IN PYTHON and following this code;

def approx_ode(h,t0,y0,tn): # Inputs - h : step size # t0 : initial t value (at step 0) # y0 : initial y value (at step 0) # tn : t value at step n

Euler's Method: Numerical integration is very important for solving ordinary differential equations which cannot be solved analytically. In such cases, an approximation will have to do, and there are many different algorithms that achieve different levels of accuracy. One of the simplest methods to understand and implement is Euler's method, which is essentially a first order approximation. In Euler's method, if we know the solution to the differential equation at time tn (y(tn)), we may estimate the solution at time tn+1 y(n+) using the following relationship (n+1)(tn) + hf(tn,y(tn)) where h tn+1- is the step size anddf(t,y). Now, write a function approx_ode by implementing the Euler's method with step size, 0.1, to find the approximate values of y(t) up to 3 decimal places for the following initial value problem (IVP): dt = 3 + e-t-i" y(0) = 1 from t 0 tot 5 at a time interval of h. Note that the above IVP can also be solved exactly by the integrating factor method 2