class_docstr: Container class for a stack of sequentially processed estimators.
  
  The Sequential class stories all layers as an ordered dictionary
  and modifies possesses a ``get_params`` method to appear as an estimator
  in the Scikit-learn API. This allows correct cloning and parameter
  updating.
  
  
  Parameters
  ----------
  stack: list, optional (default = None)
      list of estimators (i.e. layers) to build instance with.
  
  n_jobs : int (default = -1)
      Degree of concurrency. Set ``n_jobs = -1`` for maximal parallelism and
      ``n_jobs=1`` for sequential processing.
  
  backend : str, (default="threading")
      the joblib backend to use (i.e. "multiprocessing" or "threading").
  
  raise_on_exception : bool (default = False)
      raise error on soft exceptions. Otherwise issue warning.
  
  verbose : int or bool (default = False)
      level of verbosity.
  
          - ``verbose = 0`` silent (same as ``verbose = False``)
          - ``verbose = 1`` messages at start and finish
            (same as ``verbose = True``)
          - ``verbose = 2`` messages for each layer
          - etc
  
      If ``verbose >= 10`` prints to ``sys.stderr``, else ``sys.stdout``.
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docstr: Predict using sub-learners as is done during the ``fit`` call.
  
  Parameters
  -----------
  X : array-like of shape = [n_samples, n_features]
      input matrix to be used for prediction.
  
  *args : optional
      optional arguments.
  
  **kwargs : optional
      optional keyword arguments.
  
  Returns
  -------
  X_pred : array-like of shape = [n_test_samples, n_fitted_estimators]
      predictions from ``fit`` call to final layer.
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code: def transform(self, X, **kwargs):
      """Predict using sub-learners as is done during the ``fit`` call.
  
      Parameters
      -----------
      X : array-like of shape = [n_samples, n_features]
          input matrix to be used for prediction.
  
      *args : optional
          optional arguments.
  
      **kwargs : optional
          optional keyword arguments.
  
      Returns
      -------
      X_pred : array-like of shape = [n_test_samples, n_fitted_estimators]
          predictions from ``fit`` call to final layer.
      """
      if not self.__fitted__:
          NotFittedError("Instance not fitted.")
  
      f, t0 = print_job(self, "Transforming")
  
      out = self._predict(X, "transform", **kwargs)
  
      if self.verbose:
          print_time(
              t0,
              "{:<35}".format("Transform complete"),
              file=f,
              flush=True,
          )
  
      return out
  
imports: from __future__ import division, print_function, with_statement
  from abc import ABCMeta, abstractmethod
  import warnings
  from ..None import config
  from ..parallel import Layer, ParallelProcessing, make_group
  from ..parallel.base import BaseStacker
  from ..externals.sklearn.validation import check_random_state
  from ..utils import check_ensemble_build, print_time, safe_print, IdTrain, format_name
  from ..utils.exceptions import LayerSpecificationWarning, NotFittedError, NotInitializedError
  from ..metrics import Data
  from ..externals.sklearn.base import BaseEstimator, clone
label_desc: Use image node_id 6 for calling the Sequential obj's underlying member method code with example usage: obj.transform(X) and returns: out