var min, max uint64
		if unsigned {
			min, _ = constant.Uint64Val(n.Cases[0])
			max, _ = constant.Uint64Val(n.Cases[len(n.Cases)-1])
		} else {
			mn, _ := constant.Int64Val(n.Cases[0])
			mx, _ := constant.Int64Val(n.Cases[len(n.Cases)-1])
			min = uint64(mn)
			max = uint64(mx)
		}
		// Compare idx-min with max-min, to see if we can use the jump table.

    Location loc = op.getLoc();
    switch (padding_type) {
      case tensorflow::Padding::VALID: {
        auto zero =
            rewriter.create<arith::ConstantIntOp>(loc, 0, shape_scalar_type);
        *padding_low = *padding_high = zero;
        break;
      }
      case tensorflow::Padding::EXPLICIT:
        break;
      case tensorflow::Padding::SAME: {
        auto zero =

- `kubeadm`: generate CA certificates with a start time that is offset 5
  minutes in the past relative to the current system time to workaround cases of
  clock desync. ([#118922](https://github.com/kubernetes/kubernetes/pull/118922), [@champtar](https://github.com/champtar))
- `plugin_evaluation_total` metric supports prescore/score extension point.

- Previously, the scheduling queue didn't notice any extenders' failures, potentially resulting in missed cluster events and Pods rejected by Extenders being stuck in the unschedulable pod pool for up to 5 minutes in the worst-case scenario. Now, the scheduling queue notices extenders' failures and requeues Pods rejected by Extenders appropriately.
   ([#122022](https://github.com/kubernetes/kubernetes/pull/122022), [@sanposhiho](https://github.com/sanposhiho))