code: def main():
      parser = argparse.ArgumentParser()
  
      # Required parameters
      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(
          "--output_dir",
          default=None,
          type=str,
          required=True,
          help="The output directory where the model checkpoints and predictions will be written.",
      )
  
      # Other parameters
      parser.add_argument(
          "--data_dir",
          default=None,
          type=str,
          help="The input data dir. Should contain the .json files for the task."
          + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
      )
      parser.add_argument(
          "--train_file",
          default=None,
          type=str,
          help="The input training file. If a data dir is specified, will look for the file there"
          + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
      )
      parser.add_argument(
          "--predict_file",
          default=None,
          type=str,
          help="The input evaluation file. If a data dir is specified, will look for the file there"
          + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
      )
      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(
          "--version_2_with_negative",
          action="store_true",
          help="If true, the SQuAD examples contain some that do not have an answer.",
      )
      parser.add_argument(
          "--null_score_diff_threshold",
          type=float,
          default=0.0,
          help="If null_score - best_non_null is greater than the threshold predict null.",
      )
  
      parser.add_argument(
          "--max_seq_length",
          default=384,
          type=int,
          help=(
              "The maximum total input sequence length after WordPiece tokenization. Sequences "
              "longer than this will be truncated, and sequences shorter than this will be padded."
          ),
      )
      parser.add_argument(
          "--doc_stride",
          default=128,
          type=int,
          help="When splitting up a long document into chunks, how much stride to take between chunks.",
      )
      parser.add_argument(
          "--max_query_length",
          default=64,
          type=int,
          help=(
              "The maximum number of tokens for the question. Questions longer than this will "
              "be truncated to this length."
          ),
      )
      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 SQuAD 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(
          "--n_best_size",
          default=20,
          type=int,
          help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
      )
      parser.add_argument(
          "--max_answer_length",
          default=30,
          type=int,
          help=(
              "The maximum length of an answer that can be generated. This is needed because the start "
              "and end predictions are not conditioned on one another."
          ),
      )
      parser.add_argument(
          "--verbose_logging",
          action="store_true",
          help=(
              "If true, all of the warnings related to data processing will be printed. "
              "A number of warnings are expected for a normal SQuAD evaluation."
          ),
      )
      parser.add_argument(
          "--lang_id",
          default=0,
          type=int,
          help=(
              "language id of input for language-specific xlm models (see"
              " tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
          ),
      )
  
      parser.add_argument(
          "--logging_steps",
          type=int,
          default=500,
          help="Log every X updates steps.",
      )
      parser.add_argument(
          "--save_steps",
          type=int,
          default=500,
          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="Whether not to use 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(
          "--local_rank",
          type=int,
          default=-1,
          help="local_rank for distributed training on gpus",
      )
      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(
          "--server_ip",
          type=str,
          default="",
          help="Can be used for distant debugging.",
      )
      parser.add_argument(
          "--server_port",
          type=str,
          default="",
          help="Can be used for distant debugging.",
      )
  
      parser.add_argument(
          "--threads",
          type=int,
          default=1,
          help="multiple threads for converting example to features",
      )
      args = parser.parse_args()
  
      # Regularization
      if args.regularization == "null":
          args.regularization = None
  
      if args.doc_stride >= args.max_seq_length - args.max_query_length:
          logger.warning(
              "WARNING - You've set a doc stride which may be superior to the document length in some "
              "examples. This could result in errors when building features from the examples. Please reduce the doc "
              "stride or increase the maximum length to ensure the features are correctly built."
          )
  
      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 = 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)
  
      # Load pretrained model and tokenizer
      if args.local_rank not in [-1, 0]:
          # Make sure only the first process in distributed training will download model & vocab
          torch.distributed.barrier()
  
      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,
          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,
          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.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:
          # Make sure only the first process in distributed training will download model & vocab
          torch.distributed.barrier()
  
      model.to(args.device)
  
      logger.info("Training/evaluation parameters %s", args)
  
      # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set.
      # Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will
      # remove the need for this code, but it is still valid.
      if args.fp16:
          try:
              import apex
  
              apex.amp.register_half_function(torch, "einsum")
          except ImportError:
              raise ImportError(
                  "Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
              )
  
      # Training
      if args.do_train:
          train_dataset = load_and_cache_examples(
              args, tokenizer, evaluate=False, output_examples=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,
          )
  
      # Save the trained model and the tokenizer
      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()`
          # Take care of distributed/parallel training
          model_to_save = (
              model.module if hasattr(model, "module") else model
          )
          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
          )  # , force_download=True)
          tokenizer = tokenizer_class.from_pretrained(
              args.output_dir, do_lower_case=args.do_lower_case
          )
          model.to(args.device)
  
      # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
      results = {}
      if args.do_eval and args.local_rank in [-1, 0]:
          if args.do_train:
              logger.info(
                  "Loading checkpoints saved during training for evaluation"
              )
              checkpoints = [args.output_dir]
              if args.eval_all_checkpoints:
                  checkpoints = list(
                      os.path.dirname(c)
                      for c in sorted(
                          glob.glob(
                              args.output_dir + "/**/" + WEIGHTS_NAME,
                              recursive=True,
                          )
                      )
                  )
  
          else:
              logger.info(
                  "Loading checkpoint %s for evaluation",
                  args.model_name_or_path,
              )
              checkpoints = [args.model_name_or_path]
  
          logger.info(
              "Evaluate the following checkpoints: %s", checkpoints
          )
  
          for checkpoint in checkpoints:
              # Reload the model
              global_step = (
                  checkpoint.split("-")[-1]
                  if len(checkpoints) > 1
                  else ""
              )
              model = model_class.from_pretrained(
                  checkpoint
              )  # , force_download=True)
              model.to(args.device)
  
              # Evaluate
              result = evaluate(
                  args, model, tokenizer, prefix=global_step
              )
  
              result = dict(
                  (
                      k
                      + (
                          "_{}".format(global_step)
                          if global_step
                          else ""
                      ),
                      v,
                  )
                  for k, v in result.items()
              )
              results.update(result)
  
      logger.info("Results: {}".format(results))
      predict_file = list(
          filter(None, args.predict_file.split("/"))
      ).pop()
      if not os.path.exists(
          os.path.join(args.output_dir, predict_file)
      ):
          os.makedirs(os.path.join(args.output_dir, predict_file))
      output_eval_file = os.path.join(
          args.output_dir, predict_file, "eval_results.txt"
      )
      with open(output_eval_file, "w") as writer:
          for key in sorted(results.keys()):
              writer.write("%s = %s\n" % (key, str(results[key])))
  
      return results
  
imports: import argparse
  import glob
  import logging
  import os
  import random
  import timeit
  import numpy
  import torch
  from torch import nn
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm, trange
  from emmental import MaskedBertConfig, MaskedBertForQuestionAnswering
  from transformers import WEIGHTS_NAME, AdamW, BertConfig, BertForQuestionAnswering, BertTokenizer, get_linear_schedule_with_warmup, squad_convert_examples_to_features
  from transformers.data.metrics.squad_metrics import compute_predictions_log_probs, compute_predictions_logits, squad_evaluate
  from transformers.data.processors.squad import SquadResult, SquadV1Processor, SquadV2Processor
label_desc: Use image node_id 8 for calling a global function with example usage: main() and returns: results