public void onFailure(Throwable t) {}
        },
        directExecutor());
    return closingFuture;
  }

  /**
   * Starts specifying how to combine {@link ClosingFuture}s into a single pipeline.
   *
   * @throws IllegalStateException if a {@code ClosingFuture} has already been derived from any of
   *     the {@code futures}, or if any has already been {@linkplain #finishToFuture() finished}
   */

      }
      promoteAndExecute()
    }

  /**
   * The maximum number of requests for each host to execute concurrently. This limits requests by
   * the URL's host name. Note that concurrent requests to a single IP address may still exceed this
   * limit: multiple hostnames may share an IP address or be routed through the same HTTP proxy.
   *
   * If more than [maxRequestsPerHost] requests are in flight when this is invoked, those requests

    }

    // Parameterized tests for String edge cases on getName
    static Stream<Arguments> nameProvider() {
        return Stream.of(Arguments.of("", "empty string is allowed"), Arguments.of(" ", "single space is preserved"),
                Arguments.of("复杂名.txt", "unicode name is preserved"), Arguments.of(null, "null name is passed through"));
    }

    @ParameterizedTest(name = "getName returns as-set: [{0}] - {1}")

It helps a lot during **development**, but you **shouldn't** use it in **production**.

///

## Deployment Concepts { #deployment-concepts }

These examples run the server program (e.g Uvicorn), starting **a single process**, listening on all the IPs (`0.0.0.0`) on a predefined port (e.g. `80`).

This is the basic idea. But you will probably want to take care of some additional things, like:

* Security - HTTPS
* Running on startup

In this case, you can use `dict`:

{* ../../docs_src/extra_models/tutorial005_py310.py hl[6] *}

## Recap { #recap }

Use multiple Pydantic models and inherit freely for each case.

    }
    config = configFile("checkstyle.xml")
    val projectDirectory = layout.projectDirectory
    configDirectory = rules.elements.map {
        projectDirectory.dir(it.single().asFile.absolutePath).dir("checkstyle")
    }
}

plugins.withType<GroovyBasePlugin> {
    the<SourceSetContainer>().all {
        tasks.register<Checkstyle>(getTaskName("checkstyle", "groovy")) {

```

例如：

{* ../../docs_src/body_multiple_params/tutorial004_an_py310.py hl[28] *}

/// info | 注意

`Body` 也具有與 `Query`、`Path` 以及之後你會看到的其他工具相同的額外驗證與中繼資料參數。

///

## 嵌入單一 Body 參數 { #embed-a-single-body-parameter }

假設你只有一個來自 Pydantic 模型 `Item` 的單一 `item` Body 參數。

預設情況下，**FastAPI** 會直接期望該模型的內容作為請求主體。

但如果你想讓它像宣告多個 Body 參數時那樣，期望一個帶有 `item` 鍵、其內含模型內容的 JSON，你可以使用 `Body` 的特殊參數 `embed`：

```Python

`tf` instead of `tensorflow`, following the common practice of importing
TensorFlow via the command `import tensorflow as tf`.

The primary function of this module is to import all of the public TensorFlow
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.

Note that the file `__init__.py` in the TensorFlow source code tree is actually

An architecture module is responsible for providing a coherent set of features and:

- Provides a set of APIs and services for use from outside the module.
- Has a private implementation.
- Is owned by a single team. A team may own multiple architecture modules.

The modules are arranged into several different "Gradle platforms".
A Gradle platform is a logical distribution that provides support for a specific kind of automation.

            return null; // no edges - nothing to worry about
        }

        final TreeSet<MetadataGraphVertex> vertices = graph.getVertices();

        try {
            // edge case - single vertex graph
            if (vertices.size() == 1) {
                return new MetadataGraph(entry);
            }

            final ArtifactScopeEnum requestedScope = ArtifactScopeEnum.checkScope(scope);