code: def test_custom_date_features():
      """Assert that features are extracted from date features."""
      data = pycaret.datasets.get_data("juice")
      data["date"] = pd.date_range(start="1/1/2018", periods=len(data))
      pc = pycaret.classification.setup(
          data,
          target=-2,
          date_features=["date"],
          create_date_columns=["quarter"],
      )
      X, _ = pc.pipeline.transform(pc.X, pc.y)
      assert "date_quarter" in X and "day" not in X
  
imports: import io
  import numpy
  import pandas
  import pytest
  from imblearn.over_sampling import ADASYN
  from scipy.sparse import csr_matrix
  from sklearn.decomposition import PCA
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  import pycaret.classification
  import pycaret.datasets
  import pycaret.regression
label_desc: Use image node_id 18 for calling a global function with example usage: test_custom_date_features() without return types