}
  tensorflow::PartialTensorShape partial_shape;
  if (shape.num_dims != -1) {
    DCHECK(shape.dim_sizes != nullptr);
    Status status = tensorflow::PartialTensorShape::MakePartialShape(
        reinterpret_cast<int64_t*>(shape.dim_sizes), shape.num_dims,
        &partial_shape);
    if (!status.ok()) {
      tsl::Set_TF_Status_from_Status(s, status);
      return nullptr;
    }
  }
  tsl::Set_TF_Status_from_Status(

    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    size_t size = TFE_TensorHandleDeviceMemorySize(copy, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    int64_t dims[] = {2, 2};
    TFE_TensorHandle* copy_aliased = TFE_NewTensorHandleFromDeviceMemory(
        ctx, name, TF_FLOAT, dims, 2, data, size, &Deleter, copy, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_Context* ctx = TFE_NewContext(opts, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteContextOptions(opts);

  std::vector<int64_t> dims(4, 1);
  TFE_Op* op = TFE_NewOp(ctx, "AvgPool", status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TF_Tensor* tensor =

pkg math/rand/v2, func Int() int #61716
pkg math/rand/v2, func Int32() int32 #61716
pkg math/rand/v2, func Int32N(int32) int32 #61716
pkg math/rand/v2, func Int64() int64 #61716
pkg math/rand/v2, func Int64N(int64) int64 #61716
pkg math/rand/v2, func IntN(int) int #61716
pkg math/rand/v2, func N[$0 intType]($0) $0 #61716
pkg math/rand/v2, func New(Source) *Rand #61716

	// in Queue stats for bucket - from qCache
	QStat InQueueMetric `json:"queued"`
	// Deprecated fields
	// Pending size in bytes
	PendingSize int64 `json:"pendingReplicationSize"`
	// Failed size in bytes
	FailedSize int64 `json:"failedReplicationSize"`
	// Total number of pending operations including metadata updates
	PendingCount int64 `json:"pendingReplicationCount"`

		// Simple useful utilities
		repeat = func(k int64, n int) []int64 {
			a := []int64{}
			for i := 0; i < n; i++ {
				a = append(a, k)
			}
			return a
		}
		lsum = func(s []int64) int64 {
			sum := int64(0)
			for _, i := range s {
				if i < 0 {
					return -1
				}
				sum += i
			}
			return sum
		}
		esum = func(oi ObjectInfo) int64 {
			sum := int64(0)

	bts := make([]byte, 0, v.Msgsize())
	bts, _ = v.MarshalMsg(bts[0:0])
	b.SetBytes(int64(len(bts)))
	b.ReportAllocs()
	b.ResetTimer()
	for i := 0; i < b.N; i++ {
		bts, _ = v.MarshalMsg(bts[0:0])
	}
}

func BenchmarkUnmarshalallTierStats(b *testing.B) {
	v := allTierStats{}
	bts, _ := v.MarshalMsg(nil)
	b.ReportAllocs()
	b.SetBytes(int64(len(bts)))
	b.ResetTimer()
	for i := 0; i < b.N; i++ {

		{Name: "company_count_group_c"},
	})

	var count1 int64
	if err := DB.Model(&Company{}).Where("name = ?", "company_count_group_a").Group("name").Count(&count1).Error; err != nil {
		t.Errorf(fmt.Sprintf("Count should work, but got err %v", err))
	}
	if count1 != 1 {
		t.Errorf("Count with group should be 1, but got count: %v", count1)
	}

	var count2 int64

	{dataBlocks: 6, onDisks: 12, offDisks: 0, blocksize: int64(blockSizeV2), data: int64(2 * blockSizeV2), offset: 512, length: int64(blockSizeV2) + 8*1024, algorithm: DefaultBitrotAlgorithm, shouldFail: false, shouldFailQuorum: false},           // 30

	bts, _ = v.MarshalMsg(bts[0:0])
	b.SetBytes(int64(len(bts)))
	b.ReportAllocs()
	b.ResetTimer()
	for i := 0; i < b.N; i++ {
		bts, _ = v.MarshalMsg(bts[0:0])
	}
}

func BenchmarkUnmarshalBatchJobReplicateCredentials(b *testing.B) {
	v := BatchJobReplicateCredentials{}
	bts, _ := v.MarshalMsg(nil)
	b.ReportAllocs()
	b.SetBytes(int64(len(bts)))
	b.ResetTimer()
	for i := 0; i < b.N; i++ {