// or consider other ways of packing/unpacking parallel tensors.
  if (operation_name == std::string("TPUReplicatedInput")) {
    // Special-cased operation for packing per-device tensors into one parallel
    // tensor.
    if (inputs.size() != parallel_device.num_underlying_devices()) {
      std::string message(absl::StrCat(
          "The parallel device ", parallel_device_name, " expected ",
          parallel_device.num_underlying_devices(),

==============================================================================*/

#include "tensorflow/c/eager/parallel_device/parallel_device_lib.h"

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_experimental.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/parallel_device/parallel_device_testlib.h"
#include "tensorflow/c/eager/tfe_context_internal.h"

#include "tensorflow/c/eager/parallel_device/parallel_device.h"

#include <array>

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_experimental.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/immediate_execution_tensor_handle.h"
#include "tensorflow/c/eager/parallel_device/parallel_device_testlib.h"

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

#ifndef TENSORFLOW_C_EAGER_PARALLEL_DEVICE_PARALLEL_DEVICE_LIB_H_
#define TENSORFLOW_C_EAGER_PARALLEL_DEVICE_PARALLEL_DEVICE_LIB_H_

#include <memory>
#include <optional>
#include <string>
#include <utility>
#include <vector>

#include "absl/types/optional.h"

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

#include "tensorflow/c/eager/parallel_device/parallel_device_testlib.h"

#include <array>

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

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

#include "tensorflow/c/eager/parallel_device/parallel_device_lib.h"

#include <string>
#include <utility>

#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/tfe_cancellation_manager_internal.h"

<img src="/img/async/parallel-burgers/parallel-burgers-01.png" class="illustration">

Dann sind Sie endlich an der Reihe und bestellen zwei sehr leckere Burger für Ihren Schwarm und Sie.

Sie zahlen 💸.

<img src="/img/async/parallel-burgers/parallel-burgers-02.png" class="illustration">

Der Kassierer geht in die Küche.

	}
	const payloadSize = 512
	rng := rand.New(rand.NewSource(time.Now().UnixNano()))
	payload := make([]byte, payloadSize)
	_, err = rng.Read(payload)
	errFatal(err)

	// Wait for all to connect
	// Parallel writes per server.
	b.Run("bytes", func(b *testing.B) {
		for par := 1; par <= 32; par *= 2 {
			b.Run("par="+strconv.Itoa(par*runtime.GOMAXPROCS(0)), func(b *testing.B) {
				defer timeout(60 * time.Second)()

   * make it load its own version of FinalizableReferenceQueue. Then we need to interact with that
   * parallel version through reflection in order to exercise the parallel
   * FinalizableReferenceQueue, and then check that the parallel ClassLoader can be
   * garbage-collected after that.
   */

  public static class MyFinalizableWeakReference extends FinalizableWeakReference<Object> {

   * an edge connecting node A to node B if this {@link Network} has an edge connecting A to B.
   *
   * <p>If this network {@link #allowsParallelEdges() allows parallel edges}, parallel edges will be
   * treated as if collapsed into a single edge. For example, the {@link #degree(Object)} of a node
   * in the {@link Graph} view may be less than the degree of the same node in this {@link Network}.
   */