class_docstr: Search Light.
  
  Fit, predict and score a series of models to each subset of the dataset
  along the last dimension. Each entry in the last dimension is referred
  to as a task.
  
  Parameters
  ----------
  %(base_estimator)s
  %(scoring)s
  %(n_jobs)s
  %(position)s
  %(allow_2d)s
  %(verbose)s
  
  Attributes
  ----------
  estimators_ : array-like, shape (n_tasks,)
      List of fitted scikit-learn estimators (one per task).
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docstr: Estimate distances of each data slice to the hyperplanes.
  
  Parameters
  ----------
  X : array, shape (n_samples, nd_features, n_tasks)
      The input samples. For each data slice, the corresponding estimator
      outputs the distance to the hyperplane, e.g.:
      ``[estimators[ii].decision_function(X[..., ii]) for ii in range(n_estimators)]``.
      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_classes * (n_classes-1) // 2)
      Predicted distances for each estimator/data slice.
  
  Notes
  -----
  This requires base_estimator to have a ``decision_function`` method.
code: def decision_function(self, X):
      """Estimate distances of each data slice to the hyperplanes.
  
      Parameters
      ----------
      X : array, shape (n_samples, nd_features, n_tasks)
          The input samples. For each data slice, the corresponding estimator
          outputs the distance to the hyperplane, e.g.:
          ``[estimators[ii].decision_function(X[..., ii]) for ii in range(n_estimators)]``.
          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_classes * (n_classes-1) // 2)
          Predicted distances for each estimator/data slice.
  
      Notes
      -----
      This requires base_estimator to have a ``decision_function`` method.
      """  # noqa: E501
      return self._transform(X, "decision_function")
  
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 11 for calling the SlidingEstimator obj's underlying member method code with example usage: obj.decision_function(X) and returns: self