code: def forward(self, x, labels=None):
      distances = self._compute_distances(x, labels=labels)
      if not self._is_binary:
          pos_dist, neg_dist = distances
          self._compute_stats(pos_dist, neg_dist)
          hist_var = pos_dist - neg_dist
      else:
          pos_dist, neg_dist, pos_dist_b, neg_dist_b = distances
          self._compute_stats(pos_dist_b, neg_dist_b)
          hist_var = pos_dist_b - neg_dist_b
  
      PDF = self.histogram / self.histogram.sum()
      CDF = PDF.cumsum(0)
  
      # lookup weight from the CDF
      bin_idx = torch.floor(
          (hist_var - self._min_val) / self.bin_width
      ).long()
      weight = CDF[bin_idx]
  
      # Changed to an equivalent version for making same computation as in dynamic_soft_margin_loss.py
      # loss = -(neg_dist * weight).mean() + (pos_dist * weight).mean()
      loss = (hist_var * weight).mean()
      return loss.to(device=x.device, dtype=x.dtype)
  
imports: import torch
  import torch.nn
  from pytorch_metric_learning.utils import common_functions
  from pytorch_metric_learning.utils import loss_and_miner_utils
  import unittest
  from pytorch_metric_learning.losses import DynamicSoftMarginLoss
  from ..None import TEST_DEVICE, TEST_DTYPES
  from ..zzz_testing_utils.testing_utils import angle_to_coord
label_desc: Use image node_id 6 for calling the OriginalImplementationDynamicSoftMarginLoss obj's underlying member method code with example usage: obj.forward(x, labels) and returns: loss