code: def test_float_log_with_step():
      rg = hp_module.Float(
          "rg", min_value=0.01, max_value=100, step=10, sampling="log"
      )
      for _ in range(10):
          assert rg.random_sample() in [0.01, 0.1, 1.0, 10.0, 100.0]
      assert abs(rg.value_to_prob(0.1) - 0.3) < 1e-4
      assert rg.prob_to_value(0.3) == 0.1
  
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 3 for calling a global function with example usage: test_float_log_with_step() without return types