- Fix priority expander falling back to a random choice even though there is a higher priority option to choose
  - Clone `kubernetes/kubernetes` in `update-vendor.sh` shallowly, instead of fetching all revisions
  - Speed up binpacking by reducing the number of PreFilter calls (call once per pod instead of #pods*#nodes times)
  - Speed up finding unneeded nodes by 5x+ in very large clusters by reducing the number of PreFilter calls

nfiguration/assign-pod-node/). Node affinity/anti-affinity allow you to specify rules for restricting which node(s) a pod can schedule onto, based on the labels on the node. Pod affinity/anti-affinity allow you to specify rules for spreading and packing pods relative to one another, across arbitrary topologies (node, zone, etc.) These affinity rules are now be specified in a new-in-1.6 `affinity` field of the PodSpec. Kubernetes 1.6 continues to support the alpha `scheduler.alpha.kubernetes.io/affinity`...

the function completes.
(See also the section on <a href="#Handling_panics">handling panics</a>.)
</p>

<pre>
lock(l)
defer unlock(l)  // unlocking happens before surrounding function returns

// prints 3 2 1 0 before surrounding function returns
for i := 0; i &lt;= 3; i++ {
	defer fmt.Print(i)
}

// f returns 42
func f() (result int) {

  - Fix priority expander falling back to a random choice even though there is a higher priority option to choose
  - Clone `kubernetes/kubernetes` in `update-vendor.sh` shallowly, instead of fetching all revisions
  - Speed up binpacking by reducing the number of PreFilter calls (call once per pod instead of #pods*#nodes times)
  - Speed up finding unneeded nodes by 5x+ in very large clusters by reducing the number of PreFilter calls

        [example](https://github.com/tensorflow/models/blob/master/official/modeling/grad_utils.py)
        usage.
    *   Experimental support of
        [all reduce gradient packing](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/CollectiveHints)
        to allow overlapping gradient aggregation with backward path
        computation.