code: def is_pareto_efficient(points):
      """Filter a NumPy Matrix to remove rows that are strictly dominated by
      another row in the matrix. Strictly dominated means the all the row values
      are greater than the values of another row.
  
      Parameters
      ----------
      Points: NumPy array (NxM) of model metrics.
          Assumption that smaller values for metrics are preferred.
  
      Returns
      -------
      Boolean Array
          Nx1 boolean mask representing the non-dominated indices.
      """
      n, m = points.shape
      is_efficient = np.ones(n, dtype=bool)
      for i, c in enumerate(points):
          if is_efficient[i]:
              is_efficient[is_efficient] = np.any(
                  points[is_efficient] < c, axis=1
              )
              is_efficient[i] = True
      return is_efficient
  
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 9 for calling a global function with example usage: is_pareto_efficient(points) and returns: is_efficient