Advanced Python Concepts

Multithreading and Multiprocessing in Python

In Python, multithreading and multiprocessing are two ways to achieve concurrency, which is the ability to handle multiple tasks at the same time. While both can run multiple tasks, they do so in fundamentally different ways. The key difference lies in how they handle processes and threads.

• Multithreading: In Python, multithreading involves a single process with multiple threads. Threads within the same process share the same memory space. This makes communication between threads very fast and efficient. However, Python's Global Interpreter Lock (GIL) restricts the execution of only one thread at a time on a single CPU core. This means multithreading is great for I/O-bound tasks ( network requests or file reads) but not for CPU-bound tasks (tasks that require heavy computation).

• Multiprocessing: Multiprocessing, on the other hand, involves multiple independent processes, each with its own memory space and its own Python interpreter. Because they are separate processes, they can run on different CPU cores simultaneously, bypassing the GIL. This makes multiprocessing ideal for CPU-bound tasks that can be split into independent sub-tasks, as it can leverage multiple CPU cores to speed up execution.

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Example: Multithreading vs. Multiprocessing

Let's use a simple example to illustrate the difference.

• Multithreading Example (I/O-bound): A program that downloads multiple files from the internet. While one thread is waiting for a file to download, another can start downloading another file. This doesn't involve heavy computation, so multithreading is a good fit. The program's overall execution time is reduced because it's not waiting for one download to finish before starting the next.

• Multiprocessing Example (CPU-bound): A program that performs complex mathematical calculations on a large dataset. By using multiprocessing, you can split the dataset and have a separate process perform calculations on each part. This allows the program to utilize multiple CPU cores, dramatically speeding up the total calculation time.

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Synchronous vs. Asynchronous Programming

Synchronous programming is the default, sequential way of executing code. Each task must wait for the previous one to complete before it can begin. It's like a single-lane road where cars must follow one after another. If a task is slow, the entire program is blocked, and other tasks cannot proceed. This is simple and predictable but can be inefficient for tasks that involve waiting.

Asynchronous programming is a non-blocking approach that allows a program to initiate a task and then move on to other tasks without waiting for the first one to complete. It's like a chef taking an order, starting the dish, and then starting the next dish while the first one is cooking. When the first dish is ready, the chef can return to it. In Python, this is often implemented using the asyncio library, which uses an event loop to manage and schedule tasks.

Example: Sync vs. Async

Let's consider a web scraper that needs to fetch data from multiple websites.

• Synchronous Example: The program fetches data from website A. It waits for the download to complete. Then it fetches data from website B. It waits for that download to complete, and so on. This process is sequential and can be very slow if one of the websites is slow to respond.

• Asynchronous Example: The program initiates a request to website A, and while it's waiting for the response, it immediately sends a request to website B. It continues this process for all the websites. When a response from any of the websites arrives, the program can handle it. This approach is much more efficient because it uses the "waiting time" to do other work, allowing the program to fetch data from multiple sources concurrently without blocking.

Event loop in Python

In Python's asyncio, the event loop manages a single queue of tasks (coroutines) and callbacks. When a coroutine "awaits" an I/O operation (e.g., a network request), it yields control back to the event loop. The event loop then checks for completed I/O events and moves on to the next available task in its queue. The concept of "microtasks" is implicitly handled. When a task completes and schedules a callback (like the then part of an await operation), that callback is added directly to the event loop's queue and will be processed very soon.

• There isn't a separate, higher-priority "micro" queue that gets fully drained before the main queue is checked again; instead, the asyncio loop is designed to be highly responsive to events and resume tasks as soon as they become ready.

Where Python's Event Loop is Located

Unlike JavaScript's event loop, which is an integral part of the browser or Node.js runtime, Python's event loop is part of the asyncio library itself. It's a key component of the asyncio module and is run as a single-threaded process within your Python application. When you use asyncio.run(), a new event loop is created, it runs all the tasks you've scheduled, and then it shuts down when they are complete.

