code: def parse_cmdline(argv):
      """Parse the commandline into known and pipeline arguments.
  
      The known arguments are required for this specific program to function,
      and the other pipeline arguments can be used to configure beam and the
      specific beam backend being used.  See
      https://github.com/apache/beam/blob/master/sdks/python/apache_beam/options/pipeline_options.py
      for a list and description of the pipeline arguments accepted.
  
      Args:
        argv: List containing command-line arguments.
  
      Returns:
        A pair, the first of which are the known (non-pipeline) arguments
        and the second of which are the pipeline arguments.
      """
      parser = argparse.ArgumentParser()
  
      parser.add_argument(
          "--input_pattern_list",
          help="Comma-separated list of TFRecord filename patterns.",
      )
      parser.add_argument(
          "--output_pattern_prefix",
          help="Filename pattern for the output TFRecords.",
      )
      parser.add_argument(
          "--output_dataset_config_pbtxt",
          help="Optional.  If set, print out a human-readable version of "
          "DeepVariantDatasetConfig.",
      )
      parser.add_argument(
          "--output_dataset_name",
          help="Optional unless --output_dataset_config_pbtxt is set.",
      )
  
      known_args, pipeline_args = parser.parse_known_args(argv)
  
      return known_args, pipeline_args
  
file_path: deepvariant/tools/shuffle_tfrecords_beam.py
imports: import argparse
  import hashlib
  import logging
  import os
  import textwrap
  import apache_beam
  from apache_beam import coders
  from apache_beam.options.pipeline_options import PipelineOptions
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: parse_cmdline(argv) and returns: known_args,  pipeline_args