(img: Tensor)
return F_t.equalize(img)
Equalize the histogram of an image by applying
    a non-linear mapping to the input in order to create a uniform
    distribution of grayscale values in the output.

    Args:
        img (PIL Image or Tensor): Image on which equalize is applied.
            If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
            where ... means it can have an arbitrary number of leading dimensions.
            The tensor dtype must be ``torch.uint8`` and values are expected to be in ``[0, 255]``.
            If img is PIL Image, it is expected to be in mode "P", "L" or "RGB".

    Returns:
        PIL Image or Tensor: An image that was equalized.
Equalize the histogram of an image by applying
    a non-linear mapping to the input in order to create a uniform
    distribution of grayscale values in the output.
Equalize the histogram of an image by applying a non - linear mapping to the input in order to create a uniform distribution of grayscale values in the output .
def equalize(img: Tensor) -> Tensor:
    """Equalize the histogram of an image by applying
    a non-linear mapping to the input in order to create a uniform
    distribution of grayscale values in the output.

    Args:
        img (PIL Image or Tensor): Image on which equalize is applied.
            If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
            where ... means it can have an arbitrary number of leading dimensions.
            The tensor dtype must be ``torch.uint8`` and values are expected to be in ``[0, 255]``.
            If img is PIL Image, it is expected to be in mode "P", "L" or "RGB".

    Returns:
        PIL Image or Tensor: An image that was equalized.
    """
    if not isinstance(img, torch.Tensor):
        return F_pil.equalize(img)

    return F_t.equalize(img)
def equalize ( img : Tensor ) -> Tensor : if not isinstance ( img , torch . Tensor ) : return F_pil . equalize ( img ) return F_t . equalize ( img )