rng.uniform(low=0.0, high=1.0, size=static_input_shape).astype(
            np.float32
        )
    )

    def data_gen() -> repr_dataset.RepresentativeDataset:
      for _ in range(100):
        yield {
            'input_tensor': rng.uniform(
                low=0.0, high=1.0, size=static_input_shape
            ).astype(np.float32)
        }

import org.gradle.plugins.signing.signatory.SignatoryProvider;
import org.gradle.security.internal.BaseSignatoryProvider;
import org.gradle.util.internal.ConfigureUtil;

import static org.codehaus.groovy.runtime.DefaultGroovyMethods.asType;

public interface ConfigurableSignatoryProvider<T extends Signatory> extends BaseSignatoryProvider<T>, SignatoryProvider<T> {
    @Override
    default void configure(SigningExtension settings, Closure closure) {

    def data_gen():
      for _ in range(255):
        yield {
            'x': ops.convert_to_tensor(
                np.random.uniform(size=(10)).astype('f4')
            ),
            'y': ops.convert_to_tensor(
                np.random.uniform(size=(10)).astype('f4')
            ),
        }

    root = ModelWithAdd()

    temp_path = self.create_tempdir().full_path
    saved_model_save.save(

import org.objectweb.asm.Type;

import javax.lang.model.element.ExecutableElement;
import javax.lang.model.type.TypeMirror;

public class TypeUtils {
    public static Type extractType(TypeMirror typeMirror) {
        return typeMirror.accept(new TypeMirrorToType(), null);
    }

    public static Type extractReturnType(ExecutableElement methodElement) {

    private final Class<R> type;
    private final BiFunction<? super A, ? super B, ? extends R> combiner;
    private final ProviderInternal<A> left;
    private final ProviderInternal<B> right;

    public BiProvider(@Nullable Class<R> type, Provider<A> left, Provider<B> right, BiFunction<? super A, ? super B, ? extends R> combiner) {
        this.type = type;
        this.combiner = combiner;

        this.type = type;
    }

    public Type asType() {
        return type;
    }

    public TypeName asTypeName() {
        return TypeUtils.typeName(type);
    }

    public static boolean isAnyOf(Type type) {
        for (GradleLazyType gradleType : values()) {
            if (gradleType.type != null && gradleType.type.equals(type)) {
                return true;
            }
        }

        static <T> Value<T> ofNullable(@Nullable T value) {
            if (value == null) {
                return MISSING.asType();
            }
            return new Present<>(value);
        }

        static <T> Value<T> missing() {
            return MISSING.asType();
        }

        static <T> Value<T> of(T value) {
            if (value == null) {

            Value<? extends V> value = providerOfValue.calculateValue(consumer);
            if (value.isMissing()) {
                return value.asType();
            }
            collector.add(key, value.getWithoutSideEffect(), dest);
            return Value.present().withSideEffect(SideEffect.fixedFrom(value));
        }

        @Override

              np.random.uniform(size=bias_shape), dtype=dtypes.float32
          )

        self._kernel = np.random.uniform(size=y_shape).astype('f4')
        self._min = (-0.8, -0.8, -0.9)
        self._max = (0.9, 0.9, 1.0)

      @def_function.function(
          input_signature=[
              tensor_spec.TensorSpec(
                  name='x', shape=x_signature, dtype=dtypes.float32
              )
          ]

        Type parameterType = extractType(parameterElement.asType());
        ParameterKindInfo parameterKindInfo = extractParameterKind(parameterElement, isVararg);

        if (parameterKindInfo == ParameterKindInfo.VARARG_METHOD_PARAMETER) {
            if (parameterType.getSort() != Type.ARRAY) {