code: def add_train_test_metrics_parser(subparsers, formatter_class):
      subparser = subparsers.add_parser(
          "train-test-metrics",
          formatter_class=formatter_class,
          help="Compute NLU metrics training on a given dataset and testing on "
          "another",
      )
      subparser.add_argument(
          "train_dataset_path",
          type=str,
          help="Path to the dataset used for training",
      )
      subparser.add_argument(
          "test_dataset_path",
          type=str,
          help="Path to the dataset used for testing",
      )
      subparser.add_argument(
          "output_path",
          type=str,
          help="Destination path for the json metrics",
      )
      subparser.add_argument(
          "-c",
          "--config_path",
          type=str,
          help="Path to a NLU engine config file",
      )
      subparser.add_argument(
          "-s",
          "--exclude_slot_metrics",
          action="store_true",
          help="Fraction of the data that we want to use for "
          "training (between 0 and 1)",
      )
      subparser.add_argument(
          "-i",
          "--include_errors",
          action="store_true",
          help="Include parsing errors in the output",
      )
      subparser.add_argument(
          "-v",
          "--verbosity",
          action="count",
          default=0,
          help="Increase output verbosity",
      )
      subparser.set_defaults(func=_train_test_metrics)
      return subparser
  
file_path: snips-nlu/snips_nlu/cli/metrics.py
imports: from __future__ import print_function, unicode_literals
label_desc: Use image node_id 5 for calling a global function with example usage: add_train_test_metrics_parser(subparsers, formatter_class) and returns: subparser