code: 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)
  
file_path: coremltools/coremltools/converters/sklearn/_normalizer.py
imports: from ...None import SPECIFICATION_VERSION
  from ..._deps import _HAS_SKLEARN
  from ...models import MLModel
  from ...models._interface_management import set_transform_interface_params
  from ...proto import Model_pb2
  from ...proto.Normalizer_pb2 import Normalizer
label_desc: Use image node_id 1 for calling a global function with example usage: convert(model, input_features, output_features) and returns: _MLModel