* client-go: restores behavior of populating the BearerToken field in rest.Config objects constructed from kubeconfig files containing tokenFile config, or from in-cluster configuration. An additional BearerTokenFile field is now populated to enable constructed clients to periodically refresh tokens. ([#71713](https://github.com/kubernetes/kubernetes/pull/71713), [@liggitt](https://github.com/liggitt))

- Kubeadm: added the "--cleanup-tmp-dir" flag for `kubeadm reset`. It will cleanup the contents of `/etc/kubernetes/tmp`. The flag is...

- Feature gates CSIMigration to Beta (on by default) and CSIMigrationGCE to Beta (off by default since it requires installation of the GCE PD CSI Driver) ([#85231](https://github.com/kubernetes/kubernetes/pull/85231),...

- The `transformer_failures_total` metric is deprecated in favor of `transformation_operation_total`. The old metric will continue to be populated but will be removed in a future release. ([#70715](https://github.com/kubernetes/kubernetes/pull/70715), [@immutableT](https://github.com/immutableT))

* client-go: restores behavior of populating the BearerToken field in rest.Config objects constructed from kubeconfig files containing tokenFile config, or from in-cluster configuration. An additional BearerTokenFile field is now populated to enable constructed clients to periodically refresh tokens. ([#71713](https://github.com/kubernetes/kubernetes/pull/71713), [@liggitt](https://github.com/liggitt))

## Known Issues

This section contains a list of known issues reported in Kubernetes 1.8 release. The content is populated via [v1.8.x known issues and FAQ accumulator](https://github.com/kubernetes/kubernetes/issues/53004).

        Cloud TPUs.
    *   Support for `.fit`, `.evaluate`, `.predict` on TPU using numpy data, in
        addition to `tf.data.Dataset`.
    *   Keras reference implementations for many popular models are available in
        the TensorFlow
        [Model Garden](https://github.com/tensorflow/models/tree/master/official).
*   `tf.data`
    *   Changes rebatching for `tf.data datasets` + DistributionStrategy for

*P256Point, n uint8) { if n > 16 { panic("nistec: internal error: p256Table called with out-of-bounds value") } p.Set(NewP256Point()) for i := uint8(1); i <= 16; i++ { cond := subtle.ConstantTimeByteEq(i, n) p.Select(&table[i-1], p, cond) } } // Compute populates the table to the first 16 multiples of q. func (table *p256Table) Compute(q *P256Point) *p256Table { table[0].Set(q) for i := 1; i < 16; i += 2 { table[i].Double(&table[i/2]) if i+1 < 16 { table[i+1].Add(&table[i], q) } } return table } func boothW5(in...

*P256Point, n uint8) { if n > 16 { panic("nistec: internal error: p256Table called with out-of-bounds value") } p.Set(NewP256Point()) for i := uint8(1); i <= 16; i++ { cond := constanttime.ByteEq(i, n) p.Select(&table[i-1], p, cond) } } // Compute populates the table to the first 16 multiples of q. func (table *p256Table) Compute(q *P256Point) *p256Table { table[0].Set(q) for i := 1; i < 16; i += 2 { table[i].Double(&table[i/2]) if i+1 < 16 { table[i+1].Add(&table[i], q) } } return table } func boothW5(in...