}

			AssertObjEqual(t, toy, expect.Toys[idx], "ID", "CreatedAt", "UpdatedAt", "Name", "OwnerID", "OwnerType")
		}
	})

	t.Run("Company", func(t *testing.T) {
		AssertObjEqual(t, user.Company, expect.Company, "ID", "Name")
	})

	t.Run("Manager", func(t *testing.T) {
		if user.Manager != nil {
			if user.ManagerID == nil {
				t.Errorf("Manager's foreign key should be saved")
			} else {

      - run: ./gradlew ${{ matrix.bucket.tasks }} -DdisableLocalCache=true -PflakyTests=exclude ${{ needs.build.outputs.sys-prop-args }}

  unit-test-windows-arm:
    name: "Unit Test Windows ARM"
    permissions:
      contents: read
    runs-on: windows-11-arm
    needs: build
    defaults:
      run:
        shell: pwsh
    steps:
      - name: git clone

      .certificatePinner(
        CertificatePinner
          .Builder()
          .add("publicobject.com", "sha256/Vjs8r4z+80wjNcr1YKepWQboSIRi63WsWXhIMN+eWys=")
          .build(),
      ).build()

  fun run() {
    val request =
      Request
        .Builder()
        .url("https://publicobject.com/robots.txt")
        .build()

    client.newCall(request).execute().use { response ->

    ```bash
    docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/build:2.15-python3.10
    ```

    Once you have the packages installed, you can run a specific unit test in
    bazel by doing as follows:

    ```bash
    export flags="--config=linux -k"
    ```

    If the tests are to be run on the GPU:

    strategy:
      fail-fast: false
      matrix:
        pyver: ['3.9', '3.10', '3.11', '3.12']
    steps:
      - name: Stop old running containers (if any)
        shell: bash
        run: |
          running_containers=$(docker ps -q) && \
          if [[ $running_containers == "" ]]; then
            echo "No running containers";
          else
            echo "Running container(s) found" && \

```Dockerfile
FROM python:3.9

WORKDIR /code

COPY ./requirements.txt /code/requirements.txt

RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt

COPY ./app /code/app

CMD ["fastapi", "run", "app/main.py", "--port", "80"]

# Si estás detrás de un proxy como Nginx o Traefik añade --proxy-headers
# CMD ["fastapi", "run", "app/main.py", "--port", "80", "--proxy-headers"]
```

</details>

To achieve this, you will normally have a **separate program** that would make sure your application is run on startup. And in many cases, it would also make sure other components or applications are also run, for example, a database.

### Example Tools to Run at Startup { #example-tools-to-run-at-startup }

Some examples of the tools that can do this job are:

* Docker
* Kubernetes
* Docker Compose

## `fastapi run` { #fastapi-run }

Вызов `fastapi run` по умолчанию запускает FastAPI в режиме продакшн.

TensorFlow's official CI jobs run the scripts in this folder. Our internal CI
system, Kokoro, schedules our CI jobs by combining a build script with a file
from the `envs` directory that is filled with configuration options:

-   Nightly jobs (Run nightly on the `nightly` branch)
    -   Uses `wheel.sh`, `libtensorflow.sh`, `code_check_full.sh`
-   Continuous jobs (Run on every GitHub commit)
    -   Uses `pycpp.sh`

      // allocation.
      submittedTask =
          new Runnable() {
            @Override
            public void run() {
              task.run();
            }

            @Override
            public String toString() {
              return task.toString();
            }
          };
      queue.add(submittedTask);