code: def filter_dominated_rows(points):
      """Remove rows from a DataFrame that are monotonically dominated by
      another row in the DataFrame.
  
      Parameters
      ----------
      Points: DataFrame where each row represents the summarized performance
              (balanced accuracy, fairness metric) of an inner model.
  
      Returns
      -------
      pareto mask: Boolean mask representing indices of input DataFrame that are not monotonically dominated.
      masked_DataFrame: DataFrame with dominated rows filtered out.
  
      """
      pareto_mask = is_pareto_efficient(points.to_numpy())
      return pareto_mask, points.loc[pareto_mask, :]
  
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 10 for calling a global function with example usage: filter_dominated_rows(points) and returns: pareto_mask,  points