docstr: Initiate and return the GAN dataloader.
  
  Args:
      config (Coqpit): Model config.
      ap (AudioProcessor): Audio processor.
      is_eval (True): Set the dataloader for evaluation if true.
      samples (List): Data samples.
      verbose (bool): Log information if true.
      num_gpus (int): Number of GPUs in use.
      rank (int): Rank of the current GPU. Defaults to None.
  
  Returns:
      DataLoader: Torch dataloader.
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code: def get_data_loader(  # pylint: disable=no-self-use, unused-argument
      self,
      config: Coqpit,
      assets: Dict,
      is_eval: True,
      samples: List,
      verbose: bool,
      num_gpus: int,
      rank: int = None,  # pylint: disable=unused-argument
  ):
      """Initiate and return the GAN dataloader.
  
      Args:
          config (Coqpit): Model config.
          ap (AudioProcessor): Audio processor.
          is_eval (True): Set the dataloader for evaluation if true.
          samples (List): Data samples.
          verbose (bool): Log information if true.
          num_gpus (int): Number of GPUs in use.
          rank (int): Rank of the current GPU. Defaults to None.
  
      Returns:
          DataLoader: Torch dataloader.
      """
      dataset = GANDataset(
          ap=self.ap,
          items=samples,
          seq_len=config.seq_len,
          hop_len=self.ap.hop_length,
          pad_short=config.pad_short,
          conv_pad=config.conv_pad,
          return_pairs=config.diff_samples_for_G_and_D
          if "diff_samples_for_G_and_D" in config
          else False,
          is_training=not is_eval,
          return_segments=not is_eval,
          use_noise_augment=config.use_noise_augment,
          use_cache=config.use_cache,
          verbose=verbose,
      )
      dataset.shuffle_mapping()
      sampler = (
          DistributedSampler(dataset, shuffle=True)
          if num_gpus > 1
          else None
      )
      loader = DataLoader(
          dataset,
          batch_size=1 if is_eval else config.batch_size,
          shuffle=num_gpus == 0,
          drop_last=False,
          sampler=sampler,
          num_workers=config.num_eval_loader_workers
          if is_eval
          else config.num_loader_workers,
          pin_memory=False,
      )
      return loader
  
imports: from inspect import signature
  from typing import Dict, List, Tuple
  import numpy
  import torch
  from coqpit import Coqpit
  from torch import nn
  from torch.utils.data import DataLoader
  from torch.utils.data.distributed import DistributedSampler
  from trainer.trainer_utils import get_optimizer, get_scheduler
  from TTS.utils.audio import AudioProcessor
  from TTS.utils.io import load_fsspec
  from TTS.vocoder.datasets.gan_dataset import GANDataset
  from TTS.vocoder.layers.losses import DiscriminatorLoss, GeneratorLoss
  from TTS.vocoder.models import setup_discriminator, setup_generator
  from TTS.vocoder.models.base_vocoder import BaseVocoder
  from TTS.vocoder.utils.generic_utils import plot_results
label_desc: Use image node_id 15 for calling the GAN 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