code: def bootstrap_means(preds, selector, n=2500, k=25):
      grp1 = np.random.choice(preds[selector], (n, k)).mean(axis=1)
      grp2 = np.random.choice(preds[~selector], (n, k)).mean(axis=1)
      return grp1 - grp2
  
imports: from pathlib import Path
  import matplotlib.pylab
  import seaborn
  import matplotlib.pylab
  import seaborn
  from sklearn.datasets import load_boston
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.linear_model import LinearRegression
  import pandas
  from sklearn.linear_model import LogisticRegression
  from sklego.metrics import p_percent_score
  import numpy
  import pandas
  from sklego.metrics import equal_opportunity_score
  import types
  import pandas
  from sklearn.datasets import load_boston
  from sklego.preprocessing import InformationFilter
  import matplotlib.pylab
  import seaborn
  from sklearn.metrics import mean_squared_error
  import matplotlib.pylab
  import seaborn
  from sklearn.metrics import mean_squared_error
  import matplotlib.pylab
  import seaborn
  from sklearn.metrics import mean_squared_error
  from sklego.linear_model import DemographicParityClassifier
  from sklearn.linear_model import LogisticRegression
  from sklego.metrics import p_percent_score
  from sklearn.metrics import accuracy_score, make_scorer
  from sklearn.model_selection import GridSearchCV
  import matplotlib.pylab
  import seaborn
  from sklego.linear_model import EqualOpportunityClassifier
  import matplotlib.pylab
  import seaborn
label_desc: Use image node_id 2 for calling a global function with example usage: bootstrap_means(preds, selector, n, k) and returns: unknown