code: def generate_perturbed_logarithm_dataset(size):
      return np.random.randint(-50, 50, size=size) + 50.0 * np.log(
          1 + np.arange(size)
      )
  
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 1 for calling a global function with example usage: generate_perturbed_logarithm_dataset(size) and returns: unknown