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: Floating point value hyperparameter.
  
  Example #1:
  
  ```py
  hp.Float(
      "image_rotation_factor",
      min_value=0,
      max_value=1)
  ```
  
  All values in interval [0, 1] have equal probability of being sampled.
  
  Example #2:
  
  ```py
  hp.Float(
      "image_rotation_factor",
      min_value=0,
      max_value=1,
      step=0.2)
  ```
  
  `step` is the minimum distance between samples.
  The possible values are [0, 0.2, 0.4, 0.6, 0.8, 1.0].
  
  Example #3:
  
  ```py
  hp.Float(
      "learning_rate",
      min_value=0.001,
      max_value=10,
      step=10,
      sampling="log")
  ```
  
  When `sampling="log"`, the `step` is multiplied between samples.
  The possible values are [0.001, 0.01, 0.1, 1, 10].
  
  Args:
      name: A string. the name of parameter. Must be unique for each
          `HyperParameter` instance in the search space.
      min_value: Float, the lower bound of the range.
      max_value: Float, the upper bound of the range.
      step: Optional float, the distance between two consecutive samples
          in the range. If left unspecified, it is possible to sample any
          value in the interval. If `sampling="linear"`, it will be the
          minimum additve between two samples. If `sampling="log"`, it
          will be the minimum multiplier between two samples.
      sampling: String. One of "linear", "log", "reverse_log". Defaults to
          "linear". When sampling value, it always start from a value in
          range [0.0, 1.0). The `sampling` argument decides how the value
          is projected into the range of [min_value, max_value].
          "linear": min_value + value * (max_value - min_value)
          "log": min_value * (max_value / min_value) ^ value
          "reverse_log":
              (max_value -
               min_value * ((max_value / min_value) ^ (1 - value) - 1))
      default: Float, the default value to return for the parameter. If
          unspecified, the default value will be `min_value`.
      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 Float(
      self,
      name,
      min_value,
      max_value,
      step=None,
      sampling="linear",
      default=None,
      parent_name=None,
      parent_values=None,
  ):
      """Floating point value hyperparameter.
  
      Example #1:
  
      ```py
      hp.Float(
          "image_rotation_factor",
          min_value=0,
          max_value=1)
      ```
  
      All values in interval [0, 1] have equal probability of being sampled.
  
      Example #2:
  
      ```py
      hp.Float(
          "image_rotation_factor",
          min_value=0,
          max_value=1,
          step=0.2)
      ```
  
      `step` is the minimum distance between samples.
      The possible values are [0, 0.2, 0.4, 0.6, 0.8, 1.0].
  
      Example #3:
  
      ```py
      hp.Float(
          "learning_rate",
          min_value=0.001,
          max_value=10,
          step=10,
          sampling="log")
      ```
  
      When `sampling="log"`, the `step` is multiplied between samples.
      The possible values are [0.001, 0.01, 0.1, 1, 10].
  
      Args:
          name: A string. the name of parameter. Must be unique for each
              `HyperParameter` instance in the search space.
          min_value: Float, the lower bound of the range.
          max_value: Float, the upper bound of the range.
          step: Optional float, the distance between two consecutive samples
              in the range. If left unspecified, it is possible to sample any
              value in the interval. If `sampling="linear"`, it will be the
              minimum additve between two samples. If `sampling="log"`, it
              will be the minimum multiplier between two samples.
          sampling: String. One of "linear", "log", "reverse_log". Defaults to
              "linear". When sampling value, it always start from a value in
              range [0.0, 1.0). The `sampling` argument decides how the value
              is projected into the range of [min_value, max_value].
              "linear": min_value + value * (max_value - min_value)
              "log": min_value * (max_value / min_value) ^ value
              "reverse_log":
                  (max_value -
                   min_value * ((max_value / min_value) ^ (1 - value) - 1))
          default: Float, the default value to return for the parameter. If
              unspecified, the default value will be `min_value`.
          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.Float(
              name=self._get_name(name),  # Add name_scopes.
              min_value=min_value,
              max_value=max_value,
              step=step,
              sampling=sampling,
              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 14 for calling the HyperParameters obj's underlying member method code with example usage: obj.Float(name, min_value, max_value, step, sampling, default, parent_name, parent_values) and returns: self