This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

So, you might still need to use Pydantic models.

But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

///

 * }
 *
 * <p>Graph algorithms that need additional capabilities (accessing both predecessors and
 * successors, iterating over the edges, etc.) should declare their input to be of a type that
 * provides those capabilities, such as {@link Graph}, {@link ValueGraph}, or {@link Network}.
 *
 * <h3>Additional documentation</h3>
 *
 * <p>See the Guava User Guide for the {@code common.graph} package (<a

 * }
 *
 * <p>Graph algorithms that need additional capabilities (accessing both predecessors and
 * successors, iterating over the edges, etc.) should declare their input to be of a type that
 * provides those capabilities, such as {@link Graph}, {@link ValueGraph}, or {@link Network}.
 *
 * <h3>Additional documentation</h3>
 *
 * <p>See the Guava User Guide for the {@code common.graph} package (<a

import java.util.concurrent.Executor;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

/**
 * Base class for services that do not need a thread while "running" but may need one during startup
 * and shutdown. Subclasses can implement {@link #startUp} and {@link #shutDown} methods, each which
 * run in an executor which by default uses a separate thread for each method.
 *

However, this does not fully remove the prerequisite, as the Wrapper itself would still need an installed Java runtime to execute.

Gradle needs a solution that covers the use-case of starting the Wrapper itself as well given its importance.

### Running Gradle on multiple Java versions

  //  - Some providers may choose to also include alias names.
  //  - For example, the "SHA-1" algorithm might be referred to as "SHA1".
  //  - The algorithm name is not case-sensitive.
  @SuppressWarnings("deprecation") // We still need to test our deprecated APIs.
  private static final ImmutableMap<String, HashFunction> ALGORITHMS =
      new ImmutableMap.Builder<String, HashFunction>()
          .put("MD5", md5())

      private fun evictToRecoverBytes(bytesToRecover: Int): Int {
        var bytesToRecover = bytesToRecover
        var entriesToEvict = 0
        if (bytesToRecover > 0) {
          // determine how many headers need to be evicted.
          var j = dynamicTable.size - 1
          while (j >= nextHeaderIndex && bytesToRecover > 0) {
            val toEvict = dynamicTable[j]!!
            bytesToRecover -= toEvict.hpackSize

You could also stream **video** or **audio** this way, it could even be generated as you process and send it.

## A `StreamingResponse` with `yield` { #a-streamingresponse-with-yield }

If you declare a `response_class=StreamingResponse` in your *path operation function*, you can use `yield` to send each chunk of data in turn.

{* ../../docs_src/stream_data/tutorial001_py310.py ln[1:23] hl[20,23] *}

def get_query(background_tasks: BackgroundTasks, q: str | None = None):
    if q:
        message = f"found query: {q}\n"
        background_tasks.add_task(write_log, message)
    return q


@app.post("/send-notification/{email}")
async def send_notification(
    email: str, background_tasks: BackgroundTasks, q: Annotated[str, Depends(get_query)]
):
    message = f"message to {email}\n"

from tests.utils import workdir_lock

client = TestClient(app)


@workdir_lock
def test():
    log = Path("log.txt")
    if log.is_file():
        os.remove(log)  # pragma: no cover
    response = client.post("/send-notification/******@****.***")
    assert response.status_code == 200, response.text
    assert response.json() == {"message": "Notification sent in the background"}
    with open("./log.txt") as f: