code: def convert(model, input_name, output_features):
      """Convert a decision tree model to protobuf format.
  
      Parameters
      ----------
      decision_tree : DecisionTreeClassifier
          A trained scikit-learn tree model.
  
      input_name: str
          Name of the input columns.
  
      output_name: str
          Name of the output columns.
  
      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.DecisionTreeClassifier
      )
      _sklearn_util.check_fitted(
          model,
          lambda m: hasattr(m, "tree_") and model.tree_ is not None,
      )
  
      return _MLModel(
          convert_tree_ensemble(
              model,
              input_name,
              output_features,
              mode="classifier",
              class_labels=model.classes_,
          )
      )
  
imports: from ..._deps import _HAS_SKLEARN
  from ...models import MLModel
  from ._tree_ensemble import convert_tree_ensemble
label_desc: Use image node_id 1 for calling a global function with example usage: convert(model, input_name, output_features) and returns: _MLModel