All the functionality has been integrated into `fastapi`.

The only reason this package exists is as a migration path for old projects that used to depend on `fastapi-slim`, so that they can get the latest version of `fastapi`.

You **should not** install this package.

Install instead:

```bash
pip install fastapi
```

This package is deprecated and will stop receiving any updates and published versions.

// is different from regular HTTP range header. It only supports the
// form `bytes=first-last` where first and last are zero-based byte
// offsets. See
// http://docs.aws.amazon.com/AmazonS3/latest/API/mpUploadUploadPartCopy.html
// for full details. This function treats an empty rangeString as
// referring to the whole resource.
func parseCopyPartRangeSpec(rangeString string) (hrange *HTTPRangeSpec, err error) {

## GraphQL with Strawberry { #graphql-with-strawberry }

If you need or want to work with **GraphQL**, [**Strawberry**](https://strawberry.rocks/) is the **recommended** library as it has the design closest to **FastAPI's** design, it's all based on **type annotations**.

Depending on your use case, you might prefer to use a different library, but if you asked me, I would probably suggest you try **Strawberry**.

}

// Validate - validates the And field
func (a And) Validate() error {
	// > This is used in a Lifecycle Rule Filter to apply a logical AND to two or more predicates.
	// ref: https://docs.aws.amazon.com/AmazonS3/latest/API/API_LifecycleRuleAndOperator.html
	// i.e, predCount >= 2
	var predCount int
	if a.Prefix.set {
		predCount++
	}
	predCount += len(a.Tags)
	if a.ObjectSizeGreaterThan > 0 {
		predCount++
	}

        type: string

permissions:
  contents: read

jobs:
  cherrypick:
    name: Cherrypick to ${{ github.event.inputs.release_branch}} - ${{ github.event.inputs.git_commit }}
    runs-on: ubuntu-latest
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    steps:
    - name: Checkout code
      uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # v6.0.1
      with:

즉, Python에서 환경 변수로부터 읽어온 어떤 값이든 `str`이 되며, 다른 타입으로의 변환이나 검증은 코드에서 수행해야 합니다.

## Pydantic `Settings` { #pydantic-settings }

다행히 Pydantic은 환경 변수에서 오는 이러한 설정을 처리할 수 있는 훌륭한 유틸리티를 [Pydantic: Settings 관리](https://docs.pydantic.dev/latest/concepts/pydantic_settings/)로 제공합니다.

### `pydantic-settings` 설치하기 { #install-pydantic-settings }

먼저 [가상 환경](../virtual-environments.md)을 만들고 활성화한 다음, `pydantic-settings` 패키지를 설치하세요:

<div class="termy">

這表示在 Python 中自環境變數讀取到的任何值都會是 `str`，而任何轉型成其他型別或驗證都必須在程式碼中完成。

## Pydantic `Settings` { #pydantic-settings }

幸好，Pydantic 提供了很好的工具，可用來處理由環境變數而來的設定：[Pydantic：設定管理](https://docs.pydantic.dev/latest/concepts/pydantic_settings/)。

### 安裝 `pydantic-settings` { #install-pydantic-settings }

首先，請先建立你的[虛擬環境](../virtual-environments.md)，啟用它，然後安裝 `pydantic-settings` 套件：

<div class="termy">

```console

tainer:function(b){if(b.valAttr("error-msg-container"))return a(b.valAttr("error-msg-container"));var c=b.parent();return"checkbox"===b.attr("type")&&b.closest(".checkbox").length?c=b.closest(".checkbox").parent():"radio"===b.attr("type")&&b.closest(".radio").length&&(c=b.closest(".radio").parent()),c.closest(".input-group").length&&(c=c.closest(".input-group").parent()),c},applyInputErrorStyling:function(a,b){a.addClass(b.errorElementClass).removeClass(b.successElementClass),this.getParentConta...

		if versionID == "" {
			versionID = mustGetUUID()
			fi.IsLatest = true // we are creating a new version so this is latest.
		}
	}

	modTime = UTCNow() // We only preserve modTime if dstOpts.MTime is true.
	// in all other cases mtime is latest.

	fi.VersionID = versionID // set any new versionID we might have created
	fi.ModTime = modTime     // set modTime for the new versionID

{* ../../docs_src/dataclasses_/tutorial001_py310.py hl[1,6:11,18:19] *}

이는 **Pydantic** 덕분에 여전히 지원되는데, Pydantic이 [`dataclasses`에 대한 내부 지원](https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel)을 제공하기 때문입니다.

따라서 위 코드처럼 Pydantic을 명시적으로 사용하지 않더라도, FastAPI는 Pydantic을 사용해 표준 dataclasses를 Pydantic의 dataclasses 변형으로 변환합니다.

그리고 물론 다음과 같은 기능도 동일하게 지원합니다: