How to use the qpsolvers.solve_qp function in qpsolvers

m variables, 2m+1 constraints (1 equation, 2m inequations).
        :param X: array of size [n_samples, n_features] holding the training samples
        :param y: array of size [n_samples] holding the class labels
        """
        #
        m = len(y)  # m = n_samples
        K = self.kernel(X)  # gram matrix
        P = K * np.outer(y, y)
        q = -np.ones(m)
        G = np.vstack((-np.identity(m), np.identity(m)))
        h = np.hstack((np.zeros(m), np.ones(m) * self.C))
        A = y.reshape((1, -1))
        b = np.zeros(1)
        # make sure P is positive definite
        P += np.eye(P.shape[0]).__mul__(1e-3)
        self.alphas = solve_qp(P, q, G, h, A, b, sym_proj=True)

dense_instr = {
        solver: "u = solve_qp(P, q, G, h, solver='%s')" % solver
        for solver in dense_solvers}
    sparse_instr = {
        solver: "u = solve_qp(P_csc, q, G_csc, h, solver='%s')" % solver
        for solver in sparse_solvers}

    print("\nTesting all QP solvers on a dense quadratic program...")

    sol0 = solve_qp(P, q, G, h, solver=dense_solvers[0])
    for solver in dense_solvers:
        sol = solve_qp(P, q, G, h, solver=solver)
        delta = norm(sol - sol0)
        assert delta < 1e-4, "%s's solution offset by %.1e" % (solver, delta)
    for solver in sparse_solvers:
        sol = solve_qp(P_csc, q, G_csc, h, solver=solver)
        delta = norm(sol - sol0)
        assert delta < 1e-4, "%s's solution offset by %.1e" % (solver, delta)

    print("\nDense solvers\n-------------")
    for solver, instr in dense_instr.items():
        print("%s: " % solver, end='')
        get_ipython().magic('timeit %s' % instr)

    print("\nSparse solvers\n--------------")
    for solver, instr in sparse_instr.items():
        print("%s: " % solver, end='')
        get_ipython().magic('timeit %s' % instr)

def _optimize(self, digraph, v_origin):
        solver_matrices, variables = self.get_matrices(digraph, v_origin)
        P = solver_matrices.P
        q = solver_matrices.q
        G = solver_matrices.G
        h = solver_matrices.h
        A = solver_matrices.A
        b = solver_matrices.b

        # PAG  = np.concatenate([P, A, G])
        # print('rank(A)', np.linalg.matrix_rank(A))
        # print('shape(A)', A.shape)
        # print('rank([P; A; G])', np.linalg.matrix_rank(PAG))
        # print('shape([P; A; G])', PAG.shape)

        x = solve_qp(P, q, G, h, A, b, solver=self.solver)
        return x, variables
#

def check_same_solutions(tol=0.05):
    sol0 = solve_qp(P, q, G, h, solver=sparse_solvers[0])
    for solver in sparse_solvers:
        sol = solve_qp(P, q, G, h, solver=solver)
        relvar = norm(sol - sol0) / norm(sol0)
        assert relvar < tol, "%s's solution offset by %.1f%%" % (
            solver, 100. * relvar)
    for solver in dense_solvers:
        sol = solve_qp(P_array, q, G_array, h, solver=solver)
        relvar = norm(sol - sol0) / norm(sol0)
        assert relvar < tol, "%s's solution offset by %.1f%%" % (
            solver, 100. * relvar)

a0 = dot(H_lambda(s0), n0)
        b0 = dot(H_mu(s0), n0)
        c0 = dot(H_cst(s0) - p0, n0)
        h0 = takeoff_clearance
        # a0 * lambda + b0 * mu + c0 >= h0
        s1 = 3. / 4
        a1 = dot(H_lambda(s1), n1)
        b1 = dot(H_mu(s1), n1)
        c1 = dot(H_cst(s1) - p1, n1)
        h1 = landing_clearance
        # a1 * lambda + b1 * mu + c1 >= h1
        P = eye(2)
        q = zeros(2)
        G = array([[-a0, -b0], [-a1, -b1]])
        h = array([c0 - h0, c1 - h1])
        x = solve_qp(P, q, G, h)
        # H = lambda s: H_lambda(s) * x[0] + H_mu(s) * x[1] + H_cst(s)
        path = interpolate_cubic_hermite(p0, x[0] * n0, p1, x[1] * n1)
        return path

def check_same_solutions(tol=0.05):
    sol0 = solve_qp(P, q, G, h, solver=sparse_solvers[0])
    for solver in sparse_solvers:
        sol = solve_qp(P, q, G, h, solver=solver)
        relvar = norm(sol - sol0) / norm(sol0)
        assert relvar < tol, "%s's solution offset by %.1f%%" % (
            solver, 100. * relvar)
    for solver in dense_solvers:
        sol = solve_qp(P_array, q, G_array, h, solver=solver)
        relvar = norm(sol - sol0) / norm(sol0)
        assert relvar < tol, "%s's solution offset by %.1f%%" % (
            solver, 100. * relvar)

def solve_random_qp(n, solver):
    M, b = random.random((n, n)), random.random(n)
    P, q = dot(M.T, M), dot(b, M).reshape((n,))
    G = toeplitz([1., 0., 0.] + [0.] * (n - 3), [1., 2., 3.] + [0.] * (n - 3))
    h = ones(n)
    return solve_qp(P, q, G, h, solver=solver)