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.
docstr: Fit and transform pipeline.
  
  Note that the :class:`Pipeline` accepts both X and y arguments, and
  can return both X and y, depending on the transformers. The
  pipeline itself does no checks on the input.
  
  Parameters
  ----------
  X : array-like of shape [n_samples, n_features]
      Input data
  
  y : array-like of shape [n_samples, ]
      Targets
  
  Returns
  -------
  X_processed : array-like of shape [n_samples, n_preprocessed_features]
      Preprocessed input data
  
  y : array-like of shape [n_samples, ], optional
      Preprocessed targets
docstr_tok: ["Fit","and","transform","pipeline",".","Note","that","the",":","class",":","`","Pipeline","`","accepts","both","X","and","y","arguments",",","and","can","return","both","X","and","y",",","depending","on","the","transformers",".","The","pipeline","itself","does","no","checks","on","the","input",".","Parameters","--","--","--","--","--","X",":","array-like","of","shape","[","n_samples",",","n_features","]","Input","data","y",":","array-like","of","shape","[","n_samples",",","]","Targets","Returns","--","--","--","-","X_processed",":","array-like","of","shape","[","n_samples",",","n_preprocessed_features","]","Preprocessed","input","data","y",":","array-like","of","shape","[","n_samples",",","]",",","optional","Preprocessed","targets"]
code: def fit_transform(self, X, y=None):
      """Fit and transform pipeline.
  
      Note that the :class:`Pipeline` accepts both X and y arguments, and
      can return both X and y, depending on the transformers. The
      pipeline itself does no checks on the input.
  
      Parameters
      ----------
      X : array-like of shape [n_samples, n_features]
          Input data
  
      y : array-like of shape [n_samples, ]
          Targets
  
      Returns
      -------
      X_processed : array-like of shape [n_samples, n_preprocessed_features]
          Preprocessed input data
  
      y : array-like of shape [n_samples, ], optional
          Preprocessed targets
      """
      return self._run(True, True, X, y)
  
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 6 for calling the Pipeline obj's underlying member method code with example usage: obj.fit_transform(X, y) and returns: self