code: def forward(self, x):
      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)
  
      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 timm.models.layers import DropPath
  from lavis.models.ulip_models.pointbert.dvae import Group
  from lavis.models.ulip_models.pointbert.dvae import Encoder
  from lavis.models.ulip_models.pointbert.logger import print_log
  from lavis.models.ulip_models.pointbert.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message
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