Reviewed exact head d9b44cf1e1f406df2b1f5798926afba4c3e54c51 against exact base ac8d215f7d45d6601451b62e9f81622dac8aa0b4.

Removing the forced contiguous copy preserves the ordinary NumPy HWC storage as a CHW-shaped tensor with channels-last strides. In an independent matrix, standard RGB, RGBA, and float32 HWC arrays become channels-last after adding the batch dimension on this head, while base converts them to contiguous channels-first storage.

I also exercised eight NumPy layouts—including grayscale, row-padded ROIs, strided channels/columns, Fortran order, and float32—through to_image, horizontal flip, resize, dtype conversion, and normalization. Base, head, and the current GitHub merge produced identical shapes and value digests for every applicable operation; the head preserves each input's actual strides rather than changing values. The focused TestToImage slice passes 11/11 on head and merge (10/10 on base), git diff --check is clean, and the current merge tree is byte-identical to the reviewed head.

GitHub currently exposes only the green CLA/import/internal-only checks for this PR, not the normal public test matrix; the exact local functional coverage above is therefore the basis of this approval. I found no blocking issue.

AI disclosure: I used OpenAI Codex to inspect the exact revisions and repository policy, construct and run the stride/layout and downstream-transform matrix, execute the focused tests, and draft this review. I verified the results and conclusion.