code: def run():
      """Define a pipeline."""
  
      tfx.orchestration.LocalDagRunner().run(
          pipeline.create_pipeline(
              pipeline_name=configs.PIPELINE_NAME,
              pipeline_root=PIPELINE_ROOT,
              data_path=DATA_PATH,
              # NOTE: Use `query` instead of `data_path` to use BigQueryExampleGen.
              # query=configs.BIG_QUERY_QUERY,
              # NOTE: Set the path of the customized schema if any.
              # schema_path=generated_schema_path,
              preprocessing_fn=configs.PREPROCESSING_FN,
              run_fn=configs.RUN_FN,
              train_args=tfx.proto.TrainArgs(
                  num_steps=configs.TRAIN_NUM_STEPS
              ),
              eval_args=tfx.proto.EvalArgs(
                  num_steps=configs.EVAL_NUM_STEPS
              ),
              eval_accuracy_threshold=configs.EVAL_ACCURACY_THRESHOLD,
              serving_model_dir=SERVING_MODEL_DIR,
              # NOTE: Provide GCP configs to use BigQuery with Beam DirectRunner.
              # beam_pipeline_args=configs.
              # BIG_QUERY_WITH_DIRECT_RUNNER_BEAM_PIPELINE_ARGS,
              metadata_connection_config=tfx.orchestration.metadata.sqlite_metadata_connection_config(
                  METADATA_PATH
              ),
          )
      )
  
imports: import os
  from absl import logging
  from tfx import v1
  from tfx.experimental.templates.penguin.pipeline import configs
  from tfx.experimental.templates.penguin.pipeline import pipeline
label_desc: Use image node_id 1 for calling a global function with example usage: run() without return types