code: def maybe_get_metric_loss(self, embeddings, labels, indices_tuple):
      if self.loss_weights.get("metric_loss", 0) > 0:
          current_batch_size = embeddings[0].shape[0]
          indices_tuple = c_f.shift_indices_tuple(
              indices_tuple, current_batch_size
          )
          all_labels = torch.cat([labels, labels], dim=0)
          all_embeddings = torch.cat(embeddings, dim=0)
          return self.loss_funcs["metric_loss"](
              all_embeddings, all_labels, indices_tuple
          )
      return 0
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/trainers/twostream_metric_loss.py
imports: import torch
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .base_trainer import BaseTrainer
label_desc: Use image node_id 3 for calling the TwoStreamMetricLoss obj's underlying member method code with example usage: obj.maybe_get_metric_loss(embeddings, labels, indices_tuple) and returns: int, self