img      image.Image
	)
	width, height := d.width, d.height
	if d.interlace == itAdam7 && !allocateOnly {
		p := interlacing[pass]
		// Add the multiplication factor and subtract one, effectively rounding up.
		width = (width - p.xOffset + p.xFactor - 1) / p.xFactor
		height = (height - p.yOffset + p.yFactor - 1) / p.yFactor
		// A PNG image can't have zero width or height, but for an interlaced

      this.height = 1 + Math.max(height(left), height(right));
    }

    private void recompute() {
      recomputeMultiset();
      recomputeHeight();
    }

    private AvlNode<E> rebalance() {
      switch (balanceFactor()) {
        case -2:
          // requireNonNull is safe because right must exist in order to get a negative factor.
          requireNonNull(right);
          if (right.balanceFactor() > 0) {

    // Apply default weight-only quantization. Weights are quantized during
    // conversion, then dequantized during inference.
    // Activation: f32, Weight: qi8, Bias: f32
    WEIGHT_ONLY = 1;

    // Apply default dynamic range quantization. Quantized tensor value's
    // ranges are determined during graph runtime.
    // Activation: f32, Weight: qi8, Bias: f32

				"": {xdstest.LocLbEpInfo{
					LbEps: []xdstest.LbEpInfo{
						{Address: "1.2.3.4", Weight: 1},
						{Address: "2.2.2.2", Weight: 2},
					},
					Weight: 3,
				}},
				"v1": {xdstest.LocLbEpInfo{
					LbEps: []xdstest.LbEpInfo{
						{Address: "1.2.3.4", Weight: 1},
						{Address: "2.2.2.2", Weight: 1},
					},
					Weight: 2,
				}},
				"v2": {xdstest.LocLbEpInfo{
					LbEps: []xdstest.LbEpInfo{

		var nodeScore, weightSum int64
		for i := range requested {
			if allocable[i] == 0 {
				continue
			}
			weight := resources[i].Weight
			resourceScore := mostRequestedScore(requested[i], allocable[i])
			nodeScore += resourceScore * weight
			weightSum += weight
		}
		if weightSum == 0 {
			return 0
		}
		return nodeScore / weightSum
	}
}

  // valid when the quantization parameters need to be created by scanning the
  // constant content (post-training quantization or QAT without weight
  // FakeQuant).
  bool disable_per_channel = false;

  // Whether to disable per-channel weight quantization and enable legacy per
  // tensor quantization. The legacy quantization for Dense layers is

      - name: httpbin-zero
        port: 8080
        weight: 0
  - matches:
    - path:
        type: PathPrefix
        value: /weighted-100
    backendRefs:
    - filters:
      - requestHeaderModifier:
          add:
          - name: foo
            value: bar
        type: RequestHeaderModifier
      port: 8000
      name: foo-svc
      weight: 100
---
apiVersion: gateway.networking.k8s.io/v1alpha2

#SNG: from basn0g04.png
IHDR {
    width: 32; height: 32; bitdepth: 8;
    using grayscale;
}
gAMA {1.0000}
IMAGE {
    pixels hex
0000000011111111222222223333333344444444555555556666666677777777
0000000011111111222222223333333344444444555555556666666677777777
0000000011111111222222223333333344444444555555556666666677777777
0000000011111111222222223333333344444444555555556666666677777777
1111111122222222333333334444444455555555666666667777777788888888

 * @since 11.0
 */
@GwtCompatible
@ElementTypesAreNonnullByDefault
public interface Weigher<K, V> {

  /**
   * Returns the weight of a cache entry. There is no unit for entry weights; rather they are simply
   * relative to each other.
   *
   * @return the weight of the entry; must be non-negative
   */
  int weigh(K key, V value);

 */
@GwtCompatible
@FunctionalInterface
@ElementTypesAreNonnullByDefault
public interface Weigher<K, V> {

  /**
   * Returns the weight of a cache entry. There is no unit for entry weights; rather they are simply
   * relative to each other.
   *
   * @return the weight of the entry; must be non-negative
   */
  int weigh(K key, V value);