code: def get_classification_metric_data(df):
      """Compute classification metric data from a DataFrame.
  
      :param df: DataFrame
      :type df: pandas.DataFrame
      :return: pred_y, true_y, error
      :rtype: numpy.ndarray, numpy.ndarray, int
      """
      pred_y = df[PRED_Y]
      true_y = df[TRUE_Y]
      error = df[DIFF].values.sum()
      return pred_y, true_y, error
  
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 22 for calling a global function with example usage: get_classification_metric_data(df) and returns: pred_y,  true_y,  error