@ox(&D= z&zKdzq_Bfljy83H^G>f~Z@lOY5)mp&%0(Z((=5>-AKt@-QcU5yiU|p#;(P&#yfn3u z$b0(oB_hqVwa-iFd4_og=Q!Rx*E(LSWDCLKQY5sRNl>huBAgP2+S`#X}n-f6y!o~Fns0Uw3O zM6tq0eRA!WJ}e$z0pBqdbeG$5!FQ>g`D;tdv=84ZHo2FghF$F?Z>oJ3eyulee&W$m z*e^5X13|O)LXw8;jIH=_A(4_`Sg^2sU#E2(>tS~oY%7tUR<$6V_jpE*>~Xg&k49KZ zO(2WU9A<=kM}vlye4=2MO(ArPIdE!hA8khRg*ehn+Np7^4#vm@4eO~#mauF$_Ose; zrs)snBxr!Or=TthA#2-iKLQe{T=jVsdOA4+oHjjJaQBGe)~NxpX-*nWzu%N zlHLJ()a56AViR!}Fxx?8rZzj|q^(%HaQI?_K zpj-{fSiOPwR*sh6_R?j&`WNk+*KZ||ON-%M(yy%gVqM-s9R*X_kmp*6tYD|TH<-IM z|B-5zHOEFh$P;&Cbr(3NyV6WGk6w*XQj1uO`)Zdyq^!5=Q#TEp6V!;+(R9$%bh1kV z`RVp(U@M@&gvS% z@77Um((N+{*lBwfk^&M^*wQ>F%nV7jGLSVEXIG8w2RoiR)YqUKDj6mKfB(Yg!o63% zVK@%`$KkyECo!#2Rv$?8ZMOV~4-Rb(x4X}!YLp>@PfMgK40pQwaS8|JBhQ~WSXsF1 zBT6hJzhG@-gOmj0ql2IS!&ZCM!zDYK#ZVpfGSB>?mLTH82Ig>P9d9n1)%2Gn)=LL8 z5iBO%{62{ZRhX=?I_h}(8h)tv!a@BW`=g8gIPqekUss)8)NBlsY)Jh!vZWf9zQigf zDcO5Nnm3`#h_O4e!mu~H=BCfw=u$}M0`Jc2{MVuA3xZ+VG}PF&Mj(#G4L|?d(6QFa zL5<*ez5@8_(^RGGSl;7poD3X;pOtHjvf--p?HGI%qqc8Z@m6HzC|xj1%WEN>%( zw-~Inc67&KsTy1(==X8|8+aeD;uFNgPo={T<8M0u%FTpP8)foIRM(d!q)ngodw$+wD{3tju*dcYKzMtyi z89p*qW{)q8v0^y|MBJr8g0r+F>dZbuIqsy8WDPC}pPe*#o7KCr3UD(p9_2s}qq48K z3r-+f5@H9;w)2~}7VByK2V!S?=ZhCFrs#}2!L+An9l7mu{pF;+ShW%(i&Eu3;H}9B zcfS2JHV$14jE{lAR3}>rEVE`b#&8|=pxvR)nR7hbRal3c%}%Kx;W4uaF;lM}?{V_sb3If&ldSqXl_!`y1Y(TOc{4;BwJclDI| zE`_2Vg@dV~&EYNAxh8&_(@NQ+qo6+RWCz#k1U>kYzxbu(?)<&QvX}1pHo^1UuJoya zJ=28j9T#uiN4uRz*5HMV&6tX$Ir}+L(36c#DJy1XdlyioQS%e|>d*78c)ka-?q=*}p#!cLMc=?>k;1PU#SVXT| z6BuhkVBdkv$vC#&`+kEw=QF2KR?Fm5<`4N}QQ0r4{pEPV_e|6@2=+45Hf8Spc^xTj z(V*&ca#rFh>deh-x#&@%n;vyC#aL!*#12Fa(^S_^iBDVk4xKiveG>UW0(=sSM8wJA zeYv!G0}uRyQg@T+=NiwWY`?I;4r3y+@hA;G=IWtB$v76$BK7 zQ&f%1Bw6jp(`^V>6h{==XWopv%`VHk2E_`X-v`E(7k3E~62v_D{1SO7BRLk772`bp z5!@-iu<>pM1SbM9j>Xi7wXWr!aANlQTq6mkpX5koVOQ1H{?UvAt zWzC3&e)e^-54>mdHf26Z=C9^IT9~>i(%h2!U|vNdCN8O zJ8M#mc4VfyS!Ex}Pcg4?l6x15u7h@)0OaRK}3!&1X zVt6pMcE%A#n#SI|_qre5_LAf1R2BRyqTzdc(9<*(aJkwbDO?~PKoOvD+MmmPR|2uX ze-bqEsydTA6F?#0jx%)noGtfJH}@?ye0%RRM`jSxr*(o%(>5&n>%|KW79;s=7&d^J zrsu6nBda-&c1(}NEoU08jk0c&^PEmUb^voM>6IK6z? zx3F|g9;?h7bQBbBH!gy2`RHI~8N%`*J#X2d-TdroN;Rg3iFq!U)tV9f&hlVx2x4sC zw_GV8R=^W*OHDL$1M9Zo@SzX)(K+5PBHz0g+T5$}og4*hAH{X~WkgxcM$l$AcgrRO zV`F2>f(91r;WwSU5?P%xL>(?AtfiNQyFqi$G9!g#NM7zI-{1Qo8aP*auhmvY%zG={ z;K{4{q3^E*6JXduJt=b3c;&O?l3wt1_TV2LYu}qXV_hUtXafRvAwpj(-?IDxot~1ECF-Q7E+G9TFQ21CS)+PQ^8wcy8N^Qo-u zep|iM5?Pg|6mtr`l->;4BpKp^eGAy>K}7FQeoUU7)&B4zJ|WLwdwiauBH6&YT{v7j zNF3%2<*^?v1c8-&Yh~_szcHFymCKDlkn%+JMm12;sv>?clxD>nm+n{}uv5gJsai;I znd>JLF&9o4k)h>LFs(#CSj(+j}uOuh&{-_SOh(KS;aFV6=H#&Q!U zNT_0A+ksmAFGPwtsKm%%V7us6o{0&ou$Ja5y>XIeR1 z!ORfeltp+vv?;1(Pa*q|rw6iT5RispPITqPugGdYr~IGA`s)pB?TF3RcwWYZH@a56 z8LEoz1QdN4HseWgR!2*o8QSd8aGUV|7hI zm9qLg`YSL#Zns6JQHuo#i@USg_XPorCaPUeKrCJVrNXNc>jW0deUX)nk(c9!tl#)8 z#0jr0n@pD&3@gZBpkn%~&%j?z5r{eS($gP`n8!!b?&0|kjiij`!@C=@rI)&;-F1=C z-FeUQ5am3budsHh?YJdMh?$7HE6S0<^pk&F#c+vAIE(BM$7n7$ix84a$de3@WjowH zA3$jI>cuM2jGR9;kB4Xmsj#{a_C*x)Q`09$4d|%28_=$MhUxm}N9D3|T>&msoS~)z zxtR(Cls0yOfYxDkgz2*Y7N@F|I2Q|ddr%52liuVZ2@LZ2b{{fOj4_+3 zES3{aV4(gFc0fRl?m4txF){TN;yWn?02!5hx{^#k&-Ql)N9Pp$8cKU^i8TFAYleeM zvk%Zu@?(haUBQh!J-6-QM|x{w?VAh4vhex%As)+}iONH;P_xJ6*9Y3c10l&F8a?OO zG(C}z(W|R!0)>{znG+a%|Ke%TZD-n(gFti!uR}6TDWo75K+K9tkWBY5hh&P~ow(mz zcTMtly%qan+cX-19L^#|zm6F{a0YW-PnH9j#qntFX3Pii-VgGMB^WQdhrCi2wZSV< zWTHP>Y>&$p8(L)BPLBU1NvA6aN6?ZGp+RB{3^2Ij4cX`3)Amh&`Glx;{mRZReT~!t z7Y9ey!$z}ktN72SBh!q7Gj9+R7@82cXk79Y9XNPJuEyGmc!X$6`D&jDi;1qM8$1=H z9^#3gA3qdq=jm4xaKn{ZdseT1+;U-sQ>DpL*oxIijiw2owL9Hh6ut(aQvkrmKNwEn zs}rFs-+N4OIe>vUlPu_xZp{MIA<&TAo}`XOz8X}3>1feVCV~yQrNGB}Ez2v;#=Ss? z(C54l_`k-sPWmdGDu#0laUn)?Ghg6v!o2zka0-y06e9DimK5-c-hD0TFA^kh*zytX z9{76K3#qP%8UETA{x8Aez1Qlb9)6G$NV=xnziv*cvHg|GVLd;}36V`kwT-YBUdm`q z$hYZ*`6PPL3JtYIx26f&qan}pAeHdQRBwjD4t*|nb9J-GXp}FpdW=c=i2?vS62jR` zVyp{!;s%A|;;WOwFC88s3-Eibbdrd%ai*qGkKv&2U~4=&p)oF@T9s$1iPaCr$0%={ zakN@02V_fZ^?Sq!)5xDUJ7pu@f&&vyZg5b2$#*$9=IoE1i5*T^h<$QH31FM@xGJ z(EuT_2yNS7_h5g7SFhXG0x@rCd390tF*6_hAcKtJjf8|a=m#7igYkPU73agTleVbA zTYTRWXKnx_wcp}iZ1NTHoJ zY`&eRnWtG?Apqu;8&`b{WMzT((WW0xuH2C}cqv9h)0v_7f5~;f4dmD!;R2g972{@T zbx;mJK?EXiUuhE7hk40zdH03zN%!_o_r>ooQ*mw!P55Nx(3AC2Rh@Irs8urkCnil)Bp(2y z<@dtdqGM1Y8zTp#_g}EB9vSgt)2v$6W_U{jsvxE9-sR9dF)MXp zTiuhu=zBz#Sbx~7n8cO+r|6>*HS0=O>^7mf5@qF;`C27}>2SSde0x2C@DT%p)O2}N z48d)(j?Nkna>-|X3`Sa|;o0c_Z4L|(+lR~(Ac$hagto!LfdL4=$<5SoPC1eb=gbCeg!@T;pbluefE8vMyEP1 zjbktmmwqt+{4YE|s@MftBgUg`@EqRYv*ud+L*+oFSCm!uP69AQN0p1CHlt6VYQT}P zM2u!-tph;l4-04BBrII@T9wlT5qe`m4;y+7>#>0kL3HlYoKC2Ti?Xsh6yb^L>q&^P zl3v0G>jr{?*Ccx!&fK`}%rh3NlMWzM@<2QBr$hX2Gth^QFjy!Q<Fno}^oiZeTphk?3+0JF#N7Y@3#;Dm!KiMSVu4(QHj51dJKq~1|ffYC0{@on1y#Ds_xUG5h zNc{!imQlbv#>~^}pBprRKbw+{2mWk|z_t&x0FbO2&Aaishey$R3mtE{6jDDS$B+E1 zFV@+z7%6ayX&te@niiA!Ylv5b<13;<=E~?35`NI`Z3Kmn-xW?0-maj@z4=5hhs$ay zC9BU>o;JqM`hqab@|1KS&qZiEH7&;vZI7Ghue_LvZe>mw7ZSA(wg=FlAB;Cm;KPs} z5KDw@wX(}2B$Oq^GSlRb{3*dT7`$EyE0RWGNWTkPC+kzS!(x^lQ5xfD2o#iEo6kEm z{kU3Y_?1dJDqSJce(_rZ|B(G-rjyjFfbiE(Bl(un^k)6h#o*67_xLZbJu8(DKMu`9 z3b${5e6kojDQ0Bn&U;+JxZUu;3W5t*S8O7871jcxNV#bm#uW=~(qo=+DJbgRb!UR! zz)VDj1^59XC8rS9@EAaX!gSVsa?*SmAhf@{<|m_X-uh@IvqOrPiqlp$>^eX3U|QxS zmXy1inyBtDhMiXnb}2QDM)-~g%aS||Wo2$=XOL`Rw(Pe}F)p(g?c7`GVv1?->-3qu zr`Km^miqEc;>;oHf&614&ve0iK2CuAr)7dWQkLED;Bx znTeUt*cUTl+HV>QHG(q)tVRb@+^Ssmzd`xM`t5SZ2Wz04?6KC3Ss(X|d#Q;%05p?V zKQ_UTlS$j#-O>hgeK%-%W?rsH;&0wz%i}+|ZXH#r&>QkG$&NpF>TMoOY+Uv?kdfc`%lxQ(jMLc{`u zL{;ZOeo-qnO0H;r!hhJl1Yi0_9reoG+#~p)x2K?E$lqnQ1uqzzphtjbN%z&(zvx^6 zYM0Xx-VZe>oP=1stj@q$iQbExNd+`oqu#x1)`vZ(TM@?zI{!dTaw&)zx(kv!a$C(1 z?6l4RPC!z>C+f^gLx0M5<7E`>9!}uU2&L7XOd95S1El~el`>d9GH)fOC{a;QRaZ~3 z`~SiJO&v5U+O>KMczyP>6=iY-6;QqCd1ZDDuazeWQ>>&-o0+8!FL9B+mQlb*IkH@#DJ9^YGogyalaBKkL+yeocKx_a3v)H%VzI+yy9{ z51lEHd(<{h%Xtwo>hgF?D&l_k&^C#E9y|x0|MzQ4%8|*YF?Bu^a3GaS zS0Jj`O5dq`>N?aoX-~_lM8~G4b=a@yu9B4b5c2OBw#xcBc@vm=hq7+~AWn|!w{M1q%eI?|rFY4I4JsW#U?GFWJ#Qc>*gM2Po65tK^?%I0cTiJn*FKCD zv4DsTr6~$1DkZc~MUR3ChzcS|krqVhy< zZS*|v`~3Br`M#NNzT=E%kd(deec!8G*R|IAeCH2kp@L8r>&OideBctIN3X3QJg;M7 z-VpFN6?ds86A20V9v%9E<{z)H$X6G>An1vZ za{vsp_wcRDYH3l^O`KI3QU?`N+sq?`-Cs|Qb*Mu5{g~Z&k;2@g|yfp(Kv&&+<0lgTGJqvK3G zp`Ge@e`sKT>ebI$se&@yg)z}@zu|DWf8*8Zwc-hW)B3^YS(zC7kZ#3t9RH=9P!LQf z|9YnyQa*CCyfiuI1-;yK#HDX>TcMKbGGxyLwysmkto#=@+YzD3*vzw5i}0ftAAZtj zmqcj#^Y;piG39bsE9CVvhaN$&Gc~Dot0Vt&+GWqbD4ScZ`$r^q#hpfo)x@dW^-YiB zDrk!jWtJa#PIhKr9VY4VY#RETvzXYt+e%q44_te3u)kHDMWg)RWU zoLTVD4X8IaJM@zmt)9E7nQ)gq^S;&FTf-+0PCV)Uc>kPCci2bF)=ty=FAlhk^PNZA zBQ;~d3utUj_5LLFl1(eGR+R0JKlYXn0-)b^r5cK0ZzDGsFECv^YtPOt${t_25tBL) zmfy;5$UBnTc22T=^psc0fphnI_lve<#YlGO|ILIt{tG8EATIJ?c{qDw$s_EXCjlVrcDYTxF^+$A%$CEOdZ@~-tVczq=Whg zS{jF(u2*X}?7yU{N|Vg|`u@+|#Xaj>wS=6Gs?*Q?-&3m4Y}WdH>Fo|}nrM2?Qy#qx#rhb}OVBIEchp zrDIY)m8$TiRg0c)8WW~1(?d*ER{798B=OHJ8@=bckjZEf?BS`Vg@W8HCmeh<=;YEqBu*%Em7Rv+^0Z%og?rn5&fLC1uEu0 zxNX03*!s=^F9lqwmZWz?F_J_r3;oTaBCke%`Vn|zb7M%86@yR|110Ml?Hy zPNa?>KNp_?${6lm`;Ok5Az3#a%GH^bSL<;C>&bL~;+%{>JrsP?QznpS37IiZ@0#lx((}%UkMp=Dqs6{vm5hgsV~3y!Y5U73sS~;Vpz;D z(7mOuiHl34s@fkoPKqgQuA9cE^=^d1m}T|(XA0WNPlLs%``bZzwuKtaH3ifl#AJbU z%WbD2Lw9C@}G)knA~PpVQ78BSZybr9c-}DLwRy@YxC0WX80D*hnA0^mdr0T z+_i}^(OKWNm{!$#=*R+8Av_sM@wSJU*sJh(oH_mPp_9kWBCoyPIRb7^B=Nw%Y?EZc zL-1FBa@@;XIqnY0i0~v{1;3yCSrzB|ni-6a85;N$yA5HlEOSdzVUj_Vs@b&HQ6|>BQl? zeGzz$>DEKcA6AGXBtj?ar9!~@Gw0N&H_NY)xX04xjmwYRO$bR>Fin>Hv?aG-ZTM^S zv!dlT*EJ*7QtX`Gn&^zW+|9DS>rapbk>ZDdGb7Pwt#9)6s~N8@+

d2JBX2o#36n zaLBJWJN5M)-D2k{_aGBfKJ2^|vw@+pe)VC!dSUF9h6s6iW{Y`H?S|rCRYJ zpUzWqp`(L^aK~UYiEp z01!$wLAW&JM$Ou=UZ`lb7d3*-XLI1m+2swz9$WqkB`^!Fmra1rwV z@|b58#()9TORZ5BLyzq7Z!hfk%qo4|n;iT>{fPZ+^&J1bTW9NAbCe_1d~ zdm=(w2sIWnRXC4es?~^dTrD));MTGGXOj{~yLe-pimZuzIo0-ipSJ4_94IY`&}~jE zLqr2dZZ&VETVK1l9L_%*lQoSXwjeX zWhMHsT6%Q4>D%*X7amux6tM6immS4_=+&;am2=IB3W@7ar5h5`Z^^{GADa(#bDQtF z?Kx6%%e8@5;)+oDwEBbU%(>ZFb*FcgQ)Aj!#dRJiS8W@ue*4F?$v?RXhkcZIlb6vM zpbNfos!7+r3F()GjGPHfx*j*TWw)u3BWCwzc77F0Kg=HQ?b>l~dSCtD1#pH1_&K2$ zD@$L~eIsW0IrCkdcVfFEU!(YCPvXP)*h1Q9sTc3zt^uuUBBN*fe_7F1wumNBNZn7J ziCgRbi@ppH=ZlAyJ}+Jn&=iF$=&kU58`v?Ay|pmw>(q$8qtnzot>zS@3*fBJBhTKJ zPobGlHLl5T{4wtDq!bSxJ&@)hDzld_nucT zT6d>;g{T=t$`=^px<4%_#=4~46Fc4S@F&d}{knV9v4utzC(eDf8jbf(6mzT}IF{ll zyP?g^;iC9v8}oVB-iA4WJ6g<*EA_+Ey ztp0(sY1$`sS1aeCS77V>iPQ#gtbQ1xUdE}yo>b)q3V+HK_ZquO5cfRpD9HvmN2bm1 z0I{$a*&3>O@44`|BR5nypJ0eK#JRe9NPQDm!s9Ahix*a9P%zI?U-C_8ZLA4 zROJ`Gn)#ENutCjizUUSoLik*r2f1nR3yN4S@_r@xbo$XGN6<<(73T zr}$IZy;%;ota5m@Tk!VBsfL_mP8{eRAITvfnfw`#-08ax#e*d`6cts&F6oFkPH98c zzeR-=%Up?LO{_|6{4YD{G?+@QjI^2WvI=O5r`Rw)1mFP_eICD^MBSFHdhJ@2BV^rOE=dQmO3WwvUIbA6lW$|b`{o@T$4U2yjRmO> zfr0Uoy^mjBPq@|l0HOHo>#q-eqayqNaucQeI8ix)zgf!j`u(4Np6%mFrPD31HCcu^ zHLri74+!;Cy0&YXHWJaUE|PIex#ku9@YJ7oGA>K7F&3Js&xQmCCw6u|U`N~h^Y)m) zeVDBwGX}#|GGKI_DNLn#(pN@9%iV^7I*IP-`8GzA^B*xM7~9{ooO3a$-Rk2DL0iKT$(rwZ+{k_AHeIhe zGM;|d&Ig=;#`=lSAOTxd7|LZI^0*`kRX8D~2NaW4rx1#Z_D} zp2=~07t|Khtml{l@0($B5RvZz(|elo8+iFG-?dAo_7i~{-@A&}b z!$xeeHGvFl(QgEt(6#Az&Q^-=k~rW=uc~To7SA=f{EvL?tmfLzA$>oE7c+@7GghU3 zfq}$4S)^nK85%zC#IKQwFX%A6zi`>(_twkbyzn(-*~NO-p6~!x$y&k~Kj!B!<#YyDRrmKC_*mB#4?Y=u9tMpFv&?{b1i=NI)#{_hvOv`i2 z?j&5-?cLfL5LVP82l83QdoJlwnBJ{sWR%EW?FcZ|f0Zsn!x~0T;%6Ke6zt zJnkv7MpOiO<|MIe3JUOubzPttzFM|H&03!{@;zTQ|6XGYNY9Xe*!s_xnfzZr9ohrI7^s>w2P)P=drH#x2^ zbV$J*jfrdxu$31`)^6V-k6zLq@p1*&gIm6_a`-VcGRHxILWL}7dZ9ROa0;B~B;F2bVoZ8eioLkr7 zsTcIP>1&lbd`@?R;?uJ{OnMTm2#^#uu!Z>gv_e6OwTrVhZin7U)K zK~_*uAeBJXEj5ob_-R|(a3_fS6u6$$KW)!2Hzu)TiX&p9(%`f&KL$9*pz@ESN;n?S#ER)m+iqqUDmM)h~=I0s&NF^VydXuX{ zGWUOs zJ6&AEs4PmhZbt2wa zpaw7K6_{Z)3ik=(!|bcwG{sqjPr|xqS0!Rw3k+&}lRsArz3ssS!0vJR;UO>1B4FY@ z$kLZ*HE@Mp$^pMYXI5dixk$2f`SqOMu0zLhG(GtyQ;hkrpymBT5^*o+7n_%?gL&<#eEt!fSU8|~H*bPM- zjirwD@3QXsyR2N?u&k%#!*Nlbio&ZO26Zhq!s+@J$I?OD4B@D@j5@&%9}#mO1^`P! z_1C(=uBgDgJds|ThKq~gpn#&P)u zqYu}yyM?O$WFFi)m&hiNzy5c}{!aklxbb}87Gv2Y^blwRO#6{5R(hfJ9$FT2AmHIp zXos$=GUz>i?h`2cb%N)V6kF^J50(4XEbvRF&51WaAugT=EeOo}$~2i|m*d#u$7~DRauM*jRZAm4?a?3XJuYmtJ)IEp_gl)bfRoT-;zX zF8ghnj9u(tQg<+1i93!EkuWvsw0E_BWu6ozm9Ff^yn_oE<+f}?g7k4|+SOcoaxd_OGBi-0YYrO&bNZ(*>~_~7r~6T+uYe@iEM{eB9r!=Z?^ z#kSrr-vc3A+k~E4^_&*(fu?^@&72d|PC1O@E&JhI#)?%on7)9LS?eL$x_vv@{`sMa zt3;&pZAAQXQZYg|KL1Huv($81g2(zwmu@nQ6#i&VbiB*uIHP$*9_kHWyg1IZixtj{ z@NcQ1V=()dY!DBA#Gx3$F?*sTZM;Y*bog|XXazf8%=HvgXG zGjQcTaOJB1Ub(){9Ra0FPh`Ks+)*oKn{g1ZR^&Eptglk4+dmcw>bYGi9y-(*E2jsJ zI?qsRl}0n}7K64a0jB0|tCNMvZMOK+h2gY2!llqv{zrR9%AIO;^284?fR@5lakOUh zfGWJlkX0yphjIdxzjEN>;gk&Vv<&t=D6-N|T7Z-G0!!=lU@-T?zHCFBTd(Y4 z^K<9U1>6!SZ{>K(E@@}=y{0DGI$CaHxw;_m%^T|sI11{ru8U?0s;cj}&!4Zi9{KrE zy3gjY1{4_N;Pk+l2!Ez5zY=!`Q?=;ywDZeHLy)Uy&Oyn!1dJ0k?}gT(gXi_&m{!AJ zdXBfyaNIg1(y)y!cVlBCz<#{l*Wtyn{W3m5S1Slgr)7`$cD z0NZZ3GuI?AeGRlkg~QN{+3pN4udBV>vu{3d7X6Pic^!be-V2sj?Ym>0fGpXQ78Y}X z1OJ5qz%V`?iHM^)zpjz|oMiwW0DCS|Db8}O6{K!yt5c=KzDh9VxonGH@#^|K z-|U6Q!|fY8CwfaA3t9X(ksu>EWYkOe(O0^x$sN97jgjJ{7}X@lQRu&pGOvbbS2FPW zlAKF>_}KmiAe|6^aQzKH+}>NJ{xIN?mF=7DGz{$OyZik4 z^8jeRA!$)Gup3ukPMULVQT^`EF4f+)<7{cA|kDeo#PM*8p1GX zS`H_lETOfu^$qx>522xCCrD*aGah|!;*6A^FfnMoE#h$mgEhXWk1n2 zd3|;U;;jytGR>sHME-F0mL!z0PC$GkkW)7HU3<6{$~+XTtiI@2LK+_c*o})j>G?N5 zc)D-?KHWp`bYV}=h-Lm)!qLCh_n$-pwCbR!{Lhd7my~1Ae_nW)8|v@={pp#(tDUF* z{ee!^4`lrR`N^00|7m`+hwfS8!gvQiJa#6T`JbAfDT9LvP+gzK=dw17o7`}RznvG^ zcI@7-iWN4tgO+~Mu8hd{)-Vb6jUt)e6p}x2jRNshP&Meo%suCNZ*LhG$F4XVs4l0h zKQOHIYl3cq3u{y&rM_S*wU+k_vxQVuRT@dz4!Z)H)9%>so(zo!G@%6$VY>00;A=+q zG-wN{0RfX~jEn+gL9Mwax69!yNLu~693zA1P@esMd0YVbAcUIQMpx7K(6Zb(48kWJ z56hd+@4{#rXp_XCZ|*On3Q14G_L!5g=T(KB)6$y{s*6Uc&=Rc%>%dRY}K zX>5!N1%Wc~`vV-(NS2;^m81rR2!%a1Zvh;MnAARqk!cwe27@)Fw=~r&>NwSNA(jI1 zOTPKd&QIavTv_ z$2~q>OGQ%dncbdQ(g2kg*X?Jq{r#Om2VzEO&hj4Gro_{zl&A9}O%amzX`t1+ASYMJ zCKJl=N{P;ttX+qPu-+`r__h8ysOv|Z%Sk2K*w`0?5B~^vs|lkjXCBL0^hn6e)p5@) zEongDVjlPta4b{Uek-uiCR}(F?S!e8WpGZR#IE_;F&!h)bYD3lOwJ|R!^t-(zCAE7 zP_nTyE(#qaCcAPKkhJe&!$`!_%Y*kCoc{n6l#sVsd=9l_C zpF;i)#qchnBxIk&jhw2L@;c5$_e&IZ{$4DU-Zn9Fa!%iIm>;>LQ0cL**QWA($j7u&~M72g z16CTzyeB1*n@eAVK>z`9aa;;88PO2#fP)wt6*U3S#q}f-#GRbZ!HyH1O`(?Ed82t9 z4bquZQ9ybs6{4Lt0FICnMghiK+6Sq5xGAE}dKUloxB$Bl2i@1m;|7Q6R7b)&VlYLA z9Fuql0Zo`#-&~*eM0;FlNpBXsT3~ee*fA4|(#*m_8*~7&@V;XWu2DTyMkv;G3jHZ5 zX$-n9%9<;lY(YWeD}jQmc$gMDlxLr=miqg|V$CQ)YFZ8QTBLu3JFG*dO|G)3scFHn z^i)S-83pBNXMZK87Qm#t<{)6`9&m=;sqYTCj_(^6{wT^uMj|6Dx&z5aF)l&2T#*x5oX*H8R01M)%V0k30Fgg{{Xe2kra zlGzPGp7?ZqW)U;gSHt!Sx-`@%eYGIJ7L>V>8*zC9piQ)gG^)@m*YU}9!dKkEIyq;5NEBdO*W2 zf1HELg>mSWaUc4Zn9-TOMxE0s$=kktdvI{Dk+<%T^C@^GBcmZr`lv3Ua&&Z1PGn4c zm+8;A=W`!c4~nV4NYHGSwoSkR98Rx{wj@J^nOwejZaL;RjcV-j&0)N~k#UEJ^PCZY zL3%EfXvc|37{C4#0>5D6gPcc>*!K|;TOT*WRoj3zkfytAaaw*49iJNBZC@?e`%lm& z+od(S!RkCl@#r9&hU7Nz(u7NeW?dgfORynf0ICqGACdExM-l>s5B$opO`@FFt0+sy z_w?tP27OM~tK2XhH|s0wy2P)RVx@JP0D)I`pC1&JKY={VVd^bdSatW6`6in1w)KmJ zJqDIM;jl^j?Q4!`bbjsMe=JzYN){62r(2rF8hRlPI5b?Ig7l~8;#947vtnYr{3>YV z$Xt&ykkgTdBSfF7q@#j}kvD!dN*}@QN&-Puo2fCz;tk?7fBc4jG#RhlT+f0MxLmhP zXq6NzMyv^gHX&NBDB??oPNkWMws!(55Ts{ctv=3wKSN?rOwzmU#*G_yRKm@XR$=^| zdc`)wrsf;#OM0bdkm&|+AOhaYO_%)?z~I+$ZXE(&?D$tCN|Jev6$&bjWN0CVqUaue zc~oU;kJ-b@qK18B0XE!0*frT}-?1@xIK+7&g+LB-*DwU$ac~Or!>=7dl0V#F@zNf5 zfLf&M@f8FtN&zOa&mM0kLv&d{q{eQp;Vc4!SYP9c%XiQ-=3QMl4tPczL}VF2R0ra2 zD=Pkk&iNDWCZq`o*CNbOD*?@%@4LTZPWE8?N+E=3m_x%%Bg-&M=2mgXzbhOo0Y~Q_ z2>k~4?AwRBlYnHS0no>_$z-OvDjG{mXMi#Z$U!c*{FWc&GL%^ zcj8|mOi}>ENL_t?uhJI%<>N?g(g-}m64mz7L6yNpZgZ1XJvGyvj*DO&ky2RZ?M zGy5r_|81v&wsw@lYQ_tN>OAqPQq)0*MKt6tVS}T|u@1l*)ZEE7xK=6y+ssJ?w3lgfD|guY0^&eWR-FT?s*?tp9uOs4FO>h! z3;&zrczt|JfP=h-?*C0ju=N4(*Z(K!$Nw+COf}QK|IBa z%ODXh3^cW<(MeW`o*18j>8zBcBs#Av(5!621OqgfN%<7?bJd}6*m6>)L9JDgWdgB~ z?WBiHMIB_p69dZXDhDbkbz;HG&qf_Xx*>its3 z>H9zo)(4+U0MOalUW%(6-Xg}Q4POr|XxY~&L;R}FagRN9lT>{bBx;L5+a@907Qls+ z?!ov+?12P|8zSL_WPE`_Pj^`EyH6MQ^$QX{%x}Zu4{~E1P|^E|HQ&7UEx@4Nkr(?e zePKT;ENL_3Bcp-E^%s~&Ub%X8vbes-%HvygHKIUZ2PpiuEX~NnlJS@QMxW!$mx~mL^Hb zkX&{w8k`U^c$Hc7dv7@m`Zn1gfu{nCVxk$VqoeBtp@`i?rx1YHEsMW1R6oAZh^;IV zD(^<#H>?rReT%YrJ@%H z`L-kNR!J?*J3WMigieTCM3u)vsJ25cSNqGO=g_jyxDvm%RJo}R)LT-Z@e9co+vXmn zpC;|S#kR@iCCDf+F)opEXk}yJR;xb&8;atRfw#a&mC4xoS^qd2c5vCbDmm684W>_! z1hg*e2S-w>C&FHfemt`BsrF^gSI3I9bg3-6OjQW%91 zQcqlcB6MSUxF$%2S$FT=ReK<@Lzn8!Nk_P*8^r`&5zR|QC^m=PF~>D+?Z3mxpHx2y zwPRui-=ny%?|j!EH5k|D>ierlV0QZ=OtgPEy?5b8cCmsF_flN=2GJ8KN6BWcT(&&B<^EyBXzmvp@ zn{ad-`FYBp6;+#Gj;Z6OmLOc=l%<7U*PuBBYPng@Su4KbPd!>}DoYaYwF_c@Az0W~ z?T5R4KPooFkIG}D-Oo0TaKZpEVdQpT`gYi^MG&uJ0=tTBO*$%375!5z!BT-tlpYkh zXsjv0JDO74(b3T!`RhlXP;B(8SFbKC)!?VeJ+Wmca(KiE-buWh2>wynQuew_h`QPX zjV%}5^ktXGRLg)^fVC(i#Y%zg-XO0#a6&}&!GrK}>?-ELmkfd)YriYC3kmZk(YUfD zU=+W2dP5_5?eTDDKP4;Uf|fiX90f8Jb5}zQPI?145|hFoJ1fX2eHUDWp*d_k%}kSk zQj$e!BO%?WU5IEJL)uTF)o}+pv4xq~N=Fc%pF@os)l3v(T@itVL&^o)0O9Gkv_jT+ zp4^VMyyA(RhKY4Ry~If!JE2#Myq_ZcdEms%eRS&htQnRXgyn570;`m}hL_OuwDoAT zAXM9~OP9v3E>88P({ng|J)&nUHZ%%ZzqyiOW|))>uVCNOMg9B7a zpYF^~_>5}kS%h>!3ihNn=_E7r!0a?M$l9XGvsIAj775%SZtfI=KE{jv1QN)lY}VE) z*B2vkROkpM1v8)~*AeA`?Apv#4INDA1k8+OgJ@nrS}$(&fQRRp{MivfYXBCJDG&niZD4wO`g}UL zwon`l@-fsgMjHOQH%8;k-yAwC-M7!ywi`T90$fBUMQ}X$f@X6ChLBSxQCoKE?AaP? zm``1_SnaGGv5v~hqB$BF8D*AxPvHWKV~)8ho>y{!7_wil<=PV@~zT1_H;UY*5Q5IiGs8^A{7y2~J) z;X4e&DR!F|BHV8S!-w-mUy8zgp*o2b@@E#EZdhLhizFRz*0z5gsA($X8S8lq0P{^` z$DoO&(C}Y|c6X9~9N$yCW(%t3EYuLD2 z)~`B9-T>mIt~n5gtbVHBsh5-qo-qK9>M-AN^huuMLV9j^Ll}KkCj?+_2Q^Y4g8Vjo zm8pewvZuIZ;gd5o2M&P5xSO-p9|$!G4rqX?ad`nK5F;7~Hty>>t);nnf{8-7{NqC7 zF(XHEKSY7K3&0dwDMd*%SrqD5WJC&B5d{2LE6e17w9fq6d}hxfU^-XJ4RMS(@ULS< zV4sL%3xxA^brWwj;r;BO8nJfQ6l(yQN2nISbt^0*h;*|!3Z&PtpYh;qx~CEdcPZ2 zA?nk8Xc8brtvKn}W9|L963j`C+XX5sjObVxr_>Rt16YZd=>QsuHHeYR9XH(~6T@je zCt)t419W=-I@l95D?VUZw43a1%eEQ5sbTf_ zP!xgFh{JvUj;n_-E|Xd=O!*C{XDLTD8$T;{UFdl%dJ{{0?W~gPNiKlCLI~EW^PB?V zg><_p#r0kXkT%E+SDj(9yWqAtmfONv<;ECT|aYO#D{qwr@{C5Bi!Ux*ihlaPlqm*4s{|s)^-y= zZuYx6^Bp~T}XF<^^g|rrLXhd&%Zmz~&6khaSEeA9?wr;cY?6XNSpdlk#Z5mU99h=39wF=86m`tF^;2VEs!DWgG1*;PkY*BK-QMYF(`}gJ;Y(HTlMKzYo zjiC~V@m-l~(ws4GsC)B(^A~Cm1KQVdy)!g>sWcnV-$0Hc)_y~iFJ|501zqc3wvc?9 zOZY#DR2-)xPw}W(3$%dNm}`z^a6*-SsM;9~N`xHyX}I6etSZPN%t$?8UNVJkIqYP! zXx0~#ZMfw}_WL!`md#u^OKmsm^fS*xZeDnh+vPMo^$YvqvETL!?>dr6h`ccpd(k9H z#BSs#_2$7CV)mn=X}#K|`f}S;Q$tBnqa!=8_R-vJ;pqvsIZU`fPRE)AO&!{l2b)nC zmk7g8yyT;Z#2o5*e1@d;37#L5ZZSx3)y2qnGyEkOBStofUnQ2cT1z}j6e*{fM@i;* zw|f}2l?)4$K{%|Lvx|*vS{Sx@KRA$SkhFBKB5k{Ef@l>(Y%hE8PaoBm5|I=M5TKXB zI{khxH{otOSGg4e@N>?YrBjlnYaj#5ar4h{*?<8e#W94?&a=_asr=;+e_5hqxcCIo;JPV87f$yP*M8P35C`vMRNl^R%r)&?w@ZB9?R3_Ir zYvF_u$I*E0LtXAFcN-4_{TD-`u;I5iOK`RR77?D;*E>I?!uVUUFH_;7?maR%IU z(KhBi*0v_Svj$e8{}eSd+^~<0zfbmdxHElptU_)j#v{&FpRlbKOWw7e?w8qr zKg-AD{rIu9kS@1mliz;3a^BLHJ73TSji4ss;VR&Pou9ClqUp!$GRC9ygwxR|6aw0e z=bw~uu?xjLz)+U`@EeiN6DZ-VEb4OI@t_ivbt;SX3EzFdug)nUMAXmVuEw3VCWZj` zILIUbwx?Mz4sxJzrV83z*h7eohW6St4%KS@NV8~)zn1wUr}0%cSz!fl0THMbsLc{( zl8FPll!{Hv3k>C0TvV9@kDyRi;XUAyCJ>;#LtR7X6WH!Mt4q@wy0);5w`~-VEOJ?H z#x@+AdVzr~FtdZC)EC1-S>+(}pl&!}*4Ec`byaY(8YvY;8Vk4_kY(p__y(c8d5Vw}{xc!F`WGBKG1@0yJ zje8(mnz=2+`+;*vI{k@36`0Q0;o@09J!K}#9(%kR>CR6t?}sLYkP%#m_@64lG4~Df zc^ygzfQPZvL5Y4Uz>>q=K>C7^i#z6upVOib^X*gD@v+C7&YZ}V^hQ7hlXWM;gI{iv zc0=TBE8s^k#T6(A)SGV^1P!tngo zkUg0Ev1o=keSpqtDoTS>V)H9{v(x$IDyQ1gWF0@EtnAHF{dgsGGD;>IA2@|M+3Q)f zNT!+$^-=kfWef)g2T>P^Q?zLNsa4C+)YMdy{K=_RR{&+7ZmiJ|l+Y~eo(b_Dbyx4s z97t>K1yrO&Uu@aeIsZXaL#`b1nMF*dlJK^i8@t2P{Q(`Yy&s6}YP(lFfB?{iQHTyx z=RPE7xIoW}X?+V}J$$ag_RE8Ao=)g{zf80d<*u(7_3C$UB6>PNL>8=pF>ZZ*;H25l zu-+r4y9V4w*5n}ssF$V~N(K2)Y^Km*wqcsQxnD1&FZIzXD2d9ohSkm%cDl+|JRnz| zSuSLl7pPb^aLsfAVr!yt{;t@Y`dgQZzi-gJBWc{0kgGOXMHA|}8oJj3ME~Umk0#*w z`{P1V-+d^7$7g%chR-v8%Ug2p>nbM@Y(sCGQxEMd53gt9TPaGID21s+uhn&mtrRFn0}4;&4jdae$15YLOzb`hf|0yaM)<&;uhA;GD?&+eQB1v_5)Dqq zI@VI+5iv1nw18yRA)v;TlmqXzxUR*xPG!segXPFha!hBvf?HEG_JEeQ#S5$s8$M(H zoT?xvpcgV2kl)&}Z?nD~ilT=Q)n$uiNyq%D^c{#@aN6TX9q4;&3aWk00^4?MjC`?ko6jm0awhHu z2hFzPrJMLs!F?vHY*Sl1ks#oVcAm@`6UBHu_FNwDUI1#fTYnD)k4@ONV|Ql>WIpPM zqGj#{_SRQ?wV?f&n$*NQ*394WZ!hKOsYJ^t+MZ8l@X0Ng*;pP#_wA-+TZ!SSXcgX5 z8cxO?`z~dX6YKa97XHBLq}#C;o_>Nd*PzK z5u?qU72QBN#Z7hmiMM`CxQbc6P1ORVi6Z9wc)NK^wT!qod*f$nF;zycpLQLZ1S|MC zNgDiP@O|PS3uTFYKD{}v2M8h*JqJp0&+k8{7rJ5|Ho!c7*Kny6-zOdv znWa-Ih8xF}?Hx1=citUE-6!E{GMo>id5X8|u^%b?4BBXII{MoFw>vL@O2B-57v+E`jWxP9e zb?5-F{kC2{vrr;pBpF&fdUhE|PtqzTe!j=ZB@R0~;g8T^6KKu z@CS(cDWD@=vU(2XxEvP`RM}f9EejCVKuJpHYEs|+$`4&(pCMT3>zig}%3j{TcA zZ!|65j(#HVf6`m|EA|aFEp_6B|3pm3P7eZyjS01E*K=CcceBz_eJO{DPPQyZ+3Any z4MYVnEhWdzDgzU$>iG#vq`FDE7Bq7?YsGi8pC6Vw-nRp?2o`SgHRYUB6h^f4};GO z02ENNK1{%J9F}zeN*x}Al0;##q1Q3M+!6q*PZ+{5mhag}4jBWGKC&Cwfu?XMufRs@ zf{JOh`+DvgY%g}8QtjKOdrLcJflNii+`3;Q0OjFIrl#)zqX2SDDy1}L6RIby;O1)D zHGrVChIR#k3yoCJRyDS3WISk^MQmM1qhOX{sl%k14oEx=fg9x*7lWcEA%MDZz!`r5V-4p6L*&)ueap%B4HC$|JMav0HWtpo4;wB2Z z%96;_pQ%x+5Qvt_-XqV33g}Ha0f{r|EQ4i62!cM~wmL6DHyre0aP+@57`nN^lO`6@rI9*0gDJUu7 zO|zUGR%h@iVo^a=hUc%Z+d4e3m3m}bogAYd7dnVz5>k;JKSzKzF-IAE_c7C>kK;qY ze`c~mA808`HsT^8X*tFoGoqe~(Z=M!WeA{$X9v?)!=k~3bqe&7?*nJmaZGGU;64-t zd>#+X7k?>N?RL(gpC`Axs;Vl}Gx96Wt*YLllpwl{LPYMXUiCCLp5yzW{%WZqgrBku zRcg8GAA8ImPIl+(nB!dy`?!PHtURMj?Z?U4F{`ddx;sn`o&bHp$~OxOo@Kk;pIEV+ z8OlGW%%19sQ&-TN=8NMb%gV;Nx1rQ!A>w;$S17^S$aN{-Tp1TweLyg*FLNL}_jvj^ zdb&}<GZGvp8&T;Is!T``D&WdKnR!*E7ty=9oQ9hVWoGo1$Kvg8CH$NlhUGk|;H z=WZ=Pa{ih@Fgj2_K$B|t##GL2d4{@oqi4rM+wqR9*g?0UMcRzgAr_WN!$@4Dw6jg< zB%ac}jyp>|5n)*C%puH>Hqn=WK#7L+UA1nnd*yKCY!iI=`^oY58Vpw`f#C#d1T|bl z{?jV0y(b26KBd?WcXpGUCK?B(Ko5=PAm%la>2E9%6VM!ZYH4h8j-*1qIjOz95~6&~ z3JmlW5P*09B7$!(k7rwb_bHC%vg7G#G^~VMq=fWH#u}bVX1Kyq*#|WySdQc=PYwJ< zLrTlUV$~UY`U5BIx+$cnefY}8{_ye4QbHeCa}xE8{NqEj1nLdU4YjzG^V%IfJL*2L z#+!lLgrOq>_Kl()ou^3n?=zSfZj`_kFnOXb^N-gdCvv#tn?cr0o1uVtdVM@O^(%I}B@*x>3wJ#w9?p9Rn%} zuop9LOTi$?$WttMX-=q-Yy%-NLBcVts{nx*2iEX{dqF#ulT}QU#q{NZ{DE{z5HBdB zX$o*Wd(2eHcxG<(mBhrv6ewFSK}ZSdhZv6xeE5I|{;3JiE%B@{=U)7{iLL@_r+4bf zt(l4-8q!$I7o|#uucKC@qM|a83c00fKWobX|6KO#)p+Kn&kx0=MBbtM@J`gJAf4MW z(p@S}{UDB!(tE~w`iEdvi@$hZ3NrDbVs+}CzR_zjA!;5Tqz@jtjcn{>QBmA;?8{#e zpcCxE!))D|4xsSC&J_spP!WjHvE>k~zR`$9;Y82e&~=7Nnh6JSv(BFzq^Mi?)#xr& zMUw_Mizpl0-_Dj%OpPbNJrp6ONdu{K6MaprH0YPRr zbrTSgN!jp$i;c6GDLGJ-77*Op=Ng`E+mYw7?jn`=q3f7!S;WWJc-1?Y3a;FKxwV;> z9AHcW!T1)>ftWDw>Q@P3GTAbg=H_=?2!QT-|G_4Fh(6+npO8sHwww`_#TemLL~v#J z9c>mag{t(S;jyPw8=cwco|Rqs%f*0c3pg$$AWLkcZpgfR>TyDcpN*Sq$9?+IiPq0;*A4QgyEntUN7eG{ww1A7z!Gs*?_r)9djEVe2}>`L(+ zf*OkuhFt21CGBr`A`M$?m+Z&=Lt>lSp8tS~RodvqZj?42j@+cW<7@1W8vOeG2UzDP zN?CQFH18gWS0Y|JEJIe<%i?ylz1SMQ`2Vo>)=^chQQzn`kRu8zp%P*dDkv)57>IOt zDM)vB2r7ai3L?^obZxp*N~OC)kWDvR8tz<+<9Xk2jC=pN>n{0I0DmgB))USk_bFZ z1JXDK(ZXjxNaw+1Vpu@RcR|1)aRB?_d#ihgk(o&Uy9j{$|M=hkzmkBRRhJ`v;ShDc z0nL~Zxa6WF@(7t4)1hKTNXo%n2nT@BXWc_9vSV%|T@9x67%U~EqyU@`-^9oq4OOYG zp;@8@h!cEX-l-cWy3b^S$4dZ1Q3z%|8M8Vtq3k7Uq?Ux z_evQ4-tqqqV2Q}h%MwC&&OnQ7P-=jT5fBg%_+y_za)Bof!pq74DU=0^RC5@zw>*GL z$uQ?cR!~W{d;$6C=o3)=%QERSH4HD^1DHjmlKZWaJQ$V)x=;x;u`=kUu2|s{=^91t z!^S-s>D)HBYdkz#rTGQ0U*IjEC@*XIrvaQq>aNP=%_tC%>PXdjz-Zg;G9?IIVnH_ULN(=PzCZF% zPu8pDXxD)`r0W2{K(ZRZnb%WW{98ARe96-OV1<`l_6(9(I2toqUs1kdU~OIj55&=~ z1im4&@%h(j`F0vHQM+r`*(I;zKH+4t%PeNvKSqS@S>)JarER!AhB3q^gvLKeCh=*% zJ1kfv!L7QW!RY3c(%5N`pJC==?bgve*!a&odnfB{&Ha4w5@ma%nYs4#NL8kr^S|4b zBAl5gb-C{D3~Z`o;Q99=hi~oX*}7;>sD85Q6kUIMEplDMj5RG>mD=D(vYnL2Tn8;@ z(4_6KRm8Y`WI&Tk05ajok9Uk0miwE5Oprb?^6jLY^pLdun~A%1F3tAMpBpJ=`}D$? zZi(*~*}ZKVC3%b;RIpTlVW^ zKHZ$go|ZI8r8`>rcf!WkCL3?m@ea)MoKs)9U3V24kAU|*kIdWc*vibjUBU+roT~qc z@HU7gDnNz3&9D@#>RHoKvE$d1VPtLBaT{@|is;V~#$q^O&HT{Ys)($LW;!%=mM-iG@%K~0Ca*TV$VMm;Y$aycAW&;r^g2gA>zycn zeBjVHcMDe&WkKYPhVwn~A@urkxVOuu)+`SWx0({lbm~C6Qn+Y|r(@vOWp2rO(m*@r zJHqv3a<+W%gH+CR391M}^<1s}NHx|YC$7_0ec5({3t;*ouSp$!e7zk*Id7QdOgU@S zWZ#Dl)&(1u0zJlo@ZT3aOYfomHP*!0kbrJFcw{*ZuV25W;U5M!51EP!u#kGI5eBAsTAK>aH5gIUCUZEOgx;Rtz`T)lHQ!