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: Fit time series classifier to training data.
  
  Parameters
  ----------
  X : 3D np.array (any number of dimensions, equal length series)
          of shape [n_instances, n_dimensions, series_length]
      or 2D np.array (univariate, equal length series)
          of shape [n_instances, series_length]
      or pd.DataFrame with each column a dimension, each cell a pd.Series
          (any number of dimensions, equal or unequal length series)
      or of any other supported Panel mtype
          for list of mtypes, see datatypes.SCITYPE_REGISTER
          for specifications, see examples/AA_datatypes_and_datasets.ipynb
  y : 1D np.array of int, of shape [n_instances] - class labels for fitting
      indices correspond to instance indices in X
  
  Returns
  -------
  self : Reference to self.
  
  Notes
  -----
  Changes state by creating a fitted model that updates attributes
  ending in "_" and sets is_fitted flag to True.
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code: def fit(self, X, y):
      """Fit time series classifier to training data.
  
      Parameters
      ----------
      X : 3D np.array (any number of dimensions, equal length series)
              of shape [n_instances, n_dimensions, series_length]
          or 2D np.array (univariate, equal length series)
              of shape [n_instances, series_length]
          or pd.DataFrame with each column a dimension, each cell a pd.Series
              (any number of dimensions, equal or unequal length series)
          or of any other supported Panel mtype
              for list of mtypes, see datatypes.SCITYPE_REGISTER
              for specifications, see examples/AA_datatypes_and_datasets.ipynb
      y : 1D np.array of int, of shape [n_instances] - class labels for fitting
          indices correspond to instance indices in X
  
      Returns
      -------
      self : Reference to self.
  
      Notes
      -----
      Changes state by creating a fitted model that updates attributes
      ending in "_" and sets is_fitted flag to True.
      """
      fit = BaseClassifier.fit
      return fit(self, X, y)
  
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 2 for calling the BaseEarlyClassifier obj's underlying member method code with example usage: obj.fit(X, y) and returns: fit