def get_dir_from_zipfile(zip_file, fallback=None):
    """Return the name of the root folder in a zip file.

    :param zip_file: a :class:`zipfile.ZipFile` instance.
    :param fallback: if `None`, the name of the zip file is used. This is
        returned if the zip file contains more than one top-level directory,
        or none at all.
    """
    fallback = fallback or zip_file.filename
    directories = [name for name in zip_file.namelist() if name.endswith("/")
                   and len(PurePath(name).parts) == 1]
    return fallback if len(directories) > 1 else directories[0]
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