(%3^DQqDiM73niBff`w^I2g~ zmcP3Ewqs~lkziEL+8T#ID4!OtWqR3A)hwi5;P1U|O|d4yb%gHZ{?B$K$zxsfP}#8bD0^`U_WG@O=ksw7=zKK{!Kuz zasB%(>AW0K{*kq#(IT-eJ*>YbqcA6z@OA`7LRz=$nGl&Fw5D%SUr=vOxt#nH?N*%2d@Xag8lwVZ-G`}q3SrpQU{X;9+Ijno!zG%`g z_RmFWKFUi8EKTz=uGZyRO$LWkppDo|!fhtzR&5wS&M|u8_>z2*>anc>y)8FEWg)`` za3R}A=F6kKc6a~j^wfb?69Bel*H3D4tC!RW89g(3!E$!W$eaPks(cv77-X7rM@z;z z;vCg`+VP@#69#wIJJG3pqidg=;btpc z7S=k}1~s0qK&$d&9%` zSxbp9zX=iPEYliVv>CBH^zOm*9nGzqRi8qCYm3p^EwIVos(C?BRO|ShqmGI4Y*Nzu z%9D|NlJ{>5{d&Ilw&pN7*-L?YTvlTb`4P~D2O~XwpB^A*HS_}b|9sy1GjFoEI8vbv z%^%vJT3^Z~4e2nCOi>0~77JxZNVWqCVIZ%O1lcTS2*#9UOM@mgY*{auHT24#3+VXd zz1(koNlX1yU7wIu{ZpF>ZJi7OZJ6$>BYu>Lv1#w)7U1$G1IpN^G`}sy$#Gyts9X#>s|4pP`~mx{;HQk|IPB% z*Z6No=365$?}P7qK*^CHRH-?r6}f~O1(6}Migz|w@qiVXg<6eHz}Uy!)%!UGd6PCk zT6{@LO41mD#$htHldpR1uMHo2txi&7lp}dKVPwR9=|!Mi%RZ41>;C^$45qRo%o~3RG{zr+D zx`m+65&Ks1Xq4Z2V}vW_J+&*1{uOFRNoWo;*S@9!mh{VkUmJAkBQ86gW*}D9Ncp4B z=rjtuCWshrCOuKSMj2NAmPmH<`|02SqOA3%!BZOZgqN<45igFj6T+_F77SVx2pc^! zo<88BLA_oZx&D|1dRpE%M=^J{Bh+#6_io^Hme%7-zJi!xf6gg6;8e?3m#kXaYJv`p z-3dRcQ7~D^F~^&+Ti@YR&U-Fl(ja(BM$z}UeDG%(ML&UH3^O%l8U}|)jUSDWK;uWQ z++XZm+Q)gxX3cIFxLMhkSK2&Hm}B)VztJ|mtT_Q!+uHwu5zKN=?={X6##BPA3!W+G zD(9!PD3GAY8!$7XDUxU0Bbz>Aqfy^{`LXS((@ga*Qc{RXr3v0VJ-Le%cVe7oDwiMb zmdt);DrXh95gsj)*gieA=#m{|;B2a#r{HXAz~dGst0lpYA!pdR3)|>7ulT(AQrj^< z-T>o{=Y=^@yjQODYQw}A#T?s0&F&jy8OHJ7j-N){gi3Lk$OwblZt>UI%q|&?mFMW; zwpgtqS?f~InCO(=<|RI=eCY5sF{JRp^Gfu|L7<0E=7MPx1E-?r*qtQ^#{-+4AIp$k z>KAIedD-x1=n1qox|H+bR6l=xp6$TX^j@bLhNC@m0tsY4lDXYal6?zJ-5(6{v{&WP z_;C42`B=Gl|dc-zk_EX}&>uo@y$wSnsL=jJ5A#(f&4!rKG<1 z9xbZgZ2e`k{e_I9j&(WGclFaESL}=55vR^jk)1Wv<)t1EX8HnS9Tm!KKg0Nt72APB zF)>$>%_mBK@mnE%&}r@2mD~I(gVvV>>Ssf4o^ib^^Hyfp`oo{{P zpIyn6>iY)}ev=zW06RXJ=MIZ;VvymZyq(c!!(9L1J6ke&d|k78?PKAWfhVEx z07e^1{A1qhFj(i<95AT1p-y<_Rw;X!ff{s@mQOn$vd5801p`;oW zJDT9I@m6Q;9OnBG#ETUw@AKf3&FNn3d7HOAZ@u2ex=wqOf;LX6ODsjP%P=pA%FO4( z@ugM#Pqg7BCVOG6$jBpNPpe>L7-z;8qw9O!Km+TXd^<1YteHhq>PH3O7arHcp65_v z&w*TQ2kN>HT(VZ)X)=6Uoj7RmB#C79Bl)79rqE`xo9xx2NF=*9v@> zyXSk}RWoLrS==uw))VhNkJW>Pq$$Z|ovG3q(1K~g=)$A^T+%F=EF?Z_kTzi96D;~z zs=+>zDqeE2?em&vmRhbP&^mlpwB^LYKAG}=bPpcg+EvTn&7^skHn6?DmGrds`x;5K zKKbVxZGI<-mv9V6FxfD13!H=$-Qz#(zqcl*t{dWtcl%)u%D^9(#M=d}Ge8?6{d|? z=${Ww-d||DnEJ5ozLymF-b=`BgCLSRH12mn1Ycj_!lu_1Wjaa9R#fqd z;)>?+Dj0lu1Eyi64xnvUzpLqprNEgPl6f)I4LH$VZpeq(zV_c`4Bi@KoR8~Z#E=W1 zJNu4~F44HT?g)npTXn;nK$u;n%3U_0$l1CSn@E)b@7!l9G0FuL$m_o^t6mlj%h$)2 z$t_{?w!vkfZ^&2Fx!;wpMQN9!`-<3K$Q`YK8PFb+x8M>~mLsLCn2&*B34PQgy;^Y<*g@SR z-y2^X+nSD5i#Mz6Es`>-J$)M?EurT&%$NI`84Y>dn^g*=eAEyfS+X1j(kr1z$KTZyVN5R`N?eRcQ$nXEhCe)KK6K1?lReDCI% zWPOW~$)CQqr+3My&%_B3Hza$ETN|q-$GL{m9>)05BkmeGtI0Y|hr^rvSs!}d+(i|g z1T(K4x2`;`v@H&13!hKNkJDX8a@VWMyV=gdCg11?64Uxq z<2pNhLYCL@SwXGgo>?y5cW<1bdCxL{k~`OGsaM6V5k1{fkimNW44~)usXxa(;fDUQ z9}hn!I()#Ho>%mn7IZVB;^2x^tsjuARIv8_Yq&-8t~H; zdn`~ko=ABsSAM>oqi$A?Md{w2#F;}FE>?5{57m>&1F4MM8vHbIIyU~BH(RDEfo6AF z?9501r7aFrbXe<8{vz8l-ct%cKM3s7!I9VS!o+e_tT@idsrJ!P7dMC$zobTZkSMFG zBw!||NPVq(?1G;-kK5fXm~w*<_8+>kBqbwU1hO>G!>)OwK&E&Pn*zg`3YU+#@2*-^ z;#46$F}PscdoM|$>)?+7NdgS{A%qXFJp;p$-Kf^wO~4@)XCd&}i2BuOBv9%3?H4q% z2bRlmQqP?b+A+ePyiA1oegeISF8`um&$-q7nedkbx%-ZC2|;b)zaC&s6-DhDK79`{ zLys{cwE9fj36d0cX^Lv)<>)G@cl`dr2pu91`K*H_-!bggH{*BQI?W1_Xr?ErG{a0?*q`aq z-9>bn+6qpcUq+A6P=Iw>pu)N$Nw~Z9Dc)CJBR#1BXXWX_o8p; z%nOw9>VCzU@>#06K?~noW@=S+j!2i?1|T9top`!Nd~~lX-p%Kn9+~}PK*grjmmO$c zn0e#hn;Z%O6?rjau63K0(`a{iJh4j|I)_Wnr}lk$Cu)6I5pGu$bZ^%)D2PFR|K7=S!2Y(Dh15vQ;#o6ZGm_0q9@@k(mW{Ijn*ehvR?UEwxX2LB{Qco7V<3Jx zt!c}$o55v76FR{k<4Au(=FP4f`Vs9CLsZcRi^*-^~3ZyS8TGH;RiMumk^24*X?v*maIO8x`@q zA1|{ct-!wK$P*Kr%ZgSVa^KAJg(N9-Z-tcw?$(pd?|AKV8sv-x%JI*9^WL$0P?A!d z?8YwaLxB75b|9mi-TIw(vrQ9eC`pl-^|s21VZ~{x!o2rSf_p@ah>YeA2v<0-WBs>G zhd*^NB(T6#;2lMa3(7fR=a*mdtr0C*jg1E!+-D|`gqEi_#3IN*hYd+{#kX1SD zp^x4tX26E+9#%>-(c-MByx1$zW3!+9TU&3jcWrxFudL#K6aY(JDp+oy$6&Z22%KPX z&b%j8zQ?$w?O{OiXBdlcXO}%a^%~uK-|2(<@sdWT73}rL1Q=NGm=*sMbiNd;ZiY|N zepHUET=+D)TbFJ&eerE@n4S>qHtyKt3MhVk$z#Y~){eL3k}^8AL+ICe3f$Me@U=f{ z#5`f|$7Gecc@(N$I+gYxO^Cub{Mg)oz`{;I87C_kn5#ibCKJtHY3-1)ZsleaL!0|^ zLxhgx6MyQLwVfF+)2$6(7V;#2_;5}~m|TY0Hccg??2_}%o{DuMjf+6qIQIbvrKibb&znKV3zn#%QPV z%gx(%;lmr|k$?1pH7FQ3t~_4X>{Q`o=whFRmK8U6sDRo&>io#B-m1VV8ST}1(~s31 zZP)U(vmL3GRA9YPlfQ%936m(WX`^3vi+JDOKcD9Nb*o#THy!Ma;`SnEnAqNjh#kTV zdwW1T=M8Vo(E7CzY>(mA*aZ)62ff)D=cRUiV-5DfPlM>4GE__P@1wSL;?G*}Yh0oI zy_o7AIWwDa%b%R$XUc61X7PbHNV1Xj7{|p#V&}zre;y?ng)xmuhC9x`SM-hLd4K-9 zBm;rX4(OB*@6zUWj$Ic2{mH<}5lp2y8^|23reBsbtMR<}m7df*Cf0(QqOYp^joB&;Y_(T?9r_(WGasj<2{@(fJR zAIcp#mRVK?4*fGfZn(IqG6W?SfybT(_| znfQG!n5+Ib#FnIpiJ^_l%luuLcTu~p@wl}9)sT37+M?1>XS&RTfgz55olS)7wp-Te zeL;#>nvttCkVBBUJaLYPZ7amzdA!7q{u3E~9p6^jOw9KCTOBhyqK7fGW0h@uWouo= zlIpo-5BYeQWdgLC1-vnsLce3s0p(k0#ju?Oqh9JK9`cpJT);1Cwa&6UrP{ya52!}QJ*7f*i-$b*4 zo+jm5KSFKPV{5S1<79$vF6Ff!CuIMJpEq4?o?hycGr5rxs2bTjA;LMJlmD; z+5xs^3F(Mb-t}>6mZ#@lbWcTV=xL25gMX30OobJ z?Uzd*vdMi|sy+~g)ACll`dJpWcB3k%ZH<8wofqB&^F+u$sXemm#t!FKyu0&?h-4{; zT?B-{6@;(?te7hvU)9I9eb(j(?)8aPQh2>*<7Q%K^z$uB@Jr;pr!IB`b%ad&uy2di z1kH-f?wcE?zsmZRKXD8rB#iFy8Jq=QOugWbR@@P85G1&@0DCiYbEQ+r%UF7l_{htj zHSgMY%Tp6sB}#XMpeAiaY*CTk3l?=yLNW=C>=c;M?riH5jj>iMUg;ivK4TNq{nv`_ z<(d^8uxyT#M?UQ~-GdVx7nsioRv4x2b(94gE0g3Q`(An+$i;IYtVAChwPhP>ua8r^ zqf0tHx=KBx9m1w}5#5oc)50W)b$i*O)dpBu^XJum%0z#9GEK&8A8f0wEdSC3;{A9I z*^mO)p3w?}jLux)0r8q}<$zWXmt5KKep%#ok=JRea}&Y0){Eyk&i*1@&j587F! zP@OSF&c=}my~TjZ(wBfw#`V8+m6$7AL>}BSjV-xl7$@YRt&Wa2z(i0bb|j0!pmV_- zrD>IWA1DGq(%1~CP5H*JD_vv`Ei?pf;-er!5_)cm)czD6{YEpPOH*s=(4WKX4i zYJgG-=G+{2V5&%>-c)@aoK8tWYW!2>0)>f8p#f{)RD@8>pG-7g-RkM2SIc2+@Y~$n zn%a94KDBUy>c>cc12M`*iJybB#HE{55~{fgl$e2=_os)|G}RRg*kN}qX^`Db5AUql z?|9_@x$F^vFyk;s?==ny>~Qa=GF0L2>s=hHSL0Rbag3C zpN;`&)KqrEtqy>5G|)L#RX zo^gM$F}T=i&5N|D>V-Wj@IIi!F}~Y-0*9LcbaW?#$rZmoBDPx? zR0XJQH&nnhU1@y{XsD%+8Tft^?6mg&R>)>tJk*w^$S1x7BufG`8)$O9w+$GaPH3y+ z_-M1Pwl=gLk^&KJb^s)}@Uyw+YxFb2KR@~D$ATt!nv=c8w>Y`!GI1OVoY6aR@$tb`4u>%m${(tel?||6 zO#h9vgpq+cy)xuS8GFqqrOSsHLf1Yvm@Z?<BHMZBIm0y%y#cw&x{Pg*Agmj$kaU zZIl+;DBzwi!%5V#oE7ime`rn`%S^sn(PIITwnT_Lc$lgB?T&k-xvNLHz5XW+N?htT zZ>mj@3MywZRznB}itSRJp!(H5dGgJ;dcG<5bRNL(+4URG4?%TU@zS>wc7S;ai>`3n zwub^^=|~>Cb}02GKY!lwQ^g5PlFACvo3-|k)Zn`oHolGp`Rq-*$F6!EF+~(?Aso)oKKDpZGXcFd#3j;@of_Y&a1ad+6*aywT6V}!fI#5DUc1DOFZ z3&o0Z(AlDK!pslp*ELvE4!N;g7Brg%(98ICNaG<-Kg03zuf^-$wQ5&^ z)_L_yzN_ms8juN-T@J^L`7pzV3o~WTQ{(YGpCc`5K`XR{mklIq4*f(Z6jSQx+m>Ut zvGl9>cc6HqHp^nQtG$#BZ~?m1MZDh5*;&x~?`#&bTmif8mDc z)=vuj1x!Tw&V3)Ph&;Jnc{y=5LaedogPes~2^+9hi7LO2+&d4gWod1!t;P7GBO}dd zuU@$V1Pr~p?m_k}vj|%YRL!hCGG5jJO>#`2;9U%ov(WsYkunJe9n^|q3v_CULi8gK zdnW$8d9580qCUw3Vl~^gX^D(j(<%nAt}Dl9d5^HuVl1+lRzEL9MP$^fpHk0)z&yW= zWQ4hx&I%)N*GrKK?@TDh#kKW{#In^cLrG}lni`b0umvEEgZ=7t0F&N5C#U#ka$tz5P&?u&cUVf<^$^}Ewdq(%tB zUlgz5LARlMFJD4pB0|l>&|51=E48qfjU$Ie(rO2$9VAAwU=-*^u*16M3LbO?t$umw zWbp;&Mp!l3%jl`_or0b1tp{s;{$jVu#$28?k93WNMA}ymhc~+D>%14?{D&7>ADp$X zOIU)|X|mAbQ9Oe4)4Mww&_bTEObHNGO}c7oYRQxG^72i(yIc5{`#8*K5!icWJTB>v znl-giO@j-B!P>ODI9m+n2^kP@Co*cG@2VL0DcOn&j z)aTD9U4HQD^TiRUjqZ*je|6^SqtDc>FfT}>0uuQqXtNOiUG>oIR5)b z5Yp1y{#S-NV@BPg2K5qya^=JYrJcaD zitMGz>NXZmV6bI5?WprZzYgeQtHBRigNT^83!3;e9?phgrZ`#(C_&(hpFJPW4AMjq z=>!a!eJo(tO`ug&f2mUr()%BQGCQyQU~JggK7?^#rjd6ailZLcPJd{#l)}N@=s$yO zf@jXlEfUO6E%SRHXd^#;sTb7LXD4)XixVl~=-4ixZ%0(O_y(81GL%9=BbUZ;XqY}H zMSzjI!B`;^;Fc}H8nTe91H-hzHs0m!;?#BpqiDg~`RmRyE8(bUKJE9m3qfa%x-zaR zF*ESVSge^R+ECZ_hJlxIZN#~HJOrAnee^dYw6Wc;34E!WCw9+y7WYRi?V_0p7<=&P z*(r9fSO^WwKrN>)T6#HkskD@)$X{c2+Q9JHskNXcnOgYq#&+t|OWpccifg#0d2%w^ zX9qX%ls90ec)lF!1_ipNassgE~yQ(H{d?& z`F^u%bs}EdplF(q{)I;1Y4b)#ry2)PZ;>D_*7xRzSg4=^U0{k$O_5#|wCZ*u?C^09y>y^2Qd+A*5 zMPgrw@rN~NWGK@eyD~2jXj0qXRjM=(2Vff*)?qf4FvjkhUg6qvU^nP zv-(W9#o4b0SpSan$<7>PZKLkswRxVqVapJ zbbc~JI&qdOt#`B;p`e?MWgv#&6IBI*IM||RYW^aJN*1^&#EX;`5CRaU>aE_^HM0_mH!usVq<|qwe*FA@6u9_7gE;DaLi_-0x(X;9st!(H{t3|W z4RC-$1h!m^BZEoq5T{Z~Gq+8u`y;L}o^s~Dz55v{!e;22f_Q#ewl6;0NdQm zr`Nr!$ruC`eQ|-a(qnSFVH2W1E+%jfHDDZ#@BY40i{56u%^}T2| zT?KsPN5g|!=Qyy&s4&q%qyR-_&8GTVqXDCS@G+R07aX-b)1ChbeFTJVWMqD~jz=?h zFisDos{oHu^$3dIG*&=)RK^Nn#8c6VTNM`F2d4~pwK4~FuVyoH-M^>!Y~n14s_8AK zA$O+kT(v^EEyh%Rh1coefOz`7M3^xNOd-S%?>M)3?M^D2o={+zvVjtyEWxY}z3oOyK}q@qUH{k@SU)XK>RN{A^p@VPuWEc#ity*ADyk2SH;{xA zMmpQw;?veU$*ykXiBLs^LI9UE0Wcly&T(UbY=I+loHso%Fb~q{l8^H0tfT*;OHbq(mM>6b0e{FP%;fKV|$xECmT6 zW$_lXb?S!veQ?u;Q#gLEk;0lMAOPYl_$_}kJK`$WY0#2^=Az!Lge!kEZZFj|w= zZh-ZbAF-Joh@?VZypljFQ6Af@n#ftMZcuyW!C|&yi`c*!kpt$TMPx^lq;{$1DUXOR zSA06c6u0Um&ARd^av=7K$Q>>kDI_i^8BtN|*X2jlwsA-lqOPbe_jEvZ-Q0AJC46o0 z98#s~*+1%8`F62v8QOt2AQFwTz7)Fp z^Wwe&yM5M@s+oB;fvk0D?MUzlG0hRN_hrr%FD=>?Xy%iyg2Z~)yN?1+tepmf$s}yK z58;E~6lbTalEv9g`h4jP!jw+*yf}10f&XsrU%u;$3{4%VV{R=j6G`t?G&44Qh;<~` z-0njcfCPmrC7(@F%ruMAheQ}DuWtmh%6?(yM)h9QGsM3zYrGFZ)keP|XDQ9y?S3W5 zFl3>64Uzc_;>~N~qFwER%-kLEMrp%Ed1d#HY|5jmC+gYeI|=-S5*fnmRY;zk<7}(A zWpw4@MZ}7(bT6GbiQyOky{Rt3(%R3@_$i`@p*-Q$`dejC!2j)D{$|WpfViTP))xvF zT-c|Xk>uy;PO!5(XMk=pKV)?_-u#L)S$$5!)SalykEljhRcmzX_M+<$I<;DgkA3kC9>~Jw@1?Zzm_m0 zGF7xbedkZ{bhs$K?3TG~6MFHU7cfijlc6Z)9PUC+rWQXWM7~fSlZ7q~4xN)pH)LVo zGK=Vflno8(XkwciSr-J@QAzX>2?6(Js=9&AYqxYNyi3qUQXH=NsoG^R?sC z%=&Q56|Wuk439w4SrL>KDo0{r%@c-ToX&8Xf62BEuu!r{quW}+n{_fJzxylZ*p>^L zoVRi)y18z!36T-n%FOnE$j5;UvAhe19TU+wVCkB(6|plO-n9+Et}g0W70`E_@G-3#MO@(xoB$Yf^$V?+KP2Hg)xDGALqmXP^;o%xwZ*W*31xzB*~tt3NKWKQYD+Hgn#z3wwX6;K#kl6TP> z7;^9ho8M>2rxD%aj@M$cm#Q}OUkHC@ei=U!ebl-fXyw>hI#L=cNv$hcRWHh zAHyjgk*qVO2NCS3ZW@~Rfhn8Rr_{1c#!P0qw3b)aSMv}V0S*dKd&E`a^Cwh$A6a$`_XbF2cJKg{(qO0c zZEo{0xBeAvKYren*|$m-8?gTFr#VvoX|Lz1mV3DXDmyLnkuC2qB@x)(B=X-kdqeRi z)v1*GJcStYR**ya@nP^Vp1MS48VMCd9nZsiaOR>xjT>}`V7uZ|o%}SKr6+C^NNHB> zLl|48_mu}n{0ejYl#cn;Wg*>0v0Sfx)jGO_7ZDKULAEVsF>`V=`gpr)^2 zCnYWRzEs=t;V0a7NI9UR3)&@svUe~i*>k~(q1rePOCA65(7zXT&@?eAR9GdeWzSps z>IwU3Hf6`cy|xjit1u!dzikpMhcnC9F`Ip8w1fFXabOe$MJgxljh{6JnW!|^Z8MWS zc?pEK8Y1XE;zmJ^bwk=lHH#zfY7_3sLrc&9^a^O@z3a(hL$0vLyhRyNCHWLc6niue zL`-EgQ2VaF|M0%iR|*rw)IbK3shIDl|DM3RiE~Cz#KO*+^BC5eLqz5r)`VVL8&zs9 z2+awy5&C$PC@t<}R{7f#hGef%B{lv7Kub5MyJ;mpH~v1J<=xYs#)g1T^{`76C%hlRzCDT?b$;u zXpytGnA+7{Xw_vYNjrEC6?X@=PuLY7`Telshc-&{c{)h0?iYGii0Z6Nb^BN>+2UIM zjj9JrnsVKn=h~}(pGXdjQ!ntN^&DkMT5tT#)M?N#KSu&WDq%T$uMI>nrQMU_4l`NO ztZ7#1Nv9=gUEifB5#=K~2E>6$gj6E;rcHnBywcoGLKc;i4KD#o!5b%{E+1MF8_DQ3 zyTvklXj6z-@zEY_*~iE5Dy~TOa?(2fb%5x_!4qno)wVp^;A~PT=rb}{5b&} zaPD{Dn!Pym!&vU-H`ElsU~W0@Lp~mI%O-=dkLdjuqtliyd8n_#!B{!~9Xk5c50ID$ ziKSSdc*jZj;K=UBc0pAn;{UI8D_j>CyT56j$8Jteo#1;Uh=P*piHR00E2?sw`o;fO zLZ{bCt!DrtMMzgecEeVod-v}7`TEMj*YPkVV=--#BxWaV(CMLD9kYzR`v-0kh~j-N z7ZpoV?n9Ri!Er~7R4wH2@M~`;x(mT)L_|91d{mc>UU!YClWL~D!O5u%gHXP}@B=LL z-A{s&4gKW;n9m8pwXkIw-HeyM;=B~tjH78QYtG(Orz(>- zuIMHr865W!B`x-=-t72y?^4Jv7^4Sfps0$ZNiJ=}2+JpDU)RmGAkEofw2{6mRKz$> zeer2jO+Yd@*ELNb2#T~qr#N%w3q<~A2W?vNnz?C6;vq8Iw|*#Q&XoIoy80uQ%rI|I z9L_loE-06qzlJsZXjCl{Iptbd1EjThK7uXj2+G42PJ~b0-OHivT1sQ1L{6p&6rcO9 zLf_V(-@nViq;2R{D&<^;%-o33DvOvYuM)^wcgClFd<}Y-mIln*+6i1D+Us*0_YDWK8 zZ<6fS3i5ZybSWvT8$(&8oztP4$~5%5zY~&jz2Vy#q0PSVV3Ts2%tCHM$7C0igZJ`- z_YxExa+LvVLA9w05ImCSTc0DG>sfH~POx-`!WuTVu=%VcS zp$*;!hiAr>g$k%0e)*6);&-Q;h@cGMS-;YI>=y+7n{=Uzh&aDC%?K1=i(>Pj7b>j8 zq78ar+qAq9L}n4O!Pq>cL#@GO8;ss{2w?8YpEbrWFf;x6KGDQBT6zp6T~Cik{)91X z)aw@%N3u=?2P+%CLvs7zGyb)sJw;aGnb1?UN!QSDI$Xx*<;&3ev@|kFuL`8I-zs#! zFwF6cfZ#t3C9LcV(Vl9J3~CQ9 z$7fQM%z8a4p8QsKu$G{&bw_0S%U25gRg(~U?;Yt8SsZNFilnOVMfl&dezlkXpB4t) zX-e;`wMM9;PJ|M*@P;OMR|UvSD9FhL7M-hu45gVv3TE#S&FUTkd zXbm@Zmx8d6*-Ki+v^d=6)h_VAUWDCY;vcuOdSi^PLPpkcj`tsb)tFb%5ks!Uz~JJD z3-gsLV;`}4G%kPUHbYinaFVjIaf&-M{*rHQ`RqIMGff_o*a9)F>6T9>N_DrG>=nbQ zoXgdjVB`bp`T~50oz_za;0j$9gZIsP9dxueJ+!G4XZs`jdELPZka<3GPo=D7-yScI!O+!!o2OzZSQb7Nix(B@5N*kH+L)VzCr^+ER8Y zrcz-uI}54*-mr5J-N*?MozGONVEoE#WInO`%$zBpphOgjXF%Cs}^(Cgh0#!ukzNY7TAX6|~|9RvxW zpb!VGZDoJ__%X3D1R`Ozdyz9F(uyk}K=-N_o6!?O#dLC0RJd~;>2&mTaNhPIrrK>_ zFo_tvqy|nV79$uC*+T*v;t$_es1i)WzFS~rST_o-n1&R_K(`XZ`TKV?Xo`=%AQd?p2N!yv=wpL zn6GkMBCJAqe;ZdLS82u0WMn(?kbrQb?|?og$Jx5Ago=FMykQN1-U6At6qVzMYKvOj zOL)LdfaBt-R1PAvleu4Jh~D{Eh&b3QNI1Sn zCA`9hb2Vg4|EY8_75w-W0wcL$-iO=;kf6`M*5`9~l^o?$QkpvKgeDJs|Kmc2;vbtt#TFPJVHrKG>-VnFZ2O^IVm2zuCPX-bRrTL#< zP}hHy9SmPflRl@HRN%__Z|9`HpJ{UChBrP){f3e`@I ztl<6(Hg5}>wIX?saT{vIw%6dg;^Pr}91O6y%z#tKx1eM$NcaJ*&^7*1L& zR4@~?ZS*T^XVseI^gq}UjOVPs zm}cU?J{6B{!#`iq7#_{`)?-A_)NHk(4B=S;@h+hvYo18wy*I&OusIa7!x^7w7yAeQ zKAQKFcHHxE`8+F+e2Q)9_3q+aJ$95}D+Rya`zJ=je^W0A@mcHY+`W;UV|g99JaWdK zBIzVHBR+2cA@`K&&uF})oxHW?!G%A-fs`>X|K6K?PCq@n4)BvRk?Q?Sl>vgn$d#i; zsnt6!IHD%4qI$of!7mvLWl0A?$I;HE6h2SutS94#Y(}bdAY^7JQStC4!R7!Wu7D5h z7WZ>y$=nBY{8`Mk6k9Z@A-SjI1A1raD0nF!i|{TvY>*sSc|(Ik0*B*-bg9MH6D1|xA*X1J# z}T>wOtfO+*QDkCipol1WZAyp-Y$ejWK8hBQ_1?GoYxtgZh|UsYfI}6tWE%}ao)o~_u<(}45wcap@Y{2QPDftw+SQi5| zXYn+$0?x2Usq28!3|<|(?3pt&4yJ)!vzkeS3O4)EnlY2MHsn{4Fc_w=oTrg2`c?;; zCa8*}PSR#Pc%m)xM0UjDI~!0-RrktBA^JZpguEoSNj1^vO}_egYJX=dnQ|twvqHka zah0lBItB>~VY!cAk|c7!JASHtWA8@_%~`|0J1Z$Ul+sh7Zr~q8{m{g*#Swj8(j^cy zT)N<-$U|i9XEV#)dcP;(b$XKb6o&=RUdzp^%RofM-MI)LN2`;%1toC zY5(Pq{zRxQq>>@m?UI*#LGSNVZ;pIOhD=ZD)Oy!bovU7mH!S-ljRY(*nGe9~mi>UI z0yF^|l%99`m04;sY4(<;pi&p&>QJ`-ZYweF#w1(}l_Zc$Ax*7@-BS=kcIhlc9()9w zTfHQK(aX-UD=q#m{!gs_ig3kK7szby=A898l+cZC(%CbE~8yWQk> zG?G`hIXIJB1u>5cq*daGx3klG#fQ{qz4Q-ODhq5nnk~9oFjaPuQ*C^es(xxe!{REM zF8}X1uXfzcg`j5F(}jvcUuXKREL8q~ya_T%W2HK*%o(>z(On4P&@vkn7lN(TNwB$q zI-4LoES^Tv0WdDw!Z_b_Zs5_Fxn(~APx*Z7$umQ&+?s&Y<<3yL{&;+g@%k06%D;gK z$Q_3Rs>i~gmpvmkB*UU<<~H0=%M(aXK6~7qN{F5X5GxwJNZH^!yk4b)6tMg8yO>4XGS+JPD?|rEf)gPU_w(mX zw)(0=<&f9@A4;2e4NLb}+d0RCADSuab{usg?o8Bm`+GbKD()YSNj~oQ^Ps*lE30c1 zVN}=11Dzzq4I+5d!hiQ)5gd}2mH=q6rzTR!CS!U4V83fJLwa+_zSoc6PVa;!t@04M zq$j7~e8lkcknVEt7Bd-4u#eL!NlxE+OAIg%_|Q(oW0j_7DN53p%_cipm0!beJslu3 zGsOTC;pT+cSD#T!~U)1$Sw6h~U z^2A>EYa#%DK)}urWLoI~?Tmn?|6bgrnWNm|3fj2^xaL=Av#a)EA$LnCh0!vQH^$h>3{HjWGwF~LCT0!Uk$|Z9L{R{vG+~D2cn7RG*xjUp(oas=e1G>V9LT*;N zMI|G;XTCtkLA&)aRGs{%)w5TLnsB2FSN{@?OJ17l50qF(89-FDmI#`60+cUpxy#IW z>Q_Tp-+>sdw0kK`GPDi!GH6%hw_BqAKO&&A3`9if;5kaPfF<4Pe%VhXtLN%}Jpnlz zOn}-2ToH@X%r*c}5DbSc1jr9ExeGqWE|1opHRFzhY_75Ytm5&mBmrMEM{*E&oUPj{ zRz1*<0jUX$8HNX>Vkr%hg(VN<&N1o7^6p=%X+&NfHPiqKhaB$T5MGY*LfeTPCAD$D zpF7Xj*`q?jQx1egX)9zIY^t{a6oKPvZBbqv`4j`$-T$&}evzSIHq|1TaTDQ}hPESg zFOQ0E1eL)S3tl{bx658qeR;ictF zA0D_7mHx=1-)24CMZ&)0E-c1r3-iVUf48@OT`Vh@p05CjRyaQmgAS65ufXf|y5v2fy0oUNM z5Ps;(JV?p4f;dCUG`b5d>YY&w8WCufHpoF%h7GCW%Dvx>aHe4?Dbr-#$o{(uY-@I? z0WmQxHCdWYRX8Hi!*F48q*`~{v%h?1#{XWo+i_Ev#72Xj zSw?q@Az9AS6SaaWt6|AEt^o)IDddQ~>66wgubkgXe=`;J2oYT@Vts<`iE9dp{$%*L zkf3(i2;;r~v?eXcGMRh+fD+JW?lbatVeDzET&4QgayTG)knFSTw0Sy+t#}+Cdtf<4 z0Wd@6klTkW`Dz1fUfm=|O6+8DUSL30-8F>bE59oFY_2y3qnH%?5n+Ej%@VZ(rPl%_ z5=l>>nk>#o07#y??!|PZIw^VOD*(*Gc343zPnz#)feLCl90_5|V`qflI|}d|6C%hS zU%EpamHF7|Wj$wFz{%sSpWQ{F8mRi9{D#e@XRgaeJ1w_v82}BXz<_S3Ify~b9GQEF zMOXPzVO}uI;N7m98;YL%Uyeq4Uu3*XBJ-zeCsz&DyDAX>y#{gM1B3k#vd5l~cUbqx zR7rI(P|e!6wMc*GpYoqkS69=5pvWG|JotZ_`^u;)*RI`VVgV`}kwv+6BPfcSk`4<4 zB$Q48C8eYmjf$Wkpj!~65eWh5ZbiBqX+=VM(ao9nv%q(M-#5-Uf6f@^tTA}Eud?cS z?t9*IUh}%Hi6}sex9_w*zlT){VtXKAkHu-MJn}14EojI0HAY2Xr!M+dP-CDQzv{|u zad7`rAD=n#DQiRJw12QW8@>|~Q9|~agPDeN7lIOq51?t0QXTYrzd(%har`G@?vwi` z?3S0Fk0agkvy6CG7qV5Wbtm%GhdDgn@ya)odRpF+kD-&Icvk_j$IdbHehklKh??TA zdZT;T;S(DGv5(Pky*Dz~qUj{{Mxl;l>+=)P?u~5_bKb6}Mvz8tG&rMesasqgYw;Zi zRyMq<9xeIh;$27zQA~tGQ@Bve(aa?Yx*7jE%%jjP7uERBE$=U{z6j4{r4wr2EhGs| zY8y$F59i4qC3M6WbjCp$_dD3HM$n@=D`EbSY{U+H^M#wQBX*Edr2`3;MIY0*q(mxmS#v=tvoOnV1J=?9W_X1FCJ+9#t~yo!9J_yqMr@xWak9+p@t! z_>nD_dgE5*jng|g&yZi)n12}&#@4UT)|$J7`Scd>TmmT<28iRRgUfmMSy)(DHD05^ z@K?ZVTfw`x_2XSMukJ38dDG1Y%9;xoCbIex^Yio50xy`I?p-yA6wyfU!D~V{7zi1! zvFwTTb~m96ToqADXw6GY=JGIzz--qLpy)sWt2l&meJ^o0 z0;kv^$>wGb&;+c5=`~)6Fs)oq%g3O3q3@G_k%qAs5`9IDsitXxbQE{?(5N8c&89=n zEg&@+c}a>C%Q1DQpK*x7bXdM&t(6 z?;NOBQE1lj=glJRXqx}3=cpOf-ADjG`XO^b!`RgqVKN@?VgX@wBCB2{vG`Xf@P(kN zwMq!DpufI*)#zc-)!%2GS({AXXVtY-lOjM0zTnGS6F#(?pfUz40K}GwBYVHYfYMqW zu#`rSY<)!q-=Gn=hx-q4UX&JD{(z>ZzUAB_B`xI?0jXPS=&AC>Tg2JetCaXS`2$Umu^u3w%fbL70xf(cpz^m0fjxa*%WNqT+%SWi=2O$s z(0HtPn9XB1o>E??9qlqkPZ5)oFfzk%J)xs0$?NX$=@e3U^o9PtoF!S>Y;CI+Nb73N zVIPSv-n;{PS!@@&(9DdSF@)>H;{(GAqe3ZRpt_H;vT}@NLBtjJz|x`a91H5>1l?+0 zkSY1$`(Vy%&V33q&dpV8uR`UNa{S_yoMu+&-TJm*SQR+agsDR<1U@R;XV-Eqew;Ms zf2C8Zk(csGrQJInD+~x1e4NO>fAf9&Hk!AWa0PsG8~4zt-Lnyxe#8W0&k=E>jOL{P!L^h+Ue&pBoyqF>55(J85}+KTaYa0Fg>JuMJ6+16C@bL^U`m#8{^ z^5lkXQRbH~m&qB0lw@UP?FRi6>u#BV!m7h;O%5~`rN43L2VA~?5nLW*c~a4!Ha~Sj3~TvEe%8l zi~+e{@$K;l*y9iJMx-L72wL$=MPmLBsslUrNN+r>@7rcnrHA zFIXN{(k^igTzjDv_NgIhj8Z9ujNRb9(ST7WhWf~a#!kt|{`^T}2mt5=|37-(Uh6;m za+oi9#WVKlA2F?ZfEA;p|!>iP2RDm&s0C9G#=02 z{Bb&S2vx5gk5?!{>!8E&n9^UrwCItt_DTG8v`;W5MzJyE-Brq<#xMy>zJWD*#S4}|(9n`^$Gm;C>qDCqL*YHVFEoJC zAr_U1j6X?bBF6FaopqasL|aZGlOa?4N|ND6&p^erEq=rpr^Ny8znSITEe_O}15vVf z>P>8?-xe~nbe((O<41FyHe=jCK;$Wmx}rWmCD%s^K)6IU)<+|Wm7eJcue677c z^HIS)O{*bbqU&^o2pfL&BF^wc2cUEiCsbu*(Pr3|s@}E)^ryJKoO$C~Rlv@mp_n+g(-M0)Y7patX4u8ARt2adzOfI&}tE4syBR`b|NYHhK-cU z2V*jycRD(pzl?Y@?Pjk;q5=4*@a?ew_4nqG_{GS;tEdDEg2mlCnA+Asi6- zCQ73P<$=5l`X3ngXOJ#5>p{3w(41aRMv^%rwW3>)nekkDys@T&ssNXmcS~KmQ8ir; zy^{3h)hRXGYOw9-l6skC7aw^Zr;hF$tkDmPB-~4b%*am9TGraLU_JfQ@ zLL>QmLHpRg9cHT8&NYRAYOL$C&QM4Bb-Rh^Ohb9LCTs_}a00%-ca15^q#20D>18y$ zNpUQR|AOU0q=bpTIH88WUka@jHUDxD*&~1^JRd4Usn19(_K~(a0ny{^3Ty%_Gb=}Vlv4*89S%g{sI{CS3G_54>@4-t?j4OLSXoKUK;hrIdD)hw;`(^t08FQKh(VH0^o~HBAR%Nn4;w z>oMtk@`Vng*cnJ&88-+YK|GeJEv?q5rkI|n^?M5p{SoVY>pxh7v+Db0ip%Jo>EQ0% z;s93!01c|~i&BZzTNVG~mo|rbInLasTYR9JvN`|oKT)p(meVu~QE|bnBP-w(}Fz{pCs6 zxj_hg4RmKLTb!RT{8i{lN2;m&DMsvYMXE)-5~vWp><&|_T*=@+p)k0CIv9FS7CkY?7xeA}z$Z%Al zV76qhkps}=Q49NG0vY;3lmkG){mN3EZq|H>ZJ{CRUpEGE9tlyJ;Iah5N7VsPZO^sU zPW+rIPr{lEgg_9}BuUdic;rn_`t&CaPbTSH4rwHR+^(F*B4KlT>&A%hM!V(OZZeFp z1k3AMGmU9`yhWSJfCXM0D|@j!Gfk;~_)GkFbrse(s$%0N^^JIMX1u>lD(Qh!t#h-n zJozl9k~9m>6f$`16y!D2%KtCINr_82X)3-P2Bh$&QRVY!f_jA z3-k=fVtt(|0ghEl3}_#DcydNx%~xHG_L|v6q7{PWs8H z!u||W%6$smSOPSGRt%R-j2r+n=AK4uGVD^Bse?9jx$bij9*j%#XD$OdK4<{xT9|=f%TNbw8eIU-wj(+aU{$hMB+#b0)JSaA zigVp#h~ROz-J}K6pQ3;G{pB-|dV;zETvVS^+pomE&Hb>|Du>Re35aXw>HOnP<&-PB z?+2o4Y~hRFTu+zlk#Lof+InPeaK(H=gzX~S8@$#vHGxdBS}rs^LHXj47`C5MQVy}9R-jqxgX3L#S(K0 z^7ka5m30^QzV9iSOjWX*zH$$SY2FPqh>3cQ`F{Y#Ea?P+r-4*wz6G&+Z?>8h!1?fm z)U?O(b-{a{VyLTB1ipQzzaOBq4vupR=O>VE?ROl7j0QO=yzf(V|7e&%!F57;8w{*QPh~&rbms2c?q~g>_y)`WT~Z+=Vof*$ASN1hbN#DL z3a(zJRsol&n$pcRfs#=~*Es?la#CVX*adHTT*vv_2U$_m)H3nLB>)nt%gf8R8<89f zc!&NHW;oDc!%jCvkG_`aBK?~w92|__<3RwqmH??3=z{E4-!2@O9ykUR2vFb5eo63K zu`7kparX{GkG-r8DXi!3@1f2vNUtTE2i+v<@OXKR-i40szxFGBeOBCS)hdmrFj+Z4 zBRi2AYpmf=-?O{Z&GCAz@R^wd3`5x{Go-w6r}ft7-!~Waz`i5^%MfLT`8fq!_m)*r zXqS5zlA{*_TL*ntJWO* zjq!B6vN<#=$+tQYxc=PVBoXf<3g~Ty`)*WvcKN8z1-+6xivgE-xeF1axOr8HOOS z`4y7^WDHuc$eEuabxmCapxO=``uzjdxA%K}|oSJGq!R5bA%>pvm)}YuLUyE38&Qn398~-KPx>oEg#?S8Ocz zeDC)ZopNrN^vMJCU>MREr<@iIqeR0Q1g(Q-cqo`e63*O>j)%qK|%Z574#vVFF|>pTet1K=M*enGu25J>`X{X;^zm$H#4prg=n6|uOzl%w^u zq5m?J0`56_mJBz}6qz*Hi!yB5;spRg*z9TO?50C|$7S(-;cQK-7UP=!i#xnbsoIok zO;@o#2&#Tu4)mVr{_n<1kx8>)Iu%q7xPC~ZRen! zbvt3DYL6?-{P2&TgWj@2f}*x@kFz-y>G&}-TpRrC%_wZhc;(8KO=}&yD+&s22F_kE znM~1Qs76*!&X>`7&D_8J*?Jlfh67*>V-QM}9huH45WW3DZsOM@ia431U|UR{ZQ-aF zUdCqdb8(Dk$e+rd4=>?S*b#!AM>y%vs#zPKVVX<=%+KsQXIAFT5Dz0*J461teyhu& z0pdbYg_bwlo>jI7VyV!`*aA>Ao~%rcbKsw1H4d?%QJ&R|8OlYFWbRnNhC(_7jZ82pk}WX_!J9cFx~@=20TIUdxOY` zJJC5cP{Cq-6CoZ^c_Z7AwMFMiErS2;?Tk zax<p>8CwOrcP7U7Dk@vb138R8HYE_OEzgz57cUP0KyVc@POKJgc({oHz zjQSUW6z#TEsF)fZw%+3JWBy40=I{F^>ea9otj$!{j?GW?X(oY&NxA+T9f>N;4Rcd4 z>Y0)H?+OArc`as;JTlI*Y%aAO(JE^@vi^^J(yai}m!2-$t99>QxZ5FGZ6^m9hpl9P z=eYRG;}I`k9zuq4|I-$Ub0H@iruw8m{Kg3)B?&NE_B>DgI&z^KOF95qml7TtBkP6e?&0Hx{OF9S1Cr261X)MfS> z3;U3K!bh_J^_Ot@9x25QV6)c{EYGswR|HyNH}#%>umCIbx#7fDgH?kG5#crB9V0bf zffhA|bYPrpLkf#vt2g)g%O}JJZ=L&MHO@u}uZSE@%~jqS*SDy@Irg!^+~Hs~u))!W z{@vh!^xt=n>U?Lg6?3LH0g&kBRbnavBxQ~eC7IY&b7r3-1u|-m@NqZPcUvT1lH6L! zIJLRT_Y-!YUdEDuvbo#$HNn*i7rqnlQU8QatE*WrLzBSJ36nFK_BCk$3uL>*fBy>x z6MQ$27!)exdkN8w^Ir%G?g8YrQr}Ps`(SP-!%6}F$naPss@e_)%9CpGE#QaJ(ege2 zaVo|Sl}`m?p?5>tVyrswwQsdeR5pRB;TXn5pMQ;dsELMmOSaP|MC9Dyv3h}ogB;%< zuiP^u<4MGmKzLl$4)V*vAR^Ei<+6^TSc~9a8U-(s+YYz4L52~3t5xu@i!9m6W81)Q zc*l!$4xZOP{DTQV=U7OcCaP+9kbZAkhlo5H65?6ZKwuA`4?X&p_Tv%jOM!L+hLUYc|!u@&E2sKRtdBGajAKSOf_Sax*F_`8y5 z1>qP6;M$|;|3HxQQ%8t8{#T~O=oSeI6wp^+*icY}q$FImNS6O}3vY!A&uurV{uM#8 z)tBnw7+;%?YHXyjI%w;et57}+B9En~&$<%KAFYD}f`9C-{|F;)liF&rS1rsCUE5Cn zAnCvaNU+>%!#1)vFz+*-r6oSWcLxv!ejdXhWVZ@w)!`I1(Aco^ijEHH5cu(aNPfPL(O5|V^%KSRbMGtYKn_;*zLwPd!9#rD zriOIk8UL#2ptJt>Cdqs$H>Y6d8tXdx3cg?Q|7eWn$=h4PK{rS(fRX)>@sQJh_N$u0 zTr05b$`-*G34n2Y5ARJF>_@VYDHd)#F?ShZb|p-DN>lWQfS(Tl%fIm(GYIm)MGoY1 z_Qbp;Ft36ynD_(H5F4srhGl&_$LI-=xe;ifLYk93w&T-XxV%r9&bNZ1TP5YRd`Ye2 zU_2%s!?}R!qrp=W`CnTkgEuwQuR|eEv8(jB+CzljQ5l>Qy98wM?H6wPmFzUe+{#>5 zBQK3GJE`SK1K~=`3sE9}6yN80Nlt140E_FTFq!DBE)%bMBl^fi6A2lm!w*E@MP1%R zKAPUCO8Il7_sp-h(5JBT(H8tI=YqHb&eIa64n@6qsH-k!dGA9mG23oU7%+%1y{ zjaAEX@E7|>Ro@$o2myn6hi!i7{P+p_jC}Wz22wT@MdI_AO9Yr^K+3(d3)nJHX&cst zf)3T0H(j^>5_-*Pv4a;G8^ZS+2n5Nep_~ML@T%R)75?Vl#}-Sz>K#`f@C*qb zsOg}%v@%ltt!i1dE;S)vzLx^tf*9kvmrcW~LlH45k>8!t4gfioz;4s(Z^1qf&`F)b zdWqYen-YK|f6l>NMO9lc`WXcwkfpMk)mGjVp?_H6@7asv?cqd&s>%zg+VyJ9ch&`Y zNTd9Jv2&L6UsqipAd`-AXU-Wxd*6{*5?|2e5ndPX+ ztYx4gB|TY&qv1pX4s}6R^gk{z)pB7rKEwYJ!+~{VGc>T2>?suD|M+kd4h!oCqz-)@ zMPHgFLkE&@m5bIkfULs5!(#f2uKe$yTYE{zDxg5QoW*7D%5tFn)&@7bNTj$-`4%Ln z_M%D&;7F#Fg2+^uUmmtvT*eUU7RGT9ll|C_dkmqiM9MDYgt+$!w*cc8(b_OqT0juz zZzU0a%VuHK7%kral?etO!xcmu3j9ils^%_%)~)qTO+|j0GM$$Vsc8(6>*6@_m7Ez` z*fw*4m2QL@uRG2dR0%2ED@A_wz;io7C5FX{{diZ#?4LEYk;nAMK7%C^Xb6y%;mo3r z#7*or{RKjjm@by+Gg-x2D5nNHc%+gb6*}mEAGx&fVDML4BXiJG8%qOZ5w>k#764}W za5U}$?Vk8lCSay+q{STDuYcHXAJAfvJ9RCY6gOE|ergVYqp!Dvgx)z8yY>&3@%+b+ zjl=-S*UBVvJB!