jobs:
  add-reviews:
    permissions:
      contents: read  # for kentaro-m/auto-assign-action to fetch config file
      pull-requests: write  # for kentaro-m/auto-assign-action to assign PR reviewers
    runs-on: ubuntu-latest
    steps:

        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 ->

}

// Continuation Message
// ====================
// Header specification
// --------------------
// Continuation messages contain two headers, as follows:
// https://docs.aws.amazon.com/AmazonS3/latest/API/images/s3select-frame-diagram-cont.png
//
// Payload specification
// ---------------------
// Continuation messages have no payload.
func genContinuationMessage() {
	buf := new(bytes.Buffer)

{* ../../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

    types: [ closed, unlabeled, milestoned, demilestoned ]

permissions: {}

jobs:
  check_pull_metadata:
    permissions:
      issues: write
      pull-requests: write
    runs-on: ubuntu-latest
    steps:
      # Check that PRs have proper metadata: labels and milestone
      # https://github.com/gradle/issue-management-action/blob/main/src/pull-metadata.ts

name: "Conflict detector"
on:
  push:
  pull_request_target:
    types: [synchronize]

jobs:
  main:
    permissions:
      contents: read
      pull-requests: write
    runs-on: ubuntu-latest
    steps:
      - name: Check if PRs have merge conflicts
        uses: eps1lon/actions-label-merge-conflict@v3
        with:
          dirtyLabel: "conflicts"
          repoToken: "${{ secrets.GITHUB_TOKEN }}"

#!/bin/bash

function download_arch_specific_executable {
	curl -f -L -s -q \
		https://github.com/moparisthebest/static-curl/releases/latest/download/curl-$1 \
		-o /go/bin/curl || exit 1
	chmod +x /go/bin/curl
}

case $TARGETARCH in
"arm64")
	download_arch_specific_executable aarch64
	;;
"s390x")
	echo "Not downloading static cURL because it does not exist for the $TARGETARCH architecture."
	;;
*)

on:
  schedule:
    - cron: '53 3 * * 1'
  workflow_dispatch:

permissions:
  contents: write
  id-token: write
  pull-requests: write

jobs:
  update-perf-test-buckets:
    runs-on: ubuntu-latest

    if: github.repository == 'gradle/gradle'

    steps:
      - name: Checkout repository
        uses: actions/checkout@v6
      - name: configure aws credentials

typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.4, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.12.0.dev
tb-nightly ~= 2.20.0.a

/**
 * This class models the different Docker base images that are used to build Docker distributions of Elasticsearch.
 */
public enum DockerBase {
    CENTOS("centos:8", ""),

    // "latest" here is intentional, since the image name specifies "8"
    UBI("docker.elastic.co/ubi8/ubi-minimal:latest", "-ubi8"),

    // The Iron Bank base image is UBI (albeit hardened), but we are required to parameterize the Docker build