errors.app.double.submit.request = Esta solicitação pode já ter sido processada. Por favor, tente novamente.
# _/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/
# you can define your messages here:
# e.g.
# errors.xxx = ...
# info.xxx = ...
# _/_/_/_/_/_/_/_/_/_/
# ========================================================================================
# Fess
# ======

   * between pads, hoping that the JVM doesn't reorder them.
   *
   * <p>JVM intrinsics note: It would be possible to use a release-only form of CAS here, if it were
   * provided.
   */
  static final class Cell {
    volatile long p0, p1, p2, p3, p4, p5, p6;
    volatile long value;
    volatile long q0, q1, q2, q3, q4, q5, q6;

    Cell(long x) {

}

func commonETag(etags []string, quorum int) string {
	if etag, count := commonETags(etags); count >= quorum {
		return etag
	}
	return ""
}

// Beginning of unix time is treated as sentinel value here.
var (
	timeSentinel     = time.Unix(0, 0).UTC()
	timeSentinel1970 = time.Unix(0, 1).UTC() // 1970 used for special cases when xlmeta.version == 0
)

BMP, GIF, WAV, RAW, RAW\_PADDED, CSV and PROTO formats. All other input formats,
including tensorflow-io should be sandboxed if used to process untrusted data.

For example, if an attacker were to upload a malicious video file, they could
potentially exploit a vulnerability in the TensorFlow code that handles videos,
which could allow them to execute arbitrary code on the system running
TensorFlow.

    server.enqueue(
      MockResponse(
        code = 301,
        headers = headersOf("Location", "/b"),
        body = "/a has moved!",
      ),
    )
    server.enqueue(
      MockResponse(body = "/b is here"),
    )
    val request = Request(server.url("/"))
    val call = client.newCall(request)
    call.execute().use { response ->
      assertThat(
        response.body.string(),

ideias se estiver configurando **seu próprio sistema de implantação** enquanto cuida dos outros conceitos de implantação.

Confira o próximo capítulo para aprender sobre **FastAPI** com contêineres (por exemplo, Docker e Kubernetes). Você verá que essas ferramentas têm maneiras simples de resolver os outros **conceitos de implantação** também. ✨...

/// info

In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`.

The examples here use `.dict()` for compatibility with Pydantic v1, but you should use `.model_dump()` instead if you can use Pydantic v2.

///

### About `**user_in.dict()` { #about-user-in-dict }

        return (lastCheckDate == null) || policy.checkOutOfDate(lastCheckDate);
    }

    @Override
    public boolean isUpdateRequired(RepositoryMetadata metadata, ArtifactRepository repository, File file) {
        // Here, we need to determine which policy to use. Release updateInterval will be used when
        // the metadata refers to a release artifact or meta-version, and snapshot updateInterval will be used when

errors.app.double.submit.request = Il a peut-être été traité avant cette requête. Veuillez réessayer.
# _/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/
# you can define your messages here:
# e.g.
# errors.xxx = ...
# info.xxx = ...
# _/_/_/_/_/_/_/_/_/_/
# ========================================================================================
# Fess
# ======

errors.app.db.already.exists = 数据已存在。请重试。
errors.app.double.submit.request = 此请求可能已在之前处理。请重试。
# _/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/
# you can define your messages here:
# e.g.
# errors.xxx = ...
# info.xxx = ...
# _/_/_/_/_/_/_/_/_/_/
# ========================================================================================
# Fess
# ======
errors.login_error = 用户名或密码不正确。