code: def _train_worker(
      process_rank: int,
      params: Params,
      serialization_dir: Union[str, PathLike],
      include_package: List[str] = None,
      dry_run: bool = False,
      node_rank: int = 0,
      primary_addr: str = "127.0.0.1",
      primary_port: int = 29500,
      world_size: int = 1,
      distributed_device_ids: List[int] = None,
      file_friendly_logging: bool = False,
      include_in_archive: List[str] = None,
      distributed_params: Optional[Params] = None,
  ) -> Optional[Model]:
      """
      Helper to train the configured model/experiment. In distributed mode, this is spawned as a
      worker process. In a single GPU experiment, this returns the `Model` object and in distributed
      training, nothing is returned.
  
      # Parameters
  
      process_rank : `int`
          The process index that is initialized using the GPU device id.
      params : `Params`
          A parameter object specifying an AllenNLP Experiment.
      serialization_dir : `str`
          The directory in which to save results and logs.
      include_package : `List[str]`, optional
          In distributed mode, since this function would have been spawned as a separate process,
          the extra imports need to be done again. NOTE: This does not have any effect in single
          GPU training.
      dry_run : `bool`, optional (default=`False`)
          Do not train a model, but create a vocabulary, show dataset statistics and other training
          information.
      node_rank : `int`, optional
          Rank of the node.
      primary_addr : `str`, optional (default=`"127.0.0.1"`)
          Address of the primary node for distributed training.
      primary_port : `str`, optional (default=`"29500"`)
          Port of the primary node for distributed training.
      world_size : `int`, optional
          The number of processes involved in distributed training.
      distributed_device_ids: `List[str]`, optional
          IDs of the devices used involved in distributed training.
      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.
      include_in_archive : `List[str]`, optional
          Paths relative to `serialization_dir` that should be archived in addition to the default ones.
      distributed_params : `Optional[Params]`, optional
          Additional distributed params.
  
      # Returns
  
      best_model : `Optional[Model]`
          The model with the best epoch weights or `None` if in distributed training or in dry run.
      """
      common_logging.FILE_FRIENDLY_LOGGING = file_friendly_logging
  
      common_logging.prepare_global_logging(
          serialization_dir,
          rank=process_rank,
          world_size=world_size,
      )
      common_util.prepare_environment(params)
  
      distributed = world_size > 1
  
      primary = process_rank == 0
  
      include_package = include_package or []
  
      ddp_accelerator: Optional[DdpAccelerator] = None
  
      if distributed:
          assert distributed_device_ids is not None
          assert distributed_params is not None
  
          # Since the worker is spawned and not forked, the extra imports need to be done again.
          # Both the ones from the plugins and the ones from `include_package`.
          import_plugins()
          for package_name in include_package:
              common_util.import_module_and_submodules(package_name)
  
          num_procs_per_node = len(distributed_device_ids)
          # The Unique identifier of the worker process among all the processes in the
          # distributed training group is computed here. This is used while initializing
          # the process group using `init_process_group`
          global_rank = node_rank * num_procs_per_node + process_rank
  
          # Number of processes per node is useful to know if a process
          # is a primary in the local node(node in which it is running)
          os.environ["ALLENNLP_PROCS_PER_NODE"] = str(
              num_procs_per_node
          )
  
          # In distributed training, the configured device is always going to be a list.
          # The corresponding gpu id for the particular worker is obtained by picking the id
          # from the device list with the rank as index
          gpu_id = int(distributed_device_ids[process_rank])  # type: ignore
  
          # Till now, "cuda_device" might not be set in the trainer params.
          # But a worker trainer needs to only know about its specific GPU id.
          params["trainer"]["local_rank"] = process_rank
          params["trainer"]["cuda_device"] = gpu_id
          params["trainer"]["world_size"] = world_size
          params["trainer"]["distributed"] = True
  
          if gpu_id >= 0:
              torch.cuda.set_device(gpu_id)
              dist.init_process_group(
                  backend="nccl",
                  init_method=f"tcp://{primary_addr}:{primary_port}",
                  world_size=world_size,
                  rank=global_rank,
              )
          else:
              dist.init_process_group(
                  backend="gloo",
                  init_method=f"tcp://{primary_addr}:{primary_port}",
                  world_size=world_size,
                  rank=global_rank,
              )
  
          if "ddp_accelerator" in distributed_params:
              ddp_accelerator_params = distributed_params.pop(
                  "ddp_accelerator"
              )
              ddp_accelerator = DdpAccelerator.from_params(
                  ddp_accelerator_params,
                  local_rank=process_rank,
                  world_size=world_size,
                  cuda_device=gpu_id,
              )
  
          logging.info(
              f"Process group of world size {world_size} initialized "
              f"for distributed training in worker {global_rank}"
          )
  
      train_loop = TrainModel.from_params(
          params=params,
          serialization_dir=serialization_dir,
          local_rank=process_rank,
          ddp_accelerator=ddp_accelerator,
      )
  
      if dry_run:
          return None
  
      try:
          if distributed:  # let the setup get ready for all the workers
              dist.barrier()
  
          metrics = train_loop.run()
      except (KeyboardInterrupt, common_util.SigTermReceived):
          # if we have completed an epoch, try to create a model archive.
          if primary:
              best_weights_path = (
                  train_loop.trainer.get_best_weights_path()
              )
              if best_weights_path is None:
                  logging.info(
                      "Training interrupted by the user, and no best model has been saved. "
                      "No model archive created."
                  )
              else:
                  logging.info(
                      "Training interrupted by the user. Attempting to create "
                      "a model archive using the current best epoch weights."
                  )
                  archive_model(
                      serialization_dir,
                      weights=best_weights_path,
                      include_in_archive=include_in_archive,
                  )
          raise
  
      if primary:
          train_loop.finish(metrics)
  
      if not distributed:
          return train_loop.model
  
      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 4 for calling a global function with example usage: _train_worker(process_rank, params, serialization_dir, include_package, dry_run, node_rank, primary_addr, primary_port, world_size, distributed_device_ids, file_friendly_logging, include_in_archive, distributed_params) and returns: None, None, train_loop