tester.testForwarding(
        Arithmetic.class,
        new Function<Arithmetic, Arithmetic>() {
          @Override
          public Arithmetic apply(Arithmetic arithmetic) {
            return new ForwardingArithmetic(arithmetic);
          }
        });
    tester.testForwarding(
        ParameterTypesDifferent.class,
        new Function<ParameterTypesDifferent, ParameterTypesDifferent>() {
          @Override

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import java.util.Comparator;
import java.util.EnumMap;
import java.util.EnumSet;
import java.util.LinkedHashMap;
import java.util.TreeMap;
import java.util.function.BinaryOperator;
import java.util.function.Function;
import java.util.function.Supplier;
import java.util.function.ToIntFunction;
import java.util.stream.Collector;
import java.util.stream.Collectors;
import java.util.stream.Stream;
import javax.annotation.CheckForNull;

import java.util.Comparator;
import java.util.Spliterator;
import java.util.function.Consumer;
import java.util.function.DoubleConsumer;
import java.util.function.Function;
import java.util.function.IntConsumer;
import java.util.function.IntFunction;
import java.util.function.LongConsumer;
import java.util.function.Predicate;
import java.util.stream.IntStream;
import javax.annotation.CheckForNull;

  private static final List<String> SOME_STRING_LIST = asList("1", "2", "3", "4");

  private static final Function<Number, String> SOME_FUNCTION = new SomeFunction();

  private static class SomeFunction implements Function<Number, String>, Serializable {
    @Override
    public String apply(Number n) {
      return String.valueOf(n);
    }

   *
   * @param function A Function to transform the results of this future to the results of the
   *     returned future.
   * @param executor Executor to run the function in.
   * @return A future that holds result of the transformation.
   */
  public final <T extends @Nullable Object> FluentFuture<T> transform(
      Function<? super V, T> function, Executor executor) {

  // (`output_tensors`).
  //
  // If provided, a non-null `forward_function` will be used instead of the
  // backward function (`backward_function_getter` /
  // `backward_function_deleter`) to compute jvps for this operation. If
  // `forward_function` is null, a GradientTape is used on the backward function
  // to compute the jvp, which will waste computation when executing eagerly.
  //

// once (i.e., on the main function side) in running distributed functions.
// This test creates a cluster with two workers, create a variable on the
// second worker, and run a distributed function (VariableAddFunction) whose ops
// span the local and remote workers. If the graph optimization pass is executed
// on both the main function side and the component function side, an error will

      implements Serializable {
    private final Function<? super A, ? extends B> forwardFunction;
    private final Function<? super B, ? extends A> backwardFunction;

    private FunctionBasedConverter(
        Function<? super A, ? extends B> forwardFunction,
        Function<? super B, ? extends A> backwardFunction) {
      this.forwardFunction = checkNotNull(forwardFunction);

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              },
              function() {
                isMouseHover = false;
              }
            );

            this.resize();
            suggestor = this;
            $(window).resize(function() {
              suggestor.resize();
            });