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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 running_covar(xx=True, xy=False, yy=False, remove_mean=False, symmetrize=False, sparse_mode='auto', modify_data=False, column_selection=None, diag_only=False, nsave=5):
 """ Returns a running covariance estimator Returns an estimator object that can be fed chunks of X and Y data, and that can generate on-the-fly estimates of mean, covariance, running sum and second moment matrix. Parameters xx : bool Estimate the covariance of X xy : bool Estimate the cross-covariance of X and Y yy : bool Estimate the covariance of Y remove_mean : bool Remove the data mean in the covariance estimation symmetrize : bool Use symmetric estimates with sum defined by sum_t x_t + y_t and second moment matrices defined by X'X + Y'Y and Y'X + X'Y. modify_data : bool If remove_mean=True, the mean will be removed in the input data, without creating an independent copy. This option is faster but should only be selected if the input data is not used elsewhere. sparse_mode : str one of: * 'dense' : always use dense mode * 'sparse' : always use sparse mode if possible * 'auto' : automatic column_selection: ndarray(k, dtype=int) or None Indices of those columns that are to be computed. If None, all columns are computed. diag_only: bool If True, the computation is restricted to the diagonal entries (autocorrelations) only. nsave : int Depth of Moment storage. Moments computed from each chunk will be combined with Moments of similar statistical weight using the pairwise combination algorithm described in [1]_. References .. [1] http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf """

    return RunningCovar(compute_XX=xx, compute_XY=xy, compute_YY=yy, sparse_mode=sparse_mode, modify_data=modify_data,
                        remove_mean=remove_mean, symmetrize=symmetrize, column_selection=column_selection,
                        diag_only=diag_only, nsave=nsave)