code: def create_pipeline():
      """Creates the pipeline object."""
      absl.logging.set_verbosity(absl.logging.INFO)
  
      text_url = "https://raw.githubusercontent.com/karpathy/char-rnn/370cbcd/data/tinyshakespeare/input.txt"
      pattern = "art thou"
      component_instances = download_grep_print_pipeline.create_pipeline_component_instances(
          text_url=text_url,
          pattern=pattern,
      )
  
      pipeline_name = "download-grep-print-pipelin"
  
      tfx_root = os.path.join(os.environ["HOME"], "tfx_root")
      pipeline_root = os.path.join(tfx_root, "pipelines", pipeline_name)
      # Sqlite ML-metadata db path.
      metadata_path = os.path.join(
          tfx_root, "metadata", pipeline_name, "metadata.db"
      )
  
      pipeline = pipeline_module.Pipeline(
          pipeline_name=pipeline_name,
          pipeline_root=pipeline_root,
          components=component_instances,
          enable_cache=True,
          metadata_connection_config=metadata.sqlite_metadata_connection_config(
              metadata_path
          ),
      )
      return pipeline
  
file_path: tfx/tfx/examples/custom_components/container_components/download_grep_print_pipeline_on_beam_test.py
imports: import os
  import absl
  import tensorflow
  from tfx.examples.custom_components.container_components import download_grep_print_pipeline
  from tfx.orchestration import metadata
  from tfx.orchestration import pipeline
  from tfx.orchestration.beam.beam_dag_runner import BeamDagRunner
label_desc: Use image node_id 1 for calling a global function with example usage: create_pipeline() and returns: pipeline