)

// nodeStrategy implements behavior for nodes
type nodeStrategy struct {
	runtime.ObjectTyper
	names.NameGenerator
}

// Nodes is the default logic that applies when creating and updating Node
// objects.
var Strategy = nodeStrategy{legacyscheme.Scheme, names.SimpleNameGenerator}

// NamespaceScoped is false for nodes.
func (nodeStrategy) NamespaceScoped() bool {
	return false
}

    }
  }
  // BFS from nodes in 'inputs_' along out edges for the entire graph. Internal
  // output nodes are recorded during the traversal. All nodes that are output
  // nodes but not internal output nodes are considered the frontier of the
  // output nodes, and thus our stop backprop nodes.
  while (!queue.empty()) {
    std::pair<Node*, Node*> p = queue.front();

// `scope`. `enter_output` is the `loop_var_idx`-th Enter node's output.
Status CreateMerge(const Scope& scope, int loop_var_idx,
                   const Output& enter_output, Output* merge_output) {
  // The merge nodes accept the while loop's back edges as an input (i.e. the
  // not-yet-created next iteration nodes). Use the underlying NodeBuilder API
  // directly to create the back edge.

export E2E_STORAGE_TEST_ENVIRONMENT="${KUBE_E2E_STORAGE_TEST_ENVIRONMENT:-false}"

# Evict pods whenever compute resource availability on the nodes gets below a threshold.
EVICTION_HARD="${EVICTION_HARD:-memory.available<250Mi,nodefs.available<10%,nodefs.inodesFree<5%}"

# Optional: custom scheduling algorithm
SCHEDULING_ALGORITHM_PROVIDER="${SCHEDULING_ALGORITHM_PROVIDER:-}"

// The size of the image is used as the base score, scaled by a factor which considers how much nodes the image has "spread" to.
// This heuristic aims to mitigate the undesirable "node heating problem", i.e., pods get assigned to the same or
// a few nodes due to image locality.
func scaledImageScore(imageState *framework.ImageStateSummary, totalNumNodes int) int64 {

}

// Resets "_device_ordinal" attr to placeholder value for related nodes
// (XlaRecvAtHost nodes; XlaSendFromHost nodes; If/While/FuncCall nodes
// containing XlaRecvAtHost/XlaSendFromHost).
Status ResetDeviceOrdinalToPlaceholderValue(Graph* g) {
  AttrValue device_ordinal_value;
  device_ordinal_value.set_placeholder("_device_ordinal");
  for (Node* n : g->nodes()) {
    if (!HasNodeAttr(n->def(), kXlaHasHostTransferAttrName)) {

  NODE_ACCELERATORS=''
fi

# To avoid failing large tests due to some flakes in starting nodes, allow
# for a small percentage of nodes to not start during cluster startup.
ALLOWED_NOTREADY_NODES=${ALLOWED_NOTREADY_NODES:-$(($(get-num-nodes) / 100))}

# By default a cluster will be started with the master and nodes
# on Container-optimized OS (cos, previously known as gci). If

      }
    }

    // Sometimes we have VariableShape nodes in while loop (after Enter nodes).
    // They won't be constant-folded because TensorFlow constant folding does
    // not handle Enter nodes (and thus does not handle any nodes after Enter
    // nodes). We try to replace such VariableShape nodes with Const nodes here.
    if (n->type_string() == "VariableShape") {

  // Gather all outside compilation to outside compilation nodes.
  std::vector<Node*> placeholder_nodes;
  for (Node* n : g->nodes()) {
    if (n->type_string() == "Placeholder" &&
        HasNodeAttr(n->def(), kOutsideCompilationOriginalNodeAttrName)) {
      placeholder_nodes.push_back(n);
    }
  }

  // Remove the placeholder nodes, and reconnect original edge.
  auto node_name_index = g->BuildNodeNameIndex();

			logger.Info("Controller detected that some Nodes are Ready. Exiting master disruption mode")
			// When exiting disruption mode update probe timestamps on all Nodes.
			now := nc.now()
			for i := range nodes {
				v := nc.nodeHealthMap.getDeepCopy(nodes[i].Name)
				v.probeTimestamp = now
				v.readyTransitionTimestamp = now
				nc.nodeHealthMap.set(nodes[i].Name, v)
			}