code: def compute_loss(self, weights):
      squared_weight_norms = self.distance.get_norm(weights) ** 2
      deviations_from_mean = squared_weight_norms - torch.mean(
          squared_weight_norms
      )
      return {
          "loss": {
              "losses": (deviations_from_mean**2) / 4,
              "indices": c_f.torch_arange_from_size(weights),
              "reduction_type": "element",
          }
      }
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/regularizers/center_invariant_regularizer.py
imports: import torch
  from ..distances import LpDistance
  from ..utils import common_functions
  from .base_regularizer import BaseRegularizer
label_desc: Use image node_id 2 for calling the CenterInvariantRegularizer obj's underlying member method code with example usage: obj.compute_loss(weights) and returns: dict