code: def train(
      model,
      criterion,
      optimizer,
      scheduler,
      scaler,
      ap,
      global_step,
      epoch,
  ):
      data_loader = setup_loader(ap, is_val=False, verbose=(epoch == 0))
      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()
      # setup noise schedule
      noise_schedule = c["train_noise_schedule"]
      betas = np.linspace(
          noise_schedule["min_val"],
          noise_schedule["max_val"],
          noise_schedule["num_steps"],
      )
      if hasattr(model, "module"):
          model.module.compute_noise_level(betas)
      else:
          model.compute_noise_level(betas)
      for num_iter, data in enumerate(data_loader):
          start_time = time.time()
  
          # format data
          m, x = format_data(data)
          loader_time = time.time() - end_time
  
          global_step += 1
  
          with torch.cuda.amp.autocast(enabled=c.mixed_precision):
              # compute noisy input
              if hasattr(model, "module"):
                  (
                      noise,
                      x_noisy,
                      noise_scale,
                  ) = model.module.compute_y_n(x)
              else:
                  noise, x_noisy, noise_scale = model.compute_y_n(x)
  
              # forward pass
              noise_hat = model(x_noisy, m, noise_scale)
  
              # compute losses
              loss = criterion(noise, noise_hat)
          loss_wavegrad_dict = {"wavegrad_loss": loss}
  
          # check nan loss
          if torch.isnan(loss).any():
              raise RuntimeError(
                  f"Detected NaN loss at step {global_step}."
              )
  
          optimizer.zero_grad()
  
          # backward pass with loss scaling
          if c.mixed_precision:
              scaler.scale(loss).backward()
              scaler.unscale_(optimizer)
              grad_norm = torch.nn.utils.clip_grad_norm_(
                  model.parameters(), c.clip_grad
              )
              scaler.step(optimizer)
              scaler.update()
          else:
              loss.backward()
              grad_norm = torch.nn.utils.clip_grad_norm_(
                  model.parameters(), c.clip_grad
              )
              optimizer.step()
  
          # schedule update
          if scheduler is not None:
              scheduler.step()
  
          # disconnect loss values
          loss_dict = dict()
          for key, value in loss_wavegrad_dict.items():
              if isinstance(value, int):
                  loss_dict[key] = value
              else:
                  loss_dict[key] = value.item()
  
          # epoch/step timing
          step_time = time.time() - start_time
          epoch_time += step_time
  
          # get current learning rates
          current_lr = list(optimizer.param_groups)[0]["lr"]
  
          # 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 stats
          if global_step % c.print_step == 0:
              log_dict = {
                  "step_time": [step_time, 2],
                  "loader_time": [loader_time, 4],
                  "current_lr": current_lr,
                  "grad_norm": grad_norm.item(),
              }
              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 step stats
              if global_step % 10 == 0:
                  iter_stats = {
                      "lr": current_lr,
                      "grad_norm": grad_norm.item(),
                      "step_time": step_time,
                  }
                  iter_stats.update(loss_dict)
                  tb_logger.tb_train_iter_stats(global_step, iter_stats)
  
              # save checkpoint
              if global_step % c.save_step == 0:
                  if c.checkpoint:
                      # save model
                      save_checkpoint(
                          model,
                          optimizer,
                          scheduler,
                          None,
                          None,
                          None,
                          global_step,
                          epoch,
                          OUT_PATH,
                          model_losses=loss_dict,
                          scaler=scaler.state_dict()
                          if c.mixed_precision
                          else None,
                      )
  
          end_time = time.time()
  
      # print epoch stats
      c_logger.print_train_epoch_end(
          global_step, epoch, epoch_time, keep_avg
      )
  
      # Plot Training Epoch Stats
      epoch_stats = {"epoch_time": epoch_time}
      epoch_stats.update(keep_avg.avg_values)
      if args.rank == 0:
          tb_logger.tb_train_epoch_stats(global_step, epoch_stats)
      # TODO: plot model stats
      if c.tb_model_param_stats and args.rank == 0:
          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
  import numpy
  import torch
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim import Adam
  from torch.utils.data import DataLoader
  from torch.utils.data.distributed import DistributedSampler
  from TTS.utils.audio import AudioProcessor
  from TTS.utils.console_logger import ConsoleLogger
  from TTS.utils.distribute import init_distributed
  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.tensorboard_logger import TensorboardLogger
  from TTS.utils.training import setup_torch_training_env
  from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data
  from TTS.vocoder.datasets.wavegrad_dataset import WaveGradDataset
  from TTS.vocoder.utils.generic_utils import plot_results, setup_generator
  from TTS.vocoder.utils.io import save_best_model, save_checkpoint
label_desc: Use image node_id 4 for calling a global function with example usage: train(model, criterion, optimizer, scheduler, scaler, ap, global_step, epoch) and returns: keep_avg,  global_step