class_docstr: FastPitch based model that can synthesize mel spectrograms from content and speaker embeddings
  obtained from SSLDisentangler. This model can be used for voice conversion by swapping the speaker embedding 
  of a given source utterance, with the speaker embedding of a target speaker.
class_docstr_tok: ["FastPitch","based","model","that","can","synthesize","mel","spectrograms","from","content","and","speaker","embeddings","obtained","from","SSLDisentangler",".","This","model","can","be","used","for","voice","conversion","by","swapping","the","speaker","embedding","of","a","given","source","utterance",",","with","the","speaker","embedding","of","a","target","speaker","."]
code: def list_available_models(cls) -> "List[PretrainedModelInfo]":
      return []
  
imports: import random
  from typing import Iterable
  import torch
  from hydra.utils import instantiate
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from pytorch_lightning.loggers import TensorBoardLogger
  from nemo.collections.tts.losses.fastpitchloss import DurationLoss, MelLoss, PitchLoss
  from nemo.collections.tts.modules.fastpitch import FastPitchSSLModule, average_features
  from nemo.collections.tts.modules.transformer import mask_from_lens
  from nemo.collections.tts.parts.utils.helpers import plot_multipitch_to_numpy, plot_spectrogram_to_numpy
  from nemo.core.classes import ModelPT
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging, model_utils
  from nemo.utils.decorators import experimental
label_desc: Use image node_id 14 for calling the FastPitchModel_SSL obj's underlying member method code with example usage: obj.list_available_models(cls) and returns: list