code: def setUp(self):
      super().setUp()
      self._test_dir = os.path.join(
          os.environ.get(
              "TEST_UNDECLARED_OUTPUTS_DIR", self.get_temp_dir()
          ),
          self._testMethodName,
      )
      self._pipeline_name = "imdb_stub_test"
      # This example assumes that the imdb data and imdb utility function are
      # stored in tfx/examples/imdb. Feel free to customize this as needed.
      imdb_root = os.path.dirname(imdb_pipeline_native_keras.__file__)
      self._data_root = os.path.join(imdb_root, "data")
      self._module_file = os.path.join(
          imdb_root, "imdb_utils_native_keras.py"
      )
      self._serving_model_dir = os.path.join(
          self._test_dir, "serving_model"
      )
      self._pipeline_root = os.path.join(
          self._test_dir, "pipelines", self._pipeline_name
      )
      # Metadata path for recording successful pipeline run.
      self._recorded_mlmd_path = os.path.join(
          self._test_dir, "record", "metadata.db"
      )
      # Metadata path for stub pipeline
      self._metadata_path = os.path.join(
          self._test_dir, "metadata", self._pipeline_name, "metadata.db"
      )
      self._recorded_output_dir = os.path.join(
          self._test_dir, "testdata"
      )
  
      record_imdb_pipeline = imdb_pipeline_native_keras._create_pipeline(  # pylint:disable=protected-access
          pipeline_name=self._pipeline_name,
          data_root=self._data_root,
          module_file=self._module_file,
          serving_model_dir=self._serving_model_dir,
          pipeline_root=self._pipeline_root,
          metadata_path=self._recorded_mlmd_path,
          beam_pipeline_args=[],
      )
  
      BeamDagRunner().run(record_imdb_pipeline)
  
      pipeline_recorder_utils.record_pipeline(
          output_dir=self._recorded_output_dir,
          metadata_db_uri=self._recorded_mlmd_path,
          pipeline_name=self._pipeline_name,
      )
  
      # Run pipeline with stub executors.
      self.imdb_pipeline = imdb_pipeline_native_keras._create_pipeline(  # pylint:disable=protected-access
          pipeline_name=self._pipeline_name,
          data_root=self._data_root,
          module_file=self._module_file,
          serving_model_dir=self._serving_model_dir,
          pipeline_root=self._pipeline_root,
          metadata_path=self._metadata_path,
          beam_pipeline_args=[],
      )
  
imports: import os
  from absl import logging
  import tensorflow
  from tfx.dsl.compiler import compiler
  from tfx.dsl.io import fileio
  from tfx.examples.imdb import imdb_pipeline_native_keras
  from tfx.experimental.pipeline_testing import executor_verifier_utils
  from tfx.experimental.pipeline_testing import pipeline_mock
  from tfx.experimental.pipeline_testing import pipeline_recorder_utils
  from tfx.orchestration import metadata
  from tfx.orchestration.beam.beam_dag_runner import BeamDagRunner
  from ml_metadata.proto import metadata_store_pb2
label_desc: Use image node_id 1 for calling the ImdbStubPipelineRegressionEndToEndTest obj's underlying member method code with example usage: obj.setUp() without return types