code: def calculate_NMI(self, query_labels, cluster_labels, **kwargs):
      [query_labels, cluster_labels] = [
          c_f.to_numpy(x) for x in [query_labels, cluster_labels]
      ]
      return normalized_mutual_info_score(query_labels, cluster_labels)
  
imports: import torch
  from sklearn.metrics import adjusted_mutual_info_score, normalized_mutual_info_score
  from .None import common_functions
  from .inference import FaissKMeans, FaissKNN
label_desc: Use image node_id 7 for calling the AccuracyCalculator obj's underlying member method code with example usage: obj.calculate_NMI(query_labels, cluster_labels) and returns: normalized_mutual_info_score