class_docstr: UnivNet model (https://arxiv.org/abs/2106.07889) that is used to generate audio from mel spectrogram.
class_docstr_tok: ["UnivNet","model","(","https",":","\/\/arxiv.org\/abs\/2106.07889",")","that","is","used","to","generate","audio","from","mel","spectrogram","."]
code: def input_types(self):
      return {
          "spec": NeuralType(("B", "D", "T"), MelSpectrogramType()),
      }
  
imports: import itertools
  from typing import Dict
  import torch
  import torch.nn.functional
  from hydra.utils import instantiate
  from omegaconf import DictConfig, OmegaConf, open_dict
  from pytorch_lightning.loggers.wandb import WandbLogger
  from nemo.collections.tts.losses.hifigan_losses import DiscriminatorLoss, GeneratorLoss
  from nemo.collections.tts.losses.stftlosses import MultiResolutionSTFTLoss
  from nemo.collections.tts.models.base import Vocoder
  from nemo.collections.tts.modules.univnet_modules import MultiPeriodDiscriminator, MultiResolutionDiscriminator
  from nemo.collections.tts.parts.utils.helpers import get_batch_size, get_num_workers, plot_spectrogram_to_numpy
  from nemo.core import Exportable
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.neural_types.elements import AudioSignal, MelSpectrogramType
  from nemo.core.neural_types.neural_type import NeuralType
  from nemo.core.optim.lr_scheduler import compute_max_steps, prepare_lr_scheduler
  from nemo.utils import logging, model_utils
label_desc: Use image node_id 16 for calling the UnivNetModel obj's underlying member method code with example usage: obj.input_types() and returns: dict