code: def get_surrogate_booster_pyspark(
      filtered_df, analyzer, max_depth, num_leaves, min_child_samples
  ):
      """Get surrogate booster for pyspark DataFrame.
  
      Creates the surrogate model trained on errors and returns the booster.
  
      :param filtered_df: The filtered DataFrame.
      :type filtered_df: pyspark.sql.DataFrame
      :param analyzer: The error analyzer containing the categorical
          features and categories for the full dataset.
      :type analyzer: BaseAnalyzer
      :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 extracted booster from the surrogate model and the
          scored dataset.
      :rtype: (Booster, pyspark.sql.DataFrame)
      """
      # compute the pred_y column
      scored_data = analyzer.model.transform(filtered_df.to_spark())
      diff_data = scored_data.withColumn(
          DIFF,
          F.when(
              F.col(analyzer.true_y) != F.col(PREDICTION), 1
          ).otherwise(0),
      )
      if analyzer.model_task == ModelTask.CLASSIFICATION:
          diff_data = diff_data.drop(
              PREDICTION, RAW_PREDICTION, PROBABILITY
          )
      else:
          diff_data = diff_data.drop(PREDICTION)
      model = create_surrogate_model_pyspark(
          analyzer, diff_data, max_depth, num_leaves, min_child_samples
      )
      model_str = model.getNativeModel()
      booster_args = {"objective": analyzer.model_task}
      lgbm_booster = Booster(params=booster_args, model_str=model_str)
      return lgbm_booster, diff_data.to_koalas()
  
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 5 for calling a global function with example usage: get_surrogate_booster_pyspark(filtered_df, analyzer, max_depth, num_leaves, min_child_samples) and returns: lgbm_booster,  diff_data