code: def forward(
      self,
      tgt,
      reference_points,
      memory,
      memory_spatial_shapes,
      memory_level_start_index,
      memory_mask=None,
      query_pos_embed=None,
  ):
      if not self.training:
          return super(QRDeformableTransformerDecoder, self).forward(
              tgt,
              reference_points,
              memory,
              memory_spatial_shapes,
              memory_level_start_index,
              memory_mask=memory_mask,
              query_pos_embed=query_pos_embed,
          )
  
      batchsize = tgt.shape[0]
      query_list_reserve = [tgt]
      intermediate = []
      for lid, layer in enumerate(self.layers):
          start_q = self.start_q[lid]
          end_q = self.end_q[lid]
          query_list = query_list_reserve.copy()[start_q:end_q]
  
          # prepare for parallel process
          output = paddle.concat(query_list, axis=0)
          fakesetsize = int(output.shape[0] / batchsize)
          reference_points_tiled = reference_points.tile(
              [fakesetsize, 1, 1, 1]
          )
  
          memory_tiled = memory.tile([fakesetsize, 1, 1])
          query_pos_embed_tiled = query_pos_embed.tile(
              [fakesetsize, 1, 1]
          )
          memory_mask_tiled = memory_mask.tile([fakesetsize, 1])
  
          output = layer(
              output,
              reference_points_tiled,
              memory_tiled,
              memory_spatial_shapes,
              memory_level_start_index,
              memory_mask_tiled,
              query_pos_embed_tiled,
          )
  
          for i in range(fakesetsize):
              query_list_reserve.append(
                  output[batchsize * i : batchsize * (i + 1)]
              )
  
          if self.return_intermediate:
              for i in range(fakesetsize):
                  intermediate.append(
                      output[batchsize * i : batchsize * (i + 1)]
                  )
  
      if self.return_intermediate:
          return paddle.stack(intermediate)
  
      return output.unsqueeze(0)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from ppdet.core.workspace import register
  from ..layers import MultiHeadAttention
  from .position_encoding import PositionEmbedding
  from .utils import _get_clones, get_valid_ratio
  from ..initializer import linear_init_, constant_, xavier_uniform_, normal_
label_desc: Use image node_id 2 for calling the QRDeformableTransformerDecoder obj's underlying member method code with example usage: obj.forward(tgt, reference_points, memory, memory_spatial_shapes, memory_level_start_index, memory_mask, query_pos_embed) and returns: output, super, paddle