code: def create_surrogate_model_pyspark(
      analyzer, dataset, max_depth, num_leaves, min_child_samples
  ):
      """Creates and fits the surrogate lightgbm model.
  
      :param analyzer: The error analyzer containing the categorical
          features and categories for the full dataset.
      :type analyzer: BaseAnalyzer
      :param dataset: The subset of features to train the
          surrogate model on.
      :type dataset: numpy.ndarray or pandas.DataFrame
      :param diff: The difference between the true and predicted labels column.
      :type diff: numpy.ndarray
      :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.
      :type min_child_samples: int
      :return: The trained surrogate model.
      :rtype: LightGBMClassifier or LightGBMRegressor
      """
      if analyzer.model_task == ModelTask.CLASSIFICATION:
          surrogate = LightGBMClassifier(
              numIterations=1,
              maxDepth=max_depth,
              numLeaves=num_leaves,
              minDataInLeaf=min_child_samples,
              labelCol=DIFF,
          )
      else:
          surrogate = LightGBMRegressor(
              numIterations=1,
              maxDepth=max_depth,
              numLeaves=num_leaves,
              minDataInLeaf=min_child_samples,
              labelCol=DIFF,
          )
      return surrogate.fit(dataset)
  
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 6 for calling a global function with example usage: create_surrogate_model_pyspark(analyzer, dataset, max_depth, num_leaves, min_child_samples) and returns: surrogate