code: def compute_density(self, distances):
      size = distances.size(0)
      r_star = torch.floor(
          (distances.float() + 1) / self.delta
      )  # Indices of the bins containing the values of the distances
      r_star = c_f.to_device(r_star, tensor=distances, dtype=torch.long)
  
      delta_ijr_a = (distances + 1 - r_star * self.delta) / self.delta
      delta_ijr_b = (
          (r_star + 1) * self.delta - 1 - distances
      ) / self.delta
      delta_ijr_a = c_f.to_dtype(delta_ijr_a, tensor=distances)
      delta_ijr_b = c_f.to_dtype(delta_ijr_b, tensor=distances)
  
      density = torch.zeros(round(1 + 2 / self.delta))
      density = c_f.to_device(
          density, tensor=distances, dtype=distances.dtype
      )
  
      # For each node sum the contributions of the bins whose ending node is this one
      density.scatter_add_(0, r_star + 1, delta_ijr_a)
      # For each node sum the contributions of the bins whose starting node is this one
      density.scatter_add_(0, r_star, delta_ijr_b)
      return density / size
  
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 3 for calling the HistogramLoss obj's underlying member method code with example usage: obj.compute_density(distances) and returns: unknown