code: def test_nmf_fit_nn_output(Estimator, solver, init, alpha_W, alpha_H):
      # Test that the decomposition does not contain negative values
      A = np.c_[5.0 - np.arange(1, 6), 5.0 + np.arange(1, 6)]
      model = Estimator(
          n_components=2,
          init=init,
          alpha_W=alpha_W,
          alpha_H=alpha_H,
          random_state=0,
          **solver,
      )
      transf = model.fit_transform(A)
      assert not ((model.components_ < 0).any() or (transf < 0).any())
  
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 6 for calling a global function with example usage: test_nmf_fit_nn_output(Estimator, solver, init, alpha_W, alpha_H) without return types