code: def false_negative_error(y_true, y_pred):
      """Compute the standard error for the false negative rate estimate."""
      tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
      return compute_error_metric(fn, fn + tp)
  
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 4 for calling a global function with example usage: false_negative_error(y_true, y_pred) and returns: compute_error_metric