code: def compute_error_metric(metric_value, sample_size):
      """Compute standard error of a given metric based on the assumption of
      normal distribution.
  
      Parameters:
      metric_value: Value of the metric
      sample_size: Number of data points associated with the metric
  
      Returns:
      The standard error of the metric
      """
      metric_value = metric_value / sample_size
      return (
          1.96
          * np.sqrt(metric_value * (1.0 - metric_value))
          / np.sqrt(sample_size)
      )
  
imports: import numpy
  import pandas
  import matplotlib.pyplot
  import lightgbm
  from sklearn.metrics import balanced_accuracy_score, roc_auc_score
  from sklearn.metrics import confusion_matrix
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from fairlearn.metrics import MetricFrame
  from fairlearn.metrics import count, selection_rate, equalized_odds_difference, false_positive_rate, false_negative_rate
  from fairlearn.postprocessing import ThresholdOptimizer
  from fairlearn.reductions import ExponentiatedGradient
  from fairlearn.reductions import EqualizedOdds
  from sklearn.model_selection import train_test_split
  import warnings
label_desc: Use image node_id 2 for calling a global function with example usage: compute_error_metric(metric_value, sample_size) and returns: unknown