code: def compute_loss(
      self, embeddings, labels, indices_tuple, ref_emb, ref_labels
  ):
      c_f.labels_or_indices_tuple_required(labels, indices_tuple)
      c_f.ref_not_supported(embeddings, labels, ref_emb, ref_labels)
      indices_tuple = lmu.convert_to_triplets(
          indices_tuple, labels, ref_labels, t_per_anchor="all"
      )
      anchor_idx, positive_idx, negative_idx = indices_tuple
      if len(anchor_idx) == 0:
          return self.zero_losses()
      mat = self.distance(embeddings, ref_emb)
  
      anchor_positive_idx = filter_pairs(anchor_idx, positive_idx)
      anchor_negative_idx = filter_pairs(anchor_idx, negative_idx)
      ap_dists = mat[anchor_positive_idx]
      an_dists = mat[anchor_negative_idx]
  
      p_pos = self.compute_density(ap_dists)
      phi = torch.cumsum(p_pos, dim=0)
  
      p_neg = self.compute_density(an_dists)
      return {
          "loss": {
              "losses": torch.sum(p_neg * phi),
              "indices": None,
              "reduction_type": "already_reduced",
          }
      }
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/histogram_loss.py
imports: import torch
  from ..distances import CosineSimilarity
  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 HistogramLoss obj's underlying member method code with example usage: obj.compute_loss(embeddings, labels, indices_tuple, ref_emb, ref_labels) and returns: dict, self