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: Predicts labels for sequences in X.
  
  Abstract method, must be implemented.
  
  This method should update state_info with any values necessary to make future
  decisions. It is recommended that the previous time stamp used for each case
  should be stored in the state_info. The number of rows in state_info after the
  method has been called should match the number of input rows.
  
  Parameters
  ----------
  X : guaranteed to be of a type in self.get_tag("X_inner_mtype")
      if self.get_tag("X_inner_mtype") = "numpy3D":
          3D np.ndarray of shape = [n_instances, n_dimensions, series_length]
      if self.get_tag("X_inner_mtype") = "nested_univ":
          pd.DataFrame with each column a dimension, each cell a pd.Series
      for list of other mtypes, see datatypes.SCITYPE_REGISTER
      for specifications, see examples/AA_datatypes_and_datasets.ipynb
  
  Returns
  -------
  y : 1D np.array of int, of shape [n_instances] - predicted class labels
      indices correspond to instance indices in X
  decisions : 1D bool array
      An array of booleans, containing the decision of whether a prediction is
      safe to use or not.
      i-th entry is the classifier decision that i-th instance safe to use
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code: def _predict(self, X) -> Tuple[np.ndarray, np.ndarray]:
      """Predicts labels for sequences in X.
  
      Abstract method, must be implemented.
  
      This method should update state_info with any values necessary to make future
      decisions. It is recommended that the previous time stamp used for each case
      should be stored in the state_info. The number of rows in state_info after the
      method has been called should match the number of input rows.
  
      Parameters
      ----------
      X : guaranteed to be of a type in self.get_tag("X_inner_mtype")
          if self.get_tag("X_inner_mtype") = "numpy3D":
              3D np.ndarray of shape = [n_instances, n_dimensions, series_length]
          if self.get_tag("X_inner_mtype") = "nested_univ":
              pd.DataFrame with each column a dimension, each cell a pd.Series
          for list of other mtypes, see datatypes.SCITYPE_REGISTER
          for specifications, see examples/AA_datatypes_and_datasets.ipynb
  
      Returns
      -------
      y : 1D np.array of int, of shape [n_instances] - predicted class labels
          indices correspond to instance indices in X
      decisions : 1D bool array
          An array of booleans, containing the decision of whether a prediction is
          safe to use or not.
          i-th entry is the classifier decision that i-th instance safe to use
      """
      ...
  
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 15 for calling the BaseEarlyClassifier obj's underlying member method code with example usage: obj._predict(X) without return types