code: def audio_to_normal_dist(
      self, *, spec: torch.Tensor, audio: torch.Tensor
  ) -> Tuple[torch.Tensor, list, list]:
      #  Upsample spectrogram to size of audio
      spec = self.upsample(spec)
      assert spec.size(2) >= audio.size(1)
      if spec.size(2) > audio.size(1):
          spec = spec[:, :, : audio.size(1)]
  
      # logging.debug(f"spec: {spec.shape}. n_group: {self.n_group}")
      spec = split_view(spec, self.n_group, 2).permute(0, 2, 1, 3)
      spec = spec.contiguous().view(spec.size(0), spec.size(1), -1)
      spec = spec.permute(0, 2, 1)
  
      audio = split_view(audio, self.n_group, 1).permute(0, 2, 1)
      output_audio = []
      log_s_list = []
      log_det_W_list = []
  
      for k in range(self.n_flows):
          if k % self.n_early_every == 0 and k > 0:
              output_audio.append(audio[:, : self.n_early_size, :])
              audio = audio[:, self.n_early_size :, :]
  
          audio, log_det_W = self.convinv[k](audio)
          log_det_W_list.append(log_det_W)
  
          n_half = int(audio.size(1) / 2)
          audio_0 = audio[:, :n_half, :]
          audio_1 = audio[:, n_half:, :]
  
          output = self.wavenet[k]((audio_0, spec))
          log_s = output[:, n_half:, :]
          b = output[:, :n_half, :]
          audio_1 = torch.exp(log_s) * audio_1 + b
          log_s_list.append(log_s)
  
          audio = torch.cat([audio_0, audio_1], 1)
  
      output_audio.append(audio)
      return torch.cat(output_audio, 1), log_s_list, log_det_W_list
  
file_path: NeMo/nemo/collections/tts/modules/waveglow.py
imports: from typing import Tuple
  import torch
  from nemo.collections.tts.modules.submodules import Invertible1x1Conv, WaveNet
  from nemo.collections.tts.parts.utils.helpers import OperationMode, remove, split_view
  from nemo.core.classes import Exportable, NeuralModule, typecheck
  from nemo.core.neural_types.elements import AudioSignal, IntType, MelSpectrogramType, NormalDistributionSamplesType, VoidType
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 7 for calling the WaveGlowModule obj's underlying member method code with example usage: obj.audio_to_normal_dist() and returns: torch,  log_s_list,  log_det_W_list