/** The key of the configuration. e.g. 10 */
    String SUGGEST_POPULAR_WORD_SIZE = "suggest.popular.word.size";

    /** The key of the configuration. e.g. 30 */
    String SUGGEST_POPULAR_WORD_WINDOW_SIZE = "suggest.popular.word.window.size";

    /** The key of the configuration. e.g. 10 */
    String SUGGEST_POPULAR_WORD_QUERY_FREQ = "suggest.popular.word.query.freq";

 *   Added `jit_compile` as a settable property to `tf.keras.Model`.
 *   Added `synchronized` optional parameter to `layers.BatchNormalization`.
 *   Added deprecation warning to `layers.experimental.SyncBatchNormalization` and suggested to use `layers.BatchNormalization` with `synchronized=True` instead.
 *   Updated `tf.keras.layers.BatchNormalization` to support masking of the inputs (`mask` argument) when computing the mean and variance.

### Refactors

* 🔥 Remove unused NoneType. PR [#10774](https://github.com/tiangolo/fastapi/pull/10774) by [@tiangolo](https://github.com/tiangolo).

### Docs

* 📝 Tweak default suggested configs for generating clients. PR [#10736](https://github.com/tiangolo/fastapi/pull/10736) by [@tiangolo](https://github.com/tiangolo).

### Internal

mLimbs, Y) T[n+i], c = bits.Add(c1, c2, c) } // Finally for Step 7 we copy the final T window into x, and subtract m // if necessary (which as explained in maybeSubtractModulus can be the // case both if x >= m, or if x overflowed). // // The paper suggests in Section 4 that we can do an "Almost Montgomery // Multiplication" by subtracting only in the overflow case, but the // cost is very similar since the constant time subtraction tells us if // x >= m as a side effect, and taking care of the broken...