"deletecollection",
        "get",
        "list",
        "patch",
        "update",
        "watch"
      ]
    },
    {
      "group": "autoscaling",
      "kind": "Scale",
      "name": "deployments/scale",
      "namespaced": true,
      "singularName": "",
      "verbs": [
        "get",
        "patch",
        "update"
      ],
      "version": "v1"
    },
    {

func pow10Int64(b int64) int64 {
	switch b {
	case 0:
		return 1
	case 1:
		return 10
	case 2:
		return 100
	case 3:
		return 1000
	case 4:
		return 10000
	case 5:
		return 100000
	case 6:
		return 1000000
	case 7:
		return 10000000
	case 8:
		return 100000000
	case 9:
		return 1000000000
	case 10:
		return 10000000000
	case 11:
		return 100000000000
	case 12:

}

// scaledValue scales given unscaled value from scale to new Scale and returns
// an int64. It ALWAYS rounds up the result when scale down. The final result might
// overflow.
//
// scale, newScale represents the scale of the unscaled decimal.
// The mathematical value of the decimal is unscaled * 10**(-scale).
func scaledValue(unscaled *big.Int, scale, newScale int) int64 {
	dif := scale - newScale
	if dif == 0 {
		return unscaled.Int64()

// with apply.
type CustomResourceSubresourcesApplyConfiguration struct {
	Status *v1beta1.CustomResourceSubresourceStatus          `json:"status,omitempty"`
	Scale  *CustomResourceSubresourceScaleApplyConfiguration `json:"scale,omitempty"`
}

// CustomResourceSubresourcesApplyConfiguration constructs an declarative configuration of the CustomResourceSubresources type for use with
// apply.

// CHECK-LABEL: @unfuse_batch_norm
// CHECK-SAME: %[[X:[^:[:space:]]+]]
// CHECK-SAME: %[[SCALE:[^:[:space:]]+]]
// CHECK-SAME: %[[OFFSET:[^:[:space:]]+]]
// CHECK-SAME: %[[MEAN:[^:[:space:]]+]]
// CHECK-SAME: %[[VARIANCE:[^:[:space:]]+]]
func.func @unfuse_batch_norm(
    %x: tensor<4x256xf32>, %scale: tensor<256xf32>, %offset: tensor<256xf32>,
    %mean: tensor<256xf32>, %variance: tensor<256xf32>)

import (
	"math"
	"math/big"
	"testing"
)

func TestScaledValueInternal(t *testing.T) {
	tests := []struct {
		unscaled *big.Int
		scale    int
		newScale int

		want int64
	}{
		// remain scale
		{big.NewInt(1000), 0, 0, 1000},

		// scale down
		{big.NewInt(1000), 0, -3, 1},
		{big.NewInt(1000), 3, 0, 1},
		{big.NewInt(0), 3, 0, 0},

		// always round up
		{big.NewInt(999), 3, 0, 1},

    return {0, 0};
  }

  return {static_cast<int32_t>(quantized_multiplier), shift};
}

// Calculates the quantized range for a given scale, zero point, minimum and
// maximum values, and quantization range.
//
// Args:
//   scale: The scale factor for the quantized values.
//   zero_point: The zero point for the quantized values.
//   rmin: The minimum value of the quantized values.

		value  int64
		max    int64
		result int64
		scale  int32
	}{
		{100, 10, 1, 2},
		{100, 10, 1, 2},
		{100, 100, 1, 1},
		{1, 10, 1, 0},
	} {
		r, s := removeInt64Factors(test.value, test.max)
		if r != test.result {
			t.Errorf("%v: unexpected result: %d", test, r)
		}
		if s != test.scale {
			t.Errorf("%v: unexpected scale: %d", test, s)
		}
	}
}

tfr.func @tf__my_requantize(%input: !tfr.tensor) -> !tfr.tensor {
  %raw_data = tfr.quant_raw_data(%input) : (!tfr.tensor) -> !tfr.tensor
  %scale, %zp = tfr.quant_qparam(%input) : (!tfr.tensor) -> (!tfr.tensor, !tfr.tensor)
  %result = tfr.call @tf__requantize(%raw_data, %scale, %zp) : (!tfr.tensor, !tfr.tensor, !tfr.tensor) -> !tfr.tensor
  tfr.return %result : !tfr.tensor
}

// CHECK-LABEL: @tf__intermediate

		if err != nil {
			return storage, err
		}
		storage[resource] = deploymentStorage.Deployment
		storage[resource+"/status"] = deploymentStorage.Status
		storage[resource+"/scale"] = deploymentStorage.Scale
	}

	// statefulsets
	if resource := "statefulsets"; apiResourceConfigSource.ResourceEnabled(appsapiv1.SchemeGroupVersion.WithResource(resource)) {