code: def compute_loss(
      self, embeddings, labels, indices_tuple, ref_emb, ref_labels
  ):
      if len(embeddings) <= 1:
          return self.zero_losses()
      c_f.labels_required(labels)
      return self.nca_computation(
          embeddings, ref_emb, labels, ref_labels, indices_tuple
      )
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/nca_loss.py
imports: import torch
  from ..distances import LpDistance
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .base_metric_loss_function import BaseMetricLossFunction
label_desc: Use image node_id 2 for calling the NCALoss obj's underlying member method code with example usage: obj.compute_loss(embeddings, labels, indices_tuple, ref_emb, ref_labels) and returns: self, self