- [`KT-57777`](https://youtrack.jetbrains.com/issue/KT-57777) K2: Fix computing a mangled name for the synthesized `entries` property getter of an enum class
- [`KT-57433`](https://youtrack.jetbrains.com/issue/KT-57433) K2: Fix computing a mangled name for top-level functions and properties

These examples show when a Go implementation can use that instruction:
</p>

<pre>
// FMA allowed for computing r, because x*y is not explicitly rounded:
r  = x*y + z
r  = z;   r += x*y
t  = x*y; r = t + z
*p = x*y; r = *p + z
r  = x*y + float64(z)

// FMA disallowed for computing r, because it would omit rounding of x*y:
r  = float64(x*y) + z
r  = z; r += float64(x*y)
t  = float64(x*y); r = t + z

These examples show when a Go implementation can use that instruction:
</p>

<pre>
// FMA allowed for computing r, because x*y is not explicitly rounded:
r  = x*y + z
r  = z;   r += x*y
t  = x*y; r = t + z
*p = x*y; r = *p + z
r  = x*y + float64(z)

// FMA disallowed for computing r, because it would omit rounding of x*y:
r  = float64(x*y) + z
r  = z; r += float64(x*y)
t  = float64(x*y); r = t + z

* Add a Deployment example for kubectl expose. ([#23222](https://github.com/kubernetes/kubernetes/pull/23222), [@madhusudancs](https://github.com/madhusudancs))
* Use versioned object when computing patch ([#23145](https://github.com/kubernetes/kubernetes/pull/23145), [@liggitt](https://github.com/liggitt))

'../utils/computeAutoPlacement';\nimport getReferenceOffsets from '../utils/getReferenceOffsets';\nimport getPopperOffsets from '../utils/getPopperOffsets';\nimport runModifiers from '../utils/runModifiers';\n\n/**\n * Updates the position of the popper, computing the new offsets and applying\n * the new style.<br />\n * Prefer `scheduleUpdate` over `update` because of performance reasons.\n * @method\n * @memberof Popper\n */\nexport default function update() {\n  // if popper is destroyed, don't perform...

 *   Updated `tf.keras.layers.BatchNormalization` to support masking of the inputs (`mask` argument) when computing the mean and variance.
 *   Add `tf.keras.layers.Identity`, a placeholder pass-through layer.
 *   Add `show_trainable` option to `tf.keras.utils.model_to_dot` to display layer trainable status in model plots.

  - Remove redundant logging around calculating node utilization
  - Add configurable `--network` and `--rm` flags for docker in `Makefile`
  - Subtract DaemonSet pods' requests from node allocatable in the denominator while computing node utilization
  - Include taints by condition when determining if a node is unready/still starting
  - Fix `update-vendor.sh` to work on OSX and zsh

 * released multiple ListenableFuture.class files that are not byte-for-byte compatible even from
 * the beginning, thanks to using different `-source -target` values for compiling our `-jre` and
 * `-android` "flavors.")
 *
 * (We could consider releasing a listenablefuture:1.0.1 someday. But we would want to look into how

 * released multiple ListenableFuture.class files that are not byte-for-byte compatible even from
 * the beginning, thanks to using different `-source -target` values for compiling our `-jre` and
 * `-android` "flavors.")
 *
 * (We could consider releasing a listenablefuture:1.0.1 someday. But we would want to look into how

* Bug fixes:

  * Fix serialization of EnforceNodeAllocatable ([#44606](https://github.com/kubernetes/kubernetes/pull/44606), [@ivan4th](https://github.com/ivan4th))

	* Use OS-specific libs when computing client User-Agent in kubectl, etc. ([#44423](https://github.com/kubernetes/kubernetes/pull/44423), [@monopole](https://github.com/monopole))


#### Instrumentation