code: def test_transform_nan():
      # Test that SparsePCA won't return NaN when there is 0 feature in all
      # samples.
      rng = np.random.RandomState(0)
      Y, _, _ = generate_toy_data(
          3, 10, (8, 8), random_state=rng
      )  # wide array
      Y[:, 0] = 0
      estimator = SparsePCA(n_components=8)
      assert not np.any(np.isnan(estimator.fit_transform(Y)))
  
imports: import sys
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from sklearn.decomposition import PCA, MiniBatchSparsePCA, SparsePCA
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, if_safe_multiprocessing_with_blas
label_desc: Use image node_id 5 for calling a global function with example usage: test_transform_nan() without return types