code: def _resample_batch(
      self, full_images: Tensor, mc_sampling: bool = False
  ) -> Tuple[Tensor, Tensor]:
      if mc_sampling is True:
          split_ind = full_images.shape[0] // 2
          model_images = full_images[:split_ind]
          disc_images = full_images[split_ind : split_ind * 2]
      else:
          model_images = full_images
          disc_images = full_images
  
      return model_images, disc_images
  
imports: from typing import Optional, Tuple
  import hydra
  import torch
  import torch.nn
  from omegaconf import DictConfig
  from target_compression_module import TargetRateCompressionModule
  from torch import Tensor
  from neuralcompression import HyperpriorOutput
  from neuralcompression.loss_fn import DiscriminatorLoss, DistortionLoss, GeneratorLoss, TargetRateConfig
  from neuralcompression.models import HyperpriorAutoencoderBase
label_desc: Use image node_id 6 for calling the GANCompressionModule obj's underlying member method code with example usage: obj._resample_batch(full_images, mc_sampling) and returns: model_images,  disc_images