code: def test_ledoit_wolf_large():
      # test that ledoit_wolf doesn't error on data that is wider than block_size
      rng = np.random.RandomState(0)
      # use a number of features that is larger than the block-size
      X = rng.normal(size=(10, 20))
      lw = LedoitWolf(block_size=10).fit(X)
      # check that covariance is about diagonal (random normal noise)
      assert_almost_equal(lw.covariance_, np.eye(20), 0)
      cov = lw.covariance_
  
      # check that the result is consistent with not splitting data into blocks.
      lw = LedoitWolf(block_size=25).fit(X)
      assert_almost_equal(lw.covariance_, cov)
  
file_path: scikit-learn/sklearn/covariance/tests/test_covariance.py
imports: import numpy
  import pytest
  from sklearn import datasets
  from sklearn.covariance import OAS, EmpiricalCovariance, LedoitWolf, ShrunkCovariance, empirical_covariance, ledoit_wolf, ledoit_wolf_shrinkage, oas, shrunk_covariance
  from sklearn.covariance._shrunk_covariance import _ledoit_wolf
  from sklearn.utils._testing import assert_allclose, assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from .._shrunk_covariance import _oas
label_desc: Use image node_id 7 for calling a global function with example usage: test_ledoit_wolf_large() without return types