class_docstr: Abstract base class for time series classifiers.
  
  The base classifier specifies the methods and method signatures that all
  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.
docstr: Predicts labels probabilities for sequences in X.
  
  Parameters
  ----------
  X : sktime compatible time series panel data container, Panel scitype, e.g.,
      pd-multiindex: pd.DataFrame with columns = variables,
      index = pd.MultiIndex with first level = instance indices,
      second level = time indices
      numpy3D: 3D np.array (any number of dimensions, equal length series)
      of shape [n_instances, n_dimensions, series_length]
      or of any other supported Panel mtype
      for list of mtypes, see datatypes.SCITYPE_REGISTER
      for specifications, see examples/AA_datatypes_and_datasets.ipynb
  
  Returns
  -------
  y_pred : 2D np.array of int, of shape [n_instances, n_classes]
      predicted class label probabilities
      0-th indices correspond to instance indices in X
      1-st indices correspond to class index, in same order as in self.classes_
      entries are predictive class probabilities, summing to 1
docstr_tok: ["Predicts","labels","probabilities","for","sequences","in","X.","Parameters","--","--","--","--","--","X",":","sktime","compatible","time","series","panel","data","container",",","Panel","scitype",",","e.g.",",","pd-multiindex",":","pd.DataFrame","with","columns","=","variables",",","index","=","pd.MultiIndex","with","first","level","=","instance","indices",",","second","level","=","time","indices","numpy3D",":","3D","np.array","(","any","number","of","dimensions",",","equal","length","series",")","of","shape","[","n_instances",",","n_dimensions",",","series_length","]","or","of","any","other","supported","Panel","mtype","for","list","of","mtypes",",","see","datatypes.SCITYPE_REGISTER","for","specifications",",","see","examples\/AA_datatypes_and_datasets.ipynb","Returns","--","--","--","-","y_pred",":","2D","np.array","of","int",",","of","shape","[","n_instances",",","n_classes","]","predicted","class","label","probabilities","0-th","indices","correspond","to","instance","indices","in","X","1-st","indices","correspond","to","class","index",",","in","same","order","as","in","self.classes_","entries","are","predictive","class","probabilities",",","summing","to","1"]
code: def predict_proba(self, X):
      """Predicts labels probabilities for sequences in X.
  
      Parameters
      ----------
      X : sktime compatible time series panel data container, Panel scitype, e.g.,
          pd-multiindex: pd.DataFrame with columns = variables,
          index = pd.MultiIndex with first level = instance indices,
          second level = time indices
          numpy3D: 3D np.array (any number of dimensions, equal length series)
          of shape [n_instances, n_dimensions, series_length]
          or of any other supported Panel mtype
          for list of mtypes, see datatypes.SCITYPE_REGISTER
          for specifications, see examples/AA_datatypes_and_datasets.ipynb
  
      Returns
      -------
      y_pred : 2D np.array of int, of shape [n_instances, n_classes]
          predicted class label probabilities
          0-th indices correspond to instance indices in X
          1-st indices correspond to class index, in same order as in self.classes_
          entries are predictive class probabilities, summing to 1
      """
      self.check_is_fitted()
  
      # enter vectorized mode if needed
      if self._is_vectorized:
          return self._vectorize("predict_proba", X=X)
  
      self.check_is_fitted()
  
      # boilerplate input checks for predict-like methods
      X = self._check_convert_X_for_predict(X)
  
      # handle the single-class-label case
      if len(self._class_dictionary) == 1:
          return self._single_class_y_pred(X, method="predict_proba")
  
      # call internal _predict_proba
      return self._predict_proba(X)
  
imports: import time
  import numpy
  from sktime.base import BasePanelMixin
  from sktime.datatypes import VectorizedDF, check_is_scitype, convert
  from sktime.utils.sklearn import is_sklearn_transformer
  from sktime.utils.validation import check_n_jobs
  from sktime.utils.validation._dependencies import _check_estimator_deps
label_desc: Use image node_id 5 for calling the BaseClassifier obj's underlying member method code with example usage: obj.predict_proba(X) and returns: self, self, self