/** Specifies the computation of quartiles (i.e. 4-quantiles). */
  public static Scale quartiles() {
    return scale(4);
  }

  /** Specifies the computation of percentiles (i.e. 100-quantiles). */
  public static Scale percentiles() {
    return scale(100);
  }

  /**
   * Specifies the computation of q-quantiles.
   *

  public void testScale_zero() {
    assertThrows(IllegalArgumentException.class, () -> Quantiles.scale(0));
  }

  public void testScale_negative() {
    assertThrows(IllegalArgumentException.class, () -> Quantiles.scale(-4));
  }

  public void testScale_index_negative() {
    Quantiles.Scale intermediate = Quantiles.scale(10);
    assertThrows(IllegalArgumentException.class, () -> intermediate.index(-1));
  }

  public void testScale_zero() {
    assertThrows(IllegalArgumentException.class, () -> Quantiles.scale(0));
  }

  public void testScale_negative() {
    assertThrows(IllegalArgumentException.class, () -> Quantiles.scale(-4));
  }

  public void testScale_index_negative() {
    Quantiles.Scale intermediate = Quantiles.scale(10);
    assertThrows(IllegalArgumentException.class, () -> intermediate.index(-1));
  }

      Map<Integer, Double> quantiles = algorithm.multipleQuantiles(indexes, 100, dataset.clone());
      assertWithMessage("Wrong keys from " + algorithm).that(quantiles.keySet()).isEqualTo(indexes);
      for (int i : indexes) {
        assertWithMessage("Mismatch between %s and %s at %s", algorithm, REFERENCE_ALGORITHM, i)
            .that(quantiles.get(i))
            .isWithin(ALLOWED_ERROR)

import com.google.common.collect.Range;
import java.util.Random;
import org.jspecify.annotations.NullUnmarked;

/** Benchmarks some algorithms providing the same functionality as {@link Quantiles}. */
@NullUnmarked
public class QuantilesBenchmark {

  private static final ContiguousSet<Integer> ALL_DECILE_INDEXES =
      ContiguousSet.create(Range.closed(0, 10), DiscreteDomain.integers());

					}
				}
			}
		}
	}

	var memstats runtime.MemStats
	runtime.ReadMemStats(&memstats)

	gcStats := debug.GCStats{
		// If stats.PauseQuantiles is non-empty, ReadGCStats fills
		// it with quantiles summarizing the distribution of pause time.
		// For example, if len(stats.PauseQuantiles) is 5, it will be
		// filled with the minimum, 25%, 50%, 75%, and maximum pause times.
		PauseQuantiles: make([]time.Duration, 5),
	}

    * Si on compare Uvicorn, il faut le comparer à d'autre applications de serveurs comme Daphne, Hypercorn, uWSGI, etc.
* **Starlette** :

    for (int i = fromIndex; i < toIndex; i++) {
      array[i] ^= Integer.MAX_VALUE;
    }
  }

  /**
   * Returns dividend / divisor, where the dividend and divisor are treated as unsigned 32-bit
   * quantities.
   *
   * <p><b>Java 8+ users:</b> use {@link Integer#divideUnsigned(int, int)} instead.
   *
   * @param dividend the dividend (numerator)
   * @param divisor the divisor (denominator)

    for (int i = fromIndex; i < toIndex; i++) {
      array[i] ^= Long.MAX_VALUE;
    }
  }

  /**
   * Returns dividend / divisor, where the dividend and divisor are treated as unsigned 64-bit
   * quantities.
   *
   * <p><b>Java 8+ users:</b> use {@link Long#divideUnsigned(long, long)} instead.
   *
   * @param dividend the dividend (numerator)
   * @param divisor the divisor (denominator)

* GPU
    * Support for NVIDIA GPUs with compute capability 8.9 (e.g. L4 & L40) has been added to TF binary distributions (Python wheels).
* Replace `DebuggerOptions` of TensorFlow Quantizer, and migrate to `DebuggerConfig` of StableHLO Quantizer.
* Add TensorFlow to StableHLO converter to TensorFlow pip package.
* TensorRT support: this is the last release supporting TensorRT. It will be removed in the next release.