> The Gradle Build Tool repository is massive, and it will take ages to build on
> a local machine without necessary parallelization and caching.
> The full test suites are executed on the CI instance for multiple configurations,
> and you can rely on it after doing initial sanity check and targeted local testing.

### Submitting Your Change

build:unsupported_gpu_linux --config=tensorrt
build:unsupported_gpu_linux --action_env=CUDA_TOOLKIT_PATH="/usr/local/cuda-11.2"
build:unsupported_gpu_linux --action_env=LD_LIBRARY_PATH="/usr/local/cuda:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda-11.1/lib64:/usr/local/tensorrt/lib"
build:unsupported_gpu_linux --action_env=GCC_HOST_COMPILER_PATH="/dt9/usr/bin/gcc"

kube-system        coredns-5dd5756b68-nzlpw                             10.244.0.3  ambient-control-plane None                                TCP
local-path-storage local-path-provisioner-6f8956fb48-vvnpn              10.244.0.4  ambient-control-plane None                                TCP

  * It is critical that this path preserves the Service IP, if the traffic was to a Service.
* All traffic *ingressing* a pod on port 15008 in the mesh is assumed to be HBONE, and should be redirected to the node-local ztunnel on port 15008 (more on this later).

 */
private enum class ImportKind {
    /** The symbol is available from the local scope and hence cannot be imported or overwritten. */
    LOCAL,

    /** Explicitly imported by user. */
    EXPLICIT,

    /** Implicitly imported from package. */
    PACKAGE,

Você também pode declarar parâmetros de **corpo**, **rota** e **consulta**, ao mesmo tempo.

O **FastAPI** irá reconhecer cada um deles e retirar a informação do local correto.

```Python hl_lines="18"
{!../../../docs_src/body/tutorial004.py!}
```

Os parâmetros da função serão reconhecidos conforme abaixo:

En el output, hay una línea que dice más o menos:

```hl_lines="4"
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
```

Esa línea muestra la URL dónde se está sirviendo tu app en tu maquina local.

### Revísalo

Abre tu navegador en <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000</a>.

Verás la respuesta en JSON:

```JSON
{"message": "Hello World"}
```

Then, in that case, it could be simpler to have **one container** with **multiple processes**, and a local tool (e.g. a Prometheus exporter) on the same container collecting Prometheus metrics for all the internal processes and exposing those metrics on that single container.

---

Então, nesse caso, poderia ser mais simples ter **um único contêiner** com **múltiplos processos**, e uma ferramenta local (por exemplo, um exportador do Prometheus) no mesmo contêiner coletando métricas do Prometheus para todos os processos internos e expor essas métricas no único contêiner.

---