class_docstr: Super class for all numerical type hyperparameters.
code: def _check_sampling_arg(self):
      if self.min_value > self.max_value:
          raise ValueError(
              f"For HyperParameters.{self.__class__.__name__}"
              f"(name='{self.name}'), "
              f"min_value {str(self.min_value)} is greater than "
              f"the max_value {str(self.max_value)}."
          )
      sampling_values = {"linear", "log", "reverse_log"}
      # This is for backward compatibility.
      # sampling=None was allowed and was the default value.
      if self.sampling is None:
          self.sampling = "linear"
      self.sampling = self.sampling.lower()
      if self.sampling not in sampling_values:
          raise ValueError(
              f"For HyperParameters.{self.__class__.__name__}"
              f"(name='{self.name}'), sampling must be one "
              f"of {sampling_values}"
          )
  
      if (
          self.sampling in {"log", "reverse_log"}
          and self.min_value <= 0
      ):
          raise ValueError(
              f"For HyperParameters.{self.__class__.__name__}"
              f"(name='{self.name}'), "
              f"sampling='{str(self.sampling)}' does not support "
              f"negative values, found min_value: {str(self.min_value)}."
          )
      if (
          self.sampling in {"log", "reverse_log"}
          and self.step is not None
          and self.step <= 1
      ):
          raise ValueError(
              f"For HyperParameters.{self.__class__.__name__}"
              f"(name='{self.name}'), "
              f"expected step > 1 with sampling='{str(self.sampling)}'. "
              f"Received: step={str(self.step)}."
          )
  
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 2 for calling the Numerical obj's underlying member method code with example usage: obj._check_sampling_arg() without return types