"description": "NodeTaintsPolicy indicates how we will treat node taints when calculating pod topology spread skew. Options are: - Honor: nodes without taints, along with tainted nodes for which the incoming pod has a toleration, are included. - Ignore: node taints are ignored. All nodes are included.\n\nIf this value is nil, the behavior is equivalent to the Ignore policy. This is a beta-level feature default enabled by the NodeInclusionPolicyInPodTopologySpread...

                                  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

              }
            ],
            "description": "Only nodes matching the selector will be considered by the scheduler when trying to find a Node that fits a Pod when that Pod uses a ResourceClaim that has not been allocated yet.\n\nSetting this field is optional. If null, all nodes are candidates."
          }
        },
        "required": [
          "driverName"
        ],

*   `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

**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

		{"IdentifierNode.Pos", Field, 1},
		{"IfNode", Type, 0},
		{"IfNode.BranchNode", Field, 0},
		{"IsEmptyTree", Func, 0},
		{"ListNode", Type, 0},
		{"ListNode.NodeType", Field, 0},
		{"ListNode.Nodes", Field, 0},
		{"ListNode.Pos", Field, 1},
		{"Mode", Type, 16},
		{"New", Func, 0},
		{"NewIdentifier", Func, 0},
		{"NilNode", Type, 1},
		{"NilNode.NodeType", Field, 4},