class_docstr: GSA: using a  key to summarize the information for a group to be efficient.
      
code: def __init__(
      self, dim, num_heads=8, attn_drop=0.0, proj_drop=0.0, sr_ratio=1
  ):
      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
      head_dim = dim // num_heads
      self.scale = head_dim**-0.5
      self.fused_attn = use_fused_attn()
  
      self.q = nn.Linear(dim, dim, bias=True)
      self.kv = nn.Linear(dim, dim * 2, bias=True)
      self.attn_drop = nn.Dropout(attn_drop)
      self.proj = nn.Linear(dim, dim)
      self.proj_drop = nn.Dropout(proj_drop)
  
      self.sr_ratio = sr_ratio
      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
  
imports: import math
  from functools import partial
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import Mlp, DropPath, to_2tuple, trunc_normal_, use_fused_attn
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_module
  from ._registry import register_model, generate_default_cfgs
  from .vision_transformer import Attention
label_desc: Use image node_id 1 to create a new GlobalSubSampleAttn object from inherited base classes: nn with example: obj = GlobalSubSampleAttn(dim, num_heads, attn_drop, proj_drop, sr_ratio)