class_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`.
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code: def to_proto(self):
      return protos.get_proto().Float(
          name=self.name,
          min_value=self.min_value,
          max_value=self.max_value,
          step=self.step if self.step is not None else 0.0,
          sampling=hp_utils.sampling_to_proto(self.sampling),
          default=self.default,
          conditions=[c.to_proto() for c in self.conditions],
      )
  
file_path: keras-tuner/keras_tuner/engine/hyperparameters/hp_types/float_hp.py
imports: 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_utils
  from keras_tuner.engine.hyperparameters.hp_types import numerical
label_desc: Use image node_id 8 for calling the Float obj's underlying member method code with example usage: obj.to_proto() and returns: protos