code: def fix_autoregressive_output(codes, stop_token, complain=True):
      """
      This function performs some padding on coded audio that fixes a mismatch issue between what the diffusion model was
      trained on and what the autoregressive code generator creates (which has no padding or end).
      This is highly specific to the DVAE being used, so this particular coding will not necessarily work if used with
      a different DVAE. This can be inferred by feeding a audio clip padded with lots of zeros on the end through the DVAE
      and copying out the last few codes.
  
      Failing to do this padding will produce speech with a harsh end that sounds like "BLAH" or similar.
      """
      # Strip off the autoregressive stop token and add padding.
      stop_token_indices = (codes == stop_token).nonzero()
      if len(stop_token_indices) == 0:
          if complain:
              print(
                  "No stop tokens found in one of the generated voice clips. This typically means the spoken audio is "
                  "too long. In some cases, the output will still be good, though. Listen to it and if it is missing words, "
                  "try breaking up your input text."
              )
          return codes
      codes[stop_token_indices] = 83
      stm = stop_token_indices.min().item()
      codes[stm:] = 83
      if stm - 3 < codes.shape[0]:
          codes[-3] = 45
          codes[-2] = 45
          codes[-1] = 248
      return codes
  
imports: import os
  import random
  from contextlib import contextmanager
  from dataclasses import dataclass
  from time import time
  import torch
  import torch.nn.functional
  import torchaudio
  from coqpit import Coqpit
  from tqdm import tqdm
  from TTS.tts.layers.tortoise.arch_utils import TorchMelSpectrogram
  from TTS.tts.layers.tortoise.audio_utils import denormalize_tacotron_mel, load_voice, wav_to_univnet_mel
  from TTS.tts.layers.tortoise.autoregressive import UnifiedVoice
  from TTS.tts.layers.tortoise.classifier import AudioMiniEncoderWithClassifierHead
  from TTS.tts.layers.tortoise.clvp import CLVP
  from TTS.tts.layers.tortoise.diffusion import SpacedDiffusion, get_named_beta_schedule, space_timesteps
  from TTS.tts.layers.tortoise.diffusion_decoder import DiffusionTts
  from TTS.tts.layers.tortoise.random_latent_generator import RandomLatentConverter
  from TTS.tts.layers.tortoise.tokenizer import VoiceBpeTokenizer
  from TTS.tts.layers.tortoise.vocoder import VocConf, VocType
  from TTS.tts.layers.tortoise.wav2vec_alignment import Wav2VecAlignment
  from TTS.tts.models.base_tts import BaseTTS
label_desc: Use image node_id 5 for calling a global function with example usage: fix_autoregressive_output(codes, stop_token, complain) and returns: codes, codes