def deploy_tokens_and_fund_accounts(
    token_amount: int,
    number_of_tokens: int,
    deploy_service: BlockChainService,
    participants: typing.List[typing.Address],
    contract_manager: ContractManager,
) -> typing.List[typing.TokenAddress]:
    """ Deploy `number_of_tokens` ERC20 token instances with `token_amount` minted and
    distributed among `blockchain_services`. Optionally the instances will be registered with
    the raiden registry.

    Args:
        token_amount (int): number of units that will be created per token
        number_of_tokens (int): number of token instances that will be created
        deploy_service (BlockChainService): the blockchain connection that will deploy
        participants (list(address)): participant addresses that will receive tokens
    """
    result = list()
    for _ in range(number_of_tokens):
        token_address = deploy_contract_web3(
            CONTRACT_HUMAN_STANDARD_TOKEN,
            deploy_service.client,
            contract_manager=contract_manager,
            constructor_arguments=(token_amount, 2, "raiden", "Rd"),
        )

        result.append(token_address)

        # only the creator of the token starts with a balance (deploy_service),
        # transfer from the creator to the other nodes
        for transfer_to in participants:
            deploy_service.token(token_address).transfer(
                to_address=transfer_to, amount=token_amount // len(participants)
            )

    return result
def _restore_checkpoint(self,
                          master,
                          saver=None,
                          checkpoint_dir=None,
                          checkpoint_filename_with_path=None,
                          wait_for_checkpoint=False,
                          max_wait_secs=7200,
                          config=None):
    """Creates a `Session`, and tries to restore a checkpoint.


    Args:
      master: `String` representation of the TensorFlow master to use.
      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.

    Returns:
      A pair (sess, is_restored) where 'is_restored' is `True` if
      the session could be restored, `False` otherwise.

    Raises:
      ValueError: If both checkpoint_dir and checkpoint_filename_with_path are
        set.
    """
    self._target = master

    # This is required to so that we initialize the TPU device before
    # restoring from checkpoint since we'll be placing variables on the device
    # and TPUInitialize wipes out the memory of the device.
    strategy = distribution_strategy_context.get_strategy()
    if strategy and hasattr(strategy.extended,
                            "_experimental_initialize_system"):
      strategy.extended._experimental_initialize_system()  # pylint: disable=protected-access

    sess = session.Session(self._target, graph=self._graph, config=config)
    if checkpoint_dir and checkpoint_filename_with_path:
      raise ValueError("Can not provide both checkpoint_dir and "
                       "checkpoint_filename_with_path.")
    # If either saver or checkpoint_* is not specified, cannot restore. Just
    # return.
    if not saver or not (checkpoint_dir or checkpoint_filename_with_path):
      return sess, False

    if checkpoint_filename_with_path:
      _restore_checkpoint_and_maybe_run_saved_model_initializers(
          sess, saver, checkpoint_filename_with_path)
      return sess, True

    # Waits up until max_wait_secs for checkpoint to become available.
    wait_time = 0
    ckpt = checkpoint_management.get_checkpoint_state(checkpoint_dir)
    while not ckpt or not ckpt.model_checkpoint_path:
      if wait_for_checkpoint and wait_time < max_wait_secs:
        logging.info("Waiting for checkpoint to be available.")
        time.sleep(self._recovery_wait_secs)
        wait_time += self._recovery_wait_secs
        ckpt = checkpoint_management.get_checkpoint_state(checkpoint_dir)
      else:
        return sess, False

    # Loads the checkpoint.
    _restore_checkpoint_and_maybe_run_saved_model_initializers(
        sess, saver, ckpt.model_checkpoint_path)
    saver.recover_last_checkpoints(ckpt.all_model_checkpoint_paths)
    return sess, True