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));

  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);
  AbstractTensorHandle* neg_output;
  TF_RETURN_IF_ERROR(ops::Neg(ctx, inputs[0], &neg_output, "Neg"));
  tape.RecordOperation(inputs, {neg_output}, nullptr, "Neg");
  return tape.ComputeGradient(ctx,
                              /*targets=*/{neg_output},
                              /*sources=*/inputs,

    while (true) {
      if (RANDOM_SOURCE.nextBoolean()) {
        return randomNonNegativeBigInteger(numBits);
      }
      BigInteger neg = randomNonNegativeBigInteger(numBits).negate();
      if (neg.signum() != 0) {
        return neg;
      }
    }
  }

  /**
   * Generates a number in [0, 2^numBits) with an exponential distribution. The floor of the log2 of

    while (true) {
      if (RANDOM_SOURCE.nextBoolean()) {
        return randomNonNegativeBigInteger(numBits);
      }
      BigInteger neg = randomNonNegativeBigInteger(numBits).negate();
      if (neg.signum() != 0) {
        return neg;
      }
    }
  }

  /**
   * Generates a number in [0, 2^numBits) with an exponential distribution. The floor of the log2 of

	}
	return z
}

func (z *Int) Int64() int64 {
	if !z.init {
		return 0
	}
	return int64(C.mpz_get_si(&z.i[0]))
}

// Neg sets z = -x and returns z.
func (z *Int) Neg(x *Int) *Int {
	x.doinit()
	z.doinit()
	C.mpz_neg(&z.i[0], &x.i[0])
	return z
}

// Abs sets z to the absolute value of x and returns z.
func (z *Int) Abs(x *Int) *Int {
	x.doinit()
	z.doinit()

                 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"}}, {});
}

}

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) {

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               TF_Operation** op) {
  TF_OperationDescription* desc = TF_NewOperation(graph, "Neg", name);
  TF_Output neg_input = {n, 0};
  TF_AddInput(desc, neg_input);
  *op = TF_FinishOperation(desc, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  ASSERT_NE(*op, nullptr);
}

TF_Operation* Neg(TF_Operation* n, TF_Graph* graph, TF_Status* s,
                  const char* name) {
  TF_Operation* op;