code: def convert(model, feature_names, target):
      """Convert a decision tree model to protobuf format.
  
      Parameters
      ----------
      decision_tree : DecisionTreeRegressor
          A trained scikit-learn tree model.
  
      feature_names: [str]
          Name of the input columns.
  
      target: 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."
          )
  
      _sklearn_util.check_expected_type(
          model, _tree.DecisionTreeRegressor
      )
      _sklearn_util.check_fitted(
          model,
          lambda m: hasattr(m, "tree_") and model.tree_ is not None,
      )
      return _MLModel(
          _convert_tree_ensemble(model, feature_names, target)
      )
  
imports: from ._tree_ensemble import convert_tree_ensemble
  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, feature_names, target) and returns: _MLModel