message: image_ops_impl.py: fix dtype mismatch, correct typos
diff: @@ -445,7 +445,7 @@ def flip_up_down(image):
  of shape `[height, width, channels]`.
  Returns:
  - A tensor of the same type and shape as `image`.
  + A `Tensor` of the same type and shape as `image`.
  Raises:
  ValueError: if the shape of `image` not supported.
  @@ -467,7 +467,7 @@ def _flip(image, flip_index, scope_name):
  scope_name: string, scope name.
  Returns:
  - A tensor of the same type and shape as `image`.
  + A `Tensor` of the same type and shape as `image`.
  Raises:
  ValueError: if the shape of `image` not supported.
  @@ -557,8 +557,7 @@ def _rot90_4D(images, k, name_scope):
  name_scope: A valid TensorFlow name scope.
  Returns:
  - A 4-D tensor of the same type and shape as `images`.
  -
  + A 4-D `Tensor` of the same type and shape as `images`.
  """
  def _rot90():
  @@ -1505,7 +1504,7 @@ def per_image_standardization(image):
  # Apply a minimum normalization that protects us against uniform images.
  stddev = math_ops.reduce_std(image, axis=[-1, -2, -3], keepdims=True)
  - min_stddev = math_ops.rsqrt(math_ops.cast(num_pixels, dtypes.float32))
  + min_stddev = math_ops.rsqrt(math_ops.cast(num_pixels, image.dtype))
  adjusted_stddev = math_ops.maximum(stddev, min_stddev)
  image -= image_mean
  @@ -2070,7 +2069,7 @@ def is_jpeg(contents, name=None):
  is_jpeg is susceptible to false positives.
  """
  # Normal JPEGs start with \xff\xd8\xff\xe0
  - # JPEG with EXIF stats with \xff\xd8\xff\xe1
  + # JPEG with EXIF starts with \xff\xd8\xff\xe1
  # Use \xff\xd8\xff to cover both.
  with ops.name_scope(name, 'is_jpeg'):
  substr = string_ops.substr(contents, 0, 3)
  @@ -2446,8 +2445,7 @@ def sample_distorted_bounding_box(image_size,
  original image. The output is returned as 3 tensors: `begin`, `size` and
  `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
  image. The latter may be supplied to `tf.image.draw_bounding_boxes` to
  - visualize
  - what the bounding box looks like.
  + visualize what the bounding box looks like.
  Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`.
  The
  @@ -2473,7 +2471,7 @@ def sample_distorted_bounding_box(image_size,
  ```
  Note that if no bounding box information is available, setting
  - `use_image_if_no_bounding_boxes = true` will assume there is a single implicit
  + `use_image_if_no_bounding_boxes = True` will assume there is a single implicit
  bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
  false and no bounding boxes are supplied, an error is raised.
  