type: object
                    name:
                      description: Name of the subset.
                      type: string
                    trafficPolicy:
                      description: Traffic policies that apply to this subset.
                      properties:
                        connectionPool:
                          properties:

                      type: object
                    name:
                      description: Name of the subset.
                      type: string
                    trafficPolicy:
                      description: Traffic policies that apply to this subset.
                      properties:
                        connectionPool:
                          properties:

                      type: object
                    name:
                      description: Name of the subset.
                      type: string
                    trafficPolicy:
                      description: Traffic policies that apply to this subset.
                      properties:
                        connectionPool:
                          properties:

				}},
			},
		},
		"empty subsets": {
			endpoints: core.Endpoints{
				ObjectMeta: metav1.ObjectMeta{Name: "mysvc", Namespace: "namespace"},
			},
		},
		"no name required for singleton port": {
			endpoints: core.Endpoints{
				ObjectMeta: metav1.ObjectMeta{Name: "mysvc", Namespace: "namespace"},
				Subsets: []core.EndpointSubset{{

Note, most python users will want to use the Python `Tensor.__getitem__`
or `Variable.__getitem__` rather than this op directly.

The goal of this op is to produce a new tensor with a subset of
the elements from the `n` dimensional `input` tensor. The subset is chosen using
a sequence of `m` sparse range specifications encoded into the arguments
of this function. Note, in some cases

    Building signatures from these allows TF Serving to honor requests using the
    different APIs (Classify, Regress, and Predict). Furthermore,
    `serving_input_receiver_fn()` may now specify alternative subsets of nodes
    that may act as inputs. This allows, for instance, producing a prediction
    signature for a classifier that accepts raw `Tensors` instead of a
    serialized `tf.Example`.

      },
      "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": {
              "allOf": [

      },
      "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": {
              "allOf": [