code: def forward(self, x):
      with torch.cuda.amp.autocast(True):
          batch_size, num_token, embed_dim = x.shape
          # qkv is [3,batch_size,num_heads,num_token, embed_dim//num_heads]
          qkv = (
              self.qkv(x)
              .reshape(
                  batch_size,
                  num_token,
                  3,
                  self.num_heads,
                  embed_dim // self.num_heads,
              )
              .permute(2, 0, 3, 1, 4)
          )
      with torch.cuda.amp.autocast(False):
          q, k, v = qkv[0].float(), qkv[1].float(), qkv[2].float()
          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(batch_size, num_token, embed_dim)
          )
      with torch.cuda.amp.autocast(True):
          x = self.proj(x)
          x = self.proj_drop(x)
      return x
  
imports: import torch
  import torch.nn
  from timm.models.layers import DropPath, to_2tuple, trunc_normal_
  from typing import Optional, Callable
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.forward(x) and returns: x