func.func @main(%arg0 : tensor<1xf32>) -> tensor<1xf32> {
      %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
      %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

      %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {
        quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64

func.func @illegal_tf_uniform_quantize(%arg0 : tensor<1xf32>) -> tensor<1xf32> {
  %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
  %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

  // expected-error@+1 {{'tf.UniformQuantize' op is illegal as it is a UQ op or contains uq/qint types}}
  %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {

    // CHECK-NEXT: return %[[UDQ]] : tensor<1xf32>
    func.func @main(%arg0 : tensor<?xf32>) -> tensor<?xf32> {
      %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
      %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

      %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {
        quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64

    func.func @main(%arg0 : tensor<?xf32>) -> tensor<?xf32> {
      %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
      %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

      %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {
        quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64

    }

    double scale;
    int64_t nudgedZeroPoint;
    getNudgedScaleAndZeroPoint(qmin, qmax, rmin, rmax, scale, nudgedZeroPoint);
    scales.push_back(scale);
    zeroPoints.push_back(nudgedZeroPoint);
  }

  unsigned flags = isSigned ? quant::QuantizationFlags::Signed : 0;
  return quant::UniformQuantizedPerAxisType::getChecked(
      loc, flags, storageType, expressedType, scales, zeroPoints,

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

      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: