process_after: timedelta = Body(),
    repeat_at: Union[time, None] = Body(default=None),
):
    start_process = start_datetime + process_after
    duration = end_datetime - start_process
    return {
        "item_id": item_id,
        "start_datetime": start_datetime,
        "end_datetime": end_datetime,
        "process_after": process_after,
        "repeat_at": repeat_at,
        "start_process": start_process,

                            "type": "string",
                            "format": "date-time",
                        },
                        "repeat_at": IsDict(
                            {
                                "title": "Repeat At",
                                "anyOf": [
                                    {"type": "string", "format": "time"},
                                    {"type": "null"},

As Python advances, **newer versions** come with improved support for these type annotations and in many cases you won't even need to import and use the `typing` module to declare the type annotations.

If you can choose a more recent version of Python for your project, you will be able to take advantage of that extra simplicity.

In all the docs there are examples compatible with each version of Python (when there's a difference).

* Fix incorrect `Request` class import. PR [#493](https://github.com/tiangolo/fastapi/pull/493) by [@kamalgill](https://github.com/kamalgill).

## 0.38.0

* Add recent articles to [External Links](https://fastapi.tiangolo.com/external-links/) and recent opinions. PR [#490](https://github.com/tiangolo/fastapi/pull/490).

    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,
        "end_datetime": end_datetime,
        "process_after": process_after,
        "repeat_at": repeat_at,
        "start_process": start_process,

    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,
        "end_datetime": end_datetime,
        "process_after": process_after,
        "repeat_at": repeat_at,
        "start_process": start_process,

        use_cache=dependant.use_cache,
        path=dependant.path,
    )
    for sub_dependant in dependant.dependencies:
        if skip_repeats and sub_dependant.cache_key in visited:
            continue
        flat_sub = get_flat_dependant(
            sub_dependant, skip_repeats=skip_repeats, visited=visited
        )
        flat_dependant.path_params.extend(flat_sub.path_params)

```

A **context manager** in Python is something that you can use in a `with` statement, for example, `open()` can be used as a context manager:

```Python
with open("file.txt") as file:
    file.read()
```

In recent versions of Python, there's also an **async context manager**. You would use it with `async with`:

```Python
async with lifespan(app):
    await do_stuff()
```

                route=route, method=method, operation_ids=operation_ids
            )
            parameters: List[Dict[str, Any]] = []
            flat_dependant = get_flat_dependant(route.dependant, skip_repeats=True)
            security_definitions, operation_security = get_openapi_security_definitions(
                flat_dependant=flat_dependant
            )
            if operation_security:

                            "type": "string",
                            "format": "date-time",
                        },
                        "repeat_at": IsDict(
                            {
                                "title": "Repeat At",
                                "anyOf": [
                                    {"type": "string", "format": "time"},
                                    {"type": "null"},