code: def test_pipeline():
      param_grid = [{"logisticregression__C": [1, 0.1, 10]}]
      pipe = make_pipeline(
          StandardScaler(),
          CopyTransformer(),
          LogisticRegression(solver="liblinear", multi_class="ovr"),
      )
  
      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_copy_transformer.py
imports: import sys
  import numpy
  from packaging.version import Version
  from scipy.sparse import issparse
  from sklearn import __version__
  from sklearn.datasets import load_iris
  from sklearn.feature_extraction.text import TfidfTransformer
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import GridSearchCV
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from mlxtend.preprocessing import CopyTransformer
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 3 for calling a global function with example usage: test_pipeline() without return types