}~&wXAG?KdApR0iA1NRsxBQDDd1W}!+Ha04-#10s5YD;Mcdbn(B} zEI>Iun5sUMN9GB(?~!@ATP`QkfCLX$%dgz~ev!?3v-CDb%fPWJ=09zybqvnD4PoU^ z_t_=r>uZALzK1X(H{ps){@@RW6^J5IHQtXq+v3XB;&s$NoSWjS$@}ZuJ8%b+(|0Lb zlvXG>tF_G)xDCJ(rBk{bR=FdI=ogp!7B8?JfjW;pmX+_ah7#LB-<+WhsTuN+!TgkX z(amr%LI`U8PfN!v@L82eP*m1gWkGafl1a%ln1l5+AT^-ZRJ-Z`7{ekzkkRt#I#Ps5 zAL@rK3|?^?M^~$gz^U%?t}~UgcUM3eeX{4~)k|{dt93jz{0hF#18$JWx^ZYB$00*; zuw5jzAm_nn1xscLwrV^Djd>xz4{8@1SPJ4piQnS=3ssCUDz)$DVb;?^6{MApOQhSF zP^neUD93BkY2H0$S6bcYL%NzBo^fBj78o3mBZgYit=ODl%u54egbx>q0PEcX-UzZkq|yLO#u{_KWU&JCyIXD`U_2Hp z)m=Oi#%uT$kh`TPx+!P^#&!#uZA;gUSQ;JRbAR@)SELV@4eE}IK`8`xjev|QJg*0N zGtjfP-p5q}8(v zxzCG8_UK~Dx}*g|Hjq7$7LW@F0rlU&(gebqZtsbzG2Zk(30`UGa5b|O5NevUtC#wEL zo}+unlBZ6fmt6!x(azoq{X^{C4h?uwXZ2uYf3U4nL#Ft#eGvJUR}JAr&-6x5>2D*D zMUZ>+@#~MYWk%aEx25oaDLHJ9U)7atsUl7boqRk=8kKDh*e^rNtuvhP?Hv>z$QgNg zDS4|Cp9}0E%ezNO5h+^~uD9Jts;&o&%@qWv3wSwN&LKw;i`(?1-yi@adhsJYK76dJ zJ$36hl+Co#;?QW6Np9@Dd>c-rrw|UoiM{;`4LpgtJ(#Ux@fmc9v8#d>JPgoji9c#+ zgY}=ys^CKw_tjtLiF`64g}Q6tWVN)s9BcO%m_@^*{?gNiy}&tc;Y#uDF+hKVt9t_b zB;hFf<>E--9AJNm{wIGQ$Og9`#h(8c{^`au{lkoc){=ml-fe~vUG($`02~<>yX@#p z(Q&VC7qA?8HCeRj)EciS4e;#{@i7@=0}zstT|YdE_k+PXJY*Jx3NHl&1;u-^=iWf2 zH9%i+0HS@A3K2j9ggY~gV!Y^hLuWD;ruve8{=8p6ETfxQTx_>$SC4uAd|v_hql`90 zJf#3mXmlU8vXwP2&>-zusq9Rf;&1We6!orf`(|iB zCQ%O`A4PF-x2C2h-&e1uecIuZ`vXp3Tz3wTsd)HZ-H__)YA(G`crHns$!-Iu+Gc7R z8a+nE1SRv%cJzT=JQPd`+K$7&L@m(&o&0nTy#9e!3+S`qF&{5H&x!<59~{@Wt?cIk z8n6CthI|c}J+41ZQ7q+YSBrfA{-h&tmi36(MZg`8okA}bzDBToYWAxsYA6LfEuum% zc+;gxR?n)Sf5WxQ^r7X4QJ^DI$dJMI}9YhKpVcX5S8fq<#UQAZA;Jy_5@0!GD1W z1-pdAuU>^WIs{GxW(IB8#vpLnBqGW&ddAtKhztDR;605T4N@GzZ{2KB#&wD>OG|{= zfY4Bl(d8cmnBZJBud;c6tnJfTR@QrO(d)XT)pbx_IB`NOw1SFcx(rm1NwN`L6x^TYvu;0jtrG z{?AW4!R_URMIvXS=~@L0jc|f{S~cV26(JibZXL68ZO`-8|Y_FL=^<~vm-W#_wL(%Glrhi&q9HrJ#y;ixpm_BPX76U5n+7VNhv zTSq@H(kdhAT-~5QVhBbZ;NuY*$ZhZ*HBqYHo>xh}_ZXTeJl5kKM}7K=oQv8Lly&M@ z*VEcH>~GHw*SE8TIcV2yNJ>g_J2%1xGtuZ`#Vk&751xVvI6SJ{lZ4d38e^y~Bt9n0 zNd;)@fgCz+%3=#+?e*P???Jf{nEp{aXgrxWCBntXz@Yv= z_kRId1j~=}#b$clVc3sicM$ro*tdbjLz6F8sU|(_sj&aTpY`69|9hDhe+0k?eK71n zIPwvXvSfx>8 ztAtYY;RXZw{{0+NIj}m$K(=vap2($FCkIPQ?&BTi*`kB&*#~XP6iNNfh3}&@}FKiJCJ}YIW$!X$V_&SXOJN`Fs-qe~ue3)PX2a9#g^8*iC zVr7R4bl2M7UpuH*A1WN;uj!P-b@Jqwmpq^(&4Irva_C)O>@y9e+`r%4daSK>jUJHT zn6R)g3vi^*bhr?^1T);n=7>a&sRDSH^@)LOQQ1(4)4?#A0=_r&@rJ#4dq?trc(4Tn zn$0zpBWe!@_O+mbxDSl+TKkr4MXwHUr!uHsbn!5NDGx)!`rWD%U0J+gIvUVX94gTJ zY69auj{00Ud-jp_RBw+E4K?*({&zRJU@*Vo*qn58a}%IBr*yAtaVO#Es9=W|o-))G zPdVwfLNV#FLSYSMF-54|=$ToX4CwsTB_y0<6LDBF<1$(MvGV~lGjpjeC|m>;6V;SY z7FrBT=|Lc>u}_9(DRW(IsBqUyI_UrqlNgo4xcA{hhYoRXHu77J$mBRuPE$bBh+|4{ zz-c|^$Lm$9L4&E@f;!knvz7CDVU7#_v(8Rm3hd?$XZJh80B&39W2ykH+t%Zy$J;-t zDzfK9YO}Gjn!)gn)-`)R+nJw7hhI9+Mn*=~TEJ+NbxjQ!6M*S07AJd{56GVC%{JGW z&Bbld&NGaIlNEA6vbo|&W^>JvEU)3jIUQSD+q#O18?*kL!ouqphP%=YbV9jwlW>Ez zghx8v&Sg{#0~+UbYl1m{l%4x1nm1LrT4Rv}^91x5Ko2qHf96@LAT$tB!jxR#I_bXm;R{YVc`z`gs&^nsN0nNWnVFx%AKLrrk zsZJ?$o?A0YDrghZ`{NDRug^1dGw?8W?}48q@Mo9`GX90p^}%?%rZYMUz&h@O^+f0T zpo0&ia6(4H;~Cv?S*;2<$`|*r_DT;zISd{es>1Rpa-di|S9fth125u`y}Y}pgRmTC2G=YqD(VayTeZWE4nosTU8TeM`e=x>$JLM<2A$PXfN9&M^Ik1d6o#V?5U%0@<*Tg z9xoirY@>qNkWO|y+p9$@Xg3#Ydx)I;{JaBE^lzccOSY>pwg?T|v3OfKTQBNdwV3dD z^_X5YM2e3sEpqGdfz#gJTw+1Nw#@Ue8>F{tn$GJPy>?^gCwlS}(P$|YfZ1i6vrIM5 zE2n;SpkHLEQU$@z9Eg7c({6(~7?0JcTy2P;VqsxnE;OTR2^^tscIuH8Nx+&Qn1K_b znIg7UY?60@xbom9jRF;r=EXxov;3ie5OyG3h&C<64Z3RV>bbGtGi4pK%U59an~aiD z>~N@kotj;l(n>4@-l4v_P+|MH@bKcbdM z7NyjKI2kxcJ~PM+d=8QA``iOR6x{o&l}K`}$}{s0*4CM}FP}eWZO`v7fRsRsomU4o zb(>1j=Ekaet{!=J79Sl9CSn{M9u5j>3p)!NBId_xtxV_Xg;F^5`!yh+)4N`3&&)(Lp^@8_&6O8ol)|v&!IY3qHDR5>Uy2v z)VU+|wY3=Z`y0>+`4>p7xrxuOTD!r#&T}fZNL<++_A(wg4BUv<~8=uJ?I*4T}N~+ z+0k_*rZS4V7jeVKO_L2jM{6ok1SeC??RQWI*g+L=3wRmMwrX%D*VK+LPIT#s*d{^R z`eL>_jW6ish`^20dPyT_A`Vq!M!PFqnFG^)4*^3)%Pl6+sD)Wfi&xwEa zD5)3_f0PPL<&oshXL?=H9#b!%|JF{J&-ft82eB zcZ7}lEUoSjhSWQo3O329e{U3K@oFs#*#R0vr)fK#<&2CEX9=}D?H#We@}CjE#;9hQ zAj%DU(b^-6+eV_IMR%C!jvm!cG%#LT8t~bC6l#A=&of2zmcb?n;wpvuVr5UUDG0ss zfv-}@y4Q=Pr%YI>)04h5<;Ue_vsQUUzigV#=O}&S80dZV_N~I`GVHxsrM1PhOHcP2 zHokwh{GJp6s8$4oM(2!2=bSY!#R*~oR$uK*lP;eku`YCHG5pp$nT(KU@#6{9f|ZwSQ>4O z4}lmpRImgYzOwm%2G682kM(x?v;)dY4)y5Ma&8CZ6?jD-FjvjY&|Hz!ib$~36U z#Is9Z)^muoD+R4LNnGIJ#fJ&oXYw~!1w4BMr?OwyB5o?xc2?Kov@b`VB}{LM5^+Oo z5dsj3!qB>hwAHAt0YYFXzlHO3Ka8bWUz8BqfQ{V>Wj8B)_1O)`WLmQNoQ#}$!Ll;~ z)#RB|d#I~V`3PxG@Idh#1JjZWhwDNx27no#C0v>yx=tD{ZDG5)22>n4ItRu^w#zd4o6^tl+ng=QB`G-cg?cN0k(+@XGiyGIxJ#grd zr9zTM4^sFILp2BhE_F9oy^v0=*mBI~OIkr?h7nDGsAzy?SxS zlzHm6s8UEYHRvhaXCXh$n&j0P(twON%) zz{4(t30Ua3^5yOQFSQG1UcFts$e^h?+y4irfxP#&AN|+<-~Q7-ZAqF*#l>R6GZm-U z9YcYFhnQv_BQSJ}r>Mx+*Ozk@kB)rEFrp)``@H>z!6yBVvBVAVhQ&>Tf^Y}!)!bJq z>44IKNFNbHo3kcUPY$yG?O6rW@4|=~AkvHm88RFe_9Mr6kYeb>!=3BcyoFpi8ApnVtf}S-k2?N{gWd{7vtB^i| z#uP9GIeb6`ogzq`b(D%K62z%&oSdnWMRhP0F%FiP7n5kAWD!>RPKkLb3c#ehVkXeO zfEWn$-v$7qq0j|mEbG_PWer*iY!W*U9y=!kH4~a;X<-lqh?G%7_@zz=DWa2mv>ZY?1~|{2tyfm@VqbL zJ37KnpP8K%j54Bq_DWU^Z_n;K1cT{xBOaV9xdVc2;6m1(I}PkeEsN&WBAq{^5n!;K zB6}50^aRJ##sChTIWWcz(rjmS+I$FPquc>+!4{}q)Xht&huQ-`Pwcevi={T0|E|Bf zwepMdBFMdv`6hyXAd?Eo zDF7n4oB^p$XwiD;my(iJNc0@x_H?G{`IuyX-^;b2m(~5qGbdR3>Q&ceN4h=JVBs(a z)}H~QV?mIiUTD7!0|gO353<@gz(AaXFdWXq9Y3`j){Iz=IZQ9s0(#Q-n##&EM+Cu0 zb#^p9>0-3KK)DZ-b5%RC5~!)SMbTB*8B*lHVJHqpYwGGUf57T6b8~kO8iun+@rQ-e z>I5$;o6}&m*QKYY9~~C=VKjDn(Vuq?}=nbLOdym8_%>x^-yWbuW*Ma!2Eb z%O8vJlBZHnJ-2%E-HXQhL#!lg<&5^}{9ib0+?ko^T@NlYlzCqMtAgT{)*CmN-cYwf zfDu;0Y2{`0B8yp$&Z2~#p!v|Y8nt!h>fv_^JcSRxr3x{YgA>DDQ&ZEv9I){I(}x$l zyw|%K7#IxTWVU6b<24zlo0^=I`t<43awuG=kEUyIktBhKb_)W7RG;Ibdh03_r9mPq zBawj?hNh-Tc$7uWEP-7!*;?U`9KXJsii(P@IfP9J3U|(X(p;!p%*e=S6{5u9=yUGp z7gZ8)^isE8?rbpAcGvS|`=&X5UoA!Z+Q!C0mnr44a!Nes)ylUUkOsW5KYfTvBws1# z(F69#JLcf|L7q!B3xi4gf?YVRh}FuXlG(o{|H8^WS0Lvy%m5DSQg(7+{dU=|ezR1uePUNu z;GTx=)Eos9z$ZOKl((DDC zO0fwKY63XO82BjvvbInNOf8pc-1Sbt%8*!;?z}(9gO*G8r}#Wn&edKcqD%cxwIPBo zKVKFfSiV9nsHImI{H_`Y!*>gpz=y@tU&HPU6Q$6!Sc=2)%%v-95bP+I*8zkdctv!R zg3Hr)|1r8e>&L#papwX4?}{O-rd1!W-h7)35Ydj|z@VVnVE}h!W_wizSVAoCOqLuL zKHu5ZWwbYY*K&+a$fK$=jygm~_&m$qzGv?4?M7+HaQ$6rVXI=Py5-s7;ZqMGPt-Ry zt}VFG3z1zLK-4z`g@;dlS%V9xSQ8@1OSZ{M(;~_@)t!?z1$>ooq5DF#lrAd` z==R;&Pur*$Q3NIYdUzPn1%8kEV7wa=*je$5?7fO_&C`L#I*o2b5YhwLc=I zOFttHum|)Z$X619Hft#s=BcP*`=EHfarn=js;=#yZxzZRKZ+b~$t7S_Z<>4ir3#>U z%kc+?J5*1J7sj2z7I|0Vw|rZR4WZ4oS@$_f=mA!o7=T^d;=boH4u5M-U|+mqQsP-I zs@m>It(DJlyOSYAI27lw{z(;a_DeThjIf|@itsixrB!}xMj{r_9kI3f#bDF@x;k%; zW}U6GvokI{j@xX*b53p2qU-4ae`{TqzZnERAcKoS;G=CTLM`hS!`TO<)t`5S zzoU7u8sHi81KHu&Ioa6Q5W%8mc*@U+@@-0IF|!0n{%WFT=0j!1JOBooDqf#zhGtnaK*&wcXfy)Kb;bcDVwetv zZzvSzvs@F*&$TIXS}Hi8ybdVO=**f^@A~%bUAd3&LWd=QZ85OKVjywnfLuq!N7L-{ zo{xEX_Qk=lMB{sN0PhiU`m&$p1E3}R2*_LBXIi+dE5ZwBnI)jFF{-8oJb-NqdNB$M z_`vd-=h@s_PH!P8FdS^*wq^szp!Xq84IWsDC8az6#F6WPmV@7wn}=ucL`zfCKajYG z)H6IJ96HkRqYy4JoO`*sO)$y386tD=Q%%=ky-?fJ^9u`%(~d{lOp4YQ95$d3lY+ff z8!-*Fza-koi|q#tVPJ8^XSSV+ySu=HlqKwtz$QUKjq36FUaCuk%72&*Ag7maM}mp- znuS8J<=H|Yb>L5i1DoBw3h>nidhKn#C3O#GH-LHwGSITzj)1Mj@-ho?k{u}V(_c1Xs zz0Is42$br*3%Q=R7^j9~1p()Eg>tJU&3c4c9BY5$!mpm&s!haRKuE^w5|^VXVq1+E zX6?sYeZ*T|;g(|$-{-K~u?OoB_?*~7=HxMw-{t>rFIY7oii)1wlorO(_Tp(ufk$n{K4Ly9`7?R6syFq`SK$L|W-akVYgo z9p7A==RN27o%5b?{`t1)vdClv(=JJu2e0qk6j0lB7oe>pzEQ>-B zYN1dB!>3NbCrMLE!A0sZu5{*-x;^!EtM zTqY5axg0=svr;60|DvFT$gQRXgM+@cdiq<(5@NzyH*M6{qFIC3q-so@MrVd+99%xP z>F2{p{`HgAmcPS?|E~{FC~3Ny|M6eFYjqka(-ckuNNQu=@VuuMkYp{@A)ASAz?s&ztS~!<4+aa(-DEe!SdO^ zVFULVa70W6N%02!{PI-1C4{M=uTQbIwstZtJuC2vhNA#0E9*PT_!ilPZWJ zNAXCZwytjK*Vk98D~&f=V5odf+f$)>^WCWv@5}5L(+;0q4r;oSnwpwtzo@dk+QKq5 zU=R~5>_^)4J>a%KB@GI7J|Hskdg=bg(9}Y9b+4cD?I-U(mXti)x7eC)i=B_iB*kv+aE27@bi0O_m7=C`+a%5Zpy-AH=@9(>xtCmkv6?hbs9E<_6*CcgoMYV z&I?Rota`q~&I_4&?)%QA6TS?m$#0`jbPgRno3p}9*rm1dr7DKFxHytg#ZjGyc9P`^}^zN8K8!2&#F=}mq2n`i-pLVqE+g$mv1QPi9Z-SO5;MD??n z(noRG1%BY&5m`!4Cie65%UnuTY9*G}PhZ=}4MRVXqdu#&!Nh`%@*Pxp&$spKSn1ws zOT*Zh=3tQ-154Fb{3lrFQirwe-?F=h+Z`V3Rb|mr28Oeyx4df>?m*plOFvYN&ghX$qY(K?dK2^mfo3BPmwJ~&y zC$jC$NqGc*|1Ls!`t&DQm(lSVkDWQ%(z&Dv+n2A5W4@5OcN@qP1vQ-G+mqJT(CuJ- zDjUOjxMs}!Wm@sriPIlrV{iJrf8Q&$NpJjjeb}Z$)YY}(AgX&Q(_CL;c2iVbyvy%p zTAP%mWsW=5TBxRb>Uxy#j^yrkWTIQU*R%nxWaLLac&^SeJCl8b*s7&$kNtFAeSNNX zx{7@R#TGGDU(>$D%d*fs`y3lXmdP}2iJR{f6pH_OEf)#QdD@5Lj40;C>cjo)(X=M7 z4M_ z9A+vDCip1N@Z8~6*)=jv%`sy4_5+?m)P3V_y6uf2i$sT|QRmTEm7dfHmvn{9&b7d` z83titVP`9`k@emEC6DYGdht-^LT2++VH&-K-b_})^hfo15#r(p1Y@Z2g$YIJGokhfRL* zb(!s4eD?OvLiPfmP2qC#f$JX%9%qb4@lMpiYH0QBn9X>tm-D?9n8)CMi`ohA#> z&!&;WL2cdU)rm8mF{+i$cEQ zZaeE%TNDnXPRXSS3H8qIy&1|odoD|r20WX6)d%u1Lxm=aiAxevQpJC>mGhr?NVa)1 z-#^;i^H^-3rfNqFw^Qezr6mjV&IF%QlfJC!ckfOwxMWvugdgmQvs6wlZZ)H!*+;HEDdw83qCoLg4nVbip z&2YG0w9a;MDSFi7(2asNi|Xg%^CtP`uODv-=|aA+p8XLg#+MvDySBQTM|n7Y&f~`r z#Up#nPKK{LUyQGGcIHA>d6wY3kRK`2`Xwh=q@cO3WJM9Xvc;mIb^E2!%Cp&tIukuX zUZm)`IGdUFJlAdOeO(=il<%(C`b1~+%8QefDa)hpuCdv!AHWJI&dm%Kn$Vu(-HOF3 z?N_GsJF(vwF11PSCc&NVD=-fdh1D{m=Vq!g_{Sh!F=xo-9tC`M$e;Wad7Axsvt!lO zqkl&$RU^Z`Xmp}bHKnQBUMlBh{pe@pa{u@mUwAIXbhXf^E2);VMJIeezA3YKG;QW2 zdQh$Kvy9*4fq?-#jt%pXGI_3K_bX4Al-tu&uxqKLe2QvTUUi&GqM|;3;0{lIKXP1- z)fkrT%hf!N!9==rMDt8R90&{vQ5dgmVX3a@tZ4ojW1+5n_9P0WrsqHZ{?Xqb4|dx< zQptT_P}p3_9>>1a&y9HULU^R8&?IMWhBVss1|=GSkdD64l#~>)nQ(TKq~bKO(QB!M zVHdAmGg_C1M>Yv@^8L~fK`gW8N8Dz?IMWgH>b_BDLZ?AxWhJ(#prG5?yv7r6b+LF% zZ1eAk-of7XYWdi{WXF|n@2=g3EG(;PY&Gi|LXfaFmLJK&%xu5)>C3*HmmT}b{5>_d z{q0^-n};Y=B(Vtwg^InW9p!6>M)f>JhS-~}$yd2Q?tO!Kr?k+dPhqPfo`yeda5x}b z(z89AQL#OeQ_d*xd6+Bb&cPeC<7{Ei^2~-fcqrv5&Y@86o{QeeX;n=VY-3^WuKH#@ z`cqI`UfpG}aCLukO#I1{H?9C0xOChG^7NdoT9l{R4LYKO^YZf0ZC}&sR&B7`Gpcg9 zoX?vf1Ihl|YrC{?t40_b2Q0j$5d`@q>;@dp>R*Dv^YoQ~P9XV�hz;ba;Dt%@KBN zlZ--;4-5-ap5qD;jrigd)bP!poFQs=E;%OSUd+bW;%w84iVAM;{p|GB5%zlyf7SM* z(uxb+4vkT$AIk?@jePTqCfSwK%YuY>Qb_=~?GY`xL6Jll;hD-CYk-lo#^R8(C z?Ivbsl4q=EIdey<_ny0m_2;~fLbZFdqhSo6b0p>2{+37*)Vu)%&b;`I z+~SjV-J7Op9v9u(*Mzdt($eE@h0?ln;(PPy+4G|3V}}X1S|d60YhMP3kL?3qWQDv*c=l}8-Q=`+S20LTwDduNfs=rW9lSQX_m~+N zTAaTW#@+QkhN^MOeL{djkzMLdHEvh$O+{f8Mt@yGseMwpFMH_LnI{kFgEDF+m6)*9#-;>4Okutk{cm1v9aV{TzCf!utju^L%usxVoR;SfTBKiH|VQato`uf=j zgETY`2ZsWOX@6{tJ*Kavy*+Dv#MtWZ!R}HYANl$7Hx)JA3fhx*d7_NzU5o z4_)Q=v(e){_^R0&mB#BcaDe7}cbBTOXBcm|tp`NMq%>p!LLRY}MspWy+9IclL)8yM(ki zH7z?gOAarEiVbxizoDUF8YX^-8}N+{B*HdcUf${1S;MsvhcUC!GP?x=5A(I@wrRX$ zCqD1*?X?C31Wdyuj73I8nZ5V-@9E1^#9(yYXXCtEnN*AJyEr?ib-Jz9dJ$t?j7>N- zDjajS^IDj@`Y?S?2ir4kkdA|^c7MbPO<%bE9KhwVX zC%^g#V+7C!BU)Fu^<8$xBbTd*O_o*VCwSEwy`Jpx;KeV9;~mHO)G|+v8$P`P)pRL{@ zOuk(r4-~fZG0yX{GAj%4zgo!J*U)9i;Vs{H{tu!6^(*cF&J=vFGv$ad+4-kh{^$F@ z+C~1~;VHhCC-J@A>4Rl$YH11j{Q2q^Z@~6LVxpn}P~dRd&42nSByi?#3_hyHu{Bxk zh7CmOcMY!1*n99%>`F(nNR73QF_nUvn(6voBz@11>0m8b)h?~k!!I@>znBc)RYJat zd_;EW>U*4#RzLOS7}PjFh44@{W_F8%J+rrBNk2>Bp|rTC<+46-0>J*J!#lNueDq^$ zM(&Oluu1>(hyRR>C&a;j{jfiV6#nm@?(gWFy`u6}) zYT?B{kMY+HMhDWfxZ3ec>Q0G2&WUm5wetKa1OV9(^Iq@sWY|=-$m5UMt!3M+E7<8U zKw~JE#{AW(fO8Ha&gKD=q#@r!sx=wlJUlrEOjnJbZkX?6$KeRtV9oc=?Q9VaiE4#C zw~BX^Fn{L$_>2#pRSsI;7g|auV1`R|XC7|;zMx!rSTMSD?GlZ{hR;1`5%^rPp|3^G zU9!1=a)u;WEayhcC-_jFXMucXa8Ljl9_WFtDaN+MNWlfE7)cpFk2>X-FiMdh9PEKhY3WWAotcuwhudu{m@ zj}Y}W{c#m)gx~qm|CvZUbN6Z2+UcWKB zC;57EWiOQT^_vhnXJMfNBHZc+k3FF)7TDRJLRT~(P&d6fLynXndA4)1%7wLr08^#)2e%yn(cxZs9@P1UPg*q4h5?e;~hbX6= z6*-!g=E0sl)gB&Bt4*|1ek@fVgJ`9EV`3IX%jZu4{D5LWR8}_7bgW99cW+r}`)~P@ zF5vl#moD92=*tcr$T#rIH>M!f8sQ5NMvf7`dNV^FwN_XgRg;0}SD3_ynhEMHk?Yql zlEc7^gFJtnhC=e=r;Mq`$QZv|DpF7)8Y%!)${ICLwrpZ_5ct6fWyGTYNL=}{B zL7s`%B|3uMGYOt1hh*=`77u(8ihuEOe_Jvu66%f~;e46HQdTigBj*hgRnDtiPe!=e z*!ItD(?oEO3l0hYei>oR5q_2_4p!JX_XTBex>3R|HZW9?QUfZgh^hf|r zdE`Z{C##?3_}7pOHWi} zq$cf{%jVx6rjvfYzPf*hORZa&iZKf_o4_Gu>SmPvK;P%jd)Cm4pD~&%v9MT^>hM3 ztJj~S4b=%f5Rspnf@u913`e?q-z*;|MD~YWEoOF0v@RjP;MQ>zDre^R6urda#lbE^ zp;ItLOb5Ig7bi^^O1L5#T_M-rmhqG)rj?4u;W-QW-LJqKsHh1q+{K+lGBn=8KZ`x% zce<$X*0ZBsq+oW6ZPgbSFaEyA&mfcaQLOVm7g}HcxDA6g$zaV`U!PL5Kq2 zS*BGcjYo7pnsft^@(Ajc zNua?(0s5(Ob92`R7v#ahNa8k6)LjzwSORUXqrE6m(>(qL*^73<#|JLvblQX=8SsvU z)oliajE@jOlt1|SHq%n=?_l$zUEplnSp^41M>C&1N7>DEVvz@`kX7KNd_qG*9bH^n z+%`vMmUl^+RPMC<;x>_82gZ=0#+YpOc36^5rN<}SV~H6GAd~uN+@3(`np0~s3UxP7 zl|%~(N2QT^U4Kpyb?1qN@dij*m@ab6O5l>LqHuUC(U^4fcMRNLdYULQv-!J z{|urry*DKLcGJ!?3}G1KUbGg&Vlq+*{kcS5qK(`o;y5EPHz+N}Vq<}a?t6~+BHFIF zrFJMm0VEgmsQRNAh2}ih{L~+*t2@pE2Rl-_Bv-j8;e>OouB}a_CPxQCS@jkQvjXED zF7uT<*cl)e+zdg!Nb;(_x^p1nuJCi6COiF<$tL=2wyPpy>F~j8_<0`)~267XLz`GdZMM6dlbEoFBqC30k9hRIeyw zm&P=!wx`LV3hbSkX$`wAmn_*Ms>BDex-e_5)hxm!$dhE&#T`#!y8_8OXD{Yh}?ZnT}#oO?tVcX92+ZGy(mjH&kRck@e?N z4g`-oysY1yBR@$ZGlX&pxCMcY(~DYG+LS>ba70a_*AR(TR3Z|Dg&+-oQ}KqlDp~{2 z6`H4AccQ7iJ>>Q4^!K0Owywy_03keKUyweumkAD?LQ5OfxA#W<6q%{kt*>g=?RP^T%6ldN`s_nG2 zw2BL5P_d<3?je-vsOy?U*j??HC1i2|uJ!{FQK~Plej@hXXhrM$&w?DkJ zhtw@t=ud7!f@C8mwwAf;q@~D=ggtV|GJkLtPyrquUfJ7uP;-Jk_7^nE20502yPQB{;td6?l&6kooVp0hf?~eY$((zOC$t{4EYHEqS^;nc zPb<0_Yn#m4z0BRzFyyA5#0#U9<)h|fRU_lOA#1>kq+=cQ^!4Y>q(5dlHVsT&85%r? zeo-|=sALuJqSxkVpqKUfUf%UbE<7yj?q0=>?Uw6=>Ud>yGgKi{rRg={rAZ(tc-|4sigM^Zj{_zg~8+;m&N0_%ju`8}0NHu>zirho% zy2|-7PUJcG<Hk4R0<4b=3F64Gz@s19GI57cE=a;0n;EK+6mSD!nV`e^c+N|$=W${DAnz*9mw7zd4r<{hPKiLy7a9vqqeoRcvC@;ueTtCMJA7B_eNQ7y1^K%04#U@KA%?jG1Ex_1nt+f<03d z8xHy2y{uJQ>Pw(Bd>XLNl?Ttio!&n_4*Ma|ZFj>OQ=In^ zr>-A+O0`H@i=Zn0-{Pj9K^mQ$o}PCCvCYg@M+l+#L9>>x3JHIJo$Go!-5$j-Iy!2- z*7jgQ2i?Q^v@HlEb@cwb@SxNsnFl9(B%Wf~6xHp5B`-&TqI{a1~b`#$AK&1*`ma^>7~z#%|~>B0|E-YNL)jlXK!=Dk7Ztm z!NqvLIq%#0*ONCyo?~(eZV@a(NP%qnz+mjp!2C6}~TfA0C=Y9s=BJbV>6Wp(#c)aKc!U8%xkVdZc>s;ze7T?d@;> zq{r-@P1~hWbqlG;hqwG?N=6Vi1mdv}UPy$=NVolSw&I+}d`1ABEbpb=od){+`YK(?7(i$L&(G*qjgDD*U_r(t6O4wi&hP?aBN ztoHr=!7qnIi$bEq?1vTPff*@N={5S`@3AVU4%T80y9t#@;Yso&$=nY2H>X;|*m~E{ zRwi&t%jh%;hjNn#Z>p#~1ya0Yq7^uTJN085yGb*FgK(6}`wLA%M>pnsT9Bw{yr06s zn3}+(Qc&{CfOmggPJQd(*Q;~$+kG#NovfFC4DeQ%O`<_oRz-i-5gtwv_B@z)` z$})@bER#UocQ;@UclxSrtz1Hx)paN8eI&=)l?l&feWpwEMZ!_t7gS4|lcS@ENjb-B zTukqNrV*!(-@DCpYVl%DVEZ)tYzY!lXgRtkRnqP*fF{w)bV}2CE)kT_MLP^S6y3Q4 zYBx(eb7vNcf$1oWc!{lJF!9N^1q7xR=s7t#*$>|TuT`V7CP`;{M@mSCu|{HNUN^Ux zn+A0d@6PO(o}DUyM_jHTGmE(qu42>97x5#7-#Y<3(ggX>)pmyuae0|t2MP{ZV$F>S zDl-BldIJfVb@LwxXD}manle||-Bt@h{rmb{mz`K02x-iSA;o@iaE{{#QgX_5f}%2{prN{UxdIz!2+!KnXf2O?!J4ih(B-mp+7_J$pBr zl$6BfI%dtw!BM2rC%+a4>XxX!eiBj)(MUzBeOXDxFd`!B#Gl$%rfc*^(2-CnMcUJj zyK6-bzyvDg34Vyhlu?n`SFY`$ptotLh555GXSg77|Elmg!(OTYQsc1wkXhLl?8mA= zU=39H*i3pE5v=}T%vDaqX^O;YrQu2xSMCekz=F_s6fuFxw~vs`D!Y7cH^_|H#c7ql zptMx~y^@O0ynA2PfKyr+U{;|{ z1Cjdc9*|T`R)TrtBNO+45SjQx^{N!=LeEFCMp0leDjQj8k9x#VnLT(&|K*8)n)TOi z@GfDoe=9dNoE8iFjJb=z;=?m03u*v$JM%Ccqkr%#KOQOivQ|_({l|$;Rvabx%lBDB z;uVP+V{&bV8Zj<$gvZLtV!lLfY|SJt6H;b0_jam+y7X(9@SBea%zLy7d>>*X+V{Cc zt0Ko2{NJGsQLv)u0=Gqyl^FOKpg6tD#-9N2T&Rz_@0zVFFPF@2-gs!qKkBl?qi(7v zRcKkY*8XU!KlizFbP`bmfCoPu+U6$~6!DYxSg0Ccv__Zn557K0hLEEu#pmb(3jrlg zJO<`KBn^<+=m>qFpauZ3g!6tcwO-1k?_X+@65s+XFzdr@i1tdXR9rK#tZlELuJ}@<6!yZNZPV5 zL)MQmhlzT^fg-br$;nBCSw%8pDC4Ac5$S6K(aQv=n)4aD{7B~LG>=<=^d0vbqL8Jq zlW-A!#?7aMN>DSUhHWRjhaz@jo;5d4uO)=leE1q~ zign!>FepQr>}{=Z5!~nHtrF|YO>j@EU2pKa(&BNjcEg*+g@ViW<5Nnf$6WS*QiAe% z54N5U0Mutb?y@tNjCGZC`$mG0mAE~A^^TT+3B|O9zat-nUPu;&VZJcsJMxk>zJ%v4 zOPvc~%@;8WQwDne4llao#ZzERBJbr2IPGKm^q{N+*b*~K#S~ndV==}%Q0|ajRfL}B zsy18*R@4aargRpG_Be1-&eQc-|1%)h9?g^9bpvA^BGxMVfF(-ZGduM7u9Ns*3!ivn z|7PA?APDc9L&3I+XnTE$NHD3sPOUnStST=I_t z0j3E6nc!NjO46xoV9*Me)X zU}|WAB+5t=B+7fZXR}|KcHRWBVl-UQoF(PXy2%O|`0iq{_i3!1I*OI9Amv4Y% zp_G-2|H`=!-EGeXaPCFib7Vh2DL>W>1n6rCA z;fJ{4_4@pArDx17WP+Po8f*|K8F3y}n2L|@B3#c^AACkbiDPc{V~razbykDG2ca;^ z8Zr%nY5X^`iNPb`xov88DyTht-cAj^nf2K-*a#gM_+Qc5mFj1-B zLxAgAaQoTu?afJ*O*!Pip;9oOi7MdoG+&Y82VEW)pm#7)sE`{`nzr@{$5y{A?v}Oz@|9D1st1ECh>3s;UD$yaY1$6}X zueXsj)rGmEr>Bo3CeQLX6!FIKk<_VR|YL7M4eQ~W$XnnQ- zRTAnc5J)gGR7wc!47Id^R>pzt1{^YWfZd*<@_I+r;siQ5uk;#1tuDf$@uSt+-gOjh zp*iUNeFwMnpk=^#y48YE7AqSCBeP%5+yeQfC6g!zG%y;LishH!Mtc(f+}M?FlL1ffFffR#1!NZ|-zUyxvZY8yuD~CRE2{CC13VM!`+-wW zLKd6fFHbd8tYhkk!%lIDmRKA-ua7Y0{IAN^N8+C7g7H)G#bPzG1aeZm+X=3KcC1$? zMWa;Y<tYI!mV##7%vJIYFf{*Ji~=A(8byBizp!ybJS_TxpIApnjbPB4>Ms z?~s0Uw21E;*ngD~<;@AHvcP~7sNWXu!{C!|;$7Fqk2`n3re}!ZD=;#hg5^W|BpgXp zuW?KX!qFQ_gTyDVSaq;tLeIbugye7FcNr>@WecD9lTibEpJzQKX2rf?bejHX^nn%e z^EAjokY#-)tDv7%e6&&VfiaY8z782Fc97{56LLyowLwrhR&Kx8IiykP%n1n99jtW| z#uHRHt~Z5QPTTHMj;ZhxXld45$+4r~0EzxWQ!T1Yrwpc_HYbC_hY`os9#`LhvLf=I zRH|QkZuPa#QJRd(G&IOI?-8p>6U0-eh^=+vg+Q;2PTgWY5KsIBR<%2|`ve6uF}n*C`n4Mx?MhzA0UY6KOOuT=Us%Ko z7)7WqI>Q}b;kxh={3ZA?!H_I@rwsyNP||CpuZ49uBJ8$cdQ&_=M&tj_8bU!WzztvF z;)7!_f6Qq>;U(#mAI~r@xLFW83!or2(pbPy02l4sRL_Te3Y$`^T8tBb3s=+Cq`d%Q zZDA2kAVrl0=UArd11n*xqkWV2GEnwjgjwSYd2<277*D-UsXH)$l1IDv8TXIkANBp3 zrxo00K8glwIDdvTay{@Iy-iL|&QL9uM2wQ-m4Mup!NB9_%Y)}vLGs0`K&k<57BXzbY}Z7MCDdd2?r}aPiKZQ8$3|J3lh?@?lbVr zsE7LKytZI&?#Fv>X&en_iPwW^4vKV`aGUQj2v#cRr#~W5MOuB_-t3gquXC{VTWyfHL5#)LM>HF^8xw$#(2BhziNI#^8LR|o0=5xA8-0V9fUU;S?LM0?8{;;I0c`0ON5ApHZ(XFWQ z7-B)*1!=UYoh7Ra7arq9fbB8Dxs5R5Al)(Bap3BAG9u)z%(WrkELqF>iG0nP;Av4g zO$@-atY*UPF*z~E2lt=hHZ1B6Ykw9BHyTglIAqzr#R3K>JdM3_gXP@9J$fbh?)xxs zZ{Vkg0kHW~|9VI^$8aAW=VoUsGv2~g69hu`=l~-{0xpbya0GGo++=mxFdZl`3R(ws z6yfK^Ll^^~hoA*`4YZMw5f@=P&8XX^`ZhSWCp$b2@=M5e$zKXo^+?tD7Dm`4;spDf z6soV4X5QjrXW!(s_hHx5YIP2CZv7(#*c(bIw(95m0^E- zZAKhgcd(Tpor?U6t2S2O1m0|Q_REK6ph;N3R(we@2m)FSQHlVbf|}QHM&D70$K4Wj zl=rlIf15Rh9Z{RXL;nSgU>RZ0PUzo+4W6U@O4q4hD&n6v++w@%xRB=Dh?J#djHX5OLEY2JJF2%vTSQuE9*C+6JK8XBVM(;BIf2< zqbE)b%CasJ5Cj*qv^O=Sc4GBgKganM0`9zBRaGP$7d|^!m|nLAI!u|Kj$1QB0i4%# zcl?lx>s(PL5?^)H!DSL_eDTk;1o_!yaxS>rFae=b zNlMAvRq|9ZRw+O{o?Ll$3kry2x(AITAE9yaiCS1{2%*wUvAY5;o1dRE>w9DAB9??J zah*;nf(J%#DSeTo)Jq% zqa{!0Y#;zpsb=ua@++ps37QnJY=H(LLx=89vdjbp_ zErPB7g5sCX-o>uRic5UAH_w=wAoXA_au)ZPh( zsa{o!V5Bh?Dd4t25NaS%?;F_IH5G0CXxim9bQey4iD3${SA=8QZLO*dg-bv5h(%*4b?C%HNZ)Uiims!yOn~oCbcv@m`}2Uyoo?r zlNsLwwc4&so}b^-oJUD>%;FQJSuJE`#ijIJ7ryIU&yh@Uw2vBI4_(ah6>qbq@vVfT#!M-{F9IypI!M3X{`Uh!#4ZYAf&+Q-j=za_==WpmYE3Iy$}0 zc4$lLo1ej42qDOff#qw)=9vhUMB#WL67h@9|HR+y-bmx$pszX)f!o@9%ws?=6e?k< zO1Em6?mBlNE{=T4>`^HssAGZCQNsYyS710{!6V5lj%W_U15mjmAtBG|_Y;b7_y3bJ z%n5y4k=#zQWVhwCjWWBkuNS&YiW#(nHzIZ&(8^#ypePb&{Y4OI?8(!o$R6Y6(NRyn2-m z=?&3%O~&V5fp|ZjEr*mmw2h-9FCh0Fvh(IrCKBz?LfUY64l-8g#-00viJ zL_~VAv40cdHLq-IO5LtLw5Y^}AWSvV$PEqG*_%sCNCa1e{c}}YdRN3m$3;@WXD{-T z_IIczEKgq68V{VHYNq{6guq&USaI&%Kl#Wm1q{+Xr$Bs#*>y2+Ti$30=_`VzNo>`Gkwu{WQFBq{{I}Ob(`p570A+IKC`@2S z@Py&Egx{}UT9xc!*Rx+uP6dwzL3exVMj;g3>8fW*Nk{-SL2HfpE7!v+x83ViARpJ$ z_1T*%#GybWhq7=U;R+SVj^RN3b(rK{4m^fVjv2AG`=X*o%9&XwVX2hXfpHvsf*>0I zC;wSBg7SpeLlCi^m5t4gYZvq;^7Y-Qw!)}z)y)rjlPz&wQ<+y_Do5bW2%(ovLOp{c z18DX1Fo^Y*E(wv9$I8TO(9G;DO@CghsDqt1_8yH^s`s%jYd|9K*A(*qEq+x^LrF3P zc(hNo$9)-E(yAsm(+e`d$j7zqZqQBDtyw1Qt@6PLaVG2oLh3=+py`-{*ch#0vtiH> z7W9Y(J#MfV{j48zjvg_^ljVaxrFKaNtJ7$29pb@Vl|lm8Vy zVjfz>PfiW=3QmKc4{0O&`s$=O@J*RBGyke&OG5q|(`d&nX1(T5^v;jW2Jb`Wd57Ht zgDYCob;YZ|q)!suUrbql2iw~+b{~DvYYMv_*oTKoF)4I;cEen#uwJx-Dep<;rvHb# zM?U>vPZDW(mcIUoL*i&W7>)+~3Hqzj|)moss*m{!gcD7)w2jz6tj>0D?! zTp}}AY{3Fe$AOcRx&u&SJ{A^k0@pFxm6wM{9lTm8s;38CCVXfoVPqFsSXhvr{N2Ch z9pjrIN~P4UfWJu>fF{zht=?}3Qr`TMQn`KR@#Dw8Bc`3~nfbB;(|9>yDcvh?2N?6F zYp#|4Zie0y7{-^09kvtq-y(cpbYSA95w0hI4ljK}ZsBa(tIXXa5+B0LqF7r}ME z&nE2eY% zWyUVf?}j3|l!L!-9OKy&=k(SNrdRD=`qPWBYj3KndZ`d+VODyoE)6_62ZUSqh2Z;G zP-&XS>%0?la-@@0nbb;tXs`4@aKbt_ZF^znm7=PT9H*x`qun~fP8 z6iNH+%*{tMJswIx`MID1E4V>dBN$vSd_v{OlSvngwkGBRb%&PZKo!7k%@kG3V_aV|jKS3%2x zXkPEHbIMMcq$YUry6t?24(W!WAysqeCkLYM=c47##v;SDYuC@TTp2dT3mqbxih=h1VDMO}uc{!9dFYkpcXe~KGY$q($`M+UsnKXLq&pts%rmbT zxjCh@A7XA}An$3;m8G50){0$><5KKn_@>}#x1$yp`JMSLGIyvfeUV1}i8-07m=HNS zm-hzbJa-@fpCQVT*0TyI;&=^;(LbFz-@$%Hw4NDpJmWnR(yxM)<(~P2|9$1l;*^

