mutable mutex mu_;
  mutable int last_num_graph_nodes_ TF_GUARDED_BY(mu_) = 0;
};

ClientSession::ClientSession(const Scope& scope, const string& target)
    : ClientSession(scope, Impl::MakeDefaultSessionOptions(target)) {}

ClientSession::ClientSession(const Scope& scope) : ClientSession(scope, "") {}

ClientSession::ClientSession(const Scope& scope,
                             const SessionOptions& session_options) {

      TF_EXPECT_OK(session.Run(RunOptions(), ClientSession::FeedType{}, {b}, {},
                               &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}, {},

  ClientSession(const Scope& scope, const string& target);

  /// Same as above, but use the empty string ("") as the target specification.
  explicit ClientSession(const Scope& scope);

  /// Create a new session, configuring it with `session_options`.
  ClientSession(const Scope& scope, const SessionOptions& session_options);

  ~ClientSession();

  }

  template <typename T>
  void Run(const std::vector<Input::Initializer>& input_values,
           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;

        unit_dimension++;
      }
    }
  }
  return absl::OkStatus();
}

Status EvaluateGraph(ClientSession* session, const OutputList& xs,
                     const OutputList& ys, std::vector<Tensor>* x_datas,
                     std::vector<Tensor>* y_datas) {
  // Create the feed list.
  ClientSession::FeedType feed_list;
  for (int i = 0; i < x_datas->size(); i++) {
    feed_list.insert({xs[i], (*x_datas)[i]});
  }

  // Requesting the gradients for y0 and y2 should return the sum of their
  // individual gradients.
  std::vector<Output> grad_outputs;
  TF_EXPECT_OK(AddSymbolicGradients(scope_test_, {y0, y2}, {x}, &grad_outputs));
  ClientSession session(scope_test_);
  std::vector<Tensor> grad_result;
  TF_EXPECT_OK(session.Run({{x, {3.0f}}}, grad_outputs, &grad_result));
  EXPECT_EQ(grad_result.size(), 1);
  EXPECT_EQ(grad_result[0].NumElements(), 1);

    const auto zero = Const<float>(scope_, 0.0);
    const auto y = DivNoNan(scope_, x, zero);

    std::vector<Output> grad_outputs;
    TF_EXPECT_OK(AddSymbolicGradients(scope_, {y}, {x}, &grad_outputs));
    ClientSession session(scope_);
    std::vector<Tensor> grad_result;
    TF_EXPECT_OK(
        session.Run({{x, {-3.0f, 0.0f, 3.0f}}}, grad_outputs, &grad_result));
    EXPECT_EQ(grad_result.size(), 1);