code: def format_data(data):
      # return a whole audio segment
      m, x = data
      x = x.unsqueeze(1)
      if use_cuda:
          m = m.cuda(non_blocking=True)
          x = x.cuda(non_blocking=True)
      return m, x
  
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 2 for calling a global function with example usage: format_data(data) and returns: m,  x