code: def main():
      parser = argparse.ArgumentParser()
  
      # Required parameters
      parser.add_argument(
          "--data_dir",
          default=None,
          type=str,
          required=True,
          help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
      )
      parser.add_argument(
          "--model_type",
          default=None,
          type=str,
          required=True,
          help="Model type selected in the list: "
          + ", ".join(MODEL_CLASSES.keys()),
      )
      parser.add_argument(
          "--model_name_or_path",
          default=None,
          type=str,
          required=True,
          help="Path to pre-trained model or shortcut name.",
      )
      parser.add_argument(
          "--task_name",
          default=None,
          type=str,
          required=True,
          help="The name of the task to train selected in the list: "
          + ", ".join(processors.keys()),
      )
      parser.add_argument(
          "--output_dir",
          default=None,
          type=str,
          required=True,
          help="The output directory where the model predictions and checkpoints will be written.",
      )
      parser.add_argument(
          "--plot_data_dir",
          default="./plotting/",
          type=str,
          required=False,
          help="The directory to store data for plotting figures.",
      )
  
      # Other parameters
      parser.add_argument(
          "--config_name",
          default="",
          type=str,
          help="Pretrained config name or path if not the same as model_name",
      )
      parser.add_argument(
          "--tokenizer_name",
          default="",
          type=str,
          help="Pretrained tokenizer name or path if not the same as model_name",
      )
      parser.add_argument(
          "--cache_dir",
          default="",
          type=str,
          help="Where do you want to store the pre-trained models downloaded from huggingface.co",
      )
      parser.add_argument(
          "--max_seq_length",
          default=128,
          type=int,
          help=(
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          ),
      )
      parser.add_argument(
          "--do_train",
          action="store_true",
          help="Whether to run training.",
      )
      parser.add_argument(
          "--do_eval",
          action="store_true",
          help="Whether to run eval on the dev set.",
      )
      parser.add_argument(
          "--evaluate_during_training",
          action="store_true",
          help="Rul evaluation during training at each logging step.",
      )
      parser.add_argument(
          "--do_lower_case",
          action="store_true",
          help="Set this flag if you are using an uncased model.",
      )
      parser.add_argument(
          "--eval_each_highway",
          action="store_true",
          help="Set this flag to evaluate each highway.",
      )
      parser.add_argument(
          "--eval_after_first_stage",
          action="store_true",
          help="Set this flag to evaluate after training only bert (not highway).",
      )
      parser.add_argument(
          "--eval_highway",
          action="store_true",
          help="Set this flag if it's evaluating highway models",
      )
  
      parser.add_argument(
          "--per_gpu_train_batch_size",
          default=8,
          type=int,
          help="Batch size per GPU/CPU for training.",
      )
      parser.add_argument(
          "--per_gpu_eval_batch_size",
          default=8,
          type=int,
          help="Batch size per GPU/CPU for evaluation.",
      )
      parser.add_argument(
          "--gradient_accumulation_steps",
          type=int,
          default=1,
          help="Number of updates steps to accumulate before performing a backward/update pass.",
      )
      parser.add_argument(
          "--learning_rate",
          default=5e-5,
          type=float,
          help="The initial learning rate for Adam.",
      )
      parser.add_argument(
          "--weight_decay",
          default=0.0,
          type=float,
          help="Weight deay if we apply some.",
      )
      parser.add_argument(
          "--adam_epsilon",
          default=1e-8,
          type=float,
          help="Epsilon for Adam optimizer.",
      )
      parser.add_argument(
          "--max_grad_norm",
          default=1.0,
          type=float,
          help="Max gradient norm.",
      )
      parser.add_argument(
          "--num_train_epochs",
          default=3.0,
          type=float,
          help="Total number of training epochs to perform.",
      )
      parser.add_argument(
          "--max_steps",
          default=-1,
          type=int,
          help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
      )
      parser.add_argument(
          "--warmup_steps",
          default=0,
          type=int,
          help="Linear warmup over warmup_steps.",
      )
      parser.add_argument(
          "--early_exit_entropy",
          default=-1,
          type=float,
          help="Entropy threshold for early exit.",
      )
  
      parser.add_argument(
          "--logging_steps",
          type=int,
          default=50,
          help="Log every X updates steps.",
      )
      parser.add_argument(
          "--save_steps",
          type=int,
          default=50,
          help="Save checkpoint every X updates steps.",
      )
      parser.add_argument(
          "--eval_all_checkpoints",
          action="store_true",
          help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
      )
      parser.add_argument(
          "--no_cuda",
          action="store_true",
          help="Avoid using CUDA when available",
      )
      parser.add_argument(
          "--overwrite_output_dir",
          action="store_true",
          help="Overwrite the content of the output directory",
      )
      parser.add_argument(
          "--overwrite_cache",
          action="store_true",
          help="Overwrite the cached training and evaluation sets",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=42,
          help="random seed for initialization",
      )
  
