// Fills data with values [start,end) with given step size.
void Range(vector<int32_t>* data, int32_t start, int32_t end,
           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++) {

  int64_t num_values = 1;
  for (int i = 0; i < num_dims; ++i) {
    num_values *= dims[i];
  }
  TF_Tensor* t =
      TF_AllocateTensor(TF_INT32, dims, num_dims, sizeof(int32_t) * num_values);
  memcpy(TF_TensorData(t), values, sizeof(int32_t) * num_values);
  return t;
}

TF_Tensor* Int32Tensor(const std::vector<int32_t>& values) {
  int64_t dims = values.size();
  return Int32Tensor(&dims, 1, values.data());
}

      EXPECT_EQ(0, TF_NumDims(out));  // scalar
      ASSERT_EQ(sizeof(int32_t), TF_TensorByteSize(out));
      int32_t* output_contents = static_cast<int32_t*>(TF_TensorData(out));
      EXPECT_EQ(expected_results[i], *output_contents);
    }
  }

  void CompareInt32Tensor(const std::vector<int32_t>& expected, TF_Tensor* t) {
    int32_t* data = static_cast<int32_t*>(TF_TensorData(t));
    size_t size = TF_TensorByteSize(t);

  ASSERT_TRUE(out != nullptr);
  EXPECT_EQ(TF_INT32, TF_TensorType(out));
  EXPECT_EQ(0, TF_NumDims(out));  // scalar
  ASSERT_EQ(sizeof(int32_t), TF_TensorByteSize(out));
  int32_t* output_contents = static_cast<int32_t*>(TF_TensorData(out));
  EXPECT_EQ(3 + 2, *output_contents);

  // Add another operation to the graph.
  TF_Operation* neg = Neg(add, graph, s);

  // Get a list of the names of functions that have been registered.
  virtual std::vector<string> ListFunctionNames() = 0;

  struct CacheStats {
    int64_t kernel_cache_size;
    int64_t device_cache_size;
    std::map<std::string, int64_t> func_kernel_cache_entries;
    int64_t local_rendezvous_cache_active_size;
  };
  virtual CacheStats GetCacheStats() = 0;

    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

    TF_Tensor* t =
        TF_AllocateTensor(TF_INT32, nullptr, 0, 1 * sizeof(tensorflow::int32));
    *reinterpret_cast<tensorflow::int32*>(TF_TensorData(t)) = 42;
    TFE_TensorHandle* h = TFE_NewTensorHandle(t, status);
    ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
    TF_DeleteTensor(t);

template <typename... LabelType>
int64_t TFE_MonitoringCounterReader::Read(const LabelType&... labels) {
  return counter->Read(labels...);
}

TFE_MonitoringCounterReader* TFE_MonitoringNewCounterReader(const char* name) {
  auto* result = new TFE_MonitoringCounterReader(name);

  return result;
}

int64_t TFE_MonitoringReadCounter0(TFE_MonitoringCounterReader* cell_reader) {
  int64_t result = cell_reader->Read();

using tensorflow::string;

namespace {

std::vector<int64_t> TensorShapeAsVector(const tensorflow::TensorHandle& handle,
                                         absl::Status* status) {
  std::vector<int64_t> shape;
  int rank = -1;
  *status = handle.NumDims(&rank);
  if (!status->ok()) {
    return shape;
  }
  shape.reserve(rank);
  for (int i = 0; i < rank; ++i) {
    int64_t dim;
    *status = handle.Dim(i, &dim);

  return th;
}

TFE_TensorHandle* DoubleTestMatrixTensorHandle(TFE_Context* ctx) {
  int64_t dims[] = {2, 2};
  double data[] = {1.0, 2.0, 3.0, 4.0};
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, TF_DOUBLE, &dims[0],
                                        sizeof(dims) / sizeof(int64_t), status);
  memcpy(TF_TensorData(t), &data[0], TF_TensorByteSize(t));

    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_;