code: def train(
      args,
      accelerator,
      model,
      tokenizer,
      train_dataloader,
      optimizer,
      lr_scheduler,
      eval_dataloader=None,
  ):
      """Train a model on the given training data."""
  
      total_batch_size = (
          args.per_device_train_batch_size
          * accelerator.num_processes
          * args.gradient_accumulation_steps
      )
  
      logger.info("***** Running training *****")
      logger.info(
          "  Num examples = %d", args.num_examples[Split.TRAIN.value]
      )
      logger.info(
          "  Instantaneous batch size per device = %d",
          args.per_device_train_batch_size,
      )
      logger.info(
          "  Total train batch size (w. parallel, distributed & accumulation) = %d",
          total_batch_size,
      )
      logger.info(
          "  Gradient Accumulation steps = %d",
          args.gradient_accumulation_steps,
      )
      logger.info("  Total optimization steps = %d", args.max_steps)
  
      # Only show the progress bar once on each machine.
      progress_bar = tqdm(
          range(args.max_steps),
          disable=not accelerator.is_local_main_process,
      )
  
      checkpoints = None
      eval_results = None
      best_checkpoint = None
      best_eval_result = None
      early_stopping_patience_counter = 0
      should_training_stop = False
      epoch = 0
      completed_steps = 0
      train_loss = 0.0
      model.zero_grad()
  
      for _ in range(args.num_train_epochs):
          epoch += 1
          model.train()
          for step, batch in enumerate(train_dataloader):
              outputs = model(**batch)
              loss = outputs.loss
              loss = loss / args.gradient_accumulation_steps
              accelerator.backward(loss)
              train_loss += loss.item()
  
              if (
                  step % args.gradient_accumulation_steps == 0
                  or step == len(train_dataloader) - 1
              ):
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()
                  progress_bar.update(1)
                  completed_steps += 1
  
                  # Evaluate during training
                  if (
                      eval_dataloader is not None
                      and args.evaluation_strategy
                      == IntervalStrategy.STEPS.value
                      and args.eval_steps > 0
                      and completed_steps % args.eval_steps == 0
                  ):
                      accelerator.wait_for_everyone()
                      new_checkpoint = f"checkpoint-{IntervalStrategy.STEPS.value}-{completed_steps}"
                      new_eval_result = evaluate(
                          args,
                          accelerator,
                          eval_dataloader,
                          "eval",
                          model,
                          new_checkpoint,
                      )[args.eval_metric]
                      logger.info(
                          "Evaluation result at step %d: %s = %f",
                          completed_steps,
                          args.eval_metric,
                          new_eval_result,
                      )
                      if checkpoints is None:
                          checkpoints = np.array([new_checkpoint])
                          eval_results = np.array([new_eval_result])
                          best_checkpoint = new_checkpoint
                          best_eval_result = new_eval_result
                      else:
                          if (
                              new_eval_result - best_eval_result
                              > args.early_stopping_threshold
                          ):
                              best_checkpoint = new_checkpoint
                              best_eval_result = new_eval_result
                              early_stopping_patience_counter = 0
                          else:
                              if new_eval_result == best_eval_result:
                                  best_checkpoint = new_checkpoint
                                  best_eval_result = new_eval_result
                              early_stopping_patience_counter += 1
  
                          if (
                              early_stopping_patience_counter
                              >= args.early_stopping_patience
                          ):
                              should_training_stop = True
  
                          checkpoints = np.append(
                              checkpoints, [new_checkpoint], axis=0
                          )
                          eval_results = np.append(
                              eval_results, [new_eval_result], axis=0
                          )
                          sorted_ids = np.argsort(eval_results)
                          eval_results = eval_results[sorted_ids]
                          checkpoints = checkpoints[sorted_ids]
  
                      if len(checkpoints) > args.keep_checkpoint_max:
                          # Delete the current worst checkpoint
                          (
                              checkpoint_to_remove,
                              *checkpoints,
                          ) = checkpoints
                          eval_results = eval_results[1:]
                          if checkpoint_to_remove != new_checkpoint:
                              if accelerator.is_main_process:
                                  shutil.rmtree(
                                      os.path.join(
                                          args.output_dir,
                                          checkpoint_to_remove,
                                      ),
                                      ignore_errors=True,
                                  )
                              accelerator.wait_for_everyone()
  
                      if new_checkpoint in checkpoints:
                          # Save model checkpoint
                          checkpoint_output_dir = os.path.join(
                              args.output_dir, new_checkpoint
                          )
                          if accelerator.is_main_process:
                              if not os.path.exists(
                                  checkpoint_output_dir
                              ):
                                  os.makedirs(checkpoint_output_dir)
                          accelerator.wait_for_everyone()
                          unwrapped_model = accelerator.unwrap_model(
                              model
                          )
                          unwrapped_model.save_pretrained(
                              checkpoint_output_dir,
                              save_function=accelerator.save,
                          )
                          if accelerator.is_main_process:
                              tokenizer.save_pretrained(
                                  checkpoint_output_dir
                              )
                              logger.info(
                                  "Saving model checkpoint to %s",
                                  checkpoint_output_dir,
                              )
  
