message: Update docstring of tf.image.per_image_standardization.
diff: @@ -1879,15 +1879,32 @@ def per_image_standardization(image):
  - `N` is the number of elements in `x`
  - `stddev` is the standard deviation of all values in `x`
  + Example Usage:
  +
  + >>> image = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3])
  + >>> image # 3-D tensor
  + <tf.Tensor: shape=(2, 2, 3), dtype=int32, numpy=
  + array([[[ 1, 2, 3],
  + [ 4, 5, 6]],
  + [[ 7, 8, 9],
  + [10, 11, 12]]], dtype=int32)>
  + >>> new_image = tf.image.per_image_standardization(image)
  + >>> new_image # 3-D tensor with mean ~= 0 and variance ~= 1
  + <tf.Tensor: shape=(2, 2, 3), dtype=float32, numpy=
  + array([[[-1.593255 , -1.3035723 , -1.0138896 ],
  + [-0.7242068 , -0.4345241 , -0.14484136]],
  + [[ 0.14484136, 0.4345241 , 0.7242068 ],
  + [ 1.0138896 , 1.3035723 , 1.593255 ]]], dtype=float32)>
  +
  Args:
  - image: An n-D Tensor with at least 3 dimensions, the last 3 of which are the
  - dimensions of each image.
  + image: An n-D `Tensor` with at least 3 dimensions, the last 3 of which are
  + the dimensions of each image.
  Returns:
  - A `Tensor` with the same shape as `image`.
  + A `Tensor` with the same shape as `image` and its dtype is `float32`.
  Raises:
  - ValueError: if the shape of 'image' is incompatible with this function.
  + ValueError: The shape of `image` has fewer than 3 dimensions.
  """
  with ops.name_scope(None, 'per_image_standardization', [image]) as scope:
  image = ops.convert_to_tensor(image, name='image')
  