class_docstr: Generalization Light.
  
  Fit a search-light along the last dimension and use them to apply a
  systematic cross-tasks generalization.
  
  Parameters
  ----------
  %(base_estimator)s
  %(scoring)s
  %(n_jobs)s
  %(position)s
  %(allow_2d)s
  %(verbose)s
class_docstr_tok: ["Generalization","Light",".","Fit","a","search-light","along","the","last","dimension","and","use","them","to","apply","a","systematic","cross-tasks","generalization",".","Parameters","--","--","--","--","--","%","(","base_estimator",")","s","%","(","scoring",")","s","%","(","n_jobs",")","s","%","(","position",")","s","%","(","allow_2d",")","s","%","(","verbose",")","s"]
docstr: Estimate probabilistic estimates of each data slice with all possible estimators.
  
  Parameters
  ----------
  X : array, shape (n_samples, nd_features, n_slices)
      The training input samples. For each data slice, a fitted estimator
      predicts a slice of the data. The feature dimension can be
      multidimensional e.g.
      ``X.shape = (n_samples, n_features_1, n_features_2, n_estimators)``.
  
  Returns
  -------
  y_pred : array, shape (n_samples, n_estimators, n_slices, n_classes)
      The predicted values for each estimator.
  
  Notes
  -----
  This requires ``base_estimator`` to have a ``predict_proba`` method.
code: def predict_proba(self, X):
      """Estimate probabilistic estimates of each data slice with all possible estimators.
  
      Parameters
      ----------
      X : array, shape (n_samples, nd_features, n_slices)
          The training input samples. For each data slice, a fitted estimator
          predicts a slice of the data. The feature dimension can be
          multidimensional e.g.
          ``X.shape = (n_samples, n_features_1, n_features_2, n_estimators)``.
  
      Returns
      -------
      y_pred : array, shape (n_samples, n_estimators, n_slices, n_classes)
          The predicted values for each estimator.
  
      Notes
      -----
      This requires ``base_estimator`` to have a ``predict_proba`` method.
      """  # noqa: E501
      return self._transform(X, "predict_proba")
  
imports: import logging
  import numpy
  from ..fixes import _get_check_scoring
  from ..parallel import parallel_func
  from ..utils import ProgressBar, _parse_verbose, array_split_idx, fill_doc, verbose
  from .base import BaseEstimator, _check_estimator
  from .mixin import TransformerMixin
label_desc: Use image node_id 5 for calling the GeneralizingEstimator obj's underlying member method code with example usage: obj.predict_proba(X) and returns: self