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: Predict each data slice/task with a series of independent estimators.
  
  The number of tasks in X should match the number of tasks/estimators
  given at fit time.
  
  Parameters
  ----------
  X : array, shape (n_samples, nd_features, n_tasks)
      The input samples. For each data slice, the corresponding estimator
      makes the sample predictions, e.g.:
      ``[estimators[ii].predict(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_tasks).
  
  Returns
  -------
  y_pred : array, shape (n_samples, n_estimators) | (n_samples, n_tasks, n_targets)
      Predicted values for each estimator/data slice.
code: def predict(self, X):
      """Predict each data slice/task with a series of independent estimators.
  
      The number of tasks in X should match the number of tasks/estimators
      given at fit time.
  
      Parameters
      ----------
      X : array, shape (n_samples, nd_features, n_tasks)
          The input samples. For each data slice, the corresponding estimator
          makes the sample predictions, e.g.:
          ``[estimators[ii].predict(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_tasks).
  
      Returns
      -------
      y_pred : array, shape (n_samples, n_estimators) | (n_samples, n_tasks, n_targets)
          Predicted values for each estimator/data slice.
      """  # noqa: E501
      return self._transform(X, "predict")
  
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 9 for calling the SlidingEstimator obj's underlying member method code with example usage: obj.predict(X) and returns: self