return set1.contains(element) ^ set2.contains(element);
      }

      @Override
      int minSize() {
        int difference = minSize(set1) - maxSize(set2);
        return difference >= 0 ? difference : max(minSize(set2) - maxSize(set1), 0);
      }

      @Override
      int maxSize() {
        return saturatedAdd(maxSize(set1), maxSize(set2));
      }

        return set1.contains(element) ^ set2.contains(element);
      }

      @Override
      int minSize() {
        int difference = minSize(set1) - maxSize(set2);
        return difference >= 0 ? difference : max(minSize(set2) - maxSize(set1), 0);
      }

      @Override
      int maxSize() {
        return saturatedAdd(maxSize(set1), maxSize(set2));
      }

  }

  @SuppressWarnings("JUnitIncompatibleType")
  public void testWhere() {
    assertEquals(new TypeToken<Map<String, Integer>>() {}, mapOf(String.class, Integer.class));
    // Type inference is doomed here: int.class is the same as Integer.class, so this is comparing
    // TypeToken<int[]> and TypeToken<Integer[]>.
    assertEquals(new TypeToken<int[]>() {}, arrayOf(int.class));

  }

  @SuppressWarnings("JUnitIncompatibleType")
  public void testWhere() {
    assertEquals(new TypeToken<Map<String, Integer>>() {}, mapOf(String.class, Integer.class));
    // Type inference is doomed here: int.class is the same as Integer.class, so this is comparing
    // TypeToken<int[]> and TypeToken<Integer[]>.
    assertEquals(new TypeToken<int[]>() {}, arrayOf(int.class));

//   example, one cannot use the output of "switch" node as input.
// - `inputs` and `outputs` cannot have reference types. Reference types are
//   not exposed through C API and are being replaced with Resources. We support
//   reference types inside function's body to support legacy code. Do not
//   use them in new code.
// - Every node in the function's body must have all of its inputs (including

  CHECK(tensorflow::unwrap(concatOp)->OpDef());
  TFE_OpAddInput(concatOp, inputs[0], status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  EXPECT_FALSE(tensorflow::unwrap(concatOp)->OpDef())
      << "Inference context is still present";
  TFE_OpAddInput(concatOp, inputs[1], status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  tensorflow::AttrValueMap attr_values = ExtractAttrs(concatOp);

    if (reference == null) {
      throw new NullPointerException();
    }
    return reference;
  }

  /**
   * Ensures that an object reference passed as a parameter to the calling method is not null.
   *
   * @param reference an object reference
   * @param errorMessage the exception message to use if the check fails; will be converted to a

    if (reference == null) {
      throw new NullPointerException();
    }
    return reference;
  }

  /**
   * Ensures that an object reference passed as a parameter to the calling method is not null.
   *
   * @param reference an object reference
   * @param errorMessage the exception message to use if the check fails; will be converted to a

    MapDifference<Integer, String> original = Maps.difference(left, right);
    MapDifference<Integer, String> same = Maps.difference(left, right);
    MapDifference<Integer, String> reverse = Maps.difference(right, left);
    MapDifference<Integer, String> diff2 = Maps.difference(left, right2);

    new EqualsTester()
        .addEqualityGroup(original, same)

  /*
   * We avoid using a method reference or lambda here for now:
   *
   * - method reference: Inside Google, CacheBuilder is used from the implementation of a custom
   *   ClassLoader that is sometimes used as a system classloader. That's a problem because
   *   method-reference linking tries to look up the system classloader, and it fails because there
   *   isn't one yet.
   *