given:
        def repo1 = server.getRemoteIvyRepo("/repo1")
        def repo2 = server.getRemoteIvyRepo("/repo2")
        def moduleA = repo1.module('group', 'projectA')
        moduleA.publish()
        def missingModuleB = repo1.module('group', 'projectB')
        def moduleB = repo2.module('group', 'projectB')
        moduleB.publish()

        and:

                ModelMap<NativeComponentSpec> components = modelRegistry.realize("components", ModelTypes.modelMap(NativeComponentSpec.class));
                for (NativeBinarySpecInternal binary : allBinariesOf(components.withType(VariantComponentSpec.class))) {
                    state.registerBinary(binary);
                }

// vhlo.op %1 : vhlo.tensor<...>
//   ==>
// vhlo.op %1 : tensor<...>
//
// TODO: There's likely a way to make MLIR manage the unrealized cast
// conversions using a specific rewriter.
LogicalResult ApplyTypeConverter(ModuleOp op, TypeConverter &converter) {
  IRRewriter rewriter(op->getContext());

  op->walk([&](Operation *op) {
    if (op->getDialect()->getNamespace() != "vhlo") return;

    // Convert operands

                    result.allComponents {
                        if (id instanceof ModuleComponentIdentifier && id.module == 'moduleA') {
                            found = true
                            assert variants.size() == 1
                            assert variants[0].owner.module == 'moduleA'
                            def externalVariant = variants[0].externalVariant
                            assert externalVariant.present

Its property 'org.gradle.model.ModelMap<java.io.FileInputStream> map' is not a valid managed collection
A managed collection can not contain 'java.io.FileInputStream's
A valid managed collection takes the form of ModelSet<T> or ModelMap<T> where 'T' is:
        - A managed type (annotated with @Managed)""")
    }

    @Managed
    interface ManagedWithInvalidModelMap {
        ModelMap<FileInputStream> getMap()
    }

//   maybe tensor layout transformation, device specific fusion, etc.
class AlternativeSubgraphPass
    : public mlir::PassWrapper<AlternativeSubgraphPass,
                               mlir::OperationPass<ModuleOp>> {
 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(AlternativeSubgraphPass)

  llvm::StringRef getArgument() const final {
    return "tfl-get-alternative-subgraph";
  }

    ModelPath path = ModelPath.path("map")

    ModelType<ModelMap<T>> getModelMapType() {
        ModelTypes.modelMap(itemType)
    }

    void mutate(@DelegatesTo(ModelMap) Closure<?> action) {
        registry.mutate(ModelReference.of(path, modelMapType), ClosureBackedAction.of(action))
    }

    void mutateWithoutDelegation(Action<ModelMap<T>> action) {
        registry.mutate(ModelReference.of(path, modelMapType), action)

        %mul = "tf.Mul"(%cast, %scale) : (tensor<1024x24x24x3xf32>, tensor<f32>) -> tensor<1024x24x24x3xf32>
        func.return %mul : tensor<1024x24x24x3xf32>
      }
    }
  )mlir";

  OwningOpRef<ModuleOp> module_op_ref = ParseModuleOpString(kModuleCode);
  const auto test_func =
      module_op_ref->lookupSymbol<func::FuncOp>("test_fold_constant");
  ASSERT_THAT(test_func, NotNull());

                    @Model
                    List<String> strings() {
                      []
                    }

                    @Mutate
                    void addTasks(ModelMap<Task> tasks, List<String> strings) {
                        tasks.create("value") {
                            it.doLast {
                                println "value: $strings"
                            }

    // and use it for `shape_inference_graph` attribute on XlaHostCompute.
    func::FuncOp cloned_func;
    SymbolTable manager(op->getParentOfType<ModuleOp>());
    StringRef host_module = op.getHostMlirModule();
    if (!host_module.empty()) {
      mlir::OwningOpRef<mlir::ModuleOp> module_for_func;

      func::FuncOp func = op.GetHostFunc(&module_for_func);

      OpBuilder::InsertionGuard guard(rewriter);