code: def synthesis_with_waveflow(self, mel_output, sigma):
      mel_spectrogram = fluid.layers.transpose(
          fluid.layers.squeeze(mel_output, [0]), [1, 0]
      )
      mel_spectrogram = fluid.layers.unsqueeze(mel_spectrogram, [0])
  
      for layer in self.waveflow.sublayers():
          if isinstance(layer, WeightNormWrapper):
              layer.remove_weight_norm()
  
      # Run model inference.
      wav = self.waveflow.synthesize(mel_spectrogram, sigma=sigma)
      return wav.numpy()[0]
  
imports: import os
  import ast
  import argparse
  import importlib.util
  import nltk
  import paddle.fluid
  import paddle.fluid.dygraph
  import paddlehub
  from paddlehub.module.module import runnable
  from paddlehub.common.utils import mkdir
  from paddlehub.module.nlp_module import DataFormatError
  from paddlehub.common.logger import logger
  from paddlehub.module.module import moduleinfo, serving
  from paddlehub.common.dir import THIRD_PARTY_HOME
  from paddlehub.common.downloader import default_downloader
label_desc: Use image node_id 4 for calling the TransformerTTS obj's underlying member method code with example usage: obj.synthesis_with_waveflow(mel_output, sigma) and returns: wav