code: def convert(model, input_features, output_features):
      """Convert a _imputer model to the protobuf spec.
  
      Parameters
      ----------
      model: Imputer
          A trained Imputer model.
  
      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, StandardScaler)
      _sklearn_util.check_fitted(model, lambda m: hasattr(m, "mean_"))
      _sklearn_util.check_fitted(model, lambda m: hasattr(m, "scale_"))
  
      # Set the interface params.
      spec = _Model_pb2.Model()
      spec.specificationVersion = SPECIFICATION_VERSION
      spec = _set_transform_interface_params(
          spec, input_features, output_features
      )
  
      # Set the parameters
      tr_spec = spec.scaler
      for x in model.mean_:
          tr_spec.shiftValue.append(-x)
  
      for x in model.scale_:
          tr_spec.scaleValue.append(1.0 / x)
  
      return _MLModel(spec)
  
file_path: turicreate/src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_standard_scaler.py
imports: from ...None import SPECIFICATION_VERSION
  from ...models._interface_management import set_transform_interface_params
  from ...proto import Model_pb2
  from ...proto import FeatureTypes_pb2
  from ..._deps import _HAS_SKLEARN
  from ...models import MLModel
label_desc: Use image node_id 1 for calling a global function with example usage: convert(model, input_features, output_features) and returns: _MLModel