code: def _read_meta(fn):
      labels = list()
      lb_set = set()
      with open(fn) as f:
          for lb in f.readlines():
              lb = int(lb.strip())
              labels.append(lb)
              lb_set.add(lb)
      return np.array(labels), lb_set
  
file_path: dgl/examples/pytorch/hilander/utils/evaluate.py
imports: import argparse
  import inspect
  import numpy
  from clustering_benchmark import ClusteringBenchmark
  from utils import metrics, TextColors, Timer
label_desc: Use image node_id 1 for calling a global function with example usage: _read_meta(fn) and returns: np,  lb_set