please include screenshot of python code

2. Develop codes for the Jacobi algorithm You can use the idea of recasting the equations into a matrix format to compute the iteration. 4. Modify the My_gauss_seidel.py to implement the SOR method 5. Implement the Jacobi, GS and SOR and compare with direct solvers b. Use the built-in solver np. linalg. solve (A,B) to solve the linear system to compute the equivalent to the true value of the solution c. Use the built-in function to compute the LU decomposition of a matrix. P, L,U=scipylinalglu(A). Report Deliverables: 1. Determine the solution to the flowing system of linear equations using all the methods that you implemented. 10x1+2x2x33x16x2+2x3x1+x2+5x3==27=61.521.5 a. Plot the residua. norm norm (resi)=norm(Axib) for every iteration. justify. If not, why? b. un the same problem using Python's built-in function np. linalg. solve(A,B) and compare results. c. Use the Python clock function (time ()) to time the calculations What do you observe? Which method is the fastest? Why? d. Perform experiments with 1