Fix tf.image.non_max_suppression docstrings.

PiperOrigin-RevId: 235541426

tensorflow/python/ops/image_ops_impl.py

@@ -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]`.