float data[], int64_t dims[],
                                                  int num_dims);

// Get a Matrix TensorHandle with given float values and dimensions
TFE_TensorHandle* TestTensorHandleWithDimsFloat(TFE_Context* ctx, float data[],
                                                int64_t dims[], int num_dims);

    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_;

absl::Status SetAttrString(AbstractOperation*, const char* attr_name,
                           const char* data, size_t length, ForwardOperation*);
absl::Status SetAttrInt(AbstractOperation*, const char* attr_name,
                        int64_t value, ForwardOperation*);
absl::Status SetAttrFloat(AbstractOperation*, const char* attr_name,
                          float value, ForwardOperation*);

}

// Checks whether the stride array matches the layout of compact, row-majored
// data.
bool IsValidStrideCompactRowMajorData(int64_t* shape_arr, int64_t* stride_arr,
                                      int ndim) {
  bool valid = true;
  int64_t expected_stride = 1;
  for (int i = ndim - 1; i >= 0; --i) {
    // Empty tensors are always compact regardless of strides.
    if (shape_arr[i] == 0) return true;

    return absl::OkStatus();
  }
  absl::Status SetAttrShape(const char* attr_name, const int64_t* dims,
                            const int num_dims) override {
    PartialTensorShape shape;
    if (num_dims >= 0) {
      shape = PartialTensorShape(ArraySlice<int64_t>(
          reinterpret_cast<const int64_t*>(dims), num_dims));
    }
    op_->node_builder.Attr(attr_name, shape);
    return absl::OkStatus();

    mmHeap.addAll(Lists.newArrayList(1, 2, 3, 4, 47, 1, 5, 3, 0));
    assertTrue("Heap is not intact initially", mmHeap.isIntact());
    assertEquals(9, mmHeap.size());
    mmHeap.remove(5);
    assertEquals(8, mmHeap.size());
    assertTrue("Heap is not intact after remove()", mmHeap.isIntact());
    assertEquals(47, (int) mmHeap.pollLast());
    assertEquals(4, (int) mmHeap.pollLast());

// Atomically increments the value of the cell. The value must be non-negative.
TF_CAPI_EXPORT extern void TFE_MonitoringCounterCellIncrementBy(
    TFE_MonitoringCounterCell* cell, int64_t value);

// Retrieves the current value of the cell.
TF_CAPI_EXPORT extern int64_t TFE_MonitoringCounterCellValue(
    TFE_MonitoringCounterCell* cell);

// APIs for Counter without label.
typedef struct TFE_MonitoringCounter0 TFE_MonitoringCounter0;

  }
}

void TestEncodeDecode(int line, const std::vector<string>& data) {
  const int64_t n = data.size();
  absl::Status status;
  for (const std::vector<int64_t>& dims :
       std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
    // Create C++ Tensor
    Tensor src(tensorflow::DT_STRING, TensorShape(dims));
    for (int64_t i = 0; i < src.NumElements(); ++i) {
      src.flat<tstring>()(i) = data[i];
    }

 private:
  ParallelTensor(const ParallelDevice& device,
                 std::vector<TensorHandlePtr> tensors,
                 absl::Span<const int64_t> shape, const TF_DataType dtype)
      : device_(device),
        tensors_(std::move(tensors)),
        shape_(std::vector<int64_t>(shape.begin(), shape.end())),
        dtype_(dtype) {}
  ParallelTensor(const ParallelDevice& device,

		int type;
		[string] wchar_t *remark;
		uint32_t permissions;
		uint32_t max_uses;
		uint32_t current_uses;
		[string] wchar_t *path;
		[string] wchar_t *password;
		uint32_t sd_size;
		[size_is(sd_size)] uint8_t *security_descriptor;
	} ShareInfo502;

	typedef struct {
		int count;
		[size_is(count)] ShareInfo502 *array;
	} ShareInfoCtr502;

	typedef [switch_type(int)] union {
		[case(0)] ShareInfo0 *info0;