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_MEL_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, wav_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_MEL_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>).
      mel_lengths = torch.div(
          wav_lengths,
          self.mel_length_compression,
          rounding_mode="trunc",
      )
      for b in range(len(mel_lengths)):
          actual_end = (
              mel_lengths[b] + 1
          )  # Due to the convolutional nature of how these tokens are generated, it would be best if the model predicts a token past the actual last token.
          if actual_end < mel_input_tokens.shape[-1]:
              mel_input_tokens[b, actual_end:] = self.stop_mel_token
      return mel_input_tokens
  
imports: import functools
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList
  from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
  from TTS.tts.layers.tortoise.arch_utils import AttentionBlock, TypicalLogitsWarper
label_desc: Use image node_id 4 for calling the UnifiedVoice obj's underlying member method code with example usage: obj.set_mel_padding(mel_input_tokens, wav_lengths) and returns: mel_input_tokens