message: Changed example and output
diff: @@ -3252,24 +3252,19 @@ 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:
  - >>> 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)
  + >>> x = [[[0.1, 0.2, 0.3],
  + ... [0.4, 0.5, 0.6]],
  + ... [[0.7, 0.8, 0.9],
  + ... [0.10, 0.11, 0.12]]]
  + >>> tf.image.rgb_to_yuv(x)
  + <tf.Tensor: shape=(2, 2, 3), dtype=float32, numpy=
  + array([[[ 0.1815 , 0.05831515, -0.07149857],
  + [ 0.4815 , 0.05831517, -0.07149856]],
  + [[ 0.7815 , 0.05831515, -0.07149857],
  + [ 0.10815 , 0.00583152, -0.00714985]]], dtype=float32)>
  Args:
  images: 2-D or higher rank. Image data to convert. Last dimension must be
  