code: def extract_score(cv_results):
      lr = pd.DataFrame(
          {
              "positive": cv_results["test_positive_likelihood_ratio"],
              "negative": cv_results["test_negative_likelihood_ratio"],
          }
      )
      return lr.aggregate(["mean", "std"])
  
file_path: scikit-learn/examples/model_selection/plot_likelihood_ratios.py
imports: from sklearn.datasets import make_classification
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import class_likelihood_ratios
  import pandas
  from sklearn.model_selection import cross_validate
  from sklearn.dummy import DummyClassifier
  from collections import defaultdict
  import matplotlib.pyplot
  import numpy
  from sklearn.inspection import DecisionBoundaryDisplay
label_desc: Use image node_id 2 for calling a global function with example usage: extract_score(cv_results) and returns: lr