class_docstr: Container for both a hyperparameter space, and current values.
  
  A `HyperParameters` instance can be pass to `HyperModel.build(hp)` as an
  argument to build a model.
  
  To prevent the users from depending on inactive hyperparameter values, only
  active hyperparameters should have values in `HyperParameters.values`.
  
  Attributes:
      space: A list of `HyperParameter` objects.
      values: A dict mapping hyperparameter names to current values.
docstr: Choice of one value among a predefined set of possible values.
  
  Args:
      name: A string. the name of parameter. Must be unique for each
          `HyperParameter` instance in the search space.
      values: A list of possible values. Values must be int, float,
          str, or bool. All values must be of the same type.
      ordered: Optional boolean, whether the values passed should be
          considered to have an ordering. Defaults to `True` for float/int
          values.  Must be `False` for any other values.
      default: Optional default value to return for the parameter.
          If unspecified, the default value will be:
          - None if None is one of the choices in `values`
          - The first entry in `values` otherwise.
      parent_name: Optional string, specifying the name of the parent
          `HyperParameter` to use as the condition to activate the
          current `HyperParameter`.
      parent_values: Optional list of the values of the parent
          `HyperParameter` to use as the condition to activate the
          current `HyperParameter`.
  
  Returns:
      The value of the hyperparameter, or None if the hyperparameter is
      not active.
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code: def Choice(
      self,
      name,
      values,
      ordered=None,
      default=None,
      parent_name=None,
      parent_values=None,
  ):
      """Choice of one value among a predefined set of possible values.
  
      Args:
          name: A string. the name of parameter. Must be unique for each
              `HyperParameter` instance in the search space.
          values: A list of possible values. Values must be int, float,
              str, or bool. All values must be of the same type.
          ordered: Optional boolean, whether the values passed should be
              considered to have an ordering. Defaults to `True` for float/int
              values.  Must be `False` for any other values.
          default: Optional default value to return for the parameter.
              If unspecified, the default value will be:
              - None if None is one of the choices in `values`
              - The first entry in `values` otherwise.
          parent_name: Optional string, specifying the name of the parent
              `HyperParameter` to use as the condition to activate the
              current `HyperParameter`.
          parent_values: Optional list of the values of the parent
              `HyperParameter` to use as the condition to activate the
              current `HyperParameter`.
  
      Returns:
          The value of the hyperparameter, or None if the hyperparameter is
          not active.
      """
      with self._maybe_conditional_scope(parent_name, parent_values):
          hp = hp_types.Choice(
              name=self._get_name(name),  # Add name_scopes.
              values=values,
              ordered=ordered,
              default=default,
              conditions=self._conditions,
          )
          return self._retrieve(hp)
  
imports: import collections
  import contextlib
  import copy
  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 hp_types
  from keras_tuner.engine.hyperparameters import hyperparameter
label_desc: Use image node_id 12 for calling the HyperParameters obj's underlying member method code with example usage: obj.Choice(name, values, ordered, default, parent_name, parent_values) and returns: self