code: def load_and_cache_examples(args, task, tokenizer, evaluate=False):
      if args.local_rank not in [-1, 0] and not evaluate:
          torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
  
      processor = processors[task]()
      output_mode = output_modes[task]
      # Load data features from cache or dataset file
      cached_features_file = os.path.join(
          args.data_dir,
          "cached_{}_{}_{}_{}".format(
              "dev" if evaluate else "train",
              list(
                  filter(None, args.model_name_or_path.split("/"))
              ).pop(),
              str(args.max_seq_length),
              str(task),
          ),
      )
      if (
          os.path.exists(cached_features_file)
          and not args.overwrite_cache
      ):
          logger.info(
              "Loading features from cached file %s",
              cached_features_file,
          )
          features = torch.load(cached_features_file)
      else:
          logger.info(
              "Creating features from dataset file at %s", args.data_dir
          )
          label_list = processor.get_labels()
          if task in ["mnli", "mnli-mm"] and args.model_type in [
              "roberta",
              "xlmroberta",
          ]:
              # HACK(label indices are swapped in RoBERTa pretrained model)
              label_list[1], label_list[2] = (
                  label_list[2],
                  label_list[1],
              )
          examples = (
              processor.get_dev_examples(args.data_dir)
              if evaluate
              else processor.get_train_examples(args.data_dir)
          )
          features = convert_examples_to_features(
              examples,
              tokenizer,
              label_list=label_list,
              max_length=args.max_seq_length,
              output_mode=output_mode,
          )
          if args.local_rank in [-1, 0]:
              logger.info(
                  "Saving features into cached file %s",
                  cached_features_file,
              )
              torch.save(features, cached_features_file)
  
      if args.local_rank == 0 and not evaluate:
          torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
  
      # Convert to Tensors and build dataset
      all_input_ids = torch.tensor(
          [f.input_ids for f in features], dtype=torch.long
      )
      all_attention_mask = torch.tensor(
          [f.attention_mask for f in features], dtype=torch.long
      )
      all_token_type_ids = torch.tensor(
          [f.token_type_ids for f in features], dtype=torch.long
      )
      if output_mode == "classification":
          all_labels = torch.tensor(
              [f.label for f in features], dtype=torch.long
          )
      elif output_mode == "regression":
          all_labels = torch.tensor(
              [f.label for f in features], dtype=torch.float
          )
  
      dataset = TensorDataset(
          all_input_ids,
          all_attention_mask,
          all_token_type_ids,
          all_labels,
      )
      return dataset
  
imports: import argparse
  import glob
  import json
  import logging
  import os
  import random
  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 pabee.modeling_pabee_albert import AlbertForSequenceClassificationWithPabee
  from pabee.modeling_pabee_bert import BertForSequenceClassificationWithPabee
  from transformers import WEIGHTS_NAME, AdamW, AlbertConfig, AlbertTokenizer, BertConfig, 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
  from transformers.trainer_utils import is_main_process
label_desc: Use image node_id 4 for calling a global function with example usage: load_and_cache_examples(args, task, tokenizer, evaluate) and returns: dataset