code: def test_takes_log_with_nllloss(self, net_cls, module_cls, data):
      net = net_cls(module_cls, criterion=nn.NLLLoss, max_epochs=1)
      net.initialize()
  
      mock_loss = Mock(side_effect=nn.NLLLoss())
      net.criterion_.forward = mock_loss
      net.partial_fit(
          *data
      )  # call partial_fit to avoid re-initialization
  
      # check that loss was called with log-probabilities
      for (y_log, _), _ in mock_loss.call_args_list:
          assert (y_log < 0).all()
          y_proba = torch.exp(y_log)
          assert torch.isclose(
              torch.ones(len(y_proba)), y_proba.sum(1)
          ).all()
  
imports: from unittest.mock import Mock
  import numpy
  import pytest
  import torch
  from scipy.special import expit
  from sklearn.base import clone
  from torch import nn
  from skorch.tests.conftest import INFERENCE_METHODS
label_desc: Use image node_id 10 for calling the TestNeuralNet obj's underlying member method code with example usage: obj.test_takes_log_with_nllloss(net_cls, module_cls, data) without return types