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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def convert_tree_ensemble(model, input_features, output_features = ('predicted_class', float), mode = 'regressor', base_prediction = None, class_labels = None, post_evaluation_transform = None):
 """ Convert a generic tree regressor model to the protobuf spec. This currently supports: * Decision tree regression * Gradient boosted tree regression * Random forest regression * Decision tree classifier. * Gradient boosted tree classifier. * Random forest classifier. Parameters model: [DecisionTreeRegressor | GradientBoostingRegression | RandomForestRegressor] A scikit learn tree model. feature_names : list of strings, optional (default=None) Names of each of the features. target: str Name of the output column. base_prediction: double Base prediction value. mode: str in ['regressor', 'classifier'] Mode of the tree model. class_labels: list[int] List of classes post_evaluation_transform: list[int] Post evaluation transform Returns ------- model_spec: An object of type Model_pb. Protobuf representation of the model """


    num_dimensions = get_input_dimension(model)
    features = process_or_validate_features(input_features, num_dimensions)

    n_classes = None
    if mode == 'classifier':
        n_classes = model.n_classes_
        if class_labels is None:
            class_labels = range(n_classes)
        else:
            if len(class_labels) != n_classes:
                raise ValueError("Number of classes in model (%d) does not match "
                                 "length of supplied class list (%d)."
                                 % (n_classes, len(class_labels)))

        coreml_tree = TreeEnsembleClassifier(input_features, class_labels, output_features)
        if post_evaluation_transform is not None:
            coreml_tree.set_post_evaluation_transform(post_evaluation_transform)

        # Base prediction not provided
        if base_prediction is None:
            if n_classes == 2:
                base_prediction = [0.0]
            else:
                base_prediction = [0.0 for c in range(n_classes)]
        coreml_tree.set_default_prediction_value(base_prediction)
    else:
        if base_prediction is None:
            base_prediction = 0.0
        coreml_tree = TreeEnsembleRegressor(input_features, output_features)
        coreml_tree.set_default_prediction_value(base_prediction)

    # Single tree
    if hasattr(model, 'tree_'):
        _recurse(coreml_tree, model.tree_, tree_id = 0, node_id = 0,
                               mode = mode, n_classes = n_classes)

    # Multiple trees
    elif hasattr(model, 'estimators_'):
        is_ensembling_in_separate_trees = False
        if type(model.estimators_) != list:
            is_ensembling_in_separate_trees = len(model.estimators_.shape) > 0 and model.estimators_.shape[1] > 1
            estimators = model.estimators_.flatten()
        else:
            estimators = model.estimators_

        scaling = model.learning_rate if hasattr(model, 'learning_rate') else 1.0 / len(estimators)
        for tree_id, base_model in enumerate(estimators):
            if is_ensembling_in_separate_trees:
                tree_index = tree_id % n_classes
            else:
                tree_index = 0
            _recurse(coreml_tree, base_model.tree_, tree_id, node_id = 0,
                    scaling = scaling, mode = mode, n_classes = n_classes, tree_index = tree_index)
    else:
        raise TypeError('Unknown scikit-learn tree model type.')

    return coreml_tree.spec