{Name: "v1", Served: true, Storage: true, Subresources: &apiextensions.CustomResourceSubresources{Scale: &apiextensions.CustomResourceSubresourceScale{SpecReplicasPath: "spec.replicas1"}}},
						{Name: "v2", Served: false, Storage: false, Subresources: &apiextensions.CustomResourceSubresources{Scale: &apiextensions.CustomResourceSubresourceScale{SpecReplicasPath: "spec.replicas2"}}},
					},
				},
			},

  // 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.
  if (quant_params.scale.size() != 1) {

// CHECK-DAG: %[[SCALE:.*]] = "tf.Const"() <{value = dense<0.00866141729> : tensor<f32>}> : () -> tensor<f32>

// CHECK-LABEL: func private @composite_dequantize_uniform_
// CHECK-DAG: %[[SCALE:.*]] = "tf.Const"() <{value = dense<0.00866141729> : tensor<f32>}> : () -> tensor<f32>

// CHECK-LABEL: func private @composite_dequantize_uniform

// +k8s:prerelease-lifecycle-gen:deprecated=1.8
// +k8s:prerelease-lifecycle-gen:removed=1.16
// +k8s:prerelease-lifecycle-gen:replacement=autoscaling,v1,Scale

// Scale represents a scaling request for a resource.
type Scale struct {
	metav1.TypeMeta `json:",inline"`
	// Standard object metadata; More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata.
	// +optional

				})
			}
			if subresources != nil && subresources.Scale != nil {
				apiResourceDiscovery.Subresources = append(apiResourceDiscovery.Subresources, apidiscoveryv2.APISubresourceDiscovery{
					Subresource: "scale",
					ResponseKind: &metav1.GroupVersionKind{
						Group:   autoscaling.GroupName,
						Version: "v1",
						Kind:    "Scale",
					},
					Verbs: metav1.Verbs([]string{"get", "patch", "update"}),

// `key2` with given values.

module {

  // Note: following functions won't handle per-channel quantization for now.
  func.func private @internal_quantize_i8(%input : tensor<*xf32>, %scale : tensor<*xf32>, %zp : tensor<*xi32>) -> tensor<*xi8> {
    // Uses tf.floor(x + 0.5) instead of tf.round(x) since tf.round generates
    // a very expensive pattern.

      } else {
        new_output_types.push_back(result.getType());
      }
    }

    // Remove this rescale op.
    rewriter.replaceOp(op, {pre_quantized});

    // Replace the output scale of the preceding op.
    rewriter.setInsertionPointAfter(def);
    OperationState new_state(def->getLoc(), def->getName().getStringRef(),
                             def->getOperands(), new_output_types,

)

// ValidateScale validates a Scale and returns an ErrorList with any errors.
func ValidateScale(scale *autoscaling.Scale) field.ErrorList {
	allErrs := field.ErrorList{}
	allErrs = append(allErrs, apivalidation.ValidateObjectMeta(&scale.ObjectMeta, true, apimachineryvalidation.NameIsDNSSubdomain, field.NewPath("metadata"))...)

	if scale.Spec.Replicas < 0 {

  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>

		deploymentReplicas  int32
		maxSurge            intstr.IntOrString
		oldReplicas         int32
		newReplicas         int32
		scaleExpected       bool
		expectedNewReplicas int32
	}{
		{
			// Should not scale up.
			deploymentReplicas: 10,
			maxSurge:           intstr.FromInt32(0),
			oldReplicas:        10,
			newReplicas:        0,
			scaleExpected:      false,
		},
		{
			deploymentReplicas:  10,