Before anything else, what a “limited API” actually means. In programming terms, it refers to a set of functions or features that are available for use in a specific context or environment. For example, when you run Python on your computer, the standard library provides access to a wide range of built-in modules and functions. However, if you’re working with a web framework like Flask or Django, there may be certain limitations or restrictions that apply to what you can do within that context.

So without further ado, let’s take a look at some of the new functions added to Python 3.12 Limited API! ( `flask_request.get_json(strict=True)` This function allows you to retrieve JSON data from an HTTP request, but with a twist! If the input is not valid JSON or if it contains unexpected keys, this function will raise a TypeError instead of silently ignoring them like before. Because who needs error handling when you can just crash your app and confuse everyone involved?

2. `django_template.render(context)` This function allows you to render a Django template with some context data, but with a catch! If the template contains any syntax errors or if it’s missing certain variables, this function will raise an AttributeError instead of gracefully handling the error and providing helpful feedback to the user. Because who needs clear communication when you can just throw exceptions at people?

3. `sqlalchemy_session.query(Model)` This function allows you to query a SQLAlchemy database for some data, but with a twist! If there are any issues with your query or if it returns unexpected results, this function will raise an IntegrityError instead of providing helpful feedback and allowing you to debug the issue. Because who needs debugging when you can just crash your app and confuse everyone involved?

4. `pandas_read_csv(filepath)` This function allows you to read a CSV file using Pandas, but with a catch! If there are any issues with the format of the data or if it contains unexpected values, this function will raise an IndexError instead of providing helpful feedback and allowing you to debug the issue. Because who needs debugging when you can just crash your app and confuse everyone involved?

5. `numpy_array(data)` This function allows you to create a NumPy array from some data, but with a twist! If there are any issues with the shape or size of the input data, this function will raise an OverflowError instead of providing helpful feedback and allowing you to debug the issue. Because who needs debugging when you can just crash your app and confuse everyone involved?

In all seriousness though, these examples were meant to be humorous and not reflective of actual Python functions or best practices. In reality, error handling is a critical part of any programming language and should always be taken seriously!