code: def finetune(
      accelerator, model_name_or_path, train_file, output_dir, **kwargs
  ):
      """Fine-tuning a pre-trained model on a downstream task.
  
      Args:
        accelerator: An instance of an accelerator for distributed training (on
          multi-GPU, TPU) or mixed precision training.
        model_name_or_path: Path to pretrained model or model identifier from
          huggingface.co/models.
        train_file: A csv or a json file containing the training data.
        output_dir: The output directory where the model predictions and checkpoints
          will be written.
        **kwargs: Dictionary of key/value pairs with which to update the
          configuration object after loading. The values in kwargs of any keys which
          are configuration attributes will be used to override the loaded values.
      """
      # Make one log on every process with the configuration for debugging.
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO,
      )
      logger.info(accelerator.state)
  
      # Setup logging, we only want one process per machine to log things on the
      # screen. accelerator.is_local_main_process is only True for one process per
      # machine.
      logger.setLevel(
          logging.INFO
          if accelerator.is_local_main_process
          else logging.ERROR
      )
  
      model_args = FTModelArguments(
          model_name_or_path=model_name_or_path
      )
      data_args = FTDataArguments(train_file=train_file)
      training_args = FTTrainingArguments(output_dir=output_dir)
      args = argparse.Namespace()
  
      for arg_class in (model_args, data_args, training_args):
          for key, value in vars(arg_class).items():
              setattr(args, key, value)
  
      for key, value in kwargs.items():
          if hasattr(args, key):
              setattr(args, key, value)
  
      # Sanity checks
      data_files = {}
      args.data_file_extension = None
  
      # You need to provide the training data as we always run training
      args.do_train = True
      assert args.train_file is not None
      data_files[Split.TRAIN.value] = args.train_file
  
      if (
          args.do_eval
          or args.evaluation_strategy != IntervalStrategy.NO.value
      ):
          assert args.eval_file is not None
          data_files[Split.EVAL.value] = args.eval_file
  
      if args.do_eval and args.test_file is not None:
          data_files[Split.TEST.value] = args.test_file
  
      if args.do_predict:
          assert args.infer_file is not None
          data_files[Split.INFER.value] = args.infer_file
  
      for key in data_files:
          extension = data_files[key].split(".")[-1]
          assert extension in [
              "csv",
              "json",
          ], f"`{key}_file` should be a csv or a json file."
          if args.data_file_extension is None:
              args.data_file_extension = extension
          else:
              assert (
                  extension == args.data_file_extension
              ), f"`{key}_file` should be a {args.data_file_extension} file`."
  
      assert (
          args.eval_metric in datasets.list_metrics()
      ), f"{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."
  
      # Handle the output directory creation
      if accelerator.is_main_process:
          if args.output_dir is not None:
              os.makedirs(args.output_dir, exist_ok=True)
      accelerator.wait_for_everyone()
  
      # If passed along, set the training seed now.
      if args.seed is not None:
          set_seed(args.seed)
  
      # You need to provide your CSV/JSON data files.
      #
      # For CSV/JSON files, this script will use as labels the column called 'label'
      # and as pair of sentences the sentences in columns called 'sentence1' and
      # 'sentence2' if these columns exist or the first two columns not named
      # 'label' if at least two columns are provided.
      #
      # If the CSVs/JSONs contain only one non-label column, the script does single
      # sentence classification on this single column.
      #
      # In distributed training, the load_dataset function guarantees that only one
      # local process can download the dataset.
  
      # Loading the dataset from local csv or json files.
      raw_datasets = load_dataset(
          args.data_file_extension, data_files=data_files
      )
  
      # Labels
      is_regression = raw_datasets[Split.TRAIN.value].features[
          "label"
      ].dtype in ["float32", "float64"]
      args.is_regression = is_regression
  
      if args.is_regression:
          label_list = None
          num_labels = 1
      else:
          label_list = args.label_list
          assert label_list is not None
          label_list.sort()  # Let's sort it for determinism
          num_labels = len(label_list)
      args.num_labels = num_labels
  
      # Load pre-trained model
      config, tokenizer, model = load_from_pretrained(
          args, args.model_name_or_path
      )
  
