code: def _compute_l2_distances(self, x, labels=None):
      if labels is None:
          cnt = x.size(0) // 2
          a = x[:cnt, :]
          p = x[cnt:, :]
          dmat = compute_distance_matrix_unit_l2(a, p)
          return find_hard_negatives(
              dmat, output_index=False, empirical_thresh=0.008
          )
      else:
          dmat = compute_distance_matrix_unit_l2(x, x)
          dmat.fill_diagonal_(0)  # Put distance to itself to 0
          (
              anchor_idx,
              positive_idx,
              negative_idx,
          ) = lmu.convert_to_triplets(
              None, labels, labels, t_per_anchor="all"
          )
          return (
              dmat[anchor_idx, positive_idx],
              dmat[anchor_idx, negative_idx],
          )
  
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 3 for calling the OriginalImplementationDynamicSoftMarginLoss obj's underlying member method code with example usage: obj._compute_l2_distances(x, labels) and returns: find_hard_negatives, dmat,  dmat