code: def _compute_loss(self, pos_pairs, neg_pairs, indices_tuple):
      pos_loss, neg_loss = 0, 0
      if len(pos_pairs) > 0:
          mean_pos_sim = torch.mean(pos_pairs)
          pos_var = self.variance_with_eps(
              pos_pairs,
              mean_pos_sim,
              self.pos_eps,
              self.distance.is_inverted,
          )
          pos_loss = torch.nn.functional.relu(pos_var) ** 2
      if len(neg_pairs) > 0:
          mean_neg_sim = torch.mean(neg_pairs)
          neg_var = self.variance_with_eps(
              neg_pairs,
              mean_neg_sim,
              self.neg_eps,
              not self.distance.is_inverted,
          )
          neg_loss = torch.nn.functional.relu(neg_var) ** 2
      pos_pairs_idx = lmu.pos_pairs_from_tuple(indices_tuple)
      neg_pairs_idx = lmu.neg_pairs_from_tuple(indices_tuple)
      return {
          "pos_loss": {
              "losses": pos_loss,
              "indices": pos_pairs_idx,
              "reduction_type": "pos_pair",
          },
          "neg_loss": {
              "losses": neg_loss,
              "indices": neg_pairs_idx,
              "reduction_type": "neg_pair",
          },
      }
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/intra_pair_variance_loss.py
imports: import torch
  from ..distances import CosineSimilarity
  from ..losses import GenericPairLoss
  from ..utils import loss_and_miner_utils
label_desc: Use image node_id 2 for calling the IntraPairVarianceLoss obj's underlying member method code with example usage: obj._compute_loss(pos_pairs, neg_pairs, indices_tuple) and returns: dict