code: def test_scaling_fit_transform():
      alpha = 1
      rng = np.random.RandomState(0)
      Y, _, _ = generate_toy_data(3, 1000, (8, 8), random_state=rng)
      spca_lars = SparsePCA(
          n_components=3, method="lars", alpha=alpha, random_state=rng
      )
      results_train = spca_lars.fit_transform(Y)
      results_test = spca_lars.transform(Y[:10])
      assert_allclose(results_train[0], results_test[0])
  
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 10 for calling a global function with example usage: test_scaling_fit_transform() without return types