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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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@@ -12,7 +12,7 @@ build_file: "nodejs-docs-samples/.kokoro/trampoline.sh"
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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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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(`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}`);

‎ai-platform/snippets/create-training-pipeline-text-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 text classification response :');
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console.log(`Name : ${response.name}`);

‎ai-platform/snippets/create-training-pipeline-text-entity-extraction.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(`Name : ${response.name}`);

‎ai-platform/snippets/create-training-pipeline-text-sentiment-analysis.js

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

‎ai-platform/snippets/create-training-pipeline-video-action-recognition.js

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};
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const [response] = await pipelineServiceClient.createTrainingPipeline(
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const [response] =
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await pipelineServiceClient.createTrainingPipeline(request);
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console.log(`Name : ${response.name}`);

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

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

‎ai-platform/snippets/create-training-pipeline-video-object-tracking.js

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

‎ai-platform/snippets/get-hyperparameter-tuning-job.js

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name,
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request
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const [response] =
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await jobServiceClient.getHyperparameterTuningJob(request);
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‎ai-platform/snippets/get-model-evaluation-slice.js

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‎ai-platform/snippets/list-model-evaluation-slices.js

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await modelServiceClient.listModelEvaluationSlices(request);
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‎ai-platform/snippets/package.json

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"license": "Apache-2.0",
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"author": "Google LLC",
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"node": ">=12.0.0"
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"node": ">=16.0.0"
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‎appengine/hello-world/flexible/package.json

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"url": "https://github.com/GoogleCloudPlatform/nodejs-docs-samples.git"
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"node": ">=16.0.0"
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‎appengine/hello-world/standard/package.json

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‎appengine/storage/flexible/package.json

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"test": "c8 mocha -p -j 2 system-test/*.test.js --exit --timeout=30000"
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"node": ">=16.0.0"
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‎appengine/storage/standard/package.json

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‎appengine/websockets/package.json

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‎auth/package.json

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"url": "https://github.com/GoogleCloudPlatform/nodejs-docs-samples.git"
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‎automl/package.json

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‎batch/package.json

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‎cloud-language/package.json

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‎cloud-sql/mysql/mysql/package.json

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‎cloud-sql/mysql/mysql2/package.json

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‎cloud-sql/postgres/knex/package.json

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‎cloud-sql/sqlserver/mssql/package.json

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‎cloud-sql/sqlserver/tedious/package.json

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‎cloud-tasks/snippets/package.json

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‎cloud-tasks/tutorial-gcf/app/package.json

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"node": ">=16.0.0"
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