HQQsCV7eET!rV z`6z~=4!5H-7(*q?0BD-9hH#YGEc_w)aH>=GkxmTnT#0MwWEZR9 z!ntBCZE)=_S0qU=Ag)X7xh3eORAaY(7~u}wyMO$~8#UpTv9YUmpCTh8ol6z;^_^CJ zLaj0dFA4}m++~;y5Ps*6GvZz%lcAIw-&`#R&TEP)*t-qN>qP(}9jH`U*PxjK*^B8u zABaNU+n+;a_1kpfkfM^brx)6U+(`BKGj1V-Ir=sFOt=VwuNDQuJ-xJ>#kEkxLjOB! zB@+~)kP|eaA=N1AQx9O{FmO`tVY5AYmiRV@Z8|^O8egD-umQfPxOS0lR&gftZ+%bOlNzu@RFXi}Fw*(xMT&El)G_O8B4P#M*Quwfv zBkX$e>zS$GVrvL0si7vy?oW_D5QhDCQ^UYDC&tDaGxf=d~Qz zmT@haHU42B)hoSlx9fA4HtK7&54vAnf{|rIco@(8f0c`W;1vR$yTBFn41w1V0vOoT z&Pz#2DHoeZfx?iRsG+SL$EaDQR#8zg38hOx^d^J~$gK+azi~B|?ky{vJBhD4w+*1Ik~;Djh%L}Wz@mhE^N(@LOC?eT zIf>iKsV>aCDsVr6s+rr`*=bG?4O%~__qjyP!qTxb8p&y+3-}*-4bI$1c``H{{T1g+MeBxw*${~ z5Khmo{_%0K;#uj1dq^3bLQ#ya3FmPsG94@s1-c{qN@573GF8L_ix6nqmuJyUy_1n9 z9pkpQ(r7o|1F!4Qh1b@sLOnE}={zCL^I{8l0LAp!7JxwS6Q)G1jKXkK;axHJ0Cljb zjnHUCjlDTq^@oY@$^{W%nDl_+>a22eDgFKaQ3CXkv)avLqCQt?eSJw31B)AqNV8BY zQ1Jcx69<_eTy)A4J&dnZg9#3KAF<#dMBm{Uh%UO3J^>Gto}d*VGFV1B<#}c^kWllUP@744UBV zHN9T`GWj$Ios|-wv0LT)M=&faU*r57{g zkL)u>nKi5S0{dhM73D90Q`X- zzo#e43EWdt;LO{3!4qs=2y8#Rd*mM{viAmf+9#J)(!PsPYasc}By|z#RngWi61@Es zc_$0^C;kCTT zR}g>Le{&E($?%_=bZCV_46oaIZ6CaqEIS5WkW5+*-4uym#!sFhyW=m@NDAuxtNL;X zi4XK*z(%5aj!>!1)F!LGK#e{Q3;HZS0o2mwBV%@q;$X0y3Z9DG~`w$X#e+7~`oGpzS9$qbCdhz1L*|Bp(Xp8jFAi2VqR3wWB(3H~z zp53e=aus+l&SU5!rpZ(-&RXsNAFZb5VY97-O5Qjl(=Z0tsrEhlZ*mXPMG34oG)=Fs zWGCC)xz*eo0#$9z2)LZT+eJk*dAOrJVy&?UUDgIMxLOSLOBPHgEy20tdDQfE;V*kC z+Zt50klB^2Ni)f=+DTJ$7_qYkjIvPVl~yBe<_%4BQaspC%s-4Xz~2MT1D!k*L`(d| zJpTw!+-R&-!P0KlcKaCMBvejmmLH(kow~U|iw95Pm-u^zyDJmzJR1H6A-g*E#BdUd z;bF<%ba2yTiJiEHhEpED$lJ`4SwZ)$@sry_V+S-qWqka=cc8>vy=NU2n^=Toiy6fX zrMOFZX*v*X&T6**KYX2cJl6le|20&Ulo>+9$YqsHMk(1NdrOg(k&$c_4HemYyX-x( zM^siQ z4~BHq6lFJd!mvmv$<-Jn;{NL%mp7aPx=I(MDdke4V(fdTU0qysy7D}CmZUA-h>D4& z+;GsNZTO&u@3Gs&f%sV&)Q`={VBCX8fmvQ#5BWf!l-)Q&FZ9pdbi3%3m%(?NM6@ow zXCFo%%>MVQT;8x%HJg%_79UG|`J!NvE%N=J`dnFZReg}F?RE2!txXx-IEW*9S~uVi zl@;Pc*Suk`TP-c-k?_z}q6K`|H)2ebAlcwAS3H38K5)pJ6TEgBz=Xb46g&Txs-==W$Ma=+L>4- zeUo=Xw)o6sErIsAz@*&AG6u>s#I-icOO-2EQW%E z0fYo82!{mO$!v4|o2mExz_dc_l|(i3mFFFWc#_*-j=gHviG;or#RT% zRzotz#)j{dYD{SoYQq&%_>T*Q`SN8I@$Fx{vPw!}z~5|z&&iCJxq?Xf9ngoG5YiEW zc+QHuYs0<60$w|NaG15gzcB+8X#{)aHSv0I>J#z5$vJ_u&Gj?tutepyr#|3zZ=^ZQ z&AY+7)bA^ajn>&9jcI|tU+_O_-iFHerxr*0Po+*%wM<kh`r-c5nBv3YwxHty{xx5S`Fa7FuC_bC;7!;0`D&9@bFXpSiJZ*&4ELjV4)tY z9F#w&dq$g+;j`QQ!VTx-Y)df61*`QD{_TBJN(qvLoTrc03D_?c?!%jYiJ?FgJ9I)+ zh8B(>JMz>dKO*h591mW-f-8w*JiA^4AyM|&n@5(0jV#;fTTispcjwXzRDU9>6ckl7n_3n@WC zx?9d&{awxZbc*mOW;U@7e0tVh3HojC>osct3h|_?H&PBpE5zA)Atd}kBq1s*3t?W6coM_IbD zL6khXm#vlSOv@1K-@{wJqS7|oY5&Cy3ww?ReAUcO6W#<)1cafY_{H?r@c-+11;!wf zsiSr;q-W6LNXqMhl)&Zphoj8G!hKF4L8fInkdMO3Q*d2&PRmeK)DK*fqZ0Q!= zZwne6)TjBU;)q=SVs7gppy*QH6zLFMd4sGf2vCPkAY@&Iy4r-%{#Ll-98U4Q%@C#? zQ=2|Up3fCr$Hz103QJ%8NNXsto49aWJlum+I%gPC3=WB|H8A~6en%(wa2{$*+(`Fp-e}I ziyCY+&54#_t#r!54yfXfrniJEY#R<%dFD%NT(vndae7Q6JN7O3fYdRjh9C=WEmciT z7A*FCw7%W&!R(L|!4bc7mtnhn=8adKQU4FxKsc0)=iGod@GV2*ziN+QBjveMM^Ifd zJvoW(Dp7wDR-pvaG@*-XiPkmbKL;*z9t(vQEox~9Psc;l(C%NtvLRab*Fi26?X_4AR05HRX3Hzam_ zEI&l>^~|&yxWXxKGBYv#eDv%;GodP+V2k?su>P&BEkE9tB6cCtWvht899TCxJ-Yg# z3>4ScozLd~@MDT0=VN2*jm7w8wh%@yMPAfdDLXu+CNwhSaSskVdSUh#9d;5BJ*2VM zsS18iRf@%c5eHTDVzcdLDZYi~qWX~tUn%pQvq?zl^wwS+5R+Wh^F3MNNV@>YT!Zn& z%h7GiV1tzd;mipdo|^{`KH%Jld6fZXwCD15vkcZyc#DnVG&P+%vTXx*F_D#G{>x+Q z!E+*lr3_xHyn=y{+!DjbGRlj{C!UX`wq-#y4ERi7VToq~q6PAmKwC5fc}zgUaPh{A zlcD72q-JmeQytEc|h@yt z-_pj(y$(ER$3LDbD({A;Ce&@YeuG!130Z`6-am+h&Y~O=gMt4WkWm8|qhwg(Cyi!Z z*Upb5fb~Ga7E&C6#eNr#$`3VkkkU5jl5-p>2?)6laB9bfdm`j(w zN*Ta$)M=#*jwYP1AvCK)K+n(u7@17>93%n*xg{IBR&MsAC*Kj)8!%G$uv&1wS^d`I zH!NL!z^S;?+FNf=EI%Y2x z^4HLEIgY(8t@~h{rC0>G6_LsI&!0as(4%y^9aaSUp^n(H?jUT3XqCCW+q93q(52D^{54e+b2r#hd$L{gz7Yv&w(0}-lr-v!z>Eo{ZW}4 z&S|RNkP~U-<9r5OrCVz~Ws1c`%cdW{cYJ-4*A;@4vNiY2^mG|GnbS4^Q)_GBrkbh{ znZ($xW=#marE*vCLzKP8QPlAC9*bc) zAg>q)a;j9fE2Kbj*a*-Zz{-cWKrMa(l1g4zXojZWksu;nE@5C~f-UWROE6xwU`5W- z(q*Y*lLINAtk%T9P>E2Y%^e-iEhc`@8D--tVP-kJ$!~WcZ0K#hky2c6J((UYn1J$) zbBNXnU(7j)o9vTiyjmKT}OjjeP@&U=u*c(rqgUj~t;D<5Ld=WxbPR=c=T4?+EML7$s&uGcrIaGS?*Y^uI!GnbTT`HNs z%j%FKP1gx~DZf#d94PvJ0)_m-YKz=|5zYUAR_T;^xem%+}sKJ1+wi3@7ao%qoEJktUP2hhMT;Ex>!v5L=} zR(C|5#yeEKOD1{Q22F~B0C`mmO=1jh17=6m(3D$|0Fz6nYV zg&5)oqf|cPhH;%G8svR(s>7aky#;vtkPxYrqMeCJY1s0h);4piI!9`fs!rK?**&>R zXq23YRz^iMYp_zHYauu|7+X^fGY=^J8Qp$eaT$GbbOOZmr)+K0%3^07*|fbFD7f=H z-DDWKzrSN5Kk^=1Mo%z`i%B9(^gCG%05PRjJx~9QrZdB=9YnnmY6rsxx!e9)J7H)Larh z06`|ar%6Cg%=@aAfR1iefR#0E_;Z3TV-57(fyaHmn#*aT>2#!oSB;OaZ!-uKmC-St z<%BnTxMv)trHht0`7-z`0b_OEl=@<>ge5voFNdg6++o9Xu7F8B1T~Xv$|tD+OB5iU z=PHr)y#o|(bYeI4pL#4y>MH$b7qcw-d4zJ?-|Rl?bsCa9I1fI2u{zhQjBfDTw{I8z zds&)2W*_PcPDx>|K})I8HbF?ums`EA3l;gdqR^}ovA`cz5Cc6F;A}W(GWLhvbl5;_ z602u5VpH&bg)0=!6avh>93uZWwHT$RyDe%Q07IJeik_9Y4FQhUhv$xD`W4|DE=})qLnwdw_BmGKLNK~w=%0I z6naIDc!DGqneIWIme=WoZhM4}f}?xpE{Dbg?0dlIi*Q;2355rM$PaJLJ|B9g)Hac7 zlQS_DmqS8xqtqO1Q+IXaV%RSUn82bIV#h_EJ<)%?*wz3dOJ*dn1O`5>$Srk=x5j3~$RWV@8TKsgZ#0wx0s;B|EJP?3 ziHsoOg*Jk+jJd>yBi~=1IAW@7 z`5u)%>iXbrcwKILM{j9`%ZN&`gEJ)HeUK%r^3y9B5P(!G{^bx=x!&nGhM|-fmgu7w z0D{``^mR%-QM0MQr-TG{%1XDru|eeE|K|WOE=m;CU27pO3knG_Y5J}r2D_I~`>Od1 z$RwT_i%%MMk34kVu6Ld?fXYGL;4x})${(!GhJBx1cNR;eQxH_^3Y^dWD;cO-nB#h` z<8-Ub+PV1CJiM(}s}BWVAwtxhTk&NmVMK29;Sqa z(#xBg;pDZr^neXxyaYbc=XH?91WW(D6urf!Zk2`X)5V+{j++z=4ltW+HM{DM*8mLK z0C)53^S^@6|K7oqy`h$|h?KqC)j(nSz35y`k&SM968vpn#tc>yRgVB}V1T(pO%Lcr z;YaAnEmIv@Pc5xs_B%53Qxs0dh?wESr{iDJ29-Bpo#Art;oY1J4j1+1KsQR$l?Cac zkpz>RMPyD1WX}t^=cm-*mg4yz=Uv~w>N+KChec;V*r^aNZeTfA_vFwrnEGdcM+4O; z`{`Kc7T9=>a^VAqjMuOtwV*W1WD!*s zUGi}U&$m6;{^-OM)Sp1R#nF1`qbuG{L3;!;uC5%#bfFSBkgR?Kk;yRB#Y zb8C>7A@$c!<0RU7yGJP@)P@=Jj9YYK7a-e|m{MCv(|K82OuQCOUQ1~RvaKMhL{2C6k_7;T%XDy(~X<<60X9sXB5%gbSdCWHhD2d35jqs9qc2eEo0jz7-@h%UcGT}>EUlI$f2)>aS05;1kJ zBVbp_SzG+cwf9&frmA}3CBSfMRIShck(C**EX@61n_*M(--YbwS1RvExfr3aYc-6T2@y+3)4m zvsSk~Fp*WAwz~JMm#L}vbnM@H&f4M?hvbTH5!341%5lMT!`fzIhL`tkhDFqkD16ecN6VEG-V~q}l-zE(x`({v?8q3h)8DYd^m6y9)m&fdj_fh! znQU25WTaTU%R|4T&fs#qJ@?RnkIwP@M``!z#NZf?If?ejbeQ5qMSGjc+8*!LM{Z-3CiS93wyet5Dn^%LjUl-h5h9_cS><`JA;v`qkOROzG&$bHUH9`kOYx)q-Ct zZ;4mfE`^t#q+E-CVu^`UQ@J4BF(oUho{tcY2_LQqN?Nb_Szxr7Z#I4YShV$cFRGza z;(Yy2W3YZDL?|cb>Yt|Of`jsw_|9g8xTnWj9#5g3{3{z;^VP4AH@FjOL+3e{@6`Z6 z9~Mf!W96x5z855mJA=OuWKu>GKw1w5ggxCA^=_4ph*VJ7g(xJWQ#rZ8omlQ=Z9wsB z;xMC*iP>?3#zJJ8D`TFzm7XJeqaR)YKr+MQgLx)e%IxqOM>xrSD^~xI#7;mpQ~s7= z-~@Ge%NK6XKWH0maR0Za2x09i>QK=8WX zUh6s8*j41JtOSq#IH<;6FF+*)nx|?-rV4rH=&twu=CG!Y!P}QNep0ji@olW>Y-8o>((G=UX-|zBPnFg2u2nzuqx4CO zbCpA=8jFKzXp9RwR3iZ`a{k_pHpkaowl9+5&JVme zHx3@_!IDXID@wlJW$kEoiw?qP?6>ZU`}Whq!M*yrl-aL+=WhH2Wf&eGO$+=35QJf% zSxxP3R4q+v(QEah3q)_bB&hO}ZUonW)&|HK_PANw5pe;3Ez?{UlvG5o`<~lC=gnz% z2N`^?7dlLD>fMQ*ZjXv@Pqlt(%n~gaGkNr+YJL7Z{-um_)yaXM0DLTRHa0GE6mEcL zf&lO!gh7HP57NuytfN3_36;bI&nxlvMzOklr$wrJ+T*d$OnuH52GCutV$`BVenJ2ghBVNj zM>JVvm?7;{>{NV#XGH^?nLIVpK*8Hx{0l{1TJww;_RaLyLv*1hGs6N0yu<}_f zxThPia8vY(b2uT7UU*r4+7iR_T^Z$AaXaqu;(Lyb4?&|V&?&fAPjn7e%&jkJDq0L@ zJC(#ooFoxM5O@X390Tm}qIRK=UBX!J!N${mjE3?FJ%L$3w-_LS>4VWjzAMnQ{Yt0? z!C2m!GX(`j6B@?@R1|#4mCyVgS3-ZG>1$9N&#d%h;PydevpS(h=>+l`d1}L0aC^X+ z4*aa^pz$BmeOn3@7}rW$qs^xQS+{47Uo!< zr^Li$cff2YWJj7U)-|HUlU|i+p8>CG^B+%{m7|%+f3nhC;J) zD_XV;VfF%YR16wy-#@aXRJIgQJ`NYfj}S24@Yvv@xy7DYI)u7a9n<(Jaqe? z&b1}g5AM97Zw;>QOq|N8UKP?`ETJ^ueXGuzIG ziiKh*;1V(+mkSJg!!7+Z&?2qmdrsf@$Ke3#fCGlJ^XmJIwLfVWXYk##ge$1TEB(T` zn8B-N#b+l(J8tzP9Q3nvoduB!XM^sDX>t;cjHnL}L$Iqg4Wb!Ho?PkZ>E}UV=ps)# z_ZnSatC}TO&cfareAr0O1EYV2`hUZ0Dg=FNhU{Ll_G0lUA>oX*l2SW!o~dk^D+JRM5MLKQUb@@d)btMNy~H*KlQ&?7 z7?m&}978|j;FNfg2VicC6AzRAtJ9dy2$#F=Mo&^t`O~|owE~U0j*f72C83B9BE?Qn zw*#kFGPKvX-N-?eZ4NS3GFV(C_jFFGSz^iZvxCI==c1d1pDL zVL~Omx3`A>z*d0^f@R#J``=nqy5NlLOjBlzEzI>x`$r5Ph^Y0TO~_*WQyGGI`w|>4 zD|$c~Ht1sdX6-RyCEX1QL$hN;=*p zXm%wsZqtB=8}YuD5h8IbD$^k0E>Gmyn)vYWv{Fmstju230q|?)bZ#SB>e@!SAEYC~ z?#xWzUC&h(GFU^d%Axenju%u`CY@ol?R|dv$`HQkd(t5pYBH_JH;PCwQ0+mCE5sl( z^jOnC6BA{%HLyXsCmF>#?tOmqjcsmL7d*?Mh-P4=9LY18BvI?N?}>)pOoHLtfqv({ z&`9wZJ=P_7Ipp~d)glGmsvCt0QfSVF_Yo+)&+6{`9E%jmPF{Nq;qLN2PGk1~w6YkH%j@#ra@0T*yYkb7CrZelI~}5LDgPJOX7y(p^~-s zL`;fO9!+P+bm0~(K=?SHG=aI32J5S_NbU=P8tWU+~ly@d4 zCWJ)M9IQFQG?4Dhd|w4AX)_!`7#QTIn^U8#cr5fEi>V=}zk#3MJ1rhw`ZQsk3uE)t z!-G1#nu;GAv)mtMCX^|hjI-Zy2Ca9Dn>gXL;JFWB-cFn;AgXVt^-yHHU zoK{ln{{cI#ohbPqz?$jlX;xtU1wlzxr`&#$(AF;(60MQL*8Sb<=g!V*j{G^c8Eqif z;C!^bX#jmo+S9nGJ2#WIV+P!0Ai?+0*S#Os%=qgaP(#rLLD0)qX9-4jB+}E^%9e|? z58>0GM*}*)epQiGPzV7#u%@dQa35W>sEWBJ(Qw$8FOQ+gHvT>m@z}M5h7brXkBf-cRDklflOetAp)`>5%@0Zoj-Rf-Es$8g$N8EcfQ&RbO&7U8y(fBQ0k9a@3cw|!j>oS|v z8HwgK^Ea%c6@8q~5a8<<2$S1TDFdpv>L;rFg0z+vW_4lgT2W0YlwNiZdq+8f>0`Hq z*5MvlWG?nSv_As}!NMj*OnlTc!=CoAr)z3=D^M_>RbLI}tfza9>P3D2&xhKVPbyl7dcz3p#&E1Xd^@ z1?xi-XX)St#2$q@g_^3G+9X^_U%z4F;^Hbe@5so!bcc=3W8Gh@>y;Ko0hI@>|DYGb zOsgAPYjdmsBMb!;yiWd|>%JWTpPBPvUt=2ER4<-IhU^iKjabK@U$3LH67hfc5Udl; zF@;k!UN->kWb*q%Bkp`RsPoV^j)rYaBJ;9%!B$8C#M^Zqe5-?({(^@N(bq6iP&j_m z6nA>ax#s2${Zx?y@;S;qns{G?dXgw6mG4VsRvJHc3-PS>lPaPe~)Kc^SkpvaHV}CbPB%LbjV0fprX0ARP z>IXtE5ZAo%uXWnvy2#t+X>SL0TG#qBFq~l0YV*&ZpGz6U`!LWJ&65FGwRD;}Cy3+s zV6;|ma|GS%y`}d|&*Aj2=Nk&U2B)CD8LV%!c<<04rMQ8q= z-C2Far_jH>N++Th(7PMOwbFZ50NwfBgwE*1Q-IKaPc%Je?ay?Ig3GAa0NyD45DtG( zHBf;jH4J7JdZBxy35K;I)!Qg3Bd%2+OaUTD-0k(u{Ky1KmQBEJwB?;{25_S zsWJ7cJ-g`K-`R18TNL+7TX4uxt@_PSD8JPY?pR$P?SNj!RD_$!6^Vc9U`0cFZYgk* zji%)m5`_$^-1zbq0by|__WHkK!nO8(N6NiS53tI3v9Er?*HSFqCIsjE&#lGO-MmH@ z?RNBI^UGY$?8dx2aV9#Qm4gI)WYG3M20_B|2Hn+GdadGuxb3k!Q3{(ci*EzLkGGhB zotaq*K|m3v9VIXJJsMigAgr5!PJ0+ILw;Irf$NG!_-1$FllcepcAlai@4{>#rze{f zT%k`(t^^AB5D;v9d*_gh3dI^yD*oE-jh{c`T1(UqR?4}dsl^E4O=&0xaL={Juy=d* zc2!E~e7J{jig)W<^`tZ1o|1Z$-3XHLJW5b@85cEr!XbP5vp$!gp$)s^K4{$|m--Cp zW1t#c!~X$ECRrxfF%^^m`C>31$P5xVQdI1l9}x7ZqKcdvnafS5iMu<)w=_q z%mY3++CL`rhw2YW$hJwQ)XLr+APNM-lXeUgIG#O^qjN$qYbeq$AU_LCnCKZOLAE|f zUqcP4!p5_+voZUI>2aua*iq^BDo<;Z#|oxW4eqwzN0KyDdr23756=NMnTe9Cu1)7E z-Ci?3kPW1+6*tFk5>R(EGPo(gox+YN9;$oL6bh&UH9U|r9$KYrfx_|v=){8{O-4N8 zx3rj9nUVLWai7vb_SWb0=92`BEP4iyJ;lh~=keo^MI_nDGqcBs^KWfzQaTv{oxipr zEEl0$;g~V(Ps=|W)JqH9Er3w9KxT~@YT^JZxrG)0O^{|%w}X|LdFFzZG`Ez;u>Aws zPm;T3*-q)A5>xhb1(=$O>DV@Vmb5y=+g34vbrcCYtL9p-5k1#Nfs!~kE9<1H!r61A zbO!3zxhfWGe79W%?YO z+e^J@hD`}cJ9vQLZS-k}i$G`skndYjpeAg&{V#IemR`R%Z$h90)dZ$|Co&v3r1h)9y9HMp%=A&}h-Xvy*+BTZp zR>)n`X1iC}{sQ|5Vt?NmGZrY5wShdt)7RIRM)*XUiA&NoG1ITwA)%4jjbFCRUFwF9 zv95xoi~abj>1gLZ18={Y)U~ocZ5vXL^{Gg2Xv5{!NZdF3;@ z@8T^LUVLK8fC2tT{b?F6y5*qyoPr`8uYw|QPO>>xW3T5-Gh9->Hu${3Y;tOoZsK<& zCTG+3Un5TD>UmPW{gPDejA4*OhfA^>9 z+&^HkwPYxs6a8j0^1*Ph1=F+X;B}F8IgdfIGx$)cW*f6UCdib^y9xh&I_n{_3)$Orcpdy@F!8@opzSvtu1E{# zPgLi8A+|lq&9mDjrmC)bfAtjn_xcsv?JxFwl^M!&s~@|*EMXo)4IQz?2VtN=zD0VP zVDt$5f%99RUcBzLX&7^ zo4?!f=wr|Up)mi6AA1|`QQ58KO`t)FufY{j2vxv1f9Af*_9fUOe33#av3Q-cxug25 z`99=mTcv3`0%yfr6~$lyJ(KeN(|i=(Kq=#n@TAVn($|XOR-ICMVM^x+Xf@3f0#H1C zVwjd$*{eW#`L+DoCbx5Z$RcS>;EP;qZt^3=lt#8uQU~x(@5^86)&rOOXK%QA>Di}7 z_{lMS4c}*il`-4z{pkHcj*=$6dnRT}zwexeJf}{^bJ%cs)_Mpl-^#N5qxUFu%|*pc z`~1hZj6a!48?gu)4tM@EK6ouTNK5+yOsfpC$1%a0HMkU-UFsszoH?;`DGRf_zWOfW z_cic72f+XlGDb=cW>Zse25L;#9`eq-ZDmwy)OX9Zkw27;aX!ePu_ahqQ3|_UuXS_+ z)aWQRN?p_f!x?q@qbJ4{(l4NcR60Qs@{=&NO`6vPdUE);t}lsn)L^$krc0J-q@xbtQPVPw3t6D%&gi-5A9nm|U3y6Ea0BTtgZ(g~V z#9^kS`pk^hVyUl(VFt*SJ4izYR~GJoVr;^WzyxG5Y@$$EX!SilK`5C*WQ|&_U$*S%b{ykG$bDLZ^G}j5BUMR|5+y;v}IF z3`iY~?o&_`nD%690ITTxxmvAzz2G#Yu3eOC!!;Vsi^<|ih~zV0MQ@6GjAC@k#?q^* zW)onamzJxtrGmUSOOMQdrYDL*jld=^KSiBd8z#8*zS79h*{t}E;ZJtY>MzG#{@9WU zZhNI7RbP#Vvy`XPOAE%Xla0zJQI7h70c@gO{0h&HLMqqXlE?CX5nkIPexYtk%~6Le z?Dt0>M_$g);ai$cRDtsaOoTT`9x$46)~SA*h2>h__J0nE3ne0FX@aSccFl7j?Pvyo z1|kW7P5++Q{{P^T^~^1fE8Wt^b?SPrtqrR!H#yPci;o1qpk0$@Y|snKW4+lXmMRa9 zpVsaG=jQsi(x$U(^pjEkw<+!(Fb@G1pi-)EV5qG}>=&-DQkt3Hq;chRW|koQNGM4H?2bSidvS*FfqrEWkC?kDL zUf!MtToswq_r{NYy#108vH{YfsBiUbFcetBc4MC1mb&W)Rp zEXEV+GqbayFpdE!t*UqL%E`-{!hBBDj1+eNlkYid2R*apFT5QxkQdI43O?BJdXXzK zqFceKVfbgu+a#tpuJ~1L>7d|c!v%U7_}F|@26Uq%cggbaq<=rQc2m|n<^Gi3z^UQ> ztNo}!PHY&=Bv+kojk^n2xYW;w+ryI5eM?Q5aW_keQ*xp%E;nSQlA+1ju=A-S znu1;Hwu;I*$clPi&B0zqgUIHdX8bE|P-{jPjYj9omqh; zPml>Fsxxw8Siy9U5chm$DaJerJU6wyrO>-4ON$##3(UHZ-7f@F>4)E&e@oKry>5Du z%gR{G+bIau`w3Xnh>t^N%??Q4urHhI?{@4iw`~mriphRJPolYpKFQ<RrDj^#kXNK8ySPgCO;uCA(IO8X{ zyX+b!*b4JMu5P#?>UtH}0XQj5fI^0&rfZrqo5R$XK+ycO0FucuQ$quRzo^v=z_|=W zBx}WrvNNHsSid|Zew@SQ1cRD+ZUwpSJ-`26s9X#~bjeb|Qgynluh$qOd9staid~%> zZtIlonPVlhp;;1Gpmno#ar+Y(ls3JRp+p?eckl!}u9Ol2tD(FZ5xeIkZ2oy$aNGD+$ZLW^|+Rc-^Mnfy|GY#W9j|I8Ad66;JUr6&?jC zCtYnUWZtt$nN*2;ojiP_`$f)-qqjr*#v7GgxqS|k9rHQ)^eG><>F!ad)1TJ6oG1sV zC?C!nQ0&ZA_|5s5YEk#~lq4mc!z8`zq)gfP>UQuz=iE;Giqe8fkis2RR^F9e(T>(? zhmot!3xf*6U&J=5CcEq`$f=FVa`~1pTo-&C4tpj+zX?R)cc+Y?!y&yKS^-U(2~@zK z(X<0uwT*_J7Zf?^SD^K4XT`|(t+gUA)E%2!_Jg<_xQqBv&d~559DL5mbl2dkq76-d zf$oJcr4q?70kJ%n@zIm1suu6UNz8}=iO6*4!}xPY8w)gY-G*&eTLz3ye1cZRh0T) zxpCU2;|qUjt&ZC@8}Y2&tLel7-v>bkf~+ljB# znhVA{b;+~uHpEQL6KO@6P%_HeQ)1AZK_K$n_pF>5D77u|%(;dQ#hJCo%xE6ev<6h^ zkb0@#%a@mLkXE?5E7o;3vL0xaATLPe8nn@U^hE4*D~2Uek$rjQ-M8Ko=B%7as7m(E z+xPypyr#L{8sG7w$$eJq!63%9FO7sS}$U$^)h!a0=2!#JQ1 zC)#9^GSVlrL3n*8P~YuBsLuT`>L~Hp4oe?7jrP0s&zRX*6m%PXvg(JGTvf6Jq|3IHJ6BHeMo8!rwtao=OpW4_D z*orip)h>iOQ!T!0J33FDo++lY3A%E+w#(lxx-z#;dJHt>=gI;?@KgrDXr4@+WRcyH z1)KljZ0_D);oH)V(-??{qiC{DxEmVAweAh-yJ=t}A|iBc-JpPXzv()s*ghIu4(|G8 z-61a!A(v#nne4c=o+(qezvr)9cT@*mY#@ZzB&=@bhK@{5`$q2j?7w*3_C@Mshy9#kgROW zzT4kOZhOEx{``UCZBN6bddX%ir^iH}p9k;&jpay9b>u={QKM;Xi6|#$4sLQ7DyNdo zEiFMP;wpPMPJ74pcJOeKaZYaWZtGR(X_U`l6 z$fAXWs#3>rj^RgTog!B247`djxPE=*=|o$)sAScen)`#gC4mwk{8@yhnC&_8wEZnx zxeJw7esyuAnMUb5WxKl;69r{(7=L)el8Ct?qOKdJy!L*jNNNK>xs^;0hw^klG;0z1fw2kxg?C&lK%QC={ani_mtm?zX1yDUkBj= zZBzir!WgX0VHnI=T3QZm+49k+$G@n*V04G+Ms()350e;Q7jtZN`wq}IoC-p!_ffj~ zr_W5(;{{T!Tblv{Jo_#h-4iZCs!$N%C2Wk?wKfqI*wcz7=3)zmt+C z_LI0@eI>`PY7v?-zR5hPwrxF~`bteRo<(QI-K8Lyg`AecJTt@GipD_vZKn?=wm&%A zQM|xi$He?pk0wC^QOZZDCNb(q;a5B6F)IS}K^rH#bQY+M=w-qo|hn;fcL#zXSSWp~##^rbD2>Ud}#SwyAToUH02l%EeKe$&9KWs?D6;hcn2UuXNoFRtPoFzMG0G z{IzU)Uu^EZ#U~cyeBqZQqyj|hB5#b`OwJdNY^ddpjR}lHF9M$MR;2N1@HglHALn;K~7ZMWvM>7QTzur%&}@&?{0+}4T!bq>?*q0rq&ld(+! zCtv6=t)v583?^n~BnoHGi3f`?;OuyAoDgvXBMs6}lTlDGAs1aMXK9&x6*yP;XIGZA zTqoCF8sCVM~s z2C^4kgMz!haN4_oKap;22?^0Yefk96@y?V0(h-KF-=wuO5BlS>MK^64&Ic3JnhY2_ zTuH&^r(~-#t17xOs>Z#+D&E5ocy9n4y{*O8Elr3lzec~>h@mF^XwPLhvTxNlG?)TE zF*T~kZLyjaTJG<_cY?AZ1wcwee|m_)QzbMkENZ^n=WzCC`sZKe>b4%rrwX$rHR!>n zuJeM+yZK%Ft5ltyJlq0=F7ewg+o#nE#_E<1hHt>6-m534BQ-$(sd7LgIj z{hn2|FFZWTPqjbqm1=*Qtor&SGXq^_hr!ljpk9{!I9FDM*0P41P`z6qzi0&T>O&%^^gctQ{72U z1wvJf$$@Jtly|!1b-r@wj9>5NGc4v72{AbLY;VNbe}B;H(e|mCff`?QHjFmzENy=e z|Gkl=1L@C+2tFgWQ4JEm&B9tpu;O5hCma7(>IvE1digGY8JF?s$+XHLL@)MFf%p`KJufV|2s;6NdBI#$_mfX+B)wkC59@XNsi-kUJiV^rI1zEkao6`@Gp!pD14emb_!$`lnX)g5 zLlX_i6dlBk%U0}ifALY~+ne^Dc4Ou+c+bkdpTbHSYeA-Z>rtY`Td5UCh~*Sbxw<~a zmEDUk?PB7r*3@yX{&JtUVREK>_+#G8LqFmkpRxqGS9a?zZb31_{ut$S9<^W@?A!~P zkVwtdhe>Cy^&HNIJf#Kj-3nygAg?un!bc&{on>M003^4Xx)LzR3{5KE-WXs%tf83N znd&HW9Xm%t%hy!Uj>rIjOK=#h0V^d^fGmR>Ocu(76ICniya88WEH#1^A}7pnvz9k| zl#u8Bw6N462V-Y7dMnv2V|ZS!w`@wb&$h9Lq2M8vudm9@{>`cdHMfWA_?Kdapv0LkMkC^VX4LCOGD|R%47M`HYp+4Vg z?o3x^r=Rk6098cdaZ*i7-}|oVqJ45N>TEDk*RZb)Yn0fZ%;X0Jp1;nLE4Y#Ks5_M115llai7O4WkEOyQ&u&j|qgoWhkabo8&=-Bod>qyE~ha78e9wbb-2} z!%9l*qK4nu`}mwjwV&)%jlbL}9Dm6%5XekNdRCC=M|-hI+DV$6`Re8Ha!=cMi1n4M z=~%@EKFZ|@6m()Wip$TC6MQW@^E97krPn?sQrOr2Gp_@^R+z`~^oLFf&JSIcgap6U z_O?8JvH#hQjbPDVi~o}|K@}P`u)pQu)ffQX2tJCxCjj3O#qLhb$;Rc^OmP1c+P|JK z_bh3ZTuvGDVGcDAfXl7$%-oNR2GeUrqZ6skPmfa#xJyP26w&T!YL*KdODpZ>^nWJElks~4k~a>{n*0BR?vA- z7^7(nvQ9@QD*C8ZJ^{6R>tZ?o3NN}=8&D*Vr3r)ik>ESqeZM!g+MD&flX$n=? z#f7Js>sDtjKNm>}IJrSip)TG-bw{}vuqozWh;_`IZB3&vY@ETIFYHZv7Lp4sJ8tWg zY)3p0iI}&)WS(=_QlrH_>?|GEP|&>;w>k3}oXF=BX6ZN1SlSB$Xd0ygj?EUHfHQ zBl_3o0dGMd-?nw4;AE+zH;QBaS>tij&0G$e?@uuBHwZ}Pe=W$~Hz&`bdS{?=VZ>JS z(nV#(rBxfq+R;xsVU9jFnOHc0Bc*}gRxIS6audcD)@}GWAv0HLqQ-S+#%4k5;Z(nm z9HD3EEy)T*n5ASJKVF&%Im1aGoRnhlT88WAVFI%Julgl#P6nTk)kDE~+dQ|{A}gNA zd-|p?VQZ6G@Z?J9_Lup6&LJsucA}rrLy+*KXhjQkafomfQPq7te_W8UDeUdhLerHG z5Blsc=@I+2S`KR7R|+(lIva=`2vFC}y}?szb=o+nPy|vMW(&@XZN=8Fk7b4&{7nZ# z>H4;;7b&CVy9$VCazfi_m z<^p|bmd+8vlDjzEo3q=iD*gay2>d14OIeh?Juehv7NwpMg`IxnB-6mB*dq6m@0HGc zw#921hliI4{t(x0-)se?HCT}}Q6(K*9N|n%OieJ$!W3%5h=d_x>kMOJJ0TfF(_sMD zsSM-61`@XC1Gs)gas+t3$Gwqvzf`H>IsEYE5hu$Tbg2{k7B+jN@J{K~?Lto8-od@m zX#@NHmkfU+dG!1gZw6x(2(vz35;oG+%8bBHTk_M$dr1t21zsHBbx`c`$>E8Pxh!l2 zX?vIcH)E0k)|*kOuXLh9LZeO@r?Nd9BU4!8ATa!xN0!egJPVUqLV=`%#$LRECcvX# z-_IkGG+Nb^(U@^Af(X8}2CfhTSO+~3T#CS{j;MbTxFl-G7u?j&w z;KxUU<4~nGOgTuzLP9k9yl$ljaDCd zw{gaY0ep*{$*@DFfcC&s8>hupIPUvUQesE;^p6PvBS8 zx8&{XkS}EnJ0Ht#rU@R6ggvq(Nz;jp=H*YB9#iXXt&i}>C;I#eO0dgff{HNg>_yE_ z#tB(y?{=1=dzskv)-MBk4<6@d{3awI z`Cv{!dB8?ytr<$kNYX1Ek-ALpH>rn!J} zG?(aK8u>NaJPE#}cn*6~zjp(lrLh3?b6K#NQ<1NxvJhlF%!(T_ zLbw9kx%Ss5Gag#c%^^E>Z1x}np_Z%fHxWRzK`j{1wK+~2zx^hgc#-~eB=rr!11D$| zeJcN1-i1H+q{b&qEp-kBgPXw0$N;;}nDE367yo(A7RkaFp@&sY>>!w*xA)Y~DC?P3 z#<$s9M#M^i32-FwO10@_mi#H^i;s@GoA&It)%cT$1KGa|bwmA{VB0kaT-x?Z7Y9V~ z>$0VcY?nxKQe6i}kA$LTE<%OeoN}2A&F6LgdAok01Ko_uEseuZ%{?|T=HwpkO8EHW zjI7AlhpS0g?^zC2qssS0bh_JI`wUf|PuRL+oJmZovpwtMBg#Oz|gR~ zuo5tcjkG3BQQR_s7F;N_01mr#DfD%R&PB|waMtY2KVO=R-6)P2SYheA$$ftAa~Qxq z?_MwY*f&@Xx~Ef z`zYYDdC9$IL9D6Fil0`=VI${WkN<#V=-PIpI+6sqcQDr(r5^n=%ye{g)&pk6Vy>lP%vKU!i-JLh<7~*S z!L=I3sJf)~Fj5f>=uUJ%BEBk?>mJ4^P57!YhK0!}M_e`1wTqFykyg*hS}{+Kjv1wr zu3#G8=YS{G0_z;wToy?#+ly9x7R)jd5`1I!wv`|lA#QD*rw)?RA^XrDHckRCg@gQ$ zeZX8$=pE5m*8~tikh8)~HTmEw@=eJ*Z{Fjje;Cmauljs5UdtX+NoK$EMi{~`$~ zlZL8Dlp=cZg%0|SKAo`f<=|z?e!Z{p6#(G1HR0QT4S@TZ1T381tyl z>W)l<>9r%ZG6|}(6z}7C^_fA!Qp@Eb-=0T4cjN%|VTIZh;IytieQu;=HrgdYF&V z6g~KGuyyBZPl>6hil8J_`RJa_6+n|kh0v3l?&)hUFGR}uCL;H9%!>mJ9FVSOcl&>@ zNfIs#Fezh8@H!7J<+6B>|3emplMEB}PP+sDcWLo+;c|b=3(rs6>QID;Hth_DKXm(~{pd4F{Sbgx>kST0>nc$F-cMYZaS}R8T?O~y7U&OIw$sljuVx#vi=Uc;CD zyHOBpvqZk6->{?BL?YAonvUIW%zh8^#}I!dQgA%J2+t7W2W>N7mr55?iIDP}B;oPm z&WKJ?m<<;S)fyay5g*Q7@YW{rQDNIoQ#I(VS$~zeTX!MN=;If#E6KKn433@6`K8|U zU*Zt9y2AOo*Rk6Ca~i}F6H{F)j_wlEj4baP>^_;H=4BV$Yng_0M2|0H(o2_~dXaS6 z8xNFF^k7&SGat2DQ%tu;3OQChCeSa}l9H%>-l6`x8T6vFttxXyKd--Ha?yd=jETu1 zF<>;EGn2Y~LfY#jlVf7*=-txoclPVZ!!9|5Ed~#G*|F<(!z_){eoxtdZ&)=)DzEU2 z;yoWpwGv@uOZ(3$I(p(7w2p*zf{hB(lBVQEFsk;PEjVLUa6`#kcobvAKty(N{84)z zQPGup)hS^b;~?_AGl%bC*vy4yQ`T%;0866&?LKq}tB@%#?aYVv{=ZMjO`$OE^Gi35 zcFo_G;#U3`m`eIcDQSn`I*zYM39if+IxbwyQqAqJGK8f`3JF43JCd%Ol}dO&>MYuO zD&2%7yBhSD9FLfUw*Y5XL9{u?;>Q(8Xf;uEEccO?XqBqV=YOAjQQr0esb`BMq_uta z{F8oYIz)H;CsfDtd`UnS-p$@n5DC+LgRj_zgn5SMLgpkh<=4mF9JZnnQi}t;nkhHc z7_KZ~(&D-r*cLz9jECav^={1Z4|`Q#s_0)ioyJ-1eH8nXSH-Q*h zX(lmhzyenw^@CvUs;u@w7<_@_YgC}%_mIQs%uG@1riGATNGALQQX44tTg6-RrnzKM z<#FGZYP%cMkp0X48k_c)_B4JrjCgk%?>U|Ha7%RS>*?qDbgRZM`3i%n>e=$v?iQ8e z#-p3oyWgMqIiUsTy7PJK2hKV?(v(ew7CMGc!oDHa%VGPU!xgV}^u^2Ho9fj26EvC@ zrbt5WcyZfGx954?EtodpSk$)H_R%v8e&qac8NbJboJ2J+nnW%pd@3bFzj}zBSMr7J zy9b<%L^-2d5vd-faZv|rAEu_a2hX3@|DMH!H2;zv5P0`Y0TehdpK=J`o*qblTDdoI zyHwkfA~E3Cw0D>AxV{pQvNTykrCP6i8iUTiQgbzO;C0ftVyd6vY*J0BNRnBv)&_5+ zOUPex5J?NDNT5sl;=RZ5lz96zTI0Z>t@wVzc-Tk^4Du9(x#je zdTam4-70lYX6Pt%F=)WO<2Z8jtepf18A6{zB)$OgBmpfD1mlF*ks1LtKeGsQ#_13m z4{#^yfaXKX!=r-8q+>u3*jMkgU(2mqE+1howx5U9h{t$h%@C@4>L zes1|$!{~JAty!*T(DGY$QI&l2bheT~1#4Ir& zrrx+N{b5i-zPYh682t4x-I;1lZY<@C?1)E91sLV7BX$X(1}|kDrVp-Ih*}0mwwMx` zhHao&L;IGd>zc^k$kCx8QGOZ3SnNg=xVhB-0M@hPyrauUja( zxn!GX!>?kBsM(6Ob`8+3>ocKx8nC7~U%&dSyeTr?dMkTM`g<1z_ z&?^h4Pn~LjcVI>}^Nyq0l1a#ZF&yEQ9n1(JM+$SLJXpAT?lv}+eMF^ub#<{MDiocPMXv*S1Vy&GiTK+{*B9o&({>y;RR^$iZW zbO^T<$>~9XQ>M%USg?T!3A7-XaI0QMJe6UlT>9&vk}RhFeqrv<%aJKl>i00>{p&@OD5%}KHVcs1qO*G_FGNYv3S3tb#M))6_l;~3 zhQr@ZDW5E{EM4NJoa%A{a9EU(IhxIFK)i=I_y%}8DNRmJh8yYWC14JLmdw%A+-%-% zh64&dqLajN)Kh(^U>vWMt;rGwWaX%jq(9FS3h`%_aRom9x0=WtwHhv25;=ceS??I& zH9vA&HbC=0|H#wJVTMN|(QMDdPrSi&eRsy>ds4(IJWD+#XuTlZPojuqVj z#ipbSP-#p8+hZ22jg3tcCp~?VSW#kEE=$#}PG7FBHy5*bZJPegq{h9Uqhr)_L&09FT((%@$&+h{Z_fd^ zfwSw?UD$)pDIF(g0ci^`z>sST`TmOH>aPymAAb+|$m&rnQlvlWXd4TE=BfaYhSH5_ z7QX?&ObK@{3rr&$S{&PXN?v&|vZV!QRA6GLWP0px&KdB6XA0Wf1PcOV=;6Z(O(4`>yaC;Mijm6_S=KHl#T`5QAu_wZ&V2 zCkax@Bo}lo*<2YJ$#n&D+4gu*LMM=sLYW~aI^h0b z_{b^jV7bw^0cp`Zy9BA^`sS?M)V>ERSFzHvAS3l*o?4bbd?gZac|KeNv&DLZEBe38 zXuFEGfW)kUn3W+|Ww8_ra2d}~E0zZMyh%}}m_{=qhrd@Vbf`T&P-wycc2U(BH`m#Z z-i_No@4MA81*RS#MI#zs*REgJhxJPTn+bSfA%Jv5b8=un9CRuWbq)X)O44Pt2x29n+fR?2<|;7& z$~VGgWH8kp6py&0j`mtS)Qe7>Cg7a40g518ckN)Av+@g+g?;lHI9sJp78*WgX?okM z2E-eRfQvneG(M1YTUy!=8Xx`g>W<*kmxvgNn+@D9F6IKw9T3fV(HJ8zApg>H#c?r6 zj+VEP%7qfc8d~jM3GoOB$i*WKaxnWZL=r2w>Sfs~c0N}=i$-ItnpoC3gm<*a#&+uTV4@P$q82z(C7`&cC4w021Mo&Upq zKPrU&$HH-}$&rvxU*G}CkRWL^Zmb}}j7VBcPWraVGl95F_Kpo~E{D%gGVQ)kKV5(Z z{2^Lo&bw&|Un>KhUq&nLs+V#+WOkhJ7y^E4tlpnb_nSo7K@1m8V{WVbWeO7}0c9MS zvPw|1A7*Q5y;C*w@uNJ!(ur|aNN-OSrl(wCBqseqNJ)QI1Jt) zYH>tYOoh&;3G-G%a5%k_9?2LexY;T!PXH~Tt*;ketzK4T-0ovb7IS=q(~pgiqeWK@ zjv@iUXOq8i*z_Qya=YfdqTN)8Qsf+{R0!laJTr$}Y^oIHEMfW7v$XO^`S~DrjS-A% zFM*;UT$bx^an3u-OC`N4dbXElVG(LU?R*McIc#XPQmb)G7LaPtB09fUppY0C1Jh#_ zf|4)U35D;1Tv%qOO9b5ZsP>E`ypB&2i(JsP<5&U}cLel1-<2L^TixpVVVFge>XM3V z+7b4>_hjZD1N>z&$D@DVVcEvQ*^8)m{&Gp{BqJrI|66?~LcqoTgdp^mJkP+GD}($6 z@zLG4o2{^8GCh!g`urR-2h?6T5FKAA7>|GF3Pb4w#qjdE-zAP!|Nh(PE0U^@@189V4Qx)Y5cwZC#l2_Tgm zPDE<(f1va86H`I&4x=w2BV)1dc2EEL^WWCLD!3&`sh5;$Ks05wk{J;1BoZ^sb^NTO z*N?}|u2}aaCs+f?(y-H1j(4567jGhh*1^G+zkvvXC79u@N&qaQf!f=_dN8KUvfMR) z+4JvHx)4%crK|Q|2sjFap<~eN`fNVG# za>sfg#5014%W-E6u?D1pBw5@bA#><2QVdJ%Nrxc90)7akhnuYZ2C>^ES;eI>|)wRaggXp!sC3?xt1 zVOy64w>v}?vOQNoH(@_dNem^j_xqiHUsY)WTG*UgXgoM>c5S$zg{~@4;+1*&YR}fh~ z7FV&3Q#?Mhn%Y`F3ey39e?UGXz z`{YNNo;OVoNuD4d74JYqSaXpZjAOxiBN*0>7y(37soCI=j<{bSN{OyM3y0Qhhm4q+f{duOWk4*H;tE$ z_adk5^!NX)^$%oN(CV*)?F0n*aK(WhhgH?;+w=G9nHfVCf6i9@AC$s$ zyzh#klg|js$3V|wfbbxr0`OhzxTolPjptM?&SCMrHNPRncmiu!~TlmD&#d|cz93$ z6=4`>UFf8)K=gIO>Xo3Db*rWU5pMobMO+A_uX3Ll5`G$f+DF4>phY3+W5?tIYjwS? zrCb2{@NmT$v2MEnrj3rByB8v4tVy#9IcSK3)3ZK2?GH6-ntFvCS zg=ga~g=jYs6o?Ray*+SL0s%#Lwt7{>RyXx?;sOj%a{Y6XVNoj+vPnyF@li`HKslg3 z*h!7DxE9K$GJf;*3Iuqg-n)X^<_EY6VZdFe@BO&e=X9rKkl@?}C*1zw&z9DCyjDE; zA@_sxu#GTPy+u&2kZT;3hr_!%i_|EiI|S;jT>3vzvRieZlJh~fX5al2aJ3b6Cx9EfmGmQQf#g5^%? z*eeSsGniUDZ06sPbOJHD#FCN{+1L{lT92Q4R=ApJL8)>^`8Kp4kR?Eia7H;_*y9JT zkEk^V^sYX&DNjj|G{>&6=Gm{X9arv<&j8ynKGFpO{&uv>aw$@x1omGb5I7}3v1bx& zOhc>OunmA88tiXA%UW@@8auRdT+ISa{yLCU%&0^~$@~X9KUG6S z7lY*3b0;Rp>XNz(99N`Uf_-T-dfXp{g-x5U&AP+O*-iV6l4!DUWQ&7DtZQuWTIu}O z_q-bd9lthQxRKEjd}Y`-*!3I!;y^@h#i7U-zk*2b0B0F>bu>#$4)!l9EqaV?{xcwss^sHqF+H!*LtIQsZCY0 zBF#T|=BXG|#ov9{&u>&OZ29*MRO2fn0%K4d5d%0aq<0DkE!2P)7^BVt;Qis&XgeY( zO|TdAD&;X$rwzr6yGeXpSeu5JvYQ9y+i-9uuPTIXnW2sjaCo(ruS62{*_UxzQ<<@Y zmv6(#_w$NN-Tg1PO1e^ZR?md#fPoMVSkS9yFU9Rat%&}p+%CXY+Si&1JHQ?=C1HKTh7t2 zYWpO?p+y5mZ~{mrddK22?FXWGMuo3s*qRWaG0gux0Iox|Zc~Ubu#@znpZ<|%j>hu- zsM1X)n!}%A2Rum33^%^=zoN8;n8)Z3A2eoa9T5RAAZLT)IqOdnETLZ;y-MYgf^G3wKpoGc$deTgDUwzZylfp0%|Nf_a?%`sksw_kE&eO-Nf zW_mi(NK#TVv$zrjK{+O$0==I6#VR0!v%7FW4VuZbgGcSgzDVl0#wdSivq5fMGs;<; z2awFEc@LIQNN#=&Je%`$Z|D69eaVp`KYc#JnR=3J)*x;u@9XQ|_Fty{^>nK;(9cH9 z|2T%gt_9(DsH%#vzvnBm7-1pPbo{8NuU`+$Wxc@?tMfU{dWCiamYGU z6qTRQPr#`U0JrN5zriC@C_^=^!qJX;53@Ivz<&@l{v?W@!{y~5=aON4+glzIiD z`5}zO!_lx*Z;I6E_(QqAC%_6E9%QW#o4usvxjmTc3MJyw*(q>@2$lTz)3Ujwpe-?nN=DrLe-V(4omKz=zdmWHN1xZV?mhp_dLc*J+I~mTdZad8Z)!Q-VDHPs zYwL~yAq>}v{`&Z80TG=V9{fL%5u6Ks&?*znK@37#4AiV6x}hO`50aSsY4R0U!bDk? zX~K5QB(_xlet}?e@Vn`4%D59vve}xIjkdtmjTt8C6c#gzs3?3|E*X=r;w!$?l_MWj zV(GNru4%2^(j9&Bq^H5V72TB|BP||HBhfU(Rmuzl94Id;r4xJw0m-7VjKDc`HXXtH zJhJ-V00+F?LkFv;@n+Y?Ce>BAin0gYc?z7EZ3b2?U`7a^Xq|j zI+aoF&dS={M=QGF%U>4z>o0LbD8AC#zJcVa*a@_l;tw_4$fSYo=IjBo3x%;2Bb@=XN;yaZcjWWvWHx zn2F#wTTl#msaJyQMzO{eE=eoF16YGE}2dn|WBt?(1 zt~;$SDqWuXZo!QKnqXacRVsUljRev7ukr98?X)2wr(mk49=w;u$01dv;qm$0%Z-IB zUz$G&udoL6cs%0Hw7@!