code: def forward(self, query, key, value, mask=None):
      B = query.shape[0]
  
      # 1) Do all the linear projections in batch from d_model => h x d_k
      query, key, value = [
          l(x)
          .reshape([B, 0, self.headers, self.d_k])
          .transpose([0, 2, 1, 3])
          for l, x in zip(self.linears, (query, key, value))
      ]
      # 2) Apply attention on all the projected vectors in batch
      x, self.attn = self_attention(
          query, key, value, mask=mask, dropout=self.dropout
      )
      x = x.transpose([0, 2, 1, 3]).reshape(
          [B, 0, self.headers * self.d_k]
      )
      return self.linears[-1](x)
  
imports: import copy
  import math
  import paddle
  from paddle import nn
  from paddle.nn import functional
label_desc: Use image node_id 2 for calling the MultiHeadAttention obj's underlying member method code with example usage: obj.forward(query, key, value, mask) and returns: self