{* ../../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:

	// See https://docs.aws.amazon.com/AmazonS3/latest/API/API_PutBucketLifecycle.html
	// In the newer PutBucketLifecycleConfiguration API, Rule has a prefix field that is deprecated, and there exists an optional
	// Filter field, and within it, an optional Prefix field.
	// See https://docs.aws.amazon.com/AmazonS3/latest/API/API_PutBucketLifecycleConfiguration.html

    ports:
      - "127.0.0.1:9920:5432"
    environment:
      - TZ=Asia/Shanghai
      - POSTGRES_DB=gorm
      - POSTGRES_USER=gorm
      - POSTGRES_PASSWORD=gorm
  mssql:
    image: '${MSSQL_IMAGE}:latest'
    ports:
      - "127.0.0.1:9930:1433"
    environment:
      - TZ=Asia/Shanghai
      - ACCEPT_EULA=Y
      - MSSQL_SA_PASSWORD=LoremIpsum86
  tidb:
    image: 'pingcap/tidb:v6.5.0'
    ports:

name: Creates a GitHub Issue when a PR Rolled back via Commit to Master
on:
  push:
    branches:
      - master
      
permissions: {}

jobs:
  create-issue-on-pr-rollback:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      issues: write
      pull-requests: read
    if: |
      github.repository == 'tensorflow/tensorflow' &&

		var isSet bool
		for _, userMetadataPrefix := range userMetadataKeyPrefixes {
			if !stringsHasPrefixFold(k, userMetadataPrefix) {
				continue
			}
			// check the doc https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingMetadata.html
			// For metadata values like "ö", "ÄMÄZÕÑ S3", and "öha, das sollte eigentlich
			// funktionieren", tested against a real AWS S3 bucket, S3 may encode incorrectly. For

  pull_request:
    # The branches below must be a subset of the branches above
    branches: [master]
  schedule:
    - cron: '0 11 * * 4'

jobs:
  analyze:
    name: Analyze
    runs-on: ubuntu-latest

    strategy:
      fail-fast: false
      matrix:
        # Override automatic language detection by changing the below list
        # Supported options are ['csharp', 'cpp', 'go', 'java', 'javascript', 'python']

docker system prune -f || true
docker volume prune -f || true
docker volume rm $(docker volume ls -f dangling=true) || true

## change working directory
cd .github/workflows/mint

## always pull latest
docker pull docker.io/minio/mint:edge

docker-compose -f minio-${MODE}.yaml up -d
sleep 1m

docker system prune -f || true
docker volume prune -f || true

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

Felizmente, o Pydantic fornece uma ótima utilidade para lidar com essas configurações vindas de variáveis de ambiente com [Pydantic: Settings management](https://docs.pydantic.dev/latest/concepts/pydantic_settings/).

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

  schedule:
    # Execute every hour at xx:05 to avoid conflicts with other workflows
    - cron: '5 * * * *'

permissions: {}

jobs:
  stale:
    permissions:
      pull-requests: write

    runs-on: ubuntu-latest
    steps:
      - uses: actions/stale@v10
        with:
          operations-per-run: 50
          ascending: true
          exempt-all-milestones: true

 */
public interface Artifact extends Comparable<Artifact> {

    @Deprecated(since = "4.0.0")
    String RELEASE_VERSION = "RELEASE";

    @Deprecated(since = "4.0.0")
    String LATEST_VERSION = "LATEST";

    String SNAPSHOT_VERSION = "SNAPSHOT";

    Pattern VERSION_FILE_PATTERN = Pattern.compile("^(.*)-(\\d{8}\\.\\d{6})-(\\d+)$");

    // TODO into artifactScope handler

    String SCOPE_COMPILE = "compile";