description: The locality associated with the endpoint.
                      type: string
                    network:
                      description: Network enables Istio to group endpoints resident
                        in the same L3 domain/network.
                      type: string
                    ports:
                      additionalProperties:

                      description: The locality associated with the endpoint.
                      type: string
                    network:
                      description: Network enables Istio to group endpoints resident
                        in the same L3 domain/network.
                      type: string
                    ports:
                      additionalProperties:

      "io.k8s.api.apps.v1.StatefulSet": {
        "description": "StatefulSet represents a set of pods with consistent identities. Identities are defined as:\n  - Network: A single stable DNS and hostname.\n  - Storage: As many VolumeClaims as requested.\n\nThe StatefulSet guarantees that a given network identity will always map to the same storage identity.",
        "properties": {
          "apiVersion": {

    are available in Linux x86, Windows x86, and Linux aarch64 packages.

    *   **Linux x86 packages:**
        *   oneDNN optimizations are *enabled by default* on CPUs with
            neural-network-focused hardware features such as AVX512_VNNI,
            AVX512_BF16, AMX, etc.
            ([Intel Cascade Lake](https://www.intel.com/content/www/us/en/products/platforms/details/cascade-lake.html)

		{"storage", true, ""},
		{"requests.cpu", true, ""},
		{"requests.memory", true, ""},
		{"requests.storage", true, ""},
		{"limits.cpu", true, ""},
		{"limits.memory", true, ""},
		{"network", false, ""},
		{"disk", false, ""},
		{"", false, ""},
		{".", false, ""},
		{"..", false, ""},
		{"my.favorite.app.co/12345", true, ""},
		{"my.favorite.app.co/_12345", false, ""},

<tf.Tensor: shape=(), dtype=float32, numpy=1.0>
>>> tf.nn.elu(0.0)
<tf.Tensor: shape=(), dtype=float32, numpy=0.0>
>>> tf.nn.elu(-1000.0)
<tf.Tensor: shape=(), dtype=float32, numpy=-1.0>

See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
](http://arxiv.org/abs/1511.07289)
  }];

  let arguments = (ins
    TF_FloatTensor:$features
  );

  let results = (outs