class_docstr: Super class for all numerical type hyperparameters.
docstr: Sample a value with the cumulative prob in the given range.
docstr_tok: ["Sample","a","value","with","the","cumulative","prob","in","the","given","range","."]
code: def _sample_numerical_value(self, prob, max_value=None):
      """Sample a value with the cumulative prob in the given range."""
      if max_value is None:
          max_value = self.max_value
      if self.sampling == "linear":
          return prob * (max_value - self.min_value) + self.min_value
      elif self.sampling == "log":
          return self.min_value * math.pow(
              max_value / self.min_value, prob
          )
      elif self.sampling == "reverse_log":
          return (
              max_value
              + self.min_value
              - self.min_value
              * math.pow(max_value / self.min_value, 1 - prob)
          )
  
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 3 for calling the Numerical obj's underlying member method code with example usage: obj._sample_numerical_value(prob, max_value) and returns: unknown, unknown, unknown