the total number of nodes that should be running the daemon pod (i.e. status.desiredNumberScheduled) can have their a new pod created before the old pod is marked as deleted. The update starts by launching new pods on 30% of nodes. Once an updated pod is available (Ready for at least minReadySeconds) the old DaemonSet pod on that node is marked deleted. If the old pod becomes unavailable for any reason (Ready transitions to false, is evicted, or is drained) an updated pod is immediatedly created on...

is rounded first, before it's subtracted from the rounded value. With
MIN_COMBINED, a small bias is introduced where repeated iterations of quantizing
and dequantizing will introduce a larger and larger error.

*SCALED mode Example*

`SCALED` mode matches the quantization approach used in
`QuantizeAndDequantize{V2|V3}`.

*   `tf.distribute`

    *   Opened an experimental API, `tf.distribute.experimental.coordinator.get_current_worker_index`, for retrieving the worker index from within a worker, when using parameter server training with a custom training loop.

*   `tf.experimental.dtensor`

			},
			NodeName:              "foobar",
			DNSPolicy:             core.DNSClusterFirst,
			ActiveDeadlineSeconds: &activeDeadlineSeconds,
			ServiceAccountName:    "acct",
		},
		"populate all fields with larger active deadline": {
			Volumes: []core.Volume{
				{Name: "vol", VolumeSource: core.VolumeSource{EmptyDir: &core.EmptyDirVolumeSource{}}},
			},