The event loop is essentially a while True loop that continuously monitors and dispatches events. It's a central hub that:

1. Checks if any coroutines are ready to resume execution.
2. Handles completed I/O operations (e.g., from network sockets).
3. Manages scheduled callbacks and timers.

By default, there's only one event loop per thread, and it's generally recommended to run all your asyncio code within that one event loop in the main thread. To handle CPU-bound tasks without blocking the event loop, you would typically offload them to a separate thread or process using methods like loop.run_in_executor().

simple code examples to illustrate these concepts.

Multithreading vs. Multiprocessing

Multithreading is best for I/O-bound tasks (like downloading files or network requests) where the program spends most of its time waiting. The Global Interpreter Lock (GIL) limits multithreading to a single CPU core.

import threading
import time

def task(name):
    print(f"Thread {name}: Starting...")
    time.sleep(2)  # Simulate an I/O-bound task (e.g., waiting for a network response)
    print(f"Thread {name}: Finishing.")

threads = []
for i in range(3):
    t = threading.Thread(target=task, args=(i,))
    threads.append(t)
    t.start()

for t in threads:
    t.join() # Wait for all threads to complete

print("All threads have finished.")

In this example, three threads run concurrently. While one thread is sleeping (waiting), another can start, which is faster than running them sequentially.

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Multiprocessing is ideal for CPU-bound tasks (like heavy mathematical calculations) because it bypasses the GIL by using separate processes, each with its own interpreter.

import multiprocessing
import time

def task(name):
    print(f"Process {name}: Starting...")
    time.sleep(2) # Simulate a CPU-bound task (e.g., complex calculation)
    print(f"Process {name}: Finishing.")

if __name__ == "__main__":
    processes = []
    for i in range(3):
        p = multiprocessing.Process(target=task, args=(i,))
        processes.append(p)
        p.start()

    for p in processes:
        p.join() # Wait for all processes to complete

    print("All processes have finished.")

Here, three independent processes are created. They can run on different CPU cores simultaneously, offering a true speed-up for CPU-intensive work.

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Synchronous vs. Asynchronous Programming

Synchronous programming is sequential and blocking. Each task must wait for the previous one to complete.

import time

def fetch_data(url):
    print(f"Fetching data from {url}...")
    time.sleep(2) # Simulate a network request
    print(f"Finished fetching data from {url}.")
    return "Data from " + url

start_time = time.time()
data1 = fetch_data("website A")
data2 = fetch_data("website B")
data3 = fetch_data("website C")

print(f"All data fetched in {time.time() - start_time:.2f} seconds.")

The total time for this script to run will be approximately 6 seconds (2 seconds for each function call) because each call blocks the program until it finishes.

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Asynchronous programming is non-blocking and event-driven. It allows the program to switch to other tasks while waiting for a long-running operation to complete. This is typically done with Python's asyncio library.

import asyncio
import time

async def fetch_data(url):
    print(f"Fetching data from {url}...")
    await asyncio.sleep(2) # Simulate a network request
    print(f"Finished fetching data from {url}.")
    return "Data from " + url

async def main():
    start_time = time.time()

    tasks = [
        fetch_data("website A"),
        fetch_data("website B"),
        fetch_data("website C")
    ]

    await asyncio.gather(*tasks) # Run tasks concurrently

    print(f"All data fetched in {time.time() - start_time:.2f} seconds.")

if __name__ == "__main__":
    asyncio.run(main())

In this asyncio example, the await asyncio.sleep(2) tells the program to pause this specific task but not block the entire program. Instead, the event loop can switch to another task. The total execution time will be around 2 seconds, as all three tasks are initiated and run concurrently. This is highly efficient for I/O-bound operations.

• The primary benefit of asyncio is in managing concurrency, specifically for I/O-bound tasks. If you were making a hundred API calls, using asyncio would allow you to initiate all the requests concurrently, saving a significant amount of time by not waiting for each one to finish before starting the next. For a single call, there's no concurrency to manage, so the overhead of setting up an async function and an event loop provides no performance benefit.

