code: def center_crop_or_pad(
      img: torch.Tensor,
      output_size: Union[int, List[int]],
      fill: Union[int, Tuple[int, int, int]] = 0,
      padding_mode: str = "constant",
  ) -> torch.Tensor:
      """Center crops and/or pads the given image.
  
      If the image is torch Tensor, it is expected
      to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
      If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.
  
      Args:
          img (PIL Image or Tensor): Image to be cropped.
          output_size (sequence or int): (height, width) of the crop box. If int or sequence with single int,
              it is used for both directions.
          fill (int, Tuple[int]): Padding color
  
      Returns:
          PIL Image or Tensor: Cropped image.
      """
      output_size = _setup_size(output_size)
      crop_height, crop_width = output_size
      _, image_height, image_width = F.get_dimensions(img)
  
      if crop_width > image_width or crop_height > image_height:
          padding_ltrb = [
              (crop_width - image_width) // 2
              if crop_width > image_width
              else 0,
              (crop_height - image_height) // 2
              if crop_height > image_height
              else 0,
              (crop_width - image_width + 1) // 2
              if crop_width > image_width
              else 0,
              (crop_height - image_height + 1) // 2
              if crop_height > image_height
              else 0,
          ]
          img = F.pad(
              img, padding_ltrb, fill=fill, padding_mode=padding_mode
          )
          _, image_height, image_width = F.get_dimensions(img)
          if crop_width == image_width and crop_height == image_height:
              return img
  
      crop_top = int(round((image_height - crop_height) / 2.0))
      crop_left = int(round((image_width - crop_width) / 2.0))
      return F.crop(img, crop_top, crop_left, crop_height, crop_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 5 for calling a global function with example usage: center_crop_or_pad(img, output_size, fill, padding_mode) and returns: F, img