code: def initialize_active_learner(active_learner, y_train):
      # Initialize the model - This is required for model-based query strategies.
      indices_initial = multilabel_stratified_subsets_sampling(
          y_train, n_samples=200
      )
      active_learner.initialize_data(
          indices_initial, y_train[indices_initial]
      )
  
      return indices_initial
  
imports: import numpy
  from small_text import ConfidenceEnhancedLinearSVC, EmptyPoolException, PoolBasedActiveLearner, PoolExhaustedException, RandomSampling, SklearnClassifierFactory, multilabel_stratified_subsets_sampling
  from examplecode.data.example_data_multilabel import get_train_test, preprocess_data_sklearn
  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