code: def test_fitting_with_1d_target_and_pred(
      self, net_cls, module_cls, data, module_pred_1d_cls, recwarn
  ):
      # This test relates to the previous one. In general, users should fit
      # with target and prediction being 2d, even if the 2nd dimension is just
      # 1. However, in some circumstances (like when using BaggingRegressor,
      # see next test), having the ability to fit with 1d is required. In that
      # case, the module output also needs to be 1d for correctness.
      X, y = data
      X, y = X[:100], y[:100]  # less data to run faster
      y = y.flatten()
  
      net = net_cls(module_pred_1d_cls)
      net.fit(X, y)
      assert not recwarn.list
  
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 18 for calling the TestNeuralNetRegressor obj's underlying member method code with example usage: obj.test_fitting_with_1d_target_and_pred(net_cls, module_cls, data, module_pred_1d_cls, recwarn) without return types