* Stellen Sie sich einfach vor, dass `main:app` einer Python-`import`-Anweisung wie der folgenden entspricht:

        ```Python
        from main import app
        ```

    * Der Doppelpunkt in `main:app` entspricht also dem Python-`import`-Teil in `from main import app`.

* `--workers`: Die Anzahl der zu verwendenden Workerprozesse, jeder führt einen Uvicorn-Worker aus, in diesem Fall 4 Worker.

](python-types.md){.internal-link target=_blank}.

Вы пишете на стандартном Python с аннотациями типов:

```Python
from datetime import date

from pydantic import BaseModel

# Объявляем параметр user_id с типом `str`
# и получаем поддержку редактора внутри функции
def main(user_id: str):
    return user_id


# Модель Pydantic

		repairedPods: map[types.NamespacedName]types.UID{},
	}
	fieldSelectors := []string{}
	if cfg.FieldSelectors != "" {
		fieldSelectors = append(fieldSelectors, cfg.FieldSelectors)
	}
	// filter out pod events from different nodes
	fieldSelectors = append(fieldSelectors, fmt.Sprintf("spec.nodeName=%v", cfg.NodeName))
	c.pods = kclient.NewFiltered[*corev1.Pod](client, kclient.Filter{
		LabelSelector: cfg.LabelSelectors,

```

## Criando um `websocket`

Em sua aplicação **FastAPI**, crie um `websocket`:

{*../../docs_src/websockets/tutorial001.py hl[46:47]*}

/// note | Detalhes Técnicos

Você também poderia usar `from starlette.websockets import WebSocket`.

A **FastAPI** fornece o mesmo `WebSocket` diretamente apenas como uma conveniência para você, o desenvolvedor. Mas ele vem diretamente do Starlette.

///

> Object locking requires erasure coding enabled on the minio server. For more information see <https://min.io/docs/minio/linux/operations/concepts/erasure-coding.html>.

### Add MinIO as an object store for Veeam

Follow the steps from the Veeam documentation for adding MinIO as an object store - <https://helpcenter.veeam.com/docs/backup/vsphere/adding_s3c_object_storage.html?ver=100>

TFE_Op* IdentityOp(TFE_Context* ctx, TFE_TensorHandle* a);

// Return a shape op fetching the shape of `a`.
TFE_Op* ShapeOp(TFE_Context* ctx, TFE_TensorHandle* a);

// Return an allreduce op adding up input tensor `in` from `group_size` workers.
TFE_Op* AllReduceOp(TFE_Context* ctx, TFE_TensorHandle* in, int group_size);

// Return a SendOp op `op_name` with send input tensor `in` and attributes

        // Read capabilities declared in capabilities.json
        readCapabilitiesFromJson()

        withModule<ReplaceCglibNodepWithCglibRule>("org.spockframework:spock-core")
        // Prevent Spock from pulling in Groovy and third-party dependencies - see https://github.com/spockframework/spock/issues/899
        withLibraryDependencies<DependencyRemovalByNameRule>(
            "org.spockframework:spock-core",

        System.out.println("Response 2 equals Response 1? " + response1Body.equals(response2Body));
      }
    ```

            "/b",
            "Test",
            "Redirect from /a to /b",
          ),
        body = "/a has moved!",
      ),
    )
    server.enqueue(
      MockResponse(
        code = 302,
        headers =
          headersOf(
            "Location",
            "/c",
            "Test",
            "Redirect from /b to /c",
          ),
        body = "/b has moved!",

    }

    /**
     * Moves the cursor to row n, column m. The values are 1-based.
     * Any values less than 1 are mapped to 1.
     *
     * @param row    row (1-based) from top
     * @param column column (1 based) from left
     * @return this Ansi instance
     */
    public Ansi cursor(final int row, final int column) {
        return appendEscapeSequence('H', Math.max(1, row), Math.max(1, column));
    }