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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 derivative(self, x):
 """Derivative of the product space operator. Parameters x : `domain` element The point to take the derivative in Returns ------- adjoint : linear`ProductSpaceOperator` The derivative Examples -------- Example with linear operator (derivative is itself) ProductSpace(rn(3), 2).element([ [ 4., 5., 6.], [ 0., 0., 0.] ]) ProductSpace(rn(3), 2).element([ [ 4., 5., 6.], [ 0., 0., 0.] ]) Example with affine operator Calling operator gives offset by [1, 1, 1] ProductSpace(rn(3), 2).element([ [ 3., 4., 5.], [ 0., 0., 0.] ]) Derivative of affine operator does not have this offset ProductSpace(rn(3), 2).element([ [ 4., 5., 6.], [ 0., 0., 0.] ]) """

        # Lazy import to improve `import odl` time
        import scipy.sparse

        # Short circuit optimization
        if self.is_linear:
            return self

        deriv_ops = [op.derivative(x[col]) for op, col in zip(self.ops.data,
                                                              self.ops.col)]
        data = np.empty(len(deriv_ops), dtype=object)
        data[:] = deriv_ops
        indices = [self.ops.row, self.ops.col]
        shape = self.ops.shape
        deriv_matrix = scipy.sparse.coo_matrix((data, indices), shape)
        return ProductSpaceOperator(deriv_matrix, self.domain, self.range)