code: def compute_json_error_tree(
      analyzer,
      features,
      filters,
      composite_filters,
      max_depth=DEFAULT_MAX_DEPTH,
      num_leaves=DEFAULT_NUM_LEAVES,
      min_child_samples=DEFAULT_MIN_CHILD_SAMPLES,
  ):
      """Computes the error tree for the given dataset.
  
      Note: this is for backcompat for older versions
      of raiwidgets pypi package
  
      :param analyzer: The error analyzer containing the categorical
          features and categories for the full dataset.
      :type analyzer: BaseAnalyzer
      :param features: The features to train the surrogate model on.
      :type features: numpy.ndarray or pandas.DataFrame
      :param filters: The filters to apply to the dataset.
      :type filters: numpy.ndarray or pandas.DataFrame
      :param composite_filters: The composite filters to apply to the dataset.
      :type composite_filters: numpy.ndarray or pandas.DataFrame
      :param max_depth: The maximum depth of the surrogate tree trained
          on errors.
      :type max_depth: int
      :param num_leaves: The number of leaves of the surrogate tree
          trained on errors.
      :type num_leaves: int
      :param min_child_samples: The minimal number of data required to
          create one leaf.
      :return: The tree representation as a list of nodes.
      :rtype: list[dict[str, str]]
      """
      return compute_error_tree(
          analyzer,
          features,
          filters,
          composite_filters,
          max_depth,
          num_leaves,
          min_child_samples,
      )
  
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 1 for calling a global function with example usage: compute_json_error_tree(analyzer, features, filters, composite_filters, max_depth, num_leaves, min_child_samples) and returns: compute_error_tree