code: def test_nmf_non_negative_factorization_n_components_auto():
      # Check that n_components is correctly inferred from the provided
      # custom initialization.
      rng = np.random.RandomState(0)
      X = rng.random_sample((6, 5))
      W_init = rng.random_sample((6, 2))
      H_init = rng.random_sample((2, 5))
      W, H, _ = non_negative_factorization(
          X, W=W_init, H=H_init, init="custom", n_components="auto"
      )
      assert H.shape == H_init.shape
      assert W.shape == W_init.shape
  
imports: import re
  import sys
  import warnings
  from io import StringIO
  import numpy
  import pytest
  from scipy import linalg
  from sklearn.base import clone
  from sklearn.decomposition import NMF, MiniBatchNMF, non_negative_factorization
  from sklearn.decomposition import _nmf
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.utils._testing import assert_allclose, assert_almost_equal, assert_array_almost_equal, assert_array_equal, ignore_warnings
  from sklearn.utils.extmath import squared_norm
  from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
label_desc: Use image node_id 37 for calling a global function with example usage: test_nmf_non_negative_factorization_n_components_auto() without return types