}
  }
  OpBuilder builder(func);

  // For every op, add new control inputs in reverse order so that the ops don't
  // get invalidated.
  llvm::SmallVector<Value, 8> operands;
  llvm::SmallPtrSet<Operation*, 4> defining_ops;
  llvm::SmallVector<Type, 4> types;
  for (auto& item :
       llvm::make_early_inc_range(llvm::reverse(graph_op.GetBody()))) {
    auto it = new_control_inputs.find(&item);

namespace mlir {
namespace quant {

class QuantizeContext;

using AdjacentOperations = llvm::SmallVectorImpl<Operation*>;
using QuantizedMultipliers = llvm::SmallVector<QuantizedMultiplier, 4>;
using QuantizedRanges = llvm::SmallVector<QuantizedRange, 4>;
using ScaleFn = std::function<LogicalResult(QuantizeContext*, Operation*,
                                            AdjacentOperations*, bool*)>;

  //   members.
  ValuesConstraintSet constraints;
};

using Members = llvm::SmallVector<Member>;

struct ClusteringState {
  // Storage backing an array based union-find algorithm for clustering. Index
  // in this vector is the member id.
  llvm::SmallVector<Member> members;

  // Mapping from the member operation (block argument) to the member id.

==============================================================================*/

#include <functional>
#include <memory>
#include <string>
#include <vector>

#include "absl/strings/str_cat.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/StringRef.h"
#include "llvm/Support/Casting.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/Block.h"  // from @llvm-project

  const SmallVector<NamedAttribute>& attributes = {
      builder_.getNamedAttr(
          "precision_config",
          builder_.getArrayAttr(SmallVector<Attribute>(
              1, mlir::stablehlo::PrecisionAttr::get(
                     ctx_.get(), mlir::stablehlo::Precision::DEFAULT)))),
  };
  const SmallVector<Value> operands(dot_general_op->getOperands());

}

// Multiplies two 1D arrays with broadcasting support.
template <typename T>
SmallVector<T> MultiplyTwoArrays(ArrayRef<T> a, ArrayRef<T> b) {
  auto get_value_at = [](ArrayRef<T> v, size_t i) -> T {
    if (v.size() == 1) return v.front();
    return v[i];
  };

  size_t max_size = std::max(a.size(), b.size());
  SmallVector<T> result(max_size);
  for (size_t i : llvm::seq<size_t>(0, max_size)) {

#define TENSORFLOW_COMPILER_MLIR_LITE_STABLEHLO_TRANSFORMS_LEGALIZE_HLO_CONVERSIONS_SCATTER_H_

#include <cstdint>
#include <type_traits>

#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SmallVector.h"
#include "mlir/IR/BuiltinAttributes.h"  // from @llvm-project
#include "mlir/IR/BuiltinTypeInterfaces.h"  // from @llvm-project
#include "mlir/IR/ImplicitLocOpBuilder.h"  // from @llvm-project

              (std::is_integral_v<T> || std::is_same_v<T, float>), void>>
Value CreateConstValue(OpBuilder& builder, const Location loc,
                       const SmallVector<int64_t>& shape,
                       const SmallVector<T>& values) {
  if constexpr (std::is_integral_v<T>) {
    auto shape_type =
        RankedTensorType::get(shape, builder.getIntegerType(sizeof(T) * 8));

  // We apply all result attributes at once to avoid excessive allocations when
  // we have many result values.
  llvm::SmallVector<std::vector<NamedAttribute>, 8> result_attrs;
  {
    llvm::SmallVector<DictionaryAttr, 8> tmp;
    func.getAllResultAttrs(tmp);

    for (const auto& res : tmp) {
      result_attrs.push_back(res.getValue().vec());
    }
  }

    llvm::SmallSet<int, 4> resized_tensor_lists =
        GetResizedTensorListIndexes(op.else_function(), tensor_list_args);

    llvm::SmallVector<Type, 8> result_types;
    result_types.reserve(op.getNumResults());
    llvm::SmallVector<Type, 4> op_result_types;
    for (Type ty : op.getResultTypes()) {
      op_result_types.push_back(ty);
    }