csession->SetOutputs(grad_outputs_vec);
    csession->Run(s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
    TF_Tensor* out0 = csession->output_tensor(0);
    TF_Tensor* out1 = csession->output_tensor(1);

    std::vector<TF_Output> expected_grad_outputs_vec;
    expected_grad_outputs_vec.assign(expected_grad_outputs,
                                     expected_grad_outputs + 2);

    csession.Run(s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

    // Check results
    for (int i = 0; i < expected_results.size(); ++i) {
      TF_Tensor* out = csession.output_tensor(i);
      ASSERT_TRUE(out != nullptr);
      EXPECT_EQ(TF_INT32, TF_TensorType(out));
      EXPECT_EQ(1, TF_NumDims(out));
      CompareInt32Tensor(expected_results[i], out);
    }
  }

}

// Converts `noutputs` and `outputs` into `outputs_tensors` and does various
// checks while doing so.
absl::Status ProcessOutputs(const TF_Graph* fn_body, const char* fn_name,
                            int noutputs, const TF_Output* outputs,
                            std::vector<OutputTensor>* output_tensors)
    TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  output_tensors->reserve(noutputs);
  for (int i = 0; i < noutputs; ++i) {

  }

  for (int i = 0; i < nreturn_nodes; ++i) {
    opts.return_tensors.push_back(ToTensorId(nodes_to_return[i]));
  }

  // TODO(skyewm): change to OutputTensor
  tensorflow::ImportGraphDefResults results;
  TF_RETURN_IF_ERROR(
      ImportGraphDef(opts, gdef, dst_graph, dst_refiner, &results));

  for (const auto& pair : results.return_tensors) {