},
          "nodeTaintsPolicy": {

                                  properties:
                                    minimumRingSize:
                                      description: The minimum number of virtual nodes
                                        to use for the hash ring.
                                      minimum: 0
                                      type: integer
                                  type: object

                                  properties:
                                    minimumRingSize:
                                      description: The minimum number of virtual nodes
                                        to use for the hash ring.
                                      minimum: 0
                                      type: integer
                                  type: object

*   `tf.saved_model.SaveOptions`

    * `SaveOptions` now takes a new argument called
      `experimental_debug_stripper`. When enabled, this strips the debug nodes
      from both the node defs and the function defs of the graph. Note that
      this currently only strips the `Assert` nodes from the graph and
      converts them into `NoOp`s instead.

*   `tf.data`

    * `tf.data` now has an `autotune_options.initial_parallelism` option to

    "schemas": {
      "io.k8s.api.core.v1.NodeSelector": {
        "description": "A node selector represents the union of the results of one or more label queries over a set of nodes; that is, it represents the OR of the selectors represented by the node selector terms.",
        "properties": {
          "nodeSelectorTerms": {
            "description": "Required. A list of node selector terms. The terms are ORed.",
            "items": {
              "allOf": [

**A note about the source attribute:**

TensorArray gradient calls use an accumulator TensorArray object.  If
multiple gradients are calculated and run in the same session, the multiple
gradient nodes may accidentally flow through the same accumulator TensorArray.
This double counts and generally breaks the TensorArray gradient flow.

The solution is to identify which gradient call this particular