`tf.name_scope` and `tf.variable_scope`. The new argument order of
    `tf.variable_scope` is incompatible with previous versions.
*   Introducing `core/util/tensor_bundle` module: a module to efficiently
    serialize/deserialize tensors to disk. Will be used in TF's new checkpoint
    format.
*   Added tf.svd for computing the singular value decomposition (SVD) of dense

SIG Windows is also including several addition to this release:
 - Direct Server Return (DSR) mode support, allowing large numbers of services to scale up efficiently
 - Windows containers  now honor CPU limits
 - Performance improvements for collections of metrics and summary

### Increase the Kubernetes support window to one year

table[i-1], table[0], m) } out.resetFor(m) out.limbs[0] = 1 out.montgomeryRepresenta(m) tmp := NewNat().ExpandFor(m) for _, b := range e { for _, j := range []int{4, 0} { // Square four times. Optimization note: this can be implemented // more efficiently than with generic Montgomery multiplication. out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) // Select x^k in constant time from the table. k := uint((b >> j) & 0b1111)...

table[i-1], table[0], m) } out.resetFor(m) out.limbs[0] = 1 out.montgomeryRepresenta(m) tmp := NewNat().ExpandFor(m) for _, b := range e { for _, j := range []int{4, 0} { // Square four times. Optimization note: this can be implemented // more efficiently than with generic Montgomery multiplication. out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) out.montgomeryMul(out, out, m) // Select x^k in constant time from the table. k := uint((b >> j) & 0b1111)...