|-*BZlT=sdJaY+#Qu!Vr=Wr7Z`tT{#dJ<>$#P^9?_C62#L zPJV^j{+{i3%?_gY8lcvt2%05mVd1d9N{-ZxjxUb~$U_<_yberG3NLbooil-5#Iqmy)cIxfkwq)dfcb422LCGtun%u{ z+aA*63+=!56?o|V$?rEG`O*AmG$CaEiR>rSJvtg;2uHgiAYx#s1_EY;Z@=(bO-2`1 z>MS9C@QAHc&{-EcOsRk>l2D57P(o0lRtViJ;RpVF#py_esd3jX`=EqQ*jEBYG`^!Z z#g~S*2VmFC<}-br91dRkN6NRv)_y(aE;~$6)`Xg&+15M#-AjD*u}mW<4#nk=>!l)o zc0iawh|)=aoO*L^HsEQRF4A0ra^d>yAu@mB0YoG>B|gwrac$3d>9;+dDM6JyTuv{e zb*>(?uAN-Q2s*9_ztnpc_5l6PPI^Z%=nC0#ee$odLAw}V*)wdRi@v$Qn`NE`z(9J8 zx^tYxDV@1~eu^cg*oz(XZl(92M_vcK1@;693G#Lx*&v;-{QF3}TOkF-Ty>X+E*%6^ ztO39f31d}ULWh+5E2^@BT@*}{R!%!{@r-lJolj2LiQnqVwdMOY_VK>7`&i@@rTJqu zZLw4)X(7`zttJf*z)_JYsyYCjdT+JPd~Y>uXxhHGyN&*$L^{X0#nuIN&uzu5+3Mp^B;UB_zPfe0i#pflx{kxIO4M6a7fmcj|a+?=>pENIqUuC{@8z2@x zuWVuX-Y)@3Ke@P^DH+ANFRxNKlag8%hp(bWI3=&2nBR%}0;Y1(|L)(Y5$td7<$xRq)S_*F*;I-XErG4#Sf|3mU?Ai!@mOsi=A7v&B@QB6AUWN6PmK zv8I?tuB8qVm!_pR0atmLxum5n$XEsBbP^4tl+g(yPmnaQ2~L-wafV*gpqN12@f^AV zzwmDvfXV4vM#lu2M3Q(Ltt%kRGzJCYyz#~hULVk(=w979_xGFdG2nLB8xZcE zn$%}{8v)spnl~{Ck3VRulrkYW_t8h3ZDow1)*_d^hoF43f2%-Gpq zyUXf1SO7vo7kk;*8VVrnj39o_%h{Y=p5Gv|sb62_R-2g6IK8enIxkQwX%l*hk{+PO z5$G2hL7y-(oDUI6kqt+8XS1kPAC(v>Ox&XU z+WJjfnwjTz^nafHA#5^urv=!0pqFa|Dj6oBB!7i9z@U=%yZb9^8+`;RNs~K8o`kkH zobR`NMrkPf@m_nCez5J05qIxU*`Ux1H;sH`gNQDhdx^sdx~kOdvlg~hSgQ@l{5&g< zqKtkUdBtU3dk@W|vM?#Y~*um7_6HHe}ZttQWSd7GL$lacaaGB7*}%r4THO%#fV zr!|L-Nc)`)3yAyt(8UklOOvd!Wx%EpnMMEkm)`?}6RpApvKoqHfDjh%^qtF`Avx!p z{?rrL-Svoa`wQO$bdqjgY8<{s$3=5rr$(4!{S6S({9j2eB5+}G^*wfhO6`(UI1_`JI1>w9@S z&tevv8o~rbH}nC85HaGyd6b}VIhrIiI09?TM=sUfo{&K*OA~Z~gdRVTru~{Yalw7c z4k&FHX{)}8AUtny=>bGIZdS_G+su;b^AIX7^6SXdv0EuCYC182sDP2%3oZxxGIe>80?Xp9PVc{bs-e9%kz02G-{4QIri#H;R4|BR<)p|dfJ_3h1AYjy~`Iq4`FTPXx{EY2^0MkfO8 z+-}!nBKDx{`Id2?C)^C)u{~{ZPWM_E!96$5>|W0LMmwxKfIR7QCDFO#*BObXKcJ_i zB$L*{<-S%7`@eGf`^N8TK)n5VVXmH?GVP(7|0Hp?8iaru71vyJ6s132sYkJksbodQ ztajK9QMxD7JAYk+scdAIC`@n6u4wA?Lb0IemKlkdnlm3`qaZcOe1-J)3Ug7J6j%$L zVFVY$iO973>&IE&X9Dy?sw063>*^#Ksy6$kiQVRHLJC!`5){9TmgW1blg0u=DoBW@ zgE_Tgu4*||T@$O)NwZRX{9)(v-?utC1k-m^>Ba9SXxVrhIWP9qP$`ChRzFd0fr^+4 zZ(&fHpOKx{!8HyGW}z4@Q$_=>zodRgF$o!|jLc*U-<+@inv!!Bouf}@G*_`5VYHW5 z*Ba+rm2;ItFQHI9EU*=F%|Rcb3}aP>cwuVebBXoJDijVO3_nZ`K%+$ka}gb}oBqgG zVnKPlbFJhWEImo$RE_meI(g!040?=fPlLF0vBOuNUZskkN06~VN8o*a#;ZLz2ggx= zKfx+HBqE>1K!-x?!LwHdK0;P@r^10T&oi&kTIDXcH=ZcW%Fwfi#O&GO>J#kCDS^&v{wimva=~`*L-^*j&h)6Y4g<- z+3NT6x~G7iT#`c>!U$fwG{6HYfV#~T!a70hfuwn+f=@Yz!b^JZ?FgNe-4}RK zhpO)lQUQx)P-Z^FE37${mxbq1QC@qof2Ka+;{rbIMjX&i#8_wg~T5`cm?DPndUna z@0^8Xv9bhiP0FqtPL>-|)a4Ln3GK{wejrtF=2b|6KApj9H6KlS;5`*BE+}I%?I?XT zY3>nG&8PX#YpfE+R54z}qwsaNs1@~!RE)iY5&zL%QXlx2mgX4zdN#!_J!cd_M^gRM z_xFg`GHWlSj#zd#1AT9iL@J%Wy1Yb#r#nqKx!pQhFwOo;lw>4lee_?Q#BNZ&X6qvA zgsRpQc({f{SyZXG?~a`=F0uoJTon2>SPKP0SMu?TmrFteL*Ntwn278;@SwU5J`U-& z)S2yZ3}>M~!6<~=L>HJ3^)nq!qGcL=zK!6P9rCnmPSqB{Op3fbeiKQ%Q#`xg1)e92 zD^;9sK(R2(^yV=bsraa<=AvZKB@GrP9_~)-VKyF`xo*Z=3)>V63A%D;&!6FBIG<^( z?yST1>7Yn#FfHhtK2b>nx@2*Iccj})L3+J?K$YelI!MZUWn$V+LC0)u=fvpw&FU$D z3X_CLW+wTl(O(>{8WfK!W=(%no1y2TVcu=@>SIP!WXSGvbi&k|_~`MZ$nmtaHMC6- z*_wREM%T*3r%%_6y3#_Bb{FF54c+g|&JmE-QK@t;1gtb`JGd(mnsYktxAqVTk zzI)$E@HG_IL%R%{`1Wm82X{%zqe((SBa(#w;?fJjH+0Ur#oxO}t0VF$RJy4mCj4C1 zqk}Uxo2M03v**oBC{1tyad^_wpHwtL{=}<#RBh7aVE-%t0xhmbripBiMc69@epu6f z50jz9NsDcEo1yB5eTxgUbfc3#VMoHPqSGB4sDu<~9aVfbyT+MJR8r`z75vIG>}sNQ zG6Jr8nQSURzl4+FEGRyzFX?@3_r-VqY6-Sb3+%B*XfWvyXqqGyb60#~~#vc;o8ViQcq(GAt6-wChLUk=j^T`g9>y!!?d;`wJM6%O0ubcg>Y(EE6LT$@v6s@ z9K9-i7ta_plFs7bmu&1CpW&w&8{Hr&&+sK;zO0RH?kZm-DFF(2gABi-)HpkCYJuzy-Rn8) znQvJKpV({7WX`vx5Sn~=V0I>QHIacH6;v86eM3=V@{Db%xwU%9L6JigeV>-c#9TVj zZ+J0@A<^U43KWFcWVPrL^`AVB;naF8ZxB=uQ-C6bza~uiRSn3*i87g)N@OQ)iVn)@`M-@uPoNmS zDt(iqm#s>;rZVjN#WuoDi1=*?r@v!EH{O~&NL>)+3_^evli68~?Sx3jl$X?E`5coGnK-iSpSqXBfANjXECk+Alr}HK|UYp`fO= zC`up5HU@D$gEVX+c7}NDIscx~~u_t(ihkQj)6=P<4FL2dV|^ zixGw~M$s- z2l11JlNPlFmYxkp$sY`+!+>!K)aH==tu1&Hd>duOhpb~6z$5_)?(2w!5ol@2&%+}J z0q`VZuh66?@~wC@qGArv813owsvjYmo7B`# z%(rrgynAVBX~@1^c^PheTt}tIRHe{t!F}#fUr(dmn0S^32)TBzcJ9T++4{fO4;>nCR|CIlFKV z42SE~E2bs2ME9NBt6v{VrM35btfJx?({70o2-)AH8`zx7kLha2^*$s;WZ zg*mL~i0D2=XOE7;{$aNq`TW^8$8TPa%tU@0coxgu z1X9ElSx9f5^;t8hjP(JUySICV&p<7A{!`t89aTT2w)YQMcGkga4)Y$wbFAvs24cw1w1Br5^y$>rmM{XbN zt(X}Ka`bf1q*|B`QAe3cxL7K_EKVO*?u@H_wK8$WV-KUX>EUEB-_Q_(lJcl_$4QrE z!ekrFz`~p>UyKDCWV22M%u zciZ=)wFb%r`|qp;|1djW4?G)-@}=9v+5G#n@hsS4(>|SjIOd((%`_~({9!P}J>uG- ze*}C6jlkk?cbp)mk49&+FvPO5$q^-!`g=p z#DIz29727;y|sgYd_ZG(5VA%lRazzF=dxJ0t`pm7GPF*iv(PE zSB-H!8V(=kP>hV$r2s5(AJ}XGWf%P;mpHz!y! z-{YpZep2oQ1VdwNYOhW@apTFRK9XJdtR1eF{*~9HmsMrdnZ7&=$4tc#wS_&oulv0Q{C#*nGg9z z`1QHQVg019sVob`P6=G*iTQ+R8Cj~dp{nqyS}4Be=7@LwDf-%H{fvfUV%FF7!~6N> zQ-T^w*zzJj^A`d;G7!?QJt!$_<<5R&^`q;xy2i7y`TJQcn`JHQg(%cYv0!#u>{Qv~ z0em6M2}zm__?PeB>7J-7(u>e-cc)45`N$YEi{|;&_j8#ffaR)hVRk3|>%_#M_K)Q! zUp=fltaO?DTvho*U6oOU?l$NHgb=C4eM{a7zP)zw{JzR^I?3hDN__z#rHmG7oVv+7 zGp@-AHFY;FcHg>40MWdQMDc0RUir)q31U>^(tvc?+nfzo$m zU8kvCN7vn+xfz!B7m*0fxAtW1GZQmN2g7VGZ)bO-<|WKrVob`bBGfQb8$r!`T{aIu zfN4)+-9yzhou){_R)iBr;u-pj+(%01iSf&nUt|t6UIdi^2lQp@VN=r*uQD*ofEr#x$CtbA)FnxmHZ;l%eP;A`5 zHN$+%W=m|DL*nqx$?g5J~ zW8#QXR*~8F5+yCS!U=3QPg33igm`FCDyIZhN&N^Rk17~sKgMCJw>v<~W4DHS64;_y zq;e|5&I`<8Y$03Zb1>rtlA3vDu0X(MxbTut@CLw$?D~?EWMY@ulk$?__q%!clXLvI zD0-(>J`|CTTCP4iG-eAyoDAR|7FMR`b2AT->1R=vb~H+4 zyc6^H1u;<@hNanxra6q5(zGix6@zK>J*^PC#*^%E4!8N5*}1tw!YFeUNrkFt@8;+5 zR}xz`?cXa(BKC#G_}ZGA`q+xcQTf3Xs|Q;>D_|yN3l9P6`otYOFwy|vLlywV&fym) zGN2Jwh0Q0BjE~Jec1A}8ER3iShL7E_;h{SuT`}42)Fv-d==Y6YWDxkMa@~`anVnk= z=iN-^$}o|#eIq0FqLcOxrFn<=RryHH)cR!Jf_Q@L`N3pi*Lh3-NK;L&2>Z?1w)O91 zTH)U?{}1_G;?1hJD3&8AXyHY!DfqzuHTfuNcf7K)1JaRv8fgmtq;sAnC|ZU{lS%`p zL7u?{NC@c)CP+rnB=#s%a#me-?Qfv0oLD2|ObQ>HRA&Ih>^Bv8E}P+OA!9_$9V9#Q z`x)|U|9KxqDbYt_#NPN{(3y0iE#tc;cM{QvZ87P37Lb7;VjUZf_5~l= zTT1%#0e8yGloamSIW_|&Gs^e8b1aCNwD5ZozzCa2UKHXLirlRE@y%whmY-2Z)S_TT zKSIJ{z=;4VMfXKILLhQh1F|FnRSCr^rmT)rc543}j z<0N>@g~4eN0l@)}_8ky%lxxd}8#cE{+9XvoVnl{Zm^jHm< zeIrsFLU?ISRM{*W9J(u=KLT1^Okk(NwUe7STWDn61C(lwQ`UuF907yjNHtR1HIWMY z-+=n-XW!+TmFN`TCnJ?Q8G)_fK>)}hCa%V7opJYGN{VE{t5+&#lr0wF2l4%#2`r+h zcoOe({w~YkC|KNzPIa0m7f>c`uE`K5-?kWLLhMp_R8mxyRQ2znv`ZyW8b-&=XUSB# zq0ktn&J5?FA}#|k3-)AP+dQ6+Fj?TbYcs)3lCzl*L!cecu(dh)6tuliTRWg%el9A0 z>cI!^CM%KJ5NRnXE?vPD;L@uWn=j3xn3#eQV{|ZH)q|hkqPXpC3LQ0m|6`;KAs$}K z|KX;Y7@u;*fhS4iB#Lr4$rzj-psLhlixdZjeldIv>L_O>QBY6$zfn+r-tF-}o8FHA zFZbW-{|Y&g*8lVYj(?^i41Q+r@pEx+0^9GZRiFV)KLqFWzpfRM#W~7njQ;{G9-BVo z12MK)S4t7_mI3c)?ivQ(v!KfjDgnG{rV4J+ikYg2l62FpHvsrfEgAwtPq8OYg02X< zDuef~a^?|&#|;R$u<@Cb0kUX@?39jyh;`>E5-z>vVB?R)oCAv zq0_V+wj7&ZQgVl9t5dG2LXg$Xy&3Ur2LcIKZH>ZKF2sWf$sNJP2R<;wCm;xgh_j;+ z)JPxtQ449N3c+*-hQ7I1jCzHmRiT-~T>JNPqE3@_7yELwg$oanPVQJeiUYOH&;5WgA_)dP>@X`HeqfWgg_@6VhM?Z(X4v6ETl5mI$}v+^<592YZlQ-#B(fP9F%`Rt5$)y}5CEE7;8 z|5sU_ZJin}>z^!d2eLdYlI8tpKDQEYx{nnNn0H8?8~_LN`8*F$n@p;P4+5*}4>I+q z-EJFA+#1^+K*C7U>ah`pgjJ&mHdi&6y?voJCLKxdC#pbpIknpZ@Vb#z(b3VyZvDe< zI~pBFpaopIC}cYuFjoy8pXpm$usy8xY5*X-+XFa05mm5K$_KZ>O;z#;gce!DaG~Hi zo2NPgr|`r3o2Q8HluZ2T#F-Xe~T+=lS`>kMk|Wctx0jI zr>CgBr!3uU!9{q^<-0<4RdH}t52On&?Vnb<(r^f$VNR{wJ`-ccs+{wAvl@6!UHd>$ zr5r$<#?!Z82KTp7U%8^5=>e4CBn!w(Sg2iq%bIi)C`y&u9Tplo-Mj7i+ljQT5$A=> zBvup(GgTzCKYP6+TTTh*ER;Bq+Mn!i*oK4L3Y%_w1krMAcKeK0p^QIT#9yn+U7(p% z;1IH?m4rLi9~2314&gqWin<5b&3(w{&3pp_dhS3kx zFzCiYS%1EwMOFI&YO?~qN=T?2*LRn+B;^!Okw2KulH{a#>W>}gN$^dbHymuYR9;QD zq;WQkAcw%%|M}!1fAN@@O1>-M_qT=WGUMD+MbZysm`q;OQ6H{)JDUO8rSk**iV^$e z3N(pK7w04gL=CQy1v30oLvK<6Fad}1iYRh2uGGpsA7{vzznPU5e}}c0vFHCH?ybYB z+`4x0MX4wX1}ch#f=Ek;lp-RHw4@4)k`M$WBveEcq(eYLB^Ql=(x^xyNJ=Z6(p_iF zh3~ufe!uU!e&_dH=Q@Xf_Lg|o^UPBb${g?YmcaV35^F-~#kn`;9JRl2^#osu3wm(m?R+&8?wIBZEuii6BXdyg<6x;Y~ zG@rTy8J`)!FQiUEiN(>h46-blta>8o1Y> z*7ety#23>4e>`OQuchnNWQ?_XcD;%@3lwD@mdr&K%| zjNkK({Sl-1^OJHV<^TA-32!8D`nRv-a|pKn+gF?)l+XY6Z;Xd92wnaC8<_w0BAeII zkjuG>qs`EIMM_Q{4u^0xkj&WtK&5nl57C9w?}AaI`d3^~mSQ0wAV`d^heQ|O8#Fd% zidx`&I7_vlWr(4a1)xqOd;X!v*2iw9ueaFh?c2BUO0i<|&_5xksHpgIW$A+vrS&R4A))4=Ko8yB_Oz5aw{3^lIANHxht&0pcVLvJ?PvR~X{~5@4}Oj3 zHK$>+qJxvT`z6J1+}zwl5dMSWIP{{9tU+g@5@fk<;ggN8g;c%BY0&_`#Br`2K=`5_ zmi^N6PGX@@DtP8t3wCEODPn)8LM=~HE$ad`pwpHeLnMAt6T4H2h&L z7|L={qxyN#)c}4q2&4PkhQ{@RL4SD^_cnhSC9PBMS?3b*Wyz$~<~XxTy!qjknPpg(wpGX^(zR4LG7m@x)mXu6#XWP9hYw=}wmiiJTxWW-?7F9 z^}p5#mP64GztK!t{=P=${;yzBlQx-R&Jxs-A3J`$3j|H-qwJtS(zXs}Xt~ZC)npLO zM%R)iJKaH6i$l3)%L+hDKQ^Dcd5WeD3L8)G$ACQdBeJ=X}+PAj_Ly#6378^*J zK)YXlm}Y>Pmt-I%_*@K5bReaRJ`o3R8qDQ4easbAx)@<0_$K`94Elo<^n~sM%tx{| z%RzF${WEzX@{5b&Fm;{Z-TtkId>L=rHq?nUCwCyd-kQffhAxr#20H(BBa!Jixp{p$gwP7P)_}wdSx=8x8!7LRI~m2s{{i zs4J@U|LrrR+Y&d@_WoET*lbo+}}4P^J`x*h%gq3a-{!v2h$ zHmtl{0;!##SuarP+mM}HU9ee)TnWve|0X=ihMIFTOV?#fx0ZDv*}GUZ2`!6Z;Hd@M z#hn+EL5Qxdegy*Mu4T~wV0!-iIhsTy-!izh+5(B4@%+9_#r)dtZWZ5Nr?TgUpPWN)8JYEe{YebY?-jD`hvN7RtHmjzX{|gu zygnc~?QZ~@%@tKuP}d4uQ}%xT{IEOfUyB+C{sJQ#TYdfgw5!|8AIfS#aezTUKoh3i z+!|S``w7|w;ULR1w=$@P28aUI?&JnFv`v|gl~F@%1h1J&LO1Y7V$rFccyn`25DdOg z9^MX?unm5%SY_v&zsMaz5C|{HZ_Wm3)~7-o8Dlo}_4T~{K1tCI+V8~oI_@1vS)Uti zOgTy)WBU^Mv443dWR6RNnXxyRz}NHK!9~I@IXI`*;GNW@2ESBgApTC#%Mtl8rfc{U zqLfyNgumup`8!)%+j?GTbH6DgaU2z_^LM^2E_3Fn@eH*}XF%p??a0!D$mg5T zPZlxA1*n};*v>-KQD3TV$20A)F6z&@1$od`rt@+Jt*!Mg_1fQ-us{xhhH&= zBUAVe&g-(su*;mJpcGHp>#Q_$3(4x zhm{Bl4&GYUiuTpV&OwAw4f+ucmhkZH55mJvw#l#J04lnbsGZolrZw))w)FAisS}Yi zQXk=DLtbPU_7vc*5LofC-C?-|Cj}+4vW~`N99WLut6Fd`w+}0I9#(pjLZQ^=U*>jA z9URaD6k4U6Iy4tz{Z*CW)*3;o(a;M@R^4TtI(Rv~@f*YJ061~nzI_|DgqN~)yHGiKnTBd{h(&~qBycgcDr(qgm z{X;Je(&>Ei6sJuB&l;H$X3WmU##{Af!u@VG`w1rvDT((+hok+yr@NmGCG8E_D_!C#Z? zzEYjJW0Dl>kHAu8b#-6ICneQEDLQotqT^n9>6fR5AC=tQwxe!`GFV5wJE$kPJ=k0% zvVvj&VjEIHUuyBr_6AIk8?w~{%jU}_^aU(;W67r3CPyz1eE%gs|GfIzJIfu0_GeGs zLmkV;r3Phg12a`JPgu8pT{aNNAauPOLHRKSNKo<3Hgn;IPuJ;VaJ25*uT{_^i0nQ> z3cSmIzpIDJo{hmt{B=ZN>0``KR;W5us2zObSi5(S`Sj*zfQHOl#8(37 zB`pB?^c#i}>)V@R5Q;{L9{{PTsyFBR-8Rz4tvhoPECI#v8wT}D?v;Jbr>7^luZk8Q!CAok-q3VVscW7G7F{9o8BYlBLiP&j;!ebplBGyxlpNH zf$a=I@!blw-+y8Vt^wu_h?u11(e}>3habq%qJswflr7pw5O+XmWq|ihXT`<)9Y!Kd zB2QppD#|ptafcp7S=pR~q@?c68l-l$@IhNb!b9lzw>rBsCTL3TqedDDPU zk65RX7arx)t7_X?UzxEM8(UNm2lblZyouBp=ob&BVX|DuPBd1TgB))@6jdu8-ZaM? zhm;9~O}PzTK@flGwsE@nnWS}M!)=GO_Dloo4ItoIOoFu4n$r6{2Z8T^lDi1jh{c8C z^V@c>UxTJ^C(L+p7m6t=-3!GSizt3e?UyS^#kamq?EClciQ}6f$_0v8zg+Py0wtLc zATPl=bc#!Tde@-9+3#ji|5yO9KnWCyGOvKQAm>uIhh<iDXP%;nEoWKhpPAFA1fY ze|!hy$ohW>1^CBh|EEyG|Cx)Fe?eFRl28VAhO@IKT($Vf92)1bMWFb(fm{PNit$w5KF`M7PuV9 zbFgADI~K$hgAclYmpV}c{Iy{edxz^g%b*j~xzNuUToS(W3EE=XRwYXsAbT4C4EC?Y zx@FtzsSw;>%6DCzz5QOaHym7N>9$oZC=%CRLfFtTBEPeU>c?eQFT+0G{=)JxFu~j`ryn%kb z=@m9#-`FVdNM2nyW?`!H{oN9U-aaHGj;3Hu^%iv)b?LxX)39_sv(TxhlW)P*eLt^^ zg@GZyc^Z;SSyNNf^kpF7#_|7`g_eeemY@V=j?cJ7lh3M0@ThmM>q>7Tu^Ge}1!aCU zX%SOQwK3vUC1qBV@KS9fF4LYlqc>`a%FyR`ErA}t%f#=nU=&ctpW8WR<{DzQNUVMv zOtGL|0E*zn7BEK*ZRskL40DiJwS_`XEdYOGS{lmA%G0Ixq}##|ep}@H;(JmGrD`i{ z0O{khcMX<&cS#Pb5}5iCik1+;oGRXTx>c?*P*+E(Yxcu0UI&d|o61>Oq%oX7U+XWv ztu?-#S+WeGmcrq5>PT3xu6`}@Dx~~oZ5S)SB8@kox~-O1)zmG#425S2)&F89)Hk5V z%;0)K{B-`? zrI}aU@c^ELeDyg|2PsYhoc~rcs=&@~fI+^rd{}w=*Q1DKZm}v8e7*+B1YL5N3aH~K zl1HUUptGPUmo-p#*joS^i>Jn_VRL-kH4g|Qk*)Z@+Anb*8hDe!!4~@K%Of20=KuO9{NH~?oZn$i zKZ*SZPAw7re-G^b$Ibn}J?8#?EC1s=iSq;VJP)RC40RNbqb(oJK-e~>4+*8jR?Miu ze|`@>V8Ofu{Zq_r98zQ@cG7?*?~orqw9rAPYx9*7iTTR`R+#kQC=jhi6&1DFFI>1j zFgTb1v}nZ0tAXjnAV~J3r3g9tx&9O!zoH7fLf+pqYUtE|9# zH{3wtFS>3?@R<@V+lnOR8|ch<9Y0mL{c{n{q#9@eV-OZzkMA6TIaT2m6<5)qCB$O* z=1+V0@NJyI)||h4$T?}hS6888<1s=*+YrH1rS`j3>pbw>L8j0BGks`BrU56ai!m@U z$w2`GCdPfWD9g%f!pb;An}_0B6fY?N4?O`whk4-Rs@K-mPHb)Rc1|GipBhaFY=cot zSzkA>o@Sr+L*)`0q6~JT_j|0M<_yIFSg9Z%j>7AZ)(nBr&_#lt=k3s9b-#gJM>zA* zO;=H<&Kcvr9G;*Mg1>Fx6N?me$xrDifZV6xm)Gf`2W1=4Fq%;=qj+96y!$(SICK%1 zdTqDdwn8;Ax1Y|?7lKMc9EP!|m&>YP__)xZFV7H^cwxj4g%e0U4}o5rRn}-yx2iB1 z#IX|h2gd(#qaZJg5qv&Vwxcfw73+ctT&m*7J_uDT3A0da>bqlap6in~MS45Of+7E+>4dTsmSW`?*5%p6Yc;$N)2y!DHfpI-w_ zWrF1R!6M?9&Ns_(M$l$&&+KexHgCZ=;?szPN=C(9k-dcmSb*|UJ-I$;Ql+dcv~0DF zW`pPwGY1F1cnmnm)w&tAQ+EqaJr1}{N&wF@bf8St?9LB}J6PC#{Qa2%56p6_K~x&t zl(rPSmAr(a8DO0Z!@d__R65PFR@)2=9iE2zdW$CfGpId`*F60jcu%lu!d3p-EJ*dv zb@97-cT*}UfFP{E5HL!h%}b+A&Gp?t`qk5J!Xj}?@4}jo%=?SXdl&4V1L~(~z-SWV z2t*8KwO=Qdff0t;UcA)y`RM@#5sf^!Z(ixr*-LGp#&(l^c^%G+-^*JByyW;h9qY*K zC*9y1+ydqCP+&@+MzY(FKUBUY08vVb`@a65DQcPcOqv;0g?K;+N ztAfyo(H0hMy3sDystI%s#Ec>&B1(|*Iesul0U7vOG&sUIp}8g4YmVezbi^Lc8lsLU zq6K;#7o(KD{HRwxVB@Oi`RM5{Llex8pWWb4Oubhx@bkCJ^Kf(Hmd=12a#zl)S0{P& zV0SRr{l;1LmzH#GDdxJ~rJcAtH}F*jlI57CUQS2?_xhOqOjDRArRlR6*o-Oj^80%E6T zmV8jNyTg;&9`Fen$tHvQ$ORs*bp8?u{(A__Cg2|qc|8JCnH9u|#3)pmx`80< zL`egHdrkw-nTBF_Hj{Fp)dlUSJ)Mw6c~oAs_;Jw;9tFvlLMNq{>~QB!|IUVcTYK45 zP8|cdr}GMt3{X0^cOrSS;J^l;n0*562avIBSmwL21>kym$9SizsHWvcK@jz{5jgBv1UUOa^H$ZS%1d&(3_OHQ^e3IeFq+pT^yH+~$>4C+s@ zVO(1<<8Ns8*B9d-pTLbY5x;<^Y*2@&zZle8Z6RK%1=3=@zr1$o7K*- z4~A&yI8J3(837sfJbxiLy!rq)?dNZB{hK2K^AM#*y1jf;x<0}B+}}SON|4o1(Krjt zb-la|QwzH<2L)cGk}mB{2cU+>Xn}}aL^;)U3*a&ZIL>+J!u5xfvpYbuQ-1R}b-jBy z2)Mh$=>_lQK=C*{L@9l_@%v6-KEvaHaJ%=ma$?(Qr0`00$ssN20lYx;<@N%0*2g1b zwvZkF?dxa!|2MVZ0=oW$v&rB749+GKa5I1Z&i^JS;qQNf`5y&G?|v5c9e&9vByaHCz7dT05=O=bU(-SE}ucs>`$i()jo*DfmC!hX5I|}2&--)QY2}{@eGT3+y{T@VDj>qRhRuH04zB0%R6jVcvwqt22)3a@eRc=+#V(P{#WC8!1K_&weWf2{BKcZP|Z86@N=Euj*;7iR_@ zF#y<`_|brPk%NDI8fI1w)o7C*zzwW={Ey(Nf8IXE;a`(o^}xOK3~fMy5qXI6KdCOs z9>XSnBa}677r#I^%6U-soA2&JlAP#47?ZNDx^=y#2SHR7$ly6iyd@+j)K6VmyYD2DKspUh0g@+07@2l1M&+EMLYOIjkx$hREPk<_j*ulgC7?+e3^Akt@JC_vqS3n&F)!TW6u7aZMg!Sf@W-CJ40( z4+(R!fFl+2@cYVXa^aCO-ID%y8-y5-GCG2~M$bK(o8<{#D8Rbn9IgRE(Hu#n@=Cu^$LUet52S-L{s{|9%4*4kvcntc z75HrTPZXYRx&%IGD}Z>Jjm_tazR93c#q05Bujo&1pn)sE%2Ba&f@C;BXn#gz(>H6B zY8=GG7{7YVPG3d^g=j+{*#&; z9s;T=z)6>8ecok}f0W-E$3MaHNtEXq3w#(aKg+GP2#O@y57dm;zlh{tf?Ls-epdfI zQBsH%e~Fl7fZR~^CjrK*si>+BzxCk=f&fTvLCKtxZ{aH=LoR}5;&Ek~XKFc6cNPAD zHbEY3TR-~|D~#vBOz@)7p$U@0@SBd!mu8Ap$CrUW!=b5b^He^HUjYO-5(*3$s%OCY zU=F65e_aNZSdn~Au+sy;%^;H;f$z5(m@3B|GiSWPKCbu`yxdQE;N1p-@areFVp-^q zfVn4YK++gSts)SNUWM-sLvnTB#pNGXYFc(9V*fN;&-CI~4%DyXQ7J_j7W@5|Eg|7m zi{VjHDsDJn;+C&GCX%Xk|G@I*4&3x?033Ox&C@WdsQ~yaC_W2Bc@N@`A-!`z;AQR~ zol+x6jW5o*02e>SOyC1Z8irRG?ms~Pww9V3mVWm!EcabGfLyAn6+~u$#chkbJRlrZ zvlrXmkvU&8b>RKQ`ioOh9)xetMwxECO+WCJ#4Mlvy@@VPKQa4L` zp<2R=OC+i4tUi>)#D@c1V{acfj%5hmG#K@)?};+*pK?&34)22Dn`z`K)maL7mWIHbl6s7-*Di9qzl zq5YDuf3p-%sywE^U@9O++A}^5NGQYc&h+-wr0ZLM_9W%L_p>~1m?_#$rMhYd4X&o$8j~KFb2Ms>mkElBV%=HqC^G_-F9!(q zu1{~vhu7QiPdIV&!)wTmkz7hT^bV3@jJMSA@UQ}w)G)C3+O->SOUp-Ic|0P6ca&SE zc)|lgk*IF@8X(s7_7BzuW~PG9Ndt(v0>crhTf`u(9Rl%>kjlzyXS@?~ESVw83_y+s zHMF6y?X^uM)e#>HFIKV|WN%H*A;nE3Lp>r)|6aWhLY;iFucQ};kf(SWJvSpmb zz`VER_HkK?J_u6rQ@Q@==}zbPBa*Vp&6~{7!(+L>R0Zj?1Ank(fn^`7hR~5nrIUqJ zKVk7q`D9@YJ^VBv%ttJ3HFfofFilO(Iy7v6RU@s*(6BN)RcqklV$y}nfEQ5Qd_;Ou z_UlzPT3f48R~{lpd?PeaR*rj;1DP=+bfyNu zq)BLN4G0N2_FRhp3(4a}aBG;etXb~EOE0L?9Ay3Le_XOA#YmW3#f_^x4G^rb$IL>6|sG$qS3qx%_7|p&!6jFqrI#yqpn7{Xk z4m>Br)BX3vm2WCx(5CdpqB`)N!ndUNn<1@5+rj zbxb?p;&*s4br;KrM?7KMNjPpXE1rZGRIU6-b;5xSXfne}?(4x7tSuyueG!&+7WPim(M&gpx)zz!)$bi^PUhoiF9uQPz)_82jDg!Bp>`Ntn6C8x_lXT(^#>asdaX(WKC|tTyGE+m!^qLMdOj=WVKtzZ&n%j_w3HkER zte3Cjw{V(=gQhnbLhnp3p{r21!mXz=!gx9E`y=wvXYN7^odSVYrLAXPULFu54(D3I za2!>ZRl{tzt#u7-<^J&Adr#?!bm0oW_T;u8AGD7tF8TmWNr8=_wu6g*fj7^j5gzG% zsAWd97wocyoAO?0L`2FcNGF`J72UB_Z@zfE(~|5S{FUdEBcAAL^pbFnNo>qImkcZu z8(egZ3&LNBi7?AAuDG~3h3(CEpm;6Wfs)AO$&b1mXap!^lr^Zc=XfU>Rn~g zq*u^`VRFORA8mujmT)m;)^XvYf&kA{llayddf`pjIIRm%Yv9LQj&tFyPmkl4P_IZz zW@dOzFw-PmnpWB8rWyE+wph4B$&Hr_pwD-fV*GrY#dn+^~q#9`9%9VP(1v!z`=g2TEfZh=rqxPRhY?6&jNeP70 zP9IGq0x>MrcsTd>VpEzQ?^1|kbe+c&plOd6L24)`72BMZOi4}- zfJ}c9oBhHUZID3L@g5cJ2k?KnzH^YSc`W}jrzZ-lVpY)|4B7Pkk@k}aBT+nQN1(;x zA&bq}Z={<@^Da3q1$vPc!Z-~$0K5rGkPDZ+a^>+N@TSqJ3z`G(7pFRThTPD=L1Z6T zXgP6qr5xh};NVOXpX<8ab1eTCO0U;=Fgl}$$f@5UtitW*3C2!-IwfkLtIl~g;*6aU*tAtcpx_vHH98e2YJ!CD zFaL9$hZJ%x67eTDG$zh|{!R5&Hf+{W5r4@SK$&t2n4-#s!!h*6-GOK#X- zYn&&$vN#g-q!exII8h3ZHWHi9aJuFu!4%7a;V>N|&{jZNA)TF_+|G-pYYoNQ7YMnfT@#HsG3}mQIA7)WtTrI@7w8tJM_(B)D9li}7j`;|_yBeyqp=j{O z+SZIar_~1?@$<)pc6~$k0pi|WE3O>2`mNJ>ge{Rw>#I>YOnK}@?;}EP+_!{|qy<@5 zd)Q}nF~9VJA5sW~&`mwAXS_dV1P*)F6?&9Z)YTbLxZ?(14U?u4AzYMn*>0)0UI)&(q}0a{3AFiGARM za}D>T`Hqljv8>{96F-Ge!qI-=ybJ#Q?1<_+vu^eb5$i0@XGyB8h45%%1bh z*f3mAe}99MHG=^k5lLV)P8ikyxHaX8ysx^GQ$Y-X1l{PwMj;NW#Hi@#up)*jnm@@6 zwoU~&C<3u?si{kYswyhlJ&EEM<*@C-kpLAx(}jk+<5U-Kd0aikxDT#*Hk_bNfeOi~ zdfS|;#BGieFYp8zb^m3z4$x6(r_o^I*MvO@kyZ~nUOZ-<4(+us>a{9>F7Dy*vdsiw z{m1~bbOYbv*)RF3#C zS|to!{Twp~1)S~P#|FPXc7RKj5#fCpM>Xuf8z_sp8d+;M%*mY0Z9p>7{UI~v_=yvf zX22PYl0C6blun2UO1aZo19T|xmLRT58pFpUFY44DH=S7Q>wQHa7fuHs5O@u}k4vwu z)PA9m#@iE&`G(Tu#6c~7q^6~%CDNtA3B@ut2jVb6U{w=8Q(?H(^Wh_& zyRIvy!JN!ANgP=ubLCHm9VyQ>s!9wS-uh|wl}|jL;zv=tN43f)HK<7)%k1b@Gj6Ib ztEWv)cnyK+?(tf+5v~9O!|oi$zsb*oSvHU8;Pw}}Z}ElGv1KsevxrB?dwrt>j~{mi ze?mIfv45yWoXRkmd7w@6aQ3pOIJEF24x@vVfq|hKFk5ygl38n)!o)hL2)ID+>v&bg zSrlDku5uF{$M4zeHcld!v9S$|`%sWC;gXeCr*XJt^ZV~dX4hd~eYOGW2O1(ioaw%m zN_LuG9%^Wraz{A8C5AI@!azMUa1z$8r_#Y@w?LQoK__;qj0GI@!{ zWr_;{S^xTItB19NlxB;LjGwxS$-qTh4%)grArgmd{dL}#*w8j% zMuBuED9rDp1s4&p=1GsDBZ;A857y9e*aYlTlDOu63VxI z(OJvwJE*qpv#(7mxROl^A3a-u#TpaevY#X1IH7q0UYe)-=capD*svJw2an>!(UE}p zY(kxsb2F+cYrZ8oazu`Ww44w_*9?1af+|b!CQ6T%NhJ-<@u3Pm#>W?Z=;7N_nw^#n zt{Ea=W9lf2y?PuTw%Ew8gSGkkaj@Wjr?+wTN79aGpA33YNNNi9=e^Hxo}`GLNMpqg zZKI|bw8?kRk8M)PFRPUJJ;hHahr-n%Dx&U}GF0}!T4DB>PJ!OMh|tBUOIhFf?0Ux zBj^QZzfbr`c7_X9h59t!GCZqp?=yXX58l1^4GM-fH1$u!#2D3%=<83uSciDGDdI4= zBStOuJMxc*ZT~z8J~iIw{}TU6m%`~Rep_Q1x*5JJ6Ru;h;Gn%P!1G)f#DsTC;)A5S zH_)=viB$TMaa7_H$ImB!SW%bdXt6bmzAGTRZKE#P>JQ_Azxl$G)!yD$Zd+J@BBHpw z-RP!Zj+q;UPglLNbKl!+%GL;k(BnNtpcMEvZ1Lv+6iQ*zrEAckaNSP{zkc4tNw;6u z8uP{O=Ql2Ap+}zOF#3Hd{Ibmo?q2&i{*Jl?h{C?Ki?}7&G2uOs7rGHEzL-A%Nc+HU zHaO+wONP=u4?i_cO(t+^No3GH2dV+_FvAd4upw^tuQ)v@*cQlri&7K65w4%br26D^ z3*JWiGbKpqPefZpe~xGng0{GKkg3;!z8y2DJA_V5m?K@=#fdgKMDk~faeRw3ZU>&* zysmX5xP5C++5ijkxD+yAD>wLIOAFxhI_;PE75K$vY;-ZK7L3k25!3`~=7;OBFi

Y#(q5(!@(K6kAe9MD;41qpHdwamMcY9U<>yRQv)pX)`~u}{%* zLJT*GjQ{m^h|MhoNu;+vFF~bRlpe`Ija7^7xa*}zKx6blz383NNn;a}I`HMJNSQ&) zq`q1L?f$02XyP;I-vyAtfWq+8WW12Rh`K^Wl0p9@=P?nLJ!~fLq*=`MZivEZB+R2D zO^dm6%cJfKK8d^ANw&qzcMBb28PLL5t7Bc#hO@US96#>_Tq{N>;-F$1O8*$;!m8mY zz&tlCNP#LKO83#Ne(9@F`wOFXBsa@A4&gDUYv2Z!C@|V=Gv#BjheO^pre7vWHl7d283l4`f?COJ>sw78$ z4%P=j?BOE3wu00E!X@!yJNNe}0kyzhJTp7{2IPS>xxGMqK=dD^;0z!WtA3^??m>pN z`3k?HMHe9a8%qzb%w0Lzk?`27DOSICJ(c0`~aN5er+14J$xkJ*MJ?0R7sXL zf%?G?JnO_|Xa}U?Hu829p+QoMP5A%{UVwgE73d2)&2O>K9CZ>dJv{;>!?qI&CsWdL zx6{mk6BkriD6HxNIyx|9-Qd)zQ$x_pLe{km>6N|A9HcPS5BgnCv}UZYX{4%yn#u*B zar5S(5&(cQaif|*6SDfy-I^ih4$yXUtdskQz&o&@>(0)Fz}A$8foN9dswW=!0!6bi z6GokfPxGpr@T7*#;w(1HlaYKRR@8a1>BO@%gZ|C6#Y^My!AOo1n49NO2`(>X7^jDX zep_2xUv;%Pv@Pp#grh|6a<@iRf?sBQ{8eb?JJy;J6YlJWXi!jhRE=^LZJm6(3GE7$ zNiAmPnwd$?dUx;M9R>>L{;qsy@z#yEF~QDO*Ct%mW|HOsHn|Q_eTtszq1MsMH-?AX z`tt+VsZ$z*ojF@wj$C&=)HZs9#Q!%0sI7@&WfS+)k{ciIxHKubZnyP6S|>|sRH0j1 zdAIG?YNG6VoG7JCeCa{5*q4d-7V+CwZT*>VxA(O6U%AzE7ozjUe!un8`{PRmT34Q} zcdNQTc|?#1?Fk(V6V{qr2Ty~PrUJ}-P8@CPFI_)$QKkREgo~1^N%D-J1WrDEJzcq9 zXrd@dw5*N@=|fC86pa`4tX|?XZ@nPe_+Txttgfx!T&`^8@Ss%vDSnGrrFqZB8`Ec( z3#Rp4Hzu1cr*oh*FZ^VAQ}o)yGWRkULrYcn3HSb;M7?FOVG{{=+%!;`+b+0N=XZ5= zB`(8GBgu0I3R>YbU>g3NE3+%sfY<$Y@<(SS`y25#H#Z;>O^UT3@^Qd4tbhCTlRXE` z+gvCeRk!ksi$zM<2h+2%vdntt2CMewLCcQb2X#%&y?H6!z%fhi1~zd+!1QGPRO>W? zn4fe_^=>%DZaTiVq7}S%OL>F+L(f|vZFL{F?S`h3_G#dL@0`f#MZ?lATY`Fmm3d=* zJ(+nK(9!I=frE3{wi|W4Sj7UrSXl?OXuDKh^$N-b>|fjSaO{E>CmKskoUo-A>ru+8-agu5-|0=L_w2TsI7FJz?7o-No*98$gJqZVd|x zYR;Vo;*~(M>qKjc!Zd#rd-4{HL~XK!Zb6%BS6Bg=4=C3?U_J=~)-Cyviw?ytBj)*s z(D3kQJuRV&g~y3tK)p}Mi4T6OqGhM(q*Dq8dIi8YpqwY~W%9W_cNZjYuX z?3YzD-ES2+#~xcMecJWGIhJy%RC=RG*R@ng( zcULyLgYW2ja7287$t38IT7O$6d zpOaKN$@ubklts^GG&nEKQ#~&S>|E@<9{M!UOFi+Dd(wR_s_f=DK0f=xBY~;kSzprl zGnkBA*IqfBlA0P`(x2YZZL#Pq#RvtVlAy4D>8TVd6+L~HXNRVm>srf5)VR)NIx@a* zyf^nk9d~Fdz{@=JschcH+xyn*r*<>sqH zEc_V8h3>s#&|^$W*7Bqv+21*{YX0DhhH?u2>^pg|K z{2U{t(wh1KLdDv|C4;E^bSuecDP@83%tBd84%0mx8cW6LrGxp5TG-8xbIv;bRzK)M zDJcsZ{K>S8%DK-HwRap|<0u<=!Mf=3BW8;k;&any>*1W~AuDCeTQ+@e-)y)i7YPpc zL)sHfK%_gDF4|wV#SH5w>N`2|~7lClKQzNOTdsJF}wqI70a8c#bV)zUBH@R{oG%Nc^x@CCe*dbXxm_?kyIwi$Oq+TT>X@*?u0<`vtj#BZk6P8UpqbaSfBP6yF64`QXHD*yF~{|mJ*@CP ze1!Zp!RY3c*TN&W-j<_LBjjq4?;9&vDH-yLiZzyLap42CdxVs4a_+@VEG@8VEQB7k z%__WX@{aIM#JachV%f{pv}-|JrggL-2{{34PiobsuHO3@zI2Z+QvC>9ga4dD)RW@Y z)??Ho{hC)6#9sPmlUQ83=epw>rM+{{rN!+C*|hWNk*bXky_+Z9b!NFAH+SuYbTLG4 z)L_wuyBr0}`tG#~cmS|?j+w9v&c4NzW?7tP(0)Np#+WVa>rH5Dic3;Wln->Ia14yo zlR8K|78BUYYoLqeP^YZxOjV<3YY#H-RM%&{RM(oWsPrJTZ9-n-d;DVo6>5GN2Zrl8 zqL+iFG-PraufJ_zITRJS(yO^Jk+@zmQKGC$$?&V`6GiD-dE7qtTV97gUY{k=PnLL| znDg-2=+^L7WbM|O48}1XW1*3bJTtY0S>FzW_St)8k~@YEWt_;vCDkrpQK&0XSaG!^ zScz)Ck#O8~A;zO>ET!s$=uR=yRhM_SORnu05{7Mbw{BGiOj4IR4c%L-`Z7r^cCEyZ z>DHx6d$R6*B6|iM)KA}e=4Q~wRIf{)DYxK0L%heVlaMHXvyR-|fU2x0vODdLfJk4) zld41Rqg35(D{<~yU+OmfSFAe(GfP>Oen+v-d7@P4$1i;0NMbVO(^2sg2uGm6LN?{B zt)tUDU}$b`zPPAWN1h+zRhCe%6?i6gfEZqax7f2U^bot20Oba+UyGnB`{Or;A%G78 z#qnAoBXTfBAEU*t>k^zG#U1_mXr8B@D$6|D>Wus`RyDKomwRzWEst0_KbAS+Y6-7B zexXn+n=ki4?U|k2T>n&1phfs1LsUG|S|XJUzl_{sL(=&!hOdEf)+}SPBqRZ%d!$>A zHQ5X9OEc@Eh)ehmqaNSlJ4Y?h;Xszu|DQan@0F_%RC$xY7?40j4M+TIh^2xM zwFB6;^iVzzfPe~V3JI@M_(#-Tyw;`?pxl^d)SJj1Alyq+utjT7_fDLqjh;Zt{@b;i zMMYOMrqcp49m47+^e9zy+nPp>EqrSPhG~|cY`laV7;Bss)#46>emB4_ZhvA^_-RaHmH6h zk@{yL=q0_bgJe_)6dOVBI^Lu)CUF_YrjL(%A3n#Rk)lH6whK?-Spe>v**)NA=b_ME z2PADqQ20Q_H-s}lZ}amLm2nP(1rp~%f0%Z5$b_`qFmb7HOGT5zi zgNZdiv=(=Ua=tCYwlgaer=HL?-+VMd$>GJ~H^o5xXGtaZAGQ?dUd^ujRpX4Yl=SHS z{-h<6DEHwf{fR5KJFbRJ;d75J};UAZyVXF}~S_RTs?y;UuI*HI4I%~KrNhajYhys>WJ`!