code: def test_rn50_pipe():
      @pipeline_def(batch_size=10, device_id=0, num_threads=4)
      def rn50_pipe():
          enc, label = fn.readers.file(
              files=[
                  str(
                      _test_root
                      / "db/single/jpeg/113/snail-4291306_1280.jpg"
                  )
              ],
              name="FileReader",
          )
          imgs = fn.decoders.image(enc, device="mixed")
          rng = fn.random.coin_flip(probability=0.5)
          resized = fn.random_resized_crop(imgs, size=[224, 224])
          normalized = fn.crop_mirror_normalize(
              resized,
              mirror=rng,
              dtype=types.DALIDataType.FLOAT16,
              output_layout="HWC",
              crop=(224, 224),
              mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
              std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
          )
          expect_data_node(
              enc, label, imgs, rng, resized, normalized, label.gpu()
          )
          return normalized, label.gpu()
  
      pipe = rn50_pipe()
      expect_pipeline(pipe)
      pipe.build()
      imgs, labels = pipe.run()
      expect_tensor_list(imgs, labels)
      assert isinstance(imgs, tensors.TensorListGPU)
      assert imgs.dtype == types.DALIDataType.FLOAT16  # noqa: E721
      assert isinstance(labels, tensors.TensorListGPU)
  
imports: from pathlib import Path
  import numpy
  from typing import cast, Sequence, Union
  from nvidia.dali import fn, ops
  from nvidia.dali import types, tensors
  from nvidia.dali.data_node import DataNode
  from nvidia.dali.pipeline import pipeline_def, Pipeline
  from test_utils import get_dali_extra_path, check_numba_compatibility_cpu
  from nose.plugins.attrib import attr
label_desc: Use image node_id 4 for calling a global function with example usage: test_rn50_pipe() and returns: normalized,  label