k <= bias -> TMIN
            k >= bias + TMAX -> TMAX
            else -> k - bias
          }
        if (digit < t) break
        val scaleW = BASE - t
        if (w > Int.MAX_VALUE / scaleW) return false // Prevent overflow.
        w *= scaleW
      }
      bias = adapt(i - oldi, codePoints.size + 1, oldi == 0)
      val deltaN = i / (codePoints.size + 1)

	var firstErr error
	for i, mapping := range mappings {
		targetGR := mapping.Resource.GroupResource()
		scale, err := a.scaleNamespacer.Scales(namespace).Get(ctx, targetGR, name, metav1.GetOptions{})
		if err == nil {
			return scale, targetGR, nil
		}

		// if this is the first error, remember it,

  constexpr absl::string_view kModuleCode = R"mlir(
    module {
      func.func @test_fold_constant() -> (tensor<1024x24x24x3xf32>) {
        %zp = "tf.Const"() {value = dense<2> : tensor<i32>} : () -> tensor<i32>
        %scale = "tf.Const"() {value = dense<2.0> : tensor<f32>} : () -> tensor<f32>
        %weight = "tf.Const"() {value = dense<1> : tensor<1024x24x24x3xi8>} : () -> tensor<1024x24x24x3xi8>

    }
    return success(changed);
  }

 private:
  // Checks whether the operation is connected with a composite function.
  // If not, the same-scale op will not be quantized. This decision is based
  // on the current assumption that the performance gain of the same-scale
  // op itself could not beat the overhead of the quantize and dequantize
  // routines need to be added around that op. When the assumption changes,

spec:
  behavior:
    scaleDown:
      policies:
      - periodSeconds: 3
        type: typeValue
        value: 2
      selectPolicy: selectPolicyValue
      stabilizationWindowSeconds: 3
    scaleUp:
      policies:
      - periodSeconds: 3
        type: typeValue
        value: 2
      selectPolicy: selectPolicyValue
      stabilizationWindowSeconds: 3
  maxReplicas: 3
  metrics:
  - containerResource:

message HorizontalPodAutoscalerSpec {
  // reference to scaled resource; horizontal pod autoscaler will learn the current resource consumption
  // and will set the desired number of pods by using its Scale subresource.
  optional CrossVersionObjectReference scaleTargetRef = 1;

  // minReplicas is the lower limit for the number of replicas to which the autoscaler
  // can scale down.  It defaults to 1 pod.  minReplicas is allowed to be 0 if the

message HorizontalPodAutoscalerSpec {
  // reference to scaled resource; horizontal pod autoscaler will learn the current resource consumption
  // and will set the desired number of pods by using its Scale subresource.
  optional CrossVersionObjectReference scaleTargetRef = 1;

  // minReplicas is the lower limit for the number of replicas to which the autoscaler
  // can scale down.  It defaults to 1 pod.  minReplicas is allowed to be 0 if the

  // This can not be 0 if MaxSurge is 0.
  // Defaults to 25%.
  // Example: when this is set to 30%, the old ReplicaSet can be scaled down to 70% of desired pods
  // immediately when the rolling update starts. Once new pods are ready, old ReplicaSet
  // can be scaled down further, followed by scaling up the new ReplicaSet, ensuring
  // that the total number of pods available at all times during the update is at

  // This can not be 0 if MaxSurge is 0.
  // Defaults to 25%.
  // Example: when this is set to 30%, the old ReplicaSet can be scaled down to 70% of desired pods
  // immediately when the rolling update starts. Once new pods are ready, old ReplicaSet
  // can be scaled down further, followed by scaling up the new ReplicaSet, ensuring
  // that the total number of pods available at all times during the update is at

            "reason": "reasonValue",
            "fieldPath": "fieldPathValue",
            "optionalOldSelf": true
          }
        ]
      }
    },
    "subresources": {
      "status": {},
      "scale": {
        "specReplicasPath": "specReplicasPathValue",
        "statusReplicasPath": "statusReplicasPathValue",
        "labelSelectorPath": "labelSelectorPathValue"
      }
    },
    "versions": [
      {