// Create new ConstantOp-ConvertOp-Operation sequences. At this moment,
      // old ConstantOp is guaranteed to have one F32->F16 convert op regardless
      // of its number of users.
      rewriter.setInsertionPointAfter(op);
      // create new F16 constant op in that location
      ConstantOp new_const = rewriter.create<ConstantOp>(
          op->getLoc(), new_result_type, new_value_attr);

        changed = true;
      }
    }
    return changed;
  }

  // Convert ConstantOp-CastOp-Operation sequence into new ConstantOp
  // -Dequantize-Operation where the new ConstantOp has float16 data type.
  bool convertToFloat16Constant(PatternRewriter& rewriter,
                                arith::ConstantOp op) const {
    for (auto connected_op : op.getResult().getUsers()) {

class PrepareDRQQuantizableOp : public OpRewritePattern<arith::ConstantOp> {
 public:
  explicit PrepareDRQQuantizableOp(MLIRContext* context,
                                   const quant::QuantizationSpecs& quant_specs,
                                   OpSet op_set,
                                   bool enable_per_channel_quantization)
      : OpRewritePattern<arith::ConstantOp>(context),
        quant_specs_(quant_specs),

template <typename Op, typename ElementType = Type, typename ValType,
          typename Convert>
static Attribute BinaryFolder(Op *op) {
  auto lhs_op = op->getLhs().template getDefiningOp<mhlo::ConstantOp>();
  auto rhs_op = op->getRhs().template getDefiningOp<mhlo::ConstantOp>();
  if (!lhs_op || !lhs_op) return {};

  auto lhs = dyn_cast_or_null<DenseElementsAttr>(lhs_op.getValue());
  auto rhs = dyn_cast_or_null<DenseElementsAttr>(rhs_op.getValue());

    }
  });
}

arith::ConstantOp QuantizationDriver::DuplicateConstantOpIfNeeded(
    arith::ConstantOp op, Operation* target_op, const int operand_index) {
  if (op.getResult().hasOneUse()) {
    return op;
  }
  OpBuilder builder(op->getContext());
  builder.setInsertionPointAfter(op);
  arith::ConstantOp new_op = cast<arith::ConstantOp>(builder.clone(*op));

        llvm::isa<BlockArgument>(tpose_arg2.getPerm())) {
      return failure();
    }
    auto perm1 = llvm::dyn_cast_or_null<arith::ConstantOp>(
        tpose_arg1.getPerm().getDefiningOp());
    auto perm2 = llvm::dyn_cast_or_null<arith::ConstantOp>(
        tpose_arg2.getPerm().getDefiningOp());
    if (!perm1 || !perm2) {
      return failure();
    }

                                           Type type, Location loc) {
  if (arith::ConstantOp::isBuildableWith(value, type))
    return builder.create<arith::ConstantOp>(loc, type,
                                             value.cast<TypedAttr>());
  if (func::ConstantOp::isBuildableWith(value, type))
    return builder.create<func::ConstantOp>(loc, type,
                                            value.cast<FlatSymbolRefAttr>());

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tsl/platform/status_matchers.h"

namespace mlir::quant {
namespace {

using ::mlir::stablehlo::AddOp;
using ::mlir::stablehlo::ConstantOp;
using ::mlir::stablehlo::ConvolutionOp;
using ::mlir::stablehlo::DotGeneralOp;
using ::mlir::stablehlo::SubtractOp;
using ::testing::ElementsAreArray;
using ::testing::Eq;
using ::testing::IsEmpty;

  auto constant_attr = rewriter->getI32TensorAttr(values);
  return rewriter->create<arith::ConstantOp>(loc, values_type, constant_attr);
}

// Creates ConstantOp for array of int64_t.
arith::ConstantOp createI64ConstantOp(llvm::ArrayRef<int64_t> values,
                                      Location loc, PatternRewriter* rewriter) {
  auto values_type = RankedTensorType::get(

  return builder->create<arith::ConstantOp>(location, type, attr);
}

Value CreateF32SplatConst(OpBuilder* builder, ArrayRef<int64_t> shape,
                          float val, mlir::Location location) {
  auto type = RankedTensorType::get(shape, builder->getF32Type());
  auto attr = DenseElementsAttr::get(type, val);
  return builder->create<arith::ConstantOp>(location, type, attr);
}