code: def _compute_loss(self, pos_pairs, neg_pairs, indices_tuple):
      a1, p, a2, _ = indices_tuple
  
      if len(a1) > 0 and len(a2) > 0:
          dtype = neg_pairs.dtype
          # if dealing with actual distances, use negative distances
          if not self.distance.is_inverted:
              pos_pairs = -pos_pairs
              neg_pairs = -neg_pairs
  
          pos_pairs = pos_pairs.unsqueeze(1) / self.temperature
          neg_pairs = neg_pairs / self.temperature
          n_per_p = c_f.to_dtype(
              a2.unsqueeze(0) == a1.unsqueeze(1), dtype=dtype
          )
          neg_pairs = neg_pairs * n_per_p
          neg_pairs[n_per_p == 0] = c_f.neg_inf(dtype)
  
          max_val = torch.max(
              pos_pairs, torch.max(neg_pairs, dim=1, keepdim=True)[0]
          ).detach()
          numerator = torch.exp(pos_pairs - max_val).squeeze(1)
          denominator = (
              torch.sum(torch.exp(neg_pairs - max_val), dim=1)
              + numerator
          )
          log_exp = torch.log(
              (numerator / denominator) + c_f.small_val(dtype)
          )
          return {
              "loss": {
                  "losses": -log_exp,
                  "indices": (a1, p),
                  "reduction_type": "pos_pair",
              }
          }
      return self.zero_losses()
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/ntxent_loss.py
imports: import torch
  from ..distances import CosineSimilarity
  from ..utils import common_functions
  from .generic_pair_loss import GenericPairLoss
label_desc: Use image node_id 2 for calling the NTXentLoss obj's underlying member method code with example usage: obj._compute_loss(pos_pairs, neg_pairs, indices_tuple) and returns: self, dict