code: def test_rn50_pipe_mis():
      @pipeline_def(batch_size=10, device_id=0, num_threads=4)
      def rn50_pipe():
          enc_0, label_0 = fn.readers.file(
              files=[
                  str(
                      _test_root
                      / "db/single/jpeg/113/snail-4291306_1280.jpg"
                  )
              ],
              name="FileReader_0",
          )
          enc_1, label_1 = fn.readers.file(
              files=[
                  str(
                      _test_root
                      / "db/single/jpeg/113/snail-4345504_1280.jpg"
                  )
              ],
              name="FileReader_1",
          )
          imgs = fn.decoders.image([enc_0, enc_1], device="mixed")
          # Such checks are needed if we want mypy to allow unpacking of the return values from
          # MIS invocation. The overload in question returns `DataNode | [DataNode]` and the mentioned
          # operation is not available for pure DataNode (note that subscript is available, but it
          # is actual DALI operation).
          assert isinstance(imgs, list)
          rng = fn.random.coin_flip(probability=0.5)
  
          resized = fn.random_resized_crop(imgs, size=[224, 224])
          # note that mypy type-checks when we pass resized, which is of type
          # DataNode | List[DataNode] into the 1-input CMN, that expects either a DataNode in primary
          # overload, or List[DataNode] in the secondary one.
          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],
          )
          assert isinstance(resized, list)
          assert isinstance(normalized, list)
          normalized_0, normalized_1 = normalized
          label = (label_0 + label_1) * 0.5
          expect_data_node(
              enc_0,
              enc_1,
              label,
              imgs[0],
              imgs[1],
              rng,
              resized[0],
              resized[1],
              normalized_0,
              normalized_1,
              label.gpu(),
          )
          return normalized_0, normalized_1, label.gpu()
  
      pipe = rn50_pipe()
      expect_pipeline(pipe)
      pipe.build()
      imgs_0, imgs_1, labels = pipe.run()
      for imgs in [imgs_0, imgs_1]:
          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 5 for calling a global function with example usage: test_rn50_pipe_mis() and returns: normalized_0,  normalized_1,  label