TF_DeleteShapeAndTypeList(output_shapes);
  }

  absl::optional<std::vector<int64_t>> make_shape(
      std::vector<int64_t>&& dims) const {
    return absl::make_optional(dims);
  }

  absl::optional<std::vector<int64_t>> unknown_shape() const {
    return absl::nullopt;
  }

  static constexpr int64_t kUnknownDim =
      shape_inference::InferenceContext::kUnknownDim;
  TF_Status* status_;

                                 const int64_t* dims, int num_dims) {
  DCHECK(index >= 0 && index < shape_list->num_items);
  TF_ShapeAndType& shape = shape_list->items[index];
  DCHECK(shape.dims == nullptr) << "Shape at " << index << " is already set!";
  DCHECK(num_dims >= 0) << "Number of dimensions cannot be negative!";
  shape.num_dims = num_dims;
  shape.dims = new int64_t[num_dims];

TF_CAPI_EXPORT extern void TFE_CollectiveOpsCheckPeerHealth(
    TFE_Context* ctx, const char* task, int64_t timeout_in_ms,
    TF_Status* status);

// Information about the shape of a Tensor and its type.
struct TF_ShapeAndType {
  // Number of dimensions. -1 indicates unknown rank.
  int num_dims;
  // Array of dimensions. -1 indicates unknown dim.
  int64_t* dims;
  // The data type. May be 0 to denote unknown type.
  TF_DataType dtype;
};

		12: {bucketName: bucket, objName: object, inputData: []byte("ijkl"), inputMeta: map[string]string{"etag": "09a0877d04abf8759f99adec02baf579"}, inputDataSize: int64(len("ijkl"))},
		13: {bucketName: bucket, objName: object, inputData: []byte("mnop"), inputMeta: map[string]string{"etag": "e132e96a5ddad6da8b07bba6f6131fef"}, inputDataSize: int64(len("mnop"))},

		// With no metadata

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

	// 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 range n {
				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)
			for _, i := range oi.Parts {

	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++ {

	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++ {

	{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