scalar.svg...

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

  // status defines current status of the scale. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#spec-and-status. Read-only.
  // +optional
  optional ScaleStatus status = 3;
}

// ScaleSpec describes the attributes of a scale subresource
message ScaleSpec {
  // replicas is the number of observed instances of the scaled object.
  // +optional
  optional int32 replicas = 1;

  optional ScaleSpec spec = 2;

  // current status of the scale. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#spec-and-status. Read-only.
  // +optional
  optional ScaleStatus status = 3;
}

// ScaleSpec describes the attributes of a scale subresource
message ScaleSpec {
  // desired number of instances for the scaled object.
  // +optional
  optional int32 replicas = 1;
}

  // +optional
  repeated MetricSpec metrics = 4;

  // behavior configures the scaling behavior of the target
  // in both Up and Down directions (scaleUp and scaleDown fields respectively).
  // If not set, the default HPAScalingRules for scale up and scale down are used.
  // +optional
  optional HorizontalPodAutoscalerBehavior behavior = 5;
}

				p.errorf("%s doesn't support scaled register format", p.arch.Name)
			} else {
				a.Scale = int16(scale)
			}
		}
		p.get(')')
	} else if scale != 0 {
		if p.arch.Family == sys.ARM64 {
			p.errorf("arm64 doesn't support scaled register format")
		}
		// First (R) was missing, all we have is (R*scale).
		a.Reg = 0
		a.Index = r1
		a.Scale = int16(scale)
	}
}

  // +optional
  repeated MetricSpec metrics = 4;

  // behavior configures the scaling behavior of the target
  // in both Up and Down directions (scaleUp and scaleDown fields respectively).
  // If not set, the default HPAScalingRules for scale up and scale down are used.
  // +optional
  optional HorizontalPodAutoscalerBehavior behavior = 5;
}

  // 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

// Package-wide variables from generator "generated".
option go_package = "k8s.io/api/autoscaling/v2beta1";

// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in requests and limits, describing each pod in the
// current scale target (e.g. CPU or memory).  The values will be averaged
// together before being compared to the target.  Such metrics are built in to

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(
      absl::Span<const DataType> values, TFE_Context* context,
      TF_Status* status) const;

  // A parallel tensor with scalar integers numbering component devices.
  std::unique_ptr<ParallelTensor> DeviceIDs(TFE_Context* context,