code: def train_model_from_file(
      parameter_filename: Union[str, PathLike],
      serialization_dir: Union[str, PathLike],
      overrides: Union[str, Dict[str, Any]] = "",
      recover: bool = False,
      force: bool = False,
      node_rank: int = 0,
      include_package: List[str] = None,
      dry_run: bool = False,
      file_friendly_logging: bool = False,
      return_model: Optional[bool] = None,
  ) -> Optional[Model]:
      """
      A wrapper around [`train_model`](#train_model) which loads the params from a file.
  
      # Parameters
  
      parameter_filename : `str`
          A json parameter file specifying an AllenNLP experiment.
      serialization_dir : `str`
          The directory in which to save results and logs. We just pass this along to
          [`train_model`](#train_model).
      overrides : `Union[str, Dict[str, Any]]`, optional (default = `""`)
          A JSON string or a dict that we will use to override values in the input parameter file.
      recover : `bool`, optional (default=`False`)
          If `True`, we will try to recover a training run from an existing serialization
          directory.  This is only intended for use when something actually crashed during the middle
          of a run.  For continuing training a model on new data, see `Model.from_archive`.
      force : `bool`, optional (default=`False`)
          If `True`, we will overwrite the serialization directory if it already exists.
      node_rank : `int`, optional
          Rank of the current node in distributed training
      include_package : `str`, optional
          In distributed mode, extra packages mentioned will be imported in trainer workers.
      dry_run : `bool`, optional (default=`False`)
          Do not train a model, but create a vocabulary, show dataset statistics and other training
          information.
      file_friendly_logging : `bool`, optional (default=`False`)
          If `True`, we add newlines to tqdm output, even on an interactive terminal, and we slow
          down tqdm's output to only once every 10 seconds.
      return_model : `Optional[bool]`, optional (default = `None`)
          Whether or not to return the final model. If not specified, this defaults to `False` for
          distributed training and `True` otherwise.
  
      # Returns
  
      best_model : `Optional[str]`
          The path to the archived model with the best weights or `None` if in dry run.
      best_model : `Optional[Model]`
          The model with the best epoch weights or `None`, depending on the value of `return_model` and `dry_run`.
      """
      # Load the experiment config from a file and pass it to `train_model`.
      params = Params.from_file(parameter_filename, overrides)
      return train_model(
          params=params,
          serialization_dir=serialization_dir,
          recover=recover,
          force=force,
          node_rank=node_rank,
          include_package=include_package,
          dry_run=dry_run,
          file_friendly_logging=file_friendly_logging,
          return_model=return_model,
      )
  
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 2 for calling a global function with example usage: train_model_from_file(parameter_filename, serialization_dir, overrides, recover, force, node_rank, include_package, dry_run, file_friendly_logging, return_model) and returns: train_model