parameter.
                                  type: string
                                maglev:
                                  description: The Maglev load balancer implements
                                    consistent hashing to backend hosts.
                                  properties:
                                    tableSize:

                                    parameter.
                                  type: string
                                maglev:
                                  description: The Maglev load balancer implements
                                    consistent hashing to backend hosts.
                                  properties:
                                    tableSize:

        "type": "object"
      },
      "io.k8s.api.core.v1.NodeSelectorTerm": {
        "description": "A null or empty node selector term matches no objects. The requirements of them are ANDed. The TopologySelectorTerm type implements a subset of the NodeSelectorTerm.",
        "properties": {
          "matchExpressions": {
            "description": "A list of node selector requirements by node's labels.",
            "items": {

{
  "components": {
    "schemas": {
      "io.k8s.api.apps.v1.ControllerRevision": {

                        this rule.


                        Wherever possible, implementations SHOULD implement filters in the order
                        they are specified.


                        Implementations MAY choose to implement this ordering strictly, rejecting
                        any combination or order of filters that can not be supported. If implementations

        current learning rate value instead of a `LearningRateSchedule` object
        as before. If you need to access the `LearningRateSchedule` object,
        please use `optimizer._learning_rate`.
    *   **You implemented a custom optimizer based on the old optimizer.**
        Please set your optimizer to subclass
        `tf.keras.optimizer.legacy.XXX`. If you want to migrate to the new

tf.Tensor([0. 1. 1.], shape=(3,), dtype=float32)
>>> print(equality_bitcast)
tf.Tensor(
    [[  0   0   0   0]
     [  0   0 128  63]
     [  0   0 128  63]], shape=(3, 4), dtype=uint8)

*NOTE*: Bitcast is implemented as a low-level cast, so machines with different
endian orderings will give different results. A copy from input buffer to output
buffer is made on BE machines when types are of different sizes in order to get

				Name:      "123",
				Namespace: "ns",
			},
			Spec: validPodSpec(
				// TODO: Uncomment and move this block and move inside NodeAffinity once
				// RequiredDuringSchedulingRequiredDuringExecution is implemented
				//		RequiredDuringSchedulingRequiredDuringExecution: &core.NodeSelector{
				//			NodeSelectorTerms: []core.NodeSelectorTerm{
				//				{
				//					MatchExpressions: []core.NodeSelectorRequirement{

		{"FieldVal", Const, 5},
		{"Float32", Const, 5},
		{"Float64", Const, 5},
		{"Func", Type, 5},
		{"Id", Func, 5},
		{"Identical", Func, 5},
		{"IdenticalIgnoreTags", Func, 8},
		{"Implements", Func, 5},
		{"ImportMode", Type, 6},
		{"Importer", Type, 5},
		{"ImporterFrom", Type, 6},
		{"Info", Type, 5},
		{"Info.Defs", Field, 5},
		{"Info.FileVersions", Field, 22},