MethodHandle decorated = MethodHandles.insertArguments(INTERCEPTOR, 0, this, spreader, flags, caller.lookupClass().getName());
        return decorated.asCollector(Object[].class, original.type().parameterCount()).asType(original.type());
    }

    private Object interceptMethodHandle(MethodHandle original, int flags, String consumer, Object[] args) throws Throwable {
        boolean isSpread = (flags & IndyInterface.SPREAD_CALL) != 0;

    }

    @Nonnull
    private static ImplementationInfoImpl extractImplementationInfo(ExecutableElement input) {
        Type implementationOwner = extractType(input.getEnclosingElement().asType());
        String implementationName = input.getSimpleName().toString();
        String implementationDescriptor = extractMethodDescriptor(input);

            low=-1.0, high=1.0, size=(4, 3)
        ).astype('f4')
        self.bias_0 = np.random.uniform(low=-1.0, high=1.0, size=(3,)).astype(
            'f4'
        )

        self.filters_1 = np.random.uniform(
            low=-1.0, high=1.0, size=(4, 3)
        ).astype('f4')
        self.bias_1 = np.random.uniform(low=-1.0, high=1.0, size=(3,)).astype(
            'f4'
        )

        GradleLazyType gradleLazyType = GradleLazyType.from(typeName);
        switch (gradleLazyType) {
            case CONFIGURABLE_FILE_COLLECTION:
                return FILE_COLLECTION.asType();
            case DIRECTORY_PROPERTY:
            case REGULAR_FILE_PROPERTY:
                return Type.getType(File.class);
            case LIST_PROPERTY:
                return Type.getType(List.class);

                    low=-(i + 1), high=(i + 1), size=filter_shape[:-1]
                ).astype('f4')
                for i in range(self.out_channel_size)
            ],
            axis=-1,
        )

        self.bias = np.random.uniform(
            low=0, high=10, size=(self.out_channel_size)
        ).astype('f4')

      @def_function.function

  @test_util.deprecated_graph_mode_only
  def test_replace_tensors_by_numpy_ndarrays_with_tensor_list(self):
    num_samples = 8
    samples = [
        np.random.uniform(low=-1.0, high=1.0, size=(3, 3)).astype('f4')
        for _ in range(num_samples)
    ]

    repr_ds: repr_dataset.RepresentativeDataset = [
        {
            'input_tensor': ops.convert_to_tensor(sample),
        }

    }

    @Nonnull
    protected Value<? extends T> filterValue(@SuppressWarnings("unused") EvaluationContext.ScopeContext context, Value<? extends T> value) {
        if (value.isMissing()) {
            return value.asType();
        }
        T unpackedValue = value.getWithoutSideEffect();
        if (spec.isSatisfiedBy(unpackedValue)) {
            return value;
        } else {
            return Value.missing();
        }
    }

                message = message - sourceFile.absolutePath
                throw new AssertionError(message)
            }
        }

        def diagnosticListener = checkDiagnostic.asType("javax.tools.DiagnosticListener" as Class)

        def input = fileManager.getJavaFileObjectsFromFiles(Arrays.asList(sourceFile))
        compiler.getTask(null,
            fileManager,
            diagnosticListener,

            default:
                throw new UnsupportedOperationException("Generating get call for type: " + upgradedPropertyType.asType() + " is not supported");
        }
    }

    private static CodeBlock getSetCall(String propertyGetterName, PropertyUpgradeRequestExtra implementationExtra, GradleLazyType upgradedPropertyType) {

func isTypeLit(t Type) bool {
	switch Unalias(t).(type) {
	case *Named, *TypeParam:
		return false
	}
	return true
}

// isTyped reports whether t is typed; i.e., not an untyped
// constant or boolean.
// Safe to call from types that are not fully set up.
func isTyped(t Type) bool {
	// Alias and named types cannot denote untyped types
	// so there's no need to call Unalias or under, below.
	b, _ := t.(*Basic)