code: def __init__(
      self,
      n_state,
      n_head,
      is_decoder_block=False,
      max_self_attn_cache_len=None,
  ):
      self.attn = MultiHeadAttention(
          n_state,
          n_head,
          kv_caching="self" if is_decoder_block else None,
          max_self_attn_cache_len=max_self_attn_cache_len,
      )
      self.attn_ln = nn.LayerNorm(n_state)
  
      self.cross_attn = (
          MultiHeadAttention(n_state, n_head, kv_caching="cross")
          if is_decoder_block
          else None
      )
      self.cross_attn_ln = (
          nn.LayerNorm(n_state) if is_decoder_block else None
      )
  
      self.mlp = [
          nn.Linear(n_state, n_state * 4),
          Tensor.gelu,
          nn.Linear(n_state * 4, n_state),
      ]
      self.mlp_ln = nn.LayerNorm(n_state)
  
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 ResidualAttentionBlock object  with example: obj = ResidualAttentionBlock(n_state, n_head, is_decoder_block, max__attn_cache_len)