class_docstr: Transformer pipeline
  
  Pipeline class for wrapping a preprocessing pipeline of transformers.
  
  .. versionadded: 0.2.0
  
  Parameters
  ----------
  pipeline : list, instance
      A :class:`~mlens.parallel.Transformer` instance or a list of
      :class:`~mlens.parallel.Transformer`
      instances. Accepted input formats::
  
          option_1 = transformer_1
          option_2 = [transformer_1, transformer_2]
          option_3 = [("tr-1", transformer_1), ("tr-2", transformer_2)]
          option_4 = [transformer_1, ("tr-2", transformer_2)]
  
  name : str, optional
      name of pipeline.
  
  return_y : bool, default = False
      If True, both X and y will be returned in a
      :func:`~mlens.parallel.handles.Pipeline.transform` call.
code: def get_params(self, deep=True):
      out = super(Pipeline, self).get_params(deep)
      if not deep:
          return out
  
      if self.pipeline:
          for tr_name, tr in self.pipeline:
              for k, v in tr.get_params(deep=True).items():
                  out["%s__%s" % (tr_name, k)] = v
                  out[tr_name] = tr
      return out
  
imports: from .base import BaseEstimator
  from .learner import Learner, Transformer
  from ._base_functions import mold_objects, transform
  from ..utils import format_name, check_instances
  from ..utils.formatting import _check_instances
  from ..externals.sklearn.base import clone, BaseEstimator
label_desc: Use image node_id 7 for calling the Pipeline obj's underlying member method code with example usage: obj.get_params(deep) and returns: out, out