code: def __init__(
      self,
      dim_embd=512,
      n_head=8,
      n_layers=9,
      codebook_size=1024,
      latent_size=256,
      connect_list=("32", "64", "128", "256"),
      fix_modules=("quantize", "generator"),
  ):
      super(CodeFormer, self).__init__(
          512, 64, [1, 2, 2, 4, 4, 8], "nearest", 2, [16], codebook_size
      )
  
      if fix_modules is not None:
          for module in fix_modules:
              for param in getattr(self, module).parameters():
                  param.requires_grad = False
  
      self.connect_list = connect_list
      self.n_layers = n_layers
      self.dim_embd = dim_embd
      self.dim_mlp = dim_embd * 2
  
      self.position_emb = nn.Parameter(
          torch.zeros(latent_size, self.dim_embd)
      )
      self.feat_emb = nn.Linear(256, self.dim_embd)
  
      # transformer
      self.ft_layers = nn.Sequential(
          *[
              TransformerSALayer(
                  embed_dim=dim_embd,
                  nhead=n_head,
                  dim_mlp=self.dim_mlp,
                  dropout=0.0,
              )
              for _ in range(self.n_layers)
          ]
      )
  
      # logits_predict head
      self.idx_pred_layer = nn.Sequential(
          nn.LayerNorm(dim_embd),
          nn.Linear(dim_embd, codebook_size, bias=False),
      )
  
      self.channels = {
          "16": 512,
          "32": 256,
          "64": 256,
          "128": 128,
          "256": 128,
          "512": 64,
      }
  
      # after second residual block for > 16, before attn layer for ==16
      self.fuse_encoder_block = {
          "512": 2,
          "256": 5,
          "128": 8,
          "64": 11,
          "32": 14,
          "16": 18,
      }
      # after first residual block for > 16, before attn layer for ==16
      self.fuse_generator_block = {
          "16": 6,
          "32": 9,
          "64": 12,
          "128": 15,
          "256": 18,
          "512": 21,
      }
  
      # fuse_convs_dict
      self.fuse_convs_dict = nn.ModuleDict()
      for f_size in self.connect_list:
          in_ch = self.channels[f_size]
          self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)
  
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 1 to create a new CodeFormer object from inherited base classes: VQAutoEncoder with example: obj = CodeFormer(dim_embd, n_head, n_layers, codebook_size, latent_size, connect_list, fix_modules)