ConversionPatternRewriter& rewriter, int64_t const_value,
                      Type type) {
  Value result_const =
      builder.create<TF::ConstOp>(rewriter.getIntegerAttr(type, const_value));
  return result_const;
}

Value BuildIntArrayConstOp(ImplicitLocOpBuilder& builder,
                           ConversionPatternRewriter& rewriter,

class ConvertGraphOp : public OpConversionPattern<tfg::GraphOp> {
 public:
  using OpConversionPattern::OpConversionPattern;

  LogicalResult matchAndRewrite(
      tfg::GraphOp graph, OpAdaptor adaptor,
      ConversionPatternRewriter &rewriter) const final {
    Location loc = graph.getLoc();
    // To keep the import-as-graph logic taken by TFG, we create `void func()`
    // to contain the ops in the tfg::GraphOp. That means the arguments/results

 public:
  using OpConversionPattern::OpConversionPattern;

  LogicalResult matchAndRewrite(
      TF::UniformQuantizedDotHybridOp op,
      TF::UniformQuantizedDotHybridOpAdaptor adaptor,
      ConversionPatternRewriter &rewriter) const override {
    // Uniform Quantized type for the rhs.
    int64_t rhs_quantized_dimension = op.getRhsQuantizationAxis();
    // Currently for dot, PTQ supports per-tensor quantization.

    ShapedType& updates_type, ConversionPatternRewriter& rewriter);

template <typename BinaryOp, typename TfOp>
class ConvertScatterOp : public OpConversionPattern<mhlo::ScatterOp> {
 public:
  using OpConversionPattern::OpConversionPattern;

  LogicalResult matchAndRewrite(
      mhlo::ScatterOp scatter_op, OpAdaptor adaptor,
      ConversionPatternRewriter& rewriter) const final {
    {

      : ConversionPattern(converter, MatchAnyOpTypeTag(), 1, ctx) {}

  LogicalResult matchAndRewrite(
      Operation *op, ArrayRef<Value> operands,
      ConversionPatternRewriter &rewriter) const override {
    // This pattern only handle non-UQ, non-const ops.
    if (IsTFUniformQuantizedOp(op) || llvm::isa<TF::ConstOp>(op)) {
      return failure();
    }