code: def __init__(
      self,
      dim,
      num_heads=8,
      sr_ratio=1,
      linear_attn=False,
      qkv_bias=True,
      attn_drop=0.0,
      proj_drop=0.0,
  ):
      super().__init__()
      assert (
          dim % num_heads == 0
      ), f"dim {dim} should be divided by num_heads {num_heads}."
  
      self.dim = dim
      self.num_heads = num_heads
      self.head_dim = dim // num_heads
      self.scale = self.head_dim**-0.5
      self.fused_attn = use_fused_attn()
  
      self.q = nn.Linear(dim, dim, bias=qkv_bias)
      self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
      self.attn_drop = nn.Dropout(attn_drop)
      self.proj = nn.Linear(dim, dim)
      self.proj_drop = nn.Dropout(proj_drop)
  
      if not linear_attn:
          self.pool = None
          if sr_ratio > 1:
              self.sr = nn.Conv2d(
                  dim, dim, kernel_size=sr_ratio, stride=sr_ratio
              )
              self.norm = nn.LayerNorm(dim)
          else:
              self.sr = None
              self.norm = None
          self.act = None
      else:
          self.pool = nn.AdaptiveAvgPool2d(7)
          self.sr = nn.Conv2d(dim, dim, kernel_size=1, stride=1)
          self.norm = nn.LayerNorm(dim)
          self.act = nn.GELU()
  
imports: import math
  from typing import Tuple, List, Callable, Union
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, to_ntuple, trunc_normal_, LayerNorm, use_fused_attn
  from ._builder import build_model_with_cfg
  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, sr_ratio, linear_attn, qkv_bias, attn_drop, proj_drop)