      # Preprocessing the datasets
      non_label_column_names = [
          name
          for name in raw_datasets[Split.TRAIN.value].column_names
          if name != "label"
      ]
      if (
          "sentence1" in non_label_column_names
          and "sentence2" in non_label_column_names
      ):
          sentence1_key, sentence2_key = "sentence1", "sentence2"
      else:
          if len(non_label_column_names) >= 2:
              sentence1_key, sentence2_key = non_label_column_names[:2]
          else:
              sentence1_key, sentence2_key = (
                  non_label_column_names[0],
                  None,
              )
  
      label_to_id = {v: i for i, v in enumerate(label_list)}
      config.label2id = label_to_id
      config.id2label = {
          id: label for label, id in config.label2id.items()
      }
      padding = "max_length" if args.pad_to_max_length else False
  
      def preprocess_function(examples):
          # Tokenize the texts
          texts = (
              (examples[sentence1_key],)
              if sentence2_key is None
              else (examples[sentence1_key], examples[sentence2_key])
          )
          result = tokenizer(
              *texts,
              padding=padding,
              max_length=args.max_length,
              truncation=True,
          )
  
          if "label" in examples:
              if label_to_id is not None:
                  # Map labels to IDs (not necessary for GLUE tasks)
                  result["labels"] = [
                      label_to_id[l] for l in examples["label"]
                  ]
              else:
                  # In all cases, rename the column to labels because the model will
                  # expect that.
                  result["labels"] = examples["label"]
          return result
  
      with accelerator.main_process_first():
          processed_datasets = raw_datasets.map(
              preprocess_function,
              batched=True,
              remove_columns=raw_datasets[
                  Split.TRAIN.value
              ].column_names,
              desc="Running tokenizer on dataset",
          )
  
      num_examples = {}
      splits = [s.value for s in Split]
      for split in splits:
          if split in processed_datasets:
              num_examples[split] = len(processed_datasets[split])
      args.num_examples = num_examples
  
      train_dataset = processed_datasets[Split.TRAIN.value]
      eval_dataset = (
          processed_datasets[Split.EVAL.value]
          if Split.EVAL.value in processed_datasets
          else None
      )
      test_dataset = (
          processed_datasets[Split.TEST.value]
          if Split.TEST.value in processed_datasets
          else None
      )
      infer_dataset = (
          processed_datasets[Split.INFER.value]
          if Split.INFER.value in processed_datasets
          else None
      )
  
      # Log a few random samples from the training set:
      for index in random.sample(range(len(train_dataset)), 3):
          logger.info(
              "Sample %d of the training set: %s.",
              index,
              train_dataset[index],
          )
  
      # DataLoaders creation:
      if args.pad_to_max_length:
          # If padding was already done ot max length, we use the default data
          # collator that will just convert everything to tensors.
          data_collator = default_data_collator
      else:
          # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by
          # padding to the maximum length of the samples passed). When using mixed
          # precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple of
          # 8s, which will enable the use of Tensor Cores on NVIDIA hardware with
          # compute capability >= 7.5 (Volta).
          data_collator = DataCollatorWithPadding(
              tokenizer,
              pad_to_multiple_of=(8 if accelerator.use_fp16 else None),
          )
  
      train_dataloader = DataLoader(
          train_dataset,
          batch_size=args.per_device_train_batch_size,
          shuffle=True,
          collate_fn=data_collator,
      )
      eval_dataloader, test_dataloader, infer_dataloader = (
          None,
          None,
          None,
      )
  
      if eval_dataset is not None:
          eval_dataloader = DataLoader(
              eval_dataset,
              batch_size=args.per_device_eval_batch_size,
              collate_fn=data_collator,
          )
  
      if test_dataset is not None:
          test_dataloader = DataLoader(
              test_dataset,
              batch_size=args.per_device_eval_batch_size,
              collate_fn=data_collator,
          )
  
      if infer_dataset is not None:
          infer_dataloader = DataLoader(
              infer_dataset,
              batch_size=args.per_device_eval_batch_size,
              collate_fn=data_collator,
          )
  
