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

#include <memory>

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/c/eager/abstract_operation.h"

namespace tensorflow {

// Abstract interface to a context.
//

    int NT_STATUS_NOT_IMPLEMENTED = 0xC0000002;
    /** The specified information class is invalid */
    int NT_STATUS_INVALID_INFO_CLASS = 0xC0000003;
    /** Invalid access to memory location */
    int NT_STATUS_ACCESS_VIOLATION = 0xC0000005;
    /** The handle is invalid */
    int NT_STATUS_INVALID_HANDLE = 0xC0000008;
    /** The parameter is incorrect */

### Arbeitsspeicher pro Prozess { #memory-per-process }

Wenn das Programm nun Dinge in den Arbeitsspeicher lädt, zum Beispiel ein Modell für maschinelles Lernen in einer Variablen oder den Inhalt einer großen Datei in einer Variablen, verbraucht das alles **einen Teil des Arbeitsspeichers (RAM – Random Access Memory)** des Servers.

 * java.util.LinkedHashMap}. Generally speaking, this class reduces object allocation and memory
 * consumption at the price of moderately increased constant factors of CPU. Only use this class
 * when there is a specific reason to prioritize memory over CPU.
 *
 * @author Louis Wasserman
 */
@J2ktIncompatible // no support for access-order mode in LinkedHashMap delegate

   * triggered by cancel(). For details, see the comments on the fields of TimeoutFuture.
   *
   * <p>{@code volatile} is required for j2objc transpiling:
   * https://developers.google.com/j2objc/guides/j2objc-memory-model#atomicity
   */
  private volatile @Nullable InterruptibleTask<?> task;

  TrustedListenableFutureTask(Callable<V> callable) {
    this.task = new TrustedFutureInterruptibleTask(callable);
  }

Let's check back those deployment concepts from before:

* Security - HTTPS
* Running on startup
* Restarts
* **Replication (the number of processes running)**
* Memory
* Previous steps before starting

Up to this point, with all the tutorials in the docs, you have probably been running a **server program**, for example, using the `fastapi` command, that runs Uvicorn, running a **single process**.

        connection.connect();
        return connection;
    }

    @Override
    public RdmaMemoryRegion registerMemory(ByteBuffer buffer, EnumSet<RdmaAccess> access) throws IOException {
        // TCP doesn't need real memory registration
        return new TcpMemoryRegion(buffer, access);
    }

    @Override
    public String getProviderName() {
        return "TCP Fallback";
    }

    @Override

//
// Returns a TF_Buffer. The memory pointed to by the result is owned by
// lib_handle. The data in the buffer will be the serialized OpList proto for
// ops defined in the library.
TF_CAPI_EXPORT extern TF_Buffer TF_GetOpList(TF_Library* lib_handle);

// Frees the memory associated with the library handle.
// Does NOT unload the library.

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include <limits.h>
#include <memory.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <unistd.h>

#ifdef _WIN32
#include <process.h>
#endif

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

limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_C_CHECKPOINT_READER_H_
#define TENSORFLOW_C_CHECKPOINT_READER_H_

#include <memory>
#include <string>

#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/types.h"