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 pretrained model or model identifier from huggingface.co/models",
      )
      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.",
      )
      # 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="Run 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(
          "--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(
          "--learning_rate",
          default=5e-5,
          type=float,
          help="The initial learning rate for Adam.",
      )
  
      # Pruning parameters
      parser.add_argument(
          "--mask_scores_learning_rate",
          default=1e-2,
          type=float,
          help="The Adam initial learning rate of the mask scores.",
      )
      parser.add_argument(
          "--initial_threshold",
          default=1.0,
          type=float,
          help="Initial value of the threshold (for scheduling).",
      )
      parser.add_argument(
          "--final_threshold",
          default=0.7,
          type=float,
          help="Final value of the threshold (for scheduling).",
      )
      parser.add_argument(
          "--initial_warmup",
          default=1,
          type=int,
          help=(
              "Run `initial_warmup` * `warmup_steps` steps of threshold warmup during which threshold stays "
              "at its `initial_threshold` value (sparsity schedule)."
          ),
      )
      parser.add_argument(
          "--final_warmup",
          default=2,
          type=int,
          help=(
              "Run `final_warmup` * `warmup_steps` steps of threshold cool-down during which threshold stays "
              "at its final_threshold value (sparsity schedule)."
          ),
      )
  
      parser.add_argument(
          "--pruning_method",
          default="topK",
          type=str,
          help=(
              "Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
              " sigmoied_threshold = Soft movement pruning)."
          ),
      )
      parser.add_argument(
          "--mask_init",
          default="constant",
          type=str,
          help="Initialization method for the mask scores. Choices: constant, uniform, kaiming.",
      )
      parser.add_argument(
          "--mask_scale",
          default=0.0,
          type=float,
          help="Initialization parameter for the chosen initialization method.",
      )
  
      parser.add_argument(
          "--regularization",
          default=None,
          help="Add L0 or L1 regularization to the mask scores.",
      )
      parser.add_argument(
          "--final_lambda",
          default=0.0,
          type=float,
          help="Regularization intensity (used in conjunction with `regularization`.",
      )
  
      parser.add_argument(
          "--global_topk",
          action="store_true",
          help="Global TopK on the Scores.",
      )
      parser.add_argument(
          "--global_topk_frequency_compute",
          default=25,
          type=int,
          help="Frequency at which we compute the TopK global threshold.",
      )
  
      # Distillation parameters (optional)
      parser.add_argument(
          "--teacher_type",
          default=None,
          type=str,
          help=(
              "Teacher type. Teacher tokenizer and student (model) tokenizer must output the same tokenization. Only for"
              " distillation."
          ),
      )
      parser.add_argument(
          "--teacher_name_or_path",
          default=None,
          type=str,
          help="Path to the already fine-tuned teacher model. Only for distillation.",
      )
      parser.add_argument(
          "--alpha_ce",
          default=0.5,
          type=float,
          help="Cross entropy loss linear weight. Only for distillation.",
      )
      parser.add_argument(
          "--alpha_distil",
          default=0.5,
          type=float,
          help="Distillation loss linear weight. Only for distillation.",
      )
      parser.add_argument(
          "--temperature",
          default=2.0,
          type=float,
          help="Distillation temperature. Only for distillation.",
      )
  
      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(
          "--weight_decay",
          default=0.0,
          type=float,
          help="Weight decay 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(
          "--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",
      )
  
      args = parser.parse_args()
  
      # Regularization
      if args.regularization == "null":
          args.regularization = None
  
      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(
              f"Output directory ({args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to"
              " overcome."
          )
  
      # 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 = 0 if args.no_cuda else torch.cuda.device_count()
      else:  # Initializes the distributed backend which will take care of synchronizing 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 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,
          pruning_method=args.pruning_method,
          mask_init=args.mask_init,
          mask_scale=args.mask_scale,
      )
      tokenizer = tokenizer_class.from_pretrained(
          args.tokenizer_name
          if args.tokenizer_name
          else args.model_name_or_path,
          cache_dir=args.cache_dir if args.cache_dir else None,
          do_lower_case=args.do_lower_case,
      )
      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.teacher_type is not None:
          assert args.teacher_name_or_path is not None
          assert args.alpha_distil > 0.0
          assert args.alpha_distil + args.alpha_ce > 0.0
          teacher_config_class, teacher_model_class, _ = MODEL_CLASSES[
              args.teacher_type
          ]
          teacher_config = teacher_config_class.from_pretrained(
              args.teacher_name_or_path
          )
          teacher = teacher_model_class.from_pretrained(
              args.teacher_name_or_path,
              from_tf=False,
              config=teacher_config,
              cache_dir=args.cache_dir if args.cache_dir else None,
          )
          teacher.to(args.device)
      else:
          teacher = None
  
      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, teacher=teacher
          )
          logger.info(
              " global_step = %s, average loss = %s",
              global_step,
              tr_loss,
          )
  
      # 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
      ):
          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, do_lower_case=args.do_lower_case
          )
          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)
              model.to(args.device)
              result = evaluate(args, model, tokenizer, prefix=prefix)
              result = {
                  k + "_{}".format(global_step): v
                  for k, v in result.items()
              }
              results.update(result)
  
      return results
  
imports: import argparse
  import glob
  import json
  import logging
  import os
  import random
  import numpy
  import torch
  from emmental import MaskedBertConfig, MaskedBertForSequenceClassification
  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
  from transformers import WEIGHTS_NAME, AdamW, BertConfig, BertForSequenceClassification, BertTokenizer, 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
label_desc: Use image node_id 7 for calling a global function with example usage: main() and returns: results