code: def _test_do(self):
      # Run executor.
      example_gen = TestExampleGenExecutor()
      example_gen.Do({}, self._output_dict, self._exec_properties)
  
      self.assertEqual(
          artifact_utils.encode_split_names(["train", "eval"]),
          self._examples.split_names,
      )
  
      if self._exec_properties.get("format_parquet", False):
          train_output_file = os.path.join(
              self._examples.uri,
              "Split-train",
              "data_parquet-00000-of-00001.parquet",
          )
          eval_output_file = os.path.join(
              self._examples.uri,
              "Split-eval",
              "data_parquet-00000-of-00001.parquet",
          )
      else:
          train_output_file = os.path.join(
              self._examples.uri,
              "Split-train",
              "data_tfrecord-00000-of-00001.gz",
          )
          eval_output_file = os.path.join(
              self._examples.uri,
              "Split-eval",
              "data_tfrecord-00000-of-00001.gz",
          )
  
      # Check example gen outputs.
      self.assertTrue(fileio.exists(train_output_file))
      self.assertTrue(fileio.exists(eval_output_file))
  
      # Output split ratio: train:eval=2:1.
      self.assertGreater(
          fileio.open(train_output_file).size(),
          fileio.open(eval_output_file).size(),
      )
  
imports: import os
  from typing import Any, Dict, Iterable
  import apache_beam
  import pyarrow
  import tensorflow
  from tfx.components.example_gen import base_example_gen_executor
  from tfx.dsl.io import fileio
  from tfx.proto import example_gen_pb2
  from tfx.types import artifact_utils
  from tfx.types import standard_artifacts
  from tfx.types import standard_component_specs
  from tfx.utils import proto_utils
label_desc: Use image node_id 2 for calling the BaseExampleGenExecutorTest obj's underlying member method code with example usage: obj._test_do() without return types