def status(self):
        docs = {'updated'  : [],
                'committed': [],
                'to_commit': []}

        row = self.db.internal.find_one({'_id': 'last_sync'})

        if row and 'update' in row:
            for id in row['update']:
                docs['updated'].append(self.get(id, deleted=True))

        if row and 'commit' in row:
            for id in row['commit']:
                docs['committed'].append(self.get(id, deleted=True))

        to_commit =  self.get_docs_to_commit()
        for row in to_commit:
            doc = Document(**row)
            docs['to_commit'].append(doc)
        return docs
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