code: def forward(
      self,
      tgt,
      reference_points,
      memory,
      memory_spatial_shapes,
      memory_level_start_index,
      attn_mask=None,
      memory_mask=None,
      query_pos_embed=None,
  ):
      # self attention
      q = k = self.with_pos_embed(tgt, query_pos_embed)
      if self.dual_queries:
          dual_groups = self.dual_groups
          bs, num_queries, n_model = paddle.shape(q)
          q = paddle.concat(q.split(dual_groups + 1, axis=1), axis=0)
          k = paddle.concat(k.split(dual_groups + 1, axis=1), axis=0)
          tgt = paddle.concat(
              tgt.split(dual_groups + 1, axis=1), axis=0
          )
  
          g_num_queries = num_queries // (dual_groups + 1)
          if attn_mask is None or attn_mask[0] is None:
              attn_mask = None
          else:
              # [(dual_groups + 1), g_num_queries, g_num_queries]
              attn_mask = paddle.concat(
                  [sa_mask.unsqueeze(0) for sa_mask in attn_mask],
                  axis=0,
              )
              # [1, (dual_groups + 1), 1, g_num_queries, g_num_queries]
              # --> [bs, (dual_groups + 1), nhead, g_num_queries, g_num_queries]
              # --> [bs * (dual_groups + 1), nhead, g_num_queries, g_num_queries]
              attn_mask = (
                  attn_mask.unsqueeze(0)
                  .unsqueeze(2)
                  .tile([bs, 1, self.n_head, 1, 1])
              )
              attn_mask = attn_mask.reshape(
                  [
                      bs * (dual_groups + 1),
                      self.n_head,
                      g_num_queries,
                      g_num_queries,
                  ]
              )
  
      if attn_mask is not None:
          attn_mask = attn_mask.astype("bool")
  
      tgt2 = self.self_attn(q, k, value=tgt, attn_mask=attn_mask)
      tgt = tgt + self.dropout1(tgt2)
      tgt = self.norm2(tgt)
  
      # trace back
      if self.dual_queries:
          tgt = paddle.concat(
              tgt.split(dual_groups + 1, axis=0), axis=1
          )
  
      # cross attention
      tgt2 = self.cross_attn(
          self.with_pos_embed(tgt, query_pos_embed),
          reference_points,
          memory,
          memory_spatial_shapes,
          memory_level_start_index,
          memory_mask,
      )
      tgt = tgt + self.dropout2(tgt2)
      tgt = self.norm1(tgt)
  
      # ffn
      tgt2 = self.forward_ffn(tgt)
      tgt = tgt + self.dropout4(tgt2)
      tgt = self.norm3(tgt)
  
      return tgt
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from ppdet.core.workspace import register
  from ..layers import MultiHeadAttention
  from .position_encoding import PositionEmbedding
  from ..heads.detr_head import MLP
  from .deformable_transformer import MSDeformableAttention
  from ..initializer import linear_init_, constant_, xavier_uniform_, normal_, bias_init_with_prob
  from .utils import _get_clones, get_valid_ratio, get_contrastive_denoising_training_group, get_sine_pos_embed, inverse_sigmoid
label_desc: Use image node_id 5 for calling the DINOTransformerDecoderLayer obj's underlying member method code with example usage: obj.forward(tgt, reference_points, memory, memory_spatial_shapes, memory_level_start_index, attn_mask, memory_mask, query_pos_embed) and returns: tgt