code: def __call__(
      self,
      x: Tensor,
      xa: Optional[Tensor] = None,
      mask: Optional[Tensor] = None,
      len: Union[Variable, int] = None,
  ):
      if self.kv_caching == "cross":
          if xa is not None:
              k, v = self.key(xa), self.value(xa)
              if not hasattr(self, "cache_k"):
                  self.cache_k, self.cache_v = k, v
              else:
                  # see test_jitted_read_assign in test_jit.py. more context https://github.com/tinygrad/tinygrad/pull/2360#issuecomment-1817989994
                  self.cache_k.assign(k + 1 - 1).realize()
                  self.cache_v.assign(v + 1 - 1).realize()
          else:
              k, v = self.cache_k, self.cache_v
      else:
          k, v = self.key(x), self.value(x)
          if self.kv_caching == "self":
              if not hasattr(self, "cache_k"):
                  self.cache_k = Tensor.zeros(
                      x.shape[0],
                      self.max_self_attn_cache_len,
                      x.shape[2],
                  )
                  self.cache_v = Tensor.zeros(
                      x.shape[0],
                      self.max_self_attn_cache_len,
                      x.shape[2],
                  )
              k = self.cache_k.shrink((None, (0, len), None)).cat(
                  k, dim=1
              )
              v = self.cache_v.shrink((None, (0, len), None)).cat(
                  v, dim=1
              )
              padding = self.max_self_attn_cache_len - len - x.shape[1]
              self.cache_k.assign(
                  k.pad((None, (0, padding), None)).contiguous()
              ).realize()
              self.cache_v.assign(
                  v.pad((None, (0, padding), None)).contiguous()
              ).realize()
  
      q = self.query(x)
      n_ctx = q.shape[1]
      assert q.shape[-1] == k.shape[-1] == v.shape[-1]
      head_dim = q.shape[-1] // self.n_head
      q = q.reshape(*q.shape[:2], self.n_head, head_dim).permute(
          0, 2, 1, 3
      )
      k = k.reshape(*k.shape[:2], self.n_head, head_dim).permute(
          0, 2, 1, 3
      )
      v = v.reshape(*v.shape[:2], self.n_head, head_dim).permute(
          0, 2, 1, 3
      )
      attn = Tensor.scaled_dot_product_attention(
          q, k, v, mask[:n_ctx, :n_ctx] if mask is not None else None
      )
      wv = attn.permute(0, 2, 1, 3).flatten(start_dim=2)
      return self.out(wv)
  
imports: import sys
  import pathlib
  import base64
  import multiprocessing
  import numpy
  from typing import Optional, Union, Literal, List
  from tinygrad.jit import TinyJit
  from tinygrad.nn.state import torch_load, load_state_dict
  from tinygrad.helpers import getenv, DEBUG, CI, fetch
  import tinygrad.nn
  from tinygrad.shape.symbolic import Variable
  from tinygrad.tensor import Tensor
  import itertools
  import librosa
label_desc: Use image node_id 2 for calling the MultiHeadAttention obj's underlying member method code with example usage: obj.__call__(x, xa, mask, len) and returns: self