fix(MetaTensor): astype with torch dtype now returns MetaTensor preserving metadata (#8911)

## 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:

```python
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
```

Signed-off-by: Oleksandr Sanin <alexaaander.sanin@gmail.com>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>

Author: 3 people

monai/data/meta_tensor.py

@@ -495,7 +495,8 @@ def astype(self, dtype, device=None, *_args, **_kwargs):
             _kwargs: additional kwargs (currently unused).
 
         Returns:
-            data array instance
+            ``MetaTensor`` when a torch dtype is given (metadata is preserved),
+            or ``np.ndarray`` when a numpy dtype is given.
         """
         if isinstance(dtype, str):
             mod_str, *dtype = dtype.split(".", 1)
@@ -506,7 +507,7 @@ def astype(self, dtype, device=None, *_args, **_kwargs):
 
         out_type: type[torch.Tensor] | type[np.ndarray] | None
         if mod_str == "torch":
-            out_type = torch.Tensor
+            out_type = type(self)
         elif mod_str in ("numpy", "np"):
             out_type = np.ndarray
         else:

tests/data/meta_tensor/test_meta_tensor.py

@@ -435,8 +435,12 @@ def test_astype(self):
         for np_types in ("float32", "np.float32", "numpy.float32", np.float32, float, "int", np.uint16):
             self.assertIsInstance(t.astype(np_types), np.ndarray)
         for pt_types in ("torch.float", torch.float, "torch.float64"):
-            self.assertIsInstance(t.astype(pt_types), torch.Tensor)
-        self.assertIsInstance(t.astype("torch.float", device="cpu"), torch.Tensor)
+            result = t.astype(pt_types)
+            self.assertIsInstance(result, MetaTensor)
+            self.assertEqual(result.meta.get("fname"), "filename")
+        result = t.astype("torch.float", device="cpu")
+        self.assertIsInstance(result, MetaTensor)
+        self.assertEqual(result.meta.get("fname"), "filename")
 
     def test_transforms(self):
         key = "im"