code: def setup_loader(ap, r, is_val=False, verbose=False, dataset=None):
      if is_val and not c.run_eval:
          loader = None
      else:
          if dataset is None:
              dataset = MyDataset(
                  r,
                  c.text_cleaner,
                  compute_linear_spec=c.model.lower() == "tacotron",
                  meta_data=meta_data_eval
                  if is_val
                  else meta_data_train,
                  ap=ap,
                  tp=c.characters if "characters" in c.keys() else None,
                  add_blank=c["add_blank"]
                  if "add_blank" in c.keys()
                  else False,
                  batch_group_size=0
                  if is_val
                  else c.batch_group_size * c.batch_size,
                  min_seq_len=c.min_seq_len,
                  max_seq_len=c.max_seq_len,
                  phoneme_cache_path=c.phoneme_cache_path,
                  use_phonemes=c.use_phonemes,
                  phoneme_language=c.phoneme_language,
                  enable_eos_bos=c.enable_eos_bos_chars,
                  verbose=verbose,
                  speaker_mapping=speaker_mapping
                  if c.use_speaker_embedding
                  and c.use_external_speaker_embedding_file
                  else None,
              )
  
              if c.use_phonemes and c.compute_input_seq_cache:
                  # precompute phonemes to have a better estimate of sequence lengths.
                  dataset.compute_input_seq(c.num_loader_workers)
              dataset.sort_items()
  
          sampler = (
              DistributedSampler(dataset) if num_gpus > 1 else None
          )
          loader = DataLoader(
              dataset,
              batch_size=c.eval_batch_size if is_val else c.batch_size,
              shuffle=False,
              collate_fn=dataset.collate_fn,
              drop_last=False,
              sampler=sampler,
              num_workers=c.num_val_loader_workers
              if is_val
              else c.num_loader_workers,
              pin_memory=False,
          )
      return loader
  
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 1 for calling a global function with example usage: setup_loader(ap, r, is_val, verbose, dataset) and returns: loader