+4CD)liN+MJR7T5t@{u*zOl2h zy@yI8fPQfA$hAdBn8h&PKaHKQsXVe~r25=~BSS24DMw|BTToojYbdGiC(W~@HM5}5 z0q?On0*RzUiauql2_K~q>{RQqizH<=_aA8jdY$lRh=A-d?MTkGc;QSo>Vj0S z%4cD(>AiP{`cM1Vm$&7lC#cK_+lq6x1=5{!!PboR^$u0^-L$>ZC?7qJJ12XV5JPbY z!ES~%)P|y_=p=e0E+qlbq_+pu36;VN=I#qeJ0lu;A{OBehw@d}x8hQ0?LGFHl$#4-JM=-v?_lc{&;rTE>HcJPeUG)HHE0U0wF{LPm%vCcp$gGg zJRUP7OK~cb1Mpcbn+c^rC!}@APSW);n`Y%*2~!_lleeP_c_*K z7t;^vZc57O_M>UFyK*D3$K@C4{A5SR*v90tTJ22VrZ!En(a;SZD1+F@0bW~!zKWVd51;uihkktgY>>aIXFJ>adNKhmAkvqw86z@<48TSd{fkH)0%0-{HnW=JGO+^xtM$MYpArI61Gry=3Y}<`EO!90!$%4+G2Q;!dcD7 zO}l&JVpY;d2?X%!awv6k-(q$ee?Gscq!c6u zX`;qDBniT68lays(kM7chCs*|0O9!3RsT#Ih==mq{y_O;S{&pAh{5@b+wVqaT66}*5e$Yay=80uKUs9ruA7qNqVY*^ zzMJhYo!@KQ0BvH>xika?FTV5zDB()6u(D!Nw*N^}7NiAx0C#iNC%OHV`>)4lxjeS) z_;oTt366!PQ}{~e)i5r(A}(a-rtZ-km{h)riHRXl6lq=nHrfI66;2lbljZCB;pjZx zAq2Dw1h1L2t0a&OLIrh~@f1p@c0m%^tT4YMChP^)n&AMH7bLWNM>lQxe#cME%N{(zv_BPedrlA$gc>mn!^VlNuBaK>h+opeBnM$ z!zWjiQB|p1j)G;EaAKI|j4wC8MO>rlo&r@RNfHBQXx~NDJm5wGogg7RrTGc)n}r9Q zw}(-OuohOJ)z$bOg4&k|_hyfwu9;ldhq7<*M-WYTRm;HFC-6hp&iDhV85!*@5LO(g zefKyE7TFG?;q2_(A}p<~l>Tn@Nxq*kh`iH>gQAMm9i!xDFg5cqsz2fJv0i(IROqlE_d3qts5P&fPp z5x=gghJW)24GT+50>&A;SYl%0H6`_B)$Gw&&O7oFW2x+?(>9dApXIY2&O9;v^WHIckV?$ zB@~Z&j7`8x2C?Bl=*?tiW)1`rB5@vw;>jvW`<}e5Ee2X4szrfy!$AO3B6)38EV>iQ zPB)z#g@I~-hsOO^gjx8)P?L6LJy|8b{AVf(_beLM17s3Y13h63D=JEeEXV^~FqyeG z4+MNAl#=8l?r6L9IgLhiz5FL%%~9}Xvf!?Z*S~3kX9eAUc|)jImkz%iAGnCZ(&b|J z?7`4Y6mD|PK*X|S2h=O1J)JlV#KU0V{ank*$r-|>W^kw9hoaG>il=qSQW)ytB0xx*Lp$NOZxhA}??gGTDNU$+mI~~!~ zqNOIG73(|W!Lp6tm(H}>!{n*!#YT6PtAC(-*yZf*^N+?CCM!N3++yB~3fkT*QFh$W zkDqhr>x;PIO6&yJHPq91sMHxsS%`Qua8JW zL=bSG6_p(k97h}vPd+hxWGCV1JGw#bWy1>3zP&m;{n_^nr%EySwcMCpD&=i@RI{S9 z?Rm!9e*v+Z=nA2RjeZT0S~0ma_**!8!s8D|t%bT)#(2mij}WhuJSUZb5EiJk@jN+}ACdg1+_wm-fSUd^?f zM9q%^Knev2oo@4|&Ux-$%~QQO0#6Z4H?FTQ$45^?gjx9gXE#^Z{Zz;TA7gb%!*&C` zSUuoVfLz(Ex4$b;Ca_3yHZ1Hs5bh8Yz7|?F7-^LM6s_y=6<>^Jm7o}w`UBiJC=d2= z)W0I|L7zt#5@AY?2=PORfU*fXe&Pdzh?0pfP69m>#KhkbWgyWSq(ceB zm$C9RV2M4)?vmL${}4v+eH6uW+1|~E7j`xnF{a6Z#$WT)K>{s)e4SVwtv=cy$PVZp zB4RTZo*H%D`}A6g--&C7NG5(XNg?WZ>u-Z)(73qHh9~k%3=wwWH*sz>>rY);ym@lC zITc-u;`&+?h4wi`48k&im!B-RH^!mMO#0yz#3%`G-dvvl+d-<(?Th^vqk`wjF4e>G z_>XWrDSvDr{KJ9!C*?*?ssy2(7FIPFh=6|N*MNn8X<`r>zEYCjJkAVhZB;8t2aa|M z4i~h#?+?ETqHnyTw>+WsEdqZwg|pXgNT%uEzK(nwg%@fI+J**a)C0=M#59`hpZVXN zpX-M|2*AXufI66Fbr#USfaQTWJV~&kGN}i>A&j@y9yY@`IO_kHT#yj{+m`V;H=&mw z-y(`eg4i+Q12*rW)noXblfR!jD(5Di#Kil*ID6}`thTjp6j4M`0YwlbM1e(vQUXc| zih_i6gM>6lcZp)4AdS*3CDI)#jf8ZFD4inRaPBc#?>_tc&h?$^I%og2*IMu}pD{<= zh_h=wl5CYG#?R#|F|1H^z0qv7F*rO_^88R8u++ z+suoLP_AFaK5Ixhky&l1`L(-KOkpkHgQ;y(cgOC7>LP2N$EWj#{mst!TI(JqtkTv4nT z(ct70yi=?8;CFwn5=E`dhb7yq9{}FvP8LMqF4|%H;^|{Ojm!SHRz^|^-j%x^FvO+JdBM5Ec_f^SV z$$!BV`o013-Dy!|J=YB0*dT3Y_)MQv3IOB9|i+#3)TB+{M5DU39{l`12&W zYFrJ(NPbLw5`?RyIUCbr_rNT(qu5#E@MMGEEIVAT@2*7-tKk7YAc6n8U7P%{492d> zqQ|%kLLQ*4#hMg53iY(W z6CO-4aXllV;|#w!f2EQH2-pQKobKlJ=Za-3gnyxm7eyOEo44X((Y~4I0*AD z(HBU`kw^`Jb~7A}z(W~BZb$+mjJ$1)y57?6xz0VqrbT1j=zkopsrJAzM$ek;4C8r1 zkm~d&`{BHq{QwC)M%A31&Qt@i6?C=mLlvAazNS-shHw4Q0Vfld32LYnwu7di-j_4DI?{KsCQ z7buUlFJP!OWx@9X6CwdK$`-0gDu)<|$}DE#R!MG1J73(uTJ+R$2wtOT--#NoP#37l zzKfy3xcSiJzBD|WcM-<1eL)J1=guQ);KdZ;Okg6HN8$r#=BT1%sQtPfW!u_-C02b6 zV%3)_`NgDBnDmb=5ZzGCeZm&oi@ntpbaXzZ!Ikb@ro|DE-jFX8YAERyI2qv@S7F+Fa5(?9#l1d6a|K>j&Puj!dvYD$jXrad+a44*MeFW zH_?-~IL^WLh&dMK!HKVutq4@?g>*V8!ROz+Njn-~lokTfhpHW@$FQxsR&LW@fyW4l z88$mR_1gveSQ8D1OgVqy0x~h7znoBNF#_(E6R%i5zJ34x;uetdnLt2$j%ZAg0KL~= zY9LQ9OCq|QO1|@;o*s4sT0t}pxN{=cT`N-aU|+!D7c6hhNE}$Zi3(CtV00B=brj;c zsDFf08%sjBosa@zR^L0aeJG5i+442m%d+w>Mkr_*n!++NAu$kP`ScCln!HeP;_!-+kN5t?WUd1cG6LEJMYPcnPFv;6=& z$4w}U(_FMLlBWcs*sAH3iuL{Wfhq^5An(}khgMh}{*?F5VW&0~-mpB3W!e-Fr!vz} zlBgl*uw1L+<3)eEugE}qNz~Dx-VyCv*msQK-1x1u2%l!68TaZ?A+whD`W@<;4-P@- z9|N=juCfmSDY1$fiP4-NQbpt`qO0o%RJNa*cNaL6JDTx{pS;D0Rr?R|^!~jrCK|(h z?o=n|&JRJ1*6Fdn;#2u*Y{7c`hjoy5uiashtba(TM%~d+ou?7r~j7Irc^Ou7%QK%+d#F&ojO+){He2{z?gwpcX%| z4h|W%$q&{~{DX+1)Gu{MINEq>CN87qT6`hZVHI)euzlHX)aY$AuZuQqG#vR9iWTMm zJFykJqFWlwOCp&jHpc#T2Y{ZPjo>RJZB@N-Oj!kQE&@TUE_d%X{j)BkxB_2&dQP$iF3@Y3dgCMfK`(Wc(m zwFW2q4)&3Eb!Z#0%al2^(Hdti1Miq4Dlv{)AKnfp-tgu9JjoQ+J{zZl$Uo@p&Y zWKYC3gZjG~8t)DNi~Z^R8NG%wKj)c{I=V4$jLol4;*sEd@$kkYcxqek`xqh9n8o2} ze=6J{L;`>C%@2y(9cSF$hf;7HQVfuclo?{*ppcV>YETQ@tIwT68Z_$rq#poH6Kg?S z&|v|k24q?-4Ye+tdY%Lsrw$vEzn5Ar`WkOJGj@k0rW({yG@J8f-}EOP#Vli);xF* zkoRQ;GSOPKZ;KWHoo0OM1hTDx9Ko4inMf9-AM*y4MrBXi1{wYF3@u>)UPoUS-ZuK~ z@hkE0jeV4@PSikuVH^HZG3-`gwjVzQF#(J~3GSZQ-UVS#o!OflSy4Eu-GOQAs&jh^ ze;Mnv(trGT_4EfKP#Ovbw)Hdm*8xKU7_u*9R-frpqUvxudfNAPU~co9x+}qYDX7B! z{>Xfh`hJX{GBWK&XTppG2?>dVhq1x-cVupmC9j?d1_BPxt^ccrk_j9ru}R|L;kgSG zRM0lzSsMILL-u9n>oZ5z65!~WI&1Bb=lliDHvLN;37Zsgi1zE<<}0kWN}it=vIlXb zvKPI1t$*G`4ZrHW1#8rRMY#auQ&V$A&COEC=SvHmn9i|wFI^nAZDF^CsLbi85k%JI zQTiJOTC$8dbKp;c*83iCK5GF*L@Tkwv`#aQEf(#l-u;rgOaWY&sy4nWo~ZX4!-)mM zYCsI^j_W~n*d)za*{weWfH-uuZVL`VhhZFP-GMSV7y1G8JDt=bJpzD1-e91k6Ssoa zU25ou=AZygyobt4N>qwCz5ikDA`Yw{jaEvWQ_@P0U%3Pt!&uu zo_Jaf)l4WP6ZsSXz95(%3dPbL7YLo3fX2jI*sGc#7XF`HvyOPNW$?#r2G;TF9%3n^ zOT8IX_!~yhKiiufxbMul%yV&$}y_O(;;;%s0<J_8- zblN}}jt~+&ygIng%Dn^VwDIOh;9hOg$NIA`JYZEt4L9RGfiHSz^>bpw#M0Amy>PT8 zrVWe7D>GN>?k`7(QM2A=FNXxzH3R)Hf;@$@+s(juU*q{-I?>trdY^HHVj#@ARxm1o zq#msq!5rtskUFlhc(VU2;izlU*xlcr2K#G#$Mvv-E}-L2bqy*>{QUgLhGJxm8v|5% z*g zod3O*?NFvvJjA{-)!rwjw!~9Yhc5rH!a5k|&NG}idBt5@K^K4(K+_+}>tuk|}zXvGttF~B3>~t6;Yd`o3_SlBlzf1^L_MlOv1tpr*VC(s z)BN?aL@9I5_}dPH+PBFt{U6{A9TI$4NU%L1Wkf>=NX9 zJuQy-YH$>*K8-kw;~jZRm#K(C?wqX;E$kxr9-ji5yl+2(u@k&g{#mc`AU4BH7RV7NmRx;&d`Z=gcq# zQ2_24+PT_+8wDCI##69NE2cvVL&;chMKRXqi|xHqu09 z!*(qpS$=>E_qdS=WYNi9#0iZhI2h)!uN>(Yihwpj&TYem2L0T~<3-qenood2iD?E% z2w0(n^^X|Y#MD$&9+inW{n09gd`!$KY3^gWEZ9bUh=4>)^|TAaxdzWGz$7sUh7&N2 zO!Ta5FePA)<$u2}hag+(z$$t7lQ-{8W6~*Fn(z2?{PjyrW2GW63=K!rz*bW5ly^|y zTo^=6YCz0-cKk#$oW}RAT|9bJt9~^ZRz%fzMt_bdn*Qb6dRPS?0M$U%(N8}xk$)}S zwjT9!>{~s==>^9Zitf@|^ITyqrmM4roQrTvH=0@o3qPsK{1gz`DGGQdzw&>;rY~`6NG=s zFNjU1CAjvFK2bOnL=yizDUOapK(p=~dMV72@5uQy!9gi%|M%37x&g~0u#jN70*zs; zh77wLYP4G&ngc)sPp+&NK9n>d(QRHTAV5LIo5Ffdz{0BIg_Cc6_r z#SG+tVo>cBm;3?J1}EyH6cq3@ftMrdydv(P4do#YCZP^R>JOKx7~~x=OoURRAqG|f z&R)8uFOMv%?!LuLP6lI-w1}ZglHf78*=y-hn;`7XyT};@kfN&wsE}omD=uE7;A^Ej zAQ+UoHU&2yI05v3{`G;9_>Owu0F82i@6rGql4wx?x=P>zhOFQ(B4PaHRk^n#1_Tzo zYFRiVfczm7pX=&H3hG;7m{I&%s$3ig>Yksb7VIvlfI9<3YTOe#05158t{Dq6xBvA{ zGCThJ{|2X);9>(CCEbUr9uTu9Oex1nhuN=3fy&`E`L$bsBao;AkEgcpUl=F*H`i($ zZsrJFk>MVGN}t=l7DE@WSdKRaxej)8&_Ye^TJ`axEb<2y5`p$pyu)q_!0Xh|aq(eC z-^1Hv3#_Im;1b29SA!Gdz!STpRIdQ-?c>mU!l8<}=F5bY#^k%W;#64gXTbyjF&QI* z{QUg23Kp3UxOxBLS0*u#*f=)>@o5bETzY4{(a?y8+3>B$jF!qxfFB9gfDp|jZ%6`? zgdW@s&RS6Csn%K?XG7JE_gy!*K&`Ffi){J;LNdDrR>iZXAAYeiz(j{$S#m{%7D)5x zg@m;ADX@N;B)>*n^Pxmgj^fj(gM0Jbnp9Fl=8kXMoj^|-mZ6GF)BVTg4R)J0Rq4%Y z2^_C1x?4Psn9$@{m&UqQjDBC3t(rEPO{K}464H4fBtK$1VzW8b)9vQ`?)|LeHMXvX z1g37 zmzVr*$k;Wwvo(~A5by9$_Fe1FBhC?%(D5e=NSN?==DeVxcD;M&!mNOwMrlfWQ!bUA zc`tAEqR;d09SyeUkilOvgNLS#~|lZ;te=^6&6Axw;-V z&$nz1lmG0AnkW5POCmKe*7{w@^cxa;vf4a(0SCm~dR*UwerU9e@nY7OADwA+Ezk<7XuOE@F3GM8TBIEjV`F!5mgEpQO%&d^)0#q z8%WGq<*@XR{o94zi<#Xm)jB=2!{$znV6tW}qObrN9eZN!mfas7xOz@Bhk*fzwHYVu zH-OR2Ok@ak+sR1DM7$YtY#uxlm7LMWuDmuo8LXk2be@ogp|I`FK&54VXhg)l$`-ma zf9_DrPZXxTpvwv5DG^dATQj$z(fPRT!&1`}KA>$eQ#xw;fz-HZJ>KI+*#=1_o%)Y3 zhV4g&x&PeN|4l5E)2lpmRqAdVv72m=b0v*w|B7Wt(ZVnR{pG?ao?hjbnf9vkZu)^@ z`ndyj%DK81Gh4px6baDhHi!z1d^)(XR2DPok!IvCl=<9wn8UQcTEKZkppyQwfc8c6 z<~rpnPUjJ!Kob8tmXDpoooZ8VIeS?zXL%RqhBkh_8C~OfKWItevOQ_j`!e5Iv!ke| z%#89+hVQ7bl7*)ZS>{itmK@VEVaF8Djq9J=Su;h?b(XzMtI~+ZcRDVTLvwV^+^L|i z+eI(aS!=RA-8HA%ZPL2R%Y$_()~VranZ=w z7wnnO`#baMy5#BFf`8#Wkle?~9VA(EOaH0ZRYfu%tai1rcyTm))aH0qs=EH7$_Ekg z+5Gd3vBQez6u1KS--j=T)xRfDjOq!Z5BRgU=Ki&+b=1=SU7V`x$Nim+l8d!2deVPa zn&;?xYn5zNBVLw&*^Np4;u!qOJtuwc7l&!qb+0&$kj~nmp`qeMAxe9k6w{G+Tl&)N zrdw`|)zxYX_eMXmkvSRtGB*pUee&5=XU`+OnwxITHN9`)LyoC-%Ki>va#8krLe+)t z4c{4;fsoZ)>OHrH^da9^RV(YgK^~O||I90!Zj9+Bk+B(z;%k-tqZ@6p)@vIX4@yQR zJOU`0Tf8$}NCMe8pN zn7o&sh?f7ZK+Rkl+)wd2wSN!I=t9xi1LkD}LRZ}n_BL8Tv73_5^{B=cIZ5CMUM3|M zu^Fw6r%e-bb3bw#8QFtDQFk}ED%&F$HwR*+Uu7s9gbA!WOWmPoxBUHR{`>HQ-wZ(_ zFYbcF7GZ}gvWSO{BUg6jc6g?0PVWi|ZkZ@w{KhzmaIj%~WgpFi_2Ic?w^|Jzg$xzW z;5;wkv)w8~FhQrANtrb+({9RbDh<>DB- zKQh6NUkpwAcd|p-HVx$6+I594xoc*qR9TwkPpyW3$@Ivvn<*!Hnb}Qc5$f4V z#Nf8f+o|TnG+womx+kV^Jo<`*rmED;PT;Y^jVhyryHAu^!mF+nj=1ZaNzXe}td`8n zxmG!UzEnp-93SQ|D92#88(6Dd#W`N(wmW~Bdi=pkE2F~F;#Ml3w0_2F)r_)`?tV`7 z-95sQsd`SM#$!$8zlvRjyNmLxy(*Ifw|xjpm^~K);@4yIV|zYx_UKk}hUr#3G|P8j zeH+`itKNRe{otVu-m$=x80XmWXpd|{34*QnI-73yC~nHm7G(!_I$b(#w&<{eo6CQ0 zyd$qK`O$FoeM=V6j0Vz;xvk*qlX!T~jbt}p8p>2?{UF7^_2`b$@V3)O)$>BZ z;+HrPy59L%g~CTcN*r#j-QjP)Yrp#FA=@6|`L~4s=#wK|ccR$^rgo(&+REOxvpUT# z1iSu_NFUiMq;hZBc*i~KFg8*b9QsdK%Z0mZgLSEVl7V6hSN9$Y>E`{_G0oz^-!Xb! zu2d$o{fZA7fd z-&v}qvWxc@hTC>a4Wqe~@d=*o{w!U-M~$OW>G+ardp8W`ed3%>$BL1ET;~y;JLwGv zf#b1G`Gi%cw<_)Sa~Lc)H(dBQbHUSv!Ij1so*$1?Tx= z(3&+^Is7fIdCTHxw78RvEv%DCBD2$R?ldLIgT^;mbdC zC_Z^^|Dwz@OXD2+)B*1w7Y0ME;x*YkRx|QNOE(auAt{aza(pQzJLwRq%5`N3oV5^GixzaSF z9<1H3nl&0W%C0*%A@yYN1%URj1 zl_DZ|iZ$8vo#^ki`+T+6&aNEsptCaeO{da!b&MS+TO}**VlJ=Nyr6WBv$_xGUQ=W6 z?6R-zW9!E%Ny6fC=QDd)>dK3xWKsl9$?8kJKfk_Wv_F{p@Qw4$NedZumuH>XZazHS zJ&m#1e&2MTk90oq^kraXkYORDd7F2%RsBtOWAUJITtZcQbJYT6N>%Eh63aCKI_a{F z*_;SVUF+TAmy2Qpc$M^(nz7x=s!~gBi|=g(wx0K#!Y8~CCG?`ZGA z6W%PZ^3KogZ2q~{f#S|Q+Z4?}oz7{;rr6d=y&IK_Up%*Yvjvp?NpbUtV;IZ5KuB}B z_vta_;e2s3*1aPtRrviRiYacox++PPQGGKWn=O(oGXg^Nbj704XB-qN7KY;Uf=es$ zXE##HveXDk7kMv5)?In#D>>U2Z@-~p`XQaCg5%0PQ;Oi>u81C!#Z+Dy%Ju3;6vJ*( zQpGn-KV%2zX}7*h+-->H+u+X?2u{v3j8lrBZmV)|#y`pTe0=Rv>LtxGyO5IEx%&;V z(eK#bZB-`cerK=KeB$NA<+d7>ym~)9C*!U7j?*!r#ftr}b?@~;;2)&4yUb?|r0<7Y zRl4WQd|sfe9kYFJ>&D>J5>gP2Yja~R#X|CN>WBMl#c{#^IFRC}%*Jm=^W`j%`j4L> zPnaTF7~(k7Gx_?_dZ>L9+o}-{>%ekhPOM2+*_HHCyx{0C#*bwe1qvFXJ)|pD)GLqA z^%bPrS9gcw8@`nOB<-lHyPo}OII7NaGdjbPG1V!g;7?_3S3i@{(0g6QgR>v_*2s+= zo+(kE*dJ=2<-X6R64f0#ALN&z&{cLafWC?CV)BTucsZX`Qj4inWO(E|x+Z~VozrG- zo3*RhZ0Z#?Oz}MGVxu|Egrz9BJ+>D8$QzX9*)V6?>O(!6kL3~W2n}0<+43th%8g2afwR}tpxNwA33Sw%O zXR2QPgTc;L+0_E zDC{JOJ7XQGvcmRVqZB#8ndDR#WrDwjoK8(+((BC^hN=XplH{3AK6@5SJlo${cJGek zM|)|9yN(&+c?wj)v4$b%-JT@#pQ9hRCMxH#ax&OL?k&CB6Z*ldU=!)L5Y#B$nA5ajW3Ew_KVhO;j+E9ld8A=}em>Q) znW!ib(Odmbbai$bLJZV4?*Vib#$n$L1<#sUoDD>T9rYQ)6bphEkEmUBsKsdRUmj>{ zv!i5BnyfN#r5_sS`Ik;XwUk>CbW;Cou+#;gtnyYbFc_bR4YLC-qQ%Q!3AjPXb?&n&zyc4EXY($^Me%~H zqk6NB)BaZ&OA?jY4;C^CX?Bc%5g>*`R{MwwEIk3#0gm^@x7$r}o>!3DGHNd&75$5^ zzxhW6!IG)B3)w$G#<}!xx zBU`X8iIQ5a6Bl*U7`eF9UfPvsldMr z#u6mNeiy$(_og8OY=upKUV1^=4BgP4^WG__vn{wOh4(nUhBd5C3=hLfxRCttf@2iu zDpA{y_;!Y28xVonK-!Q~vAf)o2YfWtPehi~D8>&g*GGMFm1H$>2So<7t_(?=uT}J1 zS?&#?W;NYi5T!z(H26MCi42)dVH9)vEv#vD_p!=qhVyS8vB9Y<05UV9;*>Vn!KZ@lbX{ zI&vri#n^*PX#t{3!WDJ#ACDY8epdh8f zzv=}hK_Cl|AAQCe@1DPOOAMYxXczaZ(i03G2ZuR~71J0-Ytf4L#5!v*+xJr_Xy&z? z_1hfsF@GwZ245wSm{6-e%F5&`LF7yw1UI7S(WuOrlZuJ0{ZNRzskLi7m&b=Hj%L4-$&}PLXb{BKv_93`|a{@ikz%jITf(YrlZ?L4&1@gB?LNGO`tVjWACVaNiu;*3C>O4MyJi-uYQL% zf34N@Yl=Auc))me20M&>b;8(vR*g$5C&$w^@@a(sz@C?)Bd#rzYtunvWjU)ruA9xkyAp8-zq0}kRW)5k65x_`{d zL=4<@b8Yj^!uI?C{DJ+h!RbP*y-V;_iK{^d8;?ka6Nl{R@UM@3;#)k%_q(HX7lJZe zi(}=a3z}{x`48@G!Vv0*<+|rzLq?G=IB-7^%X2-)oV9-&o@c;#V`~;FV5(2kepVW9 z&jG#tE?JgNF(6OkXouyNy~)i2{=y3m&u3@hHxE@VMpQozmE=D+L$CRZ$7>ro zP7_S_($Oft1Vb;%#{Gz>kTMTU7rm!JQlVe09sus-@Jm#c#}Y=@J5Xq$37E@-Iebjr zTBVmFf`8^8Zv9st((!PU)DIJX;Sh({to~U|hu_p_cm$5f$yG|qmF1?f2$R5BdFEZu zhCrM$_<3gPbQWaX?)~`ar$eb?p@_pc4x9M#ED5*rO9&Ur;$Dxz&Bq=J_;w&aF^DmN zk0}t2*9Pbiet`do+E0UK@30`uC=(ZDu@w^*9u?} zCzm3{xQH63wJ@4C^>f{aFTU0{-9lrCfJ0CW%=5KeGpfnRz#@aa=AUSwaEP~FCqlE^l!p!b7TyB6 zxc`~fMK!0@tgyFNaEK6IiG08#A?l{=VYklu44Y)iPdY%Wg#aV_^SGbB{J$A@8Wj1} zD5bz3H`zP+RUV_8KAtyFO28dtozs?CdC8YaKX@+D(E@%Vx0#>NDBpJW0@M;;avqca z7e*%qHi?Lw=DIe^0OXGd6Q`t98u>Z3(DX&x=QcX z9RL(kT99}Q@~@r35taO{%6}SkCLt)SyToS95Xgedu%V(*fpM^L4+X=7n3JqD{aKUX z!VIS-j)X8;_c#IHB6BZyZz`oE9D8_t0w3PhlWm!GZ#z%;bk`F#W^`#?%&0Vr^@12| z6oZj&3D_niQ=dSDBSXL;&dmXlJpCV@!@2ygh=l(%1?B5^RJ$3ckEqQwVG!4eEDHmu z5|HM=U-viP_J1K{+E!OPAj*7o6%gv6&GJKf8BX@0V?d*ksnhdjZ{}O!|MAY{KaV-|SzWYa?F4^NVp@Mm3nCUDVF)^%1q`ZJ;71T~+vg=Z4dt z?ykhd#J3BFD(E(FaxVz$n<{<`03_as4zn9sOIN)8hfd5_v&--tpdd;A!v zM(#ke1j4G$ACL4I&^L29nT$W?9}iWOHEsSxS`vto454mL{y&&- zW=8ZZ?%$a;kFMJC3yE5<21F`;3iC&-KIGe9FTC*o*MD;b`ESAmlYt=Up7Wwp#`y!| zK#}iRfwad$V352Yw6n7_g0KTbvxCG>u^}38&V~X{8e$17`afO%um=+|9Il1~We%D1 zL&fdg;nC4QLa$juUr<3+)!2-aiRmpOe8RoW9bk9(03c@pad@t8HR?@N^3MULwmUgI zC6o?ZNtfwl&lx8*LN01b@_0NyC8dM07TzEOv@EO^eYR~zvUS;I)1>jG(#2#+H$x-C z7+K=QH^U{H9OSwgoQ$ zz{X4Mbj!A^-_DiSV;^@QAKDCgPCI+u=JPv)0G$6wV=606pE!Yz~=^Xasq>1Uyd z*s^>7c2H)1n4orOI^54A>d8ioBK=7K57gZ5Y8kmb+0f>fVCy|LoJwP0=s@qEIx1>v9WoHsKr3@QFX zeFyI|+7nzp4TkrKTbK^Ot5d>A~2~kgNCBdZbRXZtS$X zOn&XpZ25}VJEr~R*Gz`i=VOE42D_~G6}ac{^eROTW(CmO*e~WxQLbB(StqsZR&?6c zg{SLu9k-!5v9Dt@zIN7JKcghGrB?glzAQ=ZU4@`UUI*$Q3~$yZ7ZuGF-t*hK?W|WC zi_2eq&(5uDF(a_Ok}W(Xw$rxA2_~>5tp0V{6r1k{>2viy#X6B$lLU6{7U$@2u9Z=* z@n&b9SDpD2?^tMANTj+YY?{S{Tr?{(PvA`nNc&(#F*{x%xc_CXj zs(#kiMme&&D>OOrq3bNmFvA;%BRQt}ulA$oJQlZ9Ds=4LzH;U4aT^>pAFPbqzhS2u zJ{=W=EK56^Jj$9rbT_~7i0JF>E!uP=A|E(Z4`@K*f}tNT?6c=1AQuJnYjD(%-(k_$ zx_$c@7-C#wYBX)9(Z%_S2wLD}%!|2t#(M3$P#+^L`M5ml&)EzdQVv8qMRs^%3r5%P zpAB-yqKyH!A|TM$06*8*14w&j8}Tp}zUGDO@PWvkpO&4B ze7Q);Z-`=ki+74Fau8_^k)_Xy$mVCF@s7)Ozgob{ld&37MeP5oeDLrBiNB!pJWBim z58z`lp`6T1F^$zIni)KvuNCS#4j7>@Sbp_86H=&~6VfagvAdbqVzhEcS3Y`VgCmw>qneKMEj7~e{ z%^%nXQMn%k#jpP?X6qIsXIT8fSvV3Ir>dVbmeOG4)wxm*ZlTVz-#!zlr|m)6eQ+0_ zV8Lx)Rcz?bQvP%Mw+#uT2cfUL+OHR?pHocPkja_dTK3wzJ?pe7eBF~-{GYo&@v3`P z@O8>~O@;#Xv{?vD)O*xVyczEp^&9w1CxdR?Fe%bbIbxVm@fxpxR|ymzWXSx#U_k zIyRMTl_@~W>C&M#;~BJ=@9`^a==RM?+PeR5AQsrn7N))7*R*Zh3UiKexE!bYr+bKD zt>mRb^@_Q`X80nvaraz(wLb&13`+#(+u1^Y8Os0~-(wzbiY{)Gf@~$(_>e zqgJ_1vjoYvdH0$Qq!bpt(rb$L8u|~$1BgidYnVZ z>sOD!r~_~0V0p}zT@$}i2H|;v!o?@7aotDsddwpl6WVo864s7l>=-``4+RIDfmDOFJz608y zeis$pdMUUWR=WM`l#6bxnhTCyqrRz<|A#JpbKW|Y&)n8;b-Sl!VOjfQRi;mG^i`$L z9Z-4zo9(2y(~Etjlxs&-&SBLE?$JJKtb_7ZtwJ)B{n^_`?Xm1&ME410*$B!rcYy4} z{!?JH#d#Kz?p*nSL@f+}`bqK0%Go7sUTD0WtNE$jd?hIrz09PA z98Gf~N`$;G+{?Fa3>3QMP-WQNyB#B)0q&M#MXzp@=WvudjNj+sFgrVUu7TH@Q}(o<&#Nuos7XjJMN6&s&qB2VZdiIeg>om4Ifb@nSK z9iJPnUQ8@j%-<;gO7Y2ks7F1SE02L)&WOymC4ri9wXAjF%FLDrJ+qK>wDN?4cHZ1$ zf7hprUIKg1rJwZt(^FTToWNYuyuswA@XCIU_wM4^9t1thCGG~%v9lg`(^#Z0COv&qC+j3%dVjT| z8R0wMUaEUd!F^6I7F{gWIp1(V1k!5spWq->CM)Ags{TQ$nrSajmKIHYDM$^0$$zxG zHK(-hiqP}TsxB!f>#%JzJ3V6s4Zrd8XF68gddz2+B6W(t{SjhdFE8imR%!Vh^ZY>g z3{m5vQ*=K`ND4xx!rxcRkytqHRfLUFN7bsU8n{*~aOE?U#|JuJq%>Nb{I$r_lS-qg zs(d-ct%yQcvNb1tP;zTuTS4(*TUAQHJ?f}M-n}~)hbL?D3ang$!*uBZR-7NX7@fdT z3_zk(>stU40fGdM&vzR}8c&&GY3)LRq<7oyQ)}x+ibHmryu8idSVLE%-EDgNroXxQ zlx7cO=%qcm6HxA|FY?Y#*vAd7EIY{am}Y-Gq?5epZHc0 zeoTlO5{9Y@$CE>3&2rh9nP*-<;Z?Hk_} z9TX{UcFR~MZSAMKW_-L;sNUn@&rxWYpRv=r!s6Y$LC(9V`ABG&oKzxZFv^NWubCxS zPoUv~Gby>)xwFPpDSK`YEy^d_6WSw_3gXD>tK3WIXuu&naTds0R;=E-eTN z1P47Vk~3+rytKd@XaBS)+>0?FTPS*C#bn58YiE6Waq4?-SdwQ%u|${yZ;_%Wals5vz`OQVp*u-t1a2sc^Z!Mla}?VIGxW#@{# zv_#n=nyet|6y7|rsW5UgeKXIJNve7yluThD3L#}-77?!v%aa)g=;@lSD;F0?jJUs# zExz}PPE27>@wtq`ITHH1hh1W-Eb%8{E)iiI;V(%X>&td-Pj(cQ*IF->8_{3Og8vv# z-mW^(qr3?JPaNG6S-AaJUZyoqBW-dzTinQhKpkre%Cgau;>zb9kL|N;IeMkIXQuA3 z7^KuX+EM)NJUr*1eGwBhEWD`dN&F6P z$ad~5B_hiIHg!bnZJ5g6*xf&DMGQ9nevAcff}LC-QjFy}FX`WdWCcr8&b(^v)YMZUV80qaJhmiNm@k0C z%KZ*&qd^~9i$<=|8@g=7+Q;;sT?WfFlo3}7;K~FP$Kil0qJUadd``B-up)surv;GE z6BH^MH2pG3!3nWThGDy?v}8N7S5Z}#a3ScjxNCBY4c?5cR-ipabeyad>^G0^&-Z0i`tx^l)pi96M- zAH(I8YI-Jp^YqQrc}fCzsUZA_E0IZ2c79mq&PH4CZy|1nR963I0nE^Wl{lWYIg4o7 zwYUvuaR)PJwNrOYU!FFH4p52EWW2*+ul=K z2B2p5s{md%*4gpl!m9X82R9Z?cdZOMvHCNOVcC6%flp-~*`@0hSHx~%2jw0#>k!Oqk#Zi52STm^jfRU4Jh{NkZKZ1(u zbRGqDADy-*e};VOYo<`00puERHBWUlZAxVm8$8GPED*kHOSpAm--PBe{Eh5OeKz(MGe!|UsqscDLI;60i2(K_r z@Z9)l$gAO6Af z8Lw6Sdlw>h_Ddgtx9Ul=#jqHlTd##l%gr!QXbnT>O&{|S6P4_*aLM0=klV1%P6dbw z>|r?R_1E{(3tqyO_Q-~`Z+q#@f*8(pj7z2P+VOnU*f%J^eLqaLN8+bz#TkO!-m?bP z&vx?_c@=$&?vVS3sI`w7Sq6@#AA6{WWhSlXK^VISx8U!mB;%J3HlLrq$a8np$-VhU zTn0}Vt>IlXUY|1FJ822wH;iF-DIbHCubGP?kdKViaz+N{$YD^}*gEnu)f+?ws41wP`T> zU#$pRtr`bO`Gu90N1~7Tv(3OK3R?v-8akrA#N(LWh!G7W(XwhTQZ5b#gO^R|tDJ>-Rr^;I+A9UYzR07R^m>BBeiew-> zra!~M%-lS1JM!t%W2Fm4L-shmfPS{C;OS-m>xS6s_!Mv$*a~M3Pn@8Co89yPG<7rE zmC`wV`P4fTE>gAkreb@7qigx+0JH5Jkzb<=M91?9L2=u>J8E2--bo`_~e%pMm5`ytW$pcZDtuzQ0OXPWB>k7e)~l> z)z=Gjgp|}`uptRZ6AMxbfn4w;5A3Mai6BgnQ&d!B&}m4+<(xslgvM}bg@kB7YlN`>bf)XZsT zn*AGX;P2FeSHo?*Bu435|1o!&j725xNrZMHo*x*{le3hBZS{0(&_YODyLd>%@jvL%HlpV&PB!1Oj)o6|keK$Zqjo<~3w^&PmEon=5;g4d9vhox|5#h! zPTd*Xo@!Jk*}1*D2|N1~r0x87vfHY6Uzn;6Gk8t~IfHZ6eK9pzv_NsL1RrTpdvaCi z<!HWy{3=4={$3n3ps`usOai)qTPl~w@QnEfB@;xkudcgwBRZr z!qn9T{t`p%lR@n_Z^YCAv&HEsTVe*sA}FQsODX>a*RD(ebE2oXCN~Y`QjIr-Oh8jF z=wPS%pi4auQ0a^}hwVzHTL}pi0En6&&q@;+xWeibdVRQh>I+HoRn}7N0GOxtvH(SO ziK(y@?f`FK91Nc@107JqGl2(zAc>0cB04K*`EgpDgY@7u-@1fgIq$m$|>Z_(<3De7?wgd(+V+}!t=4=b7cArJNI)P&D#xyJCk zeGdN?oCQqmGvD)gp;WP5f%J9ULf1zOEtk>=tv3vg!Cz~EL=p~g&NCsU=OZlLevOfZ{YD#@;F#`H$Yj2G9rzqRSD|gX#2RE+XKDUXyj^BQXV3 z1{7`ZWI=`h6KguQ)CdX-LvWu~04g2Q_(J%Ymj1hHL%UzkeBcFaljoxCna0!1GqA%kbdSoVT(bYcuQJJgt6xQdtAd4&Lq6?`fRHV06;4KK zENMJlp?Mk{ZW8j+*EVopl=cg5KHuiiK~`*w9SkBU+lEccOk+J$cTWn{|Gg^5dCy#; zDbxQ3suO#%&AXVnWUem`n+cz}UE2LHG9}+)vap}c@u{ELi}#WQj@JGCa=O%I`PT;; zMtC(hyThJJ!T%c9!TL!kJ0pX@{Bb69v&()Y1bzLD%&=3KzpDHaE^}JsK$#K{D6L-Q z?$Y51S#E4DE-nfh8Z0B8JCc~e%QNr$n2IAP64m+-5UVm`krQxAUS4N+Y*Z90_z!+`PsX> z?#!&LAwbVMRv72L+!P!c$pp&wFz~>8HwaY5D6Z6OuqX!8>-!6MczB)FUS6gqCT}^b zcezZ4#Gey}fy24buWh*Z;1vc^UY8{*vHDT+fRN^E@O7Rjc4g zZ6>Q?X~KnmTCVPiC)zM`w$`;PlyAzejCkHM3-2!{ebv{+bjI!aam%4Y;aE!U`rDFb@RnvIBRuQR zE2cH!mXe$LrTdWs{mUdbFOc9_=Q~qsPfmr!vKHN6d(Zdc|6=Z~!>U@>w^2aQg^3~} zt$-+?Al<2mGy;>9kd*FD!9pdZJCvN1bcc$7gmkBLBi(SGF%rMVzFKOJC(32_M#f+29%@qE}K%iFk zz~5)fkbyQ@qH~>!Ds#|{d&WtIP;q1C@rr9}@%U!c36E|VrR%(+-3faz8d;uqfAn3K z(L?vJlQXH`eS-68wluX6ikuaJwvL^URcaAv-T`-r4U}msRIskvoc;Mw3ow?T1@oP8 zKXjmNR-?MOeMwiGfcb1H!1Lgv8%!u8c@_b!OBv7riizK7Sah*lo}8)p(#ClH9zKP^ zZjSJpifY=PR;dn&)v>R|t_^#Jwe?6ZQJYd490eR%A;RLEOc(GGafQvg}jDvSJuE(Z@qZhYu--m3uWa2ymB$RFr*dj<Zv4>(hUGD0z_McX!Ef#^r8IW{-7dF*spz?9 z-H~iYCky#vWhuN6qLn7FL1rd=hZ~(!Up?KEMJ?k>xmwcmL zPLozSlWAoCOKP`%J$dadm$Z421zyK`l+eO%<@(AMl~qnf*8$%di68yF^EwH`a)%60 z2c3zJv=mMT1kZ0WCT$F#@)>X)t`I(jM{&?Pn*7fJGzmTX$aDeQFLJb|<5T0=+}D0r z4|l^yQ+-rSst*G~JSSbxdt7n-un~87K|p(T)mEXnn>;X_|7oUOb;EYQ;IGCdYD(Kc z-7U3zTKA1z-iLEFjMZlb9DlaPUo>@)OIOc-$Piv>Xd=}x%Out-m^vdRo@IG>Iz=dRM(WB4qhIK^l&yEx;gZD?Lcw)t zpG57>_=6nUP0H)`(P5JPL8&c%J-Bo0(Zw5PyQ!X`Wi8VQ-IGT*rM9J(JD@@uOQWUY zlU_L(DHQ%rF=bvjQGTDPakfosPn)5?$vXSs$I8=?f%?&u4J z!*16UPSNjt zxWa1xsI+;{%Ohq$eE_1rdZ2ee}nOw$Ih!J+AqO<<;{P3MCr(3a(Ch9E2=PP z+*LcpfTz&!t@Q7zU=>ixOT`oTKRB5=T9vEJNs-p0rbtBGKt+&Tc>p;;k#aKhLTjb!Xu_NHA>} zn3`}C#AJPU;LeCRy@wuN+SgiL(U7@h`=&N5iheojUI#&7)_3YF5_foIZ{y=vW$Jdf zkbLSYW9Yk+T%$7o!LXH%B2Zsdjhw_esPB&UDe>+_^Ydrdsa$P5gmP~rzsbmxCOv<&7t*h_PCqd9mg#WaJ$hiWWq#4|UaYaExJg*jB+*=TQ)BG>A-mL% zg&Y0_x2?O$g9gUOs^7mGxwk1ee}4J>ORwtpxZ-(9+Z%^|N=qZRD8oAsc$ykQCu4Ao z?KmE8F2jerC^1u2KhO&4jKgGIaow%qYB^gqverFN_3L(huhph4)ca;>;ai46D@nYH z!v%@P#^Ab_yy*jE2Yl~GY`tJqE(xZpy24Mnt*8>xW~|(u3l65PCZBuV=eP?&eIgt1 z!@tpPUnIOxsrSCC)U7)8xWYK-?A&`MBf(#m{Vr|9W;+MXij(iI4-X!4L+b9vJ2|oV zz2dixE?IR(2%ws(!tJV8z10rSRXX+ zFJ(OyoKuJhy&O6LF@9_~L(6ol+2uDFYK<=g0mBxSrzN58<;B+#8X&IDb@OI@*N9#$ z%3;9$<{WDJB?Mlx8d9<_QFUbyuv`2~RwxBr;2nRyAH*STLAiSMMSUrf;}piD=%l!^ z!dmSh=F6cH^A87=pUI1xs611e@44i8P5G8=4k~Xy{#tp{g8pbCFNQmjfow9;Z&V^T zE+>3VUC)@4DElWt|73L(r%2^ml`lFe^b)yJ*CisQNoM*hZIQ5M#6AEUuZitEEvzsm>=xO(W_0gzhDlP5GvZH#k!A-`|g2m>P zdh~HRPj|}les6o$eD&9+Zcg{k>3XTOec+=MsJr^XHqI`_cF=mzNTQx6pQ8A%@?(LV zMZJ5xb;wr!p!H~Q5>3HYdCZ#6({rKN89xk|(iQ(47MUM?h1Sks5@v)jm6)`adSaKq zI{@|vsT4eQu)k7U6F)xu)KYAnXaFzr`y(DeO>1*bH0kj8ns4WB*EPzKW)e zWB6DM&9^`g>G5xJIR5y%_a{rq1j^2~7w>22OIa5J0h)-4#sy`mN=DY4^3w^867~;) z3ehrZJaCcym452ZG+WS8kn>L1d$Z0?rB^aWzOZ2L=j2KSw2ZB|-gFKh>|=VJ3Frfa zj!F>dU=fl!E};7ccHv%wKRmbcs&yWXfK|J7A1*oPl7oGS7HO;32_8s<}M<#03+ku&i#R8#gOGk~I zr_|Fgxr+@4ZQ|kWk|2DAxyAzmG!koUmu@f%JTm$Kcix`bIJH+gM48U2U#*}t1{r!` zTK=6e9~;0Rn!CLOkz*IO3c?x$Ve1>+VSI%#S9&x200V2Aa1Z=snNT)sTGeRD z3uVwNo__npDKu%+GO^(evBRrSf1hD9`Vn&+U%BrUfNrGZ_ULfEjxW&FmUQ~o={LIK z7wJD_n_@2vWSE}X*H&6QcV8@1^^wwL+>jAhDV(J3UC$qWY|g=vw80V$`mEI} z9)>e5rnWP5`u^Loj@ix8Tyh8%7Qi8f0^EJnvuudLWumw&nQ|)D-s}*jT+1xqh!Nd& z*GoAlVo~%M4WZ9)G>D!jE^Oz$uFw%xFS$SYG}tV3p!taW20hC~rF1B^THx+wz zyXo>5mlKJy!;qAgr`kxV0fZQ72+J=Z@ZdLugtS2&A-w@X2wW6GR)c@u0=g|T*#@3- zt`!03VAp#uyi~rkZzY?;r5I3VeZiWylEcnuXA!1B1psTFiIr6W;hwPN`Z;J>eFvG^ zBbh~5?4=`F#S|13zr}N-YVwAjy+)gl#;-gqQ7>V+*R#^HIm_LW*g>0Nac3gQcKvF8 zW`Jyq9xbQ$G4OjO&@0z;(0gyvi9%X;&kCUfdL@*vtaIVa4J5U$J>H1Rpn3PUy+-b5 z-a{P)>5}^|Y>TgBVWm+$dh`gvn_BA@G^`vsfVy_zUkr9YAoys`k2j8HE;`Zjz z87FoFe`UH)dV3=9r|k&uw#rOz-Cb)y1~iAlwJRNqpDy&ko=AhFWvV9SqZvTw5G!4 zPQYv3{P=Do8gRwt%k?|SnhJJ4#c;zrBBHLw zQ}lnNBb%>-dTbc!9e5g6Yv(e)s?{I)N;M2X5s}Cm ziT6_{g-xmXpr6j^oqEj@-dEiwystxFE`d;37eCR!+&S*h<>$!;oB*>Dn=;ex*| zEY+O~hYal852NbE*qsJppASS7W4u2l6-XO04bZh7f70SKnk+S=Y7=~vP1+;oztS%1 z9jOZo(q$ypE=lxh9CLKXYI#5UarlD{Js0rkj1r51tXNHvp64^YQj}=YbE_w?UJ280 zKFB2C^e`{5O&;+y8FXixg`3ygTQaGfU!Z>FXhtU@Qu_)%c4SG=wVHlg3NLf#7Uo&| z`pr1$nX=Nnh^|QEfb>`0$GJi-8Y@|$f@{XNrJ_`05?q6kZ&YrWOL3UjHrL?xOp~XO z2EA?glAU^v^G@ovIgJW=G@Za;?S7^^7)JTJRJ(u4fTHsv{p}F4Ni3{iHy+#>Be1B{ zd79og7UC6pwSR00p6FNoX;m=~ZoDTV*69bM0c4YmTxwiYQX8Q1dBT3ZwsHkX8Y+)xl?89{>YMET8T#%AwxX+LV$BlrY)|I$Da}#oVsdbCd5fQv3#4UPN-3v-oN}FOQ)<8Km;7 zUKh^!Cy769j4U}&ep0TXMlPH{FC$XIT>Q`?wotlCD|gV?UDB*%h_~X^JF-BA&tYd0 z;nvP^2!@7gFJ4xBg!zW3b@Kc|k2N@}ZNDDYc;Abd`Vv)$a|Jm&SnMtIe|QcDUhl0g zzwcl`#EpuLxSRCxv7@ev?