message: Saturate when converting image between datatypes in adjust_jpeg_quality.
diff: @@ -2309,9 +2309,9 @@ def adjust_jpeg_quality(image, jpeg_quality, name=None):
  >>> tf.image.adjust_jpeg_quality(x, 75)
  <tf.Tensor: shape=(2, 2, 3), dtype=float32, numpy=
  array([[[1., 1., 1.],
  - [0.9960785 , 0.9960785 , 0.9960785 ]],
  - [[0.98823535, 0.98823535, 0.98823535],
  - [0.98823535, 0.98823535, 0.98823535]]], dtype=float32)>
  + [1., 1., 1.]],
  + [[1., 1., 1.],
  + [1., 1., 1.]]], dtype=float32)>
  Args:
  image: 3D image. The size of the last dimension must be None, 1 or 3.
  @@ -2330,14 +2330,14 @@ def adjust_jpeg_quality(image, jpeg_quality, name=None):
  channels = image.shape.as_list()[-1]
  # Remember original dtype to so we can convert back if needed
  orig_dtype = image.dtype
  - image = convert_image_dtype(image, dtypes.uint8)
  + image = convert_image_dtype(image, dtypes.uint8, saturate=True)
  if not _is_tensor(jpeg_quality):
  # If jpeg_quality is a int (not tensor).
  jpeg_quality = ops.convert_to_tensor(jpeg_quality, dtype=dtypes.int32)
  image = gen_image_ops.encode_jpeg_variable_quality(image, jpeg_quality)
  image = gen_image_ops.decode_jpeg(image, channels=channels)
  - return convert_image_dtype(image, orig_dtype)
  + return convert_image_dtype(image, orig_dtype, saturate=True)
  @tf_export('image.random_saturation')
  