Force native grid_sample path and add docstring in Warp regression test

Address review feedback: pin USE_COMPILED=False so the test always exercises
the native grid_sample branch (the only path that normalizes the grid; the
csrc grid_pull path is unaffected), and add a Google-style docstring.

Signed-off-by: VenkateswarluNagineni <venkates2002@tamu.edu>

Author: VenkateswarluNagineni

tests/networks/blocks/warp/test_warp.py

@@ -12,6 +12,7 @@
 
 import unittest
 from pathlib import Path
+from unittest import mock
 
 import numpy as np
 import torch
@@ -138,18 +139,26 @@ def test_ill_shape(self):
         with self.assertRaisesRegex(ValueError, ""):
             warp_layer(image=torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), ddf=torch.zeros(1, 2, 3, 3))
 
+    @mock.patch("monai.networks.blocks.warp.USE_COMPILED", False)
     def test_singleton_spatial_dim(self):
-        # a single-slice volume / single-row image has a spatial dim of size 1; the grid
-        # normalization must not divide by zero (regression for native grid_sample path).
-        # "zeros" padding maps an out-of-range (here NaN, pre-fix) coordinate to 0, so it exposes
-        # the bug; "border"/"reflection" would clamp onto the lone voxel and mask it.
+        """
+        Regression test for a singleton spatial dimension (a single-slice volume or a
+        single-row/column image), where the grid normalization ``* 2 / (dim - 1)`` previously
+        divided by zero.
+
+        The native ``grid_sample`` path is forced via ``USE_COMPILED=False`` because only that
+        branch normalizes the grid; the csrc ``grid_pull`` path is unaffected. ``padding_mode``
+        is ``"zeros"`` so an out-of-range (pre-fix ``nan``) coordinate maps to 0 and exposes the
+        bug, whereas ``"border"``/``"reflection"`` would clamp onto the lone voxel and mask it.
+        For a zero displacement field the warped output must contain no ``nan`` and must equal
+        the input image.
+        """
         for shape, ndim in [((1, 1, 1, 4, 4), 3), ((1, 1, 1, 5), 2), ((1, 1, 5, 1), 2)]:
             image = torch.rand(*shape)
             ddf = torch.zeros(shape[0], ndim, *shape[2:])
             warp_layer = Warp(mode="bilinear", padding_mode="zeros")
             result = warp_layer(image, ddf)
             self.assertFalse(torch.isnan(result).any(), f"NaN in warp output for shape {shape}")
-            # a zero displacement field must reproduce the input image
             np.testing.assert_allclose(result.cpu().numpy(), image.cpu().numpy(), rtol=1e-4, atol=1e-4)
 
     def test_grad(self):