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 __init__(self, pipeline, name=None, return_y=False):
      self.name = format_name(name, "pipeline", GLOBAL_PIPELINE_NAMES)
      self.pipeline = _check_instances(pipeline)
      self.return_y = return_y
      self._pipeline = None
  
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 1 to create a new Pipeline object from inherited base classes: _BaseEstimator with example: obj = Pipeline(pipeline, name, return_y)