void* device_info) {
  TF_SetStatus(status, TF_UNIMPLEMENTED,
               "This custom device does not support packing tensors.");
  return nullptr;
}
}  // namespace

extern "C" {

bool TFE_IsCustomDevice(TFE_Context* ctx, const char* device_name) {
  return tensorflow::unwrap(ctx)->IsCustomDevice(device_name);

  So she was considering in her own mind (as well as she could,
for the hot day made her feel very sleepy and stupid), whether
the pleasure of making a daisy-chain would be worth the trouble
of getting up and picking the daisies, when suddenly a White
Rabbit with pink eyes ran close by her.

  There was nothing so VERY remarkable in that; nor did Alice
think it so VERY much out of the way to hear the Rabbit say to

- When a CNI plugin returns dual-stack pod IPs, kubelet will now try to respect the
  "primary IP family" of the cluster by picking a primary pod IP of the same family
  as the (primary) node IP, rather than assuming that the CNI plugin returned the IPs
  in the order the administrator wanted (since some CNI plugins don't allow

  // within the cluster will always get "Cluster" semantics, but clients sending to
  // a NodePort from within the cluster may need to take traffic policy into account
  // when picking a node.
  // +optional
  optional string externalTrafficPolicy = 11;

  // healthCheckNodePort specifies the healthcheck nodePort for the service.
  // This only applies when type is set to LoadBalancer and

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`...

        [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.