Exercise I For each of the following three code snippets (corresponding Python code is list in the Appendix at the end of this assignment): Calculate the theoretical time complexity by counting the number of subtractions as basic operation What do you think is the Big-Oh for the complexity? Prove how did you get the big-Oh only for Code Snippet 1 b. Plot a graph of the theoretical time complexity c. Implement the code in the language of your choice, and find the running time for several values of (for instance, for n - 1000, 2000, 3000...10000) and plot the results. (make sure not to bave other applications running in the background. You can use different values for as you see fit). Submit the full code as an appendix to your assignment d. Using the two plotted graphs, comment on the growth rate of the theoretical time complexity in comparison with the actual running times. Code Snippet 1. Negativesum - O: for - 17 n 1) Negative sum- Negativesum- Part Snippet 2. Negative Sum - 0 for Icon ) Negative Sum - Negative - i; ING Part Snippet Negativesussum - 0: tot - 13+ for li-11) Negative - Negativum - 1 document you are . and apply out Appendix A (Python code for the code snippets in Question 1) I. Code Snippet 1 Negative sun - O tot in range ( 11): Negatives = Negatives 2. Code Snippet 2 regative sun - 0 Tot in angel. +1) Negative sum = Megat Event 3. Code Snippet Negatives torin range 1 tor) in range, Negative Negativ- UNAN