class_docstr: Super class for all numerical type hyperparameters.
docstr: Convert to cumulative prob with step specified.
  
  When calling the function, no need to use (max_value + 1) since the
  function takes care of the inclusion of max_value.
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code: def _to_prob_with_step(self, value):
      """Convert to cumulative prob with step specified.
  
      When calling the function, no need to use (max_value + 1) since the
      function takes care of the inclusion of max_value.
      """
      if self.sampling == "linear":
          index = (value - self.min_value) // self.step
      if self.sampling == "log":
          index = math.log(value / self.min_value, self.step)
      if self.sampling == "reverse_log":
          index = math.log(
              (self.max_value - value + self.min_value)
              / self.min_value,
              self.step,
          )
      n_values = self._get_n_values()
      return hp_utils.index_to_prob(index, n_values)
  
file_path: keras-tuner/keras_tuner/engine/hyperparameters/hp_types/numerical.py
imports: import math
  from keras_tuner.engine.hyperparameters import hp_utils
  from keras_tuner.engine.hyperparameters import hyperparameter
label_desc: Use image node_id 9 for calling the Numerical obj's underlying member method code with example usage: obj._to_prob_with_step(value) and returns: hp_utils