for (Entry<K, V> entry : entries) {
      Entry<V, K> reversed = reverseEntry(entry);
      BiMap<V, K> inv = getMap().inverse();
      assertFalse(
          "Inverse should not contain entry " + reversed, inv.entrySet().contains(reversed));
      assertFalse(
          "Inverse should not contain key " + reversed.getKey(),
          inv.containsKey(reversed.getKey()));
      assertFalse(

    BiMap<V, K> inv = getMap().inverse();
    inv.keySet().clear();
    assertTrue(getMap().isEmpty());
    assertTrue(inv.isEmpty());
  }

  @MapFeature.Require(SUPPORTS_REMOVE)
  public void testClearInverseValuesClears() {
    BiMap<V, K> inv = getMap().inverse();
    inv.values().clear();
    assertTrue(getMap().isEmpty());
    assertTrue(inv.isEmpty());
  }

    BiMap<V, K> inv = getMap().inverse();
    inv.keySet().clear();
    assertTrue(getMap().isEmpty());
    assertTrue(inv.isEmpty());
  }

  @MapFeature.Require(SUPPORTS_REMOVE)
  public void testClearInverseValuesClears() {
    BiMap<V, K> inv = getMap().inverse();
    inv.values().clear();
    assertTrue(getMap().isEmpty());
    assertTrue(inv.isEmpty());
  }

    for (Entry<K, V> entry : entries) {
      Entry<V, K> reversed = reverseEntry(entry);
      BiMap<V, K> inv = getMap().inverse();
      assertFalse(
          "Inverse should not contain entry " + reversed, inv.entrySet().contains(reversed));
      assertFalse(
          "Inverse should not contain key " + reversed.getKey(),
          inv.containsKey(reversed.getKey()));
      assertFalse(

		// If the number is negative, we use an inversion mask to invert the
		// data bytes and treat the value as an unsigned number.
		var inv byte // 0x00 if positive or zero, 0xff if negative
		if b[0]&0x40 != 0 {
			inv = 0xff
		}

		var x uint64
		for i, c := range b {
			c ^= inv // Inverts c only if inv is 0xff, otherwise does nothing
			if i == 0 {
				c &= 0x7f // Ignore signal bit in first byte
			}
			if (x >> 56) > 0 {

minuto",mm:"%d minutos",h:"una hora",hh:"%d horas",d:"un d\xeda",dd:"%d d\xedas",w:"una semana",ww:"%d semanas",M:"un mes",MM:"%d meses",y:"un a\xf1o",yy:"%d a\xf1os"},dayOfMonthOrdinalParse:/\d{1,2}\xba/,ordinal:"%d\xba",week:{dow:0,doy:4},invalidDate:"Fecha inv\xe1lida"});var qs="ene._feb._mar._abr._may._jun._jul._ago._sep._oct._nov._dic.".split("_"),Zs="ene_feb_mar_abr_may_jun_jul_ago_sep_oct_nov_dic".split("_"),$s=[/^ene/i,/^feb/i,/^mar/i,/^abr/i,/^may/i,/^jun/i,/^jul/i,/^ago/i,/^sep/i,/^oct/i,/^nov/i,/...

`tf.sparse_reduce_sum_sparse`: `reduction_axes` becomes `axis` *
`tf.sparse_split`: `split_dim` becomes `axis` * `tf.listdiff` has been renamed
to `tf.setdiff1d` to match NumPy naming. * `tf.inv` has been renamed to be
`tf.reciprocal` (component-wise reciprocal) to avoid confusion with `np.inv`
which is matrix inversion * tf.round now uses banker's rounding (round to even)
semantics to match NumPy. * `tf.split` now takes arguments in a reversed order