(
        img: Tensor,
        top: int,
        left: int,
        height: int,
        width: int,
        size: List[int],
        interpolation: InterpolationMode=InterpolationMode.BILINEAR)
return img
Crop the given image and resize it to desired size.
    If the image is torch Tensor, it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions

    Notably used in :class:`~torchvision.transforms.RandomResizedCrop`.

    Args:
        img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image.
        top (int): Vertical component of the top left corner of the crop box.
        left (int): Horizontal component of the top left corner of the crop box.
        height (int): Height of the crop box.
        width (int): Width of the crop box.
        size (sequence or int): Desired output size. Same semantics as ``resize``.
        interpolation (InterpolationMode): Desired interpolation enum defined by
            :class:`torchvision.transforms.InterpolationMode`.
            Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
            ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.

    Returns:
        PIL Image or Tensor: Cropped image.
Crop the given image and resize it to desired size.
    If the image is torch Tensor, it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
Crop the given image and resize it to desired size . If the image is torch Tensor it is expected to have [ ... H W ] shape where ... means an arbitrary number of leading dimensions
def resized_crop(
        img: Tensor,
        top: int,
        left: int,
        height: int,
        width: int,
        size: List[int],
        interpolation: InterpolationMode=InterpolationMode.BILINEAR) -> Tensor:
    """Crop the given image and resize it to desired size.
    If the image is torch Tensor, it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions

    Notably used in :class:`~torchvision.transforms.RandomResizedCrop`.

    Args:
        img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image.
        top (int): Vertical component of the top left corner of the crop box.
        left (int): Horizontal component of the top left corner of the crop box.
        height (int): Height of the crop box.
        width (int): Width of the crop box.
        size (sequence or int): Desired output size. Same semantics as ``resize``.
        interpolation (InterpolationMode): Desired interpolation enum defined by
            :class:`torchvision.transforms.InterpolationMode`.
            Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
            ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.

    Returns:
        PIL Image or Tensor: Cropped image.
    """
    img = crop(img, top, left, height, width)
    img = resize(img, size, interpolation)
    return img
def resized_crop ( img : Tensor , top : int , left : int , height : int , width : int , size : List [ int ] , interpolation : InterpolationMode = InterpolationMode . BILINEAR ) -> Tensor : img = crop ( img , top , left , height , width ) img = resize ( img , size , interpolation ) return img