def setUpTestData(cls):
        batch_num = 0
        section_num = 0
        voter_num = 0
        party_num = 0
        position_num = 0
        candidate_num = 0
        num_elections = 2
        voters = list()
        positions = dict()
        for i in range(num_elections):
            election = Election.objects.create(name='Election {}'.format(i))
            positions[str(election.name)] = list()

            num_batches = 2
            for j in range(num_batches):
                batch = Batch.objects.create(year=batch_num, election=election)
                batch_num += 1

                num_sections = 2 if j == 0 else 1
                for k in range(num_sections):
                    section = Section.objects.create(
                        section_name=str(section_num)
                    )
                    section_num += 1

                    num_students = 2
                    for l in range(num_students):
                        voter = User.objects.create(
                            username='user{}'.format(voter_num),
                            first_name=str(voter_num),
                            last_name=str(voter_num),
                            type=UserType.VOTER
                        )
                        voter.set_password('voter')
                        voter.save()
                        voter_num += 1

                        VoterProfile.objects.create(
                            user=voter,
                            batch=batch,
                            section=section
                        )

                        voters.append(voter)

            num_positions = 3
            for i in range(num_positions):
                position = CandidatePosition.objects.create(
                    position_name='Position {}'.format(position_num),
                    election=election
                )

                positions[str(election.name)].append(position)

                position_num += 1

            num_parties = 3
            for j in range(num_parties):
                party = CandidateParty.objects.create(
                    party_name='Party {}'.format(party_num),
                    election=election
                )
                party_num += 1

                if j != 2:  # Let every third party have no candidates.
                    num_positions = 3
                    for k in range(num_positions):
                        position = positions[str(election.name)][k]

                        candidate = Candidate.objects.create(
                            user=voters[candidate_num],
                            party=party,
                            position=position,
                            election=election
                        )

                        Vote.objects.create(
                            user=voters[candidate_num],
                            candidate=candidate,
                            election=election
                        )

                        candidate_num += 1

        # Let's give one candidate an additional vote to really make sure that
        # we all got the correct number of votes.
        Vote.objects.create(
            user=voters[0],
            # NOTE: The voter in voter[1] is a Position 1 candidate of
            #       Party 1, where the voter in voter[0] is a member.
            candidate=Candidate.objects.get(user=voters[1]),
            election=Election.objects.get(name='Election 0')
        )

        _admin = User.objects.create(username='admin', type=UserType.ADMIN)
        _admin.set_password('root')
        _admin.save()
def prepare_session(self,
                      master,
                      init_op=None,
                      saver=None,
                      checkpoint_dir=None,
                      checkpoint_filename_with_path=None,
                      wait_for_checkpoint=False,
                      max_wait_secs=7200,
                      config=None,
                      init_feed_dict=None,
                      init_fn=None):
    """Creates a `Session`. Makes sure the model is ready to be used.

    Creates a `Session` on 'master'. If a `saver` object is passed in, and
    `checkpoint_dir` points to a directory containing valid checkpoint
    files, then it will try to recover the model from checkpoint. If
    no checkpoint files are available, and `wait_for_checkpoint` is
    `True`, then the process would check every `recovery_wait_secs`,
    up to `max_wait_secs`, for recovery to succeed.

    If the model cannot be recovered successfully then it is initialized by
    running the `init_op` and calling `init_fn` if they are provided.
    The `local_init_op` is also run after init_op and init_fn, regardless of
    whether the model was recovered successfully, but only if
    `ready_for_local_init_op` passes.

    If the model is recovered from a checkpoint it is assumed that all
    global variables have been initialized, in particular neither `init_op`
    nor `init_fn` will be executed.

    It is an error if the model cannot be recovered and no `init_op`
    or `init_fn` or `local_init_op` are passed.

    Args:
      master: `String` representation of the TensorFlow master to use.
      init_op: Optional `Operation` used to initialize the model.
      saver: A `Saver` object used to restore a model.
      checkpoint_dir: Path to the checkpoint files. The latest checkpoint in the
        dir will be used to restore.
      checkpoint_filename_with_path: Full file name path to the checkpoint file.
      wait_for_checkpoint: Whether to wait for checkpoint to become available.
      max_wait_secs: Maximum time to wait for checkpoints to become available.
      config: Optional `ConfigProto` proto used to configure the session.
      init_feed_dict: Optional dictionary that maps `Tensor` objects to feed
        values.  This feed dictionary is passed to the session `run()` call when
        running the init op.
      init_fn: Optional callable used to initialize the model. Called after the
        optional `init_op` is called.  The callable must accept one argument,
        the session being initialized.

    Returns:
      A `Session` object that can be used to drive the model.

    Raises:
      RuntimeError: If the model cannot be initialized or recovered.
      ValueError: If both checkpoint_dir and checkpoint_filename_with_path are
        set.
    """

    sess, is_loaded_from_checkpoint = self._restore_checkpoint(
        master,
        saver,
        checkpoint_dir=checkpoint_dir,
        checkpoint_filename_with_path=checkpoint_filename_with_path,
        wait_for_checkpoint=wait_for_checkpoint,
        max_wait_secs=max_wait_secs,
        config=config)
    if not is_loaded_from_checkpoint:
      if init_op is None and not init_fn and self._local_init_op is None:
        raise RuntimeError("Model is not initialized and no init_op or "
                           "init_fn or local_init_op was given")
      if init_op is not None:
        sess.run(init_op, feed_dict=init_feed_dict)
      if init_fn:
        init_fn(sess)

    local_init_success, msg = self._try_run_local_init_op(sess)
    if not local_init_success:
      raise RuntimeError(
          "Init operations did not make model ready for local_init.  "
          "Init op: %s, init fn: %s, error: %s" % (_maybe_name(init_op),
                                                   init_fn,
                                                   msg))

    is_ready, msg = self._model_ready(sess)
    if not is_ready:
      raise RuntimeError(
          "Init operations did not make model ready.  "
          "Init op: %s, init fn: %s, local_init_op: %s, error: %s" %
          (_maybe_name(init_op), init_fn, self._local_init_op, msg))
    return sess