spec:
  hosts:
    - {{.Host0}}
  http:
    - route:
        - destination:
            host: {{.Host0}}
          weight: {{.Weight0}}
        - destination:
            host: {{.Host1}}
          weight: {{.Weight1}}
        - destination:
            host: {{.Host2}}

		helper: network2.NewConnectionHelper(name),
	}
}

func (lb *weightedConnections) AllWeightsEqual() bool {
	if len(lb.conns) == 0 {
		return true
	}

	weight := lb.conns[0].Weight
	for _, conn := range lb.conns {
		if conn.Weight != weight {
			return false
		}
	}
	return true
}

func (lb *weightedConnections) get(index int) *WeightedConnection {
	return lb.conns[index]
}

	CallSiteOffset int // Line offset from function start line.
}

// NamedEdgeMap contains all unique call edges in the profile and their
// edge weight.
type NamedEdgeMap struct {
	Weight map[NamedCallEdge]int64

	// ByWeight lists all keys in Weight, sorted by edge weight from
	// highest to lowest.
	ByWeight []NamedCallEdge
}

func emptyProfile() *Profile {

	}
	return postProcessNamedEdgeMap(weight, totalWeight)
}

func sortByWeight(edges []NamedCallEdge, weight map[NamedCallEdge]int64) {
	sort.Slice(edges, func(i, j int) bool {
		ei, ej := edges[i], edges[j]
		if wi, wj := weight[ei], weight[ej]; wi != wj {
			return wi > wj // want larger weight first
		}
		// same weight, order by name/line number
		if ei.CallerName != ej.CallerName {

		return scaleFactor
	}
	weight := uint32(math.MaxUint32)
	if ep.GetLoadBalancingWeight().Value < math.MaxUint32/scaleFactor {
		weight = ep.GetLoadBalancingWeight().Value * scaleFactor
	}
	return weight
}

// Apply the weight for this endpoint to the network gateways.

	g, err := community.NewUndirectedLayers(friends, enemies)
	if err != nil {
		log.Fatal(err)
	}
	weights := []float64{1, -1}

	// Get the profile of internal node weight for resolutions
	// between 0.1 and 10 using logarithmic bisection.
	p, err := community.Profile(
		community.ModularMultiplexScore(g, weights, true, community.WeightMultiplex, 10, src),
		true, 1e-3, 0.1, 10,
	)
	if err != nil {
		log.Fatal(err)

// license that can be found in the LICENSE file.

package metrics

// Float64Histogram represents a distribution of float64 values.
type Float64Histogram struct {
	// Counts contains the weights for each histogram bucket.
	//
	// Given N buckets, Count[n] is the weight of the range
	// [bucket[n], bucket[n+1]), for 0 <= n < N.
	Counts []uint64

	// Buckets contains the boundaries of the histogram buckets, in increasing order.
	//

    // determined. The activation and weight are quantized to INT8 while bias is
    // quantized to INT32.
    METHOD_STATIC_RANGE_INT8 = 2;

    // Dynamic range quantization. Quantized tensor values' ranges are
    // determined in the graph executions. The weights are quantized during
    // conversion.
    METHOD_DYNAMIC_RANGE_INT8 = 3;

    // Weight-only quantization. Only weights are quantized during conversion.

// that also contains other supporting ops needed to construct the operands for
// the fused op. The caller provides the containing FuncOp as input with
// arguments specifying the input, weight, projection and bias.
// The weight, projection, bias and layer norm scale all need to be
// RankedTensorType.
// This class sets the layer norm coefficients to NoneType.
class ConvertLSTMCellSimpleToFusedLSTM {
 public:

	}
	weights[count] = uint8(highBit + 1)
	count++
	weightMark[highBit+1]++

	if weightMark[1] < 2 || weightMark[1]&1 != 0 {
		return 0, 0, r.makeError(off, "bad Huffman weights")
	}

	// Change weightMark from a count of weights to the index of
	// the first symbol for that weight. We shift the indexes to
	// also store how many we have seen so far,
	next := uint32(0)