code: def compute_metrics_local(df, metric, total, classes):
      """Compute the metric value for a given local pandas DataFrame.
  
      :param df: The DataFrame to compute the metric on.
      :type df: pandas.DataFrame
      :param metric: The metric to compute.
      :type metric: str
      :param total: The total number of rows in the DataFrame.
      :type total: int
      :return: The metric value and success/error counts.
      :rtype: tuple(float, int, int)
      """
      success = 0
      if metric != Metrics.ERROR_RATE and df.shape[0] == 0:
          metric_value = 0
          error = 0
      elif metric == Metrics.MEAN_ABSOLUTE_ERROR:
          pred_y, true_y, error = get_regression_metric_data(df)
          metric_value = mean_absolute_error(true_y, pred_y)
      elif metric == Metrics.MEAN_SQUARED_ERROR:
          pred_y, true_y, error = get_regression_metric_data(df)
          metric_value = mean_squared_error(true_y, pred_y)
      elif metric == Metrics.MEDIAN_ABSOLUTE_ERROR:
          pred_y, true_y, error = get_regression_metric_data(df)
          metric_value = median_absolute_error(true_y, pred_y)
      elif metric == Metrics.R2_SCORE:
          pred_y, true_y, error = get_regression_metric_data(df)
          metric_value = r2_score(true_y, pred_y)
      elif (
          metric in precision_metrics
          or metric in recall_metrics
          or metric in f1_metrics
          or metric == Metrics.ACCURACY_SCORE
      ):
          pred_y, true_y, error = get_classification_metric_data(df)
          func = metric_to_func[metric]
          metric_value = compute_metric_value(
              func, classes, true_y, pred_y, metric
          )
          success = total - error
      else:
          func = metric_to_func[metric]
          diff = df[DIFF]
          metric_value = func(None, None, diff)
          error = metric_value * total
          success = total - error
      return metric_value, success, 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 18 for calling a global function with example usage: compute_metrics_local(df, metric, total, classes) and returns: metric_value,  success,  error