docstr: (Re-)Initializes the current labeled pool.
  
  This methods needs to be called whenever the underlying data changes, in particularly
  before the first loop.
  
  Parameters
  ----------
  indices_initial : np.ndarray[int]
      Positional indices pointing at training examples. This is the intially labelled set
      for training an initial classifier.
  y_initial : numpy.ndarray[int] or scipy.sparse.csr_matrix
      The respective labels belonging to the examples referenced by `x_indices_initial`.
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code: def initialize_data(
      self, indices_initial, y_initial, *args, **kwargs
  ):
      """(Re-)Initializes the current labeled pool.
  
      This methods needs to be called whenever the underlying data changes, in particularly
      before the first loop.
  
      Parameters
      ----------
      indices_initial : np.ndarray[int]
          Positional indices pointing at training examples. This is the intially labelled set
          for training an initial classifier.
      y_initial : numpy.ndarray[int] or scipy.sparse.csr_matrix
          The respective labels belonging to the examples referenced by `x_indices_initial`.
      """
      pass
  
imports: import numpy
  from abc import ABC, abstractmethod
  from pathlib import Path
  from scipy.sparse import csr_matrix
  from small_text.base import LABEL_IGNORED
  from small_text.exceptions import LearnerNotInitializedException
  from small_text.utils.data import list_length
  from small_text.utils.labels import concatenate, get_ignored_labels_mask, remove_by_index
  from small_text.version import __version__
label_desc: Use image node_id 3 for calling the AbstractPoolBasedActiveLearner obj's underlying member method code with example usage: obj.initialize_data(indices_initial, y_initial) without return types