code: def main():
      parser = argparse.ArgumentParser()
      add_generic_args(parser, os.getcwd())
      parser = GLUETransformer.add_model_specific_args(
          parser, os.getcwd()
      )
      args = parser.parse_args()
  
      # If output_dir not provided, a folder will be generated in pwd
      if args.output_dir is None:
          args.output_dir = os.path.join(
              "./results",
              f"{args.task}_{time.strftime('%Y%m%d_%H%M%S')}",
          )
          os.makedirs(args.output_dir)
  
      model = GLUETransformer(args)
      trainer = generic_train(model, args)
  
      # Optionally, predict on dev set and write to output_dir
      if args.do_predict:
          checkpoints = list(
              sorted(
                  glob.glob(
                      os.path.join(
                          args.output_dir, "checkpoint-epoch=*.ckpt"
                      ),
                      recursive=True,
                  )
              )
          )
          model = model.load_from_checkpoint(checkpoints[-1])
          return trainer.test(model)
  
imports: import argparse
  import glob
  import logging
  import os
  import time
  from argparse import Namespace
  import numpy
  import torch
  from torch.utils.data import DataLoader, TensorDataset
  from lightning_base import BaseTransformer, add_generic_args, generic_train
  from transformers import glue_compute_metrics
  from transformers import glue_convert_examples_to_features
  from transformers import glue_output_modes
  from transformers import glue_processors
  from transformers import glue_tasks_num_labels
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: trainer