Можно использовать, например:

* `int`
* `float`
* `bool`
* `bytes`

{* ../../docs_src/python_types/tutorial005_py310.py hl[1] *}

### Модуль `typing` { #typing-module }

Для некоторых дополнительных сценариев может понадобиться импортировать что-то из стандартного модуля `typing`. Например, когда вы хотите объявить, что что-то имеет «любой тип», можно использовать `Any` из `typing`:

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

        }
    }

    private
    fun ComponentMetadataHandler.declareCapabilityPreference(module: String) {
        withModule<CapabilityRule>(module) {
            params(name)
            params("${providedBy.size}")
        }
    }

    /**
     * For all modules providing a capability, always use the preferred module, even if there's no conflict.

			<artifactId>tika-parser-apple-module</artifactId>
			<version>${tika.version}</version>
		</dependency>
		<dependency>
			<groupId>org.apache.tika</groupId>
			<artifactId>tika-parser-audiovideo-module</artifactId>
			<version>${tika.version}</version>
		</dependency>
		<dependency>
			<groupId>org.apache.tika</groupId>
			<artifactId>tika-parser-cad-module</artifactId>
			<version>${tika.version}</version>

    subgraph core["core platform"]

        core_runtime["core-runtime module"]
        style core_runtime stroke:#1abc9c,fill:#b1f4e7,stroke-width:2px,color:#000;

        core_configuration["core-configuration module"]
        style core_configuration stroke:#1abc9c,fill:#b1f4e7,stroke-width:2px,color:#000;

        core_execution["core-execution module"]
        style core_execution stroke:#1abc9c,fill:#b1f4e7,stroke-width:2px,color:#000;

        <configuration>
          <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
          <version>1.2.0</version>
          <models>
            <model>src/main/mdo/metadata.mdo</model>
          </models>
          <templates>
            <template>model.vm</template>
          </templates>
          <params>
            <param>packageModelV4=org.apache.maven.api.metadata</param>
          </params>

import org.gradle.api.initialization.Settings

/**
 * Defines a top-level architecture module.
 */
fun Settings.module(moduleName: String, moduleConfiguration: ArchitectureModuleBuilder.() -> Unit) {
    val module = ArchitectureModuleBuilder(moduleName, this)
    extensions.findByType(ProjectStructure::class.java)!!.architectureElements.add(module)
    module.moduleConfiguration()
}

/**
 * Defines a platform.
 */

     * Tests the formatting of an option with a module name.
     */
    @Test
    public void testModularOption() {
        String[] formatted = JavaPathType.patchModule("my.module").option(paths());
        assertEquals(2, formatted.length);
        assertEquals("--patch-module", formatted[0]);
        assertEquals(toPlatformSpecific("my.module=src/foo.java:src/bar.java"), formatted[1]);
    }

    /**

Quando você cria uma *operação de rota* no **FastAPI**, normalmente você pode retornar qualquer dado: um `dict`, uma `list`, um modelo do Pydantic, um modelo do banco de dados, etc.

Se você declarar um [Modelo de resposta](../tutorial/response-model.md), o FastAPI irá usá-lo para serializar os dados para JSON, usando o Pydantic.

Es wird von Pydantic **nicht unterstützt**, dass ein Pydantic‑v2‑Modell Felder hat, die als Pydantic‑v1‑Modelle definiert sind, und umgekehrt.

```mermaid
graph TB
    subgraph "❌ Nicht unterstützt"
        direction TB
        subgraph V2["Pydantic-v2-Modell"]
            V1Field["Pydantic-v1-Modell"]
        end
        subgraph V1["Pydantic-v1-Modell"]
            V2Field["Pydantic-v2-Modell"]
        end
    end