// Zero scales we make the minimum fp value, this is because some flatbuffers
  // contain zero scale for zero values.
  llvm::SmallVector<double> scales;
  for (float scale : quant_params.scale) {
    if (scale == 0) {
      scales.push_back(std::numeric_limits<float>::min());
      continue;
    }
    scales.push_back(scale);
  }

  // Scale size can't be zero as it is checked before.

      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,

	return false
}

// scaleUpNewReplicaSetForRecreate scales up new replica set when deployment strategy is "Recreate".
func (dc *DeploymentController) scaleUpNewReplicaSetForRecreate(ctx context.Context, newRS *apps.ReplicaSet, deployment *apps.Deployment) (bool, error) {
	scaled, _, err := dc.scaleReplicaSetAndRecordEvent(ctx, newRS, *(deployment.Spec.Replicas), deployment)
	return scaled, err

  OutputInputSameScale,
  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:

# DNS Horizontal Autoscaler

DNS Horizontal Autoscaler enables horizontal autoscaling feature for DNS service
in Kubernetes clusters. This autoscaler runs as a Deployment. It collects cluster
status from the APIServer, horizontally scales the number of DNS backends based
on demand. Autoscaling parameters could be tuned by modifying the `kube-dns-autoscaler`
ConfigMap in `kube-system` namespace.

Learn more about:

    int64_t storage_type_min, int64_t storage_type_max,
    int64_t quantized_dimension, PatternRewriter &rewriter) {
  // Check whether the scales operand has constant op.
  DenseFPElementsAttr scales;
  if (!matchPattern(scales_value, m_Constant(&scales))) {
    return rewriter.notifyMatchFailure(op, "scales must be constant");
  }

  // Check whether the zero_points operand has constant op.
  DenseIntElementsAttr zero_points;

class FixedResultScale<QuantizedType qt> : NativeOpTrait<!strconcat(
  "quant::FixedResult", qt.name, "Scale<", qt.asTraitArgsStr, ">::Impl")>;

// Specify this trait if the bias-th input of the op is a bias input, which
// needs a scale based on the scales of op1 and op2.
class AccumulatorUniformScale<int bias, int op1, int op2> : NativeOpTrait<
  !strconcat("quant::AccumulatorUniformScale<",

                   "-1.0f">), TFL_AF_None), $act)>;

// Squash tfl.dequantize and tfl.quantize pairs.
// TODO(b/185915462): Compare the scale of input and output. This can also be
// squashed to a requantize op if the scales are different.

			"The intent of this metric is to measure the rough efficacy of the LogarithmicScaleDown feature gate's effect on " +
			"the sorting (and deletion) of pods when a replicaset scales down. This only considers Ready pods when calculating and reporting.",
		Buckets:        metrics.ExponentialBuckets(0.25, 2, 6),
		StabilityLevel: metrics.ALPHA,
	},
)

// Register registers ReplicaSet controller metrics.

  CPred<"$_self.cast<ElementsAttr>().getShapedType().getElementType().isF32()">, "float constant tensor">;

// Squash tfl.dequantize and tfl.quantize pairs.
// TODO(fengliuai): Compare the scale of input and output. This can also be
// squashed to a requantize op if the scales are different.
def : Pat<(TFL_QuantizeOp (TFL_DequantizeOp $in), $qt), (replaceWithValue $in)>;

// If the tfl.dequantize op wasn't fused, we shouldn't quantize the floating