code: def test_train_step():
      input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
      input_lengths = torch.randint(100, 128, (8,)).long().to(device)
      input_lengths = torch.sort(input_lengths, descending=True)[0]
      mel_spec = torch.rand(8, 30, c.audio["num_mels"]).to(device)
      mel_postnet_spec = torch.rand(8, 30, c.audio["num_mels"]).to(
          device
      )
      mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
      mel_lengths[0] = 30
      stop_targets = torch.zeros(8, 30, 1).float().to(device)
      speaker_embeddings = torch.rand(8, 55).to(device)
  
      for idx in mel_lengths:
          stop_targets[:, int(idx.item()) :, 0] = 1.0
  
      stop_targets = stop_targets.view(
          input_dummy.shape[0], stop_targets.size(1) // c.r, -1
      )
      stop_targets = (
          (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
      )
  
      criterion = MSELossMasked(seq_len_norm=False).to(device)
      criterion_st = nn.BCEWithLogitsLoss().to(device)
      model = Tacotron2(
          num_chars=24, r=c.r, num_speakers=5, speaker_embedding_dim=55
      ).to(device)
      model.train()
      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
      optimizer = optim.Adam(model.parameters(), lr=c.lr)
      for i in range(5):
          mel_out, mel_postnet_out, align, stop_tokens = model.forward(
              input_dummy,
              input_lengths,
              mel_spec,
              mel_lengths,
              speaker_embeddings=speaker_embeddings,
          )
          assert torch.sigmoid(stop_tokens).data.max() <= 1.0
          assert torch.sigmoid(stop_tokens).data.min() >= 0.0
          optimizer.zero_grad()
          loss = criterion(mel_out, mel_spec, mel_lengths)
          stop_loss = criterion_st(stop_tokens, stop_targets)
          loss = (
              loss
              + criterion(
                  mel_postnet_out, mel_postnet_spec, mel_lengths
              )
              + stop_loss
          )
          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
          # if count not in [145, 59]:
          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 tests import get_tests_input_path
  from torch import nn, optim
  from TTS.tts.layers.losses import MSELossMasked
  from TTS.tts.models.tacotron2 import Tacotron2
  from TTS.utils.io import load_config
  from TTS.utils.audio import AudioProcessor
label_desc: Use image node_id 1 for calling the MultiSpeakeTacotronTrainTest obj's underlying member method code with example usage: obj.test_train_step() without return types