docs(transforms): update spatial_resample docstrings for None spatial_size (#9068)

Signed-off-by: Luchang Jiang <auroral.sunflower@gmail.com>

Author: FinalSunFlower

monai/transforms/spatial/functional.py

@@ -117,7 +117,9 @@ def spatial_resample(
     Args:
         img: data to be resampled, assuming `img` is channel-first.
         dst_affine: target affine matrix, if None, use the input affine matrix, effectively no resampling.
-        spatial_size: output spatial size, if the component is ``-1``, use the corresponding input spatial size.
+        spatial_size: output spatial size. Components set to ``-1`` or ``None`` use the corresponding input
+            spatial dimension. If the entire value is ``None``, the output size is computed automatically when
+            possible, otherwise the input spatial shape is used.
         mode: {``"bilinear"``, ``"nearest"``} or spline interpolation order 0-5 (integers).
             Interpolation mode to calculate output values.
             See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html

tests/transforms/test_spatial_resample.py

@@ -231,6 +231,7 @@ def test_unchange(self):
         set_track_meta(True)
 
     def test_none_spatial_size_rank_one(self):
+        """Verify that an unspecified rank-one size preserves the input shape and returns finite values."""
         img = MetaTensor(torch.randn(1, 8))
         result = SpatialResample()(img, spatial_size=None)
 
@@ -239,6 +240,7 @@ def test_none_spatial_size_rank_one(self):
         self.assertTrue(torch.isfinite(result).all())
 
     def test_partial_none_spatial_size(self):
+        """Verify that ``None`` dimensions fall back while specified dimensions produce the requested shape."""
         img = MetaTensor(torch.randn(1, 3, 6, 7))
         result = SpatialResample()(img, spatial_size=(None, 4, 5))