*   `CategoryCrossing`: create new categorical features representing
        co-occurrences of previous categorical feature values
    *   `Hashing`: the hashing trick, for large-vocabulary categorical features
    *   `Discretization`: turn continuous numerical features into categorical
        features by binning their values
*   Improved **image preprocessing** layers: `CenterCrop`, `Rescaling`

If `indices` is a vector of length `features`, the output shape will be:
```
  features x depth if axis == -1
  depth x features if axis == 0
```

If `indices` is a matrix (batch) with shape `[batch, features]`,
the output shape will be:
```
  batch x features x depth if axis == -1
  batch x depth x features if axis == 1
  depth x batch x features if axis == 0
```

Since these new workflows don’t require any new cluster features, they’re available for experimentation with your existing clusters via `kubectl alpha debug`. We’d love to hear your feedback on debugging with `kubectl`. Reach us by opening an issue, visiting [#sig-cli](https://kubernetes.slack.com/messages/sig-cli) or commenting on enhancement [#1441](https://features.k8s.io/1441).

### Structured logging

		t.Run(name, func(t *testing.T) {
			featuregatetesting.SetFeatureGateDuringTest(t, utilfeature.DefaultFeatureGate, features.RecoverVolumeExpansionFailure, scenario.enableRecoverFromExpansion)
			featuregatetesting.SetFeatureGateDuringTest(t, utilfeature.DefaultFeatureGate, features.VolumeAttributesClass, scenario.enableVolumeAttributesClass)

			scenario.oldClaim.ResourceVersion = "1"

                description: |
                  SupportedFeatures is the set of features the GatewayClass support.
                  It MUST be sorted in ascending alphabetical order.
                items:
                  description: |-
                    SupportedFeature is used to describe distinct features that are covered by
                    conformance tests.
                  type: string

Glass may be understood. But in small fragments of those Plates there is
this farther observable, that where they lie flat upon a Table, and are
turned about their centers whilst they are view'd through a Prism, they
will in some postures exhibit Waves of various Colours; and some of them
exhibit these Waves in one or two Positions only, but the most of them
do in all Positions exhibit them, and make them for the most part appear

is handled by a cluster storage driver. The volume's lifecycle is tied to the pod that defines it - it will be created before the pod starts, and deleted when the pod is removed.\n\nUse this if: a) the volume is only needed while the pod runs, b) features of normal volumes like restoring from snapshot or capacity\n   tracking are needed,\nc) the storage driver is specified through a storage class, and d) the storage driver supports dynamic volume provisioning through\n   a PersistentVolumeClaim (see...

1F643..1F644  ; valid                  ;      ; NV8    # 8.0  UPSIDE-DOWN FACE..FACE WITH ROLLING EYES
1F645..1F64F  ; valid                  ;      ; NV8    # 6.0  FACE WITH NO GOOD GESTURE..PERSON WITH FOLDED HANDS
1F650..1F67F  ; valid                  ;      ; NV8    # 7.0  NORTH WEST POINTING LEAF..REVERSE CHECKER BOARD
1F680..1F6C5  ; valid                  ;      ; NV8    # 6.0  ROCKET..LEFT LUGGAGE

Class <org.gradle.internal.instrumentation.api.annotations.features.withstaticreference.WithExtensionReferences> is not annotated (directly or via its package) with @org.gradle.api.NonNullApi in (WithExtensionReferences.java:0)