(img: Tensor, num_output_channels: int=1)
return F_t.rgb_to_grayscale(img, num_output_channels)
Convert RGB image to grayscale version of image.
    If the image is torch Tensor, it is expected
    to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions

    Note:
        Please, note that this method supports only RGB images as input. For inputs in other color spaces,
        please, consider using meth:`~torchvision.transforms.functional.to_grayscale` with PIL Image.

    Args:
        img (PIL Image or Tensor): RGB Image to be converted to grayscale.
        num_output_channels (int): number of channels of the output image. Value can be 1 or 3. Default, 1.

    Returns:
        PIL Image or Tensor: Grayscale version of the image.

        - if num_output_channels = 1 : returned image is single channel
        - if num_output_channels = 3 : returned image is 3 channel with r = g = b
Convert RGB image to grayscale version of image.
    If the image is torch Tensor, it is expected
    to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions
Convert RGB image to grayscale version of image . If the image is torch Tensor it is expected to have [ ... 3 H W ] shape where ... means an arbitrary number of leading dimensions
def rgb_to_grayscale(img: Tensor, num_output_channels: int=1) -> Tensor:
    """Convert RGB image to grayscale version of image.
    If the image is torch Tensor, it is expected
    to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions

    Note:
        Please, note that this method supports only RGB images as input. For inputs in other color spaces,
        please, consider using meth:`~torchvision.transforms.functional.to_grayscale` with PIL Image.

    Args:
        img (PIL Image or Tensor): RGB Image to be converted to grayscale.
        num_output_channels (int): number of channels of the output image. Value can be 1 or 3. Default, 1.

    Returns:
        PIL Image or Tensor: Grayscale version of the image.

        - if num_output_channels = 1 : returned image is single channel
        - if num_output_channels = 3 : returned image is 3 channel with r = g = b
    """
    if not isinstance(img, torch.Tensor):
        return F_pil.to_grayscale(img, num_output_channels)

    return F_t.rgb_to_grayscale(img, num_output_channels)
def rgb_to_grayscale ( img : Tensor , num_output_channels : int = 1 ) -> Tensor : if not isinstance ( img , torch . Tensor ) : return F_pil . to_grayscale ( img , num_output_channels ) return F_t . rgb_to_grayscale ( img , num_output_channels )