code: def test_raises_value_error_on_same_number_of_classes_and_samples(
      solver,
  ):
      """
      Tests that if the number of samples equals the number
      of classes, a ValueError is raised.
      """
      X = np.array([[0.5, 0.6], [0.6, 0.5]])
      y = np.array(["a", "b"])
      clf = LinearDiscriminantAnalysis(solver=solver)
      with pytest.raises(
          ValueError, match="The number of samples must be more"
      ):
          clf.fit(X, y)
  
imports: import numpy
  import pytest
  from scipy import linalg
  from sklearn.cluster import KMeans
  from sklearn.covariance import LedoitWolf, ShrunkCovariance, ledoit_wolf
  from sklearn.datasets import make_blobs
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis, _cov
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils import _IS_WASM, check_random_state
  from sklearn.utils._testing import _convert_container, assert_allclose, assert_almost_equal, assert_array_almost_equal, assert_array_equal
label_desc: Use image node_id 22 for calling a global function with example usage: test_raises_value_error_on_same_number_of_classes_and_samples(solver) without return types