code: def forward(self, img, target=None):
      _, height, width = F.get_dimensions(img)
      new_height = min(height, self.crop_height)
      new_width = min(width, self.crop_width)
  
      if new_height != height or new_width != width:
          offset_height = max(height - self.crop_height, 0)
          offset_width = max(width - self.crop_width, 0)
  
          r = torch.rand(1)
          top = int(offset_height * r)
          left = int(offset_width * r)
  
          img, target = self._crop(
              img, target, top, left, new_height, new_width
          )
  
      pad_bottom = max(self.crop_height - new_height, 0)
      pad_right = max(self.crop_width - new_width, 0)
      if pad_bottom != 0 or pad_right != 0:
          img, target = self._pad(
              img, target, [0, 0, pad_right, pad_bottom]
          )
  
      return img, target
  
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torchvision
  from torch import nn, Tensor
  from torchvision import ops
  from torchvision.transforms import functional, InterpolationMode, transforms
label_desc: Use image node_id 4 for calling the FixedSizeCrop obj's underlying member method code with example usage: obj.forward(img, target) and returns: img,  target