validationIgnorer *crdvalidation.ValidationIgnorer
	}{
		{name: "http"},
		{name: "tcp"},
		{name: "tls"},
		{name: "grpc"},
		{name: "mismatch"},
		{name: "weighted"},
		{name: "zero"},
		{name: "mesh"},
		{
			name: "invalid",
			validationIgnorer: crdvalidation.NewValidationIgnorer(
				"default/^invalid-backendRef-kind-",
				"default/^invalid-backendRef-mixed-",

// The scores from each plugin are added together to make the score for that node, then
// any extenders are run as well.
// All scores are finally combined (added) to get the total weighted scores of all nodes
func prioritizeNodes(
	ctx context.Context,
	extenders []framework.Extender,
	fwk framework.Framework,
	state *framework.CycleState,
	pod *v1.Pod,
	nodes []*framework.NodeInfo,

			}},
		}, valid: true},
		{name: "no destination", route: &networking.HTTPRoute{
			Route: []*networking.HTTPRouteDestination{{
				Destination: nil,
			}},
		}, valid: false},
		{name: "weighted", route: &networking.HTTPRoute{
			Route: []*networking.HTTPRouteDestination{{
				Destination: &networking.Destination{Host: "foo.baz.south"},
				Weight:      25,
			}, {

        will raise an error.
    *   `NONE`: Weighted losses with one dimension reduced (axis=-1, or axis
        specified by loss function). When this reduction type used with built-in
        Keras training loops like `fit`/`evaluate`, the unreduced vector loss is
        passed to the optimizer but the reported loss will be a scalar value.
    *   `SUM`: Scalar sum of weighted losses. 4. `SUM_OVER_BATCH_SIZE`: Scalar

  let description = [{
The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last
dimension), and each vector is normalized independently.  Within a given vector,
each component is divided by the weighted, squared sum of inputs within
`depth_radius`.  In detail,

    sqr_sum[a, b, c, d] =
        sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
    output = input / (bias + alpha * sqr_sum) ** beta

  `sync_partial_proxy_rules_duration_seconds` (in addition to the existing\n`sync_proxy_rules_duration_seconds`),
  giving better information about the duration of each \nsync type, rather than
  only giving a weighted average of the two sync types together. ([#117787](https://github.com/kubernetes/kubernetes/pull/117787), [@danwinship](https://github.com/danwinship))
- `kubeadm`: added a new "kubeadm config validate" command that can be used to

  let description = [{
The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last
dimension), and each vector is normalized independently.  Within a given vector,
each component is divided by the weighted, squared sum of inputs within
`depth_radius`.  In detail,

    sqr_sum[a, b, c, d] =
        sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
    output = input / (bias + alpha * sqr_sum) ** beta