code: def test_extract_features_with_different_datetime_parsing_options(
      df_datetime,
  ):
      X = DatetimeFeatures(
          features_to_extract=["day_of_month"], dayfirst=True
      ).fit_transform(df_datetime[["date_obj2"]])
      pd.testing.assert_frame_equal(
          X,
          pd.DataFrame({"date_obj2_day_of_month": [10, 31, 30, 17]}),
      )
  
      X = DatetimeFeatures(
          features_to_extract=["year"], yearfirst=True
      ).fit_transform(df_datetime[["date_obj2"]])
      pd.testing.assert_frame_equal(
          X,
          pd.DataFrame({"date_obj2_year": [2010, 2009, 1995, 2004]}),
      )
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.exceptions import NotFittedError
  from sklearn.pipeline import Pipeline
  from feature_engine.datetime import DatetimeFeatures
  from feature_engine.datetime._datetime_constants import FEATURES_DEFAULT, FEATURES_SUFFIXES, FEATURES_SUPPORTED
label_desc: Use image node_id 22 for calling a global function with example usage: test_extract_features_with_different_datetime_parsing_options(df_datetime) without return types