code: def forward(
      self, q, k, v, mask=None
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      heads = []
      attns = []
      vs = self.v_layer(v)
      for i in range(self.n_head):
          qs = self.q_layers[i](q)
          ks = self.k_layers[i](k)
          head, attn = self.attention(qs, ks, vs, mask)
          head_dropout = self.dropout(head)
          heads.append(head_dropout)
          attns.append(attn)
  
      head = torch.stack(heads, dim=2) if self.n_head > 1 else heads[0]
      attn = torch.stack(attns, dim=2)
  
      outputs = torch.mean(head, dim=2) if self.n_head > 1 else head
      outputs = self.w_h(outputs)
      outputs = self.dropout(outputs)
  
      return outputs, attn
  
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from darts.logging import get_logger
  from darts.utils.torch import MonteCarloDropout
label_desc: Use image node_id 3 for calling the _InterpretableMultiHeadAttention obj's underlying member method code with example usage: obj.forward(q, k, v, mask) and returns: outputs,  attn