code: def compute_loss(
      self, embeddings, labels, indices_tuple, ref_emb, ref_labels
  ):
      c_f.indices_tuple_not_supported(indices_tuple)
      c_f.labels_not_supported(labels, ref_labels)
  
      invariance_loss = self.invariance_lambda * self.invariance_loss(
          embeddings, ref_emb
      )
      variance_loss1, variance_loss2 = self.variance_loss(
          embeddings, ref_emb
      )
      covariance_loss = self.covariance_v * self.covariance_loss(
          embeddings, ref_emb
      )
      var_loss_size = c_f.torch_arange_from_size(variance_loss1)
      return {
          "invariance_loss": {
              "losses": invariance_loss,
              "indices": c_f.torch_arange_from_size(invariance_loss),
              "reduction_type": "element",
          },
          "variance_loss1": {
              "losses": self.variance_mu * variance_loss1,
              "indices": var_loss_size,
              "reduction_type": "element",
          },
          "variance_loss2": {
              "losses": self.variance_mu * variance_loss2,
              "indices": var_loss_size,
              "reduction_type": "element",
          },
          "covariance_loss": {
              "losses": covariance_loss,
              "indices": None,
              "reduction_type": "already_reduced",
          },
      }
  
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 2 for calling the VICRegLoss obj's underlying member method code with example usage: obj.compute_loss(embeddings, labels, indices_tuple, ref_emb, ref_labels) and returns: dict