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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 covar(X, remove_mean=False, modify_data=False, weights=None, sparse_mode='auto', sparse_tol=0.0):
 """ Computes the covariance matrix of X Computes .. math: C_XX &=& X^\top X while exploiting zero or constant columns in the data matrix. WARNING: Directly use moments_XX if you can. This function does an additional constant-matrix multiplication and does not return the mean. Parameters X : ndarray (T, M) Data matrix remove_mean : bool True: remove column mean from the data, False: don't remove mean. modify_data : bool If remove_mean=True, the mean will be removed in the data matrix X, without creating an independent copy. This option is faster but might lead to surprises because your input array is changed. weights : None or ndarray(T, ) weights assigned to each trajectory point of X. If None, all data points have weight one. If ndarray, each data point is assigned a separate weight. sparse_mode : str one of: * 'dense' : always use dense mode * 'sparse' : always use sparse mode if possible * 'auto' : automatic sparse_tol: float Threshold for considering column to be zero in order to save computing effort when the data is sparse or almost sparse. If max(abs(X[:, i])) < sparse_tol, then row i (and also column i if Y is not given) of the covariance matrix will be set to zero. If Y is given and max(abs(Y[:, i])) < sparse_tol, then column i of the covariance matrix will be set to zero. Returns ------- C_XX : ndarray (M, M) Covariance matrix of X See also -------- moments_XX """

    w, s, M = moments_XX(X, remove_mean=remove_mean, weights=weights, modify_data=modify_data,
                         sparse_mode=sparse_mode, sparse_tol=sparse_tol)
    return M / float(w)