code: def test_pipeline():
      rf = RandomForestClassifier(n_estimators=10)
      param_grid = [
          {"randomforestclassifier__n_estimators": [1, 5, 10]}
      ]
      pipe = make_pipeline(StandardScaler(), DenseTransformer(), rf)
      if Version(sklearn_version) < Version("0.24.1"):
          grid = GridSearchCV(
              pipe, param_grid, cv=3, n_jobs=1, iid=False
          )
      else:
          grid = GridSearchCV(pipe, param_grid, cv=3, n_jobs=1)
      grid.fit(X, y)
  
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 3 for calling a global function with example usage: test_pipeline() without return types