class_docstr: A preprocessing layer which randomly adjusts brightness during training.
  
  This layer will randomly increase/reduce the brightness for the input RGB
  images. At inference time, the output will be identical to the input.
  Call the layer with `training=True` to adjust the brightness of the input.
  
  **Note:** This layer is safe to use inside a `tf.data` pipeline
  (independently of which backend you're using).
  
  Args:
      factor: Float or a list/tuple of 2 floats between -1.0 and 1.0. The
          factor is used to determine the lower bound and upper bound of the
          brightness adjustment. A float value will be chosen randomly between
          the limits. When -1.0 is chosen, the output image will be black, and
          when 1.0 is chosen, the image will be fully white.
          When only one float is provided, eg, 0.2,
          then -0.2 will be used for lower bound and 0.2
          will be used for upper bound.
      value_range: Optional list/tuple of 2 floats
          for the lower and upper limit
          of the values of the input data.
          To make no change, use `[0.0, 1.0]`, e.g., if the image input
          has been scaled before this layer. Defaults to `[0.0, 255.0]`.
          The brightness adjustment will be scaled to this range, and the
          output values will be clipped to this range.
      seed: optional integer, for fixed RNG behavior.
  
  Inputs: 3D (HWC) or 4D (NHWC) tensor, with float or int dtype. Input pixel
      values can be of any range (e.g. `[0., 1.)` or `[0, 255]`)
  
  Output: 3D (HWC) or 4D (NHWC) tensor with brightness adjusted based on the
      `factor`. By default, the layer will output floats.
      The output value will be clipped to the range `[0, 255]`,
      the valid range of RGB colors, and
      rescaled based on the `value_range` if needed.
  
  Sample usage:
  
  ```python
  random_bright = keras.layers.RandomBrightness(factor=0.2)
  
  # An image with shape [2, 2, 3]
  image = [[[1, 2, 3], [4 ,5 ,6]], [[7, 8, 9], [10, 11, 12]]]
  
  # Assume we randomly select the factor to be 0.1, then it will apply
  # 0.1 * 255 to all the channel
  output = random_bright(image, training=True)
  
  # output will be int64 with 25.5 added to each channel and round down.
  >>> array([[[26.5, 27.5, 28.5]
              [29.5, 30.5, 31.5]]
             [[32.5, 33.5, 34.5]
              [35.5, 36.5, 37.5]]],
            shape=(2, 2, 3), dtype=int64)
  ```
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code: def call(self, inputs, training=True):
      inputs = self.backend.cast(inputs, self.compute_dtype)
      if training:
          return self._randomly_adjust_brightness(inputs)
      else:
          return inputs
  
file_path: keras/keras/layers/preprocessing/random_brightness.py
imports: from keras.api_export import keras_export
  from keras.layers.preprocessing.tf_data_layer import TFDataLayer
  from keras.random.seed_generator import SeedGenerator
label_desc: Use image node_id 5 for calling the RandomBrightness obj's underlying member method code with example usage: obj.call(inputs, training) and returns: self, inputs