{
  "kind": "Scale",
  "apiVersion": "extensions/v1beta1",
  "metadata": {
    "name": "nameValue",
    "generateName": "generateNameValue",
    "namespace": "namespaceValue",
    "selfLink": "selfLinkValue",
    "uid": "uidValue",
    "resourceVersion": "resourceVersionValue",
    "generation": 7,
    "creationTimestamp": "2008-01-01T01:01:01Z",
    "deletionTimestamp": "2009-01-01T01:01:01Z",
    "deletionGracePeriodSeconds": 10,

  OutputInputFreeScale,
  CustomScale,
};

// Each kernel signature has its own specification for scales.
struct KernelSpec {
  // Scale constraint
  ScaleConstraintType type;

  // Custom function to derive the scales. Only available when the scale
  // constraint is `CustomScale`.
  ScaleFn scale_fn;
};

class KernelSpecs {
 public:
  using Signature = llvm::SmallVector<quant::AnyQuantizedType, 4>;

SataQiu <******@****.***> 1713430345 +0800

SataQiu <******@****.***> 1713430345 +0800

UniformQuantizedPerAxisType CreateI8F32UniformQuantizedPerAxisType(
    Location loc, MLIRContext& context, ArrayRef<double> scales,
    ArrayRef<int64_t> zero_points, int quantization_dimension,
    bool narrow_range = false);

// Creates a `UniformQuantizedPerAxisType` with the given `scales` and
// `zero_points` values. The produced type has f32 as its expressed type and

// 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

      SmallVector<double>(scales), SmallVector<int64_t>(zero_points),
      quantization_dimension,
      /*storageTypeMin=*/llvm::minIntN(8) + (narrow_range ? 1 : 0),
      /*storageTypeMax=*/llvm::maxIntN(8));
}

UniformQuantizedPerAxisType CreateI32F32UniformQuantizedPerAxisType(
    const Location loc, MLIRContext& context, const ArrayRef<double> scales,

  Value scale_op = rewriter.create<TF::ConstOp>(
      loc, scale_type,
      DenseFPElementsAttr::get(scale_type,
                               {static_cast<float>(qtype.getScale())}));

  if (original_input_tensor_type.getElementType().isBF16()) {
    // Add bf16 cast op after scale to match with the next op's data
    // type.
    scale_op = rewriter.create<TF::CastOp>(

  // +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;
}

  // +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;
}