In numerical methods, one source of error occurs when we use an approximation for a mathematical expression that would otherwise be too costly to compute in terms of run-time or memory resources. One routine example is the approximation of infinite series by a finite series that mostly captures the important behavior of the infinite series. In this problem you will implement an approximation to the as represented by the following infinite series, exp(x) = summation x^n/n!Your approximation will be a truncated finite series with terms, For the first part of this problem, you are given a random real number x and will investigate how well a finite series expansion for exp(2) approximates the infinite series. • Compute exp(x) using a finite series approximation with N € (0,9) C N (i. e. N is an integer). • Save the 10 floating point values from your approximation in a numpy array named exp_approx. exp_approx should be of shape (10,) and should be ordered with increasing N (i. e. the first entry of exp_approx should correspond to exp(x, N) when N = 0 and the last entry when N = 9).