// TODO(gak): tune this condition based on empirical evidence
    return (cellList.size() > (((long) rowSpace.size() * columnSpace.size()) / 2))
        ? new DenseImmutableTable<R, C, V>(cellList, rowSpace, columnSpace)
        : new SparseImmutableTable<R, C, V>(cellList, rowSpace, columnSpace);
  }

  /** @throws IllegalArgumentException if {@code existingValue} is not null. */
  /*

  DenseImmutableTable(
      ImmutableList<Cell<R, C, V>> cellList,
      ImmutableSet<R> rowSpace,
      ImmutableSet<C> columnSpace) {
    @SuppressWarnings("unchecked")
    @Nullable
    V[][] array = (@Nullable V[][]) new Object[rowSpace.size()][columnSpace.size()];
    this.values = array;
    this.rowKeyToIndex = Maps.indexMap(rowSpace);
    this.columnKeyToIndex = Maps.indexMap(columnSpace);

  DenseImmutableTable(
      ImmutableList<Cell<R, C, V>> cellList,
      ImmutableSet<R> rowSpace,
      ImmutableSet<C> columnSpace) {
    @SuppressWarnings("unchecked")
    @Nullable
    V[][] array = (@Nullable V[][]) new Object[rowSpace.size()][columnSpace.size()];
    this.values = array;
    this.rowKeyToIndex = Maps.indexMap(rowSpace);
    this.columnKeyToIndex = Maps.indexMap(columnSpace);

  SparseImmutableTable(
      ImmutableList<Cell<R, C, V>> cellList,
      ImmutableSet<R> rowSpace,
      ImmutableSet<C> columnSpace) {
    Map<R, Integer> rowIndex = Maps.indexMap(rowSpace);
    Map<R, Map<C, V>> rows = Maps.newLinkedHashMap();
    for (R row : rowSpace) {
      rows.put(row, new LinkedHashMap<C, V>());
    }
    Map<C, Map<R, V>> columns = Maps.newLinkedHashMap();

- Kubeadm: make sure the etcd static pod startup probe uses /health?serializable=false while the liveness probe uses /health?serializable=true&exclude=NOSPACE. The NOSPACE exclusion would allow administrators to address space issues one member at a time. ([#110744](https://github.com/kubernetes/kubernetes/pull/110744), [@neolit123](https://github.com/neolit123)) [SIG Cluster Lifecycle]

* Fix panic logspam when running kubelet in standalone mode. ([#77888](https://github.com/kubernetes/kubernetes/pull/77888), [@tallclair](https://github.com/tallclair))

        dispatches the best kernel implementation based on CPU vector
        architecture. To disable them, build with
        `--define=tensorflow_mkldnn_contraction_kernel=0`.
    *   `tf.linspace(start, stop, num)` now always uses "stop" as last value
        (for num > 1)
    *   Added top-k to precision and recall to keras metrics.
    *   Add a ragged size op and register it to the op dispatcher