Python question Analysis: Computational Complexity (Q14-15) In this section, you are asked to analyse the worst-case computational time complexity of Python functions. Always identify the order of growth in the tightest and simplest possible form using big-O notation, e.g., write O(n) instead of O(2n+n/2). Question 14 (Complexity 1) State the overall worst-case time complexity of the below function and provide a short explanation of your answer. The function accepts an integer n as input. def mystery(n):     num = 1     count = 0     while num < n:         num = num*2         count += 1     return count   Question 15 (Complexity 2) State the overall worst-case time complexity of the below function and provide a short explanation of your answer. The function is supposed to return a list, res, containing the duplicated elements in the given list , lst, of size n.   Analyse the function in terms of the length of the list, n. def duplicate_values(lst):     res = []     for i in range(len(lst)):         if lst[i] in lst[i+1:]:             res.append(lst[i])     return res

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Declaring and Defining the function

A set of statements, which performs a particular task is called a function. The program gains modularity by using function. The application is quite easy to understand, read, and edit when they are created with functions. The developer/programmer creates a function and uses main() to call the function. Once the function gets executed, the control is transferred to the main program.