message: formatting changes as PR review.
diff: @@ -5537,22 +5537,22 @@ def generate_bounding_box_proposals(scores,
  """Generate bounding box proposals from encoded bounding boxes.
  Args:
  - scores: A 4-D float tensor of shape `[num_images, height, width, num_achors]`
  + scores: A 4-D float `Tensor` of shape `[num_images, height, width, num_achors]`
  containing scores of the boxes for given anchors, can be unsorted.
  - bbox_deltas: A 4-D float tensor of shape `[num_images, height, width, 4 x num_anchors]`.
  - encoding boxes with respec to each anchor. Coordinates are given in the form [dy, dx, dh, dw].
  - image_info: A 2-D float tensor of shape `[num_images, 5]` containing image information Height, Width, Scale.
  - anchors: A 2-D float tensor of shape `[num_anchors, 4]` describing the anchor boxes.
  - Boxes are formatted in the form [y1, x1, y2, x2].
  - nms_threshold: A scalar float tensor for non-maximal-suppression threshold. Defaults to 0.7.
  - pre_nms_topn: A scalar int tensor for the number of top scoring boxes to be used as input. Defaults to 6000.
  - min_size: A scalar float tensor. Any box that has a smaller size than min_size will be discarded. Defaults to 16.
  + bbox_deltas: A 4-D float `Tensor` of shape `[num_images, height, width, 4 x num_anchors]`.
  + encoding boxes with respec to each anchor. Coordinates are given in the form `[dy, dx, dh, dw]`.
  + image_info: A 2-D float `Tensor` of shape `[num_images, 5]` containing image information Height, Width, Scale.
  + anchors: A 2-D float `Tensor` of shape `[num_anchors, 4]` describing the anchor boxes.
  + Boxes are formatted in the form `[y1, x1, y2, x2]`.
  + nms_threshold: A scalar float `Tensor` for non-maximal-suppression threshold. Defaults to 0.7.
  + pre_nms_topn: A scalar int `Tensor` for the number of top scoring boxes to be used as input. Defaults to 6000.
  + min_size: A scalar float `Tensor`. Any box that has a smaller size than min_size will be discarded. Defaults to 16.
  post_nms_topn: An integer. Maximum number of rois in the output.
  name: A name for this operation (optional).
  Returns:
  rois: Region of interest boxes sorted by their scores.
  - roi_probabilities: scores of the ROI boxes in the ROIs' tensor.
  + roi_probabilities: scores of the ROI boxes in the ROIs' `Tensor`.
  """
  return gen_image_ops.generate_bounding_box_proposals(
  scores=scores,
  