Decorator application / test case annotation

Attach sample input data or configuration to a function for testing or demonstration.

Writing unit tests that need sample inputs, creating demo functions with predefined data, labeling functions with example values for documentation.

Testing suites, educational code snippets, internal libraries that provide custom decorators for behavior-driven development.

@example(; )

@example(; )

decorator_argument: expression (commonly a list literal)

• def function_name():
• assert statements
• test frameworks

• @example([4
• 5
• 6])
• @example([])
• @example({"key": "value"})

Omitting parentheses, using a non-callable decorator, forgetting to define the `example` decorator, passing incompatible argument types.

@pytest.mark.parametrize (provides multiple test cases per argument) vs @example (single configuration); @staticmethod (no argument) vs decorator with arguments.

Coming from Java: expecting annotations like @Override that cannot take arguments; in Python decorators are functions that can accept parameters.

• decorator patterns
• test annotation patterns
• parameterized testing

The decorator must be defined in scope; if not, a NameError occurs. The argument is evaluated at decoration time, so avoid expensive computations. The decorated function receives the same signature unless the decorator modifies it.

Makes test intent explicit and centralizes example data, improving readability and maintainability.

Think of @example as labeling a function with its sample input.

The example decorator shown here is a simplified illustration; real-world test frameworks like pytest provide more sophisticated decorators such as @pytest.mark.parametrize for parameterized testing.

Using @example with multiple arguments or a class‑based decorator for more complex configuration.