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Solve the following python programming problem given the following code:
# %load ../scipy2017codegen/odesys_diffusion.py
from itertools import chain
import numpy as np
import matplotlib.pyplot as plt
from scipy2017codegen.odesys import ODEsys

class MOLsys(ODEsys):
    """ System of ODEs based on method of lines on the interval x = [0, x_end] """

    def __init__(self, *args, **kwargs):
        self.x_end = kwargs.pop('x_end')
        self.n_lines = kwargs.pop('n_lines')
        self.D = kwargs.pop('D')
        self.dx = self.x_end / self.n_lines
        super(MOLsys, self).__init__(*args, **kwargs)

    def f_eval(self, y, t, *params):
        f_out = np.empty(self.ny*self.n_lines)
        for i in range(self.n_lines):
            slc = slice(i*self.ny, (i+1)*self.ny)
            y_bis = self.second_derivatives_spatial(i, y, f_out[slc])
            f_out[slc] *= self.D
            f_out[slc] += self.lambdified_f(*chain(y[slc], params))
        return f_out

    def central_reference_bin(self, i):
        return np.clip(i, 1, self.ny - 2)

    def j_eval(self, y, t, *params):
        j_out = np.zeros((self.ny*self.n_lines, self.ny*self.n_lines))  # dense matrix
        for i in range(self.n_lines):
            slc = slice(i*self.ny, (i+1)*self.ny)
            j_out[slc, slc] = self.lambdified_j(*chain(y[slc], params))
            k = self.central_reference_bin(i)
            for j in range(self.ny):
                j_out[i*self.ny + j, (k-1)*self.ny + j] +=    self.D[j]/self.dx**2
                j_out[i*self.ny + j, (k  )*self.ny + j] += -2*self.D[j]/self.dx**2
                j_out[i*self.ny + j, (k+1)*self.ny + j] +=    self.D[j]/self.dx**2
        return j_out

    def second_derivatives_spatial(self, i, y, out):
        k = self.central_reference_bin(i)
        for j in range(self.ny):
            left = y[(k-1)*self.ny + j]
            cent = y[(k  )*self.ny + j]
            rght = y[(k+1)*self.ny + j]
            out[j] = (left - 2*cent + rght)/self.dx**2

    def integrate(self, tout, y0, params=(), **kwargs):
        y0 = np.array(np.vstack(y0).T.flat)
        yout,...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

In this case, we are dealing with an equidistant grid and you may very well recognize this result from standard text books (it is actually quite easy to derive from the definition of the derivative).
The number of dependent variables in our ODE system is then the number of species multiplied by the number of bins. There is no need to create that many symbols, instead we rely on writing an outer loop calculating the local reactions rates. We create a new subclass of our ODEsys class from earlier to do this: