code: def test_float_reverse_log_with_step():
      rg = hp_module.Float(
          "rg",
          min_value=0.01,
          max_value=100,
          step=10,
          sampling="reverse_log",
      )
      for _ in range(10):
          # print(rg.random_sample())
          # assert rg.random_sample() in [0.01, 0.1, 1.0, 10.0, 100.0]
          # [0.09, 0.9, 9, 90]
          # [90, 9, 0.9, 0.09]
          sample = rg.random_sample()
          assert any(
              abs(sample - x) < 1e-4
              for x in [0.01, 90.01, 99.01, 99.91, 100.0]
          )
      assert abs(rg.value_to_prob(99.91) - 0.3) < 1e-4
      assert abs(rg.prob_to_value(0.3) - 99.91) < 1e-4
  
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 4 for calling a global function with example usage: test_float_reverse_log_with_step() without return types