code: def format_data(data):
      # setup input data
      text_input = data[0]
      text_lengths = data[1]
      speaker_names = data[2]
      linear_input = data[3] if c.model in ["Tacotron"] else None
      mel_input = data[4]
      mel_lengths = data[5]
      stop_targets = data[6]
      max_text_length = torch.max(text_lengths.float())
      max_spec_length = torch.max(mel_lengths.float())
  
      if c.use_speaker_embedding:
          if c.use_external_speaker_embedding_file:
              speaker_embeddings = data[8]
              speaker_ids = None
          else:
              speaker_ids = [
                  speaker_mapping[speaker_name]
                  for speaker_name in speaker_names
              ]
              speaker_ids = torch.LongTensor(speaker_ids)
              speaker_embeddings = None
      else:
          speaker_embeddings = None
          speaker_ids = None
  
      # set stop targets view, we predict a single stop token per iteration.
      stop_targets = stop_targets.view(
          text_input.shape[0], stop_targets.size(1) // c.r, -1
      )
      stop_targets = (
          (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze(2)
      )
  
      # dispatch data to GPU
      if use_cuda:
          text_input = text_input.cuda(non_blocking=True)
          text_lengths = text_lengths.cuda(non_blocking=True)
          mel_input = mel_input.cuda(non_blocking=True)
          mel_lengths = mel_lengths.cuda(non_blocking=True)
          linear_input = (
              linear_input.cuda(non_blocking=True)
              if c.model in ["Tacotron"]
              else None
          )
          stop_targets = stop_targets.cuda(non_blocking=True)
          if speaker_ids is not None:
              speaker_ids = speaker_ids.cuda(non_blocking=True)
          if speaker_embeddings is not None:
              speaker_embeddings = speaker_embeddings.cuda(
                  non_blocking=True
              )
  
      return (
          text_input,
          text_lengths,
          mel_input,
          mel_lengths,
          linear_input,
          stop_targets,
          speaker_ids,
          speaker_embeddings,
          max_text_length,
          max_spec_length,
      )
  
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 2 for calling a global function with example usage: format_data(data) and returns: text_input,  text_lengths,  mel_input,  mel_lengths,  linear_input,  stop_targets,  speaker_ids,  speaker_embeddings,  max_text_length,  max_spec_length