{!> ../../../docs_src/query_params/tutorial002.py!}
    ```

In this case, the function parameter `q` will be optional, and will be `None` by default.

!!! check
    Also notice that **FastAPI** is smart enough to notice that the path parameter `item_id` is a path parameter and `q` is not, so, it's a query parameter.

## Query parameter type conversion

You can also declare `bool` types, and they will be converted:

            lifespan=lifespan,
        )
        if prefix:
            assert prefix.startswith("/"), "A path prefix must start with '/'"
            assert not prefix.endswith(
                "/"
            ), "A path prefix must not end with '/', as the routes will start with '/'"
        self.prefix = prefix
        self.tags: List[Union[str, Enum]] = tags or []
        self.dependencies = list(dependencies or [])

        app = FastAPI()


        @app.middleware("http")
        async def add_process_time_header(request: Request, call_next):
            start_time = time.time()
            response = await call_next(request)
            process_time = time.time() - start_time
            response.headers["X-Process-Time"] = str(process_time)
            return response
        ```
        """

```Python hl_lines="3  5-6"
{
    "name": "Baz",
    "description": None,
    "price": 50.2,
    "tax": 10.5,
    "tags": []
}
```

FastAPI is smart enough (actually, Pydantic is smart enough) to realize that, even though `description`, `tax`, and `tags` have the same values as the defaults, they were set explicitly (instead of taken from the defaults).

As you are sitting at the table with your crush, while you wait for the burgers, you can spend that time admiring how awesome, cute and smart your crush is ✨😍✨.

<img src="/img/async/concurrent-burgers/concurrent-burgers-05.png" class="illustration">

async def read_items(
    item_id: UUID,
    start_datetime: Annotated[datetime, Body()],
    end_datetime: Annotated[datetime, Body()],
    process_after: Annotated[timedelta, Body()],
    repeat_at: Annotated[time | None, Body()] = None,
):
    start_process = start_datetime + process_after
    duration = end_datetime - start_process
    return {
        "item_id": item_id,
        "start_datetime": start_datetime,

async def read_items(
    item_id: UUID,
    start_datetime: Annotated[datetime, Body()],
    end_datetime: Annotated[datetime, Body()],
    process_after: Annotated[timedelta, Body()],
    repeat_at: Annotated[Union[time, None], Body()] = None,
):
    start_process = start_datetime + process_after
    duration = end_datetime - start_process
    return {
        "item_id": item_id,
        "start_datetime": start_datetime,

## Use Case

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

    frontmatter_end = match_pre.end()
    pre_end = match_start.end()
    post_start = match_end.start()
    template = Template(index_sponsors_template)
    message = template.render(sponsors=sponsors)
    pre_content = content[frontmatter_end:pre_end]
    post_content = content[post_start:]
    new_content = pre_content + message + post_content
    return new_content

## Run the code

All the code blocks can be copied and used directly (they are actually tested Python files).

To run any of the examples, copy the code to a file `main.py`, and start `uvicorn` with:

<div class="termy">

```console
$ uvicorn main:app --reload

<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)