<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Closures
Step1:  Decorators
Step2:  And now for the magic moment. This relies on Python's ability to rebind a reference. The function my_decorator will now go by a new name, other_func, and simultaneously other_func is used in the definition of my_decorator. In this bare metal example other_func doesn't realy do anything inside my_decorator.  In other words it isn't used, it's merely passed in as an argument (func). Code linters would call foul
Step3:  Timer decorator
Step4:  Now we're actuly going to do something with the func that is passed in. We're going to call it right in the middle of our new timer decorator. We do a little work before it is called -- we take a snapshot of the system clock with time() -- and a little more after -- another call to time(). This is the essence of a decorator.
Step5:  Sticking with the basic/explicit way to create a decorator for the moment....
Step6:  And now the fancy "syntatic sugar" way of creating the decorator.
Step7:  Stacked decorators
Step8:  Seems to me that I've seen this senario used multiple times in the context of decorators, so I suspect it is in a mooc or online tutorial somewhere. This reference might be interesting
Step9:  Let's check this out. What has the call to iter() actually returned?
Step10:  An iterator eventually expires... it runs out of stuff and then raises StopIteration. Push it beyond its limit at your own risk.
Step11:  Generators
Keep in mind that Generators are lazy and poentailly infinite. Don't let my use of for loops and range() cause you to loose sight of these two features of generators; they're helpful constructs for exploring this space.
Step12:  How might we create something similar to yield if we did not have yield? A class perhaps? Let's try it....
Step13:  Generators, comprehension style
First a list comprehension to refresh on the basic structure of a list comprehension.
Step14:  And now for the coolness... a generator comprehension
<ASSISTANT_TASK:>
Python Code:
def make_multiplier_of(n):
    def multiplier(x):
        return x * n
    return multiplier
times3 = make_multiplier_of(3)
type(times3)
times3(3)
times3(11)
times5 = make_multiplier_of(5)
times5(3)
timessomething = make_multiplier_of()
Explanation: Closures
End of explanation
def my_decorator(func):
    def inner():
        print("Running my_decorator")
    return inner
def other_func():
    print("Running other_func")
other_func()
Explanation: Decorators
End of explanation
other_func = my_decorator(other_func)
other_func()
other_func
@my_decorator
def another_func():
    print("Running another_func")
another_func()
Explanation: And now for the magic moment. This relies on Python's ability to rebind a reference. The function my_decorator will now go by a new name, other_func, and simultaneously other_func is used in the definition of my_decorator. In this bare metal example other_func doesn't realy do anything inside my_decorator.  In other words it isn't used, it's merely passed in as an argument (func). Code linters would call foul: if you're going to pass in an argument you should use it. Fair enough. We will get to more interesting examples soon.
End of explanation
from time import time
from time import sleep
Explanation: Timer decorator
End of explanation
def timer(func):
    def wrapper():
        t1 = time()
        func()
        t2 = time()
        print("{} duration is {}".format(func.__name__, t2 - t1))
    return wrapper
def slow_func():
    sleep(2)
Explanation: Now we're actuly going to do something with the func that is passed in. We're going to call it right in the middle of our new timer decorator. We do a little work before it is called -- we take a snapshot of the system clock with time() -- and a little more after -- another call to time(). This is the essence of a decorator.
End of explanation
slow_func = timer(slow_func)
slow_func()
Explanation: Sticking with the basic/explicit way to create a decorator for the moment....
End of explanation
@timer
def even_slower_func():
    sleep(5)
even_slower_func()
Explanation: And now the fancy "syntatic sugar" way of creating the decorator.
End of explanation
def italic(fn):
    def _():
        return "<i>" + fn() + "</i>"
    return _
def bold(fn):
    def _():
        return "<b>" + fn() + "</b>"
    return _
@bold
@italic
def hello():
    return "hello world"
print(hello())
Explanation: Stacked decorators
End of explanation
my_list = [2, 4, 6, 8]
my_first_iterator = iter(my_list)
Explanation: Seems to me that I've seen this senario used multiple times in the context of decorators, so I suspect it is in a mooc or online tutorial somewhere. This reference might be interesting:  https://stackoverflow.com/questions/27342149/decorator-execution-order
Iterables
End of explanation
type(my_list)
type(my_first_iterator)
dir(my_list)
dir(my_first_iterator)
for i in my_list:
    print(i)
for i in my_first_iterator:
    print(i)
Explanation: Let's check this out. What has the call to iter() actually returned?
End of explanation
next(my_first_iterator)
my_second_iterator = iter(my_list)
my_third_iterator = iter(my_list)
next(my_second_iterator)
next(my_second_iterator)
next(my_third_iterator)
my_first_iterator()
dir(my_first_iterator)
my_string = "abcdefg"
for i in my_string:
    print(i)
my_string = "ijklmno"
next(my_string)
dir(str)
my_iter_string = iter(my_string)
next(my_iter_string)
dir(my_iter_string)
Explanation: An iterator eventually expires... it runs out of stuff and then raises StopIteration. Push it beyond its limit at your own risk.
End of explanation
def gen_numbers():
    for n in range(9):
        yield n
my_gen = gen_numbers()
type(my_gen)
for i in my_gen:
    print(i)
for n in range(9):
    print(n)
Explanation: Generators
Keep in mind that Generators are lazy and poentailly infinite. Don't let my use of for loops and range() cause you to loose sight of these two features of generators; they're helpful constructs for exploring this space.
End of explanation
class MyGenNumbers(object):
    def __init__(self):
        self.max = 9
        self.current = -1
    def __next__(self):
        if self.current >= self.max:
            raise StopIteration
        self.current += 1
        return self.current
    
    def __iter__(self):
        return self
my_gen_numbers = MyGenNumbers()
type(my_gen_numbers)
for i in my_gen_numbers:
    print(i)
Explanation: How might we create something similar to yield if we did not have yield? A class perhaps? Let's try it....
End of explanation
my_list = [x * 100 for x in range(10)]
my_list
type(my_list)
Explanation: Generators, comprehension style
First a list comprehension to refresh on the basic structure of a list comprehension.
End of explanation
my_generator = (x * 100 for x in range(10))
type(my_generator)
for i in my_generator:
    print(i - 3)
Explanation: And now for the coolness... a generator comprehension:
End of explanation
<END_TASK>