code: def test_real_log_sampling_in_bounds():
      # TODO: Refactor - Use PyTest
      dim = Real(
          low=1, high=32, prior="log-uniform", transform="normalize"
      )
  
      # Round-trip a value that is within the bounds of the space
  
      # x = dim.inverse_transform(dim.transform(31.999999999999999))
      for n in (32.0, 31.999999999999999):
          round_tripped = dim.inverse_transform(dim.transform([n]))
          assert np.allclose([n], round_tripped)
          assert n in dim
          assert round_tripped in dim
  
imports: from hyperparameter_hunter.optimization.backends.skopt.engine import Optimizer
  from hyperparameter_hunter.space.dimensions import Real, Integer, Categorical
  from hyperparameter_hunter.space.space_core import Space, check_dimension, normalize_dimensions
  import numbers
  import numpy
  import pytest
  from sklearn.utils.testing import assert_array_almost_equal, assert_array_equal, assert_equal
label_desc: Use image node_id 6 for calling a global function with example usage: test_real_log_sampling_in_bounds() without return types