message: Fix tf.image.non_max_suppression docstrings.
diff: @@ -2244,9 +2244,9 @@ def non_max_suppression(boxes,
  Prunes away boxes that have high intersection-over-union (IOU) overlap
  with previously selected boxes. Bounding boxes are supplied as
  - [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
  + `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the coordinates of any
  diagonal pair of box corners and the coordinates can be provided as normalized
  - (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm
  + (i.e., lying in the interval `[0, 1]`) or absolute. Note that this algorithm
  is agnostic to where the origin is in the coordinate system. Note that this
  algorithm is invariant to orthogonal transformations and translations
  of the coordinate system; thus translating or reflections of the coordinate
  @@ -2254,10 +2254,12 @@ def non_max_suppression(boxes,
  The output of this operation is a set of integers indexing into the input
  collection of bounding boxes representing the selected boxes. The bounding
  box coordinates corresponding to the selected indices can then be obtained
  - using the `tf.gather operation`. For example:
  + using the `tf.gather` operation. For example:
  + ```python
  selected_indices = tf.image.non_max_suppression(
  boxes, scores, max_output_size, iou_threshold)
  selected_boxes = tf.gather(boxes, selected_indices)
  + ```
  Args:
  boxes: A 2-D float `Tensor` of shape `[num_boxes, 4]`.
  @@ -2301,12 +2303,14 @@ def non_max_suppression_padded(boxes,
  boxes and the number of valid indices in the index set. The bounding box
  coordinates corresponding to the selected indices can then be obtained using
  the `tf.slice` and `tf.gather` operations. For example:
  + ```python
  selected_indices_padded, num_valid = tf.image.non_max_suppression_padded(
  boxes, scores, max_output_size, iou_threshold,
  score_threshold, pad_to_max_output_size=True)
  selected_indices = tf.slice(
  selected_indices_padded, tf.constant([0]), num_valid)
  selected_boxes = tf.gather(boxes, selected_indices)
  + ```
  Args:
  boxes: A 2-D float `Tensor` of shape `[num_boxes, 4]`.
  @@ -2355,10 +2359,12 @@ def non_max_suppression_with_overlaps(overlaps,
  The output of this operation is a set of integers indexing into the input
  collection of bounding boxes representing the selected boxes. The bounding
  box coordinates corresponding to the selected indices can then be obtained
  - using the `tf.gather operation`. For example:
  + using the `tf.gather` operation. For example:
  + ```python
  selected_indices = tf.image.non_max_suppression_overlaps(
  overlaps, scores, max_output_size, iou_threshold)
  selected_boxes = tf.gather(boxes, selected_indices)
  + ```
  Args:
  overlaps: A 2-D float `Tensor` of shape `[num_boxes, num_boxes]`.
  