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: Aux. function to make parallel predictions/transformation.
code: def _transform(self, X, method):
      """Aux. function to make parallel predictions/transformation."""
      X = self._check_Xy(X)
      method = _check_method(self.base_estimator, method)
      if X.shape[-1] != len(self.estimators_):
          raise ValueError(
              "The number of estimators does not match " "X.shape[-1]"
          )
      # For predictions/transforms the parallelization is across the data and
      # not across the estimators to avoid memory load.
      parallel, p_func, n_jobs = parallel_func(
          _sl_transform,
          self.n_jobs,
          max_jobs=X.shape[-1],
          verbose=False,
      )
  
      X_splits = np.array_split(X, n_jobs, axis=-1)
      idx, est_splits = zip(*array_split_idx(self.estimators_, n_jobs))
  
      context = _create_progressbar_context(self, X, "Transforming")
      with context as pb:
          y_pred = parallel(
              p_func(est, x, method, pb.subset(pb_idx))
              for pb_idx, est, x in zip(idx, est_splits, X_splits)
          )
  
      y_pred = np.concatenate(y_pred, axis=1)
      return y_pred
  
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 7 for calling the SlidingEstimator obj's underlying member method code with example usage: obj._transform(X, method) and returns: y_pred