def timed(fn: "Function"):
    
    @wraps(fn)
    def inner(*args, **kwargs) -> "Function Output":
        """
        Inner function to calculate the time.
        """
        start = perf_counter()
        result = fn(*args, **kwargs)
        end = perf_counter()
        time_elapsed = (end - start)
        print('Run time: {0:.6f}s'.format(time_elapsed))
        return result
    return inner

profiles = []
    Profile = namedtuple('Profile', fake.profile().keys())
    for profile in range(no_profiles):
        profiles.append(Profile(**fake.profile()))
    return profiles

    no_profiles = 10000
    profiles = generate_profiles_using_namedtuple(no_profiles)
    date_today = datetime.date.today()
    blood_gp = dict()
    max_age = {'age': 0, 'profile': None}
    cur_loc_coord_sum = [0, 0]
    sum_ages = 0
    for profile in profiles:
        blood_gp[profile.blood_group] = blood_gp.get(profile.blood_group,0) + 1
        age = (date_today - profile.birthdate).days
        if  age > max_age['age']:
            max_age['age'] = age
            max_age['profile'] = profile
        cur_loc_coord_sum[0] += profile.current_location[0]
        cur_loc_coord_sum[1] += profile.current_location[1]
        sum_ages += int(age/365)

    data = namedtuple('data', 'largest_blood_type mean_current_location oldest_person average_age')
    bg_l = max(blood_gp, key=blood_gp.get)
    mean_current_location = (cur_loc_coord_sum[0]/no_profiles, cur_loc_coord_sum[1]/no_profiles)
    return data((bg_l, blood_gp[bg_l]), mean_current_location, (max_age['profile'], int(max_age['age']/365)), int(sum_ages/no_profiles))

    no_profiles = 10000
    profiles = generate_profiles_using_dictionary(no_profiles)
    date_today = datetime.date.today()
    blood_gp = dict()
    max_age = {'age': 0, 'proflie': None}
    cur_loc_coord_sum = [0, 0]
    sum_ages = 0
    for profile in profiles:
        
        blood_gp[profile['blood_group']] = blood_gp.get(profile['blood_group'],0) + 1
        age = (date_today - profile['birthdate']).days
        if  age > max_age['age']:
            max_age['age'] = age
            max_age['profile'] = profile
        cur_loc_coord_sum[0] += profile['current_location'][0]
        cur_loc_coord_sum[1] += profile['current_location'][1]
        sum_ages += int(age/365)
    bg_l = max(blood_gp, key=blood_gp.get)
    mean_current_location = (cur_loc_coord_sum[0]/no_profiles, cur_loc_coord_sum[1]/no_profiles)
    return {'largest_blood_type': (bg_l, blood_gp[bg_l]), 'mean_current_location': mean_current_location, 'oldest_person': (max_age['profile'], int(max_age['age']/365)), 'average_age': int(sum_ages/no_profiles)}

all_companies = []
    Stocks = namedtuple("Stocks", 'name symbol open high close company_weight')
    for _ in range(100):
        name = fake.company()
        open_ = round(random.uniform(41, 3999), 2)
        high_num = round(random.uniform(0.613, 1.4), 2)  # market is damn volatile
        high = open_ * high_num if high_num > 1.0 else open_
        close = random.uniform(open_ - random.randint(-10, 10), high + random.randint(-8, 10))
        if close > high:
            high = close

        all_companies.append(
            Stocks(name=name, symbol=get_capitalized_letters(name), open=open_, high=round(high, 2), close=round(close, 2), company_weight=round(random.uniform(15, 80), 3)))

    stock_index = round(sum(x.open * x.company_weight for x in all_companies), 4)
    highest_for_day = round(max(x.high * x.company_weight for x in all_companies), 2)
    lowest_close_for_day = round(min(x.close * x.company_weight for x in all_companies), 2)

    print(f"\n------------------------------------Top 100 listed companies on TSAI Stock Exchange------------------------------------")
    [print(x) for x in sorted(all_companies, key=lambda x:x.symbol)]
    print(f"\n--------------Main details on {date.today()}--------------")
    print(f"\nStock Index: {stock_index}")
    print(f"Highest for the day: {highest_for_day}")
    print(f"Lowest close for the day: {lowest_close_for_day}")