code: def train(config):
      """
      Train a Nougat model using the provided configuration.
  
      Args:
          `config` (dict): A dictionary containing configuration settings for training.
      """
      pl.seed_everything(config.get("seed", 42), workers=True)
  
      model_module = NougatModelPLModule(config)
      data_module = NougatDataPLModule(config)
  
      # add datasets to data_module
      datasets = {"train": [], "validation": []}
      for i, dataset_path in enumerate(config.dataset_paths):
          for split in ["train", "validation"]:
              datasets[split].append(
                  NougatDataset(
                      dataset_path=dataset_path,
                      nougat_model=model_module.model,
                      max_length=config.max_length,
                      split=split,
                  )
              )
      data_module.train_datasets = datasets["train"]
      data_module.val_datasets = datasets["validation"]
  
      lr_callback = LearningRateMonitor(logging_interval="step")
  
      checkpoint_callback = ModelCheckpoint(
          save_last=True,
          dirpath=Path(config.result_path)
          / config.exp_name
          / config.exp_version,
      )
      grad_norm_callback = GradNormCallback()
      custom_ckpt = CustomCheckpointIO()
  
      if not config.debug:
          logger = Logger(
              config.exp_name, project="Nougat", config=dict(config)
          )
      else:
          logger = TensorBoardLogger(
              save_dir=config.result_path,
              name=config.exp_name,
              version=config.exp_version,
              default_hp_metric=False,
          )
      trainer = pl.Trainer(
          num_nodes=config.get("num_nodes", 1),
          devices="auto",
          strategy="ddp_find_unused_parameters_true",
          accelerator="auto",
          # plugins=[SLURMEnvironment(auto_requeue=False)],
          max_epochs=config.max_epochs,
          max_steps=config.max_steps,
          val_check_interval=config.val_check_interval,
          check_val_every_n_epoch=config.check_val_every_n_epoch,
          limit_val_batches=config.val_batches,
          gradient_clip_val=config.gradient_clip_val,
          log_every_n_steps=15,
          precision="bf16-mixed",
          num_sanity_val_steps=0,
          logger=logger,
          callbacks=[
              lr_callback,
              grad_norm_callback,
              checkpoint_callback,
              GradientAccumulationScheduler(
                  {0: config.accumulate_grad_batches}
              ),
          ],
      )
  
      trainer.fit(
          model_module,
          data_module,
          ckpt_path=config.get("resume_from_checkpoint_path", None),
      )
  
imports: import argparse
  import datetime
  import os
  from os.path import basename
  from pathlib import Path
  import lightning.pytorch
  import torch
  from lightning.pytorch.callbacks import LearningRateMonitor, ModelCheckpoint, Callback, GradientAccumulationScheduler
  from lightning.pytorch.loggers.tensorboard import TensorBoardLogger
  from lightning.pytorch.plugins import CheckpointIO
  from lightning.pytorch.plugins.environments import SLURMEnvironment
  from lightning.pytorch.utilities import rank_zero_only
  from sconf import Config
  from nougat import NougatDataset
  from lightning_module import NougatDataPLModule, NougatModelPLModule
  import logging
label_desc: Use image node_id 2 for calling a global function with example usage: train(config) without return types