def METHOD_NAME(self, operators, coefficients, identity_shift=0., solver_options=None, name=None):
    if not all(isinstance(op, DuneXTMatrixOperator) for op in operators):
        return None
    if identity_shift != 0:
        return None
    if np.iscomplexobj(coefficients):
        return None
    if coefficients[0] == 1:
        matrix = operators[0].matrix.copy()
    else:
        matrix = operators[0].matrix * coefficients[0]
    for op, c in zip(operators[1:], coefficients[1:]):
        matrix.axpy(c, op.matrix)  # TODO: Not guaranteed to work for all backends! For different
        # sparsity patterns one would have to extract the patterns from the pruned
        # matrices, merge them and create a new matrix.
    return DuneXTMatrixOperator(matrix, self.source.id, self.range.id, solver_options=solver_options, name=name)