code: def test_log_sampling_random_state():
      f = hp_module.Float("f", 1e-3, 1e3, sampling="log")
      rand_sample = f.random_sample()
      assert rand_sample >= f.min_value
      assert rand_sample <= f.max_value
  
      val = 1e-3
      prob = f.value_to_prob(val)
      assert prob == 0
      new_val = f.prob_to_value(prob)
      assert np.isclose(val, new_val)
  
      val = 1
      prob = f.value_to_prob(val)
      assert prob == 0.5
      new_val = f.prob_to_value(prob)
      assert np.isclose(val, new_val)
  
      val = 1e3
      prob = f.value_to_prob(val)
      assert prob == 1
      new_val = f.prob_to_value(prob)
      assert np.isclose(val, new_val)
  
file_path: keras-tuner/keras_tuner/engine/hyperparameters/hp_types/float_hp_test.py
imports: import numpy
  from keras_tuner import protos
  from keras_tuner.engine import hyperparameters
label_desc: Use image node_id 6 for calling a global function with example usage: test_log_sampling_random_state() without return types