class_docstr: For brevity this just renames MultiHeadAttention, but ideally you'd define a new
  nn.Module with the same __init__ and forward signature as MultiHeadAttention.
class_docstr_tok: ["For","brevity","this","just","renames","MultiHeadAttention",",","but","ideally","you","'d","define","a","new","nn.Module","with","the","same","__init__","and","forward","signature","as","MultiHeadAttention","."]
code: def forward(
      self,
      query: torch.Tensor,
      key: Optional[torch.Tensor] = None,
      value: Optional[torch.Tensor] = None,
      mask: torch.Tensor = None,
      incr_state: Optional[Dict[str, torch.Tensor]] = None,
      static_kv: bool = False,
  ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
      logging.info("Custom attention called!")
      # Comment out the following line and write your custom `forward` instead.
      return super().forward(
          query,
          key=key,
          value=value,
          mask=mask,
          incr_state=incr_state,
          static_kv=static_kv,
      )
  
imports: from __future__ import annotations
  import torch
  from enum import Enum
  from typing import Dict, Optional, Tuple, Union
  from parlai.agents.transformer.modules import TransformerFFN, MultiHeadAttention, TransformerDecoder, TransformerDecoderLayer, TransformerEncoder, TransformerEncoderLayer, TransformerGeneratorModel
  from parlai.agents.transformer.transformer import TransformerGeneratorAgent
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  import parlai.utils.logging
label_desc: Use image node_id 1 for calling the MyCustomAttention obj's underlying member method code with example usage: obj.forward(query, key, value, mask, incr_state, static_kv) and returns: super