code: def f(truelabels, predictions):
      # Define race to be the sensitive variable
      sensitive = test.Race
  
      # Define a MetricFrame using a FPR of the defined sensitive features, using the true labels and
      # predictions
      fmetrics = MetricFrame(
          metrics=false_positive_rate,
          y_true=truelabels,
          y_pred=predictions,
          sensitive_features=sensitive,
      )
  
      # Compute the Relative FPR relative to white individuals.
      results = pd.DataFrame(
          [
              fmetrics.by_group,
              fmetrics.by_group / fmetrics.by_group.White,
          ],
          index=["FPR", "Relative FPR"],
      )
      return results
  
file_path: fairlearn/examples/plot_intersectional_bias.py
imports: import numpy
  import pandas
  import matplotlib.pyplot
  import sklearn.metrics
  import seaborn
  from sklearn.datasets import fetch_openml
  from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
  from sklearn.linear_model import LogisticRegression
  from fairlearn.metrics import MetricFrame, false_positive_rate
label_desc: Use image node_id 4 for calling a global function with example usage: f(truelabels, predictions) and returns: results