+i6$ihupwQ8Lu}w&BCzk9lg;$bk_BpnGCup`_(qv0UWG z&h|y_n#<4Ohh>aUe+lYfb>17aU)v}){l=^8s1&YWdi+kSBUvnlIU35KII3wk;u@Rp z*uBZ8{wnI$g_Pv#Alu^kM5zV`t0on0aIo2FzoMO_R7@6|9HW1Z1n?4-xw_FRXSfV_ z&$SJ^NM0zNoRyX4g8Dyc3*4aWdp#b(H&3MD78n=$6dzgz;^MKSfI4GlvBBQPSG~a* zK6grwMl=$G*FNEdUPaaHnd~x^8cehD1ZuOy%DtL=>by5})WOBko@x%EYue*d5zDg$ zZ}p_1!dT;5xX0U_1FIo$>C+e|H{)(9P8bJvhKz2K+yLmeu>gEkvE-r$9M`sMQW+-; zwK@5+sNA2DMY5K05kp6Z@W*$lr|;k|S-(H42TP|E=|%MT;yut0qdJo~=M#J0oIA!f zAKWJ9?qNB+1{=RbIN*LKC+&W=hiR@`u}hAgrk&GkY07SmALMOeYa1X0?Xq)bV#tB< z%GYgzwM(Q$j=t%;$2Er`Ja93-SOy)i$8c+MiUiB8j6rV=512eH`d3tY^X)764_A(Y zdiZ;OKPi1&-kVfi*lVxgOK>vVbSyrdz^duRNXiZFe3hC3>zW|1(BC^;DD!kXH1^cm z)R%DL#sL8yfBUblg+ZxD_G$NDzSt=+haHGhx?K3-_q|asTz-zC%|-0W*9N?yu$y;w zXS}at1kcIw^QV$bX*14fxs)A^;+x`3Zo6Fl)(O;M``Z_n3>wo$E`6bR5082=V}ASz zIS~|;uwwdhrcmLvfZR!X9?S^fu*7jx`(TVnMVZdieBY3;e%gX(r)rLd^5mG}R?8Qg z7#%Xpm!D_beD&3I4>}7&;qqjOpHVk-Sl)qatz!`1@&I#2M)hDLe##PkYE1f+q_ky7 z%-d5aaSd~NctcN>+mU{0I~lXI@`}TC>J8Tw2@mUZ=d4}j?f7;nCi0=&pQNyt@~3_# zE)&*GU@YDk>#{zvB(eyC#J@)CMm>)!({bgyCxEIbbFB}08@`JXYtjPH;`@e%hjTqy z(Fk{^g1kIS_uJ}`C#njD!NaE*f^s&-HJhIQK~;DbWKc+nX*;`jTenB7L;Dh4gm;k#k z453S+@;ta6d{ZtAir{tp)YQ}mTi#C|Nkp0zOAGb!bBa=v=DER95LQg{LUr85S3T2{ z+EnX!%r3FPz{`LCXvO>M*D2g!?-nRn%CbP?gsO-~85wetamD82lzX zd6i$~U4YJA=LgcuaO0C2MP|k+G(*Lf@drR%FfNz*InuDja#VZFX_)RA=DQsDr%wm9 z>C7Z;`9LSLDqTgHyaV~IfPQc9`zf)5+9wMv9oj~~;TzM*K)c-g^8kdXGU%w)e}uDY z8R)ecfE4}_+C~KY4MA*08geAmBKOZ2B6BvaK)GgBK3wGivt{EsCmv@LeR>Ew7V8&X z`k_vn>Jqy6N)uYM-`_s?!B&u>OCj&A6;RM^@@Vu30kH%|o){4;-Bx0?&?f|ehYqDO z0$C2+2ok;7nz22=hWd>^H6^7?Al&^Bz&5_ctNP@F{_l78LcAm_%bbVZbRX& zxOGozI!DGabH9}(*5Dw}fAdHSPWKap!1ooE$E`EOOzZ(b7x?heqxZ;RU^VVYG6@(i zGLOb!A3zVJq@iI-f#s;y4}tB+wjZD9>nE|t;@*DA0@9zE$#sONJFgYIg9b5CQEyCv zVLH-?_fnQ!AfOQ}2f@r8N*36T*44N3o4$W%X>D(Bw`e6HB-D$`&u3(~q?8Is^A^21 z+At*3(UFw+{3M@>XPD4XSxQNfm$SD}`i+K@1U%W?_iq< zOv5_?B_-UYlR;9GfE30R0Fb(k5anu2SOcWO-R~*@`yLCkyWN?L!0vz$L}6w+1UBUT zhzdxff64dWXGA0Frlh0aY*AD~`4m>o%z?*-E>LFVLCQg$A~-x;WkT@!_3Ji+=Eae? zx9|M4kg0j&=a;p`ug*Cl=rrvMdx*JnRf->SNBHIc#%IjVwwS z*;=0++M>q?DAc(EX#3ilb{;Hxldhe)xWH#Od+jI*J(ejWnuLq@2nCm@A$$^+wDrM7 zvZj&YR@lOb8g;V4)Ctp;32Oa1u~$-}cB+*z0P?s)l@*i>)$2Ld79C)fZLxa}R6=a1 zkwb}y=+mmJk81frqC(Xy5A={5a%){7{B)j}p!^$iD0e>u zhJae_;Qpm?Y6(Ox(lYz#vENEn!R?Wb)^$e!Akg5m%!@P+B4(g?_VCG*#LlAOa>r=n zGtnHTbmA2DA?&_?uACDGUZVZAW{v#gA*T4-!#|ze5Aih(bY2W@KdK>h?pc(xucLSd_I$-ze{A z-j2hBawe(Q&ZhopsXr1&dD%m$G@3yrme)QA=-hS}a;o(~x-XHF-$n!qnor8ew~;aQ zvClpT_x9RS@u(!+^3?XIXE-z+z&P){Ntiw)@Ki)u_0V0<`#cCsKbG@l!T7YO%t+ME z@}=*080?iioYu5ol~vvXH^j{p+Pd>>rW(~L)}gZ@I{IKt+VW+W6S~1hge4B+-aJ;V zkIm%5g)5Ezv4fk5;7e8Q%`-wzMro#M?iZyTli{+$7&r?lZ@Qt@vbcxIKy-mRG;%e%V`HE{f0gewAZI%-1g zhraVt!f|$^853vDuG~^z|MdVES)^}drg&o>W&VdtePZEdH*-i)+>1N(h@IF}JE1tW zQDr`#4DnmS(H*tCq|c*@mbhKfCyLDnr$Lh4p(&|$^tm#6heRBaB(HhGPe^R))w*i_ zE-Lk!v_5i-u;gB->7u^`4cyeBXkZK@8_}RH@4}szms6K^kjKl*l@M+mN%2yG8`WMdw<}rfHm-VM#7a2AeOVDEcUCx*G zme=d;OqqnT`%SfuxAQ}7%!;R(6z7zR%(dgB(KV?}8b>;Nl9w4NO)vU+pio zO;sBKegNk3Ck;n6`J*Pvf_i6~cF56JrO19CdWCF1HJZvsn*t$PI^qR*qu9&eZ^NryL95 zj9zoyUgp48|NQy04WMW>aE-drT)cR3Jb*!4I0>U)GRE`3$xFlpR<`f7Y9yS!l;@pf zFyc3`Fv1}FgQ81oWY7Gqv%qF_A*+M1hQzz`j^)+M+v{a#>lct9zegJH9G0*!f)KA# z55C$Dr7tFuqKw1Wu2Nz0ZeERVq!<`Q&1-;Xb_MN3nQPHlu3=vSkQ6#2O>;xC_t;3 zHDO|MEeT)0z>I)}ai_yN=4gY%5%N^`#zr%n^;1$ea?&brjCJiI__V|M(;%~`BiUcoT*dV+F+7p9UyGNchbaBC7M#^W?{DTHE`QsSq^S|GV z&$1af+GCEMEcT1^?+q5;47*}YTLXb#jUh$+o3}}OkQ4OcgR2pJ`tnc}aT_4M43=-A zciZ<4T@s{Hm=)s~Z*`u{jg=Q-GGbi|0TV_VT;Y#+q6*T$6NvN6GCna*~C=Mgk8}x?y$z$gv`6eo!83 zG5#XrFCnWu`aLIdqh^G`>Q(oX-i$hJ9-_-LSn@5}x%* zrdFjorQ0u&V7G20KbLx1wFlP$6b+dh+1Ky=uL{{j)H8M-lJtkCTftq-cM{d7NK|*> z=DYQ`$=cZDC|6m4qbdB6EyWXE`{22xs8Ync<H{03MH!*KBP|cg zdo#(1ISphV3Sw0HW?+Pnz51CF(PjxM#R!F1?7TE=SCAUztlVR8_p2gcxf ztS!XeeE8HV@cv7b(pOk=ZdHU*LFj1bW2D0`Fxz2uOtiNiR+Y}F($jZKsu~25f8~aF zs_MbZbB}2syb5*$D`ge<`~@!N|Kv|p@&ryYf{7pixQ-jeBQ6;+R$uUueji_I zDwxjgs6@qVSVUj@nfSODNs;Vuf^7WxA_zoc+wail3qj^cuM9k_$2-hf{__LaGXS&} zdO>hUco`^{fc_vOCmN#b1J$$~vVUC!{(Fudc+RuS<{2PN=aINzON zYA>y&73Zu~VwK1bKxhc*ZUclH+Lq?((DRwapa<<`DL#LH77v5(K{vFh_A%k6TI2!5 zDoE3Js(FTT7Hca!V}f+NpeB$AFcpt_xW*2`NsQwB`)}jM%mc|8E0_-u)0ONk2p=7TXzAZy($YPuv{lF!u+o%0f`95M7DpufyInT zfBo$ZElm3KUvJ#khimBqAPYnb+xCiZ1*+?a`TN_e;dvjykp&rBwj;sfSt061xDslh*^V!2mp7Hb=G3hCdjo_JC z#@05ufBn&k!3uur3VdL%k?mi9l%a|HfTN5BTN&=G1ukJubjK`QFEO59TpRg0H;BCKA`oI0azd!xoZ+Efp{Z4!QwMb(({=Td5evQ=sjh#822WrOh!r!(Z z_CxpqCIj);Z~eOTcbSLx|DXBs{}2nfYQ=q6O5_{^wX_N(&FJ30f8PPHImkqo0Yq_^ zo?f}5PK)3iyrbY#hRWrQTelkcO<48n&LZR&wRLrp3JO6O!W$rnGe!gJ$>hfX!61Z+ z4BDwJJS=r}b(4Ty3#mW?ie>s*pYTEkFq3^b5VT(!KtW3u99U6ah&>^rkcgZu6eaP~ zLnXBb4_+2LZU1txztE8^Rk(jrT^r(!=GEfw!xO*&VCDKaVKcZJNcg7U%yW9?K!6G@Bgqhp?RYjLe{$Ew+)GqcmXrfCwlhD>e>r@uCXhp2i(5Op8Zz@bwc;K>N2Kg6tuv2`us(??(?gcNpV({@W$y|36Ota48s9}6s+AZ4y z`qUWRGw03~zIgt;50VY$rf;-d=5C0OlG`*~F9M>@CbwnCSmbAm2T&rxX*oIZYxmHY z7_+zU-mxEN!y(qA1oj}dLxL+;45Q%@n^>Op$%Xn+35{F<&vx%ROieT%;^gM|3v8*b zN5VXlbf;V>6~yGWJah#6-Ds@f`+|&B}p9 z$qc&Kea&Zn#1_4N{o3S29t=ngnDu%tn)YN`XKF)}>>Ymop|FUEr@$B(pOucF(=S2I zzBmg4L*qk0i#3C$d@gqzNEq>&pXsWCXNYh1+)>(E%x{f4dfK0toK;fNcV+faAEEW^ zJhjKCtaF~e4a|JLX%MZjt$G| zt$<-rKQhP0fkj|GPa5+boLLQF!w8DAzY}`vb*jk&X%HuR_RHh+MzxA@*W=Kz4j^i1 zlGPBS*QsstuTx_#$~nJuLOZ~R10v8awTjOO<=%S{VVyp6#vC6Xf0}A;{M%d2BH&f+ zHS09{B8*ZLPEiXj(`38FSPnS8%|-Q;uFY2-ZUjVlSS;}!QsjTkd2Ae*81D+>BL4Qz zM&w{}Aw?Ue3yg7I&3oqxFJ%mz)Ptv+2X;2;@qs zQ{VxW#%dL5b@04^?4xKv$PRMuoP#UnD}ovcXr^nO%;)yEvNVdz%#Ok8o>yCOtY@vkijubak8a zZVnV%TJGrqITq7kky*cPkukku&6|JS=@id6y$Z7U$W2`)J-w>bf~+;N(z9pp9rg?7PmWHm8W>)v9c|Wd^yN4YxRJbk=`>FlRFRx4fDBesX)lG|!JFqWn`lx(pzw$o_I*#5TPQzpMHY$MxTGfRSC3y;bOE zz^*xZJF`rcW?y-+73mIV!j=ew~R)7Uy$Ux?;;Zu-x6Crv7rfQ-vDA zeMaWmfP~lpvb~0C)n~!aqyQOzFT9ePm&b}Q@U_gif;u%B5(?aJTz+;pXiYxf=4k4Pb_sB=OFjhE|P_9Q2vLp}u9Yh(b9-WVqr z`uHP+&Bkh;`Mcwk#>aW=8Ses|mrPq!Ow8knqh~`Z_2jniN~3kb(*l@lBi0cr2uJXt zfCDzUca=&EM2J)e4MdFht9HiEIKyDH0Vt?xrZeXrudlBg0A4;aGl0KQB{~d->fG_x zwJ1M|>tN7U3``;BZ=k*ytj6iV z&*^GJqcRYOu@}Ffh1difa<}262L>Y*&MB4~Qsb}*>^B|;Aa>>k=`1_pF2V?W$nxY1 zt~OU{do;gu-fnSEsjZQsiDHwfiNm%ymAihCTon6vT(W1fE1%?HjxePa@%$CTP{;Qk z9zTe+?@wex(7ciDZ3VeV?8f?!uf`sB)G%o|$&{{gu2O zq*Mc@cG~{hcJN`jl+-&DIyVCdQrKJ7Q@RFJ_Nc-Jn3tztOuJrO2Fa4Sb{v~W9k+HK zd64uy(3JjQ7+yG`EpRq1l?-YXM`%(QK!H@hPHhDDAQAaW!jT^ud;!;5MWWZ~$P#qE zfu@JkSX)8w-x4!BK)tTYa9?(6)c_@$nov8X19uq_1e5 zKLsPgOM{DtSGp*!T^b$~q#IYLjZ+!Ve(jSHol1&Gql?tJ%xGF z2=BXCUf&v4tA@kQz@hdTl2VQq)N*mp=qm3-pDp6ditTX9bQDGQU@*B50EFWqu~QHHtGBfeyKD!700{}k6}dt8n1 zt_Oi4SoE9Y>8Jl@BNpKs@K8Lu=sgksZrllaGMq1fpKs7fwGA}@GaeqEKfO6mpjRHi zQG|)W-&=6?= zel5mhGlOJ)X;wi&m$5sXNU(LrMPp++8cIqb=HSd?56httdiS}vKN00tEeVWz% ziDv4)s!R+wbn#PAn3Hjsge(D+D#!8DO{&79cC$A~S!q3TGx1sf&g#Ul;MVdmS)2PN z)M=txqv2feO@%0$0Uvv{`OSDY(8DPUG&D5kz>yKxWc{p1ZUo%G13o}iWgG-vb3>0* z_CGpDZ)lEYs4yce^X9N<%6r8Rh00IH4j2KQeawVV5p($>YxrTj(>Q*<%)a=(#J<$= zM~U7!&->V*8UBn8z^|Qu>D2gFOl9)`<0H0SRCEr2_c0m|iM9UyK3FT3P%uh07^QFx z9M&S&<h*jFC#SPSsgYQV#(EQT~ zOs3;L#Jy3km|7#*Qbh*O9YY}b4C7n2MH)FdB<8M zVI3MsVY$(Aacw8W2{ALTB*Z}}x~BJaDtrv&5Rz93%dgY1ZhThU^Qpsj zE?4N_mrs8+aOr~UZ}P()kXa+#dF|)*x7uHA?jYG{;E#U#BR(XA_IR(iS|4gLvNKei zW)U09!x~5;1@ZLmi;Fh_PPW>LI}`yD{&~CXbct0^+h$LyKH8H)S~!eHDoXK@@%2e) zC=Z4Kco+LB=&%|NA?(JI@L@dn0YN_^BuC<3Trp+R*K@9;;5X8d9f8IO1!!%I5kXkI z;T3UcN3{u-rV-p~8l)lVvCCX?p5uy^Gla=9HJgykmDJV!Ffl|zLNe_(5C$zpqYJG_ z(SYLj&RoZA6CdRM{LaA!;k-F1!Wk_y-5Spfkqg(t4@fx7b*2WW%#g9@K7U8X^}~1= zP@xT=6N}Zf=XU9s_nDX?W6ph~{scPL-yV8)?tbo@ZU2}IXdbeWTco!Hf7P6jja)*& zJ)jk&3F@~q1s>3UgCRW!(r>C9K}cMB-Tbe0{!(15#OW#IIoGx_H-?J9csvu07Cu!xtyGwBfol~$qi)Y zV5MsRhw<_2*MA_9Tg4t^;<4hc{X*Oj@ zUI8~Av_KaH3z1jdYdE=Sw6|`hHJa@;A{A!B*P5P(n<==)(D=inHKX4ny)~aVWBQ?f zp%jS{i%B+eb91%gA}vluYLw@m0R_{cHqHhjO1Q!xz+0& zF9hVuNNB2e2+$oZ{Ot~_tE&(mQzI3N&Q*lUXV6w%Di965D+A`!ZYV@k+fHFw0KwGs z0dyBsH@VK5#<~J4PQrC0!;IB+?)TbURK$Mz4$9%z5}xX;BjioM(maLE$cBqwBIHN# z3E+%O_pJbmaVWG}_3=|V^gW@ws{!a6O}l`xXXiZO$0beD5hvi<2oX3n0=hprsUI-N zj+hoVs3AwPrtr3|gWSq+9sq;UHRwTlWj0VCfduG~9?!F$cn}Hf(~rSnuz?u0SFsgD z!|AuivG~0z{t>5z_3q(Z@$%{@w$u*Z^?=_*T8|i^YlKdC5X#4jcT)zzZksmxRUIGg z+rZR#lyjs;!PB31RlxZj^==btGEE`Z1?JN}O5aUU+m7P}U?PLY5PIYwLCC*%LQRLTg|VWb;xL#O4NQnsF$>c=;@Vi3|rzhNME-PYzts;tV($Yd;u#n0eb57+}6fg`++`CS90*k$nh5k@$X{a>+F<_PF zLD5f}>*A(x@W5*+ua`svH7lnQeA`5FttaXtr$8h015h5)bqoEPHxVQF7<6Zq1vP0R zq)??q@7-%zk%r)pgxz)A_h$11!eE4)zB6#5aW9QCblu2C-)3QHzqy2sRYSJFy%PS} z=Ts9!4ARne?PA0d(B{%CRt;osB7*yiZ27XmW;iHlEJIq8SV4_SNJ<91rxA~uWb`B9 zc%t14^J`0MZTBv4s77zRd?v+Q*>#&3ptyd-@=bvn3_`#iD4oWz5o6ranpGvR)u(5( zkX0#aDA*a7QR630_ptOpo#19te${4_50-<^L$&@b7`P>{R=x=k|~^ zZ-hQb)8$WXqu@;8;9C>TGiJk}!YDrgMAGffgPvubzzIEs%t6W*g=?Lv^1+$1+&^)5 z!Ksk;jv9viyEN){7Jo7-ZDYFVWx@)I$D^>{Yz^99qR}Mbk8ONu0cDfKDCUvn%I=8k z%J)0PL%JjTP|&$c{BDUIe-YsB0xFL77Hq&li*{hYtjh`=*`&c8CP5z`_yx%c9d^|k zDyB%U8t=nR-Nn1^4>e&cRo8?NwWq%!fM(bQrh`gCl|^}!WIpe`sDNENY_e1IdnzZd_Bj7KrfN+ z1G+cYw7Xr+w^)GZsx#{?|CPDHBul~H$v0k5d-UWL#dKak;r&@Cok@R+4hu6}&Wwh0 z*nb4BQ*`S9ma}1;*_xeeY}VY^_po=FDeB1i7P;L*~0FSx6~C8`)Q!&3vMIUKn~b*7LgEbzUEjr9jEbSoL6 zwE^@vlp?bvy+}Dz^@+t|ae$xwT*tRsM|GYq*&IG&w-4R-7|Q`GvAL)b4|`+XkA&@k zI+)SdEcF3Y%;0L-^N4HK4WK_Rb*xrE3qaJo{y=Y;tnsL^G@sj!Rd7hQb(S(+cjL#< z2~G;;F8ZiYkMsLV!J)ykqfmrL`n77meT!uHfM6woyU;PQHBe9Ko9JZ&mESZjNJg*` z<8vhB4S=gdg4=Oz7N{fHiT`ljn0<0G)?etzDb=e%@|3wCctf_$)kmAmgp08N(*)RV zTcoE9*nf3ABc^b31(=%3J$^=&>=2N-D6A9Q^ zehQbP@2Y@HfnKZ@cQ@bV&GGp2Pv^cptlUYvF;&>IJo`=HT;@h4$LsdbT*(H*o1&wj zge`g&jCytCeHx+Bym#tD{7FL>%5x8I%EsIR%GJP5S-uu?owUy}Th+^{#5e7Ql}=Pa z`*`Ob76s-rzq+sqMmCnVR@I?nXIVKA3dZZ;x;?(SUahM}mR zgc2mKd>J=S%21r?Zq&VU1(B|9z%iHD9rx$JcM*?7Y>IUT$ucvH&SF} z*vlbA(uYF8w4n%vPsTajTVqt5SZOana7`w47og=~3HeF@Sg$4r=v?U=A0IaZ+nWb% zq_uMqP@WhBzxMHu&8M@v5<@ zDL)qIM2oR#Z!QH`eUB@9ZSlFoY_0ryJ#(#ndz5=|P+BUwcb=zqs&Y8@ zSQ^S58ACHL9l^Pe*jSFZ3(%{BXh$@Yb!c4uk*l7k8zh7{C@(;l+#2G4tQ7Y3sSd&@ zf>|_|W!xP;zVgOHZD`QUudc%dR+IcrM^C71G`pA{B#j1?RYRnymR~pbnDk6-ZEc^+ z_Odykpjl-y)6?g|7W^RJhRP0i&&p6~=Tb=>+=sNi-}L?=jAo$mbD`IIT@{}UD2PA zrnMNbf*$$O#SCm&m~e{meu*fkrGze|X`sgw{1|_GQuetr05eb~`6V6A*>XeJ1$-Hk z&B!>&SccckN9g&35A2ed!%A^Ev!Ysyzyci7x&1~v9i?rguvgK0`(_LYYTJ%oe_hP* z)#%O*NRRQ@n=2aVm|&kCta2~!bgA@8As*SlF)}Co#J=2l$nKGGLFKXa3!Zqo8Wr*S z*hPHzLW6+ZjqNnnL-&jW+iMv2I2s2u`_l!;!l(&BM4QUJs8c6XC{eQqE?RTdvc=`D zEt4;DQ@Ko)pXKx@Dj&(6!76#%zELNvnZtFuC#gu!yfAsj|Mly;zVe0b5WI$yQD;ci zblflh^yzMJh;rm1-511>9?`p%kbb66Gba{V#Dgl zknyNib(8!xw+2a}L1?w0$SJH%4a-4X8ksqT*qa*0zOu2S>P?~Qty%Rdadi)jM{g~Z zE-}c7=%*@f%~tg9l@{t(|HwB>KFpY!4Gr_Kgo%Mj%2d|a^ABfQiaOV47a$9Ww2?L) zZMhd_@j5!X=e8O^|8l}LFmh{j*&XqUFmolo4-@whb6s94TF%MMv3x4XTv*DCW}d#U zGlEXuVi1~LU5Cmhx&$WAP<*L`Hk zbe9y9n`P(bt(3pFb%YSG{PP2sv-W&y{#oipc#$@r-kF;kP_LJ z!)^6~hNY{)b$b)nMT za^(u4L2RiCi42g~3CX>lOocpSreHvGX{0iQ}49v4v*V;QTq) zn%JNq1!l-%)lgkj38g`NnoqNMDM+@HzY8KogD=nV*WlzfArQI{fjSP1YO)3%?sG9R zW;B{F>t2@G*_tee9X-q$vhUTTQ!@rxpi}NBxnDh#DjPe0`DWjsye6MOBDg!}2LK$i zkT}a>vF}M7hyTU{)OS>mV3rI{SUF1^C@du2T3F>_PaKZ(N1+?56UyEtzFAqy*7_r^A0dF}6?~OSe7ca= zN9$IX(f(c|qB8<3D)`04PFXVEGk#g7MO(3$*VG(~t30sclK5%ObudFjsR-ag!`+ui zjzNPLlA9sfKF`LC)!hF)LJXNX`(?<;ANbZu?j3G}A0Kgl z2O1ySl<0&Yvbo@bIZaR9(okXokCc@3bqTUm)AhuWnG+I~eC1CC^`=qHu>ikXrQ9ve z0!0NjE?kqsgGQp%jgzuj&1%tA(z3D+O66!q-CGGUGc;XT@^2SyP19GPt??m*Xt7L} z(1=H({8?D0;A~15yC!5TOG`o{Bj+|t7ajYI@6M*fNLOS>rh30fe(R=aqO24fv>}Jv znqDbWR!C!9(s14KFd+3pr7U%p6L5HCXRaA_dp&FlH8GZt^`FrhfQl*p`LAETG!GN} zwcc0A$TX&6w|U*A4P2hdIrGL^n(KyaY}nc6N=;BNh}F)IKSvMDSsbGuTYbix-c1hI zvQaWjlh(T!E2tK{0bu=}u5QbXGoMrf@;dtz^loPfqN4TPC`Q|@DP_F#w(n`q$zZ$W zsxTKRZ?1Q|tyI6LjEYlE;sltv_kMFY5^- z!wD3vX29}|ZXPWQvc_p*(ktm=SzTCw*oPLBZ`5M%LE`5d(Zo)s0F&~C&A0jK*Y1jLyRQM2fX zK*hDTRThvTxBUIIYfq;+XBfjbkJ#R{-8T`ONVu@(*mW{qMa3?Yt=2$3Bc*q zyM~2mU0ne~wN=?^*W>Gn-ANhipO3yey}p?Td7{qQxd2$ycUc5GpSt&*G9mG&ev@q3 z4fkK>j!(w`P1paN!`x#`YZae7{+&W;{&#&9!?7I47&x6|hiMcmHnNzF78Q0LY3)LmCzBg1F zbK%6bCeQ-aym{OXRcc%DQ3dy?c-`o3R;$y-POTJzRM)z5SHOBMdR^e!D)_m^-ANg0 z%UEEdy3oB@30h$p!(cgjqk5}uC4t+iDwjds$fq=^b8{UeR5ph%v|k?x^rv_|=O@NO zHB}AB3&P}s%9Yc1lMjs}G>iEPBG4X@9daEifYQ|exJ*v$_rdw+SA(vTb2;CZ=lErD z_H8Fc?@J2qR+DVXMUsSL+fZV2lXC*-Y5AlDauef{m3CU)4V0vdEVnj*6WbvZ^;U@h zW_dD@ENr=Sy(`JXCY#UvXSS>Hd_&^V7uy=#&wKXzH@k~V0k@=>WS(TUL)dWa6R@>v zqf@>N$l;9KqrCD|mD}rrN;xDyHs7i0Wi9~tdgF%Q=aZU*6M)-tjyFMNnulpv@%=2f zEu3%vyl(&bxvQy48FHpIq8yzUt%Wm)&NY4lWqk~Dre>Bh zhtPbZyq9<0^%VQ(wcBC5rOfX~svKp9f6XS_hkt%6KsE)urN!cUIT7PvJ#N+BlYyEE zQY;+MqD4OvhT|#i)|{XIZre}+Maf*JG{;#;8ZFl`Vt(~v-RvAS_0sx|`S|?uYu$;y zBi_3)b6da6pgl~+Q;}VvD86MrnfLS}Z{IV9#}!v(z8%&k)ghMnRSzTbGI$IOVs?N|_3e;yXtdATuu zqlwapngaJjJwnbZ)5wxS%ZfEE$qi6(hVug#I0OWS-j;)v%6{RY+ice0)7{gjLk-IQ za+s731KneP5QwQdK^_27SR=lR_`Q2WK%FtF2Kl(nF(S%B=O10~U4Wk>u$WVjv%NND z>#dU!+=>F9921R#_&-O+#c3_hI=JeVInK%rgQKzy=s?PU{(PVXB!7q?c~lN<*2ig)UV(-VGSo~IT(k(N0jR!2L}hr9Yj!%m){vnfGP1X zax~C_HqIKvBISUm52G}N;hcgx%b*<~5yo$f1G9TEfF-{W7}_J}$;eC)HNyrf;SwFk zc5c&suu#v~g%;BW@xl1*w6){6F1jp@47vc)Gl(dcYreZ?mfq%i^RxY%bt|7QACnk73hk<0q6&Z;Ll)y5JBrFI`&vJp+x7%ZXb{DU3@h88~nJhE2z)A2vXTZM_zmc=vlCD0_!Na&KW6{`>bsk|b*|r&!$U_y2sG zA}rCV8#7~;d}I9mFW{Da?GV}bBw3#8#Eu04veFY767p@`ZSh9r=O79_I7YfZSyBXd zB#G3w?!IV&MpuX-?S_KN=U11^Hr$}yg1RRa=hXHYO|8px!1oX_-eX*TRJI{xv7;Ke z#wDFl6=<5YtpoEW+J-_&ea>|cHV$jV%3bW=fDzRDnxqONn~kE(`N{jPMH(t-P`#$% z{6|5g;?XnMP@;`SUd_oS3JvC-Iey%JcvFBn&Bz=o)}Ttwrmv;rLN9JuUAlWnR_FCz zlJLco1ZOKtPdqg(9T@OLlh?<1aQx}r9CCA$!+8x8He^rIxxC)0-t5Yb2Y1G(+i6Pb zmVNAbk5W`PfaB*iN(a@CPl`%8*U_ufN3N;exD53_%>uMKCZjM6n?}r5lLeTcOW^QIm%HD4Gm7{yKlOd zblB%Psx!j810r~5aWiOScN+g2r2)<kMcl56A~b_kZES}8ys<|fKAugf? zehqrTgRgpC(DxwPgXPFF3ja7x!XT+sX3@hi*P9h4@j`3J41J%1+W(<`dQvM4wdH?( z9j5yKLdN9h?m#{U=-Uhyivw@K80aDYFSKbC&&kNicizER{0P@B zd;hZWZ$?CF@dRqxK9CB)OYW1>UNG>6e9|w=-#?UduEgv;2Q<}4-AJPk!~*Pq>}@JE zP@e!g;9hVvFpS254(pAGNnE=R|Nl{o{a*xI{})Qte|_S3ZkKa-QW6I;LqNg;+N)R7 zWaD_yNDT|bORrH-jQ`iyv5UvmofQ-mXb=$<`BTD{jkHVM`8tAbQ0AsVRfn|eFlPla z#{19~P^2(I2^|gyvLx(=Zh(d3P!f&`wGsqnAaDq}Qwb1crGiER<#=I%0)qPuo6FxX zpJ#Utt5OL8(`Oan0=%~b>=mnM9*AhxNEEa%T0Nj$1I(j;eT`S49YJC<0QO{IWNY&4 z#}{I9az?02BCZDUiE`e&sS|`kFhD;gjX2(ez@L73;f5$0PlH8i_(vi^89=PnJCndV z@b6cqH=Vv)R%WJTqT9^Hl0R@#Nm*Ox8pY{~LGpY7P%X|NLiOcx(2*t^gtnA*_aR3q zAg6%wi=|_J{r;X7BI7vPz6xgcn2u00215cQ_|O1}5T<4urU=teS)hKHAD|G#y-F0} z{?CgODAY?ftaf*b?6bnD{2Gubr7LfMR`NLpS(E%uc7|^}9VJr-hcMV2xzY&PaVDH z|EXw!HY_oTUu6^(7_0BH%OAfKNE)jzjO5t6imtRCc{jo!z|u z^IHiTqL^Drl2wuVCK2MkMnNev4idPhQBI(;g!mMN5`(Q?DO9Ug43Lt>S!MTNBmm~)WUcpw)T~{{ zn)ZemI(BeYC1Z8Ld!0g2_2;oxYpwP#*WRtpu70P&?k`M~c8tXY5za0~@D6t_)^u#m zD$b?#5Jgm|-0GiJ7${M3vzDE8xMjyZZ0Y#MT{)^EFF$F1Qs2eT&b`XLZhKH|R=*Zy#93Wy0Pz5GgsBe+I!oVLv<8r2mR<*hp8B&zoOG95&SjjqUtvMjc3kd z?%9U?ahW|%eDVAw&J^5GyDrGCDay$OxdjCUxl{`?FnksQ0B+!6KC+9!%dU|IAxps^ zt!D>m34#JcBgWqX;}NZHC5uTYd7b&~Dr`sFly3t|uoz|^QHv0o!$~f>zU#U;G`(7d zdgzi|C1NOi_-yO|)O4EfZOL`zH3@mt|LK#Mqpq;G3G&-<>vyr($pr;yo496LlC(&Y z3XCMufDe~0GNO~aI8cxjqiX>a%Ag|x;P5wxIw&mAH=>M9O!#f&F_;^>ky`4k)tgC~VU_pgWcf)fMPUHj*+G9F`IYN3J;zSrthc|`b%IQ2FjUgbmlD^kWnPjrmV2Z#Fg5p zY+5`Er~JD3r|QWQx;KkgrjuB_0_QGebdp_UG+^4er85^gLmc~6O4KMUHTSw&z{WcU z|7R64VH1V_u9fsAW%YRY`(ng!`O`4|fNN4&>Jd9`)&Kbq(cH44i2=rjo=zZT=GJJ% zT`9*+L7S6A!Qns8C&1^^QD(@(=CY@4&qN%X?4@~4yjo{J-^M(VptNvZ!?gT zwkMOb&~rYat-d5Qw|2%Lt^V$IbvP-N?)3O7u;=5XWb6N|24btKo#qU)c_HC4wpGRj zh$$+M#&=4#Wf(SRwCPH!D-7&{{%n@06g!wjF7UnfRtUT2C0>xH8CJZ|qS?Q`VRI+a z`JHxM`(AF-oZf1{r~KAV_lpDX^j>rpOUfhAks?%cs zQ^)YWhLMJgoK%i`lGIKW>z^J|EPrks%WUo7X5ODVTRGgjqR{#B{%||BDysF6=Q^YH zar2?`qqYSzH4($=I*04mWC?-<|IMzy(csJ*6?bN&wqRW-!j3)DQ%SV(^f@vQtuO~m zf%H|gEYG`syL_&=wAf25@xfbWHe|CaztT@hv9EH;571Rm;h=M`@uRmIm43IA!)7|(ThVrm4bY(VNBxEYTD>_GE=NtCrfIZ>7R1o%W$z zlMGC3Od)K+uUzRT?RJ-oU1xT|4Oy4_BOR%6hlfrvxz}VV8*5cVdE#!HQ-h3e1= z>_2g(vuivfH%vRU9;v$j=qu}Tj9Y!UiWIZ;EIRFs<*|EypmUqb|9xk} zdq2CUJo)(r3-4VIQ@3&6o-}>Ot>srbPMdF2w|)q=id4ioGA1~@`a~r#LN~JDHoi5R zHl({!jCs_uwRW>b^EbGxb(!X+ElzX9O#kXk44;&0F)G@XkX&t1ixs1*@Di9|f3d`) zzRJrU`BMI}>)_bZvlwDhYT9Fu-1Hpw_dZ+1F-VpP?B6e`+5C7+JM<%QkboL-@GW_% zjb)deF}6;RPjm%SdE1x`FRi_c3r{}KrtZEfXZ1s4R zPO74)PB%tPa>S=cHne-mb;-$XnJ23D+I!foJm$zl*ReZt!=jT~lJgM-(bF$GlOo?p zwfGcy$wy8p6_9A%uN&@+;GB1PF=t;&YP}`hEyBK^P-4luqH{dpWkjx?nN&w{L43Ji z#XNIflZZv+=A&P)!%g`acN)*!(u$>WObIv5s9x@I+-C_{+>zRgB@EqKKiP5V>D|GG{sx)W6%%l+gcR>Tbs zkzQ=a9zANJZ(&-WA82=&QR|F3!jTg_#wgkP{F2OSTD2o%(xb3X8v=4CD%hiHOD;PN zCb}y^f6#Oatw*QBh-mc7%*xuOxZrXG`^>u8uNTs~BAN)EJe;vBx67fup zF~cL9bG{Y~ZmgTjb|&x>@#TaPjndV)F&-?!7(qp`HeLgyh4EFG8pa&absZ-`Y#kYZ z3h2xdU<@ud?7qP&&uk?%e=}wz&arQHhd07^V=|Pp%DG>0P?vOi0P|XfQLibEZnnFa zvt@XG$SjsJ57RwlYU5#YF7KU`g1P#umVrLT$k48(UsaOkj47jg_&;j9#~oznX;Y|< zDNP%L?frqV@GPz9U75b2_z$c5rX6i~a}>R+hp^I`n3h}5f5_R|ZV^sbR#sAUmU(Wi zGIP+KhfW>zW;*N_hJ^S3tl4s0$OaPS7MKl;=w4TbhTOSYhebIH_%>g(b6s3-DAz*Q zZp)&d1x;*~aK-NA58#(Wu^a~@Xv9fnCo5b)>fWdm>Oji6uCyB7SP{uM{`-^hj@it4 zDR;RLrbc(HTD8gkh+DClnqb(JqTB1OFC5roQ_?~4G;BLbi={do)G>noPoL@pskpCT z#7pPCxp+t_yA6LQ61@AygL2Wq9?QP<>(VIENZjUgxz{e_w=C2DIq_xAdoSzMx#8@m zVvYBt=aP+5ujK`1(VQb@4YDL=>sF-W;u3S{sMZW{>|iNiNQt0*B2N3(D9z2E2dq1a zcRI}4{Eq9`jWZUBTVbrnG^I{xHZM^Z2nO}Ep3Rn>r6wjLxjN{@*j#s0&Ow>)S^?KN zT8mmN$A_hby=2mvNWGC_HJ|6Q<_vj)sWE~2xS{%G<@=&#l8ZF$p>fi-a)N{Qo?C=e z5@gS5LOO)EB_E2VE(ot49gKQ3CUU7YiT1-)x;!(HlA5gLHO1(pStrku@R{yx9SJ5T z_DJVlvub5C^WmbvPmDZaxn7GVO)+{;96V zrfzE!O>~UdEkhG+(Kk+#Ec=_aZ#J!?0=4pnonKGQu+j5Y=U5B$aob9kAX>oD zX*Hd;AAIzD!@@k%T4dwVV3hqUquLE4Wv7>^>(}g-=N}hH+lM$N@z@P&LZUFEA7QQW zVp0C`Xpz;K7q0@NUiJ*NbgX3XcWCWV($#KXRvpW@dpTs`pAK>sqwgfCMp3Kd-=}v? ztF%WfBZhPpHA_QB;+Kk7N~zf+gx*nhYtg=aW3t(jXR^!=)mVywLHAqvEiO0QF%g!n zs)%haCUHeU7anh%Dm72jwBsDl=xjqB*Py_XWYnU&tyl136K|B)MKwN~9v+L~-q??N zVQ*+>KNU}%XG?RoJjTS#`kK3`vaVv|#fI$67*Vm&A13x;k2BZgEe9PI_UWwZ*BV=+ zzw*uXE}rd?X4$T{BfgQVS!QGQDBX?Uzj^!HGVIpAddOm4@5k*Wp1R-}?YaH_ z66d(@qT~p&7mjg|izh@-vMUhVPGba6_@+eDP7f|cw zU3$G`J61j0*C{Pl!Zld*(_?UV)Jlcnj^zidZ~gTN0h=;7=y7Kp#?uZH!rT^^Ge{t$0k4obr9!JYe zNR56Vaai?U{+hQH7*#zZH_q|&qoziwgHw>c{2E~(W8UL#`a10if&-^`<_W9Lh&a_n ze`E3}R#Z8z`S=<*t(P0mw50zM8VrqLiQ`-~4!p&=W<2otFcY(L-D~ec^Cybh#+jdd z{iS5Yo{RbUOF5}O7xTwCJl81b6c{fjx)y78vr@fCZdtDDki^=*$6Yjo{yiO8_*(z? z$<_`*=dheN&I~sa(F?g4~fO0s_%MJf2O9=MOnPC`k2_2PwsmOT{|YBKT7-|A5Wt4_Hx5oI$ECQ?9F zHA|MRNF&r~sarNcT53AjuReBaW7US|%b)&Uq6E4dX;lm3QB1gjT&5&l>@DG_b_N}f z&9K74e+SD=!cv#V=uINC%0JLvm31hu)2%j)AtfUXPa5Eoqq|b&$7R=}8xU~9csHQF zt+lu~V6Ixzd4cwZ{c#7Ht2!=r<5NN=F%0?Z3wEI{t~P@-{Kq!Ehg5~{P@>sv7M_MA@z=TvW2L} zu&q}+ZZxi6>YK=M$BoUPm$KpEy{l69wbb_JoN}eCW0;HD73oA6rMoLs|Kxui9>{i- zKYlv+`f^-JXXtAqlH>oRtq(pxr#aQ=s(3LK0&c<73gbulcHdh{L z!M28b32Nuql`Zv4Jb5Z=Jl@eB8Z{lCdx`oYTf3msP5vL%{cKU)#Iu&N_DpQ_x6;Z; z$!D;Mukux1DGs@YH2>ghX&JeCY=ciN;KT`{$nsGv$@#&Jh|7_lC#c22;O2IdJ|rZqSh;-KG2)Y8#AuSF*4`^--o$38vI zG_>^QiBQIOBN8RLEN73Rh=}G5Zg2hyK}ota8^#wwcBb1bUOV?PT&xa19A5Hj@14IW z$RpjccPe9!u}pqAHDbKC&}(sAao#EH_G>yGJEnHc@`%t6hr#L0uZJ`?+c_g2jxWZ+ ziym}F13XQ?;-_cNgv!+(jpkVxs*ziDB;u1zxJVX9V>xiD0Y*+^U7 zGV|E#&oq}cUpCs%s`ktxB_|V*)r;+ZlU8am@P)1v=Yn%e-S$qXrlws1PobLO3Tubl z3Xye@Kay)2ym?x1Tg(Jxr712a(Tox?)z2Bty&0F~X2x-4Wi1Ly;&^Q6IT*E6^VFAM z5fbS<4Hsr=NtmDH&WlzZEdtB3K3jQd>6^jXrzrWl=pJhS zZWTRl>-AjBayq&4-zqy}4w5h@dFQ-O<{4xX>-Qt5g$jIR0K+G)`$IFa`H`LZ>hDr477U6$*~v2 zs+X;12QKv~UrZOW&zCZ4CApJ5qnVX1D!bpVxHo=l%qu^ZQnP9CDemXk?tJBLrobm*vMr(L!BUB_4a?z>XXe#|enf`{4>VeX0A zyBoJu_G`mCT4fW+Hs?fs7({Ui5f|2^{_{ff&g~qc zAEcZc%tu`SkavyEGZaFf417 zOl*_1bo4x;p+@WcBSKb&WEf@e%2P>dnHr17K57FUv<~S{pI5LkwCL)7dVV``uUg?+r1EOE*oKmy9qJ34IIr z1cO2fo*2*HKfk3DfBv(zh8%iKw{KMg-4VI6WYIYfS!D3{yUzDwkSh76aERP!t`e#H z&fcE2w{FOR(XjgF4Cfp9^$1k}abpit)fZv$)1T!2P(x&Co_yoVp&X*6x*gjX;IaeK zar>RiA~O=Ew8!yc(c}dFi~>7%wK|2Ba6E8OyZp+UxcWxgb)+UJOTF6=>(ZQ#CvN3gUzWFe`<$ZM4OEVO9s@YwIFwL z5Jl(vK8y5^K}2U5(x`n7zPubBordNvRfz6)3lhrht@mMoG7VDH5O$q+&ERN~%H;8T zO(k@tlR(2*y-?<2U!0f&YzAj1A-UxS@&mrDlTK3_%mMKAIC_&XvYG~k;Z1THU>DeI z{dgNp$HatT03%}%e}zDb`*E>Y{q>!68GO!|OP}pvc!=8f8v?TW`x`I1t8Jb**4d4!w;WwHA+&!ZO`GWfq%>DG`5}esk^Q0 zeD(tHawAbF9CGOk;{WDL(&mAtLd0Zb^bjDDel;YA2}0YH#(;VKef>0}OPCAh%Pkw2 zpx(awqC~Z!6>qYB+lIbTj4u+o?fNWpkFsc<7ULk2gE_a*;nDHoGtFz`1yPs#7b4<6 zWFPDJaQA7N{P2;BkWaN;VP{HQ0M7uoxfXa%?=pmCfEMzD-$|41RLqP}ta4wmiF6f^ z3Z{@;1u*p}Bd4Jc$Za)UF2ORi7BMQ6{BTH%)C9T&rC9I@SFCc}&zf(ELTN(%AoU;o}y?Wwr%d(mkoGDXD)p&*(?jirT*XKENJx+#srePot zs-Zr#g}X}XvdWjKJRqUP_-7zGx3cf{zjXVXACGEOFgCGTz3=q@yb)4(cJeCP_b21Q zEExGxz=xudLDR~FS9vI)yJBz3)aoh=sd0b?~GQBjO25KtS)6y3=52rMb0qc6$^lD$N;&xxY+pp z6G8_d&a@zJBV?z6Og`VP0nr8vjhP8BuDZcB>>^0qkYyY!XJ*872l536!!;$>894cCg&?# zFso0d(Au09j2xyHZ^^3F+A0+WZpTDd;KR)k$6%1;F`|t@36sLCc@YQ6kDM=*ARqJk z3oTIhrl1rBwE2K%C9+7y*cUm5DuNf6Hdwza!ps{@nL(y9CRCtVCiTu~ju!80IRN=- zFkO9k6`TPPn^L35(9f*~)c&i;wg6NOXtK_5zI$FJvD=G#ONu3Go*!o5!dr-v3UWxi zdk1JouD#N5kP(lM!H3j%1{`61xDZ(H+A=;|LRp{`C*Jbw4|uxMb1|3`;v7O0!3JqwpuAJnRoXC2F?gxd~gHLEA_p% zgv62bp^vaYfTeb zQx4{A{)-rr=`usrhYu@XREyqanGAzfql_{;(WVK2nFK{nYIK0bzmUiVa(OBU7%S52 zSCffy1lRra`MwvzTHB?v@UZXEU9IR^BmlC8k040QyYsYQq|;ZFJJXS1mDu#^(yYS> zP>kpxW)ms9LGsjnK_kPUgNZdnQMwGx&+UGp=mYZe=cVr5Yvj($HlJoqXmtahDTWuL zQeoe?V{~&~%{z6yig5O1x|v}(0!$lJu1pjWMjpw%xm^~L`5^rg@DDOi;}V`T6(OdJ z&-@(WdpKg_z{Ifhf1*bm;#KW`j8<1*>bkQWDc)8#2b>^b_GgCmpBw}nuZigN>r6~p zpLcTtGjefJY+4MjREoLLf(1Gd*eU*OF86YSd2%P@3}_b14} zOmm<1H&#Y33AY`%m;gbJ>O3TpLD|8kLq{9RKd0Nvj%HYL< zVW-Qx=h0zd-__BO*uzss=A~OlN5{r*VUP9sX%~=);po}eWapL4B?|#3khHy@<(7cY z3Q#yEAUgpXIqPgaSx_0iHCnh4QU6&f`y$$=&ABpt{4^Q$@9)CX*dW+1crK5A5_a)i zWDE&=EDGxa5FnBzzfv^zO=a`-CI=q$b3Re9E75~DO&vI7yFtKL8ZZ%wujPK1#s_az zz;C6nXm|&)ZQQkv;f1IOKs4 zHp*b11*W^f;~rb9(`DIH3TpNtDa9-~p8N;9*hz4j7bT2sSkh0lhS1WCbiW z1?t4`aWC-AnP1Xh3Jpf6Og!{5Hp5#**M!6vW%-avqt*UERaKSYdsA=}@IJKBj2sn) zV&dXe3mM1N-~lv+Bpw^vP7}THl)J|)a=8v+@gaoC zwc|}n{VRA=YJ76%TS8n96?RnXGe*kV_wOw}5g<)pk=eT>Jqz@4tuQgvGcv5RN#pB3}dhe4@5QFxdY>d@#=(3pYx1(4h^-mMVr%5qv@A##f;Tm_W$TdeU9TeN^m<2@{O|B1JkxumU)hc zo^4m=ht1m=?wKYvFdDgpHQBxa_100tPA6mMnrvk8oaYSzqBShX%(oy0B@L3o-Wan) zCL|+5bh4QbDGcuGQ1Iqg%}^i{BQBQ}NGUDB7hXbz;8ELDEPM`|0CCs+#D&>!cYY{8 zd$$iqlyL`m75#4&wZ(!