code: def test_fit_transform():
      alpha = 1
      rng = np.random.RandomState(0)
      Y, _, _ = generate_toy_data(
          3, 10, (8, 8), random_state=rng
      )  # wide array
      spca_lars = SparsePCA(
          n_components=3, method="lars", alpha=alpha, random_state=0
      )
      spca_lars.fit(Y)
  
      # Test that CD gives similar results
      spca_lasso = SparsePCA(
          n_components=3, method="cd", random_state=0, alpha=alpha
      )
      spca_lasso.fit(Y)
      assert_array_almost_equal(
          spca_lasso.components_, spca_lars.components_
      )
  
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 3 for calling a global function with example usage: test_fit_transform() without return types