int size = this.size;
    if (size < requireEntries().length) {
      resizeEntries(size);
    }
    int minimumTableSize = CompactHashing.tableSize(size);
    int mask = hashTableMask();
    if (minimumTableSize < mask) { // smaller table size will always be less than current mask
      resizeTable(mask, minimumTableSize, UNSET, UNSET);
    }
  }

  @Override
  public void clear() {
    if (needsAllocArrays()) {

 * good job of distributing the elements to the buckets to a distribution not far from uniform), and
 * amortized since some operations can trigger a hash table resize.
 *
 * <p>This implementation consumes significantly less memory than {@code java.util.LinkedHashSet} or
 * even {@code java.util.HashSet}, and places considerably less load on the garbage collector. Like

 * good job of distributing the elements to the buckets to a distribution not far from uniform), and
 * amortized since some operations can trigger a hash table resize.
 *
 * <p>This implementation consumes significantly less memory than {@code java.util.LinkedHashSet} or
 * even {@code java.util.HashSet}, and places considerably less load on the garbage collector. Like

    if (newEntryIndex == Integer.MAX_VALUE) {
      throw new IllegalStateException("Cannot contain more than Integer.MAX_VALUE elements!");
    }
    int newSize = newEntryIndex + 1;
    resizeMeMaybe(newSize);
    insertEntry(newEntryIndex, key, value, hash);
    this.size = newSize;
    if (newEntryIndex >= threshold) {
      resizeTable(2 * table.length);
    }
    modCount++;
    return 0;
  }

  /**

 * function doing a good job of distributing the elements to the buckets to a distribution not far
 * from uniform), and amortized since some operations can trigger a hash table resize.
 *
 * <p>As compared with {@link java.util.LinkedHashMap}, this structure places significantly reduced
 * load on the garbage collector by only using a constant number of internal objects.
 *

  DCHECK(tf_results->missing_unused_key_names_data.empty());

  size_t size = results.missing_unused_input_map_keys.size();
  tf_results->missing_unused_key_names.resize(size);
  tf_results->missing_unused_key_indexes.resize(size);

  for (int i = 0; i < size; ++i) {
    TensorId id = results.missing_unused_input_map_keys[i];
    tf_results->missing_unused_key_names_data.emplace_back(id.first);
    tf_results->missing_unused_key_names[i] =

PSED)) {\n      this.expand()\n    } else {\n      this.collapse()\n    }\n  }\n\n  autoCollapse(resize = false) {\n    if (!this._options.autoCollapseSize) {\n      return\n    }\n\n    const $bodySelector = $(SELECTOR_BODY)\n\n    if ($(window).width() <= this._options.autoCollapseSize) {\n      if (!$bodySelector.hasClass(CLASS_NAME_OPEN)) {\n        this.collapse()\n      }\n    } else if (resize === true) {\n      if ($bodySelector.hasClass(CLASS_NAME_OPEN)) {\n        $bodySelector.removeC...

		}
		// Is the buffer full? If so, resize.
		if s.end == len(s.buf) {
			// Guarantee no overflow in the multiplication below.
			const maxInt = int(^uint(0) >> 1)
			if len(s.buf) >= s.maxTokenSize || len(s.buf) > maxInt/2 {
				s.setErr(ErrTooLong)
				return false
			}
			newSize := len(s.buf) * 2
			if newSize == 0 {
				newSize = startBufSize
			}
			newSize = min(newSize, s.maxTokenSize)

- The pod `status.resize` field is now deprecated and will no longer be set. The status of a pod resize will be exposed under two new conditions: `PodResizeInProgress` and `PodResizePending` instead. ([#130733](https://github.com/kubernetes/kubernetes/pull/130733), [@natasha41575](https://github.com/natasha41575))...

- Implemented prioritization of resize requests based on `priorityClass` and QoS class when node resources are insufficient to accommodate all pending resize operations. ([#132342](https://github.com/kubernetes/kubernetes/pull/132342), [@natasha41575](https://github.com/natasha41575)) [SIG Node and Testing]