branches:
      - master
  pull_request:
  workflow_dispatch:

permissions: { }

jobs:
  code-owners-validation:
    permissions:
      contents: read
      id-token: write
    runs-on: ubuntu-latest
    steps:
      - name: Get Secrets
        uses: gradle/actions-internal/get-aws-secrets@v1

        triggerName = StageName.PULL_REQUEST_FEEDBACK.uuid,
    ) {
    init {
        id("Promotion_NightlyDocumentation")
        name = "Nightly Documentation"
        description =
            "Promotes the latest successful documentation changes on '${branch.branchName}' from Ready for Nightly as a new nightly documentation snapshot"

        triggers {
            branch.determineNightlyPromotionTriggerHour()?.let { triggerHour ->

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

這之所以可行，要感謝 **Pydantic**，因為它 [內建支援 `dataclasses`](https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel)。

所以，即使上面的程式碼沒有明確使用 Pydantic，FastAPI 仍會使用 Pydantic 將那些標準的 dataclass 轉換為 Pydantic 版本的 dataclass。

而且當然一樣支援：

- 資料驗證
- 資料序列化
- 資料文件化等

</p>
<p align="center">
<a href="https://github.com/fastapi/fastapi/actions?query=workflow%3ATest+event%3Apush+branch%3Amaster">
    <img src="https://github.com/fastapi/fastapi/actions/workflows/test.yml/badge.svg?event=push&branch=master" alt="Test">
</a>
<a href="https://coverage-badge.samuelcolvin.workers.dev/redirect/fastapi/fastapi">

                   (contains(inputs.runner, 'linux-arm64') && 'us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build-arm64:latest') ||
                   (contains(inputs.runner, 'windows-x86') && null) }}
    timeout-minutes: 300
    steps:

      #- name: Enable Long Paths
      #  if: contains(inputs.runner, 'windows-x86')

TFCI_ARTIFACT_FINAL_PYPI_ARGS="--config-file=$KOKORO_KEYSTORE_DIR/73361_tensorflow_pypirc_using_global_api_token --repository pypi-warehouse"
TFCI_ARTIFACT_FINAL_PYPI_ENABLE=1
TFCI_ARTIFACT_LATEST_GCS_URI="gs://tensorflow/nightly/latest/"
TFCI_ARTIFACT_STAGING_GCS_ENABLE=1
TFCI_ARTIFACT_STAGING_GCS_URI="gs://tensorflow-ci-staging/staging/nightly/$(git rev-parse HEAD)/"

# 2. The internal version numbers get changed
TFCI_NIGHTLY_UPDATE_VERSION_ENABLE=1

- Synchronous and asynchronous (callback) execution
- Minimal dependencies (only Apache Commons IO)

## Installation

### Maven

Add the dependency to your `pom.xml` (replace `x.y.z` with the latest version):

```xml
<dependency>
  <groupId>org.codelibs</groupId>
  <artifactId>curl4j</artifactId>
  <version>x.y.z</version>
</dependency>
```

{* ../../docs_src/settings/app02_an_py310/main.py hl[17,19:21] *}

### Ayarlar ve test { #settings-and-testing }

Ardından, `get_settings` için bir dependency override oluşturarak test sırasında farklı bir settings nesnesi sağlamak çok kolay olur:

{* ../../docs_src/settings/app02_an_py310/test_main.py hl[9:10,13,21] *}

body:
  - type: markdown
    attributes:
      value: |
        Thanks for taking the time to fill out this bug report.     

        Simple fixes in single PRs do not require issues. 
  
        **Do you use the latest project version?**

  - type: input
    id: version
    attributes:
      label: Affected version
    validations:
      required: true

  - type: textarea
    id: message
    attributes:

 *
 * <p>Caffeine defines its own interfaces (<a
 * href="https://www.javadoc.io/doc/com.github.ben-manes.caffeine/caffeine/latest/com.github.benmanes.caffeine/com/github/benmanes/caffeine/cache/Cache.html">{@code
 * Cache}</a>, <a
 * href="https://www.javadoc.io/doc/com.github.ben-manes.caffeine/caffeine/latest/com.github.benmanes.caffeine/com/github/benmanes/caffeine/cache/LoadingCache.html">{@code
 * LoadingCache}</a>, <a