code: def forward(
      self, x, w=0, detach_16=True, code_only=False, adain=False
  ):
      # ################### Encoder #####################
      enc_feat_dict = {}
      out_list = [
          self.fuse_encoder_block[f_size]
          for f_size in self.connect_list
      ]
      for i, block in enumerate(self.encoder.blocks):
          x = block(x)
          if i in out_list:
              enc_feat_dict[str(x.shape[-1])] = x.clone()
  
      lq_feat = x
      # ################# Transformer ###################
      # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
      pos_emb = self.position_emb.unsqueeze(1).repeat(1, x.shape[0], 1)
      # BCHW -> BC(HW) -> (HW)BC
      feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2, 0, 1))
      query_emb = feat_emb
      # Transformer encoder
      for layer in self.ft_layers:
          query_emb = layer(query_emb, query_pos=pos_emb)
  
      # output logits
      logits = self.idx_pred_layer(query_emb)  # (hw)bn
      logits = logits.permute(1, 0, 2)  # (hw)bn -> b(hw)n
  
      if code_only:  # for training stage II
          # logits doesn't need softmax before cross_entropy loss
          return logits, lq_feat
  
      # ################# Quantization ###################
      # if self.training:
      #     quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
      #     # b(hw)c -> bc(hw) -> bchw
      #     quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
      # ------------
      soft_one_hot = F.softmax(logits, dim=2)
      _, top_idx = torch.topk(soft_one_hot, 1, dim=2)
      quant_feat = self.quantize.get_codebook_feat(
          top_idx, shape=[x.shape[0], 16, 16, 256]
      )
      # preserve gradients
      # quant_feat = lq_feat + (quant_feat - lq_feat).detach()
  
      if detach_16:
          quant_feat = quant_feat.detach()  # for training stage III
      if adain:
          quant_feat = adaptive_instance_normalization(
              quant_feat, lq_feat
          )
  
      # ################## Generator ####################
      x = quant_feat
      fuse_list = [
          self.fuse_generator_block[f_size]
          for f_size in self.connect_list
      ]
  
      for i, block in enumerate(self.generator.blocks):
          x = block(x)
          if i in fuse_list:  # fuse after i-th block
              f_size = str(x.shape[-1])
              if w > 0:
                  x = self.fuse_convs_dict[f_size](
                      enc_feat_dict[f_size].detach(), x, w
                  )
      out = x
      # logits doesn't need softmax before cross_entropy loss
      return out, logits, lq_feat
  
imports: import math
  import torch
  from torch import nn, Tensor
  import torch.nn.functional
  from typing import Optional
  from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
  from basicsr.utils.registry import ARCH_REGISTRY
label_desc: Use image node_id 3 for calling the CodeFormer obj's underlying member method code with example usage: obj.forward(x, w, detach_16, code_only, adain) and returns: out,  logits,  lq_feat, logits,  lq_feat