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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 _update_tree_feature_weights(X, feature_names, clf, feature_weights):
 """ Update tree feature weights using decision path method. """

    tree_value = clf.tree_.value
    if tree_value.shape[1] == 1:
        squeeze_axis = 1
    else:
        assert tree_value.shape[2] == 1
        squeeze_axis = 2
    tree_value = np.squeeze(tree_value, axis=squeeze_axis)
    tree_feature = clf.tree_.feature
    _, indices = clf.decision_path(X).nonzero()
    if isinstance(clf, DecisionTreeClassifier):
        norm = lambda x: x / x.sum()
    else:
        norm = lambda x: x
    feature_weights[feature_names.bias_idx] += norm(tree_value[0])
    for parent_idx, child_idx in zip(indices, indices[1:]):
        assert tree_feature[parent_idx] >= 0
        feature_idx = tree_feature[parent_idx]
        diff = norm(tree_value[child_idx]) - norm(tree_value[parent_idx])
        feature_weights[feature_idx] += diff