How to use the quadprog.problem.OPTIMAL_INACCURATE function in quadprog

import quadprog.problem as qp
from quadprog.results import quadprogResults
import cplex as cpx
import ipdb


class CPLEX(object):
    """
    An interface for the CPLEX QP solver.
    """

    # Map of CPLEX status to CVXPY status. #TODO: add more!
    STATUS_MAP = {1: qp.OPTIMAL,
                  3: qp.INFEASIBLE,
                  2: qp.UNBOUNDED,
                  6: qp.OPTIMAL_INACCURATE}

    def __init__(self, **kwargs):
        self.options = kwargs

    def solve(self, p):

        # Convert Matrices in CSR format
        p.A = p.A.tocsr()
        p.P = p.P.tocsr()

        # Get problem dimensions
        n = p.P.shape[0]
        m = p.A.shape[0]

        # Adjust infinity values in bounds
        uA = np.copy(p.uA)

class GUROBI(object):
    """
    An interface for the Gurobi QP solver.
    """

    # Map of Gurobi status to CVXPY status.
    STATUS_MAP = {2: qp.OPTIMAL,
                  3: qp.INFEASIBLE,
                  5: qp.UNBOUNDED,
                  4: qp.SOLVER_ERROR,
                  6: qp.SOLVER_ERROR,
                  7: qp.SOLVER_ERROR,
                  8: qp.SOLVER_ERROR,
                  # TODO could be anything.
                  # means time expired.
                  9: qp.OPTIMAL_INACCURATE,
                  10: qp.SOLVER_ERROR,
                  11: qp.SOLVER_ERROR,
                  12: qp.SOLVER_ERROR,
                  13: qp.SOLVER_ERROR}

    def __init__(self, **kwargs):
        self.options = kwargs

    def solve(self, p):

        # Convert Matrices in CSR format
        p.Aeq = p.Aeq.tocsr()
        p.Aineq = p.Aineq.tocsr()

        # Convert Q matrix to COO format
        p.Q = p.Q.tocoo()