code: def parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument("--epoch", default=100, type=int)
      parser.add_argument("--batch_size", default=32, type=int)
      parser.add_argument("--initial_pool", default=1000, type=int)
      parser.add_argument(
          "--model", default="bert-base-uncased", type=str
      )
      parser.add_argument("--query_size", default=100, type=int)
      parser.add_argument("--heuristic", default="bald", type=str)
      parser.add_argument("--iterations", default=20, type=int)
      parser.add_argument("--shuffle_prop", default=0.05, type=float)
      parser.add_argument("--learning_epoch", default=20, type=int)
      return parser.parse_args()
  
file_path: baal/experiments/nlp_bert_mcdropout.py
imports: import argparse
  import random
  from copy import deepcopy
  import torch
  import torch.backends
  from tqdm import tqdm
  from datasets import load_dataset
  from transformers import BertTokenizer, TrainingArguments
  from transformers import BertForSequenceClassification
  from baal.active import get_heuristic
  from baal.active.active_loop import ActiveLearningLoop
  from baal.active.dataset.nlp_datasets import active_huggingface_dataset, HuggingFaceDatasets
  from baal.bayesian.dropout import patch_module
  from baal.transformers_trainer_wrapper import BaalTransformersTrainer
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: parser