code: def forward(self, x, register_hook=False):
      B, N, C = x.shape
      qkv = (
          self.qkv(x)
          .reshape(B, N, 3, self.num_heads, C // self.num_heads)
          .permute(2, 0, 3, 1, 4)
      )
      q, k, v = (
          qkv[0],
          qkv[1],
          qkv[2],
      )  # make torchscript happy (cannot use tensor as tuple)
  
      attn = (q @ k.transpose(-2, -1)) * self.scale
      attn = attn.softmax(dim=-1)
      attn = self.attn_drop(attn)
  
      if register_hook:
          self.save_attention_map(attn)
          attn.register_hook(self.save_attn_gradients)
  
      x = (attn @ v).transpose(1, 2).reshape(B, N, C)
      x = self.proj(x)
      x = self.proj_drop(x)
      return x
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from functools import partial
  from timm.models.vision_transformer import _cfg, PatchEmbed
  from timm.models.registry import register_model
  from timm.models.layers import trunc_normal_, DropPath
  from timm.models.helpers import named_apply, adapt_input_conv
  from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
label_desc: Use image node_id 6 for calling the Attention obj's underlying member method code with example usage: obj.forward(x, register_hook) and returns: x