*/
    public ScaleAndIndex index(int index) {
      return new ScaleAndIndex(scale, index);
    }

    /**
     * Specifies multiple quantile indexes to be calculated, each index being the k in the kth
     * q-quantile.
     *
     * @param indexes the quantile indexes, each of which must be in the inclusive range [0, q] for

  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));
  }

        `inference_output_type` for full integer quantized models. This allows
        users to modify the model input and output type to integer types
        (`tf.int8`, `tf.uint8`) instead of defaulting to float type
        (`tf.float32`).
*   NNAPI
    *   Adds NNAPI Delegation support for requantization use cases by converting
        the operation into a dequantize-quantize pair.

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

					}
				}
			}
		}
	}

	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),
	}

### Testé

* 100% <abbr title="La quantité de code qui est testé automatiquement">de couverture de test</abbr>.

	switch funcName {
	case sqlFnCast:
		return e.Cast.Expr.analyze(s)

	case sqlFnExtract:
		return e.Extract.From.analyze(s)

	case sqlFnDateAdd:
		result.combine(e.DateAdd.Quantity.analyze(s))
		result.combine(e.DateAdd.Timestamp.analyze(s))
		return result

	case sqlFnDateDiff:
		result.combine(e.DateDiff.Timestamp1.analyze(s))
		result.combine(e.DateDiff.Timestamp2.analyze(s))

Cela prendrait autant de temps pour finir avec ou sans sections (concurrence) et vous auriez effectué la même quantité de travail.

type DateAddFunc struct {
	DatePart  string       `parser:" \"DATE_ADD\" \"(\" @( \"YEAR\":Timeword | \"MONTH\":Timeword | \"DAY\":Timeword | \"HOUR\":Timeword | \"MINUTE\":Timeword | \"SECOND\":Timeword ) \",\""`
	Quantity  *Operand     `parser:" @@ \",\""`
	Timestamp *PrimaryTerm `parser:" @@ \")\""`
}

// DateDiffFunc represents the DATE_DIFF function
type DateDiffFunc struct {