code: def test_dimension_mismatch_warning(
      self, net_cls, module_cls, data, recwarn
  ):
      # When the target and the prediction have different dimensionality, mse
      # loss will broadcast them, calculating all pairwise errors instead of
      # only sample-wise. Since the errors are averaged at the end, there is
      # still a valid loss, which makes the error hard to spot. Thankfully,
      # torch gives a warning in that case. We test that this warning exists,
      # otherwise, skorch users could run into very hard to debug issues
      # during training.
      net = net_cls(module_cls)
      X, y = data
      X, y = X[:100], y[:100].flatten()  # make y 1d
      net.fit(X, y)
  
      w0, w1 = recwarn.list  # one warning for train, one for valid
      # The warning comes from PyTorch, so checking the exact wording is prone to
      # error in future PyTorch versions. We thus check a substring of the
      # whole message and cross our fingers that it's not changed.
      msg_substr = (
          "This will likely lead to incorrect results due to broadcasting. "
          "Please ensure they have the same size"
      )
      assert msg_substr in str(w0.message)
      assert msg_substr in str(w1.message)
  
imports: from functools import partial
  import numpy
  import pytest
  from sklearn.base import clone
  from skorch.tests.conftest import INFERENCE_METHODS
label_desc: Use image node_id 17 for calling the TestNeuralNetRegressor obj's underlying member method code with example usage: obj.test_dimension_mismatch_warning(net_cls, module_cls, data, recwarn) without return types