Status: api.PodStatus{
					Phase: "podPhase",
					ContainerStatuses: []api.ContainerStatus{
						{Ready: true, RestartCount: 3, State: api.ContainerState{Running: &api.ContainerStateRunning{}}},
						{State: api.ContainerState{Waiting: &api.ContainerStateWaiting{Reason: "ContainerWaitingReason"}}, RestartCount: 3},
					},
				},
			},

function kube-up() {
  kube::util::ensure-temp-dir
  detect-project

  load-or-gen-kube-basicauth
  load-or-gen-kube-bearertoken

  # Make sure we have the tar files staged on Google Storage
  find-release-tars
  upload-tars

  # ensure that environmental variables specifying number of migs to create
  set_num_migs

  if [[ ${KUBE_USE_EXISTING_MASTER:-} == "true" ]]; then

					Description: MetricDescription{
						Namespace: minioNamespace,
						Subsystem: notifySubsystem,
						Name:      "target_queue_length",
						Help:      "Number of events currently staged in the queue_dir configured for the target",
						Type:      gaugeMetric,
					},
					VariableLabels: map[string]string{"target_id": id.ID, "target_name": id.Name},
					Value:          float64(st.CurrentQueue),

    `tf.entry_function` attribute will not be pruned, as such graphs/functions may
    have been imported from a V1 TensorFlow graph, where feeds/fetches/targets are
    not provided at certain stages of IR transformation (e.g. pre-placement).

    Option `ops-to-preserve` allows to specify ops that should not be pruned,
    regardless of their reachability.

    For example, the following:

    ```mlir