class_docstr: Integer hyperparameter.
  
  Note that unlike Python's `range` function, `max_value` is *included* in
  the possible values this parameter can take on.
  
  
  Example #1:
  
  ```py
  hp.Int(
      "n_layers",
      min_value=6,
      max_value=12)
  ```
  
  The possible values are [6, 7, 8, 9, 10, 11, 12].
  
  Example #2:
  
  ```py
  hp.Int(
      "n_layers",
      min_value=6,
      max_value=13,
      step=3)
  ```
  
  `step` is the minimum distance between samples.
  The possible values are [6, 9, 12].
  
  Example #3:
  
  ```py
  hp.Int(
      "batch_size",
      min_value=2,
      max_value=32,
      step=2,
      sampling="log")
  ```
  
  When `sampling="log"` the `step` is multiplied between samples.
  The possible values are [2, 4, 8, 16, 32].
  
  Args:
      name: A string. the name of parameter. Must be unique for each
          `HyperParameter` instance in the search space.
      min_value: Integer, the lower limit of range, inclusive.
      max_value: Integer, the upper limit of range, inclusive.
      step: Optional integer, the distance between two consecutive samples in
          the range. If left unspecified, it is possible to sample any
          integers 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: Integer, default value to return for the parameter. If
          unspecified, the default value will be `min_value`.
code: def __repr__(self):
      return (
          f"Int(name: '{self.name}', min_value: {self.min_value}, "
          f"max_value: {self.max_value}, step: {self.step}, "
          f"sampling: {self.sampling}, default: {self.default})"
      )
  
file_path: keras-tuner/keras_tuner/engine/hyperparameters/hp_types/int_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 2 for calling the Int obj's underlying member method code with example usage: obj.__repr__() and returns: str, self, str, self, str, self, str, self, str, self, str, self, str