def _suppression_loop_body(boxes, iou_threshold, output_size, idx):
  """Process boxes in the range [idx*_NMS_TILE_SIZE, (idx+1)*_NMS_TILE_SIZE).

  Args:
    boxes: a tensor with a shape of [1, anchors, 4].
    iou_threshold: a float representing the threshold for deciding whether boxes
      overlap too much with respect to IOU.
    output_size: an int32 tensor of size [1]. Representing the number of
      selected boxes.
    idx: an integer scalar representing induction variable.

  Returns:
    boxes: updated boxes.
    iou_threshold: pass down iou_threshold to the next iteration.
    output_size: the updated output_size.
    idx: the updated induction variable.
  """
  num_tiles = tf.shape(boxes)[1] // _NMS_TILE_SIZE

  # Iterates over tiles that can possibly suppress the current tile.
  box_slice = tf.slice(boxes, [0, idx * _NMS_TILE_SIZE, 0],
                       [1, _NMS_TILE_SIZE, 4])
  _, box_slice, _, _ = tf.while_loop(
      lambda _boxes, _box_slice, _threshold, inner_idx: inner_idx < idx,
      _cross_suppression, [boxes, box_slice, iou_threshold,
                           tf.constant(0)])

  # Iterates over the current tile to compute self-suppression.
  iou = batch_iou(box_slice, box_slice)
  mask = tf.expand_dims(
      tf.reshape(tf.range(_NMS_TILE_SIZE), [1, -1]) > tf.reshape(
          tf.range(_NMS_TILE_SIZE), [-1, 1]), 0)
  iou *= tf.cast(tf.logical_and(mask, iou >= iou_threshold), iou.dtype)
  suppressed_iou, _, _, _ = tf.while_loop(
      lambda _iou, _threshold, loop_condition, _iou_sum: loop_condition,
      _self_suppression,
      [iou, iou_threshold,
       tf.constant(True),
       tf.reduce_sum(iou, [1, 2])])
  suppressed_box = tf.reduce_sum(suppressed_iou, 1) > 0
  box_slice *= tf.expand_dims(1.0 - tf.cast(suppressed_box, box_slice.dtype), 2)

  # Uses box_slice to update the input boxes.
  mask = tf.reshape(
      tf.cast(tf.equal(tf.range(num_tiles), idx), boxes.dtype), [1, -1, 1, 1])
  boxes = tf.tile(tf.expand_dims(box_slice, [1]),
                  [1, num_tiles, 1, 1]) * mask + tf.reshape(
                      boxes, [1, num_tiles, _NMS_TILE_SIZE, 4]) * (1 - mask)
  boxes = tf.reshape(boxes, [1, -1, 4])

  # Updates output_size.
  output_size += tf.reduce_sum(
      tf.cast(tf.reduce_any(box_slice > 0, [2]), tf.int32), [1])
  return boxes, iou_threshold, output_size, idx + 1
Process boxes in the range [idx*_NMS_TILE_SIZE, (idx+1)*_NMS_TILE_SIZE).

  Args:
    boxes: a tensor with a shape of [1, anchors, 4].
    iou_threshold: a float representing the threshold for deciding whether boxes
      overlap too much with respect to IOU.
    output_size: an int32 tensor of size [1]. Representing the number of
      selected boxes.
    idx: an integer scalar representing induction variable.

  Returns:
    boxes: updated boxes.
    iou_threshold: pass down iou_threshold to the next iteration.
    output_size: the updated output_size.
    idx: the updated induction variable.
  
inference_api/src/main/object_detection/core/post_processing.py