code: def test_net_learns(self, net_cls, module_cls, data):
      X, y = data
      net = net_cls(
          module_cls,
          max_epochs=10,
          lr=1,
          batch_size=64,
      )
      net.fit(X, y)
  
      train_losses = net.history[:, "train_loss"]
      assert train_losses[0] > 1.3 * train_losses[-1]
  
      valid_acc = net.history[-1, "valid_acc"]
      assert valid_acc > 0.65
  
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 9 for calling the TestNeuralNetBinaryClassifier obj's underlying member method code with example usage: obj.test_net_learns(net_cls, module_cls, data) without return types