code: def compute_loss(
      self, embeddings, labels, indices_tuple, ref_emb, ref_labels
  ):
      c_f.labels_required(labels)
      c_f.ref_not_supported(embeddings, labels, ref_emb, ref_labels)
      dtype, device = embeddings.dtype, embeddings.device
      self.cast_types(dtype, device)
      miner_weights = lmu.convert_to_weights(
          indices_tuple, labels, dtype=dtype
      )
      sim_to_classes = self.get_logits(embeddings)
      margin = torch.zeros(
          sim_to_classes.shape, dtype=dtype, device=embeddings.device
      )
      margin[torch.arange(0, margin.shape[0]), labels] = self.margin
      loss = F.cross_entropy(
          self.la * (sim_to_classes - margin), labels, reduction="none"
      )
      loss = loss * miner_weights
      loss_dict = {
          "loss": {
              "losses": loss,
              "indices": c_f.torch_arange_from_size(embeddings),
              "reduction_type": "element",
          }
      }
      self.add_weight_regularization_to_loss_dict(
          loss_dict, self.fc.t()
      )
      return loss_dict
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/soft_triple_loss.py
imports: import math
  import torch
  import torch.nn.functional
  from ..distances import CosineSimilarity
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .base_metric_loss_function import BaseMetricLossFunction
  from .mixins import WeightRegularizerMixin
label_desc: Use image node_id 3 for calling the SoftTripleLoss obj's underlying member method code with example usage: obj.compute_loss(embeddings, labels, indices_tuple, ref_emb, ref_labels) and returns: loss_dict