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: Remove True cases from X and y given a boolean array of decisions.
docstr_tok: ["Remove","True","cases","from","X","and","y","given","a","boolean","array","of","decisions","."]
code: def filter_X_y(X, y, decisions):
      """Remove True cases from X and y given a boolean array of decisions."""
      inv_dec = np.invert(decisions)
      return X[inv_dec], y[inv_dec]
  
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 11 for calling the BaseEarlyClassifier obj's underlying member method code with example usage: obj.filter_X_y(X, y, decisions) and returns: X,  y