const std::vector<Output>& grad_inputs,
                        std::vector<Output>* grad_outputs) {
  ops::GradFunc grad_fn;
  TF_RETURN_IF_ERROR(ops::GradOpRegistry::Global()->Lookup(
      op.node()->type_string(), &grad_fn));
  TF_RETURN_IF_ERROR(grad_fn(scope, op, grad_inputs, grad_outputs));
  TF_RETURN_IF_ERROR(scope.status());
  return absl::OkStatus();
}

REGISTER_NO_GRADIENT_OP("GetSessionTensor");
REGISTER_NO_GRADIENT_OP("DeleteSessionTensor");

Status DynamicPartitionGrad(const Scope& scope, const Operation& op,
                            const std::vector<Output>& grad_inputs,
                            std::vector<Output>* grad_outputs) {
  // DynamicPartition only moves input values into various positions
  // in the output, so the gradient operation only has to map incoming

//  public:
//   Status Compute(Context* ctx,
//                  absl::Span<AbstractTensorHandle* const> grad_inputs,
//                  absl::Span<AbstractTensorHandle*> grad_outputs) override {
//     grad_outputs[0] = grad_inputs[0];
//     grad_outputs[1] = grad_inputs[0];
//     grad_outputs[0]->Ref();
//     grad_outputs[1]->Ref();
//     return OkStatus();
//   }
//   ~AddGradientFunction() override {}

2.  Write the op gradient with the following naming scheme:

    ```
    Status OpNameGrad(const Scope& scope, const Operation& op,
                      const std::vector<Output>& grad_inputs,
                      std::vector<Output>* grad_outputs) {
      ...
      return scope.status();
    }
    REGISTER_GRADIENT_OP("OpName", OpNameGrad);
    ```

3.  Ops gradients are implemented by using the