docstr: Decode the bbox and mask.
  
  Args:
      head_out (tuple): bbox_pred, cls_logit and masks of bbox_head output.
      im_shape (Tensor): The shape of the input image without padding.
      scale_factor (Tensor): The scale factor of the input image.
      pad_shape (Tensor): The shape of the input image with padding.
  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, im_shape, scale_factor, pad_shape):
      """
      Decode the bbox and mask.
  
      Args:
          head_out (tuple): bbox_pred, cls_logit and masks of bbox_head output.
          im_shape (Tensor): The shape of the input image without padding.
          scale_factor (Tensor): The scale factor of the input image.
          pad_shape (Tensor): The shape of the input image with padding.
      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
      if self.dual_queries:
          num_queries = logits.shape[1]
          logits, bboxes = (
              logits[
                  :, : int(num_queries // (self.dual_groups + 1)), :
              ],
              bboxes[
                  :, : int(num_queries // (self.dual_groups + 1)), :
              ],
          )
  
      bbox_pred = bbox_cxcywh_to_xyxy(bboxes)
      # calculate the original shape of the image
      origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
      img_h, img_w = paddle.split(origin_shape, 2, axis=-1)
      if self.bbox_decode_type == "pad":
          # calculate the shape of the image with padding
          out_shape = pad_shape / im_shape * origin_shape
          out_shape = out_shape.flip(1).tile([1, 2]).unsqueeze(1)
      elif self.bbox_decode_type == "origin":
          out_shape = origin_shape.flip(1).tile([1, 2]).unsqueeze(1)
      else:
          raise Exception(
              f"Wrong `bbox_decode_type`: {self.bbox_decode_type}."
          )
      bbox_pred *= out_shape
  
      scores = (
          F.sigmoid(logits)
          if self.use_focal_loss
          else F.softmax(logits)[:, :, :-1]
      )
  
      if not self.use_focal_loss:
          scores, labels = scores.max(-1), scores.argmax(-1)
          if scores.shape[1] > self.num_top_queries:
              scores, index = paddle.topk(
                  scores, self.num_top_queries, axis=-1
              )
              batch_ind = (
                  paddle.arange(end=scores.shape[0])
                  .unsqueeze(-1)
                  .tile([1, self.num_top_queries])
              )
              index = paddle.stack([batch_ind, index], axis=-1)
              labels = paddle.gather_nd(labels, index)
              bbox_pred = paddle.gather_nd(bbox_pred, index)
      else:
          scores, index = paddle.topk(
              scores.flatten(1), self.num_top_queries, axis=-1
          )
          labels = index % self.num_classes
          index = index // self.num_classes
          batch_ind = (
              paddle.arange(end=scores.shape[0])
              .unsqueeze(-1)
              .tile([1, self.num_top_queries])
          )
          index = paddle.stack([batch_ind, index], axis=-1)
          bbox_pred = paddle.gather_nd(bbox_pred, index)
  
      mask_pred = None
      if self.with_mask:
          assert masks is not None
          masks = F.interpolate(
              masks,
              scale_factor=4,
              mode="bilinear",
              align_corners=False,
          )
          # TODO: Support prediction with bs>1.
          # remove padding for input image
          h, w = im_shape.astype("int32")[0]
          masks = masks[..., :h, :w]
          # get pred_mask in the original resolution.
          img_h = img_h[0].astype("int32")
          img_w = img_w[0].astype("int32")
          masks = F.interpolate(
              masks,
              size=(img_h, img_w),
              mode="bilinear",
              align_corners=False,
          )
          mask_pred, scores = self._mask_postprocess(
              masks, scores, index
          )
  
      bbox_pred = paddle.concat(
          [
              labels.unsqueeze(-1).astype("float32"),
              scores.unsqueeze(-1),
              bbox_pred,
          ],
          axis=-1,
      )
      bbox_num = paddle.to_tensor(
          self.num_top_queries, dtype="int32"
      ).tile([bbox_pred.shape[0]])
      bbox_pred = bbox_pred.reshape([-1, 6])
      return bbox_pred, bbox_num, mask_pred
  
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 3 for calling the DETRPostProcess obj's underlying member method code with example usage: obj.__call__(head_out, im_shape, scale_factor, pad_shape) and returns: bbox_pred,  bbox_num,  mask_pred