return Run(FeedType{}, fetch_outputs, {}, outputs);
}

Status ClientSession::Run(const FeedType& inputs,
                          const std::vector<Output>& fetch_outputs,
                          std::vector<Tensor>* outputs) const {
  return Run(inputs, fetch_outputs, {}, outputs);
}

Status ClientSession::Run(const FeedType& inputs,

  /// Same as above, but use the mapping in `inputs` as feeds.
  Status Run(const FeedType& inputs, const std::vector<Output>& fetch_outputs,
             std::vector<Tensor>* outputs) const;

  /// Same as above. Additionally runs the operations ins `run_outputs`.
  Status Run(const FeedType& inputs, const std::vector<Output>& fetch_outputs,
             const std::vector<Operation>& run_outputs,

                               &outputs, nullptr, thread::ThreadPoolOptions()));
      test::ExpectTensorEqual<int>(outputs[0],
                                   test::AsTensor<int>({3, 8}, {2}));
    });
  }
  auto c = Sub(root, b, a);
  std::vector<Tensor> outputs;
  TF_EXPECT_OK(session.Run(RunOptions(), ClientSession::FeedType{}, {c}, {},

           const std::vector<T>& expected_output_values) {
    ClientSession session(scope_);

    DCHECK_EQ(input_values.size(), inputs_.size());
    ClientSession::FeedType feeds;
    for (int i = 0; i < inputs_.size(); ++i) {
      feeds.emplace(inputs_[i], input_values[i]);
    }

    std::vector<Tensor> out_tensors;
    TF_ASSERT_OK(session.Run(feeds, outputs_, &out_tensors));