* prefer the simplest interface that satisfies your use case. See the <a
 * href="https://github.com/google/guava/wiki/GraphsExplained#choosing-the-right-graph-type">
 * "Choosing the right graph type"</a> section of the Guava User Guide for more details.
 *
 * <h3>Capabilities</h3>
 *
 * <p>{@code ValueGraph} supports the following use cases (<a

      /*
       * If another handleException() caller created the set, we need to use that copy in case yet
       * other callers have added to it.
       *
       * This read is guaranteed to get us the right value because we only set this once (here).
       *
       * requireNonNull is safe because either our compareAndSet succeeded or it failed because
       * another thread did it for us.
       */

                return Arrays.equals(this.ansiHash, ntlm.ansiHash) && Arrays.equals(this.unicodeHash, ntlm.unicodeHash);
                /*
                 * This still isn't quite right. If one npa object does not have external
                 * hashes and the other does then they will not be considered equal even
                 * though they may be.
                 */
            }

      + timeSeries.standardOptions.withUnit('Bps')
      + timeSeries.standardOptions.withOverrides([
        fieldOverride.byQuery.new('B')
        + fieldOverride.byQuery.withProperty('custom.axisPlacement', 'right')
        + fieldOverride.byQuery.withProperty('unit', 'c/s'),
      ])
    ,

    durationQuantile(title, targets, desc=''):
      self.base(title, targets, desc)
      + timeSeries.standardOptions.withUnit('s')

	// This matches most other JSON generators.
	// See golang.org/issue/6384 and golang.org/issue/14135.
	// Like fmt %g, but the exponent cutoffs are different
	// and exponents themselves are not padded to two digits.
	abs := math.Abs(f)
	fmt := byte('f')
	if abs != 0 {
		if abs < 1e-6 || abs >= 1e21 {
			fmt = 'e'
		}
	}
	dst = strconv.AppendFloat(dst, f, fmt, -1, 64)
	if fmt == 'e' {

    for (int i = 0; i < numInsertions * 2; i += 2) {
      bf.put(Integer.toString(i));
    }
    assertApproximateElementCountGuess(bf, numInsertions);

    // Assert that the BF "might" have all of the even numbers.
    for (int i = 0; i < numInsertions * 2; i += 2) {
      assertTrue(bf.mightContain(Integer.toString(i)));
    }

Depending on your use case, you might prefer to use a different library, but if you asked me, I would probably suggest you try **Strawberry**.

Here's a small preview of how you could integrate Strawberry with FastAPI:

    return Ints.fromBytes(b4, b3, b2, b1);
  }

  /**
   * Reads a {@code long} as specified by {@link DataInputStream#readLong()}, except using
   * little-endian byte order.
   *
   * @return the next eight bytes of the input stream, interpreted as a {@code long} in
   *     little-endian byte order
   * @throws IOException if an I/O error occurs
   */
  @CanIgnoreReturnValue // to skip some bytes
  @Override

      for (int i = n1 + 1; i <= n2; i++) {
        result *= i;
      }
      return BigInteger.valueOf(result);
    }

    /*
     * We want each multiplication to have both sides with approximately the same number of digits.
     * Currently, we just divide the range in half.
     */
    int mid = (n1 + n2) >>> 1;
    return oldSlowFactorial(n1, mid).multiply(oldSlowFactorial(mid, n2));
  }

  @Benchmark

# JSON Compatible Encoder

There are some cases where you might need to convert a data type (like a Pydantic model) to something compatible with JSON (like a `dict`, `list`, etc).

For example, if you need to store it in a database.

For that, **FastAPI** provides a `jsonable_encoder()` function.

## Using the `jsonable_encoder`

Let's imagine that you have a database `fake_db` that only receives JSON compatible data.