bool is_argmax>
class ConvertReduceOpToArgMinMax : public OpConversionPattern<mhlo::ReduceOp> {
 public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult matchAndRewrite(
      mhlo::ReduceOp reduce_op, OpAdaptor adaptor,
      ConversionPatternRewriter& rewriter) const final {
    if (reduce_op.getInputs().size() != 2) return failure();

                                                StringRef saved_model_dir)
      : OpRewritePattern<InitializeTableFromTextFileV2Op>(context),
        saved_model_dir_(saved_model_dir) {}

  LogicalResult matchAndRewrite(InitializeTableFromTextFileV2Op op,
                                PatternRewriter& rewriter) const override {
    // Now, this pattern matching only supports the following case, which is

 public:
  using OpRewritePattern<FakeQuantOp>::OpRewritePattern;

  FakeQuantRewrite(MLIRContext *ctx, bool *hadFailure)
      : OpRewritePattern<FakeQuantOp>(ctx), hadFailure(hadFailure) {}

  LogicalResult matchAndRewrite(FakeQuantOp op,
                                PatternRewriter &rewriter) const override {
    // TODO: If this pattern comes up more frequently, consider adding core
    // support for failable rewrites.

// results of the GraphOp to the operands of the FetchOp.
struct DropEmptyGraph : public OpRewritePattern<GraphOp> {
  using OpRewritePattern<GraphOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(GraphOp op,
                                PatternRewriter &rewriter) const override {
    Block &block = op.GetBody();
    // Check if graph only has one fetch.
    if (&block.front() != &block.back()) return failure();

        intAttrOne(Builder(context).getI32IntegerAttr(1)),
        allow_bf16_and_f16_type_legalization_(
            allow_bf16_and_f16_type_legalization) {}

  LogicalResult matchAndRewrite(Operation *op,
                                PatternRewriter &rewriter) const override {
    // Assumes TensorFlow convolution op is already verified to be
    // in valid form.

      : use_fake_quant_num_bits_(use_fake_quant_num_bits) {}

  FetchMinMax fetch_min_max_;

  using FetchAttrType = typename FetchMinMax::AttrType;
  LogicalResult matchAndRewrite(TFFakeQuantOp tf_op,
                                OpBuilder &rewriter) const {
    // We don't want to insert quantize/dequantize if the quantize op exists.
    auto res = tf_op.getOutputs();

 public:
  explicit HoistCwiseUnaryOutOfUnpack(MLIRContext *context)
      : OpRewritePattern<UnpackOp>(context) {}
  LogicalResult matchAndRewrite(UnpackOp op,
                                PatternRewriter &rewriter) const override;
};

LogicalResult HoistCwiseUnaryOutOfUnpack::matchAndRewrite(
    UnpackOp op, PatternRewriter &rewriter) const {
  auto loc = op.getLoc();

      : OpRewritePattern<quantfork::QuantizeCastOp>(context),
        target_opset_(target_opset) {}

 private:
  OpSet target_opset_ = OpSet::TF;

  LogicalResult matchAndRewrite(quantfork::QuantizeCastOp q_op,
                                PatternRewriter& rewriter) const override {
    auto output_type = mlir::cast<TensorType>(q_op.getType());
    auto elem_type =

                                               new_dequantize_op);
    user_op->getResult(0).setType(new_user_op_type);
  }

  LogicalResult matchAndRewrite(TF::PartitionedCallOp op,
                                PatternRewriter& rewriter) const override {
    const auto f_attr = mlir::dyn_cast<FlatSymbolRefAttr>(op.getFAttr());
    StringRef function_name = f_attr.getValue();

 public:
  explicit FoldConstantCaseOp(MLIRContext* context)
      : OpRewritePattern<TF::CaseOp>(context) {}
  LogicalResult matchAndRewrite(TF::CaseOp op,
                                PatternRewriter& rewriter) const override;
};

LogicalResult FoldConstantCaseOp::matchAndRewrite(
    TF::CaseOp op, PatternRewriter& rewriter) const {
  // Extract the constant cond value.
  DenseIntElementsAttr branch;