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.
  In this case the pipeline itself behaves as a regression model by designating
  a real valued output feature as its 'predicted feature'.
class_docstr_tok: ["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",".","In","this","case","the","pipeline","itself","behaves","as","a","regression","model","by","designating","a","real","valued","output","feature","as","its","'predicted","feature","'","."]
code: def make_updatable(self):
      super()._validate_sub_models_and_make_updatable(
          self.spec.pipelineRegressor.pipeline, self.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 3 for calling the PipelineRegressor obj's underlying member method code with example usage: obj.make_updatable() without return types