code: def __init__(
      self,
      img_size=224,
      patch_size=16,
      in_chans=3,
      num_classes=1000,
      global_pool="token",
      embed_dim=768,
      depth=12,
      num_heads=12,
      mlp_ratio=4.0,
      qkv_bias=True,
      drop_rate=0.0,
      pos_drop_rate=0.0,
      proj_drop_rate=0.0,
      attn_drop_rate=0.0,
      drop_path_rate=0.0,
      block_layers=LayerScaleBlock,
      block_layers_token=LayerScaleBlockClassAttn,
      patch_layer=PatchEmbed,
      norm_layer=partial(nn.LayerNorm, eps=1e-6),
      act_layer=nn.GELU,
      attn_block=TalkingHeadAttn,
      mlp_block=Mlp,
      init_values=1e-4,
      attn_block_token_only=ClassAttn,
      mlp_block_token_only=Mlp,
      depth_token_only=2,
      mlp_ratio_token_only=4.0,
  ):
      super().__init__()
      assert global_pool in ("", "token", "avg")
  
      self.num_classes = num_classes
      self.global_pool = global_pool
      self.num_features = self.embed_dim = embed_dim
      self.grad_checkpointing = False
  
      self.patch_embed = patch_layer(
          img_size=img_size,
          patch_size=patch_size,
          in_chans=in_chans,
          embed_dim=embed_dim,
      )
  
      num_patches = self.patch_embed.num_patches
  
      self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
      self.pos_embed = nn.Parameter(
          torch.zeros(1, num_patches, embed_dim)
      )
      self.pos_drop = nn.Dropout(p=pos_drop_rate)
  
      dpr = [drop_path_rate for i in range(depth)]
      self.blocks = nn.Sequential(
          *[
              block_layers(
                  dim=embed_dim,
                  num_heads=num_heads,
                  mlp_ratio=mlp_ratio,
                  qkv_bias=qkv_bias,
                  proj_drop=proj_drop_rate,
                  attn_drop=attn_drop_rate,
                  drop_path=dpr[i],
                  norm_layer=norm_layer,
                  act_layer=act_layer,
                  attn_block=attn_block,
                  mlp_block=mlp_block,
                  init_values=init_values,
              )
              for i in range(depth)
          ]
      )
  
      self.blocks_token_only = nn.ModuleList(
          [
              block_layers_token(
                  dim=embed_dim,
                  num_heads=num_heads,
                  mlp_ratio=mlp_ratio_token_only,
                  qkv_bias=qkv_bias,
                  norm_layer=norm_layer,
                  act_layer=act_layer,
                  attn_block=attn_block_token_only,
                  mlp_block=mlp_block_token_only,
                  init_values=init_values,
              )
              for _ in range(depth_token_only)
          ]
      )
  
      self.norm = norm_layer(embed_dim)
  
      self.feature_info = [
          dict(num_chs=embed_dim, reduction=0, module="head")
      ]
      self.head_drop = nn.Dropout(drop_rate)
      self.head = (
          nn.Linear(embed_dim, num_classes)
          if num_classes > 0
          else nn.Identity()
      )
  
      trunc_normal_(self.pos_embed, std=0.02)
      trunc_normal_(self.cls_token, std=0.02)
      self.apply(self._init_weights)
  
imports: from functools import partial
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, trunc_normal_, use_fused_attn
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new Cait object from inherited base classes: nn with example: obj = Cait(img_size, patch_size, in_chans, num_classes, global_pool, embed_dim, depth, num_heads, mlp_ratio, qkv_bias, drop_rate, pos_drop_rate, proj_drop_rate, attn_drop_rate, drop_path_rate, block_layers, block_layers_token, patch_layer, norm_layer, act_layer, attn_block, mlp_block, init_values, attn_block_token_only, mlp_block_token_only, depth_token_only, mlp_ratio_token_only)