code: def test_isotonic_sample_weight_parameter_default_value():
      # check if default value of sample_weight parameter is one
      ir = IsotonicRegression()
      # random test data
      rng = np.random.RandomState(42)
      n = 100
      x = np.arange(n)
      y = rng.randint(-50, 50, size=(n,)) + 50.0 * np.log(
          1 + np.arange(n)
      )
      # check if value is correctly used
      weights = np.ones(n)
      y_set_value = ir.fit_transform(x, y, sample_weight=weights)
      y_default_value = ir.fit_transform(x, y)
  
      assert_array_equal(y_set_value, y_default_value)
  
imports: import copy
  import pickle
  import warnings
  import numpy
  import pytest
  from scipy.special import expit
  import sklearn
  from sklearn.datasets import make_regression
  from sklearn.isotonic import IsotonicRegression, _make_unique, check_increasing, isotonic_regression
  from sklearn.utils import shuffle
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.validation import check_array
label_desc: Use image node_id 17 for calling a global function with example usage: test_isotonic_sample_weight_parameter_default_value() without return types