`tf.keras.mixed_precision.experimental.LossScaleOptimizer`, the weights
        of the `DynanmicLossScale` are copied into the `LossScaleOptimizer`
        instead of being reused. This means modifying the weights of the
        `DynamicLossScale` will no longer affect the weights of the
        LossScaleOptimizer, and vice versa.

    checkArgument(maximumSize >= 0, "maximum size must not be negative");
    this.maximumSize = maximumSize;
    return this;
  }

  /**
   * Specifies the maximum weight of entries the cache may contain. Weight is determined using the
   * {@link Weigher} specified with {@link #weigher}, and use of this method requires a

      ValueReference<K, V> previous = entry.getValueReference();
      int weight = map.weigher.weigh(key, value);
      checkState(weight >= 0, "Weights must be non-negative");

      ValueReference<K, V> valueReference =
          map.valueStrength.referenceValue(this, entry, value, weight);
      entry.setValueReference(valueReference);
      recordWrite(entry, weight, now);
      previous.notifyNewValue(value);
    }

                        if a sidecar is present in the workload.
                      maxLength: 253
                      type: string
                    weight:
                      description: The load balancing weight associated with the endpoint.
                      maximum: 4294967295
                      minimum: 0
                      type: integer
                  type: object

    checkArgument(maximumSize >= 0, "maximum size must not be negative");
    this.maximumSize = maximumSize;
    return this;
  }

  /**
   * Specifies the maximum weight of entries the cache may contain. Weight is determined using the
   * {@link Weigher} specified with {@link #weigher}, and use of this method requires a

}

// The weights of all of the matched WeightedPodAffinityTerm fields are added per-node to find the most preferred node(s)
message WeightedPodAffinityTerm {
  // weight associated with matching the corresponding podAffinityTerm,
  // in the range 1-100.
  optional int32 weight = 1;

  // Required. A pod affinity term, associated with the corresponding weight.
  optional PodAffinityTerm podAffinityTerm = 2;

    assertThat(keySet).containsExactly(0, 1, 2, 3, 4, 5, 6, 7, 8, 9);

    // add an at-the-maximum-weight entry
    getAll(cache, asList(45));
    CacheTesting.drainRecencyQueues(cache);
    assertThat(keySet).containsExactly(0, 45);

    // add an over-the-maximum-weight entry
    getAll(cache, asList(46));
    CacheTesting.drainRecencyQueues(cache);
    assertThat(keySet).contains(0);
  }

    return new IntKeyWeigher();
  }

  /** Returns a {@link Weigher} that uses the integer value as the weight. */
  static Weigher<Object, Integer> intValueWeigher() {
    return new IntValueWeigher();
  }

  static final class ConstantWeigher implements Weigher<Object, Object> {
    private final int constant;

    ConstantWeigher(int constant) {
      this.constant = constant;
    }

  - [beta] `Federated Replica Sets` create and maintain matching
    `Replica Set`s in some or all clusters in a federation, with the
    desired replica count distributed equally or according to
    specified per-cluster weights.
	([docs](http://kubernetes.io/docs/user-guide/federation/federated-replicasets.md))
	([kubernetes/features#46](https://github.com/kubernetes/enhancements/issues/46))

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