code: def test_sparse1_with_non_sparse_components():
      """Test fit_transform a sparse matrix with specifying categorical_features."""
      fit_then_transform(
          sparse1_paratial_1h.todense(),
          sparse1,
          categorical_features=[True, False],
      )
  
imports: import numpy
  import scipy.sparse
  from numpy.testing import assert_array_almost_equal
  from sklearn.datasets import load_iris, load_boston
  from sklearn.linear_model import LinearRegression
  from sklearn.pipeline import make_pipeline
  from sklearn.model_selection import cross_val_score, KFold
  from nose.tools import assert_equal
  from tpot.builtins import OneHotEncoder, auto_select_categorical_features, _transform_selected
label_desc: Use image node_id 13 for calling a global function with example usage: test_sparse1_with_non_sparse_components() without return types