code: def get_datasets(initial_pool, tokenizer):
      # To be able to support most cases, we have provided support for
      # HuggingFace datasets. You can always create a custom wrapper for
      # custom datasets.
      datasets = load_dataset("glue", "sst2")
      raw_train_set = datasets["train"]
      raw_valid_set = datasets["validation"]
  
      active_set = active_huggingface_dataset(raw_train_set, tokenizer)
      active_set.can_label = False
      valid_set = HuggingFaceDatasets(raw_valid_set, tokenizer)
  
      # We start labeling randomly.
      active_set.label_randomly(initial_pool)
      return active_set, valid_set
  
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 2 for calling a global function with example usage: get_datasets(initial_pool, tokenizer) and returns: active_set,  valid_set