docstr: Decode the mask_out and paste the mask to the origin image.
  
  Args:
      mask_out (Tensor): mask_head output with shape [N, 28, 28].
      bbox_pred (Tensor): The output bboxes with shape [N, 6] after decode
          and NMS, including labels, scores and bboxes.
      bbox_num (Tensor): The number of prediction boxes of each batch with
          shape [1], and is N.
      origin_shape (Tensor): The origin shape of the input image, the tensor
          shape is [N, 2], and each row is [h, w].
  Returns:
      pred_result (Tensor): The final prediction mask results with shape
          [N, h, w] in binary mask style.
code: def __call__(self, mask_out, bboxes, bbox_num, origin_shape):
      """
      Decode the mask_out and paste the mask to the origin image.
  
      Args:
          mask_out (Tensor): mask_head output with shape [N, 28, 28].
          bbox_pred (Tensor): The output bboxes with shape [N, 6] after decode
              and NMS, including labels, scores and bboxes.
          bbox_num (Tensor): The number of prediction boxes of each batch with
              shape [1], and is N.
          origin_shape (Tensor): The origin shape of the input image, the tensor
              shape is [N, 2], and each row is [h, w].
      Returns:
          pred_result (Tensor): The final prediction mask results with shape
              [N, h, w] in binary mask style.
      """
      num_mask = mask_out.shape[0]
      origin_shape = paddle.cast(origin_shape, "int32")
      device = paddle.device.get_device()
  
      if self.export_onnx:
          h, w = origin_shape[0][0], origin_shape[0][1]
          mask_onnx = paste_mask(
              mask_out[:, None, :, :],
              bboxes[:, 2:],
              h,
              w,
              self.assign_on_cpu,
          )
          mask_onnx = mask_onnx >= self.binary_thresh
          pred_result = paddle.cast(mask_onnx, "int32")
  
      else:
          max_h = paddle.max(origin_shape[:, 0])
          max_w = paddle.max(origin_shape[:, 1])
          pred_result = (
              paddle.zeros([num_mask, max_h, max_w], dtype="int32") - 1
          )
  
          id_start = 0
          for i in range(paddle.shape(bbox_num)[0]):
              bboxes_i = bboxes[id_start : id_start + bbox_num[i], :]
              mask_out_i = mask_out[
                  id_start : id_start + bbox_num[i], :, :
              ]
              im_h = origin_shape[i, 0]
              im_w = origin_shape[i, 1]
              pred_mask = paste_mask(
                  mask_out_i[:, None, :, :],
                  bboxes_i[:, 2:],
                  im_h,
                  im_w,
                  self.assign_on_cpu,
              )
              pred_mask = paddle.cast(
                  pred_mask >= self.binary_thresh, "int32"
              )
              pred_result[
                  id_start : id_start + bbox_num[i], :im_h, :im_w
              ] = pred_mask
              id_start += bbox_num[i]
      if self.assign_on_cpu:
          paddle.set_device(device)
  
      return pred_result
  
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 MaskPostProcess obj's underlying member method code with example usage: obj.__call__(mask_out, bboxes, bbox_num, origin_shape) and returns: pred_result