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

# CPLEX interface to solve QP problems
import numpy as np
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]

# Solve problem
        try:
            # Solve
            m.optimize()
        except:  # Error in the solution
            print "Error in Gurobi solution\n"
            return quadprogResults(qp.SOLVER_ERROR, None, None, None,
                                   None, None, None,
                                   np.inf, None)

        # Return results
        # Get status
        status = self.STATUS_MAP.get(m.Status, qp.SOLVER_ERROR)

        if (status != qp.SOLVER_ERROR) & (status != qp.INFEASIBLE):
            # Get objective value
            objval = m.objVal

            # Get solution
            sol = np.array([x[i].X for i in range(nx)])

            # Get dual variables  (Gurobi uses swapped signs (-1))
            constrs = m.getConstrs()
            sol_dual_eq = -np.array([constrs[i].Pi for i in range(neq)])
            sol_dual_ineq = -np.array([constrs[i+neq].Pi for i in range(nineq)])

            # Bounds
            sol_dual_ub = np.zeros(nx)
            sol_dual_lb = np.zeros(nx)

            RCx = [x[i].RC for i in range(nx)]  # Get reduced costs