parameters: (model, input_features, output_features)
return_statement: return _MLModel(spec)
docstring: Convert a normalizer model to the protobuf spec.
  
      Parameters
      ----------
      model: Normalizer
          A Normalizer.
  
      input_features: str
          Name of the input column.
  
      output_features: str
          Name of the output column.
  
      Returns
      -------
      model_spec: An object of type Model_pb.
          Protobuf representation of the model
docstring_summary: Convert a normalizer model to the protobuf spec.
docstring_tokens: Convert a normalizer model to the protobuf spec .
function: def convert(model, input_features, output_features):
      """Convert a normalizer model to the protobuf spec.
  
      Parameters
      ----------
      model: Normalizer
          A Normalizer.
  
      input_features: str
          Name of the input column.
  
      output_features: str
          Name of the output column.
  
      Returns
      -------
      model_spec: An object of type Model_pb.
          Protobuf representation of the model
      """
  
      if not (_HAS_SKLEARN):
          raise RuntimeError(
              "scikit-learn not found. scikit-learn conversion API is disabled."
          )
  
      # Test the scikit-learn model
      _sklearn_util.check_expected_type(model, Normalizer)
      _sklearn_util.check_fitted(model, lambda m: hasattr(m, "norm"))
  
      # Set the interface params.
      spec = _Model_pb2.Model()
      spec.specificationVersion = SPECIFICATION_VERSION
      spec = _set_transform_interface_params(spec, input_features, output_features)
  
      # Set the one hot encoder parameters
      _normalizer_spec = spec.normalizer
      if model.norm == "l1":
          _normalizer_spec.normType = _proto__normalizer.L1
      elif model.norm == "l2":
          _normalizer_spec.normType = _proto__normalizer.L2
      elif model.norm == "max":
          _normalizer_spec.normType = _proto__normalizer.LMax
      return _MLModel(spec)
function_tokens: def convert ( model , input_features , output_features ) : if not ( _HAS_SKLEARN ) : raise RuntimeError ( "scikit-learn not found. scikit-learn conversion API is disabled." ) # Test the scikit-learn model _sklearn_util . check_expected_type ( model , Normalizer ) _sklearn_util . check_fitted ( model , lambda m : hasattr ( m , "norm" ) ) # Set the interface params. spec = _Model_pb2 . Model ( ) spec . specificationVersion = SPECIFICATION_VERSION spec = _set_transform_interface_params ( spec , input_features , output_features ) # Set the one hot encoder parameters _normalizer_spec = spec . normalizer if model . norm == "l1" : _normalizer_spec . normType = _proto__normalizer . L1 elif model . norm == "l2" : _normalizer_spec . normType = _proto__normalizer . L2 elif model . norm == "max" : _normalizer_spec . normType = _proto__normalizer . LMax return _MLModel ( spec )