state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment...

            PlexusContainer container,
            MavenExecutionRequest request,
            MavenExecutionResult result,
            MavenProject project) {
        this(container, request, result, Arrays.asList(new MavenProject[] {project}));
    }

    @Deprecated
    @SuppressWarnings("checkstyle:parameternumber")
    public MavenSession(
            PlexusContainer container,
            Settings settings,

        return redirect(getClass());
    }

    // ===================================================================================
    //                                                                        Assist Logic
    //                                                                        ============
    public static OptionalEntity<DataConfig> getEntity(final CreateForm form, final String username, final long currentTime) {

  virtual std::vector<tensorflow::Device*> ListAllTfDevices() = 0;

  //===--------------------------------------------------------------------===//
  // Following are helper functions to assist integrating TFRT with current
  // TF eager runtime.
  // TODO(b/172877902): These helper functions are currently used to support
  // PyFuncOp on TFRT, and might be useful for ops that directly use low

      - [**Admission Control**](#admission-control)
      - [**API & API server**](#api-&-api-server)
      - [**Audit**](#audit)
      - [**Custom Resources**](#custom-resources)
      - [**Other**](#other)
    - [**Apps**](#apps-1)
    - [**Auth**](#auth-3)
      - [**Audit**](#audit-1)
      - [**RBAC**](#rbac)
      - [**Other**](#other-1)
      - [**GCE**](#gce)
    - [**Autoscaling**](#autoscaling)

        }
    }

    // ===================================================================================
    //                                                                        Assist Class
    //                                                                        ============

    @FunctionalInterface
    public interface ConditionOptionCall<OP extends AbstractAggregationBuilder<?>> {

      .assertNoMoreLogs()
  }

  @Test
  fun connectFail() {
    assumeNotWindows()
    server.useHttps(handshakeCertificates.sslSocketFactory())
    server.protocols = Arrays.asList(Protocol.HTTP_2, Protocol.HTTP_1_1)
    server.enqueue(
      MockResponse.Builder()
        .socketPolicy(FailHandshake)
        .build(),
    )
    url = server.url("/")
    try {

& ⤴️ 👆 💪 ⚒ `status_code` 👈 *🔀* 📨 🎚.

```Python hl_lines="1  9  12"
{!../../docs_src/response_change_status_code/tutorial001.py!}
```

& ⤴️ 👆 💪 📨 🙆 🎚 👆 💪, 👆 🛎 🔜 ( `dict`, 💽 🏷, ♒️).

& 🚥 👆 📣 `response_model`, ⚫️ 🔜 ⚙️ ⛽ & 🗜 🎚 👆 📨.

**FastAPI** 🔜 ⚙️ 👈 *🔀* 📨 ⚗ 👔 📟 (🍪 & 🎚), & 🔜 🚮 👫 🏁 📨 👈 🔌 💲 👆 📨, ⛽ 🙆 `response_model`.

And then you can set cookies in that *temporal* response object.

```Python hl_lines="1  8-9"
{!../../docs_src/response_cookies/tutorial002.py!}
```

And then you can return any object you need, as you normally would (a `dict`, a database model, etc).

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.

        target_url=run_url,
    )

    files = list(use_pr.get_files())
    docs_files = [f for f in files if f.filename.startswith("docs/")]

    deploy_url = settings.deploy_url.rstrip("/")
    lang_links: dict[str, list[str]] = {}
    for f in docs_files:
        match = re.match(r"docs/([^/]+)/docs/(.*)", f.filename)
        if not match:
            continue
        lang = match.group(1)
        path = match.group(2)