class_docstr: Fixed, untunable value.
  
  Args:
      name: A string. the name of parameter. Must be unique for each
          `HyperParameter` instance in the search space.
      value: The value to use (can be any JSON-serializable Python type).
code: def to_proto(self):
      if isinstance(self.value, bool):
          # Check bool first as bool is subclass of int.
          # So bool is also six.integer_types.
          value = protos.get_proto().Value(boolean_value=self.value)
      elif isinstance(self.value, six.integer_types):
          value = protos.get_proto().Value(int_value=self.value)
      elif isinstance(self.value, float):
          value = protos.get_proto().Value(float_value=self.value)
      elif isinstance(self.value, six.string_types):
          value = protos.get_proto().Value(string_value=self.value)
  
      return protos.get_proto().Fixed(
          name=self.name,
          value=value,
          conditions=[c.to_proto() for c in self.conditions],
      )
  
file_path: keras-tuner/keras_tuner/engine/hyperparameters/hp_types/fixed_hp.py
imports: import six
  from keras_tuner import protos
  from keras_tuner.api_export import keras_tuner_export
  from keras_tuner.engine import conditions
  from keras_tuner.engine.hyperparameters import hyperparameter
label_desc: Use image node_id 9 for calling the Fixed obj's underlying member method code with example usage: obj.to_proto() and returns: protos