code: def test_train_step():
      input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
      input_lengths = torch.randint(100, 129, (8,)).long().to(device)
      input_lengths[-1] = 128
      mel_spec = torch.rand(8, 30, c.audio["num_mels"]).to(device)
      linear_spec = torch.rand(8, 30, c.audio["fft_size"]).to(device)
      mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
      stop_targets = torch.zeros(8, 30, 1).float().to(device)
      speaker_ids = torch.randint(0, 5, (8,)).long().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 = L1LossMasked(seq_len_norm=False).to(device)
      criterion_st = nn.BCEWithLogitsLoss().to(device)
      model = Tacotron(
          num_chars=32,
          num_speakers=5,
          postnet_output_dim=c.audio["fft_size"],
          decoder_output_dim=c.audio["num_mels"],
          r=c.r,
          memory_size=c.memory_size,
      ).to(
          device
      )  # FIXME: missing num_speakers parameter to Tacotron ctor
      model.train()
      print(
          " > Num parameters for Tacotron 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
      optimizer = optim.Adam(model.parameters(), lr=c.lr)
      for _ in range(5):
          mel_out, linear_out, align, stop_tokens = model.forward(
              input_dummy,
              input_lengths,
              mel_spec,
              mel_lengths,
              speaker_ids,
          )
          optimizer.zero_grad()
          loss = criterion(mel_out, mel_spec, mel_lengths)
          stop_loss = criterion_st(stop_tokens, stop_targets)
          loss = (
              loss
              + criterion(linear_out, linear_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 L1LossMasked
  from TTS.tts.models.tacotron import Tacotron
  from TTS.utils.io import load_config
  from TTS.utils.audio import AudioProcessor
label_desc: Use image node_id 1 for calling the TacotronTrainTest obj's underlying member method code with example usage: obj.test_train_step() without return types