ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  EXPECT_EQ(assign, 1);
  int id = TF_OpIsStateful("Identity", status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  EXPECT_EQ(id, 0);
}

class ShapeInferenceTest : public ::testing::Test {
 protected:
  ShapeInferenceTest()

Status SetAttrFloat(AbstractOperation*, const char* attr_name, float value,
                    ForwardOperation*);
Status SetAttrBool(AbstractOperation*, const char* attr_name, bool value,
                   ForwardOperation*);
Status SetAttrType(AbstractOperation*, const char* attr_name, DataType value,
                   ForwardOperation*);
Status SetAttrShape(AbstractOperation*, const char* attr_name,

  ~ModularWritableFile() override { ops_->cleanup(file_.get()); }

  Status Append(StringPiece data) override;
  Status Close() override;
  Status Flush() override;
  Status Sync() override;
  Status Name(StringPiece* result) const override;
  Status Tell(int64_t* position) override;

 private:
  std::string filename_;
  std::unique_ptr<TF_WritableFile> file_;

  CHECK_EQ(actual, 1);
}

TFE_MonitoringCounter0* CreateCounter0(const char* counter_name) {
  TF_Status* status = TF_NewStatus();
  auto* counter =
      TFE_MonitoringNewCounter0(counter_name, status, "description");
  TF_DeleteStatus(status);
  return counter;
}

void IncrementCounter0(TFE_MonitoringCounter0* counter, int64_t delta) {
  auto* cell = TFE_MonitoringGetCellCounter0(counter);

using tensorflow::GraphDef;
using tensorflow::NodeDef;

static void BoolDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<bool*>(data);
}

static void Int32Deallocator(void* data, size_t, void* arg) {
  delete[] static_cast<int32_t*>(data);
}

static void DoubleDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<double*>(data);
}

static void FloatDeallocator(void* data, size_t, void* arg) {

  Status status;
  AbstractTensorPtr resolved(
      // TODO(allenl): Resolve should be const, and the caches that get updated
      // marked mutable.
      const_cast<ImmediateExecutionTensorHandle*>(this)->Resolve(&status));
  if (!status.ok()) {
    return status;
  }
  summary = resolved->SummarizeValue();
  return absl::OkStatus();
}

  forward_op_->attrs.Reset(op);
  return op_->Reset(op, raw_device_name);
}
Status AddInput(AbstractOperation* op_, AbstractTensorHandle* input,
                ForwardOperation* forward_op_) {
  TF_RETURN_IF_ERROR(op_->AddInput(input));
  forward_op_->inputs.push_back(input);
  return absl::OkStatus();
}
Status AddInputList(AbstractOperation* op_,
                    absl::Span<AbstractTensorHandle* const> inputs,

TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s,
                          const char* name = "feed",
                          TF_DataType dtype = TF_INT32,
                          const std::vector<int64_t>& dims = {});

TF_Operation* Const(TF_Tensor* t, TF_Graph* graph, TF_Status* s,
                    const char* name = "const");

TF_Operation* ScalarConst(bool v, TF_Graph* graph, TF_Status* s,

 protected:
  void SetUp() override {
    TF_StatusPtr status(TF_NewStatus());
    TF_SetTracingImplementation(std::get<0>(GetParam()), status.get());

    {
      Status s = StatusFromTF_Status(status.get());
      CHECK_EQ(errors::OK, s.code()) << s.message();
    }

    {
      AbstractContext* ctx_raw = nullptr;
      Status s =
          BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);

                       const ComputeFunc& compute_func, TF_Status* status) {
    if (max_age_ == 0) {
      return compute_func(key, value, status);
    }

    // Note: we hold onto mu_ for the rest of this function. In practice, this
    // is okay, as stat requests are typically fast, and concurrent requests are
    // often for the same file. Future work can split this up into one lock per