code: def do_training(args):
      prepare_output_path(args.output_dir, args.overwrite_output_dir)
      device, n_gpus = setup_backend(args.no_cuda)
      # Set seed
      args.seed = set_seed(args.seed, n_gpus)
      # Prepare GLUE task
      args.task_name = args.task_name.lower()
      task = get_glue_task(args.task_name, data_dir=args.data_dir)
      classifier = TransformerSequenceClassifier(
          model_type=args.model_type,
          model_name_or_path=args.model_name_or_path,
          labels=task.get_labels(),
          task_type=task.task_type,
          metric_fn=get_metric_fn(task.name),
          config_name=args.config_name,
          tokenizer_name=args.tokenizer_name,
          do_lower_case=args.do_lower_case,
          output_path=args.output_dir,
          device=device,
          n_gpus=n_gpus,
      )
  
      train_batch_size = args.per_gpu_train_batch_size * max(1, n_gpus)
  
      train_ex = task.get_train_examples()
      dev_ex = task.get_dev_examples()
      train_dataset = classifier.convert_to_tensors(
          train_ex, args.max_seq_length
      )
      dev_dataset = classifier.convert_to_tensors(
          dev_ex, args.max_seq_length
      )
      train_sampler = RandomSampler(train_dataset)
      dev_sampler = SequentialSampler(dev_dataset)
      train_dl = DataLoader(
          train_dataset,
          sampler=train_sampler,
          batch_size=train_batch_size,
      )
      dev_dl = DataLoader(
          dev_dataset,
          sampler=dev_sampler,
          batch_size=args.per_gpu_eval_batch_size,
      )
  
      total_steps, _ = classifier.get_train_steps_epochs(
          args.max_steps,
          args.num_train_epochs,
          args.per_gpu_train_batch_size,
          len(train_dataset),
      )
      classifier.setup_default_optimizer(
          weight_decay=args.weight_decay,
          learning_rate=args.learning_rate,
          adam_epsilon=args.adam_epsilon,
          warmup_steps=args.warmup_steps,
          total_steps=total_steps,
      )
      classifier.train(
          train_dl,
          dev_dl,
          None,
          gradient_accumulation_steps=args.gradient_accumulation_steps,
          per_gpu_train_batch_size=args.per_gpu_train_batch_size,
          max_steps=args.max_steps,
          num_train_epochs=args.num_train_epochs,
          max_grad_norm=args.max_grad_norm,
          logging_steps=args.logging_steps,
          save_steps=args.save_steps,
      )
      classifier.save_model(args.output_dir, args=args)
  
imports: import argparse
  import io
  import logging
  import os
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
  from nlp_architect.data.glue_tasks import get_glue_task, get_metric_fn, processors
  from nlp_architect.models.transformers import TransformerSequenceClassifier
  from nlp_architect.nn.torch import set_seed, setup_backend
  from nlp_architect.procedures.procedure import Procedure
  from nlp_architect.procedures.registry import register_inference_cmd, register_train_cmd
  from nlp_architect.procedures.transformers.base import create_base_args, inference_args, train_args
  from nlp_architect.utils.io import prepare_output_path
label_desc: Use image node_id 3 for calling a global function with example usage: do_training(args) without return types