code: def test_sparse_pca_inverse_transform():
      """Check that `inverse_transform` in `SparsePCA` and `PCA` are similar."""
      rng = np.random.RandomState(0)
      n_samples, n_features = 10, 5
      X = rng.randn(n_samples, n_features)
  
      n_components = 2
      spca = SparsePCA(
          n_components=n_components,
          alpha=1e-12,
          ridge_alpha=1e-12,
          random_state=0,
      )
      pca = PCA(n_components=n_components, random_state=0)
      X_trans_spca = spca.fit_transform(X)
      X_trans_pca = pca.fit_transform(X)
      assert_allclose(
          spca.inverse_transform(X_trans_spca),
          pca.inverse_transform(X_trans_pca),
      )
  
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 19 for calling a global function with example usage: test_sparse_pca_inverse_transform() without return types