Fix AttributeError when using torch.min and max (#8041)

Fixes #8040.

### Description

Only return values if got a namedtuple when using torch.min and max

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com>

monai/transforms/utils_pytorch_numpy_unification.py

@@ -480,7 +480,7 @@ def max(x: NdarrayTensor, dim: int | tuple | None = None, **kwargs) -> NdarrayTe
         else:
             ret = torch.max(x, int(dim), **kwargs)  # type: ignore
 
-    return ret
+    return ret[0] if isinstance(ret, tuple) else ret
 
 
 def mean(x: NdarrayTensor, dim: int | tuple | None = None, **kwargs) -> NdarrayTensor:
@@ -546,7 +546,7 @@ def min(x: NdarrayTensor, dim: int | tuple | None = None, **kwargs) -> NdarrayTe
         else:
             ret = torch.min(x, int(dim), **kwargs)  # type: ignore
 
-    return ret
+    return ret[0] if isinstance(ret, tuple) else ret
 
 
 def std(x: NdarrayTensor, dim: int | tuple | None = None, unbiased: bool = False) -> NdarrayTensor:

tests/test_utils_pytorch_numpy_unification.py

@@ -17,7 +17,7 @@
 import torch
 from parameterized import parameterized
 
-from monai.transforms.utils_pytorch_numpy_unification import mode, percentile
+from monai.transforms.utils_pytorch_numpy_unification import max, min, mode, percentile
 from monai.utils import set_determinism
 from tests.utils import TEST_NDARRAYS, assert_allclose, skip_if_quick
 
@@ -27,6 +27,13 @@
     TEST_MODE.append([p(np.array([3.1, 4.1, 4.1, 5.1])), p(4.1), False])
     TEST_MODE.append([p(np.array([3.1, 4.1, 4.1, 5.1])), p(4), True])
 
+TEST_MIN_MAX = []
+for p in TEST_NDARRAYS:
+    TEST_MIN_MAX.append([p(np.array([1, 2, 3, 4, 4, 5])), {}, min, p(1)])
+    TEST_MIN_MAX.append([p(np.array([[3.1, 4.1, 4.1, 5.1], [3, 5, 4.1, 5]])), {"dim": 1}, min, p([3.1, 3])])
+    TEST_MIN_MAX.append([p(np.array([1, 2, 3, 4, 4, 5])), {}, max, p(5)])
+    TEST_MIN_MAX.append([p(np.array([[3.1, 4.1, 4.1, 5.1], [3, 5, 4.1, 5]])), {"dim": 1}, max, p([5.1, 5])])
+
 
 class TestPytorchNumpyUnification(unittest.TestCase):
 
@@ -74,6 +81,11 @@ def test_mode(self, array, expected, to_long):
         res = mode(array, to_long=to_long)
         assert_allclose(res, expected)
 
+    @parameterized.expand(TEST_MIN_MAX)
+    def test_min_max(self, array, input_params, func, expected):
+        res = func(array, **input_params)
+        assert_allclose(res, expected, type_test=False)
+
 
 if __name__ == "__main__":
     unittest.main()