Is it possible to check if a function is decorated inside another function?

Is it possible to check if a function is decorated inside another function?

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Is it possible to check if a function is decorated inside another function?
Tag : python , By : Steve Jones
Date : November 28 2020, 04:01 AM

wish helps you To be clear, this is egregious hackery, so I don't recommend it, but since you've ruled out additional parameters, and f1 will be the same whether wrapped or not, you've left hacks as your only option. The solution is to add a local variable to the wrapper function for the sole purpose of being found by means of stack inspection:
import inspect

def out(fun):
    def inner(*args, **kwargs):
        __wrapped_by__ = out
        fun(*args, **kwargs)
    return inner

def is_wrapped_by(func):
        return inspect.currentframe().f_back.f_back.f_back.f_locals.get('__wrapped_by__') is func
    except AttributeError:
        return False

def decorated():

def not_decorated():

def f1():
    if is_wrapped_by(out):
        print('I am')
        print('I am not')

def is_wrapped_by(func):
    frame = None
        # Skip is_wrapped_by and caller 
        frame = inspect.currentframe().f_back.f_back
        while True:
            if frame.f_locals.get('__wrapped_by__') is func:
                return True
            frame = frame.f_back
    except AttributeError:
        # Leaving frame on the call stack can cause cycle involving locals
        # which delays cleanup until cycle collector runs;
        # explicitly break cycle to save yourself the headache
        del frame
    return False

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referring to the name of the decorated function inside decorator

Tag : python , By : Brian Cupps
Date : March 29 2020, 07:55 AM
With these it helps Can I, inside the decorator, refer to the name of the object being decorated? E.g., , The original name of a function is stored in the .__name__ attribute:
>>> def foo(): pass
>>> foo.__name__
>>> bar = foo
>>> bar.__name__

Decorator in class: cannot use self inside the decorated function

Tag : python , By : Jesenko Mehmedbasic
Date : March 29 2020, 07:55 AM
wish help you to fix your issue I am trying to implement a custom logger with a decorator that would collect exceptions (to save them to db later) in the following way: , There were a couple of problems with your code. Look at the comments.
import functools

class Log:
    def __init__(self):
        self.mssg = ""
        self.err = ""

class Parent(object):
    def __init__(self):
        self.logger = Log()

    @staticmethod   #You can directly use staticmethod decorator!
    def logging(fun):
        def inner(*args):
            self = args[0]  #Grab the first arg as self.
                self.logger.mssg += fun.__name__ +" :ok, "  
                return fun(self, *(args[1:]))  # Call the function using
                                               # self that we extracted.
            except Exception as e:
                self.logger.err += fun.__name__ +": error: "+str(e.args) 
        return inner

class Child(Parent):
    def __init__(self, a, b):
        super(Child, self).__init__()   #Don't forget call the parent ctor
        self.a = a
        self.b = b

    def sum_(self):
        return self.a + self.b

c = Child(3,6)
print c.sum_()  #Outputs 9

Check if a Python function is decorated with a specific decorator

Tag : python-2.7 , By : user184415
Date : March 29 2020, 07:55 AM
seems to work fine Ok, thanks to user2357112's clue, I found the answer. Even with an improvement
from functools import wraps

def applies_to(*segments):
    def applies(func):
        func.func_dict["segments"] = list(segments)
        def wrapper(*args, **kwargs):
            return func(*args, **kwargs)
        return wrapper
    return applies

@tf.function ValueError: Creating variables on a non-first call to a function decorated with tf.function, unable to unde

Tag : python , By : can
Date : March 29 2020, 07:55 AM
This might help you Using tf.function you're converting the content of the decorated function: this means that TensorFlow will try to compile your eager code into its graph representation.
The variables, however, are special objects. In fact, when you were using TensorFlow 1.x (graph mode), you were defining the variables only once and then using/updating them.
def f():
    a = tf.constant([[10,10],[11.,1.]])
    x = tf.constant([[1.,0.],[0.,1.]])
    b = tf.Variable(12.)
    y = tf.matmul(a, x) + b
    return y
b = None

def f():
    a = tf.constant([[10, 10], [11., 1.]])
    x = tf.constant([[1., 0.], [0., 1.]])
    global b
    if b is None:
        b = tf.Variable(12.)
    y = tf.matmul(a, x) + b
    print("PRINT: ", y)
    tf.print("TF-PRINT: ", y)
    return y


Creating `NumPy` arrays inside a function decorated with `numba`'s `@jit(nopython=True)`?

Tag : python , By : yatil
Date : March 29 2020, 07:55 AM
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