Comparator.comparing(PhaseId::toString, new PhaseComparator(lifecycle.getPhases())));
                mappings.put(phase, phaseBindings);
            }
        }

        /*
         * Grab plugin executions that are bound to the selected lifecycle phases from project. The effective model of
         * the project already contains the plugin executions induced by the project's packaging type. Remember, all

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    `RandomTranslation`, `RandomRotation`, `RandomHeight`, `RandomWidth`,
    `RandomZoom`, `RandomContrast`
*   Improved **`TextVectorization`** layer, which handles string tokenization,
    n-gram generation, and token encoding
    *   The `TextVectorization` layer now accounts for the mask_token as part of
        the vocabulary size when output_mode='int'. This means that, if you have

El resultado de llamarlo es algo que puede ser codificado con la función estándar de Python <a href="https://docs.python.org/3/library/json.html#json.dumps" class="external-link" target="_blank">`json.dumps()`</a>.

No devuelve un gran `str` que contenga los datos en formato JSON (como una cadena de texto). Devuelve una estructura de datos estándar de Python (por ejemplo, un `dict`) con valores y sub-valores que son todos compatibles con JSON.

/// note | Nota

そして複数のプロセスは通常、**メモリを共有しません**。これは、実行中の各プロセスがそれぞれ独自の変数やメモリ等を持っていることを意味します。つまり、コード内で大量のメモリを消費している場合、**各プロセス**は同等の量のメモリを消費することになります。

### サーバーメモリー

例えば、あなたのコードが **1GBのサイズの機械学習モデル**をロードする場合、APIで1つのプロセスを実行すると、少なくとも1GBのRAMを消費します。

また、**4つのプロセス**（4つのワーカー）を起動すると、それぞれが1GBのRAMを消費します。つまり、合計でAPIは**4GBのRAM**を消費することになります。

リモートサーバーや仮想マシンのRAMが3GBしかない場合、4GB以上のRAMをロードしようとすると問題が発生します。🚨

                                                    </c:if> <c:if test="${data.available=='false'}">
																	<span class="badge bg-gray"><la:message
                                                                            key="labels.disabled"/></span>
                                                    </c:if></td>

      } finally {
        monitor.leave();
      }
    } else {
      // It is possible due to races that we are currently in the expected state even though we
      // timed out. e.g. if we weren't event able to grab the lock within the timeout we would never
      // even check the guard. I don't think we care too much about this use case but it could lead
      // to a confusing error message.

// TODO: Not implemented
func (m *muxClient) RequestStateless(h HandlerID, req []byte, out chan<- Response) {
	if m.init {
		out <- Response{Err: errors.New("mux client already used")}
	}
	m.init = true

	// Try to grab an initial block.
	m.singleResp = false
	msg := message{
		Op:         OpConnectMux,
		Handler:    h,
		Flags:      FlagEOF,
		Payload:    req,
		DeadlineMS: uint32(m.deadline.Milliseconds()),
	}

// and RFC7239 Forwarded headers (in that order)
func GetSourceIPFromHeaders(r *http.Request) string {
	var addr string

	if enableXFFHeader {
		if fwd := r.Header.Get(xForwardedFor); fwd != "" {
			// Only grab the first (client) address. Note that '192.168.0.1,
			// 10.1.1.1' is a valid key for X-Forwarded-For where addresses after
			// the first may represent forwarding proxies earlier in the chain.
			s := strings.Index(fwd, ", ")

std::vector<std::pair<string, string>> GetGradDefs(
    const tensorflow::GraphDef& graph_def) {
  std::vector<std::pair<string, string>> grads;
  for (const tensorflow::GradientDef& grad : graph_def.library().gradient()) {
    grads.emplace_back(grad.function_name(), grad.gradient_func());
  }
  std::sort(grads.begin(), grads.end());
  return grads;
}

std::vector<string> GetFuncNames(const tensorflow::GraphDef& graph_def) {