      parser.add_argument(
          "--fp16",
          action="store_true",
          help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
      )
      parser.add_argument(
          "--fp16_opt_level",
          type=str,
          default="O1",
          help=(
              "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
              "See details at https://nvidia.github.io/apex/amp.html"
          ),
      )
      parser.add_argument(
          "--local_rank",
          type=int,
          default=-1,
          help="For distributed training: local_rank",
      )
      parser.add_argument(
          "--server_ip",
          type=str,
          default="",
          help="For distant debugging.",
      )
      parser.add_argument(
          "--server_port",
          type=str,
          default="",
          help="For distant debugging.",
      )
      args = parser.parse_args()
  
      if (
          os.path.exists(args.output_dir)
          and os.listdir(args.output_dir)
          and args.do_train
          and not args.overwrite_output_dir
      ):
          raise ValueError(
              "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
                  args.output_dir
              )
          )
  
      # Setup distant debugging if needed
      if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd
  
          print("Waiting for debugger attach")
          ptvsd.enable_attach(
              address=(args.server_ip, args.server_port),
              redirect_output=True,
          )
          ptvsd.wait_for_attach()
  
      # Setup CUDA, GPU & distributed training
      if args.local_rank == -1 or args.no_cuda:
          device = torch.device(
              "cuda"
              if torch.cuda.is_available() and not args.no_cuda
              else "cpu"
          )
          args.n_gpu = torch.cuda.device_count()
      else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
          torch.cuda.set_device(args.local_rank)
          device = torch.device("cuda", args.local_rank)
          torch.distributed.init_process_group(backend="nccl")
          args.n_gpu = 1
      args.device = device
  
      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO
          if args.local_rank in [-1, 0]
          else logging.WARN,
      )
      logger.warning(
          "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
          args.local_rank,
          device,
          args.n_gpu,
          bool(args.local_rank != -1),
          args.fp16,
      )
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(args.local_rank):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
      # Set seed
      set_seed(args)
  
      # Prepare GLUE task
      args.task_name = args.task_name.lower()
      if args.task_name not in processors:
          raise ValueError("Task not found: %s" % (args.task_name))
      processor = processors[args.task_name]()
      args.output_mode = output_modes[args.task_name]
      label_list = processor.get_labels()
      num_labels = len(label_list)
  
      # Load pretrained model and tokenizer
      if args.local_rank not in [-1, 0]:
          torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
  
      args.model_type = args.model_type.lower()
      config_class, model_class, tokenizer_class = MODEL_CLASSES[
          args.model_type
      ]
      config = config_class.from_pretrained(
          args.config_name
          if args.config_name
          else args.model_name_or_path,
          num_labels=num_labels,
          finetuning_task=args.task_name,
          cache_dir=args.cache_dir if args.cache_dir else None,
      )
      tokenizer = tokenizer_class.from_pretrained(
          args.tokenizer_name
          if args.tokenizer_name
          else args.model_name_or_path,
          do_lower_case=args.do_lower_case,
          cache_dir=args.cache_dir if args.cache_dir else None,
      )
      model = model_class.from_pretrained(
          args.model_name_or_path,
          from_tf=bool(".ckpt" in args.model_name_or_path),
          config=config,
          cache_dir=args.cache_dir if args.cache_dir else None,
      )
  
      if args.model_type == "bert":
          model.bert.encoder.set_early_exit_entropy(
              args.early_exit_entropy
          )
          model.bert.init_highway_pooler()
      elif args.model_type == "roberta":
          model.roberta.encoder.set_early_exit_entropy(
              args.early_exit_entropy
          )
          model.roberta.init_highway_pooler()
      else:
          raise NotImplementedError()
  
      if args.local_rank == 0:
          torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
  
      model.to(args.device)
  
      logger.info("Training/evaluation parameters %s", args)
  
      # Training
      if args.do_train:
          train_dataset = load_and_cache_examples(
              args, args.task_name, tokenizer, evaluate=False
          )
          global_step, tr_loss = train(
              args, train_dataset, model, tokenizer
          )
          logger.info(
              " global_step = %s, average loss = %s",
              global_step,
              tr_loss,
          )
  
          if args.eval_after_first_stage:
              result = evaluate(args, model, tokenizer, prefix="")
              print_result = get_wanted_result(result)
  
          train(
              args, train_dataset, model, tokenizer, train_highway=True
          )
  
      # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
      if args.do_train and (
          args.local_rank == -1 or torch.distributed.get_rank() == 0
      ):
          # Create output directory if needed
          if not os.path.exists(
              args.output_dir
          ) and args.local_rank in [-1, 0]:
              os.makedirs(args.output_dir)
  
          logger.info("Saving model checkpoint to %s", args.output_dir)
          # Save a trained model, configuration and tokenizer using `save_pretrained()`.
          # They can then be reloaded using `from_pretrained()`
          model_to_save = (
              model.module if hasattr(model, "module") else model
          )  # Take care of distributed/parallel training
          model_to_save.save_pretrained(args.output_dir)
          tokenizer.save_pretrained(args.output_dir)
  
          # Good practice: save your training arguments together with the trained model
          torch.save(
              args, os.path.join(args.output_dir, "training_args.bin")
          )
  
          # Load a trained model and vocabulary that you have fine-tuned
          model = model_class.from_pretrained(args.output_dir)
          tokenizer = tokenizer_class.from_pretrained(args.output_dir)
          model.to(args.device)
  
      # Evaluation
      results = {}
      if args.do_eval and args.local_rank in [-1, 0]:
          tokenizer = tokenizer_class.from_pretrained(
              args.output_dir, do_lower_case=args.do_lower_case
          )
          checkpoints = [args.output_dir]
          if args.eval_all_checkpoints:
              checkpoints = [
                  os.path.dirname(c)
                  for c in sorted(
                      glob.glob(
                          args.output_dir + "/**/" + WEIGHTS_NAME,
                          recursive=True,
                      )
                  )
              ]
  
          logger.info(
              "Evaluate the following checkpoints: %s", checkpoints
          )
          for checkpoint in checkpoints:
              global_step = (
                  checkpoint.split("-")[-1]
                  if len(checkpoints) > 1
                  else ""
              )
              prefix = (
                  checkpoint.split("/")[-1]
                  if checkpoint.find("checkpoint") != -1
                  else ""
              )
  
              model = model_class.from_pretrained(checkpoint)
              if args.model_type == "bert":
                  model.bert.encoder.set_early_exit_entropy(
                      args.early_exit_entropy
                  )
              elif args.model_type == "roberta":
                  model.roberta.encoder.set_early_exit_entropy(
                      args.early_exit_entropy
                  )
              else:
                  raise NotImplementedError()
  
              model.to(args.device)
              result = evaluate(
                  args,
                  model,
                  tokenizer,
                  prefix=prefix,
                  eval_highway=args.eval_highway,
              )
              print_result = get_wanted_result(result)
              logger.info("Result: {}".format(print_result))
              if args.eval_each_highway:
                  last_layer_results = print_result
                  each_layer_results = []
                  for i in range(model.num_layers):
                      logger.info("\n")
                      _result = evaluate(
                          args,
                          model,
                          tokenizer,
                          prefix=prefix,
                          output_layer=i,
                          eval_highway=args.eval_highway,
                      )
                      if i + 1 < model.num_layers:
                          each_layer_results.append(
                              get_wanted_result(_result)
                          )
                  each_layer_results.append(last_layer_results)
                  save_fname = (
                      args.plot_data_dir
                      + "/"
                      + args.model_name_or_path[2:]
                      + "/each_layer.npy"
                  )
                  if not os.path.exists(os.path.dirname(save_fname)):
                      os.makedirs(os.path.dirname(save_fname))
                  np.save(save_fname, np.array(each_layer_results))
                  info_str = "Score of each layer:"
                  for i in range(model.num_layers):
                      info_str += " {:.2f}".format(
                          100 * each_layer_results[i]
                      )
                  logger.info(info_str)
              result = {
                  k + "_{}".format(global_step): v
                  for k, v in result.items()
              }
              results.update(result)
  
      return results
  
imports: from __future__ import absolute_import, division, print_function
  import argparse
  import glob
  import logging
  import os
  import random
  import time
  import numpy
  import torch
  from torch import nn
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm, trange
  import transformers
  from src.modeling_highway_bert import DeeBertForSequenceClassification
  from src.modeling_highway_roberta import DeeRobertaForSequenceClassification
  from transformers import WEIGHTS_NAME, AdamW, BertConfig, BertTokenizer, RobertaConfig, RobertaTokenizer, get_linear_schedule_with_warmup
  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.trainer_utils import is_main_process
label_desc: Use image node_id 6 for calling a global function with example usage: main() and returns: results