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("--query_size", default=100, type=int)
      parser.add_argument("--lr", default=0.001)
      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/vgg_mcdropout_cifar10.py
imports: import argparse
  from pprint import pprint
  import random
  from copy import deepcopy
  import torch
  import torch.backends
  from torch import optim
  from torch.hub import load_state_dict_from_url
  from torch.nn import CrossEntropyLoss
  from torchvision import datasets
  from torchvision.models import vgg16
  from torchvision.transforms import transforms
  from tqdm import tqdm
  from baal.active import get_heuristic, ActiveLearningDataset
  from baal.active.active_loop import ActiveLearningLoop
  from baal.bayesian.dropout import patch_module
  from baal import ModelWrapper
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: parser