class_docstr: Class for generating architectures of various types.
class_docstr_tok: ["Class","for","generating","architectures","of","various","types","."]
docstr: Generate a layer instance.
  
  Parameters
  ----------
  kls : str
      class type
  
  proba : bool
      whether to set ``proba`` to ``True``
  
  preprocessing : bool
      layer with preprocessing cases
code: def get_layer(self, kls, proba, preprocessing, *args, **kwargs):
      """Generate a layer instance.
  
      Parameters
      ----------
      kls : str
          class type
  
      proba : bool
          whether to set ``proba`` to ``True``
  
      preprocessing : bool
          layer with preprocessing cases
      """
      indexer, kwargs = self.load_indexer(kls, args, kwargs)
  
      learner_kwargs = {
          "proba": kwargs.pop("proba", proba),
          "scorer": kwargs.pop("scorer", None),
      }
  
      layer = Layer(name="layer", dtype=np.float64, **kwargs)
  
      if preprocessing:
          ests = ESTIMATORS_PROBA if proba else ESTIMATORS
          prep = PREPROCESSING
      else:
          ests = ECM_PROBA if proba else ECM
          prep = []
  
      group = make_group(
          indexer,
          ests,
          prep,
          learner_kwargs=learner_kwargs,
          name="group",
      )
      layer.push(group)
      return layer
  
imports: from __future__ import division, print_function, with_statement
  import os
  import shutil
  import warnings
  from abc import abstractmethod
  import numpy
  from ..utils.utils import pickle_save, pickle_load
  from ..utils.dummy import OLS, LogisticRegression, Scale
  from ..externals.sklearn.base import clone
  from ..index import INDEXERS
  from ..ensemble.base import Sequential
  from ..estimators import LayerEnsemble
  from ..parallel import ParallelProcessing, Learner, Transformer, Layer, make_group, Pipeline
label_desc: Use image node_id 3 for calling the EstimatorContainer obj's underlying member method code with example usage: obj.get_layer(kls, proba, preprocessing) and returns: layer