code: def traverse(
      df,
      tree,
      max_split_index,
      categories,
      dict,
      feature_names,
      parent=None,
      side=TreeSide.UNKNOWN,
      metric=None,
      classes=None,
  ):
      """Traverses the current node in the tree to create a list of nodes.
  
      :param df: The DataFrame containing the features and labels.
      :type df: pandas.DataFrame
      :param tree: The current node in the tree to traverse.
      :type tree: dict
      :param max_split_index: The max split index for the tree.
      :type max_split_index: int
      :param categories: The list of categorical features and categories.
      :type categories: list[tuple]
      :param dict: The dictionary to store the nodes in.
      :type dict: dict
      :param feature_names: The list of feature names.
      :type feature_names: list[str]
      :param parent: The parent node of the current node.
      :type parent: Node or None
      :param side: The side of the parent node the current node is on.
      :type side: TreeSide
      :param metric: The metric to use for the current node.
      :type metric: str
      :param classes: The list of classes for the current node.
      :type classes: list[str]
      :return: The tree representation as a list of nodes.
      :rtype: list[dict[str, str]]
      """
      if SPLIT_INDEX in tree:
          nodeid = tree[SPLIT_INDEX]
      elif LEAF_INDEX in tree:
          nodeid = max_split_index + tree[LEAF_INDEX]
      else:
          nodeid = 0
  
      # reduce DataFrame to just features split on at each step for perf
      if not is_spark(df):
          df = filter_to_used_features(df, tree)
  
      # write current node to a dictionary that can be saved as JSON
      dict, df = node_to_dict(
          df,
          tree,
          nodeid,
          categories,
          dict,
          feature_names,
          metric,
          parent,
          side,
          classes,
      )
  
      # write children to a dictionary that can be saved as JSON
      if LEAF_VALUE not in tree:
          left_child = tree[TreeSide.LEFT_CHILD]
          right_child = tree[TreeSide.RIGHT_CHILD]
          dict = traverse(
              df,
              left_child,
              max_split_index,
              categories,
              dict,
              feature_names,
              tree,
              TreeSide.LEFT_CHILD,
              metric,
              classes,
          )
          dict = traverse(
              df,
              right_child,
              max_split_index,
              categories,
              dict,
              feature_names,
              tree,
              TreeSide.RIGHT_CHILD,
              metric,
              classes,
          )
      return dict
  
imports: import logging
  import numbers
  from enum import Enum
  import numpy
  import pandas
  from lightgbm import Booster, LGBMClassifier, LGBMRegressor
  from sklearn.metrics import mean_absolute_error, mean_squared_error, median_absolute_error, r2_score
  from erroranalysis._internal.cohort_filter import filter_from_cohort
  from erroranalysis._internal.constants import DIFF, LEAF_INDEX, METHOD, PRED_Y, ROW_INDEX, SPLIT_FEATURE, SPLIT_INDEX, TRUE_Y, CohortFilterMethods, Metrics, ModelTask, TreeNode, error_metrics, f1_metrics, metric_to_display_name, precision_metrics, recall_metrics, regression_metrics
  from erroranalysis._internal.metrics import get_ordered_classes, metric_to_func
  from erroranalysis._internal.process_categoricals import process_categoricals
  from erroranalysis._internal.utils import is_spark
  from raiutils.exceptions import UserConfigValidationException
label_desc: Use image node_id 10 for calling a global function with example usage: traverse(df, tree, max_split_index, categories, dict, feature_names, parent, side, metric, classes) and returns: dict