<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def solve_sdp(sdp, solver=None, solverparameters=None):
 """Call a solver on the SDP relaxation. Upon successful solution, it returns the primal and dual objective values along with the solution matrices. :param sdpRelaxation: The SDP relaxation to be solved. :type sdpRelaxation: :class:`ncpol2sdpa.SdpRelaxation`. :param solver: The solver to be called, either `None`, "sdpa", "mosek", "cvxpy", "scs", or "cvxopt". The default is `None`, which triggers autodetect. :type solver: str. :param solverparameters: Parameters to be passed to the solver. Actual options depend on the solver: SDPA: - `"executable"`: Specify the executable for SDPA. E.g., `"executable":"/usr/local/bin/sdpa"`, or `"executable":"sdpa_gmp"` - `"paramsfile"`: Specify the parameter file Mosek: Refer to the Mosek documentation. All arguments are passed on. Cvxopt: Refer to the PICOS documentation. All arguments are passed on. Cvxpy: Refer to the Cvxpy documentation. All arguments are passed on. SCS: Refer to the Cvxpy documentation. All arguments are passed on. :type solverparameters: dict of str. :returns: tuple of the primal and dual optimum, and the solutions for the primal and dual. :rtype: (float, float, list of `numpy.array`, list of `numpy.array`) """

    solvers = autodetect_solvers(solverparameters)
    solver = solver.lower() if solver is not None else solver
    if solvers == []:
        raise Exception("Could not find any SDP solver. Please install SDPA," +
                        " Mosek, Cvxpy, or Picos with Cvxopt")
    elif solver is not None and solver not in solvers:
        print("Available solvers: " + str(solvers))
        if solver == "cvxopt":
            try:
                import cvxopt
            except ImportError:
                pass
            else:
                raise Exception("Cvxopt is detected, but Picos is not. "
                                "Please install Picos to use Cvxopt")
        raise Exception("Could not detect requested " + solver)
    elif solver is None:
        solver = solvers[0]
    primal, dual, x_mat, y_mat, status = None, None, None, None, None
    tstart = time.time()
    if solver == "sdpa":
        primal, dual, x_mat, y_mat, status = \
          solve_with_sdpa(sdp, solverparameters)
    elif solver == "cvxpy":
        primal, dual, x_mat, y_mat, status = \
          solve_with_cvxpy(sdp, solverparameters)
    elif solver == "scs":
        if solverparameters is None:
            solverparameters_ = {"solver": "SCS"}
        else:
            solverparameters_ = solverparameters.copy()
            solverparameters_["solver"] = "SCS"
        primal, dual, x_mat, y_mat, status = \
          solve_with_cvxpy(sdp, solverparameters_)
    elif solver == "mosek":
        primal, dual, x_mat, y_mat, status = \
          solve_with_mosek(sdp, solverparameters)
    elif solver == "cvxopt":
        primal, dual, x_mat, y_mat, status = \
          solve_with_cvxopt(sdp, solverparameters)
        # We have to compensate for the equality constraints
        for constraint in sdp.constraints[sdp._n_inequalities:]:
            idx = sdp._constraint_to_block_index[constraint]
            sdp._constraint_to_block_index[constraint] = (idx[0],)
    else:
        raise Exception("Unkown solver: " + solver)
    sdp.solution_time = time.time() - tstart
    sdp.primal = primal
    sdp.dual = dual
    sdp.x_mat = x_mat
    sdp.y_mat = y_mat
    sdp.status = status
    return primal, dual, x_mat, y_mat