code: def test_mini_batch_fit_transform():
      alpha = 1
      rng = np.random.RandomState(0)
      Y, _, _ = generate_toy_data(
          3, 10, (8, 8), random_state=rng
      )  # wide array
      spca_lars = MiniBatchSparsePCA(
          n_components=3, random_state=0, alpha=alpha
      ).fit(Y)
      U1 = spca_lars.transform(Y)
      # Test multiple CPUs
      if sys.platform == "win32":  # fake parallelism for win32
          import joblib
  
          _mp = joblib.parallel.multiprocessing
          joblib.parallel.multiprocessing = None
          try:
              spca = MiniBatchSparsePCA(
                  n_components=3, n_jobs=2, alpha=alpha, random_state=0
              )
              U2 = spca.fit(Y).transform(Y)
          finally:
              joblib.parallel.multiprocessing = _mp
      else:  # we can efficiently use parallelism
          spca = MiniBatchSparsePCA(
              n_components=3, n_jobs=2, alpha=alpha, random_state=0
          )
          U2 = spca.fit(Y).transform(Y)
      assert not np.all(spca_lars.components_ == 0)
      assert_array_almost_equal(U1, U2)
      # Test that CD gives similar results
      spca_lasso = MiniBatchSparsePCA(
          n_components=3, method="cd", alpha=alpha, random_state=0
      ).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 9 for calling a global function with example usage: test_mini_batch_fit_transform() without return types