name: "ScaleIO with empty gateway",
			vol: core.Volume{
				Name: "scaleio-volume",
				VolumeSource: core.VolumeSource{
					ScaleIO: &core.ScaleIOVolumeSource{
						Gateway:    "",
						System:     "test-system",
						VolumeName: "test-vol-1",
					},
				},
			},
			errs: []verr{{
				etype: field.ErrorTypeRequired,
				field: "scaleIO.gateway",
			}},
		}, {

          },
          "scaleIO": {
            "allOf": [
              {
                "$ref": "#/components/schemas/io.k8s.api.core.v1.ScaleIOVolumeSource"
              }
            ],
            "description": "scaleIO represents a ScaleIO persistent volume attached and mounted on Kubernetes nodes."
          },
          "secret": {

MIN_COMBINED, a small bias is introduced where repeated iterations of quantizing
and dequantizing will introduce a larger and larger error.

*SCALED mode Example*

`SCALED` mode matches the quantization approach used in
`QuantizeAndDequantize{V2|V3}`.

If the mode is `SCALED`, the quantization is performed by multiplying each
input value by a scaling_factor.

	}
	// match: (LEAQ2 [off] {sym} x (MOVQconst [scale]))
	// cond: is32Bit(int64(off)+int64(scale)*2)
	// result: (LEAQ [off+int32(scale)*2] {sym} x)
	for {
		off := auxIntToInt32(v.AuxInt)
		sym := auxToSym(v.Aux)
		x := v_0
		if v_1.Op != OpAMD64MOVQconst {
			break
		}
		scale := auxIntToInt64(v_1.AuxInt)
		if !(is32Bit(int64(off) + int64(scale)*2)) {
			break
		}
		v.reset(OpAMD64LEAQ)

infinite;\n}\n\n.spinner-border-sm {\n  width: 1rem;\n  height: 1rem;\n  border-width: 0.2em;\n}\n\n@-webkit-keyframes spinner-grow {\n  0% {\n    -webkit-transform: scale(0);\n    transform: scale(0);\n  }\n  50% {\n    opacity: 1;\n  }\n}\n\n@keyframes spinner-grow {\n  0% {\n    -webkit-transform: scale(0);\n    transform: scale(0);\n  }\n  50% {\n    opacity: 1;\n  }\n}\n\n.spinner-grow {\n  display: inline-block;\n  width: 2rem;\n  height: 2rem;\n  vertical-align: text-bottom;\n  background-color:...

- If we set SelectPolicy MinPolicySelect on scaleUp behavior or scaleDown behavior,Horizontal Pod Autoscaler doesn't automatically scale the number of pods correctly ([#95647](https://github.com/kubernetes/kubernetes/pull/95647), [@JoshuaAndrew](https://github.com/JoshuaAndrew)) [SIG Apps and Autoscaling]

`Policy` optionally took in a `tf.compat.v1.mixed_precision.LossScale` in the constructor, which defaulted to a dynamic loss scale for the `"mixed_float16"` policy and no loss scale for other policies. In `Model.compile`, if the model's policy had a loss scale, the optimizer would be wrapped with a `LossScaleOptimizer`. With the non-experimental `Policy`, there is no loss scale associated with the `Policy`, and `Model.compile` wraps the optimizer with a `LossScaleOptimizer` if and only if the policy...