code: def test_subsample():
      # Make sure bin thresholds are different when applying subsampling
      mapper_no_subsample = _BinMapper(
          subsample=None, random_state=0
      ).fit(DATA)
      mapper_subsample = _BinMapper(subsample=256, random_state=0).fit(
          DATA
      )
  
      for feature in range(DATA.shape[1]):
          assert not np.allclose(
              mapper_no_subsample.bin_thresholds_[feature],
              mapper_subsample.bin_thresholds_[feature],
              rtol=1e-4,
          )
  
imports: import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_equal
  from sklearn.ensemble._hist_gradient_boosting.binning import _BinMapper, _find_binning_thresholds, _map_to_bins
  from sklearn.ensemble._hist_gradient_boosting.common import ALMOST_INF, X_BINNED_DTYPE, X_DTYPE
  from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
label_desc: Use image node_id 15 for calling a global function with example usage: test_subsample() without return types