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 __init__(self, name, value, **kwargs):
      super().__init__(name=name, default=value, **kwargs)
      self.name = name
  
      if isinstance(value, bool):
          value = bool(value)
      elif isinstance(value, six.integer_types):
          value = int(value)
      elif isinstance(value, six.string_types):
          value = str(value)
      elif not isinstance(value, (float, str)):
          raise ValueError(
              "`Fixed` value must be an `int`, `float`, `str`, "
              f"or `bool`, found {value}"
          )
      self.value = value
  
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 1 to create a new Fixed object from inherited base classes: hyperparameter with example: obj = Fixed(name, value)