code: def __call__(self, global_label):
      positive_center_label = self.positive(global_label)
      positive_center_index = (
          positive_center_label - self.positive.rank_class_start
      )
      if len(positive_center_index) > self.num_sample:
          positive_center_index = positive_center_index[
              : self.num_sample
          ]
      negative_center_index = self.negative(positive_center_index)
      #
      final_center_index = np.concatenate(
          (positive_center_index, negative_center_index)
      )
      assert (
          len(final_center_index)
          == len(np.unique(final_center_index))
          == self.num_sample
      )
      assert len(final_center_index) == self.num_sample
      return final_center_index
  
file_path: insightface/recognition/partial_fc/mxnet/memory_samplers.py
imports: import numpy
label_desc: Use image node_id 2 for calling the WeightIndexSampler obj's underlying member method code with example usage: obj.__call__(global_label) and returns: final_center_index