code: def covariance_loss(self, emb, ref_emb):
      N, D = emb.size()
      emb = emb - emb.mean(dim=0)
      ref_emb = ref_emb - ref_emb.mean(dim=0)
      cov_emb = (emb.T @ emb) / (N - 1)
      cov_ref_emb = (ref_emb.T @ ref_emb) / (N - 1)
  
      diag = torch.eye(D, device=cov_emb.device)
      cov_loss = (
          cov_emb[~diag.bool()].pow_(2).sum() / D
          + cov_ref_emb[~diag.bool()].pow_(2).sum() / D
      )
      return cov_loss
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/vicreg_loss.py
imports: import torch
  import torch.nn.functional
  from ..utils import common_functions
  from .base_metric_loss_function import BaseMetricLossFunction
label_desc: Use image node_id 5 for calling the VICRegLoss obj's underlying member method code with example usage: obj.covariance_loss(emb, ref_emb) and returns: cov_loss