code: def initialize_active_learner(active_learner, y_train):
      indices_initial = random_initialization_stratified(
          y_train, n_samples=2000
      )
      y_initial = y_train[indices_initial]
  
      active_learner.initialize_data(indices_initial, y_initial)
  
      return indices_initial
  
imports: from transformers import AutoTokenizer
  from small_text import ActiveLearnerException, EmptyPoolException, PoolBasedActiveLearner, PoolExhaustedException, RandomSampling, TransformerBasedClassificationFactory, TransformerModelArguments, list_to_csr, random_initialization_stratified
  from examplecode.data.example_data_multilabel import get_train_test
  from examplecode.data.example_data_transformers import preprocess_data
  from examplecode.shared import evaluate_multi_label
label_desc: Use image node_id 3 for calling a global function with example usage: initialize_active_learner(active_learner, y_train) and returns: indices_initial