code: def __init__(
      self,
      dim,
      num_heads,
      mlp_ratio=4.0,
      qkv_bias=False,
      proj_drop=0.0,
      attn_drop=0.0,
      drop_path=0.0,
      act_layer=nn.GELU,
      norm_layer=nn.LayerNorm,
      attn_block=ClassAttn,
      mlp_block=Mlp,
      init_values=1e-4,
  ):
      super().__init__()
      self.norm1 = norm_layer(dim)
      self.attn = attn_block(
          dim,
          num_heads=num_heads,
          qkv_bias=qkv_bias,
          attn_drop=attn_drop,
          proj_drop=proj_drop,
      )
      self.drop_path = (
          DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
      )
      self.norm2 = norm_layer(dim)
      mlp_hidden_dim = int(dim * mlp_ratio)
      self.mlp = mlp_block(
          in_features=dim,
          hidden_features=mlp_hidden_dim,
          act_layer=act_layer,
          drop=proj_drop,
      )
      self.gamma_1 = nn.Parameter(init_values * torch.ones(dim))
      self.gamma_2 = nn.Parameter(init_values * torch.ones(dim))
  
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 LayerScaleBlockClassAttn object from inherited base classes: nn with example: obj = LayerScaleBlockClassAttn(dim, num_heads, mlp_ratio, qkv_bias, proj_drop, attn_drop, drop_path, act_layer, norm_layer, attn_block, mlp_block, init_values)