message: Added preprocessing and corrected example of RGB images before feeding rgb_to_yub
diff: @@ -3252,19 +3252,24 @@ def rgb_to_yuv(images):
  Outputs a tensor of the same shape as the `images` tensor, containing the YUV
  value of the pixels.
  The output is only well defined if the value in images are in [0,1].
  + You need to scale your RGB images if their pixel values are not in the
  + required range. Below given example illustrates preprocessing of each channel
  + of images before feeding them to `rgb_to_yuv`.
  Usage Example:
  - >>> x = [[[1.0, 2.0, 3.0],
  - ... [4.0, 5.0, 6.0]],
  - ... [[7.0, 8.0, 9.0],
  - ... [10.0, 11.0, 12.0]]]
  - >>> tf.image.rgb_to_yuv(x)
  - <tf.Tensor: shape=(2, 2, 3), dtype=float32, numpy=
  - array([[[ 1.815 , 0.5831516, -0.7149856],
  - [ 4.815 , 0.5831516, -0.7149855]],
  - [[ 7.815 , 0.5831516, -0.7149856],
  - [10.815001 , 0.5831518, -0.7149852]]], dtype=float32)>
  + >>> rgb_images = tf.random.uniform(shape=[100,64,64,3], maxval=255)
  + >>> preprocessed_rgb_images = tf.truediv(
  + ... tf.subtract(
  + ... rgb_images,
  + ... tf.reduce_min(rgb_images)
  + ... ),
  + ... tf.subtract(
  + ... tf.reduce_max(rgb_images),
  + ... tf.reduce_min(rgb_images)
  + ... )
  + ... )
  + >>> yub_tensor_images = tf.image.rgb_to_yuv(preprocessed_rgb_images)
  Args:
  images: 2-D or higher rank. Image data to convert. Last dimension must be
  