code: def create_surrogate_model(
      analyzer,
      dataset_sub_features,
      diff,
      max_depth,
      num_leaves,
      min_child_samples,
      cat_ind_reindexed,
  ):
      """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_sub_features: The subset of features to train the
          surrogate model on.
      :type dataset_sub_features: 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
      :param cat_ind_reindexed: The list of categorical feature indexes.
      :type cat_ind_reindexed: list[int]
      :return: The trained surrogate model.
      :rtype: LGBMClassifier or LGBMRegressor
      """
      if analyzer.model_task == ModelTask.CLASSIFICATION:
          surrogate = LGBMClassifier(
              n_estimators=1,
              max_depth=max_depth,
              num_leaves=num_leaves,
              min_child_samples=min_child_samples,
              verbosity=-1,
          )
      else:
          surrogate = LGBMRegressor(
              n_estimators=1,
              max_depth=max_depth,
              num_leaves=num_leaves,
              min_child_samples=min_child_samples,
              verbosity=-1,
          )
      try:
          if cat_ind_reindexed:
              surrogate.fit(
                  dataset_sub_features,
                  diff,
                  categorical_feature=cat_ind_reindexed,
              )
          else:
              surrogate.fit(dataset_sub_features, diff)
      except ValueError as ve:
          # throw user exception for bad model prediction output
          if "y should be a 1d array, got an array of shape" in str(ve):
              diff_shape = diff.shape
              invalid_shape = len(diff_shape) > 2
              invalid_2d_dims = (
                  len(diff_shape) == 2 and diff_shape[1] != 1
              )
              if invalid_shape or invalid_2d_dims:
                  raise UserConfigValidationException(
                      "The surrogate model could not be trained. "
                      + "The shape of the diff array is invalid: {}. ".format(
                          diff_shape
                      )
                      + "Please check the prediction function of the model."
                  )
          # re-raise any unknown system error
          raise ve
      return surrogate
  
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 7 for calling a global function with example usage: create_surrogate_model(analyzer, dataset_sub_features, diff, max_depth, num_leaves, min_child_samples, cat_ind_reindexed) and returns: surrogate