code: def forward(self, x, ax=None):
      # x: B, H, L, 1, ax : B, H, X, L append features
      nhid, nhead, head_dim, unfold_size = (
          self.nhid,
          self.nhead,
          self.head_dim,
          self.unfold_size,
      )
      B, H, L, _ = x.shape
  
      q, k, v = self.WQ(x), self.WK(x), self.WV(x)  # x: (B,H,L,1)
  
      if ax is not None:
          aL = ax.shape[2]
          ak = self.WK(ax).view(B, nhead, head_dim, aL, L)
          av = self.WV(ax).view(B, nhead, head_dim, aL, L)
      q = q.view(B, nhead, head_dim, 1, L)
      k = F.unfold(
          k.view(B, nhead * head_dim, L, 1),
          (unfold_size, 1),
          padding=(unfold_size // 2, 0),
      ).view(B, nhead, head_dim, unfold_size, L)
      v = F.unfold(
          v.view(B, nhead * head_dim, L, 1),
          (unfold_size, 1),
          padding=(unfold_size // 2, 0),
      ).view(B, nhead, head_dim, unfold_size, L)
      if ax is not None:
          k = torch.cat([k, ak], 3)
          v = torch.cat([v, av], 3)
  
      alphas = self.drop(
          F.softmax((q * k).sum(2, keepdim=True) / np.sqrt(head_dim), 3)
      )  # B N L 1 U
      att = (alphas * v).sum(3).view(B, nhead * head_dim, L, 1)
  
      ret = self.WO(att)
  
      return ret
  
file_path: fastNLP/fastNLP/modules/torch/encoder/star_transformer.py
imports: import numpy
  import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the _MSA1 obj's underlying member method code with example usage: obj.forward(x, ax) and returns: ret