code: def test_predict_ValueError_when_any_equivalent_values_are_missing():
      """
      Test ValueError when equivalent values are missing because the offset is
      too large.
      """
      y = pd.Series(
          np.arange(51),
          index=pd.date_range(start="2000-02-25", periods=51, freq="D"),
      )
  
      forecaster = ForecasterEquivalentDate(
          offset=pd.offsets.MonthEnd(1), n_offsets=2, agg_func=np.mean
      )
      forecaster.fit(y=y)
  
      last_window = pd.Series(
          np.arange(50),
          index=pd.date_range(start="2000-02-10", periods=50, freq="D"),
      )
  
      warn_msg = re.escape(
          (
              f"Steps: ['2000-03-31', '2000-04-01', '2000-04-02'] "
              f"are calculated with less than 2 n_offsets. "
              f"To avoid this, increase the `last_window` size or decrease "
              f"the number of n_offsets. The current configuration requires "
              f"a total offset of <2 * MonthEnds>."
          )
      )
      with pytest.warns(UserWarning, match=warn_msg):
          forecaster.predict(steps=3, last_window=last_window)
  
file_path: skforecast/skforecast/ForecasterBaseline/tests/test_predict.py
imports: import re
  import pytest
  import numpy
  import pandas
  from sklearn.exceptions import NotFittedError
  from skforecast.ForecasterBaseline import ForecasterEquivalentDate
  from .fixtures_ForecasterEquivalentDate import y
label_desc: Use image node_id 8 for calling a global function with example usage: test_predict_ValueError_when_any_equivalent_values_are_missing() without return types