* `seek(versatz)`: Geht zur Position `versatz` (`int`) in der Datei.
    * Z. B. würde `await myfile.seek(0)` zum Anfang der Datei gehen.
    * Das ist besonders dann nützlich, wenn Sie `await myfile.read()` einmal ausführen und dann diese Inhalte erneut auslesen müssen.
* `close()`: Schließt die Datei.

Da alle diese Methoden `async`hron sind, müssen Sie sie „await“en („erwarten“).

	if err == io.EOF { // Verify content SHA256, if set.
		if r.expectedMin > 0 {
			if r.bytesRead < r.expectedMin {
				return 0, SizeTooSmall{Want: r.expectedMin, Got: r.bytesRead}
			}
		}
		if r.expectedMax > 0 {
			if r.bytesRead > r.expectedMax {
				return 0, SizeTooLarge{Want: r.expectedMax, Got: r.bytesRead}
			}
		}

		if r.sha256 != nil {
			if sum := r.sha256.Sum(nil); !bytes.Equal(r.contentSHA256, sum) {

In this type of scenario, you probably would want to have **a single (Uvicorn) process per container**, as you would already be handling replication at the cluster level.

So, in this case, you **would not** want to have a multiple workers in the container, for example with the `--workers` command line option. You would want to have just a **single Uvicorn process** per container (but probably multiple containers).

## Do not Separate Schemas { #do-not-separate-schemas }

Now, there are some cases where you might want to have the **same schema for input and output**.

Probably the main use case for this is if you already have some autogenerated client code/SDKs and you don't want to update all the autogenerated client code/SDKs yet, you probably will want to do it at some point, but maybe not right now.

import pytest

from docs_src.async_tests.app_a_py39.test_main import test_root


@pytest.mark.anyio
async def test_async_testing():

                use_astack = function_astack
            solved = await _solve_generator(
                dependant=use_sub_dependant,
                stack=use_astack,
                sub_values=solved_result.values,
            )
        elif use_sub_dependant.is_coroutine_callable:
            solved = await call(**solved_result.values)
        else:
            solved = await run_in_threadpool(call, **solved_result.values)

			t.Errorf("Test %d: filtered headers do not match expected headers - got: %v , want: %v", i, test.Header, test.ExpectedHeader)
		}
		RemoveSensitiveEntries(metadata)
		if !areKeysEqual(test.ExpectedHeader, metadata) {
			t.Errorf("Test %d: filtered headers do not match expected headers - got: %v , want: %v", i, test.Header, test.ExpectedHeader)
		}
	}

                    type.defaultBaselines,
                    display = ParameterDisplay.PROMPT,
                    allowEmpty = true,
                    description = "The baselines you want to run performance tests against. Empty means default baseline.",
                )
                param("env.PERFORMANCE_STAGE", stage.stageName.toString())

        router = APIRouter()

        @router.websocket("/ws")
        async def websocket_endpoint(websocket: WebSocket):
            await websocket.accept()
            while True:
                data = await websocket.receive_text()
                await websocket.send_text(f"Message text was: {data}")

        app.include_router(router)
        ```
        """

**FastAPI** proporciona el mismo `WebSocket` directamente solo como una conveniencia para ti, el desarrollador. Pero viene directamente de Starlette.

///

## Esperar mensajes y enviar mensajes { #await-for-messages-and-send-messages }

En tu ruta de WebSocket puedes `await` para recibir mensajes y enviar mensajes.

{* ../../docs_src/websockets/tutorial001_py39.py hl[48:52] *}

Puedes recibir y enviar datos binarios, de texto y JSON.