Here’s a comparison:

Synchronous Approach (Recommended for a Single Call)

This approach is straightforward and easy to read. The code executes one line at a time.

import requests

def get_data_sync(url):
    response = requests.get(url)
    return response.json()

data = get_data_sync("https://api.example.com/data/1")
print(data)

Asynchronous Approach (Unnecessary for a Single Call)

While you can write a function with async and await, it's overkill for a single call. You need to use an async library like aiohttp and run the function within an event loop.

import asyncio
import aiohttp

async def get_data_async(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.json()

async def main():
    data = await get_data_async("https://api.example.com/data/1")
    print(data)

if __name__ == "__main__":
    asyncio.run(main())

You don't put async and await in the get_data_sync function because it's a synchronous function, not an asynchronous one. It's using the requests library, which is a synchronous library.

Here's why:

• requests library is synchronous: The requests.get() function is designed to be blocking. When you call it, your program stops and waits for the entire HTTP request to complete (sending the request, waiting for the server's response, and receiving the data) before it proceeds to the next line of code.
• async and await require an asyncio ecosystem: The keywords async and await are part of Python's asyncio framework. They are used to define and manage coroutines, which are functions designed for non-blocking, asynchronous I/O operations. For these keywords to work, the function must be run within an event loop and use an asynchronous library like aiohttp that is compatible with asyncio.

Since your function uses requests, which is a synchronous library and doesn't communicate with an event loop, adding async and await would be incorrect and would lead to syntax errors or unexpected behavior.

Similarity between multithreading and asyncio in Python

Shared Goal: Concurrency in I/O-Bound Tasks

Both multithreading and asyncio are excellent for I/O-bound tasks. This is any task that spends most of its time waiting for an external operation to complete, like:

• Making network requests: Waiting for a web server to respond.
• Reading/writing files: Waiting for data to be read from or written to a disk.
• Database queries: Waiting for a database to execute a query.

In these scenarios, the CPU is largely idle. Both multithreading and asyncio prevent your program from freezing while it waits, allowing it to work on other tasks in the meantime.

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The Fundamental Difference: How They Handle Concurrency

The similarity ends at their core mechanism.

• Multithreading uses preemptive multitasking. The operating system (OS) and the Python interpreter decide when to switch between threads. The threads themselves don't give up control voluntarily. This is what makes it "preemptive"—the OS preempts a running thread to give another one a turn. Because of Python's Global Interpreter Lock (GIL), only one thread can execute Python bytecode at a time, so it's not true parallelism but a form of interleaved concurrency.

• asyncio uses cooperative multitasking. The tasks themselves (coroutines) voluntarily give up control to the event loop when they encounter an await keyword. The event loop is a single-threaded loop that checks for completed tasks and schedules the next one. This is what makes it "cooperative"—each task must cooperate by yielding control. Because it's single-threaded, it doesn't face the GIL limitations that multithreading does.

Metaprogramming in Python

• It is the creation of programs that write or manipulate other programs. In essence, it's about making code that can inspect, generate, or modify itself at runtime. It’s a powerful technique often used to reduce code duplication, create flexible APIs, and build frameworks.

Key Concepts

Metaprogramming in Python is primarily achieved through:

• Decorators: Functions that wrap other functions or classes to extend their behavior without permanent modification. They're a form of syntactic sugar for wrapping a callable.
• Class Decorators: Similar to function decorators, but they modify the behavior of a class.
• Metaclasses: The most advanced form of metaprogramming. A metaclass is the "class of a class." When you define a class, its metaclass is responsible for creating it. By creating a custom metaclass, you can control how classes are defined and how they behave.

Decorators in Python

A decorator in Python is a design pattern that allows you to dynamically extend or modify the behavior of a function or class without changing its source code. It's essentially a callable (like a function) that takes another callable as an argument and returns a new callable.

Decorators use a special syntax with the @ symbol, which is just syntactic sugar for a more verbose function call.