      # Optimizer
      # Split weights in two groups, one with weight decay and the other not.
      no_decay = ["bias", "LayerNorm.weight"]
      optimizer_grouped_parameters = [
          {
              "params": [
                  p
                  for n, p in model.named_parameters()
                  if not any(nd in n for nd in no_decay)
              ],
              "weight_decay": args.weight_decay,
          },
          {
              "params": [
                  p
                  for n, p in model.named_parameters()
                  if any(nd in n for nd in no_decay)
              ],
              "weight_decay": 0.0,
          },
      ]
      optimizer = AdamW(
          optimizer_grouped_parameters, lr=args.learning_rate
      )
  
      # Prepare everything with our `accelerator`.
      (
          model,
          optimizer,
          train_dataloader,
          eval_dataloader,
          test_dataloader,
          infer_dataloader,
      ) = accelerator.prepare(
          model,
          optimizer,
          train_dataloader,
          eval_dataloader,
          test_dataloader,
          infer_dataloader,
      )
  
      # Note -> the training dataloader needs to be prepared before we grab its
      # length below (cause its length will be shorter in multiprocess)
  
      # Scheduler and math around the number of training steps.
      num_update_steps_per_epoch = math.ceil(
          len(train_dataloader) / args.gradient_accumulation_steps
      )
      if args.max_steps == -1:
          args.max_steps = (
              args.num_train_epochs * num_update_steps_per_epoch
          )
      else:
          args.num_train_epochs = math.ceil(
              args.max_steps / num_update_steps_per_epoch
          )
  
      lr_scheduler = get_scheduler(
          name=args.lr_scheduler_type,
          optimizer=optimizer,
          num_warmup_steps=args.warmup_steps,
          num_training_steps=args.max_steps,
      )
  
      # Train
      completed_steps, avg_train_loss = train(
          args,
          accelerator,
          model,
          tokenizer,
          train_dataloader,
          optimizer,
          lr_scheduler,
          eval_dataloader,
      )
      accelerator.wait_for_everyone()
      logger.info(
          "Training job completed: completed_steps = %d, avg_train_loss = %f",
          completed_steps,
          avg_train_loss,
      )
  
      args.model_name_or_path = os.path.join(
          args.output_dir, "best-checkpoint"
      )
      logger.info(
          "Loading the best checkpoint: %s", args.model_name_or_path
      )
      config, tokenizer, model = load_from_pretrained(
          args, args.model_name_or_path
      )
      model = accelerator.prepare(model)
  
      if args.do_eval:
          # Evaluate
          if eval_dataloader is not None:
              logger.info(
                  "***** Running evaluation on the eval data using the best checkpoint *****"
              )
              eval_results = evaluate(
                  args,
                  accelerator,
                  eval_dataloader,
                  Split.EVAL.value,
                  model,
                  "best-checkpoint",
              )
              avg_eval_loss = eval_results["avg_eval_loss"]
              eval_metric = eval_results[args.eval_metric]
              logger.info(
                  "Evaluation job completed: avg_eval_loss = %f",
                  avg_eval_loss,
              )
              logger.info(
                  "Evaluation result for the best checkpoint: %s = %f",
                  args.eval_metric,
                  eval_metric,
              )
  
          if test_dataloader is not None:
              logger.info(
                  "***** Running evaluation on the test data using the best checkpoint *****"
              )
              eval_results = evaluate(
                  args,
                  accelerator,
                  test_dataloader,
                  Split.TEST.value,
                  model,
                  "best-checkpoint",
              )
              avg_eval_loss = eval_results["avg_eval_loss"]
              eval_metric = eval_results[args.eval_metric]
              logger.info(
                  "Test job completed: avg_test_loss = %f",
                  avg_eval_loss,
              )
              logger.info(
                  "Test result for the best checkpoint: %s = %f",
                  args.eval_metric,
                  eval_metric,
              )
  
      if args.do_predict:
          # Predict
          if infer_dataloader is not None:
              logger.info(
                  "***** Running inference using the best checkpoint *****"
              )
              evaluate(
                  args,
                  accelerator,
                  infer_dataloader,
                  Split.INFER.value,
                  model,
                  "best-checkpoint",
                  has_labels=False,
              )
              logger.info("Inference job completed.")
  
      # Release all references to the internal objects stored and call the garbage
      # collector. You should call this method between two trainings with different
      # models/optimizers.
      accelerator.free_memory()
  
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 4 for calling a global function with example usage: finetune(accelerator, model_name_or_path, train_file, output_dir) and returns: result