code: def test_regularization_loss(self) -> None:
      criterion = PMSNCustomLoss(
          target_distribution=_uniform_distribution
      )
      mean_anchor_probs = torch.Tensor([0.1, 0.3, 0.6])
      loss = criterion.regularization_loss(
          mean_anchor_probs=mean_anchor_probs
      )
      expected_loss = (
          1
          / 3
          * (
              math.log((1 / 3) / 0.1)
              + math.log((1 / 3) / 0.3)
              + math.log((1 / 3) / 0.6)
          )
      )
      assert loss == pytest.approx(expected_loss)
  
file_path: lightly/tests/loss/test_PMSNLoss.py
imports: import math
  import unittest
  import pytest
  import torch
  from torch import Tensor
  from lightly.loss import pmsn_loss
  from lightly.loss.pmsn_loss import PMSNCustomLoss, PMSNLoss
label_desc: Use image node_id 1 for calling the TestPMSNCustomLoss obj's underlying member method code with example usage: obj.test_regularization_loss() without return types