docstr: Register this command to argparse so it's available for the transformer-cli
  
  Args:
      parser: Root parser to register command-specific arguments
docstr_tok: ["Register","this","command","to","argparse","so","it","'s","available","for","the","transformer-cli","Args",":","parser",":","Root","parser","to","register","command-specific","arguments"]
code: def register_subcommand(parser: ArgumentParser):
      """
      Register this command to argparse so it's available for the transformer-cli
  
      Args:
          parser: Root parser to register command-specific arguments
      """
      train_parser = parser.add_parser(
          "train", help="CLI tool to train a model on a task."
      )
  
      train_parser.add_argument(
          "--train_data",
          type=str,
          required=True,
          help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.",
      )
      train_parser.add_argument(
          "--column_label",
          type=int,
          default=0,
          help="Column of the dataset csv file with example labels.",
      )
      train_parser.add_argument(
          "--column_text",
          type=int,
          default=1,
          help="Column of the dataset csv file with example texts.",
      )
      train_parser.add_argument(
          "--column_id",
          type=int,
          default=2,
          help="Column of the dataset csv file with example ids.",
      )
      train_parser.add_argument(
          "--skip_first_row",
          action="store_true",
          help="Skip the first row of the csv file (headers).",
      )
  
      train_parser.add_argument(
          "--validation_data",
          type=str,
          default="",
          help="path to validation dataset.",
      )
      train_parser.add_argument(
          "--validation_split",
          type=float,
          default=0.1,
          help="if validation dataset is not provided, fraction of train dataset to use as validation dataset.",
      )
  
      train_parser.add_argument(
          "--output",
          type=str,
          default="./",
          help="path to saved the trained model.",
      )
  
      train_parser.add_argument(
          "--task",
          type=str,
          default="text_classification",
          help="Task to train the model on.",
      )
      train_parser.add_argument(
          "--model",
          type=str,
          default="bert-base-uncased",
          help="Model's name or path to stored model.",
      )
      train_parser.add_argument(
          "--train_batch_size",
          type=int,
          default=32,
          help="Batch size for training.",
      )
      train_parser.add_argument(
          "--valid_batch_size",
          type=int,
          default=64,
          help="Batch size for validation.",
      )
      train_parser.add_argument(
          "--learning_rate",
          type=float,
          default=3e-5,
          help="Learning rate.",
      )
      train_parser.add_argument(
          "--adam_epsilon",
          type=float,
          default=1e-08,
          help="Epsilon for Adam optimizer.",
      )
      train_parser.set_defaults(func=train_command_factory)
  
file_path: transformers/src/transformers/commands/train.py
imports: import os
  from argparse import ArgumentParser, Namespace
  from ..data import SingleSentenceClassificationProcessor
  from ..pipelines import TextClassificationPipeline
  from ..utils import is_tf_available, is_torch_available, logging
  from .None import BaseTransformersCLICommand
label_desc: Use image node_id 1 for calling the TrainCommand obj's underlying member method code with example usage: obj.register_subcommand(parser) without return types