class_docstr: Abstract base class for time series clusterer.
  
  Parameters
  ----------
  n_clusters: int, defaults = None
      Number of clusters for model.
class_docstr_tok: ["Abstract","base","class","for","time","series","clusterer",".","Parameters","--","--","--","--","--","n_clusters",":","int",",","defaults","=","None","Number","of","clusters","for","model","."]
docstr: Predicts labels probabilities for sequences in X.
  
  Default behaviour is to call _predict and set the predicted class probability
  to 1, other class probabilities to 0. Override if better estimates are
  obtainable.
  
  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 : 2D array of shape [n_instances, n_classes] - predicted class probabilities
      1st dimension indices correspond to instance indices in X
      2nd dimension indices correspond to possible labels (integers)
      (i, j)-th entry is predictive probability that i-th instance is of class j
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code: def _predict_proba(self, X):
      """Predicts labels probabilities for sequences in X.
  
      Default behaviour is to call _predict and set the predicted class probability
      to 1, other class probabilities to 0. Override if better estimates are
      obtainable.
  
      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 : 2D array of shape [n_instances, n_classes] - predicted class probabilities
          1st dimension indices correspond to instance indices in X
          2nd dimension indices correspond to possible labels (integers)
          (i, j)-th entry is predictive probability that i-th instance is of class j
      """
      preds = self._predict(X)
      n_instances = len(preds)
      if hasattr(self, "n_clusters") and self.n_clusters is not None:
          n_clusters = self.n_clusters
      else:
          n_clusters = max(preds) + 1
      dists = np.zeros((X.shape[0], n_clusters))
      for i in range(n_instances):
          dists[i, preds[i]] = 1
      return dists
  
imports: import time
  from typing import Any, Union
  import numpy
  import pandas
  from sktime.base import BaseEstimator
  from sktime.datatypes import check_is_scitype, convert_to, scitype_to_mtype
  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 8 for calling the BaseClusterer obj's underlying member method code with example usage: obj._predict_proba(X) and returns: dists