text: # import tensorflow as tf
  # import numpy as np
  # import mnist_data
  # import os
  from rlkit.torch.vae.conv_vae import ConvVAE
  from rlkit.torch.vae.vae_trainer import ConvVAETrainer
  from rlkit.torch.vae.point2d_data import get_data
  # import plot_utils
  # import glob
  # import ss.path
  
  # import argparse
  from rlkit.launchers.arglauncher import run_variants
  import rlkit.torch.pytorch_util as ptu
  
  def experiment(variant):
      if variant["use_gpu"]:
          gpu_id = variant["gpu_id"]
          ptu.set_gpu_mode(True)
          ptu.set_device(gpu_id)
  
      beta = variant["beta"]
      representation_size = variant["representation_size"]
      train_data, test_data = get_data(10000)
      m = ConvVAE(representation_size)
      t = ConvVAETrainer(train_data, test_data, m, beta=beta, use_cuda=True)
      for epoch in range(10):
          t.train_epoch(epoch)
          t.test_epoch(epoch)
          t.dump_samples(epoch)
  
  if __name__ == "__main__":
      variants = []
  
      for representation_size in [2, 4, 8, 16]:
          for beta in [5.0]:
              variant = dict(
                  beta=beta,
                  representation_size=representation_size,
              )
              variants.append(variant)
      run_variants(experiment, variants, run_id=0)
  