Python contextlib for Timing Python code

If you've ever found yourself needing to measure the execution time of specific portions of your Python code, the `contextlib` module offers an elegant solution. In particular, we can use it to create a timer that functions as a context manager, simplifying the process of tracking performance without cluttering your code with timing logic. 

Consider the following example of a timer implemented as a context manager: 

from contextlib import contextmanager
import time

@contextmanager
def timer(label):
    start = time.time()
    try:
        yield
    finally:
        end = time.time()
        print(f'{label}: {end - start} seconds')

# Usage:
with timer('Model training'):
    # Your code block here, e.g., train your model

In this example, the `timer` function is decorated with `@contextmanager`, transforming it into a context manager. When you use this timer within a `with` statement, it automatically measures the time taken to execute the enclosed code block. 

This feature proves exceptionally useful for profiling the performance of various parts of your codebase. For instance, in the context of machine learning, you can easily measure the duration of model training or inference without inserting cumbersome timing statements throughout your code. 

By adopting Python's context manager and the `contextlib` module, you can streamline the process of performance profiling, making your code more readable and focused on its primary logic. Try incorporating this timer into your projects to gain valuable insights into the execution time of critical code segments.