code: def five_crop(
      img: Tensor, size: List[int]
  ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
      """Crop the given image into four corners and the central crop.
      If the image is torch Tensor, it is expected
      to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
  
      .. Note::
          This transform returns a tuple of images and there may be a
          mismatch in the number of inputs and targets your ``Dataset`` returns.
  
      Args:
          img (PIL Image or Tensor): Image to be cropped.
          size (sequence or int): Desired output size of the crop. If size is an
              int instead of sequence like (h, w), a square crop (size, size) is
              made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
  
      Returns:
         tuple: tuple (tl, tr, bl, br, center)
         Corresponding top left, top right, bottom left, bottom right and center crop.
      """
      if not torch.jit.is_scripting() and not torch.jit.is_tracing():
          _log_api_usage_once(five_crop)
      if isinstance(size, numbers.Number):
          size = (int(size), int(size))
      elif isinstance(size, (tuple, list)) and len(size) == 1:
          size = (size[0], size[0])
  
      if len(size) != 2:
          raise ValueError(
              "Please provide only two dimensions (h, w) for size."
          )
  
      _, image_height, image_width = get_dimensions(img)
      crop_height, crop_width = size
      if crop_width > image_width or crop_height > image_height:
          msg = "Requested crop size {} is bigger than input size {}"
          raise ValueError(
              msg.format(size, (image_height, image_width))
          )
  
      tl = crop(img, 0, 0, crop_height, crop_width)
      tr = crop(
          img, 0, image_width - crop_width, crop_height, crop_width
      )
      bl = crop(
          img, image_height - crop_height, 0, crop_height, crop_width
      )
      br = crop(
          img,
          image_height - crop_height,
          image_width - crop_width,
          crop_height,
          crop_width,
      )
  
      center = center_crop(img, [crop_height, crop_width])
  
      return tl, tr, bl, br, center
  
imports: import math
  import numbers
  import sys
  import warnings
  from enum import Enum
  from typing import Any, List, Optional, Tuple, Union
  import numpy
  import torch
  from PIL import Image
  from torch import Tensor
  from ..utils import _log_api_usage_once
  from .None import _functional_pil, _functional_tensor
label_desc: Use image node_id 22 for calling a global function with example usage: five_crop(img, size) and returns: tl,  tr,  bl,  br,  center