code: def nan_accuracy(unique_labels, return_per_class):
      if return_per_class:
          return [float("nan") for _ in range(len(unique_labels))]
      return float("nan")
  
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 11 for calling a global function with example usage: nan_accuracy(unique_labels, return_per_class) and returns: float, unknown