'Returns a regression target for each anchor.
Args:
anchors: a BoxList representing N anchors
groundtruth_boxes: a BoxList representing M groundtruth_boxes
match: a matcher.Match object
Returns:
reg_targets: a float32 tensor with shape [N, box_code_dimension]'

def _create_regression_targets(self, anchors, groundtruth_boxes, match):

    matched_anchor_indices = match.matched_column_indices()
    unmatched_ignored_anchor_indices = match.unmatched_or_ignored_column_indices()
    matched_gt_indices = match.matched_row_indices()
    matched_anchors = box_list_ops.gather(anchors, matched_anchor_indices)
    matched_gt_boxes = box_list_ops.gather(groundtruth_boxes, matched_gt_indices)
    matched_reg_targets = self._box_coder.encode(matched_gt_boxes, matched_anchors)
    unmatched_ignored_reg_targets = tf.tile(self._default_regression_target(), tf.stack([tf.size(unmatched_ignored_anchor_indices), 1]))
    reg_targets = tf.dynamic_stitch([matched_anchor_indices, unmatched_ignored_anchor_indices], [matched_reg_targets, unmatched_ignored_reg_targets])
    return reg_targets
