code: def forward(self, x, y, mask=None):
      # x: B, H, 1, 1, 1 y: B H L 1
      nhid, nhead, head_dim, unfold_size = (
          self.nhid,
          self.nhead,
          self.head_dim,
          self.unfold_size,
      )
      B, H, L, _ = y.shape
  
      q, k, v = self.WQ(x), self.WK(y), self.WV(y)
  
      q = q.view(B, nhead, 1, head_dim)  # B, H, 1, 1 -> B, N, 1, h
      k = k.view(B, nhead, head_dim, L)  # B, H, L, 1 -> B, N, h, L
      v = v.view(B, nhead, head_dim, L).permute(
          0, 1, 3, 2
      )  # B, H, L, 1 -> B, N, L, h
      pre_a = torch.matmul(q, k) / np.sqrt(head_dim)
      if mask is not None:
          pre_a = pre_a.masked_fill(
              mask[:, None, None, :], -float("inf")
          )
      alphas = self.drop(F.softmax(pre_a, 3))  # B, N, 1, L
      att = torch.matmul(alphas, v).view(
          B, -1, 1, 1
      )  # B, N, 1, h -> B, N*h, 1, 1
      return self.WO(att)
  
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 _MSA2 obj's underlying member method code with example usage: obj.forward(x, y, mask) and returns: self