code: def crop(image, target, region):
      cropped_image = F.crop(image, *region)
  
      target = target.copy()
      i, j, h, w = region
  
      # should we do something wrt the original size?
      target["size"] = torch.tensor([h, w])
  
      fields = ["labels", "area", "iscrowd"]
  
      if "boxes" in target:
          boxes = target["boxes"]
          max_size = torch.as_tensor([w, h], dtype=torch.float32)
          cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
          cropped_boxes = torch.min(
              cropped_boxes.reshape(-1, 2, 2), max_size
          )
          cropped_boxes = cropped_boxes.clamp(min=0)
          area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(
              dim=1
          )
          target["boxes"] = cropped_boxes.reshape(-1, 4)
          target["area"] = area
          fields.append("boxes")
  
      if "masks" in target:
          # FIXME should we update the area here if there are no boxes?
          target["masks"] = target["masks"][:, i : i + h, j : j + w]
          fields.append("masks")
  
      # remove elements for which the boxes or masks that have zero area
      if "boxes" in target or "masks" in target:
          # favor boxes selection when defining which elements to keep
          # this is compatible with previous implementation
          if "boxes" in target:
              cropped_boxes = target["boxes"].reshape(-1, 2, 2)
              keep = torch.all(
                  cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1
              )
          else:
              keep = target["masks"].flatten(1).any(1)
  
          for field in fields:
              target[field] = target[field][keep]
  
      return cropped_image, target
  
imports: import random
  import PIL
  import torch
  import torchvision.transforms
  import torchvision.transforms.functional
  from util.box_ops import box_xyxy_to_cxcywh
  from util.misc import interpolate
label_desc: Use image node_id 1 for calling a global function with example usage: crop(image, target, region) and returns: cropped_image,  target