code: def _pad(self, img, target, padding):
      # Taken from the functional_tensor.py pad
      if isinstance(padding, int):
          pad_left = pad_right = pad_top = pad_bottom = padding
      elif len(padding) == 1:
          pad_left = pad_right = pad_top = pad_bottom = padding[0]
      elif len(padding) == 2:
          pad_left = pad_right = padding[0]
          pad_top = pad_bottom = padding[1]
      else:
          pad_left = padding[0]
          pad_top = padding[1]
          pad_right = padding[2]
          pad_bottom = padding[3]
  
      padding = [pad_left, pad_top, pad_right, pad_bottom]
      img = F.pad(img, padding, self.fill, self.padding_mode)
      if target is not None:
          target["boxes"][:, 0::2] += pad_left
          target["boxes"][:, 1::2] += pad_top
          if "masks" in target:
              target["masks"] = F.pad(
                  target["masks"], padding, 0, "constant"
              )
  
      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 2 for calling the FixedSizeCrop obj's underlying member method code with example usage: obj._pad(img, target, padding) and returns: img,  target