class_docstr: Abstract base class for early time series classifiers.
  
  The base classifier specifies the methods and method signatures that all
  early classifiers have to implement. Attributes with an underscore suffix are set in
  the method fit.
  
  Parameters
  ----------
  classes_            : ndarray of class labels, possibly strings
  n_classes_          : integer, number of classes (length of classes_)
  fit_time_           : integer, time (in milliseconds) for fit to run.
  _class_dictionary   : dictionary mapping classes_ onto integers 0...n_classes_-1.
  _threads_to_use     : number of threads to use in fit as determined by n_jobs.
  state_info          : An array containing the state info for each decision in X.
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docstr: Return the state information generated from the last predict/update call.
  
  Returns
  -------
  An array containing the state info for each decision in X from update and
  predict methods. Contains classifier dependent information for future decisions
  on the data and information on when a cases decision has been made. Each row
  contains information for a case from the latest decision on its safety made in
  update/predict. Successive updates are likely to remove rows from the
  state_info, as it will only store as many rows as there are input instances to
  update/predict.
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code: def get_state_info(self):
      """Return the state information generated from the last predict/update call.
  
      Returns
      -------
      An array containing the state info for each decision in X from update and
      predict methods. Contains classifier dependent information for future decisions
      on the data and information on when a cases decision has been made. Each row
      contains information for a case from the latest decision on its safety made in
      update/predict. Successive updates are likely to remove rows from the
      state_info, as it will only store as many rows as there are input instances to
      update/predict.
      """
      return self.state_info
  
imports: from abc import ABC, abstractmethod
  from typing import Tuple
  import numpy
  from sktime.base import BaseEstimator
  from sktime.classification import BaseClassifier
label_desc: Use image node_id 8 for calling the BaseEarlyClassifier obj's underlying member method code with example usage: obj.get_state_info() and returns: self