fix(MetaTensor): astype with torch dtype now returns MetaTensor preserving metadata

[AlexanderSanin]

Summary

• MetaTensor.astype() called with a torch dtype (e.g. torch.int32, torch.float16) was silently returning a plain torch.Tensor, discarding all metadata (affine matrix, spacing, applied_operations, and any custom keys).
• Root cause: out_type was hardcoded to torch.Tensor instead of type(self) (MetaTensor), so convert_data_type set track_meta=False and stripped the metadata.
• Fix: use out_type = type(self) when mod_str == "torch", so convert_data_type receives output_type=MetaTensor, sets track_meta=True, and the dtype cast is performed while preserving all metadata.
• The auto3dseg/analyzer.py module already annotated label_tensor.astype(torch.int16) as returning a MetaTensor (line 493), relying on this contract.

Closes #8202

Test plan

• Existing test_astype test updated to assert isinstance(result, MetaTensor) and that metadata keys survive the cast.
• All 96 tests/data/meta_tensor/ tests pass locally (0 failures).
• Manual verification:

import torch
from monai.data import MetaTensor

t = MetaTensor(torch.tensor([1., 2., 3.]), meta={"fname": "scan.nii"})
result = t.astype(torch.int32)
assert isinstance(result, MetaTensor)          # was torch.Tensor before
assert result.meta["fname"] == "scan.nii"      # metadata preserved
assert result.dtype == torch.int32             # dtype correctly cast

[AlexanderSanin]

Hey @ericspod @garciadias. Could you, please, have a look at this?

[coderabbitai]

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ℹ️ Recent review info

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📥 Commits

Reviewing files that changed from the base of the PR and between eccefc5 and deba0f1.

📒 Files selected for processing (2)

• monai/data/meta_tensor.py
• tests/data/meta_tensor/test_meta_tensor.py

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📝 Walkthrough

Walkthrough

The PR updates MetaTensor.astype() to preserve the MetaTensor subclass when casting to PyTorch dtypes. Previously, the method returned a plain torch.Tensor, losing metadata. The implementation now uses type(self) instead of torch.Tensor as the output type for torch dtype casts. The docstring clarifies the return behavior: torch dtypes return MetaTensor with metadata preserved, while numpy dtypes return np.ndarray. Test assertions were updated to verify the MetaTensor return type and metadata preservation across PyTorch dtype casts.

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~5 minutes

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 33.33% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title directly and clearly describes the main change: MetaTensor.astype() with torch dtypes now returns MetaTensor while preserving metadata.
Description check	✅ Passed	The description covers the bug, root cause, fix, and includes test details and manual verification examples. All key template sections are addressed.
Linked Issues check	✅ Passed	The PR addresses issue #8202 by making MetaTensor.astype() return MetaTensor instead of plain torch.Tensor, preserving metadata as expected [#8202].
Out of Scope Changes check	✅ Passed	All changes are scoped to fixing MetaTensor.astype() behavior and updating tests; no unrelated modifications detected.

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✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests

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