code: def convert(model, feature_names, target):
      """Convert a boosted tree model to protobuf format.
  
      Parameters
      ----------
      decision_tree : RandomForestRegressor
          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, _ensemble.RandomForestRegressor
      )
  
      def is_rf_model(m):
          if len(m.estimators_) == 0:
              return False
          if hasattr(m, "estimators_") and m.estimators_ is not None:
              for t in m.estimators_:
                  if not hasattr(t, "tree_") or t.tree_ is None:
                      return False
              return True
          else:
              return False
  
      _sklearn_util.check_fitted(model, is_rf_model)
      return _MLModel(
          _convert_tree_ensemble(model, feature_names, target)
      )
  
imports: from ._tree_ensemble import convert_tree_ensemble
  from ._tree_ensemble import get_input_dimension
  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, False, True, False, False