code: def _parse_flags(
      argv: List[str],
  ) -> Tuple[argparse.Namespace, List[str]]:
      """Parses command line arguments.
  
      Args:
        argv: Unparsed arguments for run_executor.py. Known argument names include
          --executor_class_path: Python class of executor in format of
            <module>.<class>.
          --json_serialized_invocation_args: Full JSON-serialized parameters for
            this execution.
  
          The remaining part of the arguments will be parsed as the beam args used
          by each component executors. Some commonly used beam args are as follows:
            --runner: The beam pipeline runner environment. Can be DirectRunner (for
              running locally) or DataflowRunner (for running on GCP Dataflow
              service).
            --project: The GCP project ID. Neede when runner==DataflowRunner
            --direct_num_workers: Number of threads or subprocesses executing the
              work load.
          For more about the beam arguments please refer to:
          https://cloud.google.com/dataflow/docs/guides/specifying-exec-params
  
      Returns:
        Tuple of an argparse result and remaining beam args.
      """
  
      parser = argparse_flags.ArgumentParser()
      parser.add_argument(
          "--executor_class_path",
          type=str,
          required=True,
          help="Python class of executor in format of <module>.<class>.",
      )
      parser.add_argument(
          "--json_serialized_invocation_args",
          type=str,
          required=True,
          help="JSON-serialized metadata for this execution.",
      )
  
      return parser.parse_known_args(argv)
  
imports: import argparse
  import os
  from typing import List, Tuple
  from absl import app
  from absl import logging
  from absl.flags import argparse_flags
  from kfp.pipeline_spec import pipeline_spec_pb2
  from tfx.components.evaluator import executor
  from tfx.dsl.components.base import base_beam_executor
  from tfx.dsl.components.base import base_executor
  from tfx.dsl.io import fileio
  from tfx.orchestration.kubeflow.v2.container import kubeflow_v2_entrypoint_utils
  from tfx.orchestration.portable import outputs_utils
  from tfx.types import artifact_utils
  from tfx.types import standard_artifacts
  from tfx.types import standard_component_specs
  from tfx.types import value_artifact
  from tfx.utils import import_utils
  from google.protobuf import json_format
label_desc: Use image node_id 2 for calling a global function with example usage: _parse_flags(argv) and returns: parser