code: def initialize_active_learner(active_learner, y_train):
      # Initialize the model. This is required for model-based query strategies.
      indices_pos_label = np.where(y_train == 1)[0]
      indices_neg_label = np.where(y_train == 0)[0]
  
      indices_initial = np.concatenate(
          [
              np.random.choice(indices_pos_label, 10, replace=False),
              np.random.choice(indices_neg_label, 10, replace=False),
          ],
          dtype=int,
      )
  
      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
  from examplecode.data.example_data_binary import get_train_test, preprocess_data
  from examplecode.shared import evaluate
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