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, c.audio["num_mels"], 30).to(device)
      linear_spec = torch.rand(8, 30, c.audio["fft_size"]).to(device)
      mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
      speaker_ids = torch.randint(0, 5, (8,)).long().to(device)
  
      criterion = criterion = GlowTTSLoss()
  
      # model to train
      model = GlowTts(
          num_chars=32,
          hidden_channels_enc=128,
          hidden_channels_dec=128,
          hidden_channels_dp=32,
          out_channels=80,
          encoder_type="rel_pos_transformer",
          encoder_params={
              "kernel_size": 3,
              "dropout_p": 0.1,
              "num_layers": 6,
              "num_heads": 2,
              "hidden_channels_ffn": 768,  # 4 times the hidden_channels
              "input_length": None,
          },
          use_encoder_prenet=True,
          num_flow_blocks_dec=12,
          kernel_size_dec=5,
          dilation_rate=5,
          num_block_layers=4,
          dropout_p_dec=0.0,
          num_speakers=0,
          c_in_channels=0,
          num_splits=4,
          num_squeeze=1,
          sigmoid_scale=False,
          mean_only=False,
      ).to(device)
  
      # reference model to compare model weights
      model_ref = GlowTts(
          num_chars=32,
          hidden_channels_enc=128,
          hidden_channels_dec=128,
          hidden_channels_dp=32,
          out_channels=80,
          encoder_type="rel_pos_transformer",
          encoder_params={
              "kernel_size": 3,
              "dropout_p": 0.1,
              "num_layers": 6,
              "num_heads": 2,
              "hidden_channels_ffn": 768,  # 4 times the hidden_channels
              "input_length": None,
          },
          use_encoder_prenet=True,
          num_flow_blocks_dec=12,
          kernel_size_dec=5,
          dilation_rate=5,
          num_block_layers=4,
          dropout_p_dec=0.0,
          num_speakers=0,
          c_in_channels=0,
          num_splits=4,
          num_squeeze=1,
          sigmoid_scale=False,
          mean_only=False,
      ).to(device)
  
      model.train()
      print(
          " > Num parameters for GlowTTS model:%s"
          % (count_parameters(model))
      )
  
      # pass the state to ref model
      model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
  
      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):
          (
              z,
              logdet,
              y_mean,
              y_log_scale,
              alignments,
              o_dur_log,
              o_total_dur,
          ) = model.forward(
              input_dummy, input_lengths, mel_spec, mel_lengths, None
          )
          optimizer.zero_grad()
          loss_dict = criterion(
              z,
              y_mean,
              y_log_scale,
              logdet,
              mel_lengths,
              o_dur_log,
              o_total_dur,
              input_lengths,
          )
          loss = loss_dict["loss"]
          loss.backward()
          optimizer.step()
  
      # check parameter changes
      count = 0
      for param, param_ref in zip(
          model.parameters(), model_ref.parameters()
      ):
          assert (
              param != param_ref
          ).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
              count, param.shape, param, param_ref
          )
          count += 1
  
file_path: TTS/tests/test_glow_tts.py
imports: import copy
  import os
  import unittest
  import torch
  from tests import get_tests_input_path
  from torch import optim
  from TTS.tts.layers.losses import GlowTTSLoss
  from TTS.tts.models.glow_tts import GlowTts
  from TTS.utils.io import load_config
  from TTS.utils.audio import AudioProcessor
label_desc: Use image node_id 1 for calling the GlowTTSTrainTest obj's underlying member method code with example usage: obj.test_train_step() without return types