class_docstr: ### Replay Buffer
  
  Replay buffer is used to train the discriminator.
  Generated images are added to the replay buffer and sampled from it.
  
  The replay buffer returns the newly added image with a probability of $0.5$.
  Otherwise, it sends an older generated image and replaces the older image
  with the newly generated image.
  
  This is done to reduce model oscillation.
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docstr: Add/retrieve an image
code: def push_and_pop(self, data: torch.Tensor):
      """Add/retrieve an image"""
      data = data.detach()
      res = []
      for element in data:
          if len(self.data) < self.max_size:
              self.data.append(element)
              res.append(element)
          else:
              if random.uniform(0, 1) > 0.5:
                  i = random.randint(0, self.max_size - 1)
                  res.append(self.data[i].clone())
                  self.data[i] = element
              else:
                  res.append(element)
      return torch.stack(res)
  
imports: import itertools
  import random
  import zipfile
  from typing import Tuple
  import torch
  import torch.nn
  import torchvision.transforms
  from PIL import Image
  from torch.utils.data import DataLoader, Dataset
  from torchvision.transforms import InterpolationMode
  from torchvision.utils import make_grid
  from labml import lab, tracker, experiment, monit
  from labml.configs import BaseConfigs
  from labml.utils.download import download_file
  from labml.utils.pytorch import get_modules
  from labml_helpers.device import DeviceConfigs
  from labml_helpers.module import Module
label_desc: Use image node_id 2 for calling the ReplayBuffer obj's underlying member method code with example usage: obj.push_and_pop(data) and returns: torch