LogicalResult ConvertSSDPostProcessFunc::RewriteFunc() {
  func_.eraseBody();
  func_.addEntryBlock();
  func_->setAttr(kTFImplements,
                 StringAttr::get(func_.getContext(), kCustomSSDPostprocessing));

  OpBuilder builder(func_.getBody());
  std::string custom_option_buffer;
  if (failed(CreateNMSCustomOptions(func_, attr_.getAttrs(),
                                    custom_option_buffer))) {
    return failure();
  }

    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();
  }

  // function name can be changed by those passes.
  call_op->setAttr(TF::kStablehloEntryFunctionAttrName,
                   FlatSymbolRefAttr::get(builder.getStringAttr(func_name)));
  // Store the custom attribute to restore the function name when loading it
  // back in the post calibration stage. As mentioned above, the above entry
  // function attribute is not reliable.
  call_op->setAttr(kOriginalStablehloEntryFunctionAttrName,

      return true;
    }
  }

  SymbolTable symbol_table(op->getParentOfType<ModuleOp>());
  // Check the functions associated to CaseOp, IfOp and WhileOp.
  for (const NamedAttribute& attr : op->getAttrs()) {
    FlatSymbolRefAttr symbol_attr =
        dyn_cast_or_null<FlatSymbolRefAttr>(attr.getValue());
    if (!symbol_attr) continue;

    func::FuncOp target_func = dyn_cast_or_null<func::FuncOp>(

            Set<JavaMethod> setters = gettersAndSetters.get(Boolean.FALSE).stream().map(Accessor::getMethod).collect(toSet());
            if (!getters.isEmpty() && !setters.isEmpty()) {
                return new Accessors(owningClass, propertyName, getters, setters);
            }

_initializers = _KerasLazyLoader(
    globals(), submodule="initializers", name="initializers", mode="v2")
setattr(_current_module, "losses", _losses)
setattr(_current_module, "metrics", _metrics)
setattr(_current_module, "optimizers", _optimizers)

func Match(label labels.Selector, field fields.Selector) storage.SelectionPredicate {
	return storage.SelectionPredicate{
		Label:    label,
		Field:    field,
		GetAttrs: GetAttrs,
	}
}

// GetAttrs returns labels and fields of a given object for filtering purposes.
func GetAttrs(obj runtime.Object) (labels.Set, fields.Set, error) {
	parameters, ok := obj.(*resource.ResourceClassParameters)
	if !ok {

func Match(label labels.Selector, field fields.Selector) storage.SelectionPredicate {
	return storage.SelectionPredicate{
		Label:    label,
		Field:    field,
		GetAttrs: GetAttrs,
	}
}

// GetAttrs returns labels and fields of a given object for filtering purposes.
func GetAttrs(obj runtime.Object) (labels.Set, fields.Set, error) {
	parameters, ok := obj.(*resource.ResourceClaimParameters)
	if !ok {

// Copies attributes that satisfy the given predicate from `from` to `to`.
template <typename Predicate>
void CopyAttributes(Operation *from, Operation *to, Predicate P) {
  for (const NamedAttribute &attr : from->getAttrs())
    if (P(attr)) to->setAttr(attr.getName(), attr.getValue());
}

// Copies attributes whose name begins with an _ from `from` to `to`.
inline void CopyUnderscoredAttributes(Operation *from, Operation *to) {

  auto cluster = builder.create<mlir::tf_device::ClusterOp>(
      mlir::UnknownLoc::get(&context), result_types);
  cluster->setAttr(kNumCoresPerReplicaAttr,
                   builder.getIntegerAttr(builder.getIntegerType(64), 1));
  cluster->setAttr(kTopologyAttr, builder.getStringAttr(""));
  cluster->setAttr(kDeviceAssignmentAttr, builder.getArrayAttr({}));

  EXPECT_FALSE(HasModelParallelism(cluster));
}