code: def __init__(
      self,
      n_mels,
      n_audio_ctx,
      n_audio_state,
      n_audio_head,
      n_audio_layer,
      **_,
  ):
      self.conv1 = nn.Conv1d(
          n_mels, n_audio_state, kernel_size=3, padding=1
      )
      self.conv2 = nn.Conv1d(
          n_audio_state,
          n_audio_state,
          kernel_size=3,
          stride=2,
          padding=1,
      )
      self.blocks = [
          ResidualAttentionBlock(n_audio_state, n_audio_head)
          for _ in range(n_audio_layer)
      ]
      self.ln_post = nn.LayerNorm(n_audio_state)
      self.positional_embedding = Tensor.empty(
          n_audio_ctx, n_audio_state
      )
      self.encode = TinyJit(self.__call__)
  
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 AudioEncoder object  with example: obj = AudioEncoder(n_mels, n_audio_ctx, n_audio_state, n_audio_head, n_audio_layer)