// unchanged.
class CommuteBothInputsTransposedWithEwiseOps : public RewritePattern {
 public:
  explicit CommuteBothInputsTransposedWithEwiseOps(MLIRContext *context)
      : RewritePattern(MatchAnyOpTypeTag(), /*benefit=*/1, context) {}
  LogicalResult matchAndRewrite(Operation *op,
                                PatternRewriter &rewriter) const override {
    if (!IsElementwise(op)) {
      return failure();
    }

  }

  void runOnOperation() override;
};

class RemoveTpuOp : public RewritePattern {
 public:
  explicit RemoveTpuOp(MLIRContext* context)
      : RewritePattern(MatchAnyOpTypeTag(), /*benefit=*/1, context) {}

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

                                const std::string& device_type,
                                bool prefer_tf2xla)
      : ConversionPattern(converter, MatchAnyOpTypeTag(), /*benefit=*/1, ctx),
        device_type_(device_type),
        prefer_tf2xla_(prefer_tf2xla) {}

  LogicalResult matchAndRewrite(
      Operation* op, ArrayRef<Value> operands,

    }
  }

  return std::nullopt;
}

class AddQuantizationUnitLoc : public RewritePattern {
 public:
  explicit AddQuantizationUnitLoc(MLIRContext* context)
      : RewritePattern(MatchAnyOpTypeTag(), /*benefit=*/1, context) {}

 private:
  LogicalResult matchAndRewrite(Operation* op,
                                PatternRewriter& rewriter) const override {
    if (!IsOpWithQuantizableTrait(op) ||

  TFQuantTypeConverter &converter_;
};

class TFQuantTypePattern : public ConversionPattern {
 public:
  TFQuantTypePattern(MLIRContext *ctx, TypeConverter &converter)
      : 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.

namespace mlir {
namespace {

class ConvertResultsBroadcastableShapeOp : public RewritePattern {
 public:
  ConvertResultsBroadcastableShapeOp(MLIRContext* context)
      : RewritePattern(MatchAnyOpTypeTag(), 1, context) {}

  LogicalResult matchAndRewrite(Operation* op,
                                PatternRewriter& rewriter) const override;

 private:
  template <typename Op>

  BFloat16TypeConverter& converter_;
};

class BFloat16TypePattern : public ConversionPattern {
 public:
  BFloat16TypePattern(TypeConverter& converter, MLIRContext* ctx)
      : ConversionPattern(converter, MatchAnyOpTypeTag(), /*benefit=*/1, ctx) {}

  LogicalResult matchAndRewrite(
      Operation* op, const ArrayRef<Value> operands,
      ConversionPatternRewriter& rewriter) const override {
    if (getTypeConverter()->isLegal(op)) {

  //    implemented in C++ don't do so this comparison doesn't work in those
  //    cases.
  // 2) Descending pattern benefit.
  // 3) Op specific patterns over patterns with MatchAnyOpTypeTag.
  // 4) Order of patterns in `RewritePatternSet`.

  // Add TF->HLO legalization patterns.
  PopulateLegalizeTfPatterns(context, &legalize_lower_patterns);

  // Add TF->TF lowering patterns.

  // outlives this object.
  explicit AddCustomAggregationOp(MLIRContext *context,
                                  const CalibrationOptions &calib_opts)
      : RewritePattern(MatchAnyOpTypeTag(), /*benefit=*/1, context),
        calib_opts_(calib_opts) {}

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

// identifying `erf` ops and validate the structure around them.
class OutlineGELU : public RewritePattern {
 public:
  explicit OutlineGELU(MLIRContext* context)
      : RewritePattern(MatchAnyOpTypeTag(), /*benefit=*/1, context) {}

  LogicalResult matchAndRewrite(Operation* op,
                                PatternRewriter& rewriter) const override {
    if (!MatchERF(op)) return failure();
    // `add 1`