docstr: Converts a mel spectrogram to one that can be saved as an image
  sample  = sqrt(exp(mel))
  Arguments
  ---------
  mel: torch.Tensor
      the mel spectrogram (as used in the model)
  len: int
      length of the mel spectrogram
  index: int
      batch index
  Returns
  -------
  mel: torch.Tensor
      the spectrogram, for image saving purposes
docstr_tok: ["Converts","a","mel","spectrogram","to","one","that","can","be","saved","as","an","image","sample","=","sqrt","(","exp","(","mel",")",")","Arguments","--","--","--","--","-","mel",":","torch.Tensor","the","mel","spectrogram","(","as","used","in","the","model",")","len",":","int","length","of","the","mel","spectrogram","index",":","int","batch","index","Returns","--","--","--","-","mel",":","torch.Tensor","the","spectrogram",",","for","image","saving","purposes"]
code: def process_mel(self, mel, len, index=0):
      """Converts a mel spectrogram to one that can be saved as an image
      sample  = sqrt(exp(mel))
      Arguments
      ---------
      mel: torch.Tensor
          the mel spectrogram (as used in the model)
      len: int
          length of the mel spectrogram
      index: int
          batch index
      Returns
      -------
      mel: torch.Tensor
          the spectrogram, for image saving purposes
      """
      assert mel.dim() == 3
      return torch.sqrt(torch.exp(mel[index][: len[index]]))
  
imports: import os
  import sys
  import torch
  import logging
  import torchaudio
  import numpy
  import speechbrain
  from speechbrain.inference.vocoders import HIFIGAN
  from pathlib import Path
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.data_utils import scalarize
label_desc: Use image node_id 6 for calling the FastSpeech2Brain obj's underlying member method code with example usage: obj.process_mel(mel, len, index) and returns: torch