docstr: Given mel tokens that are derived from a padded audio clip and the actual lengths of each batch element in
  that audio clip, reformats the tokens with stop_audio_token in place of the zero padding. This is required
  preformatting to create a working TTS model.
docstr_tok: ["Given","mel","tokens","that","are","derived","from","a","padded","audio","clip","and","the","actual","lengths","of","each","batch","element","in","that","audio","clip",",","reformats","the","tokens","with","stop_audio_token","in","place","of","the","zero","padding",".","This","is","required","preformatting","to","create","a","working","TTS","model","."]
code: def set_mel_padding(self, mel_input_tokens, code_lengths):
      """
      Given mel tokens that are derived from a padded audio clip and the actual lengths of each batch element in
      that audio clip, reformats the tokens with stop_audio_token in place of the zero padding. This is required
      preformatting to create a working TTS model.
      """
      # Set padding areas within MEL (currently it is coded with the MEL code for <zero>).
      for b in range(len(code_lengths)):
          actual_end = code_lengths[b]
          if actual_end < mel_input_tokens.shape[-1]:
              mel_input_tokens[b, actual_end:] = self.stop_audio_token
      return mel_input_tokens
  
imports: import functools
  import math
  import random
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import GPT2Config
  from TTS.tts.layers.xtts.gpt_inference import GPT2InferenceModel
  from TTS.tts.layers.xtts.latent_encoder import ConditioningEncoder
  from TTS.tts.layers.xtts.perceiver_encoder import PerceiverResampler
label_desc: Use image node_id 5 for calling the GPT obj's underlying member method code with example usage: obj.set_mel_padding(mel_input_tokens, code_lengths) and returns: mel_input_tokens