// requests been enqueued.
    for (int i = 0; i < 50; ++i) {
      handles_task0.push_back(TestMatrixTensorHandle(ctx));
    }
  }
  const char task1_name[] = "/job:localhost/replica:0/task:1/device:CPU:0";
  const char task2_name[] = "/job:localhost/replica:0/task:2/device:CPU:0";

  std::vector<TFE_TensorHandle*> handles_task2;
  for (auto* h_task0 : handles_task0) {
    handles_task2.push_back(

  // Remote device on `worker2`.
  const char remote_device[] = "/job:localhost/replica:0/task:2/device:CPU:0";
  // `ctx_0`, `ctx_1` contains `remote_device`.
  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_0);
    ASSERT_TRUE(std::find(device_names.begin(), device_names.end(),
                          remote_device) != device_names.end());
  }

  {

      } else {
        EXPECT_EQ(dltensor_out->strides[i],
                  dltensor_out->shape[i + 1] * dltensor_out->strides[i + 1]);
      }
    }
  }
  const float* data_in = static_cast<const float*>(dltensor_in->data);
  const float* data_out = static_cast<const float*>(dltensor_out->data);
  for (size_t j = 0; j < num_elements; ++j) {
    EXPECT_EQ(data_out[j], data_in[j]);
  }

  TFE_CallDLManagedTensorDeleter(dlm_out);

// along with this program.  If not, see <http://www.gnu.org/licenses/>.

package cmd

// Format related consts
const (
	// Format config file carries backend format specific details.
	formatConfigFile = "format.json"
)

const (
	// Version of the formatMetaV1
	formatMetaVersionV1 = "1"
)

// format.json currently has the format:
// {
//   "version": "1",

					panic(to)
				}
			}
		}()
	}
	wg.Wait()
}

func TestDynamicTimeoutHitMinimum(t *testing.T) {
	const minimum = 30 * time.Second
	timeout := newDynamicTimeout(time.Minute, minimum)

	initial := timeout.Timeout()

	const successTimeout = 20 * time.Second
	for range 100 {
		for range dynamicTimeoutLogSize {
			timeout.LogSuccess(successTimeout)
		}
	}

func TestScanLineNoNewline(t *testing.T) {
	const text = "abcdefghijklmn\nopqrstuvwxyz"
	lines := []string{
		"abcdefghijklmn",
		"opqrstuvwxyz",
	}
	testNoNewline(text, lines, t)
}

// Test that the line splitter handles a final line with a carriage return but no newline.
func TestScanLineReturnButNoNewline(t *testing.T) {
	const text = "abcdefghijklmn\nopqrstuvwxyz\r"
	lines := []string{

	"github.com/minio/minio/internal/logger"
	"github.com/minio/pkg/v3/env"
)

const (
	logSubsys = "locking"
)

func lockLogIf(ctx context.Context, err error) {
	logger.LogIf(ctx, logSubsys, err)
}

// Enabled indicates object locking is enabled
const Enabled = "Enabled"

// RetMode - object retention mode.
type RetMode string

const (
	// RetGovernance - governance mode.
	RetGovernance RetMode = "GOVERNANCE"

  AbstractFunctionKind getKind() const { return kind_; }

  // Returns the AbstractFunction as a FunctionDef.
  virtual absl::Status GetFunctionDef(const FunctionDef**) = 0;

  // Returns a shared reference to the wrapped function.
  virtual absl::StatusOr<core::RefCountPtr<FunctionRecord>>
  GetFunctionRecord() = 0;

 private:
  const AbstractFunctionKind kind_;
};

using AbstractFunctionPtr =

  TF_DeviceList* devices = TFE_ContextListDevices(ctx, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  const int num_devices = TF_DeviceListCount(devices);

  const char* kCPUDevice = "CPU:0";
  for (int i = 0; i < num_devices; ++i) {
    const string name(TF_DeviceListName(devices, i, status.get()));
    if (TF_GetCode(status.get()) != TF_OK) {

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

void RegisterLoggingDevice(TFE_Context* context, const char* name,
                           bool strict_scope_placement, bool* arrived_flag,
                           bool* executed_flag, TF_Status* status);
void AllocateLoggingDevice(const char* name, bool* arrived_flag,
                           bool* executed_flag, TFE_CustomDevice** device,