if (ce1.size() == ce2.size()) {
            Iterator<Exclusion> i1 = ce1.iterator();
            Iterator<Exclusion> i2 = ce2.iterator();
            while (i1.hasNext() && i2.hasNext()) {
                if (!equals(i1.next(), i2.next())) {
                    return false;
                }
            }
            return !i1.hasNext() && !i2.hasNext();
        }
        return false;
    }

                        if (i1.hasNext()) {
                            res1 = i1.next();
                        } else {
                            done = true;
                        }
                    }
                } else {
                    // move on to next in r1
                    if (i1.hasNext()) {
                        res1 = i1.next();
                    } else {

    // Test data
    List<String> list = Lists.newArrayList(asList("a", "b"));

    // Test & Verify
    Iterator<String> i1 = Iterables.consumingIterable(list).iterator();
    Iterator<String> i2 = Iterables.consumingIterable(list).iterator();

    i1.next();
    assertThrows(ConcurrentModificationException.class, () -> i2.next());
  }

  public void testConsumingIterable_queue_iterator() {

    // Test data
    List<String> list = Lists.newArrayList("a", "b");

    // Test & Verify
    Iterator<String> i1 = Iterators.consumingIterator(list.iterator());
    Iterator<String> i2 = Iterators.consumingIterator(list.iterator());

    i1.next();
    assertThrows(ConcurrentModificationException.class, () -> i2.next());
  }

  public void testIndexOf_consumedData() {

    // Test data
    List<String> list = Lists.newArrayList("a", "b");

    // Test & Verify
    Iterator<String> i1 = Iterators.consumingIterator(list.iterator());
    Iterator<String> i2 = Iterators.consumingIterator(list.iterator());

    i1.next();
    assertThrows(ConcurrentModificationException.class, () -> i2.next());
  }

  public void testIndexOf_consumedData() {

        *   `tf.unsortedsegmentmax` op is supported.
        *   `tf.unsortedsegmentsum` op is supported.
    *   Updates to existing operations:
        *   `tfl.scatter_nd` now supports I1 for the `update` arg.
    *   Upgrade Flatbuffers v2.0.5 from v1.12.0

*   `tf.keras`:

    *   `EinsumDense` layer is moved from experimental to core. Its import path