type: object
                        loadBalancer:
                          description: Settings controlling the load balancer algorithms.
                          oneOf:
                          - not:
                              anyOf:
                              - required:
                                - simple
                              - required:

    private List<ModelProblem> problems;

    private String source;

    private Model sourceModel;

    private Model rootModel;

    private Set<ModelProblem.Severity> severities = EnumSet.noneOf(ModelProblem.Severity.class);

    DefaultModelProblemCollector(ModelBuildingResult result) {
        this.result = result;
        this.problems = result.getProblems();

    EnumSet<SomeEnum> noUnits = Sets.complementOf(allUnits, SomeEnum.class);
    verifySetContents(noUnits, EnumSet.noneOf(SomeEnum.class));
  }

  @J2ktIncompatible
  @GwtIncompatible // complementOf
  public void testComplementOfEmptyEnumSetWithoutType() {
    Set<SomeEnum> noUnits = EnumSet.noneOf(SomeEnum.class);
    EnumSet<SomeEnum> allUnits = Sets.complementOf(noUnits);

            new TestEnumSetGenerator() {
              @Override
              public Set<AnEnum> create(AnEnum[] elements) {
                return (elements.length == 0)
                    ? EnumSet.noneOf(AnEnum.class)
                    : EnumSet.copyOf(MinimalCollection.of(elements));
              }
            })
        .named("EnumSet")
        .withFeatures(
            SetFeature.GENERAL_PURPOSE,

            new TestEnumSetGenerator() {
              @Override
              public Set<AnEnum> create(AnEnum[] elements) {
                return (elements.length == 0)
                    ? EnumSet.noneOf(AnEnum.class)
                    : EnumSet.copyOf(MinimalCollection.of(elements));
              }
            })
        .named("EnumSet")
        .withFeatures(
            SetFeature.GENERAL_PURPOSE,

            uprank rank 1 inputs, so these layers can now multi-hot encode
            unbatched multi-dimensional samples.
        *   Added a new output mode `"one_hot"` for `CategoryEncoding`,
            `StringLookup`, `IntegerLookup`, which will encode each element in
            an input batch individually, and automatically append a new output