_test.go","kids":[],"cl_weight":0.1,"touches":1,"min_t":1316289444,"max_t":1316289444,"mean_t":1316289444},{"name":"transport.go","kids":[],"cl_weight":0.1,"touches":1,"min_t":1316289444,"max_t":1316289444,"mean_t":1316289444}],"cl_weight":0.9999999999999999,"touches":1,"min_t":1316289444,"max_t":1316289444,"mean_t":1316289444},{"name":"draw","kids":[{"name":"Makefile","kids":[],"cl_weight":1,"touches":1,"min_t":1258062920,"max_t":1258062920,"mean_t":1258062920}],"cl_weight":1,"touches":1,"min_t...

type codeResponse struct {
	Tree     *codeNode `json:"tree"`
	Username string    `json:"username"`
}

type codeNode struct {
	Name     string      `json:"name"`
	Kids     []*codeNode `json:"kids"`
	CLWeight float64     `json:"cl_weight"`
	Touches  int         `json:"touches"`
	MinT     int64       `json:"min_t"`
	MaxT     int64       `json:"max_t"`
	MeanT    int64       `json:"mean_t"`
}

var codeJSON []byte

  %weight = arith.constant dense<1.0> : tensor<144x12xf32>
  %q_weight = "quantfork.qcast"(%weight) : (tensor<144x12xf32>) -> tensor<144x12x!quant.uniform<i8:f32, 0.074855112561992565:-1>>
  %dq_weight = "quantfork.dcast"(%q_weight) : (tensor<144x12x!quant.uniform<i8:f32, 0.074855112561992565:-1>>) -> tensor<144x12xf32>
  %9 = "tf.MatMul"(%7, %dq_weight) {transpose_a = false, transpose_b = false} : (tensor<*xf32>, tensor<144x12xf32>) -> tensor<*xf32>

module {
  // CHECK-LABEL: dot_general
  func.func private @dot_general(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
    // CHECK: %[[q_weight:.*]] = "quantfork.qcast"
    // CHECK-SAME: -> tensor<2x2x!quant.uniform<i8<-127:127>:f32:1, {0.049663885371891529,0.060200210631363035}>>
    // CHECK: %[[dq_weight:.*]] = "quantfork.dcast"(%[[q_weight]])

  %dq_input= "quantfork.dcast"(%q_input) : (tensor<1x3x4x3x!quant.uniform<i8:f32, 0.58810077742034317:-128>>) -> tensor<1x3x4x3xf32>
  %q_weight = "quantfork.qcast"(%weight) : (tensor<2x3x3x2xf32>) -> tensor<2x3x3x2x!quant.uniform<i8:f32, 0.074855112561992565:-1>>
  %dq_weight = "quantfork.dcast"(%q_weight) : (tensor<2x3x3x2x!quant.uniform<i8:f32, 0.074855112561992565:-1>>) -> tensor<2x3x3x2xf32>

func TestCpuWeightToCpuShares(t *testing.T) {
	testCases := []struct {
		cpuWeight         uint64
		expectedCpuShares uint64
	}{
		{
			cpuWeight:         1,
			expectedCpuShares: 2,
		},
		{
			cpuWeight:         2,
			expectedCpuShares: 28,
		},
		{
			cpuWeight:         3,
			expectedCpuShares: 54,
		},
		{
			cpuWeight:         4,
			expectedCpuShares: 80,
		},
		{

  } else if (!isa<TF::ConstOp>(op_input)) {
    return false;
  }
  if (isa<TF::CastOp>(op_weight)) {
    op_weight = op_weight->getOperand(0).getDefiningOp();
  } else if (!isa<TF::ConstOp>(op_weight)) {
    return false;
  }

  if (!IsRankedInt(op_weight->getResult(0), 8)) return false;
  if (!IsRankedInt(op_input->getResult(0), 8)) return false;

  return true;
}

		weight:   weight,
		deadline: e.currentDeadline + 1/weight,
		index:    e.currentIndex,
	})
}

// PickAndAdd picks an available entry and re-adds it with the given weight calculation
func (e *EDF) PickAndAdd(calcWeight func(prevWeight float64, value any) float64) any {
	// if no available entry, return nil
	if len(*e.pq) == 0 {
		return nil
	}
	entry := heap.Pop(e.pq).(*Entry)

		//   JOB_OBJECT_CPU_RATE_CONTROL_WEIGHT_BASED or JOB_OBJECT_CPU_RATE_CONTROL_HARD_CAP. This is why the settings are mutually exclusive.
		// Part two - Docker (doc: https://docs.docker.com/engine/api/v1.30)
		//   If both CpuWeight and CpuMaximum are passed to Docker, then it sets
		//   JOB_OBJECT_CPU_RATE_CONTROL_ENABLE = JOB_OBJECT_CPU_RATE_CONTROL_WEIGHT_BASED ignoring CpuMaximum.

// https://github.com/kubernetes/enhancements/tree/master/keps/sig-node/2254-cgroup-v2#phase-1-convert-from-cgroups-v1-settings-to-v2
func CpuWeightToCpuShares(cpuWeight uint64) uint64 {
	return uint64((((cpuWeight - 1) * 262142) / 9999) + 2)
}

func getCgroupv1CpuConfig(cgroupPath string) (*ResourceConfig, error) {
	cpuQuotaStr, errQ := fscommon.GetCgroupParamString(cgroupPath, "cpu.cfs_quota_us")