scipy How to write a Jacobian function for optimize.minimize Rosenbrock function

Example

Think this could example could be better but you get the gist

import numpy as np
from scipy.optimize import _minimize
from scipy import special
import matplotlib.pyplot as plt
from matplotlib import cm
from numpy.random import randn

x, y = np.mgrid[-2:2:100j, -2:2:100j]
plt.pcolor(x, y, optimize.rosen([x, y]))
plt.plot(1, 1, 'xw')

# Make plot with vertical (default) colorbar
data = np.clip(randn(100, 100), -1, 1)
cax = plt.imshow(data, cmap=cm.coolwarm)

# Add colorbar, make sure to specify tick locations to match desired  ticklabels 
cbar = plt.colorbar(cax, ticks=[-2, 0, 2])  # vertically oriented colorbar
plt.axis([-2, 2, -2, 2])
plt.title('Rosenbrock function') #add title if desired
plt.xlabel('x')
plt.ylabel('y')

plt.show()  #generate