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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_errors(params, data, errs, B, C=None):
 """ Take a set of parameters that were fit with lmfit, and replace the errors with the 1\sigma errors calculated using the covariance matrix. Parameters params : lmfit.Parameters Model data : 2d-array Image data errs : 2d-array ? Image noise. B : 2d-array B matrix. C : 2d-array C matrix. Optional. If supplied then Bmatrix will not be used. Returns ------- params : lmfit.Parameters Modified model. """


    mask = np.where(np.isfinite(data))

    # calculate the proper parameter errors and copy them across.
    if C is not None:
        try:
            J = lmfit_jacobian(params, mask[0], mask[1], errs=errs)
            covar = np.transpose(J).dot(inv(C)).dot(J)
            onesigma = np.sqrt(np.diag(inv(covar)))
        except (np.linalg.linalg.LinAlgError, ValueError) as _:
            C = None

    if C is None:
        try:
            J = lmfit_jacobian(params, mask[0], mask[1], B=B, errs=errs)
            covar = np.transpose(J).dot(J)
            onesigma = np.sqrt(np.diag(inv(covar)))
        except (np.linalg.linalg.LinAlgError, ValueError) as _:
            onesigma = [-2] * len(mask[0])

    for i in range(params['components'].value):
        prefix = "c{0}_".format(i)
        j = 0
        for p in ['amp', 'xo', 'yo', 'sx', 'sy', 'theta']:
            if params[prefix + p].vary:
                params[prefix + p].stderr = onesigma[j]
                j += 1

    return params