code: def generate_logistic_dataset(size):
      X = np.sort(np.random.normal(size=size))
      return np.random.random(size=size) < expit(X)
  
file_path: scikit-learn/benchmarks/bench_isotonic.py
imports: import argparse
  import gc
  from datetime import datetime
  import matplotlib.pyplot
  import numpy
  from scipy.special import expit
  from sklearn.isotonic import isotonic_regression
label_desc: Use image node_id 2 for calling a global function with example usage: generate_logistic_dataset(size) and returns: unknown