`@lru_cache` is part of `functools` which is part of Python's standard library, you can read more about it in the <a href="https://docs.python.org/3/library/functools.html#functools.lru_cache" class="external-link" target="_blank">Python docs for `@lru_cache`</a>.

## Recap { #recap }

`@lru_cache` es parte de `functools`, que es parte del paquete estándar de Python, puedes leer más sobre él en las <a href="https://docs.python.org/3/library/functools.html#functools.lru_cache" class="external-link" target="_blank">docs de Python para `@lru_cache`</a>.

## Resumen { #recap }

`@lru_cache` — часть `functools`, что входит в стандартную библиотеку Python. Подробнее можно прочитать в <a href="https://docs.python.org/3/library/functools.html#functools.lru_cache" class="external-link" target="_blank">документации Python по `@lru_cache`</a>.

## Итоги { #recap }

`@lru_cache` ist Teil von `functools`, welches Teil von Pythons Standardbibliothek ist. Weitere Informationen dazu finden Sie in der <a href="https://docs.python.org/3/library/functools.html#functools.lru_cache" class="external-link" target="_blank">Python Dokumentation für `@lru_cache`</a>.

## Zusammenfassung { #recap }

这样，它的行为几乎就像是一个全局变量。但是由于它使用了依赖项函数，因此我们可以轻松地进行测试时的覆盖。

`@lru_cache` 是 `functools` 的一部分，它是 Python 标准库的一部分，您可以在<a href="https://docs.python.org/3/library/functools.html#functools.lru_cache" class="external-link" target="_blank">Python 文档中了解有关 `@lru_cache` 的更多信息</a>。

## 小结

您可以使用 Pydantic 设置处理应用程序的设置或配置，利用 Pydantic 模型的所有功能。

* 通过使用依赖项，您可以简化测试。
* 您可以使用 `.env` 文件。

`@lru_cache` faz parte de `functools`, que faz parte da biblioteca padrão do Python; você pode ler mais sobre isso na <a href="https://docs.python.org/3/library/functools.html#functools.lru_cache" class="external-link" target="_blank">documentação do Python para `@lru_cache`</a>.

## Recapitulando { #recap }

import re
import warnings
from collections.abc import Sequence
from copy import copy, deepcopy
from dataclasses import dataclass, is_dataclass
from enum import Enum
from functools import lru_cache
from typing import (
    Annotated,
    Any,
    Union,
    cast,
)

from fastapi._compat import shared
from fastapi.openapi.constants import REF_TEMPLATE
from fastapi.types import IncEx, ModelNameMap, UnionType

import json
import secrets
import subprocess
from collections.abc import Iterable
from functools import lru_cache
from os import sep as pathsep
from pathlib import Path
from typing import Annotated

import git
import typer
import yaml
from github import Github
from pydantic_ai import Agent
from rich import print

non_translated_sections = (
    f"reference{pathsep}",
    "release-notes.md",
    "fastapi-people.md",

### 10.1 Handle Caching Strategy
```java
public class HandleCache {
    private final int maxHandles = 1000;
    private final LinkedHashMap<String, HandleInfo> lruCache;
    
    public HandleCache() {
        this.lruCache = new LinkedHashMap<String, HandleInfo>(maxHandles, 0.75f, true) {
            @Override
            protected boolean removeEldestEntry(Map.Entry<String, HandleInfo> eldest) {

String last = CollectionsUtil.getLast(sequenced);
SequencedCollection<String> reversed = CollectionsUtil.reversed(sequenced);

// Specialized collections
LruHashMap<String, Object> lruCache = new LruHashMap<>(100); // LRU cache with max 100 entries
CaseInsensitiveMap<String> configMap = new CaseInsensitiveMap<>();
```

### Resource Management
```java
import org.codelibs.core.io.*;