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) {
	claim, ok := obj.(*resource.ResourceClaim)
	if !ok {
		return nil, nil, errors.New("not a resourceclaim")

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) {
	scheduling, ok := obj.(*resource.PodSchedulingContext)
	if !ok {

	slice, ok := obj.(*resource.ResourceSlice)
	if !ok {
		return nil, fmt.Errorf("not a ResourceSlice")
	}
	return []string{slice.NodeName}, nil
}

// GetAttrs returns labels and fields of a given object for filtering purposes.
func GetAttrs(obj runtime.Object) (labels.Set, fields.Set, error) {
	slice, ok := obj.(*resource.ResourceSlice)
	if !ok {
		return nil, nil, fmt.Errorf("not a ResourceSlice")
	}

	Field               fields.Selector
	GetAttrs            AttrFunc
	IndexLabels         []string
	IndexFields         []string
	Limit               int64
	Continue            string
	AllowWatchBookmarks bool
}

// Matches returns true if the given object's labels and fields (as
// returned by s.GetAttrs) match s.Label and s.Field. An error is
// returned if s.GetAttrs fails.

      {StringAttr::get(context, "outputs"),
       StringAttr::get(context, absl::StrJoin(output_names, ","))});
  auto dictAttr = DictionaryAttr::get(context, func_attrs);
  main_func->setAttr(StringAttr::get(context, kEntryFunctionAttr), dictAttr);
  main_func->setAttr(
      kTfSavedModelExportedNamesAttr,
      builder.getStrArrayAttr({kImportModelDefaultGraphFuncName}));

  if (input_names.size() != main_func.getNumArguments() ||

  auto operands = llvm::to_vector<4>(op->getOperands());
  operands.push_back(operand);
  auto new_op = builder->create<OpT>(op->getLoc(), op->getResultTypes(),
                                     operands, attr.getAttrs());
  op->replaceAllUsesWith(new_op.getOperation()->getResults());
  op->erase();
  return new_op;
}

// Returns success if the function has at most one op of the template type and

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


# Do an eager load for Keras' code so that any function/method that needs to

      added_func_op->getRegion(0).getBlocks().front().front().getAttr(
          kInferenceType));
  added_call_op->setAttr(kDevice, device);
  added_call_op->setAttr(kInferenceType, inference_type);
  added_func_op->setAttr(kDevice, device);
  added_func_op->setAttr(kInferenceType, inference_type);

  std::string function_name = absl::StrCat(interface_name.getValue().str(), "_",

    }
    OpBuilder builder(call);
    auto new_call = builder.create<CallOp>(
        call.getLoc(), info.decomposed_callee.getFunctionType().getResults(),
        new_operands, call->getAttrs());
    new_call->setAttr(
        "f", SymbolRefAttr::get(
                 builder.getContext(),
                 const_cast<func::FuncOp&>(info.decomposed_callee).getName()));
    for (int64_t i = 0; i < call.getNumResults(); ++i) {

    }
    OpBuilder builder(call);
    auto new_call = builder.create<CallOp>(
        call.getLoc(), info.decomposed_callee.getFunctionType().getResults(),
        new_operands, call->getAttrs());
    new_call->setAttr(
        "f", SymbolRefAttr::get(
                 builder.getContext(),
                 const_cast<func::FuncOp&>(info.decomposed_callee).getName()));