code: def get_sample_coco_image(tensor=True):
      """
      Args:
          tensor (bool): if True, returns 3xHxW tensor.
              else, returns a HxWx3 numpy array.
  
      Returns:
          an image, in BGR color.
      """
      try:
          file_name = DatasetCatalog.get("coco_2017_val_100")[0][
              "file_name"
          ]
          if not PathManager.exists(file_name):
              raise FileNotFoundError()
      except IOError:
          # for public CI to run
          file_name = PathManager.get_local_path(
              "http://images.cocodataset.org/train2017/000000000009.jpg"
          )
      ret = read_image(file_name, format="BGR")
      if tensor:
          ret = torch.from_numpy(
              np.ascontiguousarray(ret.transpose(2, 0, 1))
          )
      return ret
  
imports: import io
  import numpy
  import os
  import re
  import tempfile
  import unittest
  from typing import Callable
  import torch
  import torch.onnx.symbolic_helper
  from packaging import version
  from torch._C import ListType
  from torch.onnx import register_custom_op_symbolic
  from detectron2 import model_zoo
  from detectron2.config import CfgNode, LazyConfig, instantiate
  from detectron2.data import DatasetCatalog
  from detectron2.data.detection_utils import read_image
  from detectron2.modeling import build_model
  from detectron2.structures import Boxes, Instances, ROIMasks
  from detectron2.utils.file_io import PathManager
label_desc: Use image node_id 3 for calling a global function with example usage: get_sample_coco_image(tensor) and returns: ret