code: def compute_loss(self, weights):
      center_similarities = self.distance(weights)
      small_val = c_f.small_val(weights.dtype)
      center_similarities_masked = torch.clamp(
          2.0 * center_similarities[self.same_class_mask], max=2
      )
      divisor = 2 * torch.sum(self.same_class_mask)
      reg = torch.sqrt(2.0 + small_val - center_similarities_masked)
      self.set_stats(center_similarities)
      return {
          "loss": {
              "losses": reg,
              "indices": c_f.torch_arange_from_size(reg),
              "reduction_type": "element",
              "divisor": divisor,
          }
      }
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/regularizers/sparse_centers_regularizer.py
imports: import torch
  from ..distances import CosineSimilarity
  from ..reducers import DivisorReducer
  from ..utils import common_functions
  from .base_regularizer import BaseRegularizer
label_desc: Use image node_id 2 for calling the SparseCentersRegularizer obj's underlying member method code with example usage: obj.compute_loss(weights) and returns: dict