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refactor: linting errors, updates to node 16 (#3464)
* refactor: updates samples to node 16 and resolves linting errors * fix: adding in a skip for the gaming samples as it is referencing a project that seems to either not exist or has strict permissions, preventing the tests from running refactor: move dataproc and media-livestream back to node 12
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‎.github/workflows/game-servers-snippets.yaml‎

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schedule:
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- cron: '0 0 * * 0'
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jobs:
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test:
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permissions:
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contents: 'read'
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id-token: 'write'
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if: github.event.action != 'labeled' || github.event.label.name == 'actions:force-run'
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uses: ./.github/workflows/test.yaml
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with:
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name: 'game-servers-snippets'
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path: 'game-servers/snippets'
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# -----------------
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# Issue to revisit skip: https://github.com/GoogleCloudPlatform/nodejs-docs-samples/issues/3466
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# -----------------
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# test:
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# permissions:
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# contents: 'read'
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# id-token: 'write'
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# if: github.event.action != 'labeled' || github.event.label.name == 'actions:force-run'
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# uses: ./.github/workflows/test.yaml
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# with:
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# name: 'game-servers-snippets'
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# path: 'game-servers/snippets'
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flakybot:
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permissions:
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contents: 'read'
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id-token: 'write'
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if: github.event_name == 'schedule' && always() # always() submits logs even if tests fail
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uses: ./.github/workflows/flakybot.yaml
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needs: [test]
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# needs: [test]

‎.kokoro/appengine/test-deployment/common.cfg‎

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# Configure the docker image for kokoro-trampoline.
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env_vars: {
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key: "TRAMPOLINE_IMAGE"
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value: "gcr.io/cloud-devrel-kokoro-resources/node"
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value: "gcr.io/cloud-devrel-kokoro-resources/node:16-user"
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}
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# Tell the trampoline which build file to use.

‎.kokoro/common.cfg‎

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# Configure the docker image for kokoro-trampoline.
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env_vars: {
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key: "TRAMPOLINE_IMAGE"
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value: "gcr.io/cloud-devrel-kokoro-resources/node:14-user"
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value: "gcr.io/cloud-devrel-kokoro-resources/node:16-user"
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}
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# Export XUnit test results for further analysis
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action {
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define_artifacts {
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regex: "**/*sponge_log.xml"
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}
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}
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}

‎.kokoro/functions/billing-periodic.cfg‎

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# Configure the docker image for kokoro-trampoline.
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env_vars: {
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key: "TRAMPOLINE_IMAGE"
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value: "gcr.io/cloud-devrel-kokoro-resources/node:14-user"
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value: "gcr.io/cloud-devrel-kokoro-resources/node:16-user"
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}
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# Tell the trampoline which build file to use.

‎.kokoro/functions/ocr-app.cfg‎

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# Configure the docker image for kokoro-trampoline.
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env_vars: {
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key: "TRAMPOLINE_IMAGE"
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value: "gcr.io/cloud-devrel-kokoro-resources/node:14-user"
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value: "gcr.io/cloud-devrel-kokoro-resources/node:16-user"
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}
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# Tell the trampoline which build file to use.

‎.kokoro/run/common.cfg‎

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# Configure the docker image for kokoro-trampoline.
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env_vars: {
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key: "TRAMPOLINE_IMAGE"
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value: "gcr.io/cloud-devrel-kokoro-resources/node:14-user"
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value: "gcr.io/cloud-devrel-kokoro-resources/node:16-user"
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}
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# Export XUnit test results for further analysis

‎ai-platform/snippets/create-training-pipeline-image-classification.js‎

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const request = {parent, trainingPipeline};
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// Create training pipeline request
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const [response] = await pipelineServiceClient.createTrainingPipeline(
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request
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);
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const [response] =
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await pipelineServiceClient.createTrainingPipeline(request);
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console.log('Create training pipeline image classification response');
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console.log(`Name : ${response.name}`);

‎ai-platform/snippets/create-training-pipeline-image-object-detection.js‎

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};
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// Create training pipeline request
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const [response] = await pipelineServiceClient.createTrainingPipeline(
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request
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);
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const [response] =
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await pipelineServiceClient.createTrainingPipeline(request);
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console.log('Create training pipeline image object detection response');
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console.log(`Name : ${response.name}`);

‎ai-platform/snippets/create-training-pipeline-tabular-classification.js‎

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};
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// Create training pipeline request
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const [response] = await pipelineServiceClient.createTrainingPipeline(
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request
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);
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const [response] =
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await pipelineServiceClient.createTrainingPipeline(request);
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console.log('Create training pipeline tabular classification response');
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console.log(`Name : ${response.name}`);

‎ai-platform/snippets/create-training-pipeline-tabular-regression.js‎

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};
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// Create training pipeline request
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const [response] = await pipelineServiceClient.createTrainingPipeline(
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request
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);
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const [response] =
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await pipelineServiceClient.createTrainingPipeline(request);
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console.log('Create training pipeline tabular regression response');
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console.log(`Name : ${response.name}`);

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