Mentioning the padding policy in transforms.GaussianBlur docs

torchvision/transforms/functional.py

@@ -1301,7 +1301,7 @@ def erase(img: Tensor, i: int, j: int, h: int, w: int, v: Tensor, inplace: bool
 
 
 def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Tensor:
-    """Performs Gaussian blurring on the image by given kernel.
+    """Performs Gaussian blurring on the image by given kernel, using reflection padding corresponding to the kernel size, to maintain the input shape. 
     If the image is torch Tensor, it is expected
     to have [..., H, W] shape, where ... means at most one leading dimension.
 

torchvision/transforms/v2/_misc.py

@@ -166,7 +166,7 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
 
 
 class GaussianBlur(Transform):
-    """Blurs image with randomly chosen Gaussian blur.
+    """Blurs image with randomly chosen Gaussian blur kernel. The convolution will be using reflection padding corresponding to the kernel size, to maintain the input shape.
 
     If the input is a Tensor, it is expected
     to have [..., C, H, W] shape, where ... means an arbitrary number of leading dimensions.