code: def get_data_loader(
      self,
      config: Coqpit,
      assets: Dict,
      is_eval: bool,
      samples: Union[List[Dict], List[List]],
      verbose: bool,
      num_gpus: int,
      rank: int = None,
  ) -> "DataLoader":  # pylint: disable=W0613
      if is_eval and not config.run_eval:
          loader = None
      else:
          # init dataloader
          dataset = XTTSDataset(
              self.config,
              samples,
              self.xtts.tokenizer,
              config.audio.sample_rate,
              is_eval,
          )
  
          # wait all the DDP process to be ready
          if num_gpus > 1:
              torch.distributed.barrier()
  
          # sort input sequences from short to long
          # dataset.preprocess_samples()
  
          # get samplers
          sampler = self.get_sampler(dataset, num_gpus)
  
          # ignore sampler when is eval because if we changed the sampler parameter we will not be able to compare previous runs
          if sampler is None or is_eval:
              loader = DataLoader(
                  dataset,
                  batch_size=config.eval_batch_size
                  if is_eval
                  else config.batch_size,
                  shuffle=False,
                  drop_last=False,
                  collate_fn=dataset.collate_fn,
                  num_workers=config.num_eval_loader_workers
                  if is_eval
                  else config.num_loader_workers,
                  pin_memory=False,
              )
          else:
              loader = DataLoader(
                  dataset,
                  sampler=sampler,
                  batch_size=config.eval_batch_size
                  if is_eval
                  else config.batch_size,
                  collate_fn=dataset.collate_fn,
                  num_workers=config.num_eval_loader_workers
                  if is_eval
                  else config.num_loader_workers,
                  pin_memory=False,
              )
      return loader
  
imports: from dataclasses import dataclass, field
  from typing import Dict, List, Tuple, Union
  import torch
  import torch.nn
  import torchaudio
  from coqpit import Coqpit
  from torch.nn import functional
  from torch.utils.data import DataLoader
  from trainer.torch import DistributedSampler
  from trainer.trainer_utils import get_optimizer, get_scheduler
  from TTS.tts.configs.xtts_config import XttsConfig
  from TTS.tts.datasets.dataset import TTSDataset
  from TTS.tts.layers.tortoise.arch_utils import TorchMelSpectrogram
  from TTS.tts.layers.xtts.dvae import DiscreteVAE
  from TTS.tts.layers.xtts.tokenizer import VoiceBpeTokenizer
  from TTS.tts.layers.xtts.trainer.dataset import XTTSDataset
  from TTS.tts.models.base_tts import BaseTTS
  from TTS.tts.models.xtts import Xtts, XttsArgs, XttsAudioConfig
  from TTS.utils.io import load_fsspec
label_desc: Use image node_id 15 for calling the GPTTrainer obj's underlying member method code with example usage: obj.get_data_loader(config, assets, is_eval, samples, verbose, num_gpus, rank) and returns: loader