code: def forward(
      self,
      x: Tensor,
      xa: Optional[Tensor] = None,
      mask: Optional[Tensor] = None,
      kv_cache: Optional[dict] = None,
  ):
      q = self.query(x)
  
      if kv_cache is None or xa is None or self.key not in kv_cache:
          # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
          # otherwise, perform key/value projections for self- or cross-attention as usual.
          k = self.key(x if xa is None else xa)
          v = self.value(x if xa is None else xa)
      else:
          # for cross-attention, calculate keys and values once and reuse in subsequent calls.
          k = kv_cache[self.key]
          v = kv_cache[self.value]
  
      wv, qk = self.qkv_attention(q, k, v, mask)
      return self.out(wv), qk
  
imports: import base64
  import gzip
  from dataclasses import dataclass
  from typing import Dict, Iterable, Optional
  import numpy
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
  from .decoding import decode
  from .decoding import detect_language
  from .transcribe import transcribe
label_desc: Use image node_id 2 for calling the MultiHeadAttention obj's underlying member method code with example usage: obj.forward(x, xa, mask, kv_cache) and returns: self,  qk