# This:
@my_decorator
def my_function():
    # ...

# Is equivalent to this:
def my_function():
    # ...

my_function = my_decorator(my_function)

The core idea is to "wrap" a function or class to add new functionality before or after the original code runs.

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Function Decorators

A function decorator is a function that takes another function as an argument, adds some new functionality, and returns the modified function. This is most commonly used for tasks like logging, timing, authentication, or validation.

Example: A Simple Timer Decorator

This decorator measures how long a function takes to execute.

import time

def timing_decorator(func):
    """A decorator that prints the execution time of a function."""
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        print(f"'{func.__name__}' ran in {end_time - start_time:.4f} seconds.")
        return result
    return wrapper

@timing_decorator
def complex_calculation(n):
    """Simulates a complex calculation."""
    sum_val = 0
    for i in range(n):
        sum_val += i
    return sum_val

# Calling the decorated function
result = complex_calculation(10000000)
print(f"Calculation result: {result}")

In this example, timing_decorator is the decorator function. It defines a new function wrapper that encapsulates the original complex_calculation function. When you call complex_calculation(10000000), you are actually calling the wrapper function. The wrapper executes its own timing logic, calls the original function, and then prints the elapsed time before returning the result.

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Class Decorators

A class decorator is a callable that takes a class object as an argument and returns a new class object. Class decorators are used to modify or extend the behavior of an entire class. Common use cases include enforcing a class structure, adding attributes to all instances of a class, or automatically registering a class with a registry.

Example: A Class Decorator for Enforcing an Interface

This decorator checks if a class implements certain methods and raises an error if it doesn't.

def enforce_interface(cls):
    """A class decorator to ensure 'start' and 'stop' methods exist."""
    if not hasattr(cls, 'start') or not callable(getattr(cls, 'start')):
        raise TypeError(f"Class '{cls.__name__}' must have a 'start' method.")
    if not hasattr(cls, 'stop') or not callable(getattr(cls, 'stop')):
        raise TypeError(f"Class '{cls.__name__}' must have a 'stop' method.")
    return cls

@enforce_interface
class Car:
    def __init__(self, model):
        self.model = model

    def start(self):
        print(f"Starting the {self.model}.")

    def stop(self):
        print(f"Stopping the {self.model}.")

# This class will raise a TypeError when it's defined because it lacks a 'stop' method
try:
    @enforce_interface
    class Motorcycle:
        def __init__(self, model):
            self.model = model

        def start(self):
            print(f"Starting the {self.model}.")
except TypeError as e:
    print(f"\nCaught an error: {e}")

In this example, @enforce_interface is the class decorator. When the Car class is defined, the enforce_interface function is called with Car as an argument. The decorator checks for the required methods and returns the class. When Motorcycle is defined, the check fails, and the TypeError is raised immediately, providing static analysis at runtime.

Python Internals: A Deeper Look

Python is an interpreted, high-level, and dynamically typed language. Its inner workings, often referred to as "Python Internals," involve several key components that manage how your code is executed, including the CPython interpreter, the compilation process, memory management, and how core data types are handled. Understanding these can help you write more efficient and robust code.

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The CPython Interpreter

When people talk about Python, they are usually referring to CPython, which is the reference implementation of the language written in C. It's the most widely used interpreter and is what you get when you download Python from python.org. Its main job is to take your Python source code and translate it into a language the computer can understand.

The execution of a Python script by the CPython interpreter follows these general steps:

1. Lexing and Parsing: The interpreter's front end reads your .py file. It first tokenizes the source code into a stream of tokens (lexing), such as keywords, identifiers, and operators. These tokens are then structured into an Abstract Syntax Tree (AST) , which represents the code's grammatical structure.
2. Compilation to Bytecode: The AST is then compiled into Python bytecode. Bytecode is a low-level, platform-independent set of instructions. It's not machine code but is a set of instructions for the Python Virtual Machine (PVM). You can see this bytecode using the dis module in Python.
3. Execution by the Python Virtual Machine (PVM): The PVM is the runtime engine of the CPython interpreter. It's a loop that reads and executes the bytecode instructions one by one. The PVM is also responsible for managing the call stack, memory, and objects.

