code: def test_traverse_tree(self, string_labels, metric):
      (
          X_train,
          X_test,
          y_train,
          y_test,
          categorical_features,
      ) = create_adult_census_data(string_labels)
      model = create_kneighbors_classifier(X_train, y_train)
      feature_names = list(X_train.columns)
      error_analyzer = ModelAnalyzer(
          model,
          X_test,
          y_test,
          feature_names,
          categorical_features,
          metric=metric,
      )
      categorical_info = get_categorical_info(
          error_analyzer, feature_names
      )
      cat_ind_reindexed, categories_reindexed = categorical_info
      pred_y = model.predict(X_test)
      diff = pred_y != y_test
      max_depth = 3
      num_leaves = 31
      min_child_samples = 20
      surrogate = create_surrogate_model(
          error_analyzer,
          X_test,
          diff,
          max_depth,
          num_leaves,
          min_child_samples,
          cat_ind_reindexed,
      )
      model_json = surrogate._Booster.dump_model()
      tree_structure = model_json["tree_info"][0]["tree_structure"]
      max_split_index = get_max_split_index(tree_structure) + 1
      filtered_indexed_df = X_test
      filtered_indexed_df[DIFF] = diff
      filtered_indexed_df[TRUE_Y] = y_test
      filtered_indexed_df[PRED_Y] = pred_y
      cache_subtree_features(tree_structure, feature_names)
      tree = traverse(
          filtered_indexed_df,
          tree_structure,
          max_split_index,
          (categories_reindexed, cat_ind_reindexed),
          [],
          feature_names,
          metric=error_analyzer.metric,
      )
      # create dictionary from json tree id to values
      tree_dict = {}
      for entry in tree:
          tree_dict[entry["id"]] = entry
      validate_traversed_tree(
          tree_structure,
          tree_dict,
          max_split_index,
          feature_names,
          filtered_indexed_df,
      )
  
imports: import re
  import time
  from enum import Enum
  import numpy
  import pandas
  import pytest
  from common_utils import replicate_dataset
  from erroranalysis._internal.cohort_filter import filter_from_cohort
  from erroranalysis._internal.constants import ARG, COLUMN, COMPOSITE_FILTERS, DIFF, LEAF_INDEX, METHOD, OPERATION, PRED_Y, ROW_INDEX, SPLIT_FEATURE, SPLIT_INDEX, TRUE_Y, CohortFilterMethods, CohortFilterOps, Metrics, ModelTask, regression_metrics
  from erroranalysis._internal.error_analyzer import ModelAnalyzer, PredictionsAnalyzer
  from erroranalysis._internal.surrogate_error_tree import TreeSide, cache_subtree_features, compute_error_tree, create_surrogate_model, get_categorical_info, get_max_split_index, traverse
  from rai_test_utils.datasets.tabular import create_adult_census_data, create_binary_classification_dataset, create_cancer_data, create_diabetes_data, create_iris_data, create_simple_titanic_data
  from rai_test_utils.models.model_utils import create_models_classification
  from rai_test_utils.models.sklearn import create_kneighbors_classifier, create_sklearn_random_forest_regressor, create_titanic_pipeline
  from raiutils.exceptions import UserConfigValidationException
label_desc: Use image node_id 5 for calling the TestSurrogateErrorTree obj's underlying member method code with example usage: obj.test_traverse_tree(string_labels, metric) without return types