code: def add_subparser(
      self, parser: argparse._SubParsersAction
  ) -> argparse.ArgumentParser:
      description = (
          """Train the specified model on the specified dataset."""
      )
      subparser = parser.add_parser(
          self.name, description=description, help="Train a model."
      )
  
      subparser.add_argument(
          "param_path",
          type=str,
          help="path to parameter file describing the model to be trained",
      )
  
      subparser.add_argument(
          "-s",
          "--serialization-dir",
          required=True,
          type=str,
          help="directory in which to save the model and its logs",
      )
  
      subparser.add_argument(
          "-r",
          "--recover",
          action="store_true",
          default=False,
          help="recover training from the state in serialization_dir",
      )
  
      subparser.add_argument(
          "-f",
          "--force",
          action="store_true",
          required=False,
          help="overwrite the output directory if it exists",
      )
  
      subparser.add_argument(
          "-o",
          "--overrides",
          type=str,
          default="",
          help=(
              "a json(net) structure used to override the experiment configuration, e.g., "
              "'{\"iterator.batch_size\": 16}'.  Nested parameters can be specified either"
              " with nested dictionaries or with dot syntax."
          ),
      )
  
      subparser.add_argument(
          "--node-rank",
          type=int,
          default=0,
          help="rank of this node in the distributed setup",
      )
  
      subparser.add_argument(
          "--dry-run",
          action="store_true",
          help=(
              "do not train a model, but create a vocabulary, show dataset statistics and "
              "other training information"
          ),
      )
      subparser.add_argument(
          "--file-friendly-logging",
          action="store_true",
          default=False,
          help="outputs tqdm status on separate lines and slows tqdm refresh rate",
      )
  
      subparser.set_defaults(func=train_model_from_args)
  
      return subparser
  
imports: import argparse
  import logging
  import os
  from os import PathLike
  from typing import Any, Dict, List, Optional, Union
  import warnings
  import torch
  import torch.distributed
  import torch.multiprocessing
  from allennlp.commands.subcommand import Subcommand
  from allennlp.common import Params, Registrable, Lazy
  from allennlp.common.checks import check_for_gpu, ConfigurationError
  from allennlp.common.meta import Meta, META_NAME
  from allennlp.common import logging
  from allennlp.common import util
  from allennlp.common.plugins import import_plugins
  from allennlp.data import DatasetReader, Vocabulary
  from allennlp.data import DataLoader
  from allennlp.models.archival import archive_model, CONFIG_NAME, verify_include_in_archive
  from allennlp.models.model import Model
  from allennlp.nn.parallel import DdpAccelerator
  from allennlp.training.trainer import Trainer
  from allennlp.training import util
label_desc: Use image node_id 1 for calling the Train obj's underlying member method code with example usage: obj.add_subparser(parser) and returns: subparser