```console
$ pip install fastapi

---> 100%
```

</div>

Per il rilascio in produzione, sarà necessario un server ASGI come <a href="http://www.uvicorn.org" class="external-link" target="_blank">Uvicorn</a> oppure <a href="https://github.com/pgjones/hypercorn" class="external-link" target="_blank">Hypercorn</a>.

<div class="termy">

```console
$ pip install uvicorn[standard]

---> 100%
```

</div>

		return args, err
	}

	opaURL := env.Get(EnvIamOpaURL, "")
	if opaURL == "" {
		opaURL = env.Get(EnvPolicyOpaURL, kv.Get(URL))
		if opaURL == "" {
			return args, nil
		}
	}
	authToken := env.Get(EnvIamOpaAuthToken, "")
	if authToken == "" {
		authToken = env.Get(EnvPolicyOpaAuthToken, kv.Get(AuthToken))
	}

	u, err := xnet.ParseHTTPURL(opaURL)
	if err != nil {
		return args, err
	}

if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  python3 tensorflow/tools/ci_build/update_version.py --nightly
fi

# This generates a pure python wheel of the format "*-py3-none-any.whl"
python3 tensorflow/tools/pip_package/setup.py bdist_wheel --dist-dir "$TFCI_OUTPUT_DIR"

# Get the name of the pure python wheel that was built. This should
# resolve to either
# 1. tensorflow-a.b.c-py3-none-any.whl  or, for nightly builds,

 *
 * @author Kevin Damm
 * @author Kyle Maddison
 */
@ElementTypesAreNonnullByDefault
final class LittleEndianByteArray {

  /** The instance that actually does the work; delegates to Unsafe or a pure-Java fallback. */
  private static final LittleEndianBytes byteArray;

  /**
   * Load 8 bytes into long in a little endian manner, from the substring between position and

namespace tensorflow {
namespace parallel_device {
namespace {

class OpDeleter {
 public:
  void operator()(TFE_Op* to_delete) const { TFE_DeleteOp(to_delete); }
};

using OpPtr = std::unique_ptr<TFE_Op, OpDeleter>;

class StatusDeleter {
 public:
  void operator()(TF_Status* to_delete) const { TF_DeleteStatus(to_delete); }
};

using StatusPtr = std::unique_ptr<TF_Status, StatusDeleter>;

    if (UTF_8.equals(charset)) {
      int utf16Length = input.length();
      int h1 = seed;
      int i = 0;
      int len = 0;

      // This loop optimizes for pure ASCII.
      while (i + 4 <= utf16Length) {
        char c0 = input.charAt(i);
        char c1 = input.charAt(i + 1);
        char c2 = input.charAt(i + 2);
        char c3 = input.charAt(i + 3);

namespace tensorflow {
namespace parallel_device {
namespace {

class OpDeleter {
 public:
  void operator()(TFE_Op* to_delete) const { TFE_DeleteOp(to_delete); }
};

using OpPtr = std::unique_ptr<TFE_Op, OpDeleter>;

using MaybeParallelTensorOwned =
    absl::variant<std::unique_ptr<ParallelTensor>, TensorHandlePtr>;

using MaybeParallelTensorUnowned =

{!> ../../docs_src/body_nested_models/tutorial007.py!}
```

////

/// info

Notice how `Offer` has a list of `Item`s, which in turn have an optional list of `Image`s

///

## Bodies of pure lists

If the top level value of the JSON body you expect is a JSON `array` (a Python `list`), you can declare the type in the parameter of the function, the same as in Pydantic models:

```Python
images: List[Image]

### Configuração de Plugins para OpenSearch

Execute antrun:run para baixar os plugins no diretório de plugins:

    $ mvn antrun:run

### Executar o Fess

Execute ou depure `org.codelibs.fess.FessBoot` no seu IDE, e depois acesse http://localhost:8080/

### Criar Pacote

Execute o objetivo `package` e o arquivo de lançamento será criado em target/releases.

    $ mvn package

            pass  # pragma: no cover
    assert e.value.code == status.WS_1000_NORMAL_CLOSURE


def websocket_middleware(middleware_func):
    """
    Helper to create a Starlette pure websocket middleware
    """

    def middleware_constructor(app):
        @functools.wraps(app)
        async def wrapped_app(scope, receive, send):
            if scope["type"] != "websocket":