Joe Tsai <******@****.***> 1446603151 -0800

    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

    absl::Span<AbstractTensorHandle*> outputs) {
  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,

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

  // 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"}, {{"negation_0", DT_INT32}}, {{"negation", DT_INT32}},

  // Add another oper to the graph.
  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));

    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

                            const string& op_device, TF_Status* s,
                            const char* name = "min");

TF_Operation* Neg(TF_Operation* n, TF_Graph* graph, TF_Status* s,
                  const char* name = "neg");

TF_Operation* LessThan(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s);

TF_Operation* RandomUniform(TF_Operation* shape, TF_DataType dtype,

                                                           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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	case ppc64.ACMP, ppc64.ACMPU, ppc64.ACMPW, ppc64.ACMPWU, ppc64.AFCMPO, ppc64.AFCMPU:
		return true
	}
	return false
}

// IsPPC64NEG reports whether the op (as defined by an ppc64.A* constant) is
// one of the NEG-like instructions that require special handling.
func IsPPC64NEG(op obj.As) bool {
	switch op {
	case ppc64.AADDMECC, ppc64.AADDMEVCC, ppc64.AADDMEV, ppc64.AADDME,
		ppc64.AADDZECC, ppc64.AADDZEVCC, ppc64.AADDZEV, ppc64.AADDZE,