TF_GraphProperties* TF_NewGraphProperties(const TF_GrapplerItem* item) {
  return reinterpret_cast<TF_GraphProperties*>(
      new tensorflow::grappler::GraphProperties(
          *reinterpret_cast<const tensorflow::grappler::GrapplerItem*>(item)));
}

void TF_DeleteGraphProperties(TF_GraphProperties* graph_properties) {
  if (graph_properties == nullptr) return;
  delete reinterpret_cast<tensorflow::grappler::GraphProperties*>(
      graph_properties);

    TF_OpKernelContext* ctx) {
  auto* cc_ctx = reinterpret_cast<tensorflow::OpKernelContext*>(ctx);
  return reinterpret_cast<TF_CoordinationServiceAgent*>(
      cc_ctx->coordination_service_agent());
}

bool TF_CoordinationServiceIsInitialized(TF_CoordinationServiceAgent* agent) {
  if (agent == nullptr) return false;
  auto* cc_agent = reinterpret_cast<tsl::CoordinationServiceAgent*>(agent);

    if (num_dims == kUnknownRank) {
      return absl::OkStatus();
    }

    std::vector<int64_t> dims(num_dims, kUnknownDim);
    TF_GraphGetTensorShape(graph_, output_,
                           reinterpret_cast<int64_t*>(dims.data()), num_dims,
                           &status);
    TF_RETURN_IF_ERROR(StatusFromTF_Status(&status));
    TF_RETURN_IF_ERROR(tensorflow::TensorShapeUtils::MakeShape(dims, shape));

void ParallelTensorDeallocator(void* data) {
  delete reinterpret_cast<ParallelTensor*>(data);
}

// Used as an argument to TFE_NewCustomDeviceTensorHandle, for computing the
// number of dimensions of a parallel tensor.
int ParallelTensorNumDims(void* data, TF_Status* status) {
  const std::vector<int64_t>* shape;
  Status s = reinterpret_cast<ParallelTensor*>(data)->Shape(&shape);
  if (!s.ok()) {

TEST(TF_GrapplerItem, NodesToPreserve) {
  GrapplerItem item;
  item.fetch = std::vector<string>{"Conv", "BiasAdd"};
  std::unordered_set<string> nodes_preserved = item.NodesToPreserve();
  TF_GrapplerItem* c_item = reinterpret_cast<TF_GrapplerItem*>(&item);

  int list_total_size = 0;
  for (const string& s : nodes_preserved) {
    list_total_size += s.size();
  }

  size_t storage_size = 0;
  int num_values = 0;

  }
  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;
    }
  }

        methods_.summarize(data_, &c_status), TF_DeleteBuffer);
    if (!c_status.status.ok()) {
      return c_status.status;
    }
    summary = std::string(reinterpret_cast<const char*>(summary_buffer->data),
                          summary_buffer->length);
    return absl::OkStatus();
  }

 private:
  void* const data_;

  TF_Buffer* result1 = TF_NewBuffer();
  TFE_MonitoringSamplerCellValue(cell1, result1);
  tensorflow::HistogramProto histogram1;
  EXPECT_TRUE(histogram1.ParseFromString(
      {reinterpret_cast<const char*>(result1->data), result1->length}));
  EXPECT_EQ(histogram1.sum(), 3.0);
  TF_DeleteBuffer(result1);
  TFE_MonitoringDeleteSampler1(sampler1);

// may lead to collisions. Introduce another way to get a unique id for this
// tensor.
int64_t ToId(const AbstractTensorHandle* t) {
  return static_cast<int64_t>(reinterpret_cast<uintptr_t>(t));
}

Status ZerosLike(AbstractContext* ctx, AbstractTensorHandle* t,
                 AbstractTensorHandle** result) {
  AbstractOperationPtr op(ctx->CreateOperation());

                                     const int64_t* values, int num_values) {
  forward_op_.attrs.Set(
      attr_name, gtl::ArraySlice<const int64_t>(
                     reinterpret_cast<const int64_t*>(values), num_values));
  return parent_op_->SetAttrIntList(attr_name, values, num_values);
}
Status TapeOperation::SetAttrTypeList(const char* attr_name,