--dry-run=client -oyaml \
    --from-file=istio-workload-dashboard.json="${TMP}/istio-workload-dashboard.json" \
    --from-file=istio-service-dashboard.json="${TMP}/istio-service-dashboard.json" \
    --from-file=istio-mesh-dashboard.json="${TMP}/istio-mesh-dashboard.gen.json" \
    --from-file=istio-extension-dashboard.json="${TMP}/istio-extension-dashboard.json"
} > "${ADDONS}/grafana.yaml"

# Set up loki
{

Refer to the dashboard [json file here](https://raw.githubusercontent.com/minio/minio/master/docs/metrics/prometheus/grafana/minio-dashboard.json).

![Grafana](https://raw.githubusercontent.com/minio/minio/master/docs/metrics/prometheus/grafana/grafana-minio.png)

        with:
          name: SARIF file
          path: results.sarif
          retention-days: 5

      # Upload the results to GitHub's code scanning dashboard (optional).
      # Commenting out will disable upload of results to your repo's Code Scanning dashboard
      - name: "Upload to code-scanning"
        uses: github/codeql-action/upload-sarif@662472033e021d55d94146f66f6058822b0b39fd # v3.27.0
        with:

# Declare default permissions as read only.
permissions: read-all

jobs:
  analysis:
    name: Scorecard analysis
    runs-on: ubuntu-latest
    permissions:
      # Needed to upload the results to code-scanning dashboard.
      security-events: write
      # Needed to publish results and get a badge (see publish_results below).
      id-token: write
      # Uncomment the permissions below if installing in a private repository.

You also define in some way at which **moments** your app will send those requests or events.

And **your users** define in some way (for example in a web dashboard somewhere) the **URL** where your app should send those requests.

    now allows you to instrument a TensorFlow program and dump debugging
    information to a directory on the file system. The directory can be read and
    visualized by a new interactive dashboard in TensorBoard 2.3 called
    [Debugger V2](https://www.tensorflow.org/tensorboard/debugger_v2), which
    reveals the details of the TensorFlow program including graph structures,