type.getNumElements());
  }

  return sparsity;
}

typedef struct InspectResult {
  // Whether the weight tensor is sparse enough to be compressed.
  bool can_compress;
  // If the weight tensor cannot be encoded in a block configuration that the op
  // supports, a Densify() op will be inserted afterwards to fall back to dense
  // execution.
  bool needs_densify;

		// encountered, let the individual Score weight take precedence.
		if _, ok := f.scorePluginWeight[e.Name]; ok {
			continue
		}
		// a weight of zero is not permitted, plugins can be disabled explicitly
		// when configured.
		f.scorePluginWeight[e.Name] = int(e.Weight)
		if f.scorePluginWeight[e.Name] == 0 {
			f.scorePluginWeight[e.Name] = 1
		}

// resource ops are considered duplicated if they have the same `shared_name`.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateMergeDuplicateResourceOpsPass();

// Apply quantization to weights based on the provided schemes.
std::unique_ptr<OperationPass<ModuleOp>> CreateQuantizeWeightsPass(
    const tensorflow::quantization::QuantizationOptions& quant_options);

// Propagate quantized type through allowed ops.

//       value {dimension_specs {dimension: 3}}}
//   }}
// }
// ```
//
// This preset:
//   * Applies per-channel quantization for weights (input index 1) of
//     convolution quantizable unit family. The quantization dimension is 3, the
//     channel dimension, which assumes the weight tensor is in NHWC format.
//   * Applies static-range PTQ for all other ops.
//
// Next ID: 4
message StaticRangePtqPreset {

constexpr mlir::StringRef kUnidirectionalSequenceRnnOp =
    "name: 'UnidirectionalSequenceRnn' input_arg: {name: 'Input' type: "
    "DT_FLOAT} input_arg: { name: 'Weights' type: DT_FLOAT } "
    "input_arg: { name: 'RecurrentWeights' type: DT_FLOAT } input_arg: { "
    "name: 'Bias' type: DT_FLOAT} "
    "input_arg: { name: 'HiddenState' type: DT_FLOAT} "
    "output_arg: { name: "

	}
	return
}

func validateRouteDestinations(weights []*networking.RouteDestination, gatewaySemantics bool) (errs error) {
	var totalWeight int32
	for _, weight := range weights {
		if weight == nil {
			errs = multierror.Append(errs, errors.New("weight may not be nil"))
			continue
		}
		if weight.Destination == nil {

  // prepare_quantize_ptq_per_channel.mlir.
  Option<bool> enable_per_channel_quantization_{
      *this, "enable-per-channel-quantization", llvm::cl::init(false),
      llvm::cl::desc("Whether enable per-channel quantized weights.")};
};

bool PrepareQuantizePass::SetInputNodesQuantizationParams(func::FuncOp func) {
  StringRef func_name = func.getName();
  auto has_quantize_op = [&](const Value arg) {
    return (arg.hasOneUse() &&

	return buildScoreConfigWithWeights(defaultWeights, ps...)
}

func buildScoreConfigWithWeights(weights map[string]int32, ps ...string) *config.Plugins {
	var plugins []config.Plugin
	for _, p := range ps {
		plugins = append(plugins, config.Plugin{Name: p, Weight: weights[p]})
	}
	return &config.Plugins{Score: config.PluginSet{Enabled: plugins}}
}

type injectedResult struct {

		ret += edge.Weight
	}
	return ret
}

type edgeList []*Edge

func (el edgeList) Len() int {
	return len(el)
}

func (el edgeList) Less(i, j int) bool {
	if el[i].Weight != el[j].Weight {
		return abs64(el[i].Weight) > abs64(el[j].Weight)
	}

	from1 := el[i].Src.Info.PrintableName()
	from2 := el[j].Src.Info.PrintableName()
	if from1 != from2 {

					{Address: "20.0.0.1", Weight: 6},
					{Address: "20.0.0.2", Weight: 6},
					{Address: "20.0.0.3", Weight: 6},
					// 2 endpoint on network1 with weight aggregated at the gateway
					{Address: "1.1.1.1", Weight: 12},
					// 1 endpoint on network4 with no gateway (i.e. directly accessible)
					{Address: "40.0.0.1", Weight: 6},
				},
				Weight: 36,
			},
		},
		wantWorkloadMetadata: []string{