auto outputs = parallel_device.Join(
      /*expected_output_shapes=*/{PartialTensorShape({})}, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  const std::vector<std::unique_ptr<ParallelTensor>>& handles = *outputs;
  const std::vector<int64_t>* shape;
  absl::Status s = handles[0]->Shape(&shape);
  ASSERT_TRUE(s.ok());
  EXPECT_EQ(0, shape->size());
}

	"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"

    // 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(

#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,

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

package config

// Config value separator
const (
	ValueSeparator = ","
)

// Top level common ENVs
const (
	EnvAccessKey    = "MINIO_ACCESS_KEY"
	EnvSecretKey    = "MINIO_SECRET_KEY"
	EnvRootUser     = "MINIO_ROOT_USER"
	EnvRootPassword = "MINIO_ROOT_PASSWORD"

	// Legacy files

//
// The filled `device` struct and the allocated `device_info` struct may be
// passed to TFE_RegisterCustomDevice. The `device_name` arguments must match.
void AllocateParallelDevice(const char* device_name,
                            const char* const* underlying_devices,
                            int num_underlying_devices,
                            TFE_CustomDevice* device, void** device_info);

}  // namespace parallel_device

	// Output:
	// true
}

// Use a Scanner to implement a simple word-count utility by scanning the
// input as a sequence of space-delimited tokens.
func ExampleScanner_words() {
	// An artificial input source.
	const input = "Now is the winter of our discontent,\nMade glorious summer by this sun of York.\n"
	scanner := bufio.NewScanner(strings.NewReader(input))
	// Set the split function for the scanning operation.
	scanner.Split(bufio.ScanWords)

	go func() {
		for {
			rb.Read(buf)
		}
	}()

	b.ResetTimer()
	for i := 0; i < b.N; i++ {
		rb.Write(data)
	}
}

func BenchmarkRingBuffer_AsyncReadBlocking(b *testing.B) {
	const sz = 512
	const buffers = 10
	rb := New(sz * buffers)
	rb.SetBlocking(true)
	data := []byte(strings.Repeat("a", sz))
	buf := make([]byte, sz)

	go func() {
		for {
			rb.Read(buf)
		}
	}()

					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 l := 0; l < 100; l++ {
		for i := 0; i < dynamicTimeoutLogSize; i++ {
			timeout.LogSuccess(successTimeout)
		}

           int32_t step = 1) {
  for (int32_t i = start; i < end; i += step) {
    (*data)[i] = i;
  }
}

// Fills out_dims with the dimensions of the given tensor.
void GetDims(const TF_Tensor* t, int64_t* out_dims) {
  int num_dims = TF_NumDims(t);
  for (int i = 0; i < num_dims; i++) {
    out_dims[i] = TF_Dim(t, i);
  }
}

// Runs model as is if output is a scalar,