human
What is a the Python code you requested given the following:
3. Affine Decomposition
In order to obtain an affine decomposition, we recast the problem on a fixed, parameter independent, reference domain $\Omega$. We choose the reference domain characterized by $\mu_0$=1 which we generate through the generate_mesh notebook provided in the data folder.
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#Here is the Python code you requested.
@PullBackFormsToReferenceDomain()
@ShapeParametrization(
    ("x[0]", "x[1]"),  # subdomain 1
    ("mu[0] * (x[0] - 1) + 1", "x[1]"),  # subdomain 2
)
class EllipticOptimalControl(EllipticOptimalControlProblem):

    # Default initialization of members
    def __init__(self, V, **kwargs):
        # Call the standard initialization
        EllipticOptimalControlProblem.__init__(self, V, **kwargs)
        # ... and also store FEniCS data structures for assembly
        assert "subdomains" in kwargs
        assert "boundaries" in kwargs
        self.subdomains, self.boundaries = kwargs["subdomains"], kwargs["boundaries"]
        yup = TrialFunction(V)
        (self.y, self.u, self.p) = split(yup)
        zvq = TestFunction(V)
        (self.z, self.v, self.q) = split(zvq)
        self.dx = Measure("dx")(subdomain_data=subdomains)
        self.ds = Measure("ds")(subdomain_data=boundaries)
        # Regularization coefficient
        self.alpha = 0.01
        # Desired state
        self.y_d = Constant(1.0)
        # Customize linear solver parameters
        self._linear_solver_parameters.update({
            "linear_solver": "mumps"
        })

    # Return custom problem name
    def name(self):
        return "EllipticOptimalControl1RB"

    # Return stability factor
    def get_stability_factor_lower_bound(self):
        return 1.

    # Return theta multiplicative terms of the affine expansion of the problem.
    def compute_theta(self, term):
        mu = self.mu
        if term in ("a", "a*"):
            theta_a0 = 1.0
            return (theta_a0,)
        elif term in ("c", "c*"):
            theta_c0 = 1.0
            return (theta_c0,)
        elif term == "m":
            theta_m0 = 1.0
            return (theta_m0,)
        elif term == "n":
            theta_n0 = self.alpha
            return (theta_n0,)
        elif term == "f":
            theta_f0 = 1.0
            return (theta_f0,)
        elif term == "g":
            theta_g0 = 1.0
            theta_g1 = mu[1]
            return (theta_g0,...