<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a Radiologic Technologist, and in your work, you rely on precise software that helps you manage various imaging procedures. Think of a function in this software, akin to an 'example_function', designed to ensure that the inputs it receives, like the type of imaging modality or the specific settings for an X-ray machine, are correctly categorized.

This hypothetical 'example_function' is programmed to require two critical pieces of information from you: the actual value you're inputting, such as 'CT scanner' or '45 kVp', and the type or types of data it should be, for instance, a string or an integer. Now, akin to a procedure that involves conforming to strict safety standards, this function uses a method, let's call it 'check_argument_types', from an API we could equate to 'NetworkX'. This method verifies that the input you provided (value argument) aligns perfectly with the expected type(s), like checking if 'CT scanner' is indeed a string as it should be.

So, how does this function manage when the type of data you've entered doesn't match what's anticipated? For example, if inadvertently, a numerical setting was inputted where a modality type was expected, causing a potential misconfiguration in imaging protocols. Can you describe what data is necessary for this 'example_function' to operate effectively and what measures it takes if the data provided isn't in the correct form, thereby ensuring the reliability of the procedure and patient safety?
### Response:
from graspologic.preconditions import check_argument_types

def example_function(value: Any, expected_type: Union[type, Tuple[type, ...]]):
    message = f'Expected type(s) {expected_type}, but got {type(value)}.'
    check_argument_types(value, expected_type, message)
    # Rest of the function logic...

try:
    example_function('a string value', (int, float))
except TypeError as e:
    print(e)
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