It is quite an extensive specification and covers several complex use cases.

It includes ways to authenticate using a "third party".

That's what all the systems with "login with Facebook, Google, X (Twitter), GitHub" use underneath.

### OAuth 1 { #oauth-1 }

There was an OAuth 1, which is very different from OAuth2, and more complex, as it included direct specifications on how to encrypt the communication.

But there are situations where you might need to access the `Request` object directly.

## Details about the `Request` object { #details-about-the-request-object }

As **FastAPI** is actually **Starlette** underneath, with a layer of several tools on top, you can use Starlette's <a href="https://www.starlette.dev/requests/" class="external-link" target="_blank">`Request`</a> object directly when you need to.

        // The actual user home dir will be a subfolder using the name of the distribution.
        gradleUserHomeDir = intTestHomeDir
        // The user home dir is not wiped out by clean. Move the daemon working space underneath the build dir so they don't pile up on CI.
        // The actual daemon registry dir will be a subfolder using the name of the distribution.
        daemonRegistry = repoRoot().dir("build/daemon")

## HTTPX { #httpx }

Even if your **FastAPI** application uses normal `def` functions instead of `async def`, it is still an `async` application underneath.

func unwrapErrs(err error) (leafErr error) {
	uerr := errors.Unwrap(err)
	depth := 1
	for uerr != nil {
		// Save the current `uerr`
		leafErr = uerr
		// continue to look for leaf errors underneath
		uerr = errors.Unwrap(leafErr)
		depth++
		if depth == unwrapErrsDepth {
			// If we have reached enough depth we
			// do not further recurse down, this
			// is done to avoid any unnecessary

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

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.

		return http.ErrServerClosed
	}
	srv.listenerMutex.Unlock()

	if atomic.AddUint32(&srv.inShutdown, 1) > 1 {
		// shutdown in progress
		return http.ErrServerClosed
	}

	// Close underneath HTTP listener.
	srv.listenerMutex.Lock()
	err := srv.listener.Close()
	srv.listenerMutex.Unlock()
	if err != nil {
		return err
	}

	// Wait for opened connection to be closed up to Shutdown timeout.

		return deadlineconn.New(result.conn).WithReadDeadline(listener.opts.IdleTimeout).WithWriteDeadline(listener.opts.IdleTimeout), result.err
	case <-listener.ctxDoneCh:
	}
	return nil, syscall.EINVAL
}

// Close - closes underneath all TCP listeners.
func (listener *httpListener) Close() (err error) {
	listener.ctxCanceler()

	for i := range listener.listeners {
		listener.listeners[i].Close()
	}

	return nil
}

				// been seen recently due to hardware issues
				// causes Reads() to go slower than Writes().
				//
				// Send such results anyways as this shall
				// expose a problem underneath.
				if totalPut > throughputHighestPut {
					throughputHighestResults = results
					throughputHighestPut = totalPut
					// let the client see lower value as well
					throughputHighestGet = totalGet
				}

		}
		return eErr
	}

	if err := target.send(eventData); err != nil {
		return err
	}

	// Delete the event from store.
	return target.store.Del(key)
}

// Close - closes underneath connections to NSQD server.
func (target *NSQTarget) Close() (err error) {
	close(target.quitCh)
	if target.producer != nil {
		// this blocks until complete:
		target.producer.Stop()
	}
	return nil
}