code: def test_operator_coco_reader_same_images():
      file_root = os.path.join(
          test_data_root, "db", "coco_pixelwise", "images"
      )
      train_annotations = os.path.join(
          test_data_root, "db", "coco_pixelwise", "instances.json"
      )
  
      coco_dir = os.path.join(test_data_root, "db", "coco")
      coco_dir_imgs = os.path.join(coco_dir, "images")
      coco_pixelwise_dir = os.path.join(
          test_data_root, "db", "coco_pixelwise"
      )
      coco_pixelwise_dir_imgs = os.path.join(
          coco_pixelwise_dir, "images"
      )
  
      for file_root, _ in [
          (coco_dir_imgs, os.path.join(coco_dir, "instances.json")),
          (
              coco_pixelwise_dir_imgs,
              os.path.join(coco_pixelwise_dir, "instances.json"),
          ),
          (
              coco_pixelwise_dir_imgs,
              os.path.join(
                  coco_pixelwise_dir, "instances_rle_counts.json"
              ),
          ),
      ]:
          pipe = Pipeline(batch_size=1, num_threads=4, device_id=0)
          with pipe:
              inputs1, boxes1, labels1, *_ = fn.readers.coco(
                  file_root=file_root,
                  annotations_file=train_annotations,
                  name="reader1",
                  seed=1234,
              )
              inputs2, boxes2, labels2, *_ = fn.readers.coco(
                  file_root=file_root,
                  annotations_file=train_annotations,
                  polygon_masks=True,
                  name="reader2",
              )
              inputs3, boxes3, labels3, *_ = fn.readers.coco(
                  file_root=file_root,
                  annotations_file=train_annotations,
                  pixelwise_masks=True,
                  name="reader3",
              )
              pipe.set_outputs(
                  inputs1,
                  boxes1,
                  labels1,
                  inputs2,
                  boxes2,
                  labels2,
                  inputs3,
                  boxes3,
                  labels3,
              )
          pipe.build()
  
          epoch_sz = pipe.epoch_size("reader1")
          assert epoch_sz == pipe.epoch_size("reader2")
          assert epoch_sz == pipe.epoch_size("reader3")
  
          for _ in range(epoch_sz):
              (
                  inputs1,
                  boxes1,
                  labels1,
                  inputs2,
                  boxes2,
                  labels2,
                  inputs3,
                  boxes3,
                  labels3,
              ) = pipe.run()
              np.testing.assert_array_equal(
                  inputs1.at(0), inputs2.at(0)
              )
              np.testing.assert_array_equal(
                  inputs1.at(0), inputs3.at(0)
              )
              np.testing.assert_array_equal(
                  labels1.at(0), labels2.at(0)
              )
              np.testing.assert_array_equal(
                  labels1.at(0), labels3.at(0)
              )
              np.testing.assert_array_equal(boxes1.at(0), boxes2.at(0))
              np.testing.assert_array_equal(boxes1.at(0), boxes3.at(0))
  
imports: import numpy
  import nvidia.dali.fn
  import os
  import tempfile
  import json
  from nvidia.dali import Pipeline, pipeline_def
  from nose_utils import raises
  from nose2.tools import params
  from test_utils import compare_pipelines, get_dali_extra_path
label_desc: Use image node_id 4 for calling a global function with example usage: test_operator_coco_reader_same_images() without return types