// 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{

```

!!! tip "豆知識"
    JSONはキーとして`str`しかサポートしていないことに注意してください。

    しかしPydanticには自動データ変換機能があります。

    これは、APIクライアントがキーとして文字列しか送信できなくても、それらの文字列に純粋な整数が含まれている限り、Pydanticが変換して検証することを意味します。

    そして、`weights`として受け取る`dict`は、実際には`int`のキーと`float`の値を持つことになります。

## まとめ

**FastAPI** を使用すると、Pydanticモデルが提供する最大限の柔軟性を持ちながら、コードをシンプルに短く、エレガントに保つことができます。

以下のような利点があります:

* エディタのサポート（どこでも補完！）
* データ変換（別名：構文解析・シリアライズ）

# Mapping of signature def key -> SignatureDef.
_SignatureDefMap = Mapping[str, meta_graph_pb2.SignatureDef]

# Default minimum number of elements in the weights for them to be quantized
# during dynamic range quantization (DRQ) and weight-only quantization.
_DYNAMIC_RANGE_DEFAULT_MIN_NUM_ELEMENTS_FOR_WEIGHTS = 1024


def _is_qat_saved_model(saved_model_path: str):

  // broadcasting support. This needs to be run immediately after HLO->TF
  // legalization; otherwise other passes like `ConvertTFBroadcastTo` will
  // constant fold the newly generated TF broadcast ops and materialize the
  // weights.
  pass_manager.addNestedPass<mlir::func::FuncOp>(
      mlir::TF::CreateBroadcastFoldPass());

  // Canonicalization after TF legalization.
  pass_manager.addNestedPass<mlir::func::FuncOp>(

                        if a sidecar is present in the workload.
                      type: string
                    weight:
                      description: The load balancing weight associated with the endpoint.
                      maximum: 4294967295
                      minimum: 0
                      type: integer
                  type: object

      ValueReference<K, V> previous = entry.getValueReference();
      int weight = map.weigher.weigh(key, value);
      checkState(weight >= 0, "Weights must be non-negative");

      ValueReference<K, V> valueReference =
          map.valueStrength.referenceValue(this, entry, value, weight);
      entry.setValueReference(valueReference);
      recordWrite(entry, weight, now);
      previous.notifyNewValue(value);
    }

                        if a sidecar is present in the workload.
                      type: string
                    weight:
                      description: The load balancing weight associated with the endpoint.
                      maximum: 4294967295
                      minimum: 0
                      type: integer
                  type: object