code: def forward(
      self, x: Tensor, mask_future_timesteps: bool = True
  ) -> Tuple[Tensor, Tensor]:
      # [Batch,Time,MultiHead,AttDim] := [N,T,M,AD]
      bs, t, h_size = x.shape
      qkv = self.qkv_linears(x)
      q, k, v = qkv.split(
          (
              self.n_head * self.d_head,
              self.n_head * self.d_head,
              self.d_head,
          ),
          dim=-1,
      )
      q = q.view(bs, t, self.n_head, self.d_head)
      k = k.view(bs, t, self.n_head, self.d_head)
      v = v.view(bs, t, self.d_head)
  
      # [N,T1,M,Ad] x [N,T2,M,Ad] -> [N,M,T1,T2]
      # attn_score = torch.einsum('bind,bjnd->bnij', q, k)
      attn_score = torch.matmul(
          q.permute((0, 2, 1, 3)), k.permute((0, 2, 3, 1))
      )
      attn_score.mul_(self.scale)
  
      if mask_future_timesteps:
          attn_score = attn_score + self._mask
  
      attn_prob = F.softmax(attn_score, dim=3)
      attn_prob = self.attn_dropout(attn_prob)
  
      # [N,M,T1,T2] x [N,M,T1,Ad] -> [N,M,T1,Ad]
      # attn_vec = torch.einsum('bnij,bjd->bnid', attn_prob, v)
      attn_vec = torch.matmul(attn_prob, v.unsqueeze(1))
      m_attn_vec = torch.mean(attn_vec, dim=1)
      out = self.out_proj(m_attn_vec)
      out = self.out_dropout(out)
  
      return out, attn_vec
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import LayerNorm
  import logging
  import warnings
  from typing import Tuple, Optional
  from ..losses.pytorch import MAE
  from ..common._base_windows import BaseWindows
label_desc: Use image node_id 2 for calling the InterpretableMultiHeadAttention obj's underlying member method code with example usage: obj.forward(x, mask_future_timesteps) and returns: out,  attn_vec