) {
      events.add(AssertionError("onReset"))
      notifyAll()
    }
  }

  companion object {
    fun roundUp(
      num: Int,
      divisor: Int,
    ): Int = (num + divisor - 1) / divisor

    val IGNORE =
      object : PushObserver {
        override fun onRequest(
          streamId: Int,
          requestHeaders: List<Header>,
        ) = false

*Oui, c'est toute l'histoire*.

---

Il n'y a plus d'attente 🕙 nulle part, juste beaucoup de travail à effectuer, dans différentes pièces de la maison.

Vous pourriez diviser en différentes sections comme avec les burgers, d'abord le salon, puis la cuisine, etc. Mais vous n'attendez 🕙 rien, vous ne faites que nettoyer et nettoyer, la séparation en sections ne changerait rien au final.

///

### <a href="https://github.com/encode/apistar" class="external-link" target="_blank">APIStar</a> (<= 0.5)

Juste avant de décider de développer **FastAPI**, j'ai trouvé le serveur **APIStar**. Il contenait presque tout ce
que je recherchais et avait un beau design.

Build And Host Fast Data Science Applications Using FastAPI - author: Navule Pavan Kumar Rao author_link: https://www.linkedin.com/in/navule/ link: https://www.tutlinks.com/deploy-fastapi-on-azure/ title: Deploy FastAPI on Azure App Service - author: Davide Fiocco author_link: https://github.com/davidefiocco link: https://davidefiocco.github.io/streamlit-fastapi-ml-serving/ title: Machine learning model serving in Python using FastAPI and streamlit - author: Netflix author_link: https://netflixtechblog.com/...

 *
 * <ul>
 *   <li>automatic loading of entries into the cache
 *   <li>least-recently-used eviction when a maximum size is exceeded (note that the cache is
 *       divided into segments, each of which does LRU internally)
 *   <li>time-based expiration of entries, measured since last access or last write
 *   <li>keys automatically wrapped in {@code WeakReference}

      int start = index * size;
      int end = min(start + size, list.size());
      return list.subList(start, end);
    }

    @Override
    public int size() {
      return IntMath.divide(list.size(), size, RoundingMode.CEILING);
    }

    @Override
    public boolean isEmpty() {
      return list.isEmpty();
    }
  }

        input.equals(".%2e", ignoreCase = true) ||
        input.equals("%2e%2e", ignoreCase = true)

    /**
     * Cuts this string up into alternating parameter names and values. This divides a query string
     * like `subject=math&easy&problem=5-2=3` into the list `["subject", "math", "easy", null,
     * "problem", "5-2=3"]`. Note that values may be null and may contain '=' characters.
     */

int16                  -32768
int32             -2147483648
int64    -9223372036854775808
</pre>

<p>
If the divisor is a <a href="#Constants">constant</a>, it must not be zero.
If the divisor is zero at run time, a <a href="#Run_time_panics">run-time panic</a> occurs.
If the dividend is non-negative and the divisor is a constant power of 2,
the division may be replaced by a right shift, and computing the remainder may

        passed to the optimizer but the reported loss will be a scalar value.
    *   `SUM`: Scalar sum of weighted losses. 4. `SUM_OVER_BATCH_SIZE`: Scalar
        `SUM` divided by number of elements in losses. This reduction type is
        not supported when used with `tf.distribute.Strategy` outside of
        built-in training loops like `tf.keras` `compile`/`fit`.

to compute (y * q) / 2ᵈ, rounded to nearest integer, with 1/2 // rounding up (see FIPS 203, Section 2.3). dividend := uint32(y) * q quotient := dividend >> d // (y * q) / 2ᵈ // The d'th least-significant bit of the dividend (the most significant bit // of the remainder) is 1 for the top half of the values that divide to the // same quotient, which are the ones that round up. quotient += dividend >> (d - 1) & 1 // quotient is at most (2¹¹-1) * q / 2¹¹ + 1 = 3328, so it didn't overflow. return fieldElement(quotient)...