code: def train(
      data_loader,
      model,
      criterion,
      optimizer,
      optimizer_st,
      scheduler,
      ap,
      global_step,
      epoch,
      scaler,
      scaler_st,
  ):
      model.train()
      epoch_time = 0
      keep_avg = KeepAverage()
      if use_cuda:
          batch_n_iter = int(
              len(data_loader.dataset) / (c.batch_size * num_gpus)
          )
      else:
          batch_n_iter = int(len(data_loader.dataset) / c.batch_size)
      end_time = time.time()
      c_logger.print_train_start()
      for num_iter, data in enumerate(data_loader):
          start_time = time.time()
  
          # format data
          (
              text_input,
              text_lengths,
              mel_input,
              mel_lengths,
              linear_input,
              stop_targets,
              speaker_ids,
              speaker_embeddings,
              max_text_length,
              max_spec_length,
          ) = format_data(data)
          loader_time = time.time() - end_time
  
          global_step += 1
  
          # setup lr
          if c.noam_schedule:
              scheduler.step()
  
          optimizer.zero_grad()
          if optimizer_st:
              optimizer_st.zero_grad()
  
          with torch.cuda.amp.autocast(enabled=c.mixed_precision):
              # forward pass model
              if (
                  c.bidirectional_decoder
                  or c.double_decoder_consistency
              ):
                  (
                      decoder_output,
                      postnet_output,
                      alignments,
                      stop_tokens,
                      decoder_backward_output,
                      alignments_backward,
                  ) = model(
                      text_input,
                      text_lengths,
                      mel_input,
                      mel_lengths,
                      speaker_ids=speaker_ids,
                      speaker_embeddings=speaker_embeddings,
                  )
              else:
                  (
                      decoder_output,
                      postnet_output,
                      alignments,
                      stop_tokens,
                  ) = model(
                      text_input,
                      text_lengths,
                      mel_input,
                      mel_lengths,
                      speaker_ids=speaker_ids,
                      speaker_embeddings=speaker_embeddings,
                  )
                  decoder_backward_output = None
                  alignments_backward = None
  
              # set the [alignment] lengths wrt reduction factor for guided attention
              if mel_lengths.max() % model.decoder.r != 0:
                  alignment_lengths = (
                      mel_lengths
                      + (
                          model.decoder.r
                          - (mel_lengths.max() % model.decoder.r)
                      )
                  ) // model.decoder.r
              else:
                  alignment_lengths = mel_lengths // model.decoder.r
  
              # compute loss
              loss_dict = criterion(
                  postnet_output,
                  decoder_output,
                  mel_input,
                  linear_input,
                  stop_tokens,
                  stop_targets,
                  mel_lengths,
                  decoder_backward_output,
                  alignments,
                  alignment_lengths,
                  alignments_backward,
                  text_lengths,
              )
  
          # check nan loss
          if torch.isnan(loss_dict["loss"]).any():
              raise RuntimeError(
                  f"Detected NaN loss at step {global_step}."
              )
  
          # optimizer step
          if c.mixed_precision:
              # model optimizer step in mixed precision mode
              scaler.scale(loss_dict["loss"]).backward()
              scaler.unscale_(optimizer)
              optimizer, current_lr = adam_weight_decay(optimizer)
              grad_norm, _ = check_update(
                  model, c.grad_clip, ignore_stopnet=True
              )
              scaler.step(optimizer)
              scaler.update()
  
              # stopnet optimizer step
              if c.separate_stopnet:
                  scaler_st.scale(loss_dict["stopnet_loss"]).backward()
                  scaler.unscale_(optimizer_st)
                  optimizer_st, _ = adam_weight_decay(optimizer_st)
                  grad_norm_st, _ = check_update(
                      model.decoder.stopnet, 1.0
                  )
                  scaler_st.step(optimizer)
                  scaler_st.update()
              else:
                  grad_norm_st = 0
          else:
              # main model optimizer step
              loss_dict["loss"].backward()
              optimizer, current_lr = adam_weight_decay(optimizer)
              grad_norm, _ = check_update(
                  model, c.grad_clip, ignore_stopnet=True
              )
              optimizer.step()
  
              # stopnet optimizer step
              if c.separate_stopnet:
                  loss_dict["stopnet_loss"].backward()
                  optimizer_st, _ = adam_weight_decay(optimizer_st)
                  grad_norm_st, _ = check_update(
                      model.decoder.stopnet, 1.0
                  )
                  optimizer_st.step()
              else:
                  grad_norm_st = 0
  
          # compute alignment error (the lower the better )
          align_error = 1 - alignment_diagonal_score(alignments)
          loss_dict["align_error"] = align_error
  
          step_time = time.time() - start_time
          epoch_time += step_time
  
          # aggregate losses from processes
          if num_gpus > 1:
              loss_dict["postnet_loss"] = reduce_tensor(
                  loss_dict["postnet_loss"].data, num_gpus
              )
              loss_dict["decoder_loss"] = reduce_tensor(
                  loss_dict["decoder_loss"].data, num_gpus
              )
              loss_dict["loss"] = reduce_tensor(
                  loss_dict["loss"].data, num_gpus
              )
              loss_dict["stopnet_loss"] = (
                  reduce_tensor(
                      loss_dict["stopnet_loss"].data, num_gpus
                  )
                  if c.stopnet
                  else loss_dict["stopnet_loss"]
              )
  
