def average_weights(state_dicts: List[dict]) -> OrderedDict:
    """
    Averaging of input weights.

    Args:
        state_dicts: Weights to average

    Raises:
        KeyError: If states do not match

    Returns:
        Averaged weights
    """
    # source https://gist.github.com/qubvel/70c3d5e4cddcde731408f478e12ef87b
    params_keys = None
    for i, state_dict in enumerate(state_dicts):
        model_params_keys = list(state_dict.keys())
        if params_keys is None:
            params_keys = model_params_keys
        elif params_keys != model_params_keys:
            raise KeyError(
                "For checkpoint {}, expected list of params: {}, "
                "but found: {}".format(i, params_keys, model_params_keys)
            )

    average_dict = OrderedDict()
    for k in state_dicts[0].keys():
        average_dict[k] = torch.div(
            sum(state_dict[k] for state_dict in state_dicts), len(state_dicts)
        )

    return average_dict

    Averaging of input weights.

    Args:
        state_dicts: Weights to average

    Raises:
        KeyError: If states do not match

    Returns:
        Averaged weights
    
catalyst/contrib/utils/swa.py