code: def __init__(
      self,
      dim,
      num_heads=8,
      head_dim_ratio=1.0,
      attn_drop=0.0,
      proj_drop=0.0,
  ):
      super().__init__()
      self.dim = dim
      self.num_heads = num_heads
      head_dim = round(dim // num_heads * head_dim_ratio)
      self.head_dim = head_dim
      self.scale = head_dim**-0.5
      self.fused_attn = use_fused_attn(experimental=True)
  
      self.qkv = nn.Conv2d(
          dim,
          head_dim * num_heads * 3,
          1,
          stride=1,
          padding=0,
          bias=False,
      )
      self.attn_drop = nn.Dropout(attn_drop)
      self.proj = nn.Conv2d(
          self.head_dim * self.num_heads,
          dim,
          1,
          stride=1,
          padding=0,
          bias=False,
      )
      self.proj_drop = nn.Dropout(proj_drop)
  
imports: import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import to_2tuple, trunc_normal_, DropPath, PatchEmbed, LayerNorm2d, create_classifier, 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 Attention object from inherited base classes: nn with example: obj = Attention(dim, num_heads, head_dim_ratio, attn_drop, proj_drop)