docstr: Decode the bbox.
  Args:
      head_out (tuple): bbox_pred, cls_logit and masks of bbox_head output.
      im_shape (Tensor): The shape of the input image.
      scale_factor (Tensor): The scale factor of the input image.
  Returns:
      bbox_pred (Tensor): The output prediction with shape [N, 6], including
          labels, scores and bboxes. The size of bboxes are corresponding
          to the input image, the bboxes may be used in other branch.
      bbox_num (Tensor): The number of prediction boxes of each batch with
          shape [bs], and is N.
code: def __call__(self, head_out):
      """
      Decode the bbox.
      Args:
          head_out (tuple): bbox_pred, cls_logit and masks of bbox_head output.
          im_shape (Tensor): The shape of the input image.
          scale_factor (Tensor): The scale factor of the input image.
      Returns:
          bbox_pred (Tensor): The output prediction with shape [N, 6], including
              labels, scores and bboxes. The size of bboxes are corresponding
              to the input image, the bboxes may be used in other branch.
          bbox_num (Tensor): The number of prediction boxes of each batch with
              shape [bs], and is N.
      """
      bboxes, logits, masks = head_out
      bbox_pred = bboxes
  
      scores = F.softmax(logits, axis=2)
  
      import copy
  
      soft_scores = copy.deepcopy(scores)
      scores, index = paddle.topk(scores.max(-1), 300, axis=-1)
  
      batch_ind = (
          paddle.arange(end=scores.shape[0])
          .unsqueeze(-1)
          .tile([1, 300])
      )
      index = paddle.stack([batch_ind, index], axis=-1)
      labels = paddle.gather_nd(soft_scores.argmax(-1), index).astype(
          "int32"
      )
      score_class = paddle.gather_nd(soft_scores, index)
      bbox_pred = paddle.gather_nd(bbox_pred, index)
      bbox_pred = paddle.concat(
          [
              labels.unsqueeze(-1).astype("float32"),
              score_class,
              scores.unsqueeze(-1),
              bbox_pred,
          ],
          axis=-1,
      )
      bbox_num = paddle.to_tensor(
          bbox_pred.shape[1], dtype="int32"
      ).tile([bbox_pred.shape[0]])
      bbox_pred = bbox_pred.reshape([-1, bbox_pred.shape[-1]])
      return bbox_pred, bbox_num
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from ppdet.modeling.bbox_utils import nonempty_bbox
  from .transformers import bbox_cxcywh_to_xyxy
label_desc: Use image node_id 2 for calling the DETRBBoxSemiPostProcess obj's underlying member method code with example usage: obj.__call__(head_out) and returns: bbox_pred,  bbox_num