code: def forward(
      self,
      mlvl_feats,
      mlvl_masks,
      query_embed,
      mlvl_pos_embeds,
      dn_label_query,
      dn_bbox_query,
      attn_mask,
      reg_branches=None,
      cls_branches=None,
      **kwargs,
  ):
      assert (
          self.as_two_stage and query_embed is None
      ), "as_two_stage must be True for DINO"
  
      feat_flatten = []
      mask_flatten = []
      lvl_pos_embed_flatten = []
      spatial_shapes = []
      for lvl, (feat, mask, pos_embed) in enumerate(
          zip(mlvl_feats, mlvl_masks, mlvl_pos_embeds)
      ):
          bs, c, h, w = feat.shape
          spatial_shape = (h, w)
          spatial_shapes.append(spatial_shape)
          feat = feat.flatten(2).transpose(1, 2)
          mask = mask.flatten(1)
          pos_embed = pos_embed.flatten(2).transpose(1, 2)
          lvl_pos_embed = pos_embed + self.level_embeds[lvl].view(
              1, 1, -1
          )
          lvl_pos_embed_flatten.append(lvl_pos_embed)
          feat_flatten.append(feat)
          mask_flatten.append(mask)
      feat_flatten = torch.cat(feat_flatten, 1)
      mask_flatten = torch.cat(mask_flatten, 1)
      lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
      spatial_shapes = torch.as_tensor(
          spatial_shapes, dtype=torch.long, device=feat_flatten.device
      )
      level_start_index = torch.cat(
          (
              spatial_shapes.new_zeros((1,)),
              spatial_shapes.prod(1).cumsum(0)[:-1],
          )
      )
      valid_ratios = torch.stack(
          [self.get_valid_ratio(m) for m in mlvl_masks], 1
      )
  
      reference_points = self.get_reference_points(
          spatial_shapes, valid_ratios, device=feat.device
      )
  
      feat_flatten = feat_flatten.permute(
          1, 0, 2
      )  # (H*W, bs, embed_dims)
      lvl_pos_embed_flatten = lvl_pos_embed_flatten.permute(
          1, 0, 2
      )  # (H*W, bs, embed_dims)
      memory = self.encoder(
          query=feat_flatten,
          key=None,
          value=None,
          query_pos=lvl_pos_embed_flatten,
          query_key_padding_mask=mask_flatten,
          spatial_shapes=spatial_shapes,
          reference_points=reference_points,
          level_start_index=level_start_index,
          valid_ratios=valid_ratios,
          **kwargs,
      )
      memory = memory.permute(1, 0, 2)
      bs, _, c = memory.shape
  
      (
          output_memory,
          output_proposals,
      ) = self.gen_encoder_output_proposals(
          memory, mask_flatten, spatial_shapes
      )
      enc_outputs_class = cls_branches[self.decoder.num_layers](
          output_memory
      )
      enc_outputs_coord_unact = (
          reg_branches[self.decoder.num_layers](output_memory)
          + output_proposals
      )
      cls_out_features = cls_branches[
          self.decoder.num_layers
      ].out_features
      topk = self.two_stage_num_proposals
      # NOTE In DeformDETR, enc_outputs_class[..., 0] is used for topk
      topk_indices = torch.topk(
          enc_outputs_class.max(-1)[0], topk, dim=1
      )[1]
  
      topk_score = torch.gather(
          enc_outputs_class,
          1,
          topk_indices.unsqueeze(-1).repeat(1, 1, cls_out_features),
      )
      topk_coords_unact = torch.gather(
          enc_outputs_coord_unact,
          1,
          topk_indices.unsqueeze(-1).repeat(1, 1, 4),
      )
      topk_anchor = topk_coords_unact.sigmoid()
      topk_coords_unact = topk_coords_unact.detach()
  
      query = (
          self.query_embed.weight[:, None, :]
          .repeat(1, bs, 1)
          .transpose(0, 1)
      )
      # NOTE the query_embed here is not spatial query as in DETR.
      # It is actually content query, which is named tgt in other
      # DETR-like models
      if dn_label_query is not None:
          query = torch.cat([dn_label_query, query], dim=1)
      if dn_bbox_query is not None:
          reference_points = torch.cat(
              [dn_bbox_query, topk_coords_unact], dim=1
          )
      else:
          reference_points = topk_coords_unact
      reference_points = reference_points.sigmoid()
      # decoder
      query = query.permute(1, 0, 2)
      memory = memory.permute(1, 0, 2)
      inter_states, inter_references = self.decoder(
          query=query,
          key=None,
          value=memory,
          attn_masks=attn_mask,
          key_padding_mask=mask_flatten,
          reference_points=reference_points,
          spatial_shapes=spatial_shapes,
          level_start_index=level_start_index,
          valid_ratios=valid_ratios,
          reg_branches=reg_branches,
          **kwargs,
      )
  
      inter_references_out = inter_references
  
      return (
          inter_states,
          inter_references_out,
          topk_score,
          topk_anchor,
          memory,
      )
  
imports: import math
  import warnings
  import torch
  import torch.nn
  from mmcv.cnn import build_norm_layer
  from mmcv.cnn.bricks.transformer import BaseTransformerLayer, TransformerLayerSequence, build_transformer_layer_sequence
  from mmcv.ops import MultiScaleDeformableAttention
  from mmengine.model import BaseModule
  from mmengine.model.weight_init import xavier_init
  from torch.nn.init import normal_
  from mmdet.models.layers.transformer import inverse_sigmoid
  from mmdet.registry import MODELS
label_desc: Use image node_id 5 for calling the CoDinoTransformer obj's underlying member method code with example usage: obj.forward(mlvl_feats, mlvl_masks, query_embed, mlvl_pos_embeds, dn_label_query, dn_bbox_query, attn_mask, reg_branches, cls_branches) and returns: inter_states,  inter_references_out,  topk_score,  topk_anchor,  memory