code: def test_train_step():
      config = TacotronConfig(
          num_chars=32,
          num_speakers=10,
          use_speaker_embedding=True,
          out_channels=513,
          decoder_output_dim=80,
          use_capacitron_vae=True,
          capacitron_vae=CapacitronVAEConfig(),
          optimizer="CapacitronOptimizer",
          optimizer_params={
              "RAdam": {"betas": [0.9, 0.998], "weight_decay": 1e-6},
              "SGD": {"lr": 1e-5, "momentum": 0.9},
          },
      )
  
      batch = dict({})
      batch["text_input"] = (
          torch.randint(0, 24, (8, 128)).long().to(device)
      )
      batch["text_lengths"] = (
          torch.randint(100, 129, (8,)).long().to(device)
      )
      batch["text_lengths"] = torch.sort(
          batch["text_lengths"], descending=True
      )[0]
      batch["text_lengths"][0] = 128
      batch["linear_input"] = torch.rand(
          8, 120, config.audio["fft_size"] // 2 + 1
      ).to(device)
      batch["mel_input"] = torch.rand(
          8, 120, config.audio["num_mels"]
      ).to(device)
      batch["mel_lengths"] = (
          torch.randint(20, 120, (8,)).long().to(device)
      )
      batch["mel_lengths"] = torch.sort(
          batch["mel_lengths"], descending=True
      )[0]
      batch["mel_lengths"][0] = 120
      batch["stop_targets"] = torch.zeros(8, 120, 1).float().to(device)
      batch["stop_target_lengths"] = torch.randint(0, 120, (8,)).to(
          device
      )
      batch["speaker_ids"] = torch.randint(0, 5, (8,)).long().to(device)
      batch["d_vectors"] = None
  
      for idx in batch["mel_lengths"]:
          batch["stop_targets"][:, int(idx.item()) :, 0] = 1.0
  
      batch["stop_targets"] = batch["stop_targets"].view(
          batch["text_input"].shape[0],
          batch["stop_targets"].size(1) // config.r,
          -1,
      )
      batch["stop_targets"] = (
          (batch["stop_targets"].sum(2) > 0.0)
          .unsqueeze(2)
          .float()
          .squeeze()
      )
      model = Tacotron(config).to(device)
      criterion = model.get_criterion()
      optimizer = model.get_optimizer()
      model.train()
      print(
          " > Num parameters for Tacotron with Capacitron VAE model:%s"
          % (count_parameters(model))
      )
      model_ref = copy.deepcopy(model)
      count = 0
      for param, param_ref in zip(
          model.parameters(), model_ref.parameters()
      ):
          assert (param - param_ref).sum() == 0, param
          count += 1
      for _ in range(10):
          _, loss_dict = model.train_step(batch, criterion)
          optimizer.zero_grad()
          loss_dict["capacitron_vae_beta_loss"].backward()
          optimizer.first_step()
          loss_dict["loss"].backward()
          optimizer.step()
      # check parameter changes
      count = 0
      for param, param_ref in zip(
          model.parameters(), model_ref.parameters()
      ):
          # ignore pre-higway layer since it works conditional
          assert (
              param != param_ref
          ).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
              count, param.shape, param, param_ref
          )
          count += 1
  
imports: import copy
  import os
  import unittest
  import torch
  from torch import nn, optim
  from tests import get_tests_input_path
  from TTS.tts.configs.shared_configs import CapacitronVAEConfig, GSTConfig
  from TTS.tts.configs.tacotron_config import TacotronConfig
  from TTS.tts.layers.losses import L1LossMasked
  from TTS.tts.models.tacotron import Tacotron
  from TTS.utils.audio import AudioProcessor
label_desc: Use image node_id 1 for calling the TacotronCapacitronTrainTest obj's underlying member method code with example usage: obj.test_train_step() without return types