code: def __init__(
      self,
      dim: int,
      num_heads: int,
      qkv_bias: bool = False,
      mlp_ratio: float = 4.0,
      scale_mlp: bool = False,
      swiglu_mlp: bool = False,
      proj_drop: float = 0.0,
      attn_drop: float = 0.0,
      drop_path: float = 0.0,
      init_values: Optional[float] = None,
      act_layer: Callable = nn.GELU,
      norm_layer: Callable = LayerNorm,
      window_size: Optional[Tuple[int, int]] = None,
      attn_head_dim: Optional[int] = None,
  ):
      super().__init__()
      self.norm1 = norm_layer(dim)
      self.attn = Attention(
          dim,
          num_heads=num_heads,
          qkv_bias=qkv_bias,
          attn_drop=attn_drop,
          proj_drop=proj_drop,
          window_size=window_size,
          attn_head_dim=attn_head_dim,
      )
      # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
      self.drop_path1 = (
          DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
      )
  
      self.norm2 = norm_layer(dim)
      if swiglu_mlp:
          self.mlp = SwiGLU(
              in_features=dim,
              hidden_features=int(dim * mlp_ratio),
              norm_layer=norm_layer if scale_mlp else None,
              drop=proj_drop,
          )
      else:
          self.mlp = Mlp(
              in_features=dim,
              hidden_features=int(dim * mlp_ratio),
              act_layer=act_layer,
              norm_layer=norm_layer if scale_mlp else None,
              drop=proj_drop,
          )
      self.drop_path2 = (
          DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
      )
  
      if init_values:
          self.gamma_1 = nn.Parameter(init_values * torch.ones(dim))
          self.gamma_2 = nn.Parameter(init_values * torch.ones(dim))
      else:
          self.gamma_1, self.gamma_2 = None, None
  
imports: import math
  from typing import Callable, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.checkpoint import checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, SwiGLU, LayerNorm, DropPath, trunc_normal_, use_fused_attn
  from timm.layers import resample_patch_embed, resample_abs_pos_embed, resize_rel_pos_bias_table
  from ._builder import build_model_with_cfg
  from ._registry import generate_default_cfgs, register_model
  from .vision_transformer import checkpoint_filter_fn
label_desc: Use image node_id 1 to create a new Block object from inherited base classes: nn with example: obj = Block(dim, num_heads, qkv_bias, mlp_ratio, scale_mlp, swiglu_mlp, proj_drop, attn_drop, drop_path, init_values, act_layer, norm_layer, window_size, attn_head_dim)