code: def __init__(
      self,
      n_state,
      n_head,
      kv_caching: Literal["cross", "self"] = None,
      max_self_attn_cache_len=None,
  ):
      self.n_head = n_head
      self.query = nn.Linear(n_state, n_state)
      self.key = nn.Linear(n_state, n_state, bias=False)
      self.value = nn.Linear(n_state, n_state)
      self.out = nn.Linear(n_state, n_state)
  
      self.kv_caching = kv_caching
      self.max_self_attn_cache_len = max_self_attn_cache_len
  
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 1 to create a new MultiHeadAttention object  with example: obj = MultiHeadAttention(n_state, n_head, kv_caching, max__attn_cache_len)