TFE_TensorHandle* input_eager =
      TestTensorHandleWithDims<T, datatype>(eager_ctx, data, dims, num_dims);
  *tensor =
      unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get()));
  return absl::OkStatus();
}

// Return a scalar tensor handle with given value.
template <class T, TF_DataType datatype>
Status TestScalarTensorHandle(AbstractContext* ctx, const T value,

        op_type, output_tensors, input_tensor_id, input_dtypes,
        backward_function_getter, backward_function_deleter);
    return absl::OkStatus();
  }
  if (!ShouldRecord(input_tensor_id, input_dtypes)) {
    return absl::OkStatus();
  }

  // We may need to allocate zero inputs for trainable dtypes we don't have JVPs
  // for. Make sure they get cleaned up.

  bool UsesFunctionLibrary() const override { return false; }
  Status Init(
      const tensorflow::RewriterConfig_CustomGraphOptimizer* config) override {
    return OkStatus();
  }
  Status Optimize(Cluster* cluster, const GrapplerItem& item,
                  GraphDef* optimized_graph_def) override;

  ~CGraphOptimizer() override {
    if (optimizer_.destroy_func != nullptr) {

//     grad_outputs[0] = grad_inputs[0];
//     grad_outputs[1] = grad_inputs[0];
//     grad_outputs[0]->Ref();
//     grad_outputs[1]->Ref();
//     return OkStatus();
//   }
//   ~AddGradientFunction() override {}
// };
//
// GradientFunction* AddRegisterer(const ForwardOperation& op) {
//   // More complex gradient functions can use inputs/attrs etc. from the