}

  // Replace old while op with new while op.
  auto new_while_op = builder.create<TF::WhileOp>(
      while_op.getLoc(), new_result_types, new_operands, while_op->getAttrs());
  auto new_while_wrapper =
      CreateIsland(new_while_op, while_wrapper.getControlInputs(), builder);
  for (auto result : while_wrapper.getOutputs()) {
    result.replaceAllUsesWith(

				}
				originalRevision = out.(metav1.Object).GetResourceVersion()
			}

			// prepare result and pred
			pred := storage.SelectionPredicate{
				Label: tc.selector,
				Field: fields.Everything(),
				GetAttrs: func(obj runtime.Object) (labels.Set, fields.Set, error) {
					pod, ok := obj.(*examplev1.Pod)
					if !ok {
						return nil, nil, fmt.Errorf("not a pod")
					}
					return pod.ObjectMeta.Labels, fields.Set{

    ElementsAttr input, int column) {
  auto int_attr = mlir::cast<DenseIntElementsAttr>(input);
  auto shaped_type = int_attr.getType();
  auto shape = shaped_type.getShape();

  if (shape.size() != 2) return DenseIntElementsAttr();

  llvm::SmallVector<int64_t, 4> values;
  values.reserve(shaped_type.getNumElements() / shape[1]);

  for (const auto &it : llvm::enumerate(int_attr.getValues<APInt>())) {

    }
    // Recreate the attributes dictionary to only contain "value".
    NamedAttrList attributes;
    attributes.append(NamedAttribute(value_str_attr, cst->getAttr("value")));
    cst->setAttrs(attributes.getDictionary(&getContext()));
  });

  auto& side_effect_analysis = getAnalysis<mlir::TF::SideEffectAnalysis>();
  for (auto func : getOperation().getOps<mlir::func::FuncOp>())

	}
	return storage.SelectionPredicate{
		Label:    labels.Everything().Add(*l),
		Field:    fields.Everything(),
		GetAttrs: getPodAttrs,
	}
}

func matchEverything() storage.SelectionPredicate {
	return storage.SelectionPredicate{
		Label: labels.Everything(),
		Field: fields.Everything(),
		GetAttrs: func(obj runtime.Object) (label labels.Set, field fields.Set, err error) {
			return nil, nil, nil
		},
	}
}

    // folding), but we can still derive the shape of a constant tensor for
    // its attribute type.
    auto tensor_attr = mlir::cast<mlir::TypedAttr>(inst->getAttr("value"));
    llvm::ArrayRef<int64_t> shape_ref =
        mlir::cast<TensorType>(tensor_attr.getType()).getShape();
    if (mlir::failed(check_shape(shape_ref))) return std::nullopt;

	}
	for name, tc := range cases {
		t.Run(name, func(t *testing.T) {
			if tc.job == nil {
				_, _, err := GetAttrs(tc.nonJobObject)
				if diff := cmp.Diff(tc.wantErr, err.Error()); diff != "" {
					t.Errorf("Unexpected errors (-want,+got):\n%s", diff)
				}
			} else {
				gotLabels, _, err := GetAttrs(tc.job)
				if err != nil {
					t.Errorf("Error %s supposed to be nil", err.Error())
				}

//sysnb	Getpid() (pid int)
//sysnb	Getppid() (ppid int)
//sys	Getpriority(which int, who int) (prio int, err error)
//sysnb	Getrusage(who int, rusage *Rusage) (err error)
//sysnb	Gettid() (tid int)
//sys	Getxattr(path string, attr string, dest []byte) (sz int, err error)
//sys	InotifyAddWatch(fd int, pathname string, mask uint32) (watchdesc int, err error)
//sysnb	InotifyInit1(flags int) (fd int, err error)

	sz = int(r0)
	if int64(r0) == -1 {
		err = errnoErr2(e1, e2)
	}
	return
}

//go:nosplit
func get_FgetxattrAddr() *(func(fd int, attr string, dest []byte) (sz int, err error))

var Fgetxattr = enter_Fgetxattr

func enter_Fgetxattr(fd int, attr string, dest []byte) (sz int, err error) {
	funcref := get_FgetxattrAddr()
	if funcptrtest(GetZosLibVec()+SYS___FGETXATTR_A<<4, "") == 0 {
		*funcref = impl_Fgetxattr

        output_uniform_quantized_tensor_type};
    auto new_conv_op_with_output_type =
        rewriter.create<stablehlo::ConvolutionOp>(
            op.getLoc(), new_conv_output_types, op.getOperands(),
            op->getAttrs());

    rewriter.replaceAllUsesWith(op.getResult(),
                                new_conv_op_with_output_type.getResult());

    auto new_output_dequant_op =