{d`qH_36(b#P^Te=SlQ|jKBSM*_ed@Tc)-e`)J8F4XXMQd zBZLoOFCj{cCx}QP+ohFK5Fa&^*d@l~)jNAbQ^N^(*fZaPb0hi}ONL-edq%$SbkjHK z8e2(A!6jzm!lPmMwaASlQ0K&#v~;M9iaSVqG_3s+j6W}kpjz%;r8-9GdimMWV=dpP z!aNb1m7qQ%L`nCAF1^N*45G{_+ee;I*Vw|dGar6*e|s5W$G_o+f0}Q19|=n5m=L7XPm!uuhsWglE)7)eyRvsA@gDUrg0m|J zp^1if@s3g7WJXn4ALjJvBdSz^mbu=p)v1OWpKp5`Fd?ac7eDcZK7|%x?w?6TCU&+p z3lF&FKOtq^?)Qk$oBc@!2XB9|%??Dg^s@TRl{;L0rl@{=dbx@P30 zJH=>qJ!0Z7H;lK?(+z?11Vyp<+=i-VM6>YLXpxXFez%%WT^;que}2!896N@w78569 z#akKm>MF|k8dX*HGH85Kh7a`!By-+Z&}I&-Sa7YO?uP9JE?_(Mkpc&&q7Et40NS3$ z(MAU+2|-!p1u};Bk=bu#?m_|{WEv8Z2l6fh2Z^(Ul8TzvONj(eP9~@~rTYB_Wysw2 ztCT!=*|Kc&k{1`vT|8vFKtm;YM)Nj=oh5Gvaw1Q=-Kz=duF$ezO8G$=9eGtQZ zsegsRirxh_wg=6*j|1^%#NF=bM?lm~&d>cv@1VFLsEj+vtR@(Ns_8DC`Zk9gKA@9*H3Sf+POObp`FO2clC`9vY;lB-b~qY8>=d;W4jA5ffv#eA+SwdS%UdVrn!;rTsy zd>aME@NSNE?@4_hAhwXv)^j-wJ_v}94@JNrzPV-ZHHp=TT8h5EwK`S_Ml0Mc+DI*a zbxR6rbXlZ80LJ)@og>9B<3*b|@d-T2Dt}YSYYeFD{?{#MVr6$^f)R)yKM!2xu`567%`e4J7@gr!eV{W}wQ&l*f(Gq5jzVqtV(Mz`;0=~M4;F;NIwhwTf4H^A?S9IrU3{9 zAX+jPp*X#XTpwj9 z{*;;c8z$@+inDwL8s$Od_wbjWQV!O>!zmz{P9_BfnKNKk#+m?pnQkc3zmt9&S zTHix6ctjxK4L==U^NIjV{nrfeAtU&yhKaP#IED61^9ROXljo1*Dm(%(!#v>L%L@jH zW*zZ!#f#u3907rj{E+P+ORTEXX{N^uQN6*#ykNt*CZyj$U~z)e;p8ZhFZ{yWz+-LB zI`Q}Z8Bw{mmcEog!{s~p4-Y*re##?Wu(o1@kZGCn+igINra_hNlsjHx7ChpP%QM|d zkF#~)N5LgCMP55D8S6+P04xh6rkz5h;at6HmplZx6N4|zMiCf~Mj3qr~#a;xClZ_kKMz^#ZwoMhLpO#I12Bar!2h8j zmOycr09Nx+ts$rztX%Vl*m790I`u_+IP?sV^Q6AW%L}evW~5 z%ap&`=OFA+`w*5B#X(XWg|grpdrsz~G<4Ycq$4Oc9QL>S$lpz=EE|Le5X`}ZW)Lqg zi`4bcR)2eTNhUOc!LU6^WIR_87|uDo;b%` zg+X8;wSOc5kwK!{k;4ZkvaV;iY6CNp&_eEAk*cm4N}HshzR2_78nb=A4;enG$VWiB zlgY`0kruvbZ)&JTh@x=|w><`MEq-T6kulCQ1eT z2Y&)R{HW;$xyI-PptgN?SoL~6>?j1zMFrjZYQ@uXV(^zr)xX>LvN}t_ENP@90Dbrt zGx^HLqrj=MSkIZzKPZ#LpV1#;VP+M3^WpBn)^tyV(BKw+K|y#bzIwdgP*#&A`oM0UsV;F)=Yo1%=l#v0Rux8X-!D&)0+VD1kH7)ymT}7tovhPx)&H(eIL_ z(=#HtN~rJnely#0JZ(XREX9Ob-5sV1OhfB@WB>xbGU*{Wv>|^I-7AA%d=o}*W>3<|Q*Yn_mN;@2> z1B(<6glnyo?^7~Ah5C@S7CMg9@$o_caJrB&3JnF#Lx};B8WS=mEZ&;JQ|9 zJyHJm9`@>h_+eP^EW(uk8#4!wWM)7KX6}bwylL8;eCH`0505I?b4-=?$c2Fgw6aGV zKq1x}J-h3C!h4YXMDCsEqXKF0PbLFyz(9@QVTb!aH0UEUM`hVDd|^v8L+Xq6^0U{{ zWnyUIk+RPVszL%JNT)@^8RU3Q3Z{y%<*=dJuPvTm35;789ok^hGX*td2n3GsgMS{X zCqPGY3AKr=TIDjRrCm9;5(1fx$}V?`fcWw|7j{*Gy;gy}@E9$7BN}wKfp>fabVdII zg5|v*y05Hxe@yu0!4A;oo?&nTt?aYckDj4sg*_w!_Q??;0hfhqets%?1jvR8Qzs=S z7YT;*;xNFo?8mLbwl(!8ke#y=X&#Lb-h;+ZV2T#@Jgu+17ii_vFanE2k{;w>>!@u+ zevX3G;L07vKb>EdRqqs)J`euoTmo17N9X`r`&w$xZ3!J}r>pr7lkaf|s zut)>`GA&uB$fi{uEnIvK~+A!pWJsv%a;)i$CDDZHQ1R9)Gu&?NrB8ox+t@Bx{gj5JtE zFh&!zM$y-M4=VJbzQe;v$#NUhWGejlm&xEgKL5)XO-^B~7`2F;7dt^q<)~TjG^d!c zsQT^Ow{B4Xgn5>kwo|Mevkx%{nqGJkKnD#K!9(^z#m72!r&x=$ci{Suk!$cg5d0fs z>1YiWYUw(ZdmRc!Az=Ox1{pskav9V@pcpM{8azmoeyrM1 zjBm?*Ttf{L>(Y_9CKcsI_{U>s;0L6%SBucz>zeFw0?=-g?amPq@|m;dQX z;dAFeH6_TgEh1f!K}z?nSl*L&?|mVu91KoZA8h; z;9ETdm~26-7XZTTC93ts)J7su9oBs|5-I}Pt)|N7YUTHdu;CKhH3LwBo zSig_q(Fd`>T)CjOj+VGf>H4=;W8Y&RgDcG_gncaT2;q%e2m#YVIE9a$!py%!jrfNl zlY9qcLaH~aZ{inXq9RqRn)lEM$7zx&|- z{Zzdt7oue7A@lG#erC}1wNZmvKWpJ|`W0xje%>plKO~(~dRgBcMBfxgU_-dZp4IGg z=HPu1-u7; zD75SV*gcJE0jT0%ym}RJ=jqWF&;#q@)=~jjM#m0P-c!2&NI@+Fagb?S^!@Ke04hfz zmMbcW(PR*eZxQ=7H8+=*eMKUsO57G(2N#oU2@jMCJW##x7AGPmCTJ|qQ!Tl~!wiH{ zDCOA7$j_UN^kn1$AMa*%L)D|i1C0?V>>Xv^^neM+Re(xXK{(WmltcgxuYNTOcJs_f zDH7m((ydwX4LucLa&ZToBxG0BK)8r}1Hv!aR^9=NI2x3-GOij_Ok_>pbniEU8VL4i z**TRrOrz9@)6hfVvsp^(;V$Q<9T5UP@$is5=9GAFt^9AP8=DIz7bt@>t+Z4bEG?K| ztEhED`GZb#-Qp-tuB1Z=9l$yH@1&4l))stlQ;=tnvl2ZyX^1q(GOK-XpkF~t8B?hV zz1JyfA$5dVxGk0qeA3f6R_=t)jOGpuELCn`#uo)X6}e=C_)g&?&A}2uLo^=-D;Nrg zMnupfr!{1bO0Osk=zyt4SfNnbaV5Yea<#z^8y%C!iSqe_qQM;>*^?lbQ)b=4Ipya_ zmz+1;_0@PPa@-*EpuD^ZwN~RczOtRDO;v~TxRDPqPrirE#gFfXn4A#Bm&ZWNZ%Kt{Pt;yx!Hx9OWsPN4e6D-L&6U4TMSsA;22&!~cfS{pJWUxO zWfNqA-^Jg-0C!%}DVr6Xk(GjfH4bX!2m}C|Kx%eJ`5QA0q&zACoAh*9qhFW?dKUyL6AgGuJLeb6aF0+ zG>H5?h$iytvW^E$PY<7w)X)$=2erW_UtphNh>2ai#fn&CFq8ewUwE<*ou47MMrpW5RXhp%XNI6Mw+QB#%xPt{;700jVY$uhc>#l>JAStWt4&1PgxpF!+`NgP7Kudpc?`54}Pz1$B%wf%Gox zdP5tDQRb_Srwn(K#g5*bhV_#996uKDIq?v9E5gD@lDhIRrJl7_mBQaQdRgVqz(_+N zIQ%qM&S))1xh5-Y*_>21b`bq+>D#Uqo05|PdQZs51MWAY3 zi8*}Od=ZLCg~9(Rv@&f8S^XEY%o>-vkHe^^S4fekiS!bfo z3?Rd6smaN=QLrD?ht*FP(5Z-+OK97QdcDb=;W+XSx}%vF@O`L!jDU0Y2-teUb`snS zycax}i4))sh-`2iKiO6>L{FbrNd+Hw<#oxy+a*KNgoUS+En$1lr+~iAR-DguNgD|; z69(iH1n9#UWV@Ttcsynktd8#3Wo0#{VRMY2Bh>$Ji)az$KR56D9#`*Uc%Q;JgO)mb^F91K0;Av7)j^Ee2@1 z-Rc&v+5CHmpz^g;o$Aqg)%3%vG=l(b&u4?>@Gikzq?T>Sm$wpF)c8U!W+bZyEldqK z?I*>&@=9UQY8LmQ8-Cw+p<53oe8%Jxg*3*zHg&v4pn^cI5Ag*zC|<2XwB($W*8FTc zw6SPeKn&(<7<%CBUqKwR=l!|!zUagBBxI#;vUWUaatQ8DDLRDm!Ru`f?4ki1|B<~g zUo{BlLw^0I%)}S~@!xz%8i1$Bp#Y}2l92b)Th1;E>Yf+YUNvmo(zf$`rTtBCLeD$Ky6%So*wKqY7Vdan7WCd=2t*dxNQ;smP@N|AJ_F_q5mo8b>RGZ(1qlXF z=NiLI8ClS`0J+GCn{}OL9&EO|rpNv{?CE&R;WNDt89DXBmm+uzzeAtvZ%RZA5~`vH ze&UHDS%K8COxV-Ya~e>d(JBxiRGEFIZQgOeiX4m!5gm!uCnx22Mger^LIf z-rHNF@_~V$o}RL@a+*iIxNvVQ97d4ia47fdgY&20^I>LgK6}l8;pvpd!nRO#!_3?j z_(u_E7|>v0ME{oM!)2j|3Kev@&%u{SIh(WJufGgay7sWCh_z_&5jc?tVP2csgtqt{k23n{%5b3*+H>N2uBtXX2z2#;M95`f)?K#%02BC$T6O8PSu z5)Nw5oH;|TM`p++S$#C5W6egw0tSfGi0aUNCA)ray})*W910w81yza}0D7>BZQYhB6EkxtkN^W)jEVL7(=0Fq zRxGFrt<;yQQ#@Kfqj@M0zfQUPp1gKLV@(q=KVs-^2Bz??Tre*lrqz3P&(wwOet{ zCo6QJ63H1jqHz-H&6#rzPwwF#>=~B8qnA!oQ=y8Rqk={#P9A@rsA_7)=4w^sw>b}3 z&2!h!BGMgr+crNteFXbN01&K~Bk))nUjMyUl0fu|w$c3GvpKbr-u4fc zTPWvx`C9R(TL)T@h8zymm$FdZsc@C7Q0B-mEU;|q20W4CS`#!Yzf|u7@KrM=g#)jd zvH5osG*3&d&N2xGCG^b>E8fK4mqX~j%xnI=FU&WG7dpP{UtI%O%%}DF345D^VZ2Xl zSDppt(g&L?4g8yPCSwupV$Em!_vvB$3i5deokMi%8@QGT;%WHrID9A1{YIK3ilF~K zde*f6&HeXI$-AS4_|{Y5?jhEv5>0S#Cah>-FHFD}tG8eM=%@SaR)c$3uCAS^2eX0L zJFw<(VuAQWv5%CDtg1cd;1}?R98_NRixl^s&y8#+%?Fgs8kcAIA0fD%@G;;lS59rS zo3&xeS=euf;55V^@G1p95qJWb4@F9LX@%W|Iic}LsY{{+1eO%=j7XtOfiVPnN}`3Sh* z1WFTFZN4ZmNi>4@KW`xZugTDK^poH4Hv$DR(>M>6FAcYu1L2cXzURv%d00DLp%gVx znHLu+!~_Ni@-}D(9ci0-?xG^-ip4+elf~+iX>geP%y(uzd3x&a;#553LiWLdPYaB} zAHr46pCGH7|9Y*}t&W?!=k51HaPYy;zB+iEv&p6pA5;qLF zaADuVl_lpJ{6Ad1cRba7{62mfQWB*b5rv{MOHpLIOGrlN*fT4;jO@`KitHpS>o~_u zhiodzi0tgWj*RT>_k11QpU?OAx&P|%=#F#V@7L=&uIF`KPu$)*jRd;w=<>~$bUE-L zdRBhCun+bjaw=&2bk`Iz4RrS>A`I?DdI3jH1B;zq|5k3FR|Bqmyl591Y{Uec>z~uT zU>~|n_p~cmS>Q81gzk8lh-DpH{z9vs z3!VTqg1B24;MFLNA@k#4Zsj4sY+Or9fY*g02^bqWIIG#fheK?@9LxIfmATOx?@irGNJGF@69pP6)ze ztL-4bJYEs-zTwUIpCSjXv#j3L9xdD2IXqd5#>%FE)!<`pEKC)ew`B6SYUMuqlW=Z6?%?e zx+4RQecbW!@dXRUR#0ZPLYcK?=%wQIZ~ZrtPl&$vLQ8>CxIEAyd!9ai9JZzd z@UTa0bhJT#Z0~<1)ficyl=O-MtrFkWkv@^QAq>bYjOeO!$~xwj@hpDk(sAwkNIUhj z3?+@S(QIB6>Ua28$U!vVJ*Q`d6ib9DKtMtPK2!$Ey&3T0#mz`yT*3liI5I-h`U3ri zMq9;`s6@jz4e2zfT(Ew^c~)kX``hJut=}q3D1PAifE+8lga5Dq6sj1)*8$-a8jBn7 zzIjcidy0j@|2NgBw4^rbqAC~H3!)6ku2T~+(8cckep3S3*g5j7$%8o`Lqo&-*oePP z663Ki(0lRXSSIAOEPmZND}Nl`>b2O`yCf+T%nGb!I^9-GD))I81*4NA@&Fgl`>WV9 z57r%Q1tDrLP;M)3Ur>V5&>URm5To?Z6{i0x4Loa3%{$&p|Gj=R6n@@f9ftVNUvL@r z`NK@bMzD~m=b}T8{&XNfgHxko8F{xGNApfK~G^|9uMuYN5D;;@$vClD0oz-ikA$>8xo#^SD2!VjGq_$>rwA|@Kjx} zsmn_xzb`S`;V%XS7yeS^x`KCqro>TE+n;N|eUJ90=>u%`lV` zOfCWwyYEmi^1=pQWQEzS=|0|=gbo`Dw#Z16aWf4?e<#Wj~4mbA_K}x)9D6%mH;>D(S_%SPf_-mLem%Qv$3iOwDbj+ z1urt_#zJ7v9mMJd8tv4`m0-(k#W&9tBH^8gn9D@%UUA$wWJ$%3Gn1o&iJ45xO!NCE zsk76(elEX%o%_>LA(wE!3Z9uuJG`5rjKV2~!tRWR+Jxv2jC>1Hd&U)3|1B$z4e$B_$V4%OY z&=b)y@(&Z!^YpCm?)Hi3A-i`pXARu{+8MmMXb|BD+5Gq-gL*XN@jp!ah z_?$6f4|=F@FoCQ>M;r(ujp?-DV5;#4_XG$ilO0daz7j?Kt4ONhC&hw--CbH!NeAGQ zwe&|B-JjC{40csE0ulP2Wo9vjW3AP0LtvEY7KJ5(!jm>^P^I(07TJPfN8pM}U9F&u z_ONY0X;{!y-}6v5j|;135c1}rU|~RPOXb=PN;!hJ+ST#3rZqul)vcn(nZ;MfMwK44 zAF)?3#3w)pMd9_$4+~VUBxz+DGWs~cZLB{!rgZ&!DD-?nsXj?)EbmuQp=Og;#>LXz}?3BeGYZrF^NPP~BUC}e$FPo4GPZ;h8}b2FB5znB~S za{tWMefY<_XsXQ0pgY=97plisMticF^Y$Xzbm|gXG6HLeynEJQ6+srVU_@@Q0PhAY zGx*oY#Zv~IWWg4XvwXy5Jv*@Z6j%}=7tk1ki_F*q>MXDu77!|$dC*A$P;x?3yi{aq z_7#T73H1>Ge_H+5Mzj&HNbWngN9jIE%^`};{95T(H&uG5hGT~!cBJj1rn?5Fph-O; z5fQFEp75IAucKQDH}^Z>U8iAi7q~?e=1pD#M)?mVQYl&x&QdI)R zGX>^#@aQ;Thx{R{sRuo)Fg&dwJi8!*BK*6Wj<1(@X%iQdJUuR<%T`Kj+!9Jz z?Gmwc;U<+KYp~VfpyTIN3icg zmpTh;u5OfiTGwa4oP7GGElQ|7UvIy*Sol0N6FHD?Tb;^AX_!Q8WjcdjXzAU|IeEUG z>zoNsU;vzAqpxkV#A~E)XlVkFx~?I9!+39L=`t^Bx~>!1Kmn2P))IQ!q+BRB$Cn6Y zVT7(!Hc;Dj)w}9v-d-zx+nQ^qS1j$b{%M&t4-+0l>DOJjc|vz*EGC70Y=QtF^JDDi zS&e}c4;Hcs5(VPJA5m}Tr+N#Ar$N~Ym6-kzJL^JiGV-r_McNSA67EZbk!rI`ccE+B z8XC3dTat9mp&kbFM=sb}J6AULuduN2yp%-O65;3@yL(V}QhqUslxn0=j>DlwLgoj} z-o3AP*bTfJQYo6-R4{An>mH=^CR332 z-XPox)Vu;9Cn)SJhNz?nVIyW5e=z(M*gh*vdjcV=M1Ke0Bg)-u34f!;u-&tlJw6v_ z_rNR4`TFt9u85KGerS$E_8Q}4Ei95L^|iz3Am0vtqB5q&Z12n%gx#{oDI=%RH9;$b z(5g5V5$n(rA9C}VNZ7h7Y_|Y>mG=tA1ZDyC*OJZb6)&W|@YQATDVK3Y?ZQQEsDug3hlvjyY$1d^T51o`-b6 zup^CEjoyRIkcZJc7zRWp`~<8CH3zT}`n|ld+-gMF24VmrlhVCaeRoOSh)+OFti3%s z>m$No0@-&gRE3((%PQvOaF`EmW-fE-ko$nn`NL2b1uSTC{|^PTuY35sS8I7(J8Xi0Ueg zK4zoy@7=AL^wxDlz1#9Xu@E=K9RC&COAsC;EF7>d*~x@HCq{_8#R@8I@~k$2n8z-!)XOH996b+P`A5GL#Wogvm z%;C$FJhe3KnQUym%ka+;j@-{&Rn>Xbxy}6J4gB9Iy@cg*GeHe5Ruc5nzwZ^MJk?0i zHs~1MjlcoF{ZI0*cU$C!uTNWF*gbPV7NQGlW3tbqxl7!T6X1uxfFCZvb?KUiXy&Md zQU1jr2yrNDb2I2jhRSmm_+{0AXiG}|Q<~OlOp7oaKeDmlU}>#j{Jq2R_t4LSd^;u4 zLz$TsvDSa{>Vyo^&?ij}d^j#tG#J$H3r;j9CGejMoC%M;iXX@0lBH*Y4hDY$x@OGrzVF{{C#H zawjmnuF@ToIK8#nR{GUev<8@0{J^&lK}&nlY+-ynG2P~Eg?aXCNaert$lAT!+QRYlEp5fglPy*!=<1{Q|IKmc!aeEVKf0$u$~byz%P(;75hg~0L} zs--HZRXM;8*AX1^l?T7QGKY0wF~@F2zvoe-<6+vr1A`YG5#Fq_&Dln(Aa#_X3aIhP z*~jRk7{0PJE#U)PdV^u`-Kg@t=y}w<&2Sr*3O5(H*sM$#ZSeweEF2uYRgnWWNcGUu zN^do$fQlk7@?UP9)iW5uQeYlyEOfp{KYOR_MI8j6o9KvV##Hl9_w_G}hi~OXNX$Vn z9o5p8Z}dkM_2%@}rYfr8D+Aa=(6)OZgGorHiC2(??+UYS!-!;7`1D_Xw?4>9yaG%l z3?WlxBN(GuovKd(53p?Ttm<2$S;rRST4YC^Gp@94CNiOFi{-`otrZlY9fIgx>%vehtowI9* zzsmAUR5pA{Dye?dP7|edxk#;=yctq6+api+f z(8zi5`L(3pJVimDL8AB?a9psf?AHD8ag*)KPOg`X-yH1~S5d(X3f zm{P5$t~f4VkI}pfKLB?C5_a_<5sxm@W zy*671(Ggf?H;l{$?;O-VSD{`FKgb@AJX5 zl5XRt4cN~EYN-)jWqkHK>^d+9mw{vKrUv#ArhmuG%i?f^YecsFE{f3pO61^LHwU%t z7@5{O^Eza~9^Wl*`hbJO@raPo69@-F!R(p(9ul9`iRTre;~FP0jPp>+Kj;ih2eV@kUo*)II;8 z4+izTpa^K%{}2_c3YE`3FaYE}DIGSL8wY2*XjlC=a=3}0-Cp0WyN#18;N^a2)Wzy( z%T_)VJEO%8B;5*&oEGw%^)r6vi3V^nTRuZbi-hv&?pYL#i+t+9r*c$63wNZM%f!p8 zNJ^^`a>HETUiBDJaGd<`LsTrxZV|;A869}edKtfem94zvl47NqO4-!!l!g?Z`y{1?^S)+Q(iXd1vRY-JI z6dMw9;{N^hrLN9@pR#aEas6_SaeRTAKewYXuUZ5SmCHI_SzE1}ThR;jM0kzc`ApB? z3qI>^ky73TfYmnXT-6UdEfV!(Hm`I;1$@b=^Wm9Ez~QR+{TDeSJccMJ+=MEL*mJNI z)g5enhk>6K4k`e3a4j;2F|fS=4WNdT4d>o_J>S<~H|TAJuFH%leEA8xy{bP^RXMkp zL1+$*qoFk!3iMr1fIUNFHfWL2J1KA7h?BUG4{*>so4%yoOcOlJ9TIOVZ4cGWT@(g9 zG5%_o;q2JLQYZxR0AVOKCdV6anZ`KTUEb5+fFSI-XzLEeo}}dU&;*r@y#NKiluD+Y zI23~F`{)+9zaBXa6T&LNtg32nE8$P_f4W%Uk=(^7C-p&1xQhk!WcM7D?S)9jKSSm?;Cz4e#9 zpYJK5qz7OOL3j#}BqXusb!sQj}gvfuQ#9;C~7w_1FQ@(K2E6bbx?a%k{sN z@)<|&CGEoWoyXd+;SMOnc;IzenauNmfW`T&nWKzZY>SlMDYGIcyTDw#?OPqDb%6cU z0`sAV4{2kLp``aXjPuiiHRY?z9Qep_VHSfvW**pZAo~wc#w!BfXg)chg#>g>PY<_C zf%gt@MOU7xf6Fad`5To1nB|9j;wP_-<&PS)TSTS(3YkI^)jk&q!yQDpA_Z(#=J+-k za_eWETRG~FS|D^>t4#zS&dN=eV;QM1WyEytY&k%A#JlM%zdk#JmR@*Ef`kOtEMJCE zay&Bqacnn_aQDEUPLjoDH3!m$-)yfaCnkQ0bi|#~?StTc2C@^qNiqjZS9VEluZMnY zeeb<@Vn^T{FqIVm+*NbE79oz{C-NE!S~LfUrY~#0co;0gxr#j&W`Ew^h@;vrhaczD zvSG(vs8N&WmWRwU$ZeUhcYN0Wf=z4fWDk_tW)Le8i=Z6_IF;X?&8FtN23nl^aC=nI zJI47DDkoyT=A7F)S7dX-5@YkHE=0qaq`Vi`2Wd10(+VuL-}g3AqhtqFgd>P&3c7E$ zAz(F9!rf6Iu+ev?_P4ldZ)GTnd@J?$&{+_b@zInbMMYpQK}=_SChUEN%A2#YF}|Db zfiDk~W-2%>p}(JDi+QyYC_6c!{HBV-ovCo>jok+?)wlXh`SB@FAzQW06okctk&Nah z^w$xQp=ZE!izET7PG>sPnHYF}s@faO3w$$)s&ID3?S96|3j1z)r4q5gUJ+0b zhLZR@s%J5zHCtQY@6Jy9Zs{Q}0SHnpjGuTr_cY+HUVS0~R1}YZBW!GGOTH^LMkmjQ z#N`pyLS!KaX}DuaoKV(t7YW+s!0mhP?|*sP+a<8%r-*`N+=qOLL}5z1sfz;teoEnz z`nddJkDEM@eX6_GAn^_Zsc>GKik}qs3x+_EqIPeOyt>MA?$#r;GNtaF*a<)ntRve> z0g6Vz*MBZCNIR+n%MMYq+v71Ikm?USh(ndEHo5IQYfVlPh+(AWU;hnDm5yfgh`M;q z+S-~V#V09g0Qgb*=o3(4I08DU1TwTD&`Y3fOBOq#L_5*(-M#2`r6P0^9+b#?I53Zc zU*Oz1b+oiTABZp9TwI?g4e#B{y&fxN0Pxdf_MaA!*3ZF82WTe2#S4E7WmeKo&1XRf zYwodyh8{&bL_Yx&7FC$G4eFoO&#`!gz{Kg6kvIY`{_&^%1}oN3B_jYDD10sDyjr~4 z!@O|ww5m12Sba8EEXQFwUV_ZpH$2fc$kjQvQX_*yhuBU|k`q!AuRp;V#F|nDDmIYr#UCX}z=akMmA*UhRoISc zqdjhMAH!jb&{_%Nklv)QWs`cojSSy*P1XlB*_oTksGLZGGF8~V>rx)Iaje(ubJz%{ z0TuJ`xJfIEW})ZvdzAPo?G%ahp#TNrnK^J#(Qwtrg=b(7lR^?;fVmeDH4hdfD;XNb z82-fC-QiJrn|S&B`*w56b`uFdboEL(@mE(TnNLo}8mPO@j zKGAUEBIS3$dYc~omaCYF{Lz>u+!5F=JSp7IcLis8C+VAMUgrW^-JucR|2cGSqg)J5ks7BL0=Geq(f26b zWaQ2$npHCBmlfb4IPeJq%%f$kCg9whN7o{kB9!&3dlXxa3bHa{s@Us1b$EtlDI*$y zZd*R?IS&z{SjZtGDI*mVPE*}hfze#DI4A5hc*qj97jD!>?xM*k2Y48|K^7PnI$WT^ zcoq3^8DuMONbz2Oax2fMLrzLN+vvrMe-~3mtI45F!f|#*l#o~HZh%onbny9m17G)v zIgh~K3o8y9_P;{Z zcsheK1e)+w%ajLtaDEpepX!Bc#4j4l0VuNk2vX_<73m>0J$Vj5JJdO)=pAg$1;C!1 zD`73g1d$batW@|jD+5fHaoYuuqifLFMrYR7)`r5znHcmJEWp=}qP3LurOjU8Gnaz| zST5;1!@o=&ZVz_IR>nW)O@xQI%fVN4Bn$kdia{x0nZMNH!dRwZr1|gjk-H|sc!LM) z@7^WXTc(L!Lyz3yfUy|<`I|hIZ{)X)Fv~w~y#PYqyt&r=T9{YhnfpQI+D~hAh8*QX zNGvEwmeXe3XPnyj8>G-eP6i};w5}29OoBr~TwM0x3~Kt3MHzyyfdLua&j-;=E^jy3 zmP8zTn7KPkzmuM~T$Xd+aQ6~Jz6rx2~3TQ(LlHzrjVWS+enH$I-*YFJ- zE1HGB_WPTyW%K7Jnx~<|f${2VfAi2(Drw~U@Lhe^N8i;30!jpcHOgIhL&@5r4kkQ%45w&grv$$9}-OyWeY#Lz{oWGbjk0UB;~Ux5Rf)-vg2V@#9b#el}I> z6|{vRea5wZ{MCM&9sC{QP^WccmER#99>DHsVVXxzf}9R{8TO<(7poqp`>!yFC@LrCXC9Pmx^m_zl-&QhhIq~r`tT-CneRBY6i*MDh zV{K z72hC(O2&d4q+_+!)vMjJWKgg-54W&0rV(y|57-B=wqPg2F3fAuf`9Dqrt-30$AcI> zGv&c`*uW_^y#bTSDkw^fh^89pZ`h}b=IVr(T1c?^vGD`5IXu^d3^fI$>JSf8o3!3t-LbeMG&{2YPp^)(2>CRC1q55=;`Dsvws75 zDE0KQFXY#;P6qrZQE=S~)yy29B@a^-BjX3}b&wA#K{Z{?uAuU&ylJ@mhh_vOyg`jm z8e$K&8>wKyLub%^n#kk_qGXly07YDZV{IUInAixJGDhG_MK;`YS z_PZciVGd9rIID@~QIg`I+wIe1n>$nauBvxLqXh@3Yn!XXZ;37ER}Nl53;;jER2fb( z&^1|Zhryi|IwIznh;E6-bmTeHC-YO?de#rS?c3};L4!l|O}37|*_P_CD)xrbXShIh zG@%euhn|7ZWP<7o$f#2NS+6`*dWbg`bI-lx7Uq2NCO~Es zdIm=7J!ROBcG*o|&d^Gc^D=N%=w%Z2ym?p6ZZMv<-Til(v$g)k(HOX!UKr&t94v&W z=N1vcv5MNBhV*8W?z^kOVW;-T8URyIjR>QiZ$m>vO2w7xO5G&Vs{+lQTl%^wq_djx~ozR;FPT&mUEy?GIpt0 zColP2W3kRlTQQ9;y45BU^{`RG-n1Q<^$@L0Iv5jpuF3`jrIS9c`Qk?N%15gv3CWM* zUtF?ZGeOV4{7s(+>|BLtH7$;Ufrd{ejs6t{1*5E4GHj&`^@8TOF-_;Z4%j~%{+@rz zFpY32w(Wa0hm`*9Xd7N}^zAuVQ3n=)DI`J&glg39XgpYadZ7czZa3VZPCCsqcJ zZ{aJKAcTB+Jl#^n50-cKr5pG1jG3wn5>%_^3LShUd307ZDY#H+h96jY7}&c!gaM{X z$@;8vo&y;zC#In0$e*SrA#IH@i-O4>cVby6vjG8Y!AlPy{dfHzNDpl?>*7{P+sP zkugKWB5qE%G$jlaSf81U+lf_*mGXYqhJ9oBxcHxza2;HT>w!;&0qRukqqUI<^13iR z&Cd9k5fg}rM8Iqd;|**wqMc~HmR#F=hQ(d8DzC%5E;+%m%}V3=KjAf& z(iCfk`3o5cAH!K@Ge@*&y+mxVGNBRSfTyYfxfe396J=m%kq<-(=e@-D*cKuvy*0os zV+cChX8-#*_AdxdL#$x1aX{M;E82LC8(IN4!B#>aeG=f+5}>VB0~Os2s3NFmMCPXp z3ysIT=hatW^Kc?q>J@m`!HUF%&+*ogbMW|e$OZN)2^B$k)uNpzxdkZf&9dp{#hi1v zNNbpc)%|FY>sWZ}p>hRJp0qAW8d2f>zJQu9P0euBX{>tK+S-!7k?G}L?#Ukx4i>uQ zle_qut^)G0L(wglF(C0|uFu78g@Uf9ihv`&Yik+0>OD6NfJCrmjQdqkuBQ`um%n^T z=(y#Q-YSEnNb9!*&jT>l{o)Q(AqdR9oEaaM(c*5D{saETBd68K&9+G(ZG4}qSi6EYjXkR%pKws*bMtr_cb;w1F#DG=vCMq zU$Cp}~?h>x$RC96wm`e(7a!6dd)**5__0u0dJAmi$bM@aq-JK$@RZ2>2 z`=lw4Ys1DBOYoW{@jg8Uq&zKl+#2Zd4SX98GCHj#349GAcE-l={f9AHM{A|gyZ^|A~3Bt7^-COuGveQ~SBh!{4nd8|cg1{Ar@g%F3O z5#}QShP4I70fZ+>x|0&%?}DT=Zbdj#^Ec@k|3lt(@VF2d6nQjoY%uW)1>)RosM{u_HQp(aUr)|#OeZ*<4nGDs4GO1rr{1j z7}SD@Q6vKK3HbFtslmM7+4CsllhyQ@u!Kj2G&jZYgS~t!8KO9Hu>|zFtpc>*xXmps zZ3HD6#dl@XB2nlZ@YqccZ%b5hffn50U!rQET#{sF55xlN2qDR%@;_K%$fNu9@%Z3p zt{~TlB+6{InKx61U1K8@!JpkXb#NwXjECTXWb$$0b1zumzXNzq%rT2-QxdaSpbiM0 z;>du(Tx?VOJ4ez2y##{=Z-$&T2+M#hA9D=&2tI(f?xm%D{b!f1Sbb^79qoX3RdZu+ z0MSq}%oe}k&?8~+VjfaV9wOoFp*<2;6HLfG4-G;dv^ol_i?Bq!7y2egGWPC@QuFUy z@y{vn)jlVdHwM6fq0ECwRR@o77)1RGg>irxg^9QffOmO+xbj=EPC5eDL5IzcZ4^#; zt6K&6HF*nh+rU<>VSGtdYK0_#h9Zd=DFa#7(K)tq%-9Y;h{aAlVNAs$!5+~7=>dlXA6vjv? zOuPXNjyUE5?g>|}I06MOuMLLpHR_ z@5oYwp6zo~W1*uP3Lc(~VFAA53^JRnIqQOx?U-F7hE(gj`xc#w<<@FXXieY&U% zgw-OTdpAPsj}~ZyChHmqPi`z`!uSTS10!YK>4nNCRm zzSM6k@1`>PWc%3Ow=_Nd1CG_iKK;~YCp-Y#;ED8)hzbUkM{1xiaO(e1 zF-6c&UMB`ynof}*VI;J6)R;^(@MklJd;Nh15kunR;E*|1np`~RrQ`V4R>JLDf+;kD za<^RYPgq`EEna%O_%Mli|Enh$%8a>8D-vC*zuJ_{0@3H75Dd(7tQA#Mtobwl8h+w> z2cUV4O-V}QAFp^ypngQ~;O@LKVU{bjIa~m^}*vG?)1y)e7qg>(Y9TqD7qc& zoBSWcBX6;nJ;G*2i9eWio!+l*>h45{+UN6`+TGXFoUIrp$4wwuER+28zE#895qC>k zx9Jxg@8vPPo(3i{pf!98F{l~tIe&x}sWo8%)O$LEZs#VISyYIXfNwgD z_acC$Ks_h&=om2VSJ_i?0EqG+z9A~_mpg`xh25?@n@znwvxM<#yXi@1;=&9XB`kcr`Ap9aRhcWL>D3X5r~StGIQ@!O|t&k20$E zW5xSbpdngW6TqmNQ$VhVK?O!F&8F!#i<6bp=*m&)$&d{zF^UdFKNiMM~vyBFx5V`4<&R6wAxQGQ;dPa_@PA|_^T$b~%-hD=hqzi5Ut zjT}PcS}wDKme=pA%7IQ$(Xs(K3082QlqwlA3C#P@q0A#PuLFq_-{D30J}jpG_sE?x zXfBF*6h&M2xcC5dJtco)4hG>5Cae!W5v-eYp|1PBWhXJva1PiDH_;si#_`P?FaRI% z08vL5n8g>+d}ep;;@tDFJf&lJ+lOklR&?nF4G$%&9ZM=P`<3GIZqADe)qj|xZ?6Rp z40qHWcsIJGpQTKR*4LqR*sb}LU<~*_5Zn%rgQE)48#%&QRvE+xhyncp%sSBEavcaR z3`Tt&4_;Fw5M-(jMihiGZasdv++pJs#+F+I^Ht}d?|mHJUZA1aTDDI?Y#bR_k9g6f-8tlfP4#=XH4=z?K?aeC>;(}jSd$V*Vi^Q-1-|(m{W5_GZ3@@zs6i} zN18!py^?NjJ7q*9^L&pw=PbO9ccXK8>=E3TRXv>H3t_eHemoK;Y*BlpxMkae29Z)_ z@9$vvN;w^a42QWAO|Z}2UDE5Svv5%=csbVal`@?u6U$q$7jz2IF#l3c>%F;@-#Oga zn$5s+YkvMGygvrbr|a4#CPCVhSL&QjP_9;(1!7z$bBx@ClhMP$!Qm&0PK_b|*|Rd6 zA_QWN%-26@JKa|*&Cs~QkJt3^_duQf%A^{TatN<+y3ip;s-kCTe)MygXHya$Kji-m ze!xtT=)#HN`+N-1qvuY&8mhLS{HJU2q5giMccI zOC1g!Kg7>|^om$LQmn$PxtW=DOOimG^Qy06itJmfTP!|GgzZrVjRH1N8*6 z%-iNL6p23#SFaDZC)^tSW6ZSE-5PC{&b-HS>p7&S=8MS*lxr7OK@)U5CzRl4PCFC` zpHjwU1l+D5fzJr;g6hI%F;;L+0wO-9r+?gpJ;}4__UKA)Njp#H%^V4F9-tyVWaN^z zZ~?n^pw`{LzsTg9D(OYictkX|DG8XG5i%*Jn4S9`- z_RpyVM_T@&yw-y|wS8)>K@NKP*uU^9;>hTwAhMZT*uex@kGz*IF_~Qoh;D{)XgSG) z?O7kZGJt2fJ9NE7Vip(t4^w!W739Qp%Ddrid$nitG&~50|3?$@96UooBUA%~65b6l z0KL% zov{GcR?T2ru4~NJhf^wsMg?ss0mgAX;GO4fRzxx#N;e?{PFz8H4#hYc;DN$Cn&J3y zA>|0cFfa!p|7`yM&uvT@hcr0TW@hfJp!p}L{UYEqjc-7ASL5vqOjI+N0-9%lAF995 zla6gW5br%7&j7hk^6eQNcg^Q?=P8#DbE$M+#Hs4=6Oprn0U@jUR9OWV{lm-IS=~Ht zV=(WScksj8+Xv&aQEPXQ?1)bTtJks3!1Xymin3|y30KD_P=ZD5V8ElyU{#aw?VoWp z-aXtA`dpMznJ4X@hb74m43MD-T79xgnW|(q0+pTvoaVBZg+ZiNE?ZG54KJ)nA?2z% z1G9Z_Q$CY=w@vGB)d)@lR*LEZbBP|u=}Z36Z9ZLv`yef;WwchKr8v70OMT7%(;ZcM zSU%?XhtXSlglStV=`#o!Clthv)!=aQ4x|_&ga|_q#pA?-npZ&DQw6Vc!BXtAmXfh? z+;yPIBYY4XLAIycwVH)LGSR`apt?~#bYZ^@tLTy>!@ncF0Jz%l^HxQ<(v zQQW;6v;4pma`+6F#mO#09_aR^mk;paFya)$u*e_s zu#p`4kasu{{RY?&nevmsCS3W^MhLG1QD``D7EwPDFaNX}Mld#ZX(dc$;?Cl#bg^}F zs3XUyodX=2- z(pIRQM|OgWs#_aMDu_ie1V=vtM6_rY==|CTm=_;lxe&X3DcJ1Wm>2bAs5|3N@;p*j z(#?=E$`(@j_LVe)iZrl1Q8j3jrAr*GRxMN=pK}NaP<+kJ$@-Azp;oy@rV$M0Gg|MU zYzd?T7fjmw|C#DAziqd0Jw{DYUx*lkh6~(AV!ul+bITC+x1&BE_EfCvxB1Xb;PAJVblmxO^UubO zGraE^m|z_UCH5m+^V(BD$N0b$<_u_9@XfgZW$GS{?Y5`rGa%NvFcKRXGyY-x?g0SH z5HvMy=`LUdn4}sjg>D4mug=T$OK+{H{vpw5^PuAw_ZAca!$9c?fuVdA6a<@5nchHs zncsx%0YLz>h-ZQ~n-;3%B~uWp>{?#=n-7AJmHek$4h8>cD?gZrmoZ~c7-*q()#s1+ zm^NCp_=o&(wlT2k5xghE`%I5~YRRnIf&r;P7-k2*d#9heG&DRs?*0F&Jp}A)-pi?j zCQ=V{5D4kd+=U}hnV3r;s2J@9f)A(Nzl7{(x+VxTt&c5$RL^SmsZScL9h&X7mMf*r zfb58Rxn~Wx*MG#ds!DGp2Es(fV5FpA5qX*-4ooMDT%vE3+ddK2lfBjvY&k07(~2QB z;uq()Pn9gNl_I647+B`|#oIkDC>yO!fB0wLdDfwNt#B`(P+?R#Tm~!knfFsV!^=Ql zna?ZW%{c(8tV*@0GC&Hs@CTUSH5*=)3rqsKSP$F(i`;*$jKH$sPb(}u0Z`sQ6SvKi z#|NOfK7?QAi84T-e_u2r?p(3R(E%MuB3wE1sioiTO)HV7nXHAshp|sw2A=Q2s})Gj z7^Q!o95Mf9P@E+HC0Ho%hF+0^lzw(^!XQL?l(tC`p@S3(y;#@jRVu4_$9b11N&R}=MM!l3uTB2FcP-p^O5ujC#6R?pnhvTfPTy9L_+=t^K}CsJZ9{fm9)t1JhF!dmxn*VCue_HxH& zNDejJg@MRXzJGmcNjt+S;L!3b2z28cfFIR#-Av!LpN0l40BPh*d!d2LjPaUPEdHLN zOHEjOh(oIRJi>tz>~`426Htu~`-C{4Yz~7>o>z8h6TF?=^^}xe zG&4TzICbq)>L)wrmq!T0HdMES4J1$w^pGs%O5n!81aUi`BM*hrn@i>6+mZ(hU!SX z1q)8_YQ{D&X*bUx{=thrsRl9R=@KSudOBXiS}4l!w74H+bm>ry0GRRUcz?4M=hcV$ z-KqEIym~}C5aO~-c+$W*yRxjDr0!hBtk|nvptUJ{{<%^L#!r_SyC%)h!&1iOPYn(= z&mi!RH>ChKp^FQ2N8w1+#Je9fszKnNhC0X+OaFtM@OJDxI9{qxx!|aXf-J4+*%Zal*2js^Ast%=i-B`c2>S_HGacpG zay2qyS!(c5ROE}$Qrx6ilU!eG( z5jGoMT~~}qc%Ol_xqSu4;j()O?FK^(6#zi>V~@idEdtMkj7(5N zKHjChdCR!a?Kv%7ux*aYZ2G+wtEDq6J?YsLLW7_6qpT92xYFy(wjpUZ#L_eEBtkh) z^y+XoRT^+T-&bK|2*s-$m)~%ue&_HT3J0k7FPfb7Ia7$2m-nJJu4kb)fnbrEE{JI9 z80!Z#$|%+i_uQ1_uas0cO$4z80~;GZc;1W0wLn{$w`AK>q%)1HQ2zueDRZhzrld!| zOV_g}oWg|1cL_(?-Nn|IAN4 z+;!g5B7;G$gc`i1ppem7bGP>=>d={klgGhX`{E$Z`WAv z)H0K3sx}1E(jb_mC?m!eq>)fmP(dC8ig^{x&l2UePb*9AFU)n?HHxUglzBg?4Fd^=319&;YUs63obavP{=TG_B3?D-<#ww zS2IJ3c%sf(H(|3B8^oC@5HX$0HaE5mYOq}>O<41tAtLiSOx$d(pg=U^VN^?EVuibs z4)nh$wh9iT^oo831fwXom42a!s;rDx@7n5^ehu{t`G!3$`Neg(dY^mM+(|8@_{1GlJD(t*O(a!czS0?#iPif^iIKGtBbA?&-ys> ztnm+11VZwBdrna$5qfrE)DEE}(MBa7!dXVqWX*uSzI->R+c91NG(U~nNZ=phMG4@| z?r;ufeQ=5C%R1?tRZBHMG zob;j2JE+CPcr|5ZaNF%gc;-ue|83l%qk&)a`sQ>=$?UYc0r#Rl4mAW7uNLVQ7&oaUA$Asb+{6TMh z?US<$BUp;s9dnEU;K?=@yOYYt3-x0@ z3hxfna(<+7gu!j<@{6bSLsdeajTDwM#_j%0A4k5c@%ywl7qny8dlbnEdoEAwOkNST zoKw&LGl_HjqDM3vdGZAah@Q?x7)(8svMJ3Mmzy7zl~WjoagDXP!B5XnPTY*0%EaR; z9Q)Br_0l>{KPlkfC~_W07{EuEgQ4OPHcKENLDAZJxMOb~AmDjXmwR&|I`3b+T(y6n6jD(u&QX>vM;Ro^`l{m}QjyYCX96FI_hG)eWfpMuWk7%N|>foY@KV zY3lIY5S`ZlUQBakO>onYj7P@D7xWag-p?P%4=fUHS-@n` zL=!F~MFaL`HKI0v0ZCIElrILU^=y~#+2*$;BqZ3}ZpPcB@7rq5OX~7abXvJvxv#s# zW8?n$x;20TguPpK6fY>h3^^)f%>{uf6u@DH$^)2Bz)x8@(?-^!p^Rt=%w@tzOadM? z3o0)|0P!>HO&zeNip>x^S#Q`|MS(U2VS@c$UK1j07|3uHq1h?ftme!74wYgup9%E+ zX&SKzlr@TLcrSR9N-$%(N816`tquctH#G4>fx9hBKhcniGt5;5s6u+&mBm$k6g-BmzR~$7#hQ_xn2V7|1F9D&Ff2m|} z6+l3~g?!XOgoggJkB%-JYTX*}%i!kZjNfyW);DP+fI07FOO&-$I>F$Fic;8>%c@E- z%Ko3zyojipbpWW_(P~0FpJ2MHSj7Lp+}JcKB>?5D27;>cMy15r*VL#UX#ryiWlI#+ z1{b9SKvFpr(*9wpjAgt@ypEz%o@Pg8O#r9pPI*@@0I3W|b^;da(7jDyDpteP>5OZ) zi&j`A&7~s;?mJ$2y6RDr^9wYNH)jnr2>iVvQ>~=$V7o+3U`Kvi5lZy+Hfse@-H^w#2(<2Jh@HzX(ph;S^}Oab&c5B+N25EOb9Th3|*KD^HDeHnNnh0(>PF z6DIWPy*y=rrS`DLZSa-w4g&Pf$ai-Lm@Q#w1BS*9{>*0&GChcOWn6Y?Q%q{` zp8m$Z>0#> zSOr~WHPnDi|C4!K5`k&y$+xMY~1BWU9`Ge&Z*$5^p%8=%UZu`OeAh$zF&MeE> zhCu+v_==TR)f-dqhF%~tC;RRJu_}C449;gBAgE@melA9##T`r$)*#D+DKh>!(<6#z zn*7$Ip9h8K_1P-VBZ`FE(twl*K zeGA;6&>1Bx=*XztWYqB_J!^^5&mb5vDgx?BcPmw`bwr-!gLLsHc1tU;G14hm3+-X! zdhjq&{>wG?Z{+6FN*xzphUOmNT5thBr9TcotTnb|3!yUtdFoX}=>QoKqfsB=)QG7h z)AGhZ$-C(V9NReif&>SMMto3n*op<8>5i&!qnIOc1L*CpWW4?QNU3lS zNDgK1auB^AUfVb?ry@a3P`vVI#8N5{$?@2Gr^Mkf;2m=xY}