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The Global Interpreter Lock (GIL)

The Global Interpreter Lock (GIL) is one of the most talked-about aspects of CPython. It's a mutex (a lock) that protects access to Python objects, ensuring that only one thread can execute Python bytecode at a time. This simplifies memory management and prevents race conditions but means that multithreading in CPython cannot achieve true parallelism on multi-core processors for CPU-bound tasks.

• Impact on I/O-Bound Tasks: The GIL is released during I/O operations (e.g., waiting for network data or disk reads). This allows other threads to run while the first is blocked, making multithreading still very effective for I/O-bound tasks.
• Impact on CPU-Bound Tasks: For tasks that require heavy computation, the GIL prevents multiple threads from running simultaneously, negating the benefits of multi-core CPUs. In these cases, multiprocessing is the preferred approach, as each process has its own GIL.

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Memory Management

Python's memory management is handled automatically. It uses a private heap to store objects and data structures. This heap is managed by the Python memory manager.

• Reference Counting: The primary memory management strategy is reference counting. Each object has a counter that tracks the number of references pointing to it. When the reference count drops to zero, the object is deallocated, and its memory is returned to the heap.
• Garbage Collection: Reference counting alone cannot handle circular references, where objects refer to each other but are no longer accessible from the main program. To solve this, Python's garbage collector periodically scans for these cycles and cleans up the orphaned objects.

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Python Objects and Core Data Types

Everything in Python is an object, including integers, strings, functions, and even types themselves. Each object has:

• PyObject_HEAD: A header that contains the reference count and a pointer to the object's type.
• Type Pointer: A pointer to the object's type. For example, an integer object's type pointer would point to the PyInt_Type object.
• Value: The actual data stored in the object.

Due to this object-oriented nature, even simple operations can be more complex than in low-level languages. For example, when you reassign a variable, you aren't changing the value in memory but rather changing the reference to point to a new object.

When you run a .py file

When you run a .py file, Python doesn't directly compile it into machine code like C or C++. Instead, it follows a multi-step process. Here is a step-by-step breakdown of how a Python program is compiled and how memory is allocated.

Step 1: Lexing and Parsing (The Frontend)

The CPython interpreter first reads your .py source file.

1. Lexing: The source code is broken down into a series of small, meaningful units called tokens. For example, a line of code like x = 10 + y would be broken into tokens for x, =, 10, +, and y.
2. Parsing: These tokens are then structured into an Abstract Syntax Tree (AST). The AST is a hierarchical tree representation of your code that reflects its grammatical structure and is easier for the interpreter to work with.

Step 2: Compilation to Bytecode

The AST is then passed to the compiler, which translates it into Python bytecode.

• Bytecode is a low-level, platform-independent set of instructions. It's not machine code, but rather an instruction set for the Python Virtual Machine (PVM).
• The .py file is not a binary executable; it's the source code. The compiled bytecode is usually saved in a .pyc file (Python compiled file) or the __pycache__ directory. This is done to speed up future executions of the same file, as the compilation step can be skipped.

Step 3: Execution by the Python Virtual Machine (PVM)

The PVM is the runtime engine of the Python interpreter. It is a loop that reads and executes the bytecode instructions one by one.

• The PVM manages the program's call stack, which keeps track of active function calls.
• It is also responsible for managing the program's memory.

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Step 4: Memory Allocation and Object Creation

Python manages memory automatically using a private heap. All Python objects, including integers, strings, lists, and functions, are stored in this heap. When a variable is assigned to an object, two things happen:

1. Object Creation: An object is created in memory (on the heap) to store the data. The object's memory is allocated by the Python memory manager.
2. Reference Creation: A variable name is created in the current scope. This variable is a reference (a pointer) that points to the object in the heap.

Example:
When you run x = 10, the following happens:

• The PVM sees the instruction to create an integer object with the value 10.
• The memory manager finds a space in the heap and allocates memory for a new integer object.
• A reference named x is created in the current scope, and it's set to point to the newly created object.

