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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 k_fold_cross_validation( fitters, df, duration_col, event_col=None, k=5, evaluation_measure=concordance_index, predictor="predict_expectation", predictor_kwargs={}, fitter_kwargs={}, ):
 # pylint: disable=dangerous-default-value,too-many-arguments,too-many-locals """ Perform cross validation on a dataset. If multiple models are provided, all models will train on each of the k subsets. Parameters fitters: model one or several objects which possess a method: ``fit(self, data, duration_col, event_col)`` Note that the last two arguments will be given as keyword arguments, and that event_col is optional. The objects must also have the "predictor" method defined below. df: DataFrame a Pandas DataFrame with necessary columns `duration_col` and (optional) `event_col`, plus other covariates. `duration_col` refers to the lifetimes of the subjects. `event_col` refers to whether the 'death' events was observed: 1 if observed, 0 else (censored). duration_col: (n,) array the column in DataFrame that contains the subjects lifetimes. event_col: (n,) array the column in DataFrame that contains the subject's death observation. If left as None, assumes all individuals are non-censored. k: int the number of folds to perform. n/k data will be withheld for testing on. evaluation_measure: function a function that accepts either (event_times, predicted_event_times), or (event_times, predicted_event_times, event_observed) and returns something (could be anything). Default: statistics.concordance_index: (C-index) between two series of event times predictor: string a string that matches a prediction method on the fitter instances. For example, ``predict_expectation`` or ``predict_percentile``. Default is "predict_expectation" The interface for the method is: ``predict(self, data, **optional_kwargs)`` fitter_kwargs: keyword args to pass into fitter.fit method predictor_kwargs: keyword args to pass into predictor-method. Returns ------- results: list (k,1) list of scores for each fold. The scores can be anything. """

    # Make sure fitters is a list
    try:
        fitters = list(fitters)
    except TypeError:
        fitters = [fitters]
    # Each fitter has its own scores
    fitterscores = [[] for _ in fitters]

    n, _ = df.shape
    df = df.copy()

    if event_col is None:
        event_col = "E"
        df[event_col] = 1.0

    df = df.reindex(np.random.permutation(df.index)).sort_values(event_col)

    assignments = np.array((n // k + 1) * list(range(1, k + 1)))
    assignments = assignments[:n]

    testing_columns = df.columns.drop([duration_col, event_col])

    for i in range(1, k + 1):

        ix = assignments == i
        training_data = df.loc[~ix]
        testing_data = df.loc[ix]

        T_actual = testing_data[duration_col].values
        E_actual = testing_data[event_col].values
        X_testing = testing_data[testing_columns]

        for fitter, scores in zip(fitters, fitterscores):
            # fit the fitter to the training data
            fitter.fit(training_data, duration_col=duration_col, event_col=event_col, **fitter_kwargs)
            T_pred = getattr(fitter, predictor)(X_testing, **predictor_kwargs).values

            try:
                scores.append(evaluation_measure(T_actual, T_pred, E_actual))
            except TypeError:
                scores.append(evaluation_measure(T_actual, T_pred))

    # If a single fitter was given as argument, return a single result
    if len(fitters) == 1:
        return fitterscores[0]
    return fitterscores