Unbdray does not give me correct answer

Hello,

I am trying to solve a problem in two ways with and without Benders decomposition. extract unbdrays and add them to my Master problem but when I extract them and put them into (w)(d-Bx)=<0 is not working and gives me an error. I would be highly thankful if you help me in this regard.

def solve_Dual_subproblem(z):
    
    
    # defining all variables
    Dualproblem = grb.Model()
    #Dualproblem.setParam ('LogToConsole',0)
    sumpc = Dualproblem.addVars(Tx,vtype='c',lb=0) #sum power channel
    powerupperbound = Dualproblem.addVars(Tx,range(numChannels),vtype='c',lb=0) #upperbound power variable
    qdef = Dualproblem.addVars(Tx,Rx,range(numChannels),vtype='c',lb=-grb.GRB.INFINITY, ub= grb.GRB.INFINITY) #qdef
    qsens = Dualproblem.addVars(Tx,Rx,range(numChannels),vtype='c',lb=0) #qsens
    qint = Dualproblem.addVars(Tx,Rx,range(numChannels),vtype='c',lb=0) #qint
    qsnr = Dualproblem.addVars(Tx,Rx,range(numChannels),vtype='c',lb=0) #qsnr
    
    # defining all the constraints
   
    # constraint(0)
    sump=[]
    for i in list(range(numDevices)):
        for n in range(numChannels):
            lhs = - sumpc[i] - powerupperbound[i,n] - sum( dist[i,j] * qdef[i,j,n] for j in list(range(numDevices)) )
            rhs = 1
            sump.append(Dualproblem.addConstr(lhs <= rhs , name = 'sump')) 
            
    # constraint(1) 
    sumq=[]
    for i in list(range(numDevices)):
        for j in list(range(numDevices)):
            for n in range(numChannels):
                lhs=qdef[i,j,n] + qsens[i,j,n] - (sum(qint[k,j,n] for k in list(range(numDevices)) ) - qint[i,j,n]) + qsnr[i,j,n] - delta * (sum(qsnr[k,j,n] for k in list(range(numDevices)) ) - qsnr[i,j,n] )
                rhs=0
                sumq.append(Dualproblem.addConstr(lhs == rhs , name = 'sumq')) 
    

    
    expr = sum(-Pmax_radio * sumpc[i] for i in list(range(numDevices))) + sum( -Pmax_channel * sum( z[i,j,n] for j in Rx ) * powerupperbound[i,n] for i in list(range(numDevices)) for n in range(numChannels)) + sum( Psense * z[i,j,n] * qsens[i,j,n] for i in list(range(numDevices)) for j in list(range(numDevices)) for n in range(numChannels)) + sum( (-Pinterfere -(1-z[i,j,n])*M) * qint[i,j,n] for i in list(range(numDevices)) for j in list(range(numDevices)) for n in range(numChannels)) + sum( (delta*gamma-M*(1-z[i,j,n])) *qsnr[i,j,n] for i in list(range(numDevices)) for j in list(range(numDevices)) for n in range(numChannels))
    Dualproblem.setObjective(sense=grb.GRB.MAXIMIZE,expr=(expr))  
    Dualproblem.setParam('InfUnbdInfo', 1) #for feasibility cut
    Dualproblem.setParam('Method',0) #simplex method for dual
    Dualproblem.update()
    Dualproblem.optimize()
    
    #print(Dualproblem.display())
    print('Dualproblem.status',Dualproblem.status)
    print('Dualproblem.ObjVal= ', Dualproblem.ObjVal)
    if Dualproblem.status==grb.GRB.UNBOUNDED:
        #print('in',Dualproblem.getConstrByName('sumq').getAttr("FarkasDual"))
        dualsp=Dualproblem.getAttr('UnbdRay', Dualproblem.getVars())
        
        sumpc_sol = Dualproblem.getAttr('UnbdRay', sumpc)
        
        dual_sumpc = np.array([sumpc_sol[(i)] for i in range(numDevices)])
    
        powerupperbound_sol = Dualproblem.getAttr('UnbdRay', powerupperbound)
        dual_powerupperbound = np.array([powerupperbound_sol[(i),n] for i in range(numDevices) for n in range(numChannels)])

        qdef_sol = Dualproblem.getAttr('UnbdRay', qdef)
        dual_qdef = np.array([qdef_sol[(i),(j),n] for i in range(numDevices) for j in range(numDevices) for n in range(numChannels)])

        qsens_sol = Dualproblem.getAttr('UnbdRay', qsens)
        dual_qsens = np.array([qsens_sol[(i),(j),n] for i in range(numDevices) for j in range(numDevices) for n in range(numChannels)])

        qint_sol = Dualproblem.getAttr('UnbdRay', qint)
        dual_qint = np.array([qint_sol[(i),(j),n] for i in range(numDevices) for j in range(numDevices) for n in range(numChannels)])

        qsnr_sol = Dualproblem.getAttr('UnbdRay', qsnr)
        dual_qsnr = np.array([qsnr_sol[(i),(j),n] for i in range(numDevices) for j in range(numDevices) for n in range(numChannels)])
    return dual_sumpc,dual_powerupperbound,dual_qdef,dual_qsens,dual_qint,dual_qsnr,Dualproblem.status,dualsp

alpha = sum( Pmax_radio * dual_sumpc[i] for i in range(numDevices)) + sum((Pinterfere + M) * dual_qint[i,j,n] for i in range(numDevices) for j in range(numDevices) for n in range(numChannels)) +sum(((delta * gamma) - M) * dual_qsnr[i,j,n] for i in range(numDevices) for j in range(numDevices) for n in range(numChannels))
        beta_z=np.zeros((numDevices,numDevices,numChannels))
        for j in range(numDevices):
            for n in range(numChannels):
                for i in range(numDevices):
                    beta_z[i,j,n] = Pmax_channel * dual_powerupperbound[i,n]  + Psense * dual_qsens[i,j,n]- M * dual_qint[i,j,n] + M * dual_qsnr[i,j,n]
                    #beta_z[i,j,n] = Pmax_channel * dual_powerupperbound[i,n]  + (Psense * dual_qsens[i,j,n]-M*dual_qint[i,j,n] )

        #check the correctness of the cuts

                #checking subproblem status and adding cuts
        cutname='cut'+ str(outeriter)
        if dual_feas == 5: #infeasible sub problem  & unbounded dual
       

           master.addConstr(alpha + sum(beta_z[i,j,n]*z[(i),(j),n] for i in range(numDevices) for j in range(numDevices) for n in range(numChannels)) <= 0 , name='feas'+str(outeriter))