How to use the symengine.DenseMatrix function in symengine

#num_samples = YBase.shape[0]/idf.model.num_dofs
                l = (float(idf.base_error) / len(p_nid)) * idf.opt['regularizationFactor']

                #TODO: also use symengine to gain speedup?
                #p = BlockMatrix([[(K*delta)], [delta_nonid]])
                #Y = BlockMatrix([[Matrix(R1),             ZeroMatrix(R1.shape[0], len(p_nid))],
                #                 [ZeroMatrix(len(p_nid), R1.shape[1]), l*Identity(len(p_nid))]])
                Y = BlockMatrix([[R1*(K*delta)],[l*Identity(len(p_nid))*delta_nonid]]).as_explicit()
                rho1_hat = np.concatenate((rho1, l*idf.model.xStdModel[p_nid]))
                e_rho1 = (Matrix(rho1_hat - contactForces) - Y)
            else:
                try:
                    from symengine import DenseMatrix as eMatrix
                    if idf.opt['verbose']:
                        print('using symengine')
                    edelta = eMatrix(delta.shape[0], delta.shape[1], delta)
                    eK = eMatrix(K.shape[0], K.shape[1], K)
                    eR1 = eMatrix(R1.shape[0], R1.shape[1], Matrix(R1))
                    Y = eR1*eK*edelta
                    e_rho1 = Matrix(eMatrix(rho1) - contactForces - Y)
                except ImportError:
                    if idf.opt['verbose']:
                        print('not using symengine')
                    Y = R1*(K*delta)
                    e_rho1 = Matrix(rho1 - contactForces) - Y

            if idf.opt['verbose'] > 1:
                print("Step 2...", time.ctime())

            # minimize estimation error of to-be-found parameters delta
            # (regressor dot std variables projected to base - contacts should be close to measured torques)
            u = Symbol('u')

l = (float(idf.base_error) / len(p_nid)) * idf.opt['regularizationFactor']

                #TODO: also use symengine to gain speedup?
                #p = BlockMatrix([[(K*delta)], [delta_nonid]])
                #Y = BlockMatrix([[Matrix(R1),             ZeroMatrix(R1.shape[0], len(p_nid))],
                #                 [ZeroMatrix(len(p_nid), R1.shape[1]), l*Identity(len(p_nid))]])
                Y = BlockMatrix([[R1*(K*delta)],[l*Identity(len(p_nid))*delta_nonid]]).as_explicit()
                rho1_hat = np.concatenate((rho1, l*idf.model.xStdModel[p_nid]))
                e_rho1 = (Matrix(rho1_hat - contactForces) - Y)
            else:
                try:
                    from symengine import DenseMatrix as eMatrix
                    if idf.opt['verbose']:
                        print('using symengine')
                    edelta = eMatrix(delta.shape[0], delta.shape[1], delta)
                    eK = eMatrix(K.shape[0], K.shape[1], K)
                    eR1 = eMatrix(R1.shape[0], R1.shape[1], Matrix(R1))
                    Y = eR1*eK*edelta
                    e_rho1 = Matrix(eMatrix(rho1) - contactForces - Y)
                except ImportError:
                    if idf.opt['verbose']:
                        print('not using symengine')
                    Y = R1*(K*delta)
                    e_rho1 = Matrix(rho1 - contactForces) - Y

            if idf.opt['verbose'] > 1:
                print("Step 2...", time.ctime())

            # minimize estimation error of to-be-found parameters delta
            # (regressor dot std variables projected to base - contacts should be close to measured torques)
            u = Symbol('u')
            U_rho = BlockMatrix([[Matrix([u - rho2_norm_sqr]), e_rho1.T],

#TODO: also use symengine to gain speedup?
                #p = BlockMatrix([[(K*delta)], [delta_nonid]])
                #Y = BlockMatrix([[Matrix(R1),             ZeroMatrix(R1.shape[0], len(p_nid))],
                #                 [ZeroMatrix(len(p_nid), R1.shape[1]), l*Identity(len(p_nid))]])
                Y = BlockMatrix([[R1*(K*delta)],[l*Identity(len(p_nid))*delta_nonid]]).as_explicit()
                rho1_hat = np.concatenate((rho1, l*idf.model.xStdModel[p_nid]))
                e_rho1 = (Matrix(rho1_hat - contactForces) - Y)
            else:
                try:
                    from symengine import DenseMatrix as eMatrix
                    if idf.opt['verbose']:
                        print('using symengine')
                    edelta = eMatrix(delta.shape[0], delta.shape[1], delta)
                    eK = eMatrix(K.shape[0], K.shape[1], K)
                    eR1 = eMatrix(R1.shape[0], R1.shape[1], Matrix(R1))
                    Y = eR1*eK*edelta
                    e_rho1 = Matrix(eMatrix(rho1) - contactForces - Y)
                except ImportError:
                    if idf.opt['verbose']:
                        print('not using symengine')
                    Y = R1*(K*delta)
                    e_rho1 = Matrix(rho1 - contactForces) - Y

            if idf.opt['verbose'] > 1:
                print("Step 2...", time.ctime())

            # minimize estimation error of to-be-found parameters delta
            # (regressor dot std variables projected to base - contacts should be close to measured torques)
            u = Symbol('u')
            U_rho = BlockMatrix([[Matrix([u - rho2_norm_sqr]), e_rho1.T],
                                 [e_rho1,            I(e_rho1.shape[0])]])

#p = BlockMatrix([[(K*delta)], [delta_nonid]])
                #Y = BlockMatrix([[Matrix(R1),             ZeroMatrix(R1.shape[0], len(p_nid))],
                #                 [ZeroMatrix(len(p_nid), R1.shape[1]), l*Identity(len(p_nid))]])
                Y = BlockMatrix([[R1*(K*delta)],[l*Identity(len(p_nid))*delta_nonid]]).as_explicit()
                rho1_hat = np.concatenate((rho1, l*idf.model.xStdModel[p_nid]))
                e_rho1 = (Matrix(rho1_hat - contactForces) - Y)
            else:
                try:
                    from symengine import DenseMatrix as eMatrix
                    if idf.opt['verbose']:
                        print('using symengine')
                    edelta = eMatrix(delta.shape[0], delta.shape[1], delta)
                    eK = eMatrix(K.shape[0], K.shape[1], K)
                    eR1 = eMatrix(R1.shape[0], R1.shape[1], Matrix(R1))
                    Y = eR1*eK*edelta
                    e_rho1 = Matrix(eMatrix(rho1) - contactForces - Y)
                except ImportError:
                    if idf.opt['verbose']:
                        print('not using symengine')
                    Y = R1*(K*delta)
                    e_rho1 = Matrix(rho1 - contactForces) - Y

            if idf.opt['verbose'] > 1:
                print("Step 2...", time.ctime())

            # minimize estimation error of to-be-found parameters delta
            # (regressor dot std variables projected to base - contacts should be close to measured torques)
            u = Symbol('u')
            U_rho = BlockMatrix([[Matrix([u - rho2_norm_sqr]), e_rho1.T],
                                 [e_rho1,            I(e_rho1.shape[0])]])

            if idf.opt['verbose'] > 1: