error encountered using decorator to update wrapper

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  • Techknowledgy :

Your implementation of decorator is unnecessary. Just use functools.wraps:

def f(fn):
   ""
"Converts the string fn to a function and returns it."
""
@functools.wraps(fn)
def _f(fn):
   return eval(fn)
return _f

However, you don't want a decorator in this case but a function factory.

def factory(exp):
   def f( ** kwargs):
   return eval(exp, globals(), kwargs)
f.__name__ = exp
return f

Now you can use this like this:

>>> x_squared = factory("x**2") >>>
   x_squared(x = 7)
49

Suggestion : 2

Last Updated : 21 Sep, 2021,GATE CS 2021 Syllabus

Output:

Decorator called
inner
function
outer
function

Output :

4.5
441
0.5
wrong data types.enter numeric

Suggestion : 3

without needing to explicitly close page. Even if an error occurs, page.close() will be called when the with block is exited.,Changed in version 3.10: Async context managers created with asynccontextmanager() can be used as decorators.,Context managers defined with asynccontextmanager() can be used either as decorators or with async with statements:,Context managers created using contextmanager() are also single use context managers, and will complain about the underlying generator failing to yield if an attempt is made to use them a second time:

from contextlib
import contextmanager

@contextmanager
def managed_resource( * args, ** kwds):
   # Code to acquire resource, e.g.:
   resource = acquire_resource( * args, ** kwds)
try:
yield resource
finally:
# Code to release resource, e.g.:
   release_resource(resource)

   >>>
   with managed_resource(timeout = 3600) as resource:
   ...# Resource is released at the end of this block,
   ...# even
if code in the block raises an exception
from contextlib
import asynccontextmanager

@asynccontextmanager
async def get_connection():
   conn = await acquire_db_connection()
try:
yield conn
finally:
await release_db_connection(conn)

async def get_all_users():
   async with get_connection() as conn:
      return conn.query('SELECT ...')
import time
from contextlib
import asynccontextmanager

@asynccontextmanager
async def timeit():
   now = time.monotonic()
try:
yield
finally:
print(f 'it took {time.monotonic() - now}s to run')

@timeit()
async def main():
   #...async code...
from contextlib
import contextmanager

@contextmanager
def closing(thing):
   try:
   yield thing
finally:
thing.close()
from contextlib
import closing
from urllib.request
import urlopen

with closing(urlopen('https://www.python.org')) as page:
   for line in page:
   print(line)
from contextlib
import asynccontextmanager

@asynccontextmanager
async def aclosing(thing):
   try:
   yield thing
finally:
await thing.aclose()

Suggestion : 4

May 13, 2020 • 24 min read

def func_a():
   return "I was angry with my friend."

def func_b():
   return "I told my wrath, my wrath did end"

def func_c( * funcs):
   for func in funcs:
   print(func())

main_func = func_c
main_func(func_a, func_b)
>>> I was angry with my friend. >>>
   I told my wrath, my wrath did end
def higher(func):
   ""
"This is a higher order function.
It returns another
function.
""
"

return func

def lower():
   return "I'm hunting high and low"

higher(lower)
>>> <function __main__.lower()>
h = higher(lower)
h()
>>> "I'm hunting high and low"

Suggestion : 5

Calling the function with a different list length also works well. Let’s try again without the decorator, and with the decorator applied while using a longer list of names.,We now see how the plumbing works with decorators. Our current decorator is not modifying any behavior for the functions that it decorates. Let’s change that now. Here is an updated version of the my_decorator() function.,Up to this point, we have intentionally left the decorator function in a state that does not actually modify the effect of the function that it decorates. Something interesting has occurred however if we run this code above. Let’s see what has changed.,Do you notice what has changed? Before adding the decorator to the to_be_decorated() function, if we simply printed out that function we see:

The first step in understanding Decorators in Python is understanding that a function is an object. Just like a string, float, int, and so on is an object in Python, so too is the function. Let’s use the Python type() function to demonstrate this.

some_str = 'some string'
a_bool = True
a_float = 1.0
a_int = 1
a_list = [1, 2, 3]
a_dict = {
   'a': 1,
   'b': 2
}
a_tuple = (1, 2, 3)
a_set = {
   1,
   2,
   3
}

print(f 'some_str: {type(some_str)}')
print(f 'a_bool: {type(a_bool)}')
print(f 'a_float: {type(a_float)}')
print(f 'a_int: {type(a_int)}')
print(f 'a_list: {type(a_list)}')
print(f 'a_dict: {type(a_dict)}')
print(f 'a_tuple: {type(a_tuple)}')
print(f 'a_set: {type(a_set)}')
some_str: <class 'str'>
   a_bool: <class 'bool'>
      a_float: <class 'float'>
         a_int: <class 'int'>
            a_list: <class 'list'>
               a_dict: <class 'dict'>
                  a_tuple: <class 'tuple'>
                     a_set: <class 'set'>

You can see that every one of these has a class keyword in its type output. In other words, they are all objects. Now check this out.

def my_func():
   print('my_func')

print(f 'my_func: {type(my_func)}')

It is perfectly legal to define a function inside of another function in Python. In this example here we simply define an inner function and then return it when the outer function is called.

def my_func():
   def inner_func():
   pass

return inner_func

result = my_func()
print(result)
def my_func():
    def inner_func():
        pass

    return inner_func


result = my_func()
print(result)
<function my_func.<locals>.inner_func at 0x000002D87FF46310>

Suggestion : 6

To overcome this issue, we will have to import another wrapper function called wraps and then wrap our decorator functions using that.,As discussed above, Python decorators function is nothing but a function which takes another function as an argument. So we can create it using the following syntax :-,If you have a look at the above output, you will see that the print statement from the wrapper function of the python decorator ran first. But this is a longer way to apply a decorator to a function. The general syntax to use is to add @decorator_function at the very top of the function.,To apply the python decorators to a function first we will create a simple function which will just have a print statement:-

As discussed above, Python decorators function is nothing but a function which takes another function as an argument. So we can create it using the following syntax :-

def decorator_function(func):
   def wrapper_function():
   print(f 'Wrapper function ran before {func.__name__}.')
return func()
return wrapper_function

To apply the python decorators to a function first we will create a simple function which will just have a print statement:-

def a_function():
   print('A function ran.')

Now, to decorate it we can do the following :-

decorated_function = decorator_function(a_function)
decorated_function()

# Output

Wrapper
function ran before a_function.
A
function ran.

In the above example our function didn’t take any argument but if we have a following function which takes certain arguments :-

def b_function(name, age):
   print(f '{name} is {age} years old.')

Now, if we try to decorate it with our python decorators and run it, we will encounter an error:-

@decorator_function
def b_function(name, age):
   print(f '{name} is {age} years old.')

b_function('Saral', 30)

TypeError: wrapper_function() takes 0 positional arguments but 2 were given