          # detach loss values
          loss_dict_new = dict()
          for key, value in loss_dict.items():
              if isinstance(value, (int, float)):
                  loss_dict_new[key] = value
              else:
                  loss_dict_new[key] = value.item()
          loss_dict = loss_dict_new
  
          # update avg stats
          update_train_values = dict()
          for key, value in loss_dict.items():
              update_train_values["avg_" + key] = value
          update_train_values["avg_loader_time"] = loader_time
          update_train_values["avg_step_time"] = step_time
          keep_avg.update_values(update_train_values)
  
          # print training progress
          if global_step % c.print_step == 0:
              log_dict = {
                  "max_spec_length": [
                      max_spec_length,
                      1,
                  ],  # value, precision
                  "max_text_length": [max_text_length, 1],
                  "step_time": [step_time, 4],
                  "loader_time": [loader_time, 2],
                  "current_lr": current_lr,
              }
              c_logger.print_train_step(
                  batch_n_iter,
                  num_iter,
                  global_step,
                  log_dict,
                  loss_dict,
                  keep_avg.avg_values,
              )
  
          if args.rank == 0:
              # Plot Training Iter Stats
              # reduce TB load
              if global_step % c.tb_plot_step == 0:
                  iter_stats = {
                      "lr": current_lr,
                      "grad_norm": grad_norm,
                      "grad_norm_st": grad_norm_st,
                      "step_time": step_time,
                  }
                  iter_stats.update(loss_dict)
                  tb_logger.tb_train_iter_stats(global_step, iter_stats)
  
              if global_step % c.save_step == 0:
                  if c.checkpoint:
                      # save model
                      save_checkpoint(
                          model,
                          optimizer,
                          global_step,
                          epoch,
                          model.decoder.r,
                          OUT_PATH,
                          optimizer_st=optimizer_st,
                          model_loss=loss_dict["postnet_loss"],
                          scaler=scaler.state_dict()
                          if c.mixed_precision
                          else None,
                      )
  
                  # Diagnostic visualizations
                  const_spec = postnet_output[0].data.cpu().numpy()
                  gt_spec = (
                      linear_input[0].data.cpu().numpy()
                      if c.model in ["Tacotron", "TacotronGST"]
                      else mel_input[0].data.cpu().numpy()
                  )
                  align_img = alignments[0].data.cpu().numpy()
  
                  figures = {
                      "prediction": plot_spectrogram(
                          const_spec, ap, output_fig=False
                      ),
                      "ground_truth": plot_spectrogram(
                          gt_spec, ap, output_fig=False
                      ),
                      "alignment": plot_alignment(
                          align_img, output_fig=False
                      ),
                  }
  
                  if (
                      c.bidirectional_decoder
                      or c.double_decoder_consistency
                  ):
                      figures["alignment_backward"] = plot_alignment(
                          alignments_backward[0].data.cpu().numpy(),
                          output_fig=False,
                      )
  
                  tb_logger.tb_train_figures(global_step, figures)
  
                  # Sample audio
                  if c.model in ["Tacotron", "TacotronGST"]:
                      train_audio = ap.inv_spectrogram(const_spec.T)
                  else:
                      train_audio = ap.inv_melspectrogram(const_spec.T)
                  tb_logger.tb_train_audios(
                      global_step,
                      {"TrainAudio": train_audio},
                      c.audio["sample_rate"],
                  )
          end_time = time.time()
  
      # print epoch stats
      c_logger.print_train_epoch_end(
          global_step, epoch, epoch_time, keep_avg
      )
  
      # Plot Epoch Stats
      if args.rank == 0:
          epoch_stats = {"epoch_time": epoch_time}
          epoch_stats.update(keep_avg.avg_values)
          tb_logger.tb_train_epoch_stats(global_step, epoch_stats)
          if c.tb_model_param_stats:
              tb_logger.tb_model_weights(model, global_step)
      return keep_avg.avg_values, global_step
  
imports: import argparse
  import glob
  import os
  import sys
  import time
  import traceback
  from random import randrange
  import numpy
  import torch
  from torch.utils.data import DataLoader
  from TTS.tts.datasets.preprocess import load_meta_data
  from TTS.tts.datasets.TTSDataset import MyDataset
  from TTS.tts.layers.losses import TacotronLoss
  from TTS.tts.utils.generic_utils import check_config_tts, setup_model
  from TTS.tts.utils.io import save_best_model, save_checkpoint
  from TTS.tts.utils.measures import alignment_diagonal_score
  from TTS.tts.utils.speakers import parse_speakers
  from TTS.tts.utils.synthesis import synthesis
  from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
  from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
  from TTS.utils.audio import AudioProcessor
  from TTS.utils.console_logger import ConsoleLogger
  from TTS.utils.distribute import DistributedSampler, apply_gradient_allreduce, init_distributed, reduce_tensor
  from TTS.utils.generic_utils import KeepAverage, count_parameters, create_experiment_folder, get_git_branch, remove_experiment_folder, set_init_dict
  from TTS.utils.io import copy_model_files, load_config
  from TTS.utils.radam import RAdam
  from TTS.utils.tensorboard_logger import TensorboardLogger
  from TTS.utils.training import NoamLR, adam_weight_decay, check_update, gradual_training_scheduler, set_weight_decay, setup_torch_training_env
label_desc: Use image node_id 3 for calling a global function with example usage: train(data_loader, model, criterion, optimizer, optimizer_st, scheduler, ap, global_step, epoch, scaler, scaler_st) and returns: keep_avg,  global_step