code: def train_model(
      params: Params,
      serialization_dir: Union[str, PathLike],
      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]:
      """
      Trains the model specified in the given [`Params`](../common/params.md#params) object, using the data
      and training parameters also specified in that object, and saves the results in `serialization_dir`.
  
      # Parameters
  
      params : `Params`
          A parameter object specifying an AllenNLP Experiment.
      serialization_dir : `str`
          The directory in which to save results and logs.
      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 : `List[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[Model]`
          The model with the best epoch weights or `None`, depending on the value of `return_model` and `dry_run`.
      """
      common_logging.FILE_FRIENDLY_LOGGING = file_friendly_logging
  
      training_util.create_serialization_dir(
          params, serialization_dir, recover, force
      )
      params.to_file(os.path.join(serialization_dir, CONFIG_NAME))
  
      # Change Author: Gabe Orlanski
      # Placeholder for the time being to make sure no errors are raised b/c of
      # the evaluator.
      params.pop("evaluation", None)
  
      meta = Meta.new()
      meta.to_file(os.path.join(serialization_dir, META_NAME))
  
      include_in_archive = params.pop("include_in_archive", None)
      verify_include_in_archive(include_in_archive)
  
      model: Optional[Model] = None
  
      distributed_params = params.params.pop("distributed", None)
      # If distributed isn't in the config and the config contains strictly
      # one cuda device, we just run a single training process.
      if distributed_params is None:
          model = _train_worker(
              process_rank=0,
              params=params,
              serialization_dir=serialization_dir,
              include_package=include_package,
              dry_run=dry_run,
              file_friendly_logging=file_friendly_logging,
          )
      # Otherwise, we are running multiple processes for training.
      else:
          common_logging.prepare_global_logging(
              serialization_dir,
              rank=0,
              world_size=1,
          )
  
          # We are careful here so that we can raise a good error if someone
          # passed the wrong thing - cuda_devices are required.
          device_ids = distributed_params.pop("cuda_devices", None)
          multi_device = (
              isinstance(device_ids, list) and len(device_ids) > 1
          )
          num_nodes = distributed_params.pop("num_nodes", 1)
  
          if not (multi_device or num_nodes > 1):
              raise ConfigurationError(
                  "Multiple cuda devices/nodes need to be configured to run distributed training."
              )
          check_for_gpu(device_ids)
  
          primary_addr = distributed_params.pop(
              "primary_address", "127.0.0.1"
          )
          if primary_addr in ("127.0.0.1", "0.0.0.0", "localhost"):
              # If running locally, we can automatically find an open port if one is not specified.
              primary_port = (
                  distributed_params.pop("primary_port", None)
                  or common_util.find_open_port()
              )
          else:
              # Otherwise we require that the port be specified.
              primary_port = distributed_params.pop("primary_port")
  
          num_procs = len(device_ids)
          world_size = num_nodes * num_procs
  
          # Creating `Vocabulary` objects from workers could be problematic since
          # the data loaders in each worker will yield only `rank` specific
          # instances. Hence it is safe to construct the vocabulary and write it
          # to disk before initializing the distributed context. The workers will
          # load the vocabulary from the path specified.
          vocab_dir = os.path.join(serialization_dir, "vocabulary")
          if recover:
              vocab = Vocabulary.from_files(vocab_dir)
          else:
              vocab = training_util.make_vocab_from_params(
                  params.duplicate(),
                  serialization_dir,
                  print_statistics=dry_run,
              )
          params["vocabulary"] = {
              "type": "from_files",
              "directory": vocab_dir,
              "padding_token": vocab._padding_token,
              "oov_token": vocab._oov_token,
          }
  
          logging.info(
              "Switching to distributed training mode since multiple GPUs are configured | "
              f"Primary is at: {primary_addr}:{primary_port} | Rank of this node: {node_rank} | "
              f"Number of workers in this node: {num_procs} | Number of nodes: {num_nodes} | "
              f"World size: {world_size}"
          )
  
          mp.spawn(
              _train_worker,
              args=(
                  params.duplicate(),
                  serialization_dir,
                  include_package,
                  dry_run,
                  node_rank,
                  primary_addr,
                  primary_port,
                  world_size,
                  device_ids,
                  file_friendly_logging,
                  include_in_archive,
                  Params(distributed_params),
              ),
              nprocs=num_procs,
          )
  
      if not dry_run:
          archive_model(
              serialization_dir, include_in_archive=include_in_archive
          )
      else:
          return None
  
      if return_model is None:
          return model  # model may or may not be `None`.
      elif return_model is True:
          return (
              model
              if model is not None
              else Model.load(params, serialization_dir)
          )
      else:
          return None
  
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 3 for calling a global function with example usage: train_model(params, serialization_dir, recover, force, node_rank, include_package, dry_run, file_friendly_logging, return_model) and returns: None, model, unknown, None