class_docstr: Layer of preprocessing pipes and estimators.
  
  Layer is an internal class that holds a layer and its associated data
  including an estimation procedure. It behaves as an estimator from an
  Scikit-learn API point of view.
  
  Parameters
  ----------
  propagate_features : list, range, optional
      Features to propagate from the input array to the output array.
      Carries input features to the output of the layer, useful for
      propagating original data through several stacked layers. Propagated
      features are stored in the left-most columns.
  
  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 preprocessing and estimators
  
          - ``verbose = 3`` messages for completed job
  
      If ``verbose >= 10`` prints to ``sys.stderr``, else ``sys.stdout``.
  
  shuffle : bool (default = False)
      Whether to shuffle data before fitting layer.
  
  random_state : obj, int, optional
      Random seed number to use for shuffling inputs
  
  **kwargs : optional
      optional arguments to :class:`BaseParallel`.
class_docstr_tok: ["Layer","of","preprocessing","pipes","and","estimators",".","Layer","is","an","internal","class","that","holds","a","layer","and","its","associated","data","including","an","estimation","procedure",".","It","behaves","as","an","estimator","from","an","Scikit-learn","API","point","of","view",".","Parameters","--","--","--","--","--","propagate_features",":","list",",","range",",","optional","Features","to","propagate","from","the","input","array","to","the","output","array",".","Carries","input","features","to","the","output","of","the","layer",",","useful","for","propagating","original","data","through","several","stacked","layers",".","Propagated","features","are","stored","in","the","left-most","columns",".","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","preprocessing","and","estimators","-","``","verbose","=","3","``","messages","for","completed","job","If","``","verbose",">","=","10","``","prints","to","``","sys.stderr","``",",","else","``","sys.stdout","``",".","shuffle",":","bool","(","default","=","False",")","Whether","to","shuffle","data","before","fitting","layer",".","random_state",":","obj",",","int",",","optional","Random","seed","number","to","use","for","shuffling","inputs","*","*","kwargs",":","optional","optional","arguments","to",":","class",":","`","BaseParallel","`","."]
docstr: Cross validated scores
docstr_tok: ["Cross","validated","scores"]
code: def data(self):
      """Cross validated scores"""
      return Data(self.raw_data)
  
imports: from __future__ import division, print_function
  from .base import OutputMixin, IndexMixin, BaseStacker
  from ..utils import time, print_time, safe_print, format_name
  from ..utils.exceptions import NotFittedError
  from ..externals.joblib import delayed
  from ..metrics import Data
label_desc: Use image node_id 10 for calling the Layer obj's underlying member method code with example usage: obj.data() and returns: Data