code: def forward_aux(
      self,
      mlvl_feats,
      mlvl_masks,
      query_embed,
      mlvl_pos_embeds,
      pos_anchors,
      pos_feats=None,
      reg_branches=None,
      cls_branches=None,
      return_encoder_output=False,
      attn_masks=None,
      head_idx=0,
      **kwargs,
  ):
      feat_flatten = []
      mask_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)
          feat_flatten.append(feat)
          mask_flatten.append(mask)
      feat_flatten = torch.cat(feat_flatten, 1)
      mask_flatten = torch.cat(mask_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
      )
  
      feat_flatten = feat_flatten.permute(
          1, 0, 2
      )  # (H*W, bs, embed_dims)
  
      memory = feat_flatten
      memory = memory.permute(1, 0, 2)
      bs, _, c = memory.shape
  
      topk_coords_unact = inverse_sigmoid(pos_anchors)
      reference_points = pos_anchors
      if self.num_co_heads > 0:
          pos_trans_out = self.aux_pos_trans_norm[head_idx](
              self.aux_pos_trans[head_idx](
                  self.get_proposal_pos_embed(topk_coords_unact)
              )
          )
          query = pos_trans_out
          if self.with_coord_feat:
              query = query + self.pos_feats_norm[head_idx](
                  self.pos_feats_trans[head_idx](pos_feats)
              )
  
      # 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=None,
          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
  
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 6 for calling the CoDinoTransformer obj's underlying member method code with example usage: obj.forward_aux(mlvl_feats, mlvl_masks, query_embed, mlvl_pos_embeds, pos_anchors, pos_feats, reg_branches, cls_branches, return_encoder_output, attn_masks, head_idx) and returns: inter_states,  inter_references_out