}

SmallVector<Value> CloneOpWithReplacedOperands(
    OpBuilder& builder, Operation* op, const ArrayRef<Value> new_operands) {
  IRMapping mapping;
  for (const auto& arg : enumerate(new_operands)) {
    mapping.map(op->getOperand(arg.index()), arg.value());
  }
  return builder.clone(*op, mapping)->getResults();
}

FailureOr<int32_t> CastI64ToI32(const int64_t value) {

    return "runs passes registered as tensorflow::GraphOptimizationPass";
  }

 private:
  void runOnOperation() override {
    // Verify all passes requested were registered/found.
    for (auto pass_it : llvm::enumerate(passes_)) {
      if (pass_it.value() == nullptr) {
        mlir::emitError(mlir::UnknownLoc::get(&getContext()))
            << "could not find pass " << cl_pass_list[pass_it.index()];
        return signalPassFailure();

              // filter.
              return a_is_function_exclude > b_is_function_exclude;
            });

  for (const auto& tac_filter : llvm::enumerate(tac_filters_->tac_filters())) {
    SmallVector<Operation*> filtered_ops;
    ApplyTacFilter(module, tac_filter.value(), filtered_ops, builder);
    PrintTacFilterResult(module.getLoc(), tac_filter.value(),

  for (int i = 0; i < op->getNumRegions(); ++i) {
    state.addRegion();
  }
  Operation* new_op = builder.create(state);
  for (const auto& indexed_regions : llvm::enumerate(op->getRegions())) {
    Region& region = op->getRegion(indexed_regions.index());
    IRMapping mapping;
    indexed_regions.value().cloneInto(&region, mapping);
  }
  int new_position = 0;

  const TorchAvgPoolData pool = GetTorchAvgPoolData(op);

  const auto values = GetPadOpPaddingValues(pool);

  llvm::SmallVector<int32_t> padding_vec(8, 0);  // NHWC
  for (auto [ind, val] : llvm::enumerate(values)) {
    padding_vec[ind + 2] = val;
  }

  auto padding_shape = RankedTensorType::get({4, 2}, builder.getI32Type());
  return DenseIntElementsAttr::get(padding_shape, padding_vec);
}