code: def train(
      data_loader,
      model,
      criterion,
      optimizer,
      scheduler,
      ap,
      global_step,
      epoch,
  ):
      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()
      scaler = (
          torch.cuda.amp.GradScaler() if c.mixed_precision else None
      )
      for num_iter, data in enumerate(data_loader):
          start_time = time.time()
  
          # format data
          (
              text_input,
              text_lengths,
              mel_input,
              mel_lengths,
              speaker_c,
              avg_text_length,
              avg_spec_length,
              attn_mask,
              item_idx,
          ) = format_data(data)
  
          loader_time = time.time() - end_time
  
          global_step += 1
          optimizer.zero_grad()
  
          # forward pass model
          with torch.cuda.amp.autocast(enabled=c.mixed_precision):
              (
                  z,
                  logdet,
                  y_mean,
                  y_log_scale,
                  alignments,
                  o_dur_log,
                  o_total_dur,
              ) = model.forward(
                  text_input,
                  text_lengths,
                  mel_input,
                  mel_lengths,
                  attn_mask,
                  g=speaker_c,
              )
  
              # compute loss
              loss_dict = criterion(
                  z,
                  y_mean,
                  y_log_scale,
                  logdet,
                  mel_lengths,
                  o_dur_log,
                  o_total_dur,
                  text_lengths,
              )
  
          # backward pass with loss scaling
          if c.mixed_precision:
              scaler.scale(loss_dict["loss"]).backward()
              scaler.unscale_(optimizer)
              grad_norm = torch.nn.utils.clip_grad_norm_(
                  model.parameters(), c.grad_clip
              )
              scaler.step(optimizer)
              scaler.update()
          else:
              loss_dict["loss"].backward()
              grad_norm = torch.nn.utils.clip_grad_norm_(
                  model.parameters(), c.grad_clip
              )
              optimizer.step()
  
          # setup lr
          if c.noam_schedule:
              scheduler.step()
  
          # current_lr
          current_lr = optimizer.param_groups[0]["lr"]
  
          # compute alignment error (the lower the better )
          align_error = 1 - alignment_diagonal_score(
              alignments, binary=True
          )
          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["log_mle"] = reduce_tensor(
                  loss_dict["log_mle"].data, num_gpus
              )
              loss_dict["loss_dur"] = reduce_tensor(
                  loss_dict["loss_dur"].data, num_gpus
              )
              loss_dict["loss"] = reduce_tensor(
                  loss_dict["loss"].data, num_gpus
              )
  
          # 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 = {
                  "avg_spec_length": [
                      avg_spec_length,
                      1,
                  ],  # value, precision
                  "avg_text_length": [avg_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,
                      "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,
                          1,
                          OUT_PATH,
                          model_loss=loss_dict["loss"],
                      )
  
                  # wait all kernels to be completed
                  torch.cuda.synchronize()
  
                  # Diagnostic visualizations
                  # direct pass on model for spec predictions
                  target_speaker = (
                      None if speaker_c is None else speaker_c[:1]
                  )
  
                  if hasattr(model, "module"):
                      spec_pred, *_ = model.module.inference(
                          text_input[:1],
                          text_lengths[:1],
                          g=target_speaker,
                      )
                  else:
                      spec_pred, *_ = model.inference(
                          text_input[:1],
                          text_lengths[:1],
                          g=target_speaker,
                      )
  
                  spec_pred = spec_pred.permute(0, 2, 1)
                  gt_spec = mel_input.permute(0, 2, 1)
                  const_spec = spec_pred[0].data.cpu().numpy()
                  gt_spec = gt_spec[0].data.cpu().numpy()
                  align_img = alignments[0].data.cpu().numpy()
  
                  figures = {
                      "prediction": plot_spectrogram(const_spec, ap),
                      "ground_truth": plot_spectrogram(gt_spec, ap),
                      "alignment": plot_alignment(align_img),
                  }
  
                  tb_logger.tb_train_figures(global_step, figures)
  
                  # Sample audio
                  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 torch
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import DataLoader
  from torch.utils.data.distributed import DistributedSampler
  from TTS.tts.datasets.preprocess import load_meta_data
  from TTS.tts.datasets.TTSDataset import MyDataset
  from TTS.tts.layers.losses import GlowTTSLoss
  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 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, setup_torch_training_env
label_desc: Use image node_id 4 for calling a global function with example usage: train(data_loader, model, criterion, optimizer, scheduler, ap, global_step, epoch) and returns: keep_avg,  global_step