LocalCache<String, String> cache = makeLocalCache(builder, loader);

      assertThat(cache.getOrLoad("test")).isEqualTo("testLoad");

      ticker.advance(10, MILLISECONDS); // so that the next call will trigger refresh
      assertThat(cache.getOrLoad("test")).isEqualTo("testLoad");
      reloadStarted.await();
      ticker.advance(500, MILLISECONDS); // so that the entry expires during the reload

          "Operation '", op.node.DebugString(), "' was changed by ",
          mutation_type,
          " after it was run by a session. This mutation will have no effect, "
          "and will trigger an error in the future. Either don't modify "
          "nodes after running them or create a new session.");
    }
  }
}

namespace {

  }

  public void testReloadAfterFailure() throws ExecutionException {
    AtomicInteger count = new AtomicInteger();
    Exception e = new IllegalStateException("exception to trigger failure on first load()");
    CacheLoader<Integer, String> failOnceFunction =
        new CacheLoader<Integer, String>() {

          @Override
          public String load(Integer key) throws Exception {

In an expression switch,
the switch expression is evaluated and
the case expressions, which need not be constants,
are evaluated left-to-right and top-to-bottom; the first one that equals the
switch expression
triggers execution of the statements of the associated case;
the other cases are skipped.
If no case matches and there is a "default" case,
its statements are executed.

* 🔧 Update sponsors: Coherence. PR [#12093](https://github.com/fastapi/fastapi/pull/12093) by [@tiangolo](https://github.com/tiangolo).
* 📝 Fix async test example not to trigger DeprecationWarning. PR [#12084](https://github.com/fastapi/fastapi/pull/12084) by [@marcinsulikowski](https://github.com/marcinsulikowski).

        [`FTRL`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/raw_ops/ApplyFtrl)/[`FTRLV2`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/raw_ops/ApplyFtrlV2)
        that can triggerred by `multiply_linear_by_lr` allowing a learning rate
        of zero.

### `tf.data`:

*   `tf.data.experimental.dense_to_ragged_batch` works correctly with tuples.