code: def crop_or_pad(
      img: torch.Tensor,
      top: int,
      left: int,
      height: int,
      width: int,
      fill: Union[int, Tuple[int, int, int]] = 0,
      padding_mode: str = "constant",
  ) -> torch.Tensor:
      """Crops and/or pads image to meet target size, with control over fill and padding_mode."""
      _, image_height, image_width = F.get_dimensions(img)
      right = left + width
      bottom = top + height
      if (
          left < 0
          or top < 0
          or right > image_width
          or bottom > image_height
      ):
          padding_ltrb = [
              max(-left + min(0, right), 0),
              max(-top + min(0, bottom), 0),
              max(right - max(image_width, left), 0),
              max(bottom - max(image_height, top), 0),
          ]
          img = F.pad(
              img, padding_ltrb, fill=fill, padding_mode=padding_mode
          )
  
      top = max(top, 0)
      left = max(left, 0)
      return F.crop(img, top, left, height, width)
  
imports: import math
  import numbers
  import random
  import warnings
  from typing import List, Sequence, Tuple, Union
  import torch
  import torchvision.transforms.functional
  from PIL import Image
  import numpy
label_desc: Use image node_id 6 for calling a global function with example usage: crop_or_pad(img, top, left, height, width, fill, padding_mode) and returns: F