simplify docstrings

Author: AntoineSimoulin

torchvision/transforms/v2/_type_conversion.py

@@ -96,38 +96,21 @@ def transform(self, inpt: Any, params: dict[str, Any]) -> torch.Tensor:
 
 
 class ToCVCUDATensor(Transform):
-    """Convert a ``torch.Tensor`` with NCHW layout to a ``cvcuda.Tensor`` with NHWC layout.
+    """Convert a ``torch.Tensor`` with NCHW shape to a ``cvcuda.Tensor``.
     If the input tensor is on CPU, it will automatically be transferred to GPU.
     Only 1-channel and 3-channel images are supported.
 
     This transform does not support torchscript.
-
-    Example:
-        >>> import torch
-        >>> from torchvision.transforms import v2
-        >>> img_tensor = torch.randint(0, 256, (1, 3, 320, 240), dtype=torch.uint8)
-        >>> img_cvcuda = v2.ToCVCUDATensor()(img_tensor)
-        >>> print(img_cvcuda.shape)
-        (1, 3, 240, 320)
     """
 
     def transform(self, inpt: torch.Tensor, params: dict[str, Any]) -> "cvcuda.Tensor":
         return F.to_cvcuda_tensor(inpt)
 
 
 class CVCUDAToTensor(Transform):
-    """Convert a ``cvcuda.Tensor`` with NHWC layout to a ``torch.Tensor`` with NCHW layout.
+    """Convert a ``cvcuda.Tensor`` to a ``torch.Tensor`` with NCHW shape.
 
     This function does not support torchscript.
-
-    Example:
-        >>> import cvcuda
-        >>> from torchvision.transforms import v2
-        >>> img_tensor = torch.randint(0, 255, (1, 240, 320, 3), dtype=torch.uint8, device="cuda")
-        >>> img_cvcuda = cvcuda.as_tensor(img_tensor, cvcuda.TensorLayout.NHWC)
-        >>> img_tensor = v2.CVCUDAToTensor()(img_cvcuda)
-        >>> print(img_tensor.shape)
-        torch.Size([1, 3, 240, 320])
     """
 
     try: