code: def test_corr_nearest_factor_sparse(self, dm):
      # Test that result is the same if the input is dense or sparse
      d = 200
  
      # Generate a test matrix of factors
      X = np.zeros((d, dm), dtype=np.float64)
      x = np.linspace(0, 2 * np.pi, d)
      rs = np.random.RandomState(10)
      for j in range(dm):
          X[:, j] = np.sin(x * (j + 1)) + rs.randn(d)
  
      # Get the correlation matrix
      _project_correlation_factors(X)
      X *= 0.7
      mat = np.dot(X, X.T)
      np.fill_diagonal(mat, 1)
  
      # Threshold it
      mat.flat[np.abs(mat.flat) < 0.35] = 0.0
      smat = sparse.csr_matrix(mat)
  
      dense_rslt = corr_nearest_factor(mat, dm, maxiter=10000)
      sparse_rslt = corr_nearest_factor(smat, dm, maxiter=10000)
  
      mat_dense = dense_rslt.corr.to_matrix()
      mat_sparse = sparse_rslt.corr.to_matrix()
  
      assert dense_rslt.Converged is sparse_rslt.Converged
      assert dense_rslt.Converged is True
  
      assert_allclose(mat_dense, mat_sparse, rtol=0.25, atol=1e-3)
  
imports: import warnings
  import numpy
  from numpy.testing import assert_almost_equal, assert_allclose
  import scipy.sparse
  import pytest
  from statsmodels.stats.correlation_tools import corr_nearest, corr_clipped, cov_nearest, _project_correlation_factors, corr_nearest_factor, _spg_optim, corr_thresholded, cov_nearest_factor_homog, FactoredPSDMatrix
  from statsmodels.tools.testing import Holder
label_desc: Use image node_id 3 for calling the Test_Factor obj's underlying member method code with example usage: obj.test_corr_nearest_factor_sparse(dm) without return types