* hashmaps. Using a static ThreadLocal to avoid that overhead would mean that different
   * ExecutionSequencer objects interfere with each other, which would be undesirable, in addition
   * to increasing the memory footprint of every thread that interacted with it. In order to release
   * entries in thread-specific maps when the ThreadLocal object itself is no longer referenced,

    }

    private void appendJvmStats(final StringBuilder buf) {
        buf.append("\"jvm\":{");
        final JvmStats jvmStats = JvmStats.jvmStats();
        final Mem mem = jvmStats.getMem();
        buf.append("\"memory\":{");
        buf.append("\"heap\":{");
        append(buf, "used", () -> mem.getHeapUsed().getBytes()).append(',');
        append(buf, "committed", () -> mem.getHeapCommitted().getBytes()).append(',');

 */
package okhttp3.internal.tls

import java.security.cert.X509Certificate
import javax.security.auth.x500.X500Principal

/** A simple index that of trusted root certificates that have been loaded into memory. */
class BasicTrustRootIndex(vararg caCerts: X509Certificate) : TrustRootIndex {
  private val subjectToCaCerts: Map<X500Principal, Set<X509Certificate>>

  init {

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_GRADIENT_CHECKER_H_
#define TENSORFLOW_C_EAGER_GRADIENT_CHECKER_H_

#include <memory>

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/unified_api_testutil.h"

namespace tensorflow {
namespace gradients {

 */
package org.apache.maven.settings.building;

import org.apache.maven.building.Source;

/**
 * Provides access to the contents of settings independently of the backing store (e.g. file system, database, memory).
 *
 *
 * @deprecated instead use {@link Source}
 */
@Deprecated

#ifndef TENSORFLOW_C_EAGER_CUSTOM_DEVICE_TESTUTIL_H_
#define TENSORFLOW_C_EAGER_CUSTOM_DEVICE_TESTUTIL_H_

// A simple logging device to test custom device registration.
#include <memory>

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/tf_status.h"

  // ExtendSessionGraphHelper manually.
  std::atomic<bool> extend_before_run;
};

struct TF_ImportGraphDefOptions {
  tensorflow::ImportGraphDefOptions opts;

  // Backing memory for TensorId fields in opts.
  // TODO(skyewm): it'd be better if ImportGraphDefOptions owned this.
  std::vector<tensorflow::string> tensor_id_data;
};

struct TF_ImportGraphDefResults {

// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in the requests and limits, describing a single container in
// each of the pods of the current scale target(e.g. CPU or memory). The values will be
// averaged together before being compared to the target. Such metrics are built into
// Kubernetes, and have special scaling options on top of those available to

如果您需要执行繁重的后台计算，并且不一定需要由同一进程运行（例如，您不需要共享内存、变量等），那么使用其他更大的工具（如 <a href="https://docs.celeryq.dev" class="external-link" target="_blank">Celery</a>）可能更好。

它们往往需要更复杂的配置，即消息/作业队列管理器，如RabbitMQ或Redis，但它们允许您在多个进程中运行后台任务，甚至是在多个服务器中。

要查看示例，查阅 [Project Generators](../project-generation.md){.internal-link target=_blank}，它们都包括已经配置的Celery。

但是，如果您需要从同一个**FastAPI**应用程序访问变量和对象，或者您需要执行小型后台任务（如发送电子邮件通知），您只需使用 `BackgroundTasks` 即可。

## 回顾

  // this doc comment is a modified copy of the inherited doc comment,
  // without the reference to unlimited queues.
  /**
   * Returns the number of additional elements that this queue can ideally (in the absence of memory
   * or resource constraints) accept without blocking. This is always equal to the initial capacity
   * of this queue less the current {@code size} of this queue.
   *