class_docstr: A pipeline model that exposes a sequence of models as a single model,
  It requires a set of inputs, a sequence of other models and a set of outputs.
  
  This class is the base class for :py:class:`PipelineClassifier` and
  :py:class:`PipelineRegressor`, which contain a sequence ending in a classifier
  or regressor and themselves behave like a classifier or regressor.  This class
  may be used directly for a sequence of feature transformer objects.
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docstr: Create a pipeline of models to be executed sequentially.
  
  Parameters
  ----------
  
  input_features: [list of 2-tuples]
      Name(s) of the input features, given as a list of `('name', datatype)`
      tuples.  The datatypes entry can be any of the data types defined in the
      :py:mod:`models.datatypes` module.
  
  output_features: [list of features]
      Name(s) of the output features, given as a list of
      `('name',datatype)` tuples.  The datatypes entry can be any of the
      data types defined in the :py:mod:`models.datatypes` module.  All features
      must be either defined in the inputs or be produced by one of the
      contained models.
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code: def __init__(
      self, input_features, output_features, training_features=None
  ):
      """
      Create a pipeline of models to be executed sequentially.
  
      Parameters
      ----------
  
      input_features: [list of 2-tuples]
          Name(s) of the input features, given as a list of `('name', datatype)`
          tuples.  The datatypes entry can be any of the data types defined in the
          :py:mod:`models.datatypes` module.
  
      output_features: [list of features]
          Name(s) of the output features, given as a list of
          `('name',datatype)` tuples.  The datatypes entry can be any of the
          data types defined in the :py:mod:`models.datatypes` module.  All features
          must be either defined in the inputs or be produced by one of the
          contained models.
  
      """
      spec = _Model_pb2.Model()
      spec.specificationVersion = _SPECIFICATION_VERSION
  
      # Access this to declare it as a pipeline
      spec.pipeline
  
      spec = set_transform_interface_params(
          spec, input_features, output_features, training_features
      )
  
      # Save the spec as a member variable.
      self.spec = spec
  
imports: from ..None import SPECIFICATION_VERSION
  from ..proto import Model_pb2
  from .None import _feature_management
  from .None import model
  from ._interface_management import set_classifier_interface_params, set_regressor_interface_params, set_training_features, set_transform_interface_params
label_desc: Use image node_id 1 to create a new Pipeline object  with example: obj = Pipeline(input_features, output_features, training_features)