code: def forward(self, x, H, W):
      B, N, C = x.shape
      q = (
          self.q(x)
          .reshape(B, N, self.num_heads, C // self.num_heads)
          .permute(0, 2, 1, 3)
      )
  
      if self.sr_ratio > 1:
          x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
          x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
          x_ = self.norm(x_)
          kv = (
              self.kv(x_)
              .reshape(B, -1, 2, self.num_heads, C // self.num_heads)
              .permute(2, 0, 3, 1, 4)
          )
      else:
          kv = (
              self.kv(x)
              .reshape(B, -1, 2, self.num_heads, C // self.num_heads)
              .permute(2, 0, 3, 1, 4)
          )
      k, v = kv[0], kv[1]
  
      attn = (q @ k.transpose(-2, -1)) * self.scale
      attn = attn.softmax(dim=-1)
      attn = self.attn_drop(attn)
  
      x = (attn @ v).transpose(1, 2).reshape(B, N, C)
      x = self.proj(x)
      x = self.proj_drop(x)
  
      return x
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from functools import partial
  from torch.nn.init import trunc_normal_
  from megatron.model.transformer import DropPath
  from megatron.model import LayerNorm
label_desc: Use image node_id 3 for calling the Attention obj's underlying member method code with example usage: obj.forward(x, H, W) and returns: x