code: def random_brightness(x, brightness_range, scale=True):
      """Performs a random brightness shift.
  
      # Arguments
          x: Input tensor. Must be 3D.
          brightness_range: Tuple of floats; brightness range.
          scale: Whether to rescale the image such that minimum and maximum values
              are 0 and 255 respectively.
              Default: True.
  
      # Returns
          Numpy image tensor.
  
      # Raises
          ValueError if `brightness_range` isn't a tuple.
      """
      if len(brightness_range) != 2:
          raise ValueError(
              "`brightness_range should be tuple or list of two floats. "
              "Received: %s" % (brightness_range,)
          )
  
      u = np.random.uniform(brightness_range[0], brightness_range[1])
      return apply_brightness_shift(x, u, scale)
  
file_path: keras-preprocessing/keras_preprocessing/image/affine_transformations.py
imports: import numpy
  from .utils import array_to_img, img_to_array
label_desc: Use image node_id 9 for calling a global function with example usage: random_brightness(x, brightness_range, scale) and returns: apply_brightness_shift