code: def train_model(
      config: PyTextConfig,
      dist_init_url: str = None,
      device_id: int = 0,
      rank: int = 0,
      world_size: int = 1,
      metric_channels: Optional[List[Channel]] = None,
      metadata: CommonMetadata = None,
  ) -> Tuple:
      task, training_state = prepare_task(
          config,
          dist_init_url,
          device_id,
          rank,
          world_size,
          metric_channels,
          metadata,
      )
      trained_model, best_metric = task.train(
          config, rank, world_size, training_state
      )
      # Only rank 0 gets to finalize the job and export the model
      if rank == 0:
          save_and_export(config, task, metric_channels)
      print("Training timings")
      timing.report()
      return trained_model, best_metric
  
imports: import gzip
  import json
  import os
  import sys
  import traceback
  from typing import Any, Dict, get_type_hints, IO, Iterator, List, Optional, Tuple, Union
  import torch
  from pytext.common.constants import Stage
  from pytext.config import ExportConfig, PyTextConfig, TestConfig
  from pytext.config.component import ComponentType, create_component, create_exporter
  from pytext.data.data import Batcher
  from pytext.data.data_handler import CommonMetadata
  from pytext.metric_reporters.channel import Channel
  from pytext.task import create_task, get_latest_checkpoint_path, load, NewTask, save, Task_Deprecated
  from pytext.task.disjoint_multitask import NewDisjointMultitask
  from pytext.trainers import TrainingState
  from pytext.utils import cuda, distributed, precision, round_seq, set_random_seeds, timing
  from pytext.utils.file_io import PathManager
label_desc: Use image node_id 5 for calling a global function with example usage: train_model(config, dist_init_url, device_id, rank, world_size, metric_channels, metadata) and returns: trained_model,  best_metric