def setup_keyring(keyring_name):
    '''Setup the keyring'''
    keyring_path = os.path.join("test", "outputdata", keyring_name)
    # Delete the entire keyring
    shutil.rmtree(keyring_path, ignore_errors=True)
    os.makedirs(keyring_path)
    gpg = GPG(gnupghome=keyring_path, gpgbinary="gpg")
    for key_name in ["key1_private", "key1_public"]:
        with open(os.path.join("test", "inputdata", key_name + ".txt"), "r") as keyfile:
            key_str = "".join(keyfile.readlines())
        import_result = gpg.import_keys(key_str)
        print("Import result:", type(import_result))
        print(import_result.__dict__)
        if import_result.count == 1 and len(set(import_result.fingerprints)) == 1:
            print("Got one import result")
    return gpg
def run_model(prior, images, model, disc_reg_weight):
  """Run the model with new data and samples.

  Args:
    prior: the noise source as the generator input.
    images: images sampled from dataset.
    model: a GAN model defined in gan.py.
    disc_reg_weight: regularisation weight for discrmininator gradients.

  Returns:
    debug_ops: statistics from the model, see gan.py for more detials.
    disc_grads: discriminator gradients.
    gen_grads: generator gradients.
  """
  generator_inputs = prior.sample(FLAGS.batch_size)
  model_output = model.connect(images, generator_inputs)
  optimization_components = model_output.optimization_components

  disc_grads = tf.gradients(
      optimization_components['disc'].loss,
      optimization_components['disc'].vars)

  gen_grads = tf.gradients(
      optimization_components['gen'].loss,
      optimization_components['gen'].vars)

  if disc_reg_weight > 0.0:
    reg_grads = _compute_reg_grads(gen_grads,
                                   optimization_components['disc'].vars)
    disc_grads = _acc_grads(disc_grads, disc_reg_weight, reg_grads)

  debug_ops = model_output.debug_ops

  return debug_ops, disc_grads, gen_grads