docstr: Parameters
  ----------
  active_learner : small_text.active_learner.PoolBasedActiveLearner
      An active learner instance.
  predictions : np.ndarray[int]
      Predictions for a fixed subset (usually the full train set).
  proba : np.ndarray[float]
      Probability distribution over the possible classes for a fixed subset. This is expected
      to have the same length as `predictions` unless one of `predictions` and `proba`
      is `None`.
  indices_stopping : np.ndarray[int]
      Uses the given indices to select a subset for stopping from either `predictions`
      or `proba` if not `None`. The indices are relative to `predictions` and `proba`.
code: def stop(
      self,
      active_learner=None,
      predictions=None,
      proba=None,
      indices_stopping=None,
  ):
      """
      Parameters
      ----------
      active_learner : small_text.active_learner.PoolBasedActiveLearner
          An active learner instance.
      predictions : np.ndarray[int]
          Predictions for a fixed subset (usually the full train set).
      proba : np.ndarray[float]
          Probability distribution over the possible classes for a fixed subset. This is expected
          to have the same length as `predictions` unless one of `predictions` and `proba`
          is `None`.
      indices_stopping : np.ndarray[int]
          Uses the given indices to select a subset for stopping from either `predictions`
          or `proba` if not `None`. The indices are relative to `predictions` and `proba`.
      """
      pass
  
file_path: small-text/small_text/stopping_criteria/base.py
imports: import numpy
  from abc import ABC, abstractmethod
label_desc: Use image node_id 1 for calling the StoppingCriterion obj's underlying member method code with example usage: obj.stop(active_learner, predictions, proba, indices_stopping) without return types