class_docstr: Super class for all numerical type hyperparameters.
code: def __init__(
      self,
      name,
      min_value,
      max_value,
      step=None,
      sampling="linear",
      default=None,
      **kwargs,
  ):
      super().__init__(name=name, default=default, **kwargs)
      self.max_value = max_value
      self.min_value = min_value
      self.step = step
      self.sampling = sampling
      self._check_sampling_arg()
  
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 1 to create a new Numerical object from inherited base classes: hyperparameter with example: obj = Numerical(name, min_value, max_value, step, sampling, default)