code: def forward(
      self,
      query,
      *args,
      reference_points=None,
      valid_ratios=None,
      reg_branches=None,
      **kwargs,
  ):
      output = query
      intermediate = []
      intermediate_reference_points = [reference_points]
      for lid, layer in enumerate(self.layers):
          if reference_points.shape[-1] == 4:
              reference_points_input = (
                  reference_points[:, :, None]
                  * torch.cat([valid_ratios, valid_ratios], -1)[:, None]
              )
          else:
              assert reference_points.shape[-1] == 2
              reference_points_input = (
                  reference_points[:, :, None] * valid_ratios[:, None]
              )
  
          query_sine_embed = self.gen_sineembed_for_position(
              reference_points_input[:, :, 0, :], self.embed_dims // 2
          )
          query_pos = self.ref_point_head(query_sine_embed)
  
          query_pos = query_pos.permute(1, 0, 2)
          output = layer(
              output,
              *args,
              query_pos=query_pos,
              reference_points=reference_points_input,
              **kwargs,
          )
          output = output.permute(1, 0, 2)
  
          if reg_branches is not None:
              tmp = reg_branches[lid](output)
              assert reference_points.shape[-1] == 4
              new_reference_points = tmp + inverse_sigmoid(
                  reference_points, eps=1e-3
              )
              new_reference_points = new_reference_points.sigmoid()
              reference_points = new_reference_points.detach()
  
          output = output.permute(1, 0, 2)
          if self.return_intermediate:
              intermediate.append(self.norm(output))
              intermediate_reference_points.append(new_reference_points)
              # NOTE this is for the "Look Forward Twice" module,
              # in the DeformDETR, reference_points was appended.
  
      if self.return_intermediate:
          return torch.stack(intermediate), torch.stack(
              intermediate_reference_points
          )
  
      return output, reference_points
  
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 4 for calling the DinoTransformerDecoder obj's underlying member method code with example usage: obj.forward(query) and returns: output,  reference_points, torch,  torch