code: def test_sparse_to_dense():
      todense = DenseTransformer()
      tfidf = TfidfTransformer()
      X_t = tfidf.fit_transform([[1, 2, 3]])
      assert issparse(X_t)
      X_dense = todense.transform(X_t)
      expect = np.array([[0.26726124, 0.53452248, 0.80178373]])
      assert np.allclose(X_dense, expect)
  
file_path: mlxtend/mlxtend/preprocessing/tests/test_dense_transformer.py
imports: import numpy
  from packaging.version import Version
  from scipy.sparse import issparse
  from sklearn import __version__
  from sklearn.datasets import load_iris
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.feature_extraction.text import TfidfTransformer
  from sklearn.model_selection import GridSearchCV
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from mlxtend.preprocessing import DenseTransformer
label_desc: Use image node_id 2 for calling a global function with example usage: test_sparse_to_dense() without return types