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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def load_arff_files_standardized(path_training, attr_labels, pos_label=None, path_testing=None, survival=True, standardize_numeric=True, to_numeric=True):
 """Load dataset in ARFF format. Parameters path_training : str Path to ARFF file containing data. attr_labels : sequence of str Names of attributes denoting dependent variables. If ``survival`` is set, it must be a sequence with two items: the name of the event indicator and the name of the survival/censoring time. pos_label : any type, optional Value corresponding to an event in survival analysis. Only considered if ``survival`` is ``True``. path_testing : str, optional Path to ARFF file containing hold-out data. Only columns that are available in both training and testing are considered (excluding dependent variables). If ``standardize_numeric`` is set, data is normalized by considering both training and testing data. survival : bool, optional, default: True Whether the dependent variables denote event indicator and survival/censoring time. standardize_numeric : bool, optional, default: True Whether to standardize data to zero mean and unit variance. See :func:`sksurv.column.standardize`. to_numeric : boo, optional, default: True Whether to convert categorical variables to numeric values. See :func:`sksurv.column.categorical_to_numeric`. Returns ------- x_train : pandas.DataFrame, shape = (n_train, n_features) Training data. y_train : pandas.DataFrame, shape = (n_train, n_labels) Dependent variables of training data. x_test : None or pandas.DataFrame, shape = (n_train, n_features) Testing data if `path_testing` was provided. y_test : None or pandas.DataFrame, shape = (n_train, n_labels) Dependent variables of testing data if `path_testing` was provided. """

    dataset = loadarff(path_training)
    if "index" in dataset.columns:
        dataset.index = dataset["index"].astype(object)
        dataset.drop("index", axis=1, inplace=True)

    x_train, y_train = get_x_y(dataset, attr_labels, pos_label, survival)

    if path_testing is not None:
        x_test, y_test = _load_arff_testing(path_testing, attr_labels, pos_label, survival)

        if len(x_train.columns.symmetric_difference(x_test.columns)) > 0:
            warnings.warn("Restricting columns to intersection between training and testing data",
                          stacklevel=2)

            cols = x_train.columns.intersection(x_test.columns)
            if len(cols) == 0:
                raise ValueError("columns of training and test data do not intersect")

            x_train = x_train.loc[:, cols]
            x_test = x_test.loc[:, cols]

        x = safe_concat((x_train, x_test), axis=0)
        if standardize_numeric:
            x = standardize(x)
        if to_numeric:
            x = categorical_to_numeric(x)

        n_train = x_train.shape[0]
        x_train = x.iloc[:n_train, :]
        x_test = x.iloc[n_train:, :]
    else:
        if standardize_numeric:
            x_train = standardize(x_train)
        if to_numeric:
            x_train = categorical_to_numeric(x_train)

        x_test = None
        y_test = None

    return x_train, y_train, x_test, y_test