Fix type hint for image reader

[Kirscher]

Description

This PR fixes the type hint for image reader to correctly handle Cupy arrays and removes the TODO.

Types of changes

• Non-breaking change (fix or new feature that would not break existing functionality).

[coderabbitai]

Walkthrough

The change adds a NdarrayOrCupy type alias in monai/data/image_reader.py (defined under a TYPE_CHECKING block to reference cupy.ndarray when available, falling back otherwise) and imports Union. Type annotations for internal image data containers are updated from list[np.ndarray] to list[NdarrayOrCupy] in the relevant get_data implementations. No runtime logic is changed; this is a static typing update to allow NumPy or CuPy arrays.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~8 minutes

• Homogeneous, localized annotation changes in a single file
• Verify TYPE_CHECKING block and alias resolve correctly and do not introduce runtime dependency
• Confirm Union import and any added typing imports are used and lint-clean

Pre-merge checks and finishing touches

✅ Passed checks (3 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title accurately describes the main change: updating type hints in the image reader to support CuPy arrays.
Description check	✅ Passed	Description covers the change objective and marks it as non-breaking, but lacks detail about the specific implementation and omits testing sections.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.

✨ Finishing touches

• 📝 Generate docstrings

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment

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Reviewing files that changed from the base of the PR and between e242c44 and 1a58715.

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• monai/data/image_reader.py (4 hunks)

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• monai/data/image_reader.py

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Actionable comments posted: 0

🧹 Nitpick comments (1)

monai/data/image_reader.py (1)

25-25: NdarrayOrCupy alias looks correct; only minor typing/style nits.

The TYPE_CHECKING/runtime-Any pattern for NdarrayOrCupy cleanly supports optional CuPy and avoids runtime import issues. This should fix the local type errors around CuPy arrays without changing behavior. As a small follow‑up you could (optionally) switch to the | union syntax (NdarrayOrCupy = np.ndarray | cupy.ndarray) and then drop the Union import for consistency with the rest of the file, and consider whether you eventually want public get_data return annotations/docstrings to reflect that GPU paths may yield CuPy arrays rather than strictly NumPy.

Also applies to: 60-65

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Cache: Disabled due to data retention organization setting

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📥 Commits

Reviewing files that changed from the base of the PR and between e267705 and e242c44.

📒 Files selected for processing (1)

• monai/data/image_reader.py (4 hunks)

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**/*.py

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Files:

• monai/data/image_reader.py

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🔇 Additional comments (2)

monai/data/image_reader.py (2)

673-701: PydicomReader local buffer typing now matches possible NumPy/CuPy values.

Updating img_array to list[NdarrayOrCupy] matches the actual values appended in both CPU and GPU (to_gpu) paths and removes the previous type mismatch while leaving runtime unchanged.

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1111-1138: NibabelReader local buffer typing correctly generalized to NumPy/CuPy.

Using list[NdarrayOrCupy] for img_array here is consistent with _get_array_data and _stack_images when to_gpu=True and aligns the internal typing with existing GPU behavior.

[ericspod]

Hi @Kirscher thanks for these updates, I think it's fine though I agree we need a better way of doing the type checking when optional libraries are used.

[ericspod]

This was done to get around Cupy being an optional dependency, this makes it necessary when type checking but this might not be avoidable right now. We do need to consider a way of defining placeholder types for optional dependencies so we can type check correctly without these workarounds.