              if completed_steps >= args.max_steps:
                  break
  
              if should_training_stop:
                  break
  
          # Evaluate during training
          if (
              eval_dataloader is not None
              and args.evaluation_strategy
              == IntervalStrategy.EPOCH.value
          ):
              accelerator.wait_for_everyone()
              new_checkpoint = (
                  f"checkpoint-{IntervalStrategy.EPOCH.value}-{epoch}"
              )
              new_eval_result = evaluate(
                  args,
                  accelerator,
                  eval_dataloader,
                  "eval",
                  model,
                  new_checkpoint,
              )[args.eval_metric]
              logger.info(
                  "Evaluation result at epoch %d: %s = %f",
                  epoch,
                  args.eval_metric,
                  new_eval_result,
              )
  
              if checkpoints is None:
                  checkpoints = np.array([new_checkpoint])
                  eval_results = np.array([new_eval_result])
                  best_checkpoint = new_checkpoint
                  best_eval_result = new_eval_result
              else:
                  if (
                      new_eval_result - best_eval_result
                      > args.early_stopping_threshold
                  ):
                      best_checkpoint = new_checkpoint
                      best_eval_result = new_eval_result
                      early_stopping_patience_counter = 0
                  else:
                      if new_eval_result == best_eval_result:
                          best_checkpoint = new_checkpoint
                          best_eval_result = new_eval_result
                      early_stopping_patience_counter += 1
  
                  if (
                      early_stopping_patience_counter
                      >= args.early_stopping_patience
                  ):
                      should_training_stop = True
  
                  checkpoints = np.append(
                      checkpoints, [new_checkpoint], axis=0
                  )
                  eval_results = np.append(
                      eval_results, [new_eval_result], axis=0
                  )
                  sorted_ids = np.argsort(eval_results)
                  eval_results = eval_results[sorted_ids]
                  checkpoints = checkpoints[sorted_ids]
  
              if len(checkpoints) > args.keep_checkpoint_max:
                  # Delete the current worst checkpoint
                  checkpoint_to_remove, *checkpoints = checkpoints
                  eval_results = eval_results[1:]
                  if checkpoint_to_remove != new_checkpoint:
                      if accelerator.is_main_process:
                          shutil.rmtree(
                              os.path.join(
                                  args.output_dir, checkpoint_to_remove
                              ),
                              ignore_errors=True,
                          )
                      accelerator.wait_for_everyone()
  
              if new_checkpoint in checkpoints:
                  # Save model checkpoint
                  checkpoint_output_dir = os.path.join(
                      args.output_dir, new_checkpoint
                  )
                  if accelerator.is_main_process:
                      if not os.path.exists(checkpoint_output_dir):
                          os.makedirs(checkpoint_output_dir)
                  accelerator.wait_for_everyone()
                  unwrapped_model = accelerator.unwrap_model(model)
                  unwrapped_model.save_pretrained(
                      checkpoint_output_dir,
                      save_function=accelerator.save,
                  )
                  if accelerator.is_main_process:
                      tokenizer.save_pretrained(checkpoint_output_dir)
                      logger.info(
                          "Saving model checkpoint to %s",
                          checkpoint_output_dir,
                      )
  
          if completed_steps >= args.max_steps:
              break
  
          if should_training_stop:
              break
  
      if best_checkpoint is not None:
          # Save the best checkpoint
          logger.info("Best checkpoint: %s", best_checkpoint)
          logger.info(
              "Best evaluation result: %s = %f",
              args.eval_metric,
              best_eval_result,
          )
          best_checkpoint_output_dir = os.path.join(
              args.output_dir, best_checkpoint
          )
          if accelerator.is_main_process:
              shutil.move(
                  best_checkpoint_output_dir,
                  os.path.join(args.output_dir, "best-checkpoint"),
              )
              shutil.rmtree(
                  best_checkpoint_output_dir, ignore_errors=True
              )
          accelerator.wait_for_everyone()
  
      else:
          # Assume that the last checkpoint is the best checkpoint and save it
          checkpoint_output_dir = os.path.join(
              args.output_dir, "best-checkpoint"
          )
          if not os.path.exists(checkpoint_output_dir):
              os.makedirs(checkpoint_output_dir)
  
          accelerator.wait_for_everyone()
          unwrapped_model = accelerator.unwrap_model(model)
          unwrapped_model.save_pretrained(
              checkpoint_output_dir, save_function=accelerator.save
          )
          if accelerator.is_main_process:
              tokenizer.save_pretrained(checkpoint_output_dir)
              logger.info(
                  "Saving model checkpoint to %s", checkpoint_output_dir
              )
      return completed_steps, train_loss / completed_steps
  
imports: import argparse
  import dataclasses
  import json
  import logging
  import math
  import os
  import random
  import shutil
  from typing import List, Optional
  import datasets
  import numpy
  import pandas
  import torch
  from datasets import load_dataset, load_metric
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  from transformers import AdamW, AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, default_data_collator, get_scheduler, set_seed
  from transformers.file_utils import ExplicitEnum
  from transformers.trainer_utils import IntervalStrategy
label_desc: Use image node_id 1 for calling a global function with example usage: train(args, accelerator, model, tokenizer, train_dataloader, optimizer, lr_scheduler, eval_dataloader) and returns: completed_steps,  unknown