Wenn Sie sehen möchten, was sich in verschiedenen Versionen von FastAPI geändert hat, lesen Sie mehr dazu im fortgeschrittenen Teil, unter [Fortgeschrittene Abhängigkeiten – Abhängigkeiten mit `yield`, `HTTPException`, `except` und Hintergrundtasks](../../advanced/advanced-dependencies.md#dependencies-with-yield-httpexception-except-and-background-tasks).

    * If you know Python types you know how to use Pydantic.
* Plays nicely with your **<abbr title="Integrated Development Environment: similar to a code editor">IDE</abbr>/<dfn title="A program that checks for code errors">linter</dfn>/brain**:
    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:

* Vai bem com o/a seu/sua **<abbr title="Integrated Development Environment - Ambiente de Desenvolvimento Integrado: similar a um editor de código">IDE</abbr>/<dfn title="Um programa que confere erros de código">linter</dfn>/cérebro**:
    * Como as estruturas de dados do Pydantic são apenas instâncias de classes que você define, o preenchimento automático, linting, mypy e a sua intuição devem funcionar corretamente com seus dados validados.

	return mac, err
}

func (k *KMS) updateMetrics(err error, latency time.Duration) {
	// First, update the latency histogram
	// Therefore, find the first bucket that holds the counter for
	// requests with a latency at least as large as the given request
	// latency and update its and all subsequent counters.
	bucket := slices.IndexFunc(k.latencyBuckets, func(b time.Duration) bool { return latency < b })

* **不烧脑**：
    * 没有新的模式定义 micro-language 需要学习。
    * 如果你知道 Python types，你就知道如何使用 Pydantic。
* 和你 **<abbr title="Integrated Development Environment - 集成开发环境: 类似于代码编辑器">IDE</abbr>/<dfn title="一个检查代码错误的程序">linter</dfn>/brain** 适配:
    * 因为 pydantic 数据结构仅仅是你定义的类的实例；自动补全，linting，mypy 以及你的直觉应该可以和你验证的数据一起正常工作。
* 验证**复杂结构**:
    * 使用分层的 Pydantic 模型，Python `typing` 的 `List` 和 `Dict` 等等。
    * 验证器使我们能够简单清楚地将复杂的数据模式定义、检查并记录为 JSON Schema。

 * <p>
 * There are several ways to set jCIFS properties. See
 * the <a href="../overview-summary.html#scp">overview page of the API
 * documentation</a> for details.
 */

public class Config {

    /**
     * Counter for tracking socket connections.
     */
    public static int socketCount = 0;

    /**
     * The static <code>Properties</code>.
     */

    private static Properties prp = new Properties();

/*
 * Copyright 2012-2025 CodeLibs Project and the Others.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,

					</la:link>
				</div>
			</c:if>
		</c:if>
	</aside>
</div>
<div class="row">
	<nav id="subfooter" class="mx-auto">
		<ul class="pagination justify-content-center">
			<c:if test="${existPrevPage}">
				<li class="page-item"><la:link styleClass="page-link" aria-label="Previous"

* **更簡單**：
    * 不需要學習新的 micro-language 來定義結構。
    * 如果你知道 Python 型別，你就知道如何使用 Pydantic。
* 和你的 **<abbr title="Integrated Development Environment - 整合開發環境: 類似於程式碼編輯器">IDE</abbr>/<dfn title="檢查程式碼錯誤的程式">linter</dfn>/brain** 都能好好配合：
    * 因為 Pydantic 的資料結構其實就是你自己定義的類別實例，所以自動補齊、linting、mypy 以及你的直覺都能很好地在經過驗證的資料上發揮作用。
* 驗證**複雜結構**：
    * 使用 Pydantic 模型時，你可以把資料結構分層設計，並且用 Python 的 `List` 和 `Dict` 等型別來定義。

/*
 * Copyright (C) 2007 The Guava Authors
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,