LogicalResult match(Operation* op) const override {
    if (op->getNumResults() != 1) {
      return failure();
    }
    auto type = mlir::cast<TensorType>(op->getResult(0).getType());
    if (!type || !type.getElementType().isF32()) {
      return failure();
    }
    return success(
        op->hasOneUse() &&
        IsWeightQuantizableFunction(*op->getUses().begin(), type.getRank()));
  }

  // Runs the analysis and populates `control_predecessors_` for `op`.
  void AnalyzeOp(Operation* op);

  // Updates `control_predecessors_` for given `resource_id` and `op`.
  void AddPredecessorsForAccess(ResourceId resource_id, Operation* op,
                                bool read_only);

  // Updates resource access for given `resource_id` and `op` in
  // `per_resource_access_info_` and `op_to_resource_ids_`.

	"github.com/zeebo/xxh3"
)

// Op is operation type.
//
//go:generate msgp -unexported -file=$GOFILE
//go:generate stringer -type=Op -output=msg_string.go -trimprefix=Op $GOFILE

// Op is operation type messages.
type Op uint8

// HandlerID is the ID for the handler of a specific type.
type HandlerID uint8

const (
	// OpConnect is a connect request.
	OpConnect Op = iota + 1

// Returns the entry function name for the given tf.XlaCallModule op. Returns
// empty string if such attribute does not exist.
StringRef GetEntryFunctionName(TF::XlaCallModuleOp op);

// Checks whether the given op contains QuantizationTrait::FullyQuantizable.
inline bool HasQuantizableTrait(Operation* op) {
  return op->hasAttrOfType<StringAttr>(kQuantTraitAttrName) &&
         op->getAttrOfType<StringAttr>(kQuantTraitAttrName).getValue().str() ==

SmallVector<Value> ConstantFoldOpIfPossible(Operation* op) {
  if (!IsOperationFoldable(op)) return op->getResults();

  OpBuilder builder(op);
  SmallVector<Value> results;
  if (failed(FoldOperation(builder, op, results))) {
    return op->getResults();
  }
  return results;
}

LogicalResult ConstantFoldQuantizableOperands::matchAndRewrite(
    Operation* op, PatternRewriter& rewriter) const {

// to an instance of an operation (e.g. TensorFlow runtime operation caching),
// `op` can be set instead.
struct ResourceHandle {
  ResourceHandle(StringRef container, StringRef name, StringRef device,
                 Operation* op)
      : container(container), name(name), device(device), op(op) {}

  bool operator==(const ResourceHandle& rhs) const {
    return container == rhs.container && name == rhs.name &&

  return BinaryGradCommon(scope, op, grad_outputs, gx_1, gx_2);
}

Status MaximumGrad(const Scope& scope, const Operation& op,
                   const std::vector<Output>& grad_inputs,
                   std::vector<Output>* grad_outputs) {
  auto comparator = GreaterEqual(scope, op.input(0), op.input(1));
  return MaximumMinimumGradCommon(scope, op, grad_inputs, grad_outputs,

func rewriteValuePPC64(v *Value) bool {
	switch v.Op {
	case OpAbs:
		v.Op = OpPPC64FABS
		return true
	case OpAdd16:
		v.Op = OpPPC64ADD
		return true
	case OpAdd32:
		v.Op = OpPPC64ADD
		return true
	case OpAdd32F:
		v.Op = OpPPC64FADDS
		return true
	case OpAdd64:
		v.Op = OpPPC64ADD
		return true
	case OpAdd64F:
		v.Op = OpPPC64FADD
		return true
	case OpAdd8:
		v.Op = OpPPC64ADD

		v.Op = OpS390XMULLD
		return true
	case OpMul64F:
		v.Op = OpS390XFMUL
		return true
	case OpMul64uhilo:
		v.Op = OpS390XMLGR
		return true
	case OpMul8:
		v.Op = OpS390XMULLW
		return true
	case OpNeg16:
		v.Op = OpS390XNEGW
		return true
	case OpNeg32:
		v.Op = OpS390XNEGW
		return true
	case OpNeg32F:
		v.Op = OpS390XFNEGS
		return true
	case OpNeg64:
		v.Op = OpS390XNEG

      rewriter.replaceOpWithNewOp<tf_device::ReturnOp>(return_op, cluster_vals);

  // Rewrite the cluster op.
  rewriter.setInsertionPoint(op);
  auto new_op = rewriter.create<tf_device::ClusterOp>(
      op->getLoc(), new_return.getOperandTypes(), op->getOperands(),
      op->getAttrs());
  rewriter.inlineRegionBefore(op.getBodyRegion(), new_op.getBodyRegion(),
                              new_op.getBodyRegion().end());

  int idx = 0;