}
        bitsChanged |= bits.set(combinedHash % bitSize);
      }
      return bitsChanged;
    }

    @Override
    public <T extends @Nullable Object> boolean mightContain(
        @ParametricNullness T object,
        Funnel<? super T> funnel,
        int numHashFunctions,
        LockFreeBitArray bits) {
      long bitSize = bits.bitSize();
      long hash64 = murmur3_128().hashObject(object, funnel).asLong();

   * @since 22.0
   */
  public long approximateElementCount() {
    long bitSize = bits.bitSize();
    long bitCount = bits.bitCount();

    /*
     * Each insertion is expected to reduce the # of clear bits by a factor of
     * `numHashFunctions/bitSize`. So, after n insertions, expected bitCount is `bitSize * (1 - (1 -
     * numHashFunctions/bitSize)^n)`. Solving that for n, and approximating `ln x` as `x - 1` when x

    numBits++;

    LockFreeBitArray bitArray = new LockFreeBitArray(numBits);
    assertTrue(
        "BitArray.bitSize() must return a positive number, but was " + bitArray.bitSize(),
        bitArray.bitSize() > 0);

    // Ideally we would also test the bitSize() overflow of this BF, but it runs out of heap space
    // BloomFilter.create(Funnels.unencodedCharsFunnel(), 244412641, 1e-11);
  }

  /**

#### Union { #union }

You can declare that a variable can be any of **several types**, for example, an `int` or a `str`.

To define it you use the <dfn title='also called "bitwise or operator", but that meaning is not relevant here'>vertical bar (`|`)</dfn> to separate both types.

This is called a "union", because the variable can be anything in the union of those two sets of types.

    * 这个 `dict` 的键是 `str` 类型（比如，每个条目的名称）。
    * 这个 `dict` 的值是 `float` 类型（比如，每个条目的价格）。

#### Union { #union }

你可以声明一个变量可以是若干种类型中的任意一种，比如既可以是 `int` 也可以是 `str`。

定义时使用<dfn title='也叫“按位或运算符（bitwise or operator）”，但这里与该含义无关'>竖线（`|`）</dfn>把两种类型分开。

这称为“联合类型”（union），因为变量可以是这两类类型集合的并集中的任意一个。

```Python hl_lines="1"
{!> ../../docs_src/python_types/tutorial008b_py310.py!}
```

这表示 `item` 可以是 `int` 或 `str`。

	SEXTB	X16, X17				// 93184860
	SEXTH	X17, X18				// 13995860
	XNOR	X18, X19, X20				// 33ca2941 or 33ca2901134afaff
	XNOR	X18, X19				// b3c92941 or b3c9290193c9f9ff
	ZEXTH	X19, X20				// 3bca0908

	// 28.4.2: Bitwise Rotation (Zbb)
	ROL	X8, X9, X10				// 33958460 or b30f8040b3dff4013395840033e5af00
	ROL	X8, X9					// b3948460 or b30f8040b3dff401b3948400b3e49f00
	ROLW	X9, X10, X11				// bb159560 or b30f9040bb5ff501bb159500b3e5bf00

*    product                integers, floats, complex values
/    quotient               integers, floats, complex values
%    remainder              integers

&    bitwise AND            integers
|    bitwise OR             integers
^    bitwise XOR            integers
&^   bit clear (AND NOT)    integers

<<   left shift             integer << integer >= 0

*   Adds `tf.sparse.map_values` to apply a function to the `.value`s of
    `SparseTensor` arguments.
*   The Python bitwise operators for `Tensor` (`__and__`, `__or__`, `__xor__`
    and `__invert__` now support non-`bool` arguments and apply the
    corresponding bitwise ops. `bool` arguments continue to be supported and
    dispatch to logical ops. This brings them more in line with Python and NumPy
    behavior.