fix the comment by ai review

Signed-off-by: sewon.jeon <sewon.jeon@connecteve.com>

Author: eclipse0922

monai/transforms/post/array.py

@@ -843,11 +843,14 @@ def __call__(self, points: NdarrayOrTensor, spatial_shape: Sequence[int] | None
                 continue
             if not ((center >= 0).all() and (center < bounds_t).all()):
                 continue
-            # Round to nearest integer for impulse placement
+            # Round to nearest integer for impulse placement, then clamp to valid index range
             center_int = center.round().long()
+            # Clamp indices to [0, size-1] to avoid out-of-bounds (e.g., 9.7 rounds to 10 in size-10 array)
+            bounds_max = (bounds_t - 1).long()
+            center_int = torch.minimum(torch.maximum(center_int, torch.zeros_like(center_int)), bounds_max)
             # Place impulse (use maximum in case of overlapping landmarks)
             current_val = heatmap[idx][tuple(center_int)]
-            heatmap[idx][tuple(center_int)] = max(current_val, torch.tensor(1.0, dtype=self.torch_dtype, device=device))
+            heatmap[idx][tuple(center_int)] = torch.maximum(current_val, torch.tensor(1.0, dtype=self.torch_dtype, device=device))
 
         # Apply Gaussian blur using GaussianFilter
         # Reshape to (num_points, 1, *spatial) for per-channel filtering

monai/transforms/post/dictionary.py

@@ -680,7 +680,7 @@ def _determine_shape(
         self, points: Any, static_shape: tuple[int, ...] | None, data: Mapping[Hashable, Any], ref_key: Hashable | None
     ) -> tuple[int, ...]:
         points_t = convert_to_tensor(points, dtype=torch.float32, track_meta=False)
-        if points_t.ndim not in (2, 3):
+        if points_t.ndim != 2:
             raise ValueError(f"{self._ERR_INVALID_POINTS} Got {points_t.ndim}D tensor.")
         spatial_dims = int(points_t.shape[-1])
         if static_shape is not None: