code: def parse_args(argv):
      """Parses command line arguments."""
      parser = argparse_flags.ArgumentParser(
          formatter_class=argparse.ArgumentDefaultsHelpFormatter
      )
  
      # High-level options.
      parser.add_argument(
          "--verbose",
          "-V",
          action="store_true",
          help="Report progress and metrics when training or compressing.",
      )
      parser.add_argument(
          "--model_path",
          default="bls2017",
          help="Path where to save/load the trained model.",
      )
      subparsers = parser.add_subparsers(
          title="commands",
          dest="command",
          help="What to do: 'train' loads training data and trains (or continues "
          "to train) a new model. 'compress' reads an image file (lossless "
          "PNG format) and writes a compressed binary file. 'decompress' "
          "reads a binary file and reconstructs the image (in PNG format). "
          "input and output filenames need to be provided for the latter "
          "two options. Invoke '<command> -h' for more information.",
      )
  
      # 'train' subcommand.
      train_cmd = subparsers.add_parser(
          "train",
          formatter_class=argparse.ArgumentDefaultsHelpFormatter,
          description="Trains (or continues to train) a new model. Note that this "
          "model trains on a continuous stream of patches drawn from "
          "the training image dataset. An epoch is always defined as "
          "the same number of batches given by --steps_per_epoch. "
          "The purpose of validation is mostly to evaluate the "
          "rate-distortion performance of the model using actual "
          "quantization rather than the differentiable proxy loss. "
          "Note that when using custom training images, the validation "
          "set is simply a random sampling of patches from the "
          "training set.",
      )
      train_cmd.add_argument(
          "--lambda",
          type=float,
          default=0.01,
          dest="lmbda",
          help="Lambda for rate-distortion tradeoff.",
      )
      train_cmd.add_argument(
          "--train_glob",
          type=str,
          default=None,
          help="Glob pattern identifying custom training data. This pattern must "
          "expand to a list of RGB images in PNG format. If unspecified, the "
          "CLIC dataset from TensorFlow Datasets is used.",
      )
      train_cmd.add_argument(
          "--num_filters",
          type=int,
          default=128,
          help="Number of filters per layer.",
      )
      train_cmd.add_argument(
          "--train_path",
          default="/tmp/train_bls2017",
          help="Path where to log training metrics for TensorBoard and back up "
          "intermediate model checkpoints.",
      )
      train_cmd.add_argument(
          "--batchsize",
          type=int,
          default=8,
          help="Batch size for training and validation.",
      )
      train_cmd.add_argument(
          "--patchsize",
          type=int,
          default=256,
          help="Size of image patches for training and validation.",
      )
      train_cmd.add_argument(
          "--epochs",
          type=int,
          default=1000,
          help="Train up to this number of epochs. (One epoch is here defined as "
          "the number of steps given by --steps_per_epoch, not iterations "
          "over the full training dataset.)",
      )
      train_cmd.add_argument(
          "--steps_per_epoch",
          type=int,
          default=1000,
          help="Perform validation and produce logs after this many batches.",
      )
      train_cmd.add_argument(
          "--max_validation_steps",
          type=int,
          default=16,
          help="Maximum number of batches to use for validation. If -1, use one "
          "patch from each image in the training set.",
      )
      train_cmd.add_argument(
          "--preprocess_threads",
          type=int,
          default=16,
          help="Number of CPU threads to use for parallel decoding of training "
          "images.",
      )
      train_cmd.add_argument(
          "--precision_policy",
          type=str,
          default=None,
          help="Policy for `tf.keras.mixed_precision` training.",
      )
      train_cmd.add_argument(
          "--check_numerics",
          action="store_true",
          help="Enable TF support for catching NaN and Inf in tensors.",
      )
  
      # 'compress' subcommand.
      compress_cmd = subparsers.add_parser(
          "compress",
          formatter_class=argparse.ArgumentDefaultsHelpFormatter,
          description="Reads a PNG file, compresses it, and writes a TFCI file.",
      )
  
      # 'decompress' subcommand.
      decompress_cmd = subparsers.add_parser(
          "decompress",
          formatter_class=argparse.ArgumentDefaultsHelpFormatter,
          description="Reads a TFCI file, reconstructs the image, and writes back "
          "a PNG file.",
      )
  
      # Arguments for both 'compress' and 'decompress'.
      for cmd, ext in (
          (compress_cmd, ".tfci"),
          (decompress_cmd, ".png"),
      ):
          cmd.add_argument("input_file", help="Input filename.")
          cmd.add_argument(
              "output_file",
              nargs="?",
              help=f"Output filename (optional). If not provided, appends '{ext}' to "
              f"the input filename.",
          )
  
      # Parse arguments.
      args = parser.parse_args(argv[1:])
      if args.command is None:
          parser.print_usage()
          sys.exit(2)
      return args
  
imports: import argparse
  import glob
  import sys
  from absl import app
  from absl.flags import argparse_flags
  import tensorflow
  import tensorflow_compression
  import tensorflow_datasets
label_desc: Use image node_id 10 for calling a global function with example usage: parse_args(argv) and returns: args