code: def test_pipeline(df):
      X, y = df[["a", "b", "c", "d"]], df[["e"]].values.ravel()
  
      pipe = Pipeline(
          [
              ("design", FormulaicTransformer("a + np.log(a) + b - 1")),
              ("scale", StandardScaler()),
              ("model", LogisticRegression(solver="lbfgs")),
          ]
      )
      assert pipe.fit(X, y).predict(X).shape[0] == X.shape[0]
  
file_path: scikit-lego/tests/test_preprocessing/test_formulaic_transformer.py
imports: import joblib
  import numpy
  import pandas
  import pytest
  from scipy.sparse import spmatrix
  from sklearn.linear_model import LogisticRegression
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from sklego.preprocessing import FormulaicTransformer
label_desc: Use image node_id 4 for calling a global function with example usage: test_pipeline(df) without return types