code: def test_covariance():
      x, y = make_blobs(
          n_samples=100, n_features=5, centers=1, random_state=42
      )
  
      # make features correlated
      x = np.dot(
          x, np.arange(x.shape[1] ** 2).reshape(x.shape[1], x.shape[1])
      )
  
      c_e = _cov(x, "empirical")
      assert_almost_equal(c_e, c_e.T)
  
      c_s = _cov(x, "auto")
      assert_almost_equal(c_s, c_s.T)
  
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 21 for calling a global function with example usage: test_covariance() without return types