code: def _crop(self, img, target, top, left, height, width):
      img = F.crop(img, top, left, height, width)
      if target is not None:
          boxes = target["boxes"]
          boxes[:, 0::2] -= left
          boxes[:, 1::2] -= top
          boxes[:, 0::2].clamp_(min=0, max=width)
          boxes[:, 1::2].clamp_(min=0, max=height)
  
          is_valid = (boxes[:, 0] < boxes[:, 2]) & (
              boxes[:, 1] < boxes[:, 3]
          )
  
          target["boxes"] = boxes[is_valid]
          target["labels"] = target["labels"][is_valid]
          if "masks" in target:
              target["masks"] = F.crop(
                  target["masks"][is_valid], top, left, height, width
              )
  
      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 3 for calling the FixedSizeCrop obj's underlying member method code with example usage: obj._crop(img, target, top, left, height, width) and returns: img,  target