*
 * Start a {@code ClosingFuture} pipeline {@linkplain #submit(ClosingCallable, Executor) from a
 * callable block} that may capture objects for later closing. To start a pipeline from a {@link
 * ListenableFuture} that doesn't create resources that should be closed later, you can use {@link
 * #from(ListenableFuture)} instead.
 *
 * <h3>Derived steps</h3>
 *

   *
   * <p><b>{@code Stream} equivalent:</b> {@code stream.filter(type::isInstance).map(type::cast)}.
   * This does perform a little more work than necessary, so another option is to insert an
   * unchecked cast at some later point:
   *
   * {@snippet :
   * @SuppressWarnings("unchecked") // safe because of ::isInstance check
   * ImmutableList<NewType> result =

   *
   * <p><b>{@code Stream} equivalent:</b> {@code stream.filter(type::isInstance).map(type::cast)}.
   * This does perform a little more work than necessary, so another option is to insert an
   * unchecked cast at some later point:
   *
   * {@snippet :
   * @SuppressWarnings("unchecked") // safe because of ::isInstance check
   * ImmutableList<NewType> result =

  }

  /**
   * Computes the difference between two maps. This difference is an immutable snapshot of the state
   * of the maps at the time this method is called. It will never change, even if the maps change at
   * a later time.
   *
   * <p>Since this method uses {@code HashMap} instances internally, the keys of the supplied maps
   * must be well-behaved with respect to {@link Object#equals} and {@link Object#hashCode}.
   *

  }

  /**
   * Computes the difference between two maps. This difference is an immutable snapshot of the state
   * of the maps at the time this method is called. It will never change, even if the maps change at
   * a later time.
   *
   * <p>Since this method uses {@code HashMap} instances internally, the keys of the supplied maps
   * must be well-behaved with respect to {@link Object#equals} and {@link Object#hashCode}.
   *

    * Added `DynamicEmbedding` Keras layer
    * Added 'UpdateEmbeddingCallback`
    * `DynamicEmbedding` layer allows for the continuous updating of the
      vocabulary and embeddings during the training process. This layer
      maintains a hash table to track the most up-to-date vocabulary based on
      the inputs received by the layer and the eviction policy. When this layer

The expression <code>x</code> is evaluated and saved during the evaluation of the
method value; the saved copy is then used as the receiver in any calls,
which may be executed later.
</p>

<pre>
type S struct { *T }
type T int
func (t T) M() { print(t) }

t := new(T)
s := S{T: t}
f := t.M                    // receiver *t is evaluated and stored in f

    volatile int count;

    /** The weight of the live elements in this segment's region. */
    @GuardedBy("this")
    long totalWeight;

    /**
     * Number of updates that alter the size of the table. This is used during bulk-read methods to
     * make sure they see a consistent snapshot: If modCounts change during a traversal of segments

* 👥 Update FastAPI People. PR [#9999](https://github.com/tiangolo/fastapi/pull/9999) by [@tiangolo](https://github.com/tiangolo).
* 🐳 Update Dockerfile with compatibility versions, to upgrade later. PR [#9998](https://github.com/tiangolo/fastapi/pull/9998) by [@tiangolo](https://github.com/tiangolo).