code: def test_XdawnTransformer():
      """Test _XdawnTransformer."""
      pytest.importorskip("sklearn")
      # Get data
      raw, events, picks = _get_data()
      raw.del_proj()
      epochs = Epochs(
          raw,
          events,
          event_id,
          tmin,
          tmax,
          picks=picks,
          preload=True,
          baseline=None,
          verbose=False,
      )
      X = epochs._data
      y = epochs.events[:, -1]
      # Fit
      xdt = _XdawnTransformer()
      xdt.fit(X, y)
      pytest.raises(ValueError, xdt.fit, X, y[1:])
      pytest.raises(ValueError, xdt.fit, "foo")
  
      # Provide covariance object
      signal_cov = compute_raw_covariance(raw, picks=picks)
      xdt = _XdawnTransformer(signal_cov=signal_cov)
      xdt.fit(X, y)
      # Provide ndarray
      signal_cov = np.eye(len(picks))
      xdt = _XdawnTransformer(signal_cov=signal_cov)
      xdt.fit(X, y)
      # Provide ndarray of bad shape
      signal_cov = np.eye(len(picks) - 1)
      xdt = _XdawnTransformer(signal_cov=signal_cov)
      pytest.raises(ValueError, xdt.fit, X, y)
      # Provide another type
      signal_cov = 42
      xdt = _XdawnTransformer(signal_cov=signal_cov)
      pytest.raises(ValueError, xdt.fit, X, y)
  
      # Fit with y as None
      xdt = _XdawnTransformer()
      xdt.fit(X)
  
      # Compare xdawn and _XdawnTransformer
      xd = Xdawn(correct_overlap=False)
      xd.fit(epochs)
  
      xdt = _XdawnTransformer()
      xdt.fit(X, y)
      assert_array_almost_equal(
          xd.filters_["cond2"][:2, :], xdt.filters_.reshape(2, 2, 8)[0]
      )
  
      # Transform testing
      xdt.transform(X[1:, ...])  # different number of epochs
      xdt.transform(X[:, :, 1:])  # different number of time
      pytest.raises(ValueError, xdt.transform, X[:, 1:, :])
      Xt = xdt.transform(X)
      pytest.raises(ValueError, xdt.transform, 42)
  
      # Inverse transform testing
      Xinv = xdt.inverse_transform(Xt)
      assert Xinv.shape == X.shape
      xdt.inverse_transform(Xt[1:, ...])
      xdt.inverse_transform(Xt[:, :, 1:])
      # should raise an error if not correct number of components
      pytest.raises(ValueError, xdt.inverse_transform, Xt[:, 1:, :])
      pytest.raises(ValueError, xdt.inverse_transform, 42)
  
file_path: mne-python/mne/preprocessing/tests/test_xdawn.py
imports: from pathlib import Path
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from scipy import stats
  from mne import Epochs, EpochsArray, compute_raw_covariance, create_info, pick_types, read_events
  from mne.decoding import Vectorizer
  from mne.fixes import _safe_svd
  from mne.io import read_raw_fif
  from mne.preprocessing.xdawn import Xdawn, _XdawnTransformer
label_desc: Use image node_id 7 for calling a global function with example usage: test_XdawnTransformer() without return types