code: def input_types(self):
      if self.mode == OperationMode.infer:
          return {
              "spec": NeuralType(("B", "D", "T"), MelSpectrogramType()),
              "z": NeuralType(
                  ("B", "D", "T"), MelSpectrogramType(), optional=True
              ),
              "sigma": NeuralType(optional=True),
          }
      else:
          return {
              "spec": NeuralType(("B", "D", "T"), MelSpectrogramType()),
              "z": NeuralType(
                  ("B", "D", "T"), MelSpectrogramType(), optional=True
              ),
              "audio": NeuralType(
                  ("B", "T"), AudioSignal(), optional=True
              ),
              "run_inverse": NeuralType(
                  elements_type=IntType(), optional=True
              ),
              "sigma": NeuralType(optional=True),
          }
  
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 4 for calling the WaveGlowModule obj's underlying member method code with example usage: obj.input_types() and returns: dict, dict