input_ = fused_func_op_.getArgument(0);
  bias_ = fused_func_op_.getArgument(2);

  weight_ = fused_func_op_.getArgument(1);
  weight_type_ = mlir::cast<RankedTensorType>(weight_.getType());

  if (weight_type_.getRank() != 2) {
    return fused_func_op_.emitError() << "The weight tensor was not of rank 2";
  }

  if (weight_type_.getDimSize(1) % num_gates_ != 0) {

    checkArgument(maximumSize >= 0, "maximum size must not be negative");
    this.maximumSize = maximumSize;
    return this;
  }

  /**
   * Specifies the maximum weight of entries the cache may contain. Weight is determined using the
   * {@link Weigher} specified with {@link #weigher}, and use of this method requires a

    checkArgument(maximumSize >= 0, "maximum size must not be negative");
    this.maximumSize = maximumSize;
    return this;
  }

  /**
   * Specifies the maximum weight of entries the cache may contain. Weight is determined using the
   * {@link Weigher} specified with {@link #weigher}, and use of this method requires a

	}
	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 {

    - `apiserver_flowcontrol_demand_seats_high_watermark`: High watermark, over last adjustment period, of demand_seats
    - `apiserver_flowcontrol_demand_seats_average`: Time-weighted average, over last adjustment period, of demand_seats
    - `apiserver_flowcontrol_demand_seats_stdev`: Time-weighted standard deviation, over last adjustment period, of demand_seats
    - `apiserver_flowcontrol_demand_seats_smoothed`: Smoothed seat demands

  const bool disable_per_channel_;

  // We should distinguish weights and bias constants. Biases are specified by
  // the quantization spec or are the operands of ops with same scale spec. The
  // rest are weights.
  DenseSet<Operation*> weights_;

  // The weights require narrow_range quantization. This map collects all the
  // weight operands defined by the op quant spec. The value of each entry is

		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.

        `tf.keras.mixed_precision.experimental.LossScaleOptimizer`, the weights
        of the `DynanmicLossScale` are copied into the `LossScaleOptimizer`
        instead of being reused. This means modifying the weights of the
        `DynamicLossScale` will no longer affect the weights of the
        LossScaleOptimizer, and vice versa.

  let description = [{
      This pass encodes sparse weights in the model in the proper format, and adds
      Densify() op if necessary. The general algorithm is:
        1. Get list of operands (weights) of an op that can be sparse.
        2. Get list of supported block configurations of the op.
        3. Calculate random sparsity of the weight.
          3.1. If sparsity level is below the encoding threshold, keep in dense.

	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)