cluster_op->setAttr(
      builder->getStringAttr(kFuncAttr),
      mlir::SymbolRefAttr::get(builder->getContext(), outlined_func.getName()));

  builder->setInsertionPoint(cluster_op);
  auto cluster_func_op = builder->create<tf_device::ClusterFuncOp>(
      cluster_op.getLoc(), outlined_func.getFunctionType().getResults(),
      live_ins.getArrayRef(), cluster_op->getAttrs());

  added_func_op->setAttr(kInterfaceNameAttr, interface_name);
  added_call_op->setAttr(kInterfaceNameAttr, interface_name);

  StringAttr device = mlir::cast<StringAttr>(
      added_func_op->getRegion(0).getBlocks().front().front().getAttr(kDevice));
  StringAttr inference_type = mlir::cast<StringAttr>(
      added_func_op->getRegion(0).getBlocks().front().front().getAttr(
          kInferenceType));

    }
  }
  OpBuilder builder(op);
  builder.setInsertionPointAfter(op);
  OperationState state(op->getLoc(), op->getName().getStringRef(),
                       op->getOperands(), result_types, op->getAttrs());
  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())) {

  // use it in exports or if there are breaking changes
  module->setAttr("tfl.schema_version",
                  builder.getI32IntegerAttr(model->version));
  if (!model->description.empty()) {
    module->setAttr("tfl.description",
                    builder.getStringAttr(model->description));
  }

  if (!metadata_attrs.empty()) {
    module->setAttr("tfl.metadata", builder.getDictionaryAttr(metadata_attrs));
  }

    }
  });
  return entry_funcs;
}

LogicalResult GetCallees(SymbolUserOpInterface op, SymbolTable &symtab,
                         llvm::SmallVector<func::FuncOp> &callees) {
  for (auto attr : op->getAttrs()) {
    auto sym = mlir::dyn_cast<SymbolRefAttr>(attr.getValue());
    if (!sym) continue;
    auto callee = symtab.lookup<func::FuncOp>(sym.getRootReference());
    if (!callee) {

    return func_.emitWarning()
           << "Invalid number of results from " << kMaxUnpooling << ": "
           << func_.getFunctionType().getNumResults();
  }

  auto attrs = attr_.getAttrs();

  if (failed(HasIntegerArrayWithSize(&func_, attrs, "pool_size", 2))) {
    return failure();
  }

  if (failed(HasIntegerArrayWithSize(&func_, attrs, "strides", 2))) {
    return failure();
  }

 private:
  LogicalResult matchAndRewrite(TF::TPUPartitionedCallOp call_op,
                                PatternRewriter& rewriter) const override {
    auto f_attr = mlir::dyn_cast<FlatSymbolRefAttr>(call_op.getFAttr());
    auto module_op = call_op->getParentOfType<ModuleOp>();
    SymbolTable symbol_table(module_op);

    auto f_name = f_attr.getValue();
    func::FuncOp float_func =

    if (op->getDialect() != tf_dialect) return WalkResult::advance();

    if (parallel_group_attr) {
      op->setAttr(TF::kParallelExecAnnotation, parallel_group_attr);
    }
    auto device_attr = op->getAttr(kDeviceAttr);
    if (!device_attr) {
      op->setAttr(kDeviceAttr, launch.getDeviceAttr());
      return WalkResult::advance();
    }

  auto spec = std::make_unique<OpQuantSpec>();
  if (auto call_op = dyn_cast<TF::PartitionedCallOp>(op)) {
    StringRef function_name =
        mlir::cast<FlatSymbolRefAttr>(call_op.getFAttr()).getValue();
    if (!function_name.starts_with("composite_")) {
      return spec;
    }
    if (function_name.contains("depthwise_conv2d")) {
      spec->coeff_op_quant_dim[1] = 3;

    BlockArgument block_arg) {
  assert(block_arg.getOwner() == &GetBody());

  unsigned arg_number = block_arg.getArgNumber();
  unsigned num_replicated_args = GetNumReplicatedBlockArguments();
  int32_t num_replicas = getNAttr().getInt();
  MutableArrayRef<OpOperand> operands = getOperation()->getOpOperands();

  // All replicated arguments are before packed arguments so return replicated