* Elements are added in encounter order.
   *
   * <p>If the mapped elements contain duplicates (according to {@link Object#equals}), the element
   * will be added more than once, with the count summed over all appearances of the element.
   *
   * <p>Note that {@code stream.collect(toMultiset(function, e -> 1, supplier))} is equivalent to

* The alternative documentation will also reflect the new query parameter and body:

![ReDoc](https://fastapi.tiangolo.com/img/index/index-06-redoc-02.png)

### Recap

In summary, you declare **once** the types of parameters, body, etc. as function parameters.

You do that with standard modern Python types.

You don't have to learn a new syntax, the methods or classes of a specific library, etc.

    // The Dolphin algorithm is attractive because it does the fewest array reads and writes: each
    // array slot is read and written exactly once. However, it can have very poor memory locality:
    // benchmarking shows it can take 7 times longer than the other two in some cases. The other two

         * TODO(cpovirk): Consider checking onlyOnRight.containsKey instead of right.containsKey.
         * That could change behavior if the input maps use different equivalence relations (and so
         * a key that appears once in `right` might appear multiple times in `left`). We don't
         * guarantee behavior in that case, anyway, and the current behavior is likely undesirable.

         * TODO(cpovirk): Consider checking onlyOnRight.containsKey instead of right.containsKey.
         * That could change behavior if the input maps use different equivalence relations (and so
         * a key that appears once in `right` might appear multiple times in `left`). We don't
         * guarantee behavior in that case, anyway, and the current behavior is likely undesirable.

* The alternative documentation will also reflect the new query parameter and body:

![ReDoc](https://fastapi.tiangolo.com/img/index/index-06-redoc-02.png)

### Recap { #recap }

In summary, you declare **once** the types of parameters, body, etc. as function parameters.

You do that with standard modern Python types.

You don't have to learn a new syntax, the methods or classes of a specific library, etc.

  }

  /**
   * Tests for Predicates.contains(Pattern) and .containsPattern(String). We assume the regex level
   * works, so there are only trivial tests of that aspect. TODO: Fix comment style once annotation
   * stripper is fixed.
   */
  @GwtIncompatible // Predicates.containsPattern
  public void testContainsPattern_apply() {
    Predicate<CharSequence> isFoobar = Predicates.containsPattern("^Fo.*o.*bar$");

The fast food store has 8 processors (cashiers/cooks). While the concurrent burgers store might have had only 2 (one cashier and one cook).

But still, the final experience is not the best. 😞

---

This would be the parallel equivalent story for burgers. 🍔

For a more "real life" example of this, imagine a bank.

   * errors, for better performance.
   *
   * <p>For a new instance, where the arrays above have not yet been allocated, the value of {@code
   * metadata} is the size that the arrays should be allocated with. Once the arrays have been
   * allocated, the value of {@code metadata} combines the number of bits in the "short hash", in
   * its bottom {@value CompactHashing#HASH_TABLE_BITS_MAX_BITS} bits, with a modification count in

     * builder after this call. This does not change semantics, but may improve performance if
     * {@code expectedValuesPerKey} is a good estimate.
     *
     * <p>This may be called more than once; each newly added key will use the most recent call to
     * {@link #expectedValuesPerKey} as its hint.
     *
     * @throws IllegalArgumentException if {@code expectedValuesPerKey} is negative
     * @since 33.3.0