code: def forward(self, x, mask=None):
      assert x.dim() == 3
      qkv = self.to_qvk(x)  # [batch, tokens, dim*3*heads ]
      # decomposition to q,v,k and cast to tuple
      # the resulted shape before casting to tuple will be: [3, batch, heads, tokens, dim_head]
  
      shape = qkv.shape
      k = 3
      h = self.heads
  
      d = paddle.reshape(qkv, [shape[0], shape[1], -1, k, h])
      d = paddle.transpose(d, perm=[3, 0, 4, 1, 2])
  
      q, k, v = tuple(d)
  
      out = compute_mhsa(
          q, k, v, mask=mask, scale_factor=self.scale_factor
      )
  
      # re-compose: merge heads with dim_head
  
      out = paddle.flatten(
          paddle.transpose(out, perm=[0, 2, 1, 3]),
          start_axis=2,
          stop_axis=3,
      )
  
      return self.W_0(out)
  
imports: from typing import List, Tuple
  import numpy
  import paddle
  import paddle.nn
  from paddle import Tensor
  from medicalseg.cvlibs import manager
label_desc: Use image node_id 2 for calling the MultiHeadSelfAttention obj's underlying member method code with example usage: obj.forward(x, mask) and returns: self