How to use the symengine.Lambdify function in symengine

def time_roots_symengine(self):
        from symengine import Lambdify
        x, new_inits, success = self.eqsys.roots(
            self.c0, np.logspace(-3, 0, 50), 'NH3',
            lambdify=Lambdify, lambdify_unpack=False)
        assert all(success)

import symengine as se
import warnings

src = os.path.join(os.path.dirname(__file__), '6_links_rhs.txt')
serial = open(src, 'tr').read()
parsed = parse_expr(serial, transformations=standard_transformations)
vec = sp.Matrix(1, 14, parsed)
args = tuple(sorted(vec.free_symbols, key=lambda arg: arg.name))
exprs = vec, vec.jacobian(args[:-14])
inp = np.ones(len(args))
assert inp.size == 26


lmb_sp = sp.lambdify(args, exprs, modules=['math', 'sympy'])
lmb_se = se.Lambdify(args, *exprs)
lmb_se_llvm = se.Lambdify(args, *exprs, backend='llvm')


lmb_sp(*inp)
tim_sympy = clock()
for i in range(500):
    v, m = lmb_sp(*inp)
tim_sympy = clock() - tim_sympy

lmb_se(inp)
tim_se = clock()
for i in range(500):
    v, m = lmb_se(inp)
tim_se = clock() - tim_se


lmb_se_llvm(inp)

from sympy.parsing.sympy_parser import standard_transformations
import symengine as se
import warnings

src = os.path.join(os.path.dirname(__file__), '6_links_rhs.txt')
serial = open(src, 'tr').read()
parsed = parse_expr(serial, transformations=standard_transformations)
vec = sp.Matrix(1, 14, parsed)
args = tuple(sorted(vec.free_symbols, key=lambda arg: arg.name))
exprs = vec, vec.jacobian(args[:-14])
inp = np.ones(len(args))
assert inp.size == 26


lmb_sp = sp.lambdify(args, exprs, modules=['math', 'sympy'])
lmb_se = se.Lambdify(args, *exprs)
lmb_se_llvm = se.Lambdify(args, *exprs, backend='llvm')


lmb_sp(*inp)
tim_sympy = clock()
for i in range(500):
    v, m = lmb_sp(*inp)
tim_sympy = clock() - tim_sympy

lmb_se(inp)
tim_se = clock()
for i in range(500):
    v, m = lmb_se(inp)
tim_se = clock() - tim_se

from time import clock
import numpy as np
import sympy as sp
import symengine as se

# Real-life example (ion speciation problem in water chemistry)

x = sp.symarray('x', 14)
p = sp.symarray('p', 14)
args = np.concatenate((x, p))
exp = sp.exp
exprs = [x[0] + x[1] - x[4] + 36.252574322669, x[0] - x[2] + x[3] + 21.3219379611249, x[3] + x[5] - x[6] + 9.9011158998744, 2*x[3] + x[5] - x[7] + 18.190422234653, 3*x[3] + x[5] - x[8] + 24.8679190043357, 4*x[3] + x[5] - x[9] + 29.9336062089226, -x[10] + 5*x[3] + x[5] + 28.5520551531262, 2*x[0] + x[11] - 2*x[4] - 2*x[5] + 32.4401680272417, 3*x[1] - x[12] + x[5] + 34.9992934135095, 4*x[1] - x[13] + x[5] + 37.0716199972041, p[0] - p[1] + 2*p[10] + 2*p[11] - p[12] - 2*p[13] + p[2] + 2*p[5] + 2*p[6] + 2*p[7] + 2*p[8] + 2*p[9] - exp(x[0]) + exp(x[1]) - 2*exp(x[10]) - 2*exp(x[11]) + exp(x[12]) + 2*exp(x[13]) - exp(x[2]) - 2*exp(x[5]) - 2*exp(x[6]) - 2*exp(x[7]) - 2*exp(x[8]) - 2*exp(x[9]), -p[0] - p[1] - 15*p[10] - 2*p[11] - 3*p[12] - 4*p[13] - 4*p[2] - 3*p[3] - 2*p[4] - 3*p[6] - 6*p[7] - 9*p[8] - 12*p[9] + exp(x[0]) + exp(x[1]) + 15*exp(x[10]) + 2*exp(x[11]) + 3*exp(x[12]) + 4*exp(x[13]) + 4*exp(x[2]) + 3*exp(x[3]) + 2*exp(x[4]) + 3*exp(x[6]) + 6*exp(x[7]) + 9*exp(x[8]) + 12*exp(x[9]), -5*p[10] - p[2] - p[3] - p[6] - 2*p[7] - 3*p[8] - 4*p[9] + 5*exp(x[10]) + exp(x[2]) + exp(x[3]) + exp(x[6]) + 2*exp(x[7]) + 3*exp(x[8]) + 4*exp(x[9]), -p[1] - 2*p[11] - 3*p[12] - 4*p[13] - p[4] + exp(x[1]) + 2*exp(x[11]) + 3*exp(x[12]) + 4*exp(x[13]) + exp(x[4]), -p[10] - 2*p[11] - p[12] - p[13] - p[5] - p[6] - p[7] - p[8] - p[9] + exp(x[10]) + 2*exp(x[11]) + exp(x[12]) + exp(x[13]) + exp(x[5]) + exp(x[6]) + exp(x[7]) + exp(x[8]) + exp(x[9])]
lmb_symengine = se.Lambdify(args, exprs)
lmb_sympy = sp.lambdify(args, exprs)

inp = np.ones(28)

tim_symengine = clock()
res_symengine = np.empty(len(exprs))
for i in range(500):
    res_symengine = lmb_symengine(inp)
    #lmb_symengine.unsafe_real_real(inp, res_symengine)
tim_symengine = clock() - tim_symengine

tim_sympy = clock()
for i in range(500):
    res_sympy = lmb_sympy(*inp)
tim_sympy = clock() - tim_sympy

def ManualLLVM(inputs, *outputs):
    outputs_ravel = list(itertools.chain(*outputs))
    cb = se.Lambdify(inputs, outputs_ravel, backend="llvm")
    def func(*args):
        result = []
        n = np.empty(len(outputs_ravel))
        t = cb.unsafe_real(np.concatenate([arg.ravel() for arg in args]), n)
        start = 0
        for output in outputs:
            elems = reduce(mul, output.shape)
            result.append(n[start:start+elems].reshape(output.shape))
            start += elems
        return result
    return func

translates the symbolic Jacobain to a function using SymEngines `Lambdify` or `LambdifyCSE`. If the symbolic Jacobian has not been generated, it is generated by calling `generate_jac_sym`.
		
		Parameters
		----------
		do_cse : boolean
			Whether a common-subexpression detection, namely `LambdifyCSE`, should be used.
		"""
		
		self._prepare_lambdas()
		
		jac_matrix = symengine.Matrix([
				[ordered_subs(entry,self._lambda_subs) for entry in line]
				for line in self.jac_sym
			])
		
		lambdify = symengine.LambdifyCSE if do_cse else symengine.Lambdify
		core_jac = lambdify(self._lambda_args,jac_matrix)
		self.jac = lambda t,Y: core_jac(np.hstack([t,Y,self.control_par_values]))
		
		self.compile_attempt = False