static {
    ImmutableSet.Builder<Integer> intValues = ImmutableSet.builder();
    // Add boundary values manually to avoid over/under flow (this covers 2^N for 0 and 31).
    intValues.add(Integer.MAX_VALUE - 1, Integer.MAX_VALUE);
    // Add values up to 40. This covers cases like "square of a prime" and such.
    for (int i = 1; i <= 40; i++) {
      intValues.add(i);
    }
    // Now add values near 2^N for lots of values of N.

                        "content": {
                            "application/json": {



...
```

#### What is OpenAPI for { #what-is-openapi-for }

The OpenAPI schema is what powers the two interactive documentation systems included.

And there are dozens of alternatives, all based on OpenAPI. You could easily add any of those alternatives to your application built with **FastAPI**.

  /**
   * Returns the smallest power of two greater than or equal to {@code x}. This is equivalent to
   * {@code checkedPow(2, log2(x, CEILING))}.
   *
   * @throws IllegalArgumentException if {@code x <= 0}
   * @throws ArithmeticException of the next-higher power of two is not representable as a {@code
   *     long}, i.e. when {@code x > 2^62}
   * @since 20.0
   */

> Modern versions of Python have support for **"asynchronous code"** using something called **"coroutines"**, with **`async` and `await`** syntax.

That should make more sense now. ✨

All that is what powers FastAPI (through Starlette) and what makes it have such an impressive performance.

## Very Technical Details { #very-technical-details }

/// warning

You can probably skip this.

    abstract double expectedAsymptotic(int n);
  }

  /**
   * Returns a list of objects with the same hash code, of size 2^power, counting calls to equals,
   * hashCode, and compareTo in counter.
   */
  private static List<CountsHashCodeAndEquals> createAdversarialInput(
      int power, CallsCounter counter) {
    String str1 = "Aa";
    String str2 = "BB";
    assertEquals(str1.hashCode(), str2.hashCode());

  }

  public void testCountOverflow_doesNotThrow() {
    PairedStatsAccumulator accumulator = new PairedStatsAccumulator();
    accumulator.add(ONE_VALUE, OTHER_ONE_VALUE);
    for (int power = 1; power < Long.SIZE - 1; power++) {
      accumulator.addAll(accumulator.snapshot());
    }
    // Should overflow without throwing.
    accumulator.addAll(accumulator.snapshot());
    assertThat(accumulator.count()).isLessThan(0L);

// handleAssociationForOwners is a helper function that handles associations for all owners
func (s setCreateOrUpdateG[T]) handleAssociationForOwners(base *DB, ctx context.Context, handler func(owner T, association *Association) error, associationName string) error {
	var owners []T
	if err := base.Find(&owners).Error; err != nil {
		return err
	}

	for _, owner := range owners {

  }

  public void testCountOverflow_doesNotThrow() {
    StatsAccumulator accumulator = new StatsAccumulator();
    accumulator.add(ONE_VALUE);
    for (int power = 1; power < Long.SIZE - 1; power++) {
      accumulator.addAll(accumulator.snapshot());
    }
    // Should overflow without throwing.
    accumulator.addAll(accumulator.snapshot());
    assertThat(accumulator.count()).isLessThan(0L);

  static boolean isFinite(double d) {
    return getExponent(d) <= MAX_EXPONENT;
  }

  static boolean isNormal(double d) {
    return getExponent(d) >= MIN_EXPONENT;
  }

  /*
   * Returns x scaled by a power of 2 such that it is in the range [1, 2). Assumes x is positive,
   * normal, and finite.
   */
  static double scaleNormalize(double x) {
    long significand = doubleToRawLongBits(x) & SIGNIFICAND_MASK;

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

So, you might still need to use Pydantic models.

But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

///

## Dataclasses in `response_model` { #dataclasses-in-response-model }

You can also use `dataclasses` in the `response_model` parameter: