code: def compare_metricframe_results(mframe_1, mframe_2, metrics, names):
      """Concatenate the results of two MetricFrames along a subset of metrics.
  
      Parameters
      ----------
      mframe_1: First MetricFrame for comparison
      mframe_2: Second MetricFrame for comparison
      metrics: The subset of metrics for comparison
      names: The names of the selected metrics
  
      Returns
      -------
      MetricFrame : MetricFrame
          The concatenation of the two MetricFrames, restricted to the metrics
          specified.
  
      """
      return pd.concat(
          [mframe_1.by_group[metrics], mframe_2.by_group[metrics]],
          keys=names,
          axis=1,
      )
  
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 7 for calling a global function with example usage: compare_metricframe_results(mframe_1, mframe_2, metrics, names) and returns: pd