Python Object Identity

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But what about those ***** Easter eggs? Well, let’s just say that Python’s developers aren’t afraid to poke fun at their own language. The code from __future__ import braces raises an error message summarizing their feelings on block delimiters. And if you try to import the antigravity module, it opens a web browser to xkcd comic 353 a humorous fictional use for such a module that demonstrates Python’s ease of adding functionality through modules.
But let’s not forget about the Zen of Python, which is displayed when trying to import __hello__. This summary of Python design philosophy reminds us that readability counts and encourages us to write code that is clear and concise. And with new features like structural pattern matching (introduced in PEP 622), we can continue to simplify our code while maintaining type safety.

In terms of the new context provided, let’s dive deeper into how Python’s duck typing allows functionality to be added at run time through decorators. Decorators are a powerful feature in Python that allow us to modify or enhance existing functions without changing their original implementation. They work by wrapping the function with another function and returning the wrapped function as a result.
For example, let’s say we have a simple function called `square`:

# Defining a function called "square" that takes in a parameter "x"
def square(x):
    # Returning the square of the parameter "x"
    return x ** 2

We can create a decorator that adds logging functionality to this function by wrapping it with another function that logs the input and output values. Here’s how we would do that:

# Import the logging module
import logging

# Define a decorator function that adds logging functionality to a given function
def log_square(func):
    # Define a wrapper function that takes in any number of arguments and keyword arguments
    def wrapper(*args, **kwargs):
        # Store the first argument in a variable
        x = args[0]
        # Call the original function with the given arguments and store the result
        result = func(*args, **kwargs)
        # Log the input value using the logging module
        logging.debug("Input: {}".format(x))
        # Log the output value using the logging module
        logging.debug("Output: {}".format(result))
        # Return the result of the original function
        return result
    # Return the wrapper function
    return wrapper

We can then apply this decorator to the `square` function using the `@` syntax:

# Define a decorator function called log_square
def log_square(func):
    # Define a wrapper function that takes in any number of arguments
    def wrapper(*args, **kwargs):
        # Print a message before executing the function
        print("Executing function...")
        # Call the original function with the given arguments
        result = func(*args, **kwargs)
        # Print the result of the function
        print("Result:", result)
        # Return the result
        return result
    # Return the wrapper function
    return wrapper

# Define the square function
def square(x):
    # Calculate the square of the given number
    result = x ** 2
    # Return the result
    return result

# Apply the log_square decorator to the square function using the @ syntax
square = log_square(square)

# Call the square function with the argument 5
square(5)

# Output:
# Executing function...
# Result: 25

# Explanation:
# The log_square decorator function takes in a function as an argument and returns a wrapper function.
# The wrapper function takes in any number of arguments and executes the original function with those arguments.
# Before executing the function, a message is printed and after the function is executed, the result is printed.
# The wrapper function then returns the result.
# The square function is defined and the log_square decorator is applied to it using the @ syntax.
# This means that whenever the square function is called, it will first go through the log_square decorator and then execute the original function.
# Finally, the square function is called with the argument 5 and the result is printed.

Now, whenever we call the `square` function with an input value, it will log both the input and output values. This is just one example of how decorators can be used to add functionality at run time in Python without changing the original implementation of a function.

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