How to resolve conflict between Pylance type detection error and custom decorator?

Solutions to conflict between PyLance type check and custom decorator

In Python development, static type checking tools (such as PyLance) often generate type checking errors or warnings with custom decorators. This article will use an example to demonstrate how to resolve the type mismatch problem reported by PyLance in a custom decorator.

Question example:

The following code snippet shows a custom decorator execute and the decorated function query_data_source :

 def execute(func):
    def inner_wrapper(*args, **kwargs) -> result[any]: # The problem lies with session.begin() as session:
            result = session.execute(func(*args, **kwargs))
            return result

    return inner_wrapper


@execute
def query_data_source(
    start_id: int = 1, max_results_amount: int = 10
) -> select: # PyLance believes that the return type here is select
    stmt = (
        select(
            datasource.id,
            datasource.name,
            datasource.source_url,
            datasource.author,
            datasource.description,
            datasource.cover_image_url,
            datasource.start_date,
            datasource.end_date,
        )
        .where(datasource.id >= start_id)
        .limit(max_results_amount)
        .order_by(datasource.id)
    )
    return stmt

There is no problem with the code at runtime, but PyLance warns query_data_source 's return type mismatch, because it infers that the execute decorator changes the return type to result[any] .

Solution:

To solve this problem, we need to add more precise type hints to the decorator definition to clarify the return type of the decorating function. The modified code is as follows:

 from typing import Callable, Any

def execute(func: Callable[..., Result]) -> Callable[..., Result]: # Add type prompt def inner_wrapper(*args, **kwargs) -> Result[Any]:
        with Session.begin() as session:
            result = session.execute(func(*args, **kwargs))
            return result

    return inner_wrapper

By adding the Callable[..., Result] type prompt in the execute decorator, we tell PyLance to return Result type. ... means that the number and type of parameters are uncertain, and Result assumes that it is a custom type or an imported type. This resolves PyLance's type checking error and ensures that the type information is accurate.

This method is suitable for type checking issues with many custom decorators. By adding appropriate type prompts, you can work effectively with type checking tools to improve code quality and maintainability. Remember to replace Result and session with the actual types and objects used in your code based on your actual situation.

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