If you then run y = x, a new object is not created. Instead, a new reference y is created, and it also points to the same integer object 10.

Memory Deallocation

Python's memory management also handles deallocation automatically, primarily through reference counting.

• Every object has a reference count that tracks how many variables are pointing to it.
• When the reference count of an object drops to zero (e.g., when a variable goes out of scope or is reassigned), the memory manager deallocates the object and returns the memory to the heap.
• Python also has a garbage collector to handle circular references (e.g., two objects that point to each other) that reference counting alone can't detect.

Sure, Ashutosh! Let's dive deep into context managers and generators in Python — two powerful features that help write clean, efficient, and readable code.

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🧠 1. Context Managers

🔍 What is a Context Manager?

A context manager is a Python construct that allows you to allocate and release resources precisely when you want. The most common use case is with file operations, where you want to ensure a file is closed after you're done with it.

✅ Benefits:

• Automatic resource management
• Cleaner syntax using with statement
• Exception-safe

🔧 Built-in Example:

with open('example.txt', 'r') as file:
    content = file.read()
print(content)  # File is automatically closed after the block

🛠 Custom Context Manager using __enter__ and __exit__:

class MyContext:
    def __enter__(self):
        print("Entering context")
        return "Resource"

    def __exit__(self, exc_type, exc_value, traceback):
        print("Exiting context")

# Usage
with MyContext() as resource:
    print(f"Using {resource}")

🧹 Cleaner Custom Context Manager using contextlib:

from contextlib import contextmanager

@contextmanager
def managed_resource():
    print("Setup")
    yield "Resource"
    print("Cleanup")

with managed_resource() as res:
    print(f"Using {res}")

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🔁 2. Generators

🔍 What is a Generator?

A generator is a function that returns an iterator and allows you to iterate over data without storing the entire sequence in memory. It uses the yield keyword.

✅ Benefits:

• Memory-efficient
• Lazy evaluation (generates values on the fly)
• Useful for large datasets or streams

🔧 Simple Generator Example:

def count_up_to(max):
    count = 1
    while count <= max:
        yield count
        count += 1

for num in count_up_to(5):
    print(num)

🧠 Generator Expression (like list comprehension):

squares = (x*x for x in range(5))
for s in squares:
    print(s)

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🔗 Combining Context Managers and Generators

You can use generators inside context managers or vice versa. For example, reading large files line-by-line:

def read_large_file(file_name):
    with open(file_name, 'r') as f:
        for line in f:
            yield line.strip()

for line in read_large_file('bigfile.txt'):
    print(line)

The with keyword in Python is specifically tied to context management. It is not a general-purpose OOP feature like class, def, or self. You can only use with on objects that implement the context management protocol, i.e., they define:

• __enter__(self)
• __exit__(self, exc_type, exc_value, traceback)

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🔍 What Does with Actually Do?

When you write:

with managed_resource() as res:
    # do something with res

Python does the following under the hood:

1. Calls managed_resource().__enter__() and assigns its return value to res.
2. Executes the block under the with statement.
3. When the block finishes (even if an exception occurs), it calls __exit__() to clean up.

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🧪 Can You Use with on Any Class?

Not directly. You must implement the context manager protocol in your class. Here's an example:

class MyClass:
    def __enter__(self):
        print("Entering context")
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        print("Exiting context")

    def do_something(self):
        print("Doing something")

# Now you can use it with `with`
with MyClass() as obj:
    obj.do_something()

If your class doesn't implement __enter__ and __exit__, using with will raise a TypeError.

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🧹 Shortcut: contextlib.contextmanager

If you don’t want to write a full class, you can use the @contextmanager decorator from contextlib to turn a generator function into a context manager:

from contextlib import contextmanager

@contextmanager
def simple_context():
    print("Setup")
    yield "Resource"
    print("Cleanup")

with simple_context() as res:
    print(f"Using {res}")

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