TF_Operation* neg = Neg(add, graph, s, "neg");
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  NodeDef node_def_neg;
  ASSERT_TRUE(GetNodeDef(neg, &node_def_neg));
  EXPECT_EQ(string("add"), node_def_neg.input(0));

  // update edge of neg
  TF_UpdateEdge(graph, TF_Output{one, 0}, TF_Input{neg, 0}, s);

  ASSERT_TRUE(GetNodeDef(neg, &node_def_neg));

                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    /* Given upstream grad U and a Neg op Y = -X, the gradients are:
     *
     *    dX =  -U
     *
     */

    std::string name = "Neg_Grad";
    TF_RETURN_IF_ERROR(
        ops::Neg(ctx, grad_outputs[0], &grad_inputs[0], name.c_str()));
    return absl::OkStatus();
  }
  ~NegGradientFunction() override {}
};

  TF_Operation* neg = Neg(feed, func_graph_, s_);
  Define(-1, {}, {feed}, {neg}, {});

  // Use, run, and verify
  TF_Operation* func_feed = Placeholder(host_graph_, s_);
  TF_Operation* func_op = Use({func_feed});
  Run({{func_feed, Int32Tensor(3)}}, func_op, -3);
  VerifyFDef({"neg_0"}, {{"feed", DT_INT32}}, {{"neg", DT_INT32}},
             {{"feed", "neg_0:0"}, {"neg_0:y:0", "neg"}}, {});
}

                                                           TF_DeleteStatus);

  TF_Operation* feed = Placeholder(func_graph.get(), s.get());
  TF_Operation* neg = Neg(feed, func_graph.get(), s.get());

  TF_Output inputs[] = {{feed, 0}};
  TF_Output outputs[] = {{neg, 0}};
  *func = TF_GraphToFunction(func_graph.get(), name, append_hash, -1,
                             /*opers=*/nullptr, 1, inputs, 1, outputs,

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}

Status NegModel(AbstractContext* ctx,
                absl::Span<AbstractTensorHandle* const> inputs,
                absl::Span<AbstractTensorHandle*> outputs) {
  return ops::Neg(ctx, inputs[0], &outputs[0], "Neg");
}

Status SubModel(AbstractContext* ctx,
                absl::Span<AbstractTensorHandle* const> inputs,
                absl::Span<AbstractTensorHandle*> outputs) {