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 pabee.modeling_pabee_albert import AlbertForSequenceClassificationWithPabee
  from pabee.modeling_pabee_bert import BertForSequenceClassificationWithPabee
  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 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