}
	node := nodeInfo.Node()

	s, err := getPreScoreState(cycleState)
	if err != nil {
		return 0, framework.AsStatus(err)
	}
	var score int64
	for tpKey, tpValues := range s.topologyScore {
		if v, exist := node.Labels[tpKey]; exist {
			score += tpValues[v]
		}
	}

	return score, nil
}

// NormalizeScore normalizes the score for each filteredNode.

			_, pvs := schema.GetIdentityFieldValuesMap(association.DB.Statement.Context, reflectValue, rel.Schema.PrimaryFields)
			if pcolumn, pvalues := schema.ToQueryValues(rel.Schema.Table, rel.Schema.PrimaryFieldDBNames, pvs); len(pvalues) > 0 {
				conds = append(conds, clause.IN{Column: pcolumn, Values: pvalues})
			} else {
				return ErrPrimaryKeyRequired
			}

  %1 = "tf.AddV2"(%arg1, %cst_1) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  %3 = "tf.RaggedTensorToVariant"(%arg7) {RAGGED_RANK = 0 : i64, Tsplits = i64, Tvalues = i32, batched_input = false, device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<*xi32>) -> tensor<!tf_type.variant>

  %35:2 = "tf.RaggedGather"(%32, %34, %0) {OUTPUT_RAGGED_RANK = 1 : i64, PARAMS_RAGGED_RANK = 1 : i64, Tindices = i64, Tsplits = i64, Tvalues = i64, device = ""} : (tensor<3xi64>, tensor<?xi64>, tensor<2xi64>) -> (tensor<?xi64>, tensor<?xi64>)

    Res<TF_Tensor, [{Tensor of all values in the table. Indexed in parallel with `keys`.}]>:$values
  );

  TF_DerivedResultTypeAttr Tkeys = TF_DerivedResultTypeAttr<0>;
  TF_DerivedResultTypeAttr Tvalues = TF_DerivedResultTypeAttr<1>;
}

def TF_LookupTableFindOp : TF_Op<"LookupTableFind", []> {
  let summary = "Looks up keys in a table, outputs the corresponding values.";

  let description = [{