code: def test_lda_coefs():
      # Test if the coefficients of the solvers are approximately the same.
      n_features = 2
      n_classes = 2
      n_samples = 1000
      X, y = make_blobs(
          n_samples=n_samples,
          n_features=n_features,
          centers=n_classes,
          random_state=11,
      )
  
      clf_lda_svd = LinearDiscriminantAnalysis(solver="svd")
      clf_lda_lsqr = LinearDiscriminantAnalysis(solver="lsqr")
      clf_lda_eigen = LinearDiscriminantAnalysis(solver="eigen")
  
      clf_lda_svd.fit(X, y)
      clf_lda_lsqr.fit(X, y)
      clf_lda_eigen.fit(X, y)
  
      assert_array_almost_equal(
          clf_lda_svd.coef_, clf_lda_lsqr.coef_, 1
      )
      assert_array_almost_equal(
          clf_lda_svd.coef_, clf_lda_eigen.coef_, 1
      )
      assert_array_almost_equal(
          clf_lda_eigen.coef_, clf_lda_lsqr.coef_, 1
      )
  
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 4 for calling a global function with example usage: test_lda_coefs() without return types