return;
    }

    const auto [min_value, max_value] = *min_max_values;
    mlir::OpBuilder builder(aggregator_op);
    aggregator_op->setAttr("min", builder.getF32FloatAttr(min_value));
    aggregator_op->setAttr("max", builder.getF32FloatAttr(max_value));
  });
  return status;
}

bool IsCalibrationRequired(mlir::ModuleOp module_op) {
  bool calibration_required = false;

    }

    public void setId(final String id) {
        this.id = id;
    }

    public void setCss(final String css) {
        this.css = css;
    }

    public void setAttr(final String name, final String value) {
        this.attrName = name;
        this.attrValue = value;
    }

    @Override
    public String toString() {

                          default_value);
}

string AttrView::SetterMethod() const {
  if (!attr_.is_list()) {
    return absl::StrCat("SetAttr", toUpperCamel(attr_.full_type()));
  } else {
    return absl::StrCat("SetAttr", toUpperCamel(attr_.base_type()), "List");
  }
}

std::vector<string> AttrView::SetterArgs() const {
  if (attr_.full_type() == "string") {

  ConstOp zero = builder.create<ConstOp>(loc, zero_attr);
  zero->setAttr(kICIWeightDistributionMlirBridgeMarker,
                builder.getBoolAttr(true));
  ConstOp shape = builder.create<ConstOp>(loc, shape_attr);
  shape->setAttr(kICIWeightDistributionMlirBridgeMarker,
                 builder.getBoolAttr(true));
  FillOp fill = builder.create<FillOp>(loc, shape, zero);
  fill->setAttr(kICIWeightDistributionMlirBridgeMarker,

  // Both the device attribute and compile_device_type attribute should be
  // propagated to the reduce function call.
  reduce_call->setAttr(kDeviceAttr,
                       reduce_dataset->getAttrOfType<StringAttr>(kDeviceAttr));
  reduce_call->setAttr(
      TF::kCompileDeviceTypeAttr,
      reduce_dataset->getAttrOfType<StringAttr>(TF::kCompileDeviceTypeAttr));

  SmallVector<Value, 4> if_returns;

namespace mlir {
namespace TFL {
namespace tac {

// Cost attribute string on the TFL dialect.
constexpr char kCost[] = "tac.cost";

inline void UpdateCost(Operation* op, float cost, OpBuilder* builder) {
  op->setAttr(kCost, builder->getF32FloatAttr(cost));
}

// Get the cost annotated with kCost.
inline bool GetCostOnOp(Operation* op, float* cost) {
  auto cost_type = op->getAttrOfType<FloatAttr>(kCost);
  if (cost_type == nullptr) {

    if (auto device = res->getAttrOfType<StringAttr>("device")) {
      if (!device.getValue().empty())
        result_id->setAttr("device", device);
      else
        result_id->setAttr("device", compile_device_op);
    } else if (compile_device_op) {
      result_id->setAttr("device", compile_device_op);
    }
    res.getOutput().replaceAllUsesWith(compile_op->getResult(0));
  }

      /*config_proto=*/builder.getStringAttr(""),
      /*executor_type=*/builder.getStringAttr(""));
  caller.setFAttr(symbol);

  // Set an attribute that our inliner will look for when choosing which
  // TF::StatefulPartitionedCallOps to inline.
  if (flag_for_inlining)
    caller->setAttr(kEmbeddingPipeliningInlineAttr, builder.getBoolAttr(true));
  return caller;
}

  auto func = builder.create<func::FuncOp>(module->getLoc(),
                                           kSessionInitFuncName, func_type);
  func->setAttr(kTfSavedModelExportedNamesAttr,
                builder.getStrArrayAttr({kSessionInitFuncName}));
  func->setAttr(kTfSavedModelInitializerTypeAttr,
                builder.getStringAttr(kTfSavedModelInitializerRestoreType));
  func.setVisibility(mlir::func::FuncOp::Visibility::Public);

                            live_ins);

  func::FuncOp outlined_func =
      BuildFunction(live_ins.getArrayRef(), cluster_op, symbol_table, builder);
  cluster_op->setAttr(
      builder->getStringAttr(kFuncAttr),
      mlir::SymbolRefAttr::get(builder->getContext(), outlined_func.getName()));

  builder->setInsertionPoint(cluster_op);