code: def test_predict_ValueError_when_all_equivalent_values_are_missing():
      """
      Test ValueError when equivalent values are missing because the offset is
      too large.
      """
      y = pd.Series(
          np.arange(7),
          index=pd.date_range(start="2000-02-25", periods=7, freq="D"),
      )
  
      forecaster = ForecasterEquivalentDate(
          offset=pd.offsets.MonthEnd(1), n_offsets=1
      )
      forecaster.fit(y=y)
  
      last_window = pd.Series(
          np.arange(5),
          index=pd.date_range(start="2000-02-10", periods=5, freq="D"),
      )
  
      err_msg = re.escape(
          (
              f"All equivalent values are missing. This is caused by using "
              f"an offset (<MonthEnd>) larger than the available data. "
              f"Try to decrease the size of the offset (<MonthEnd>), "
              f"the number of n_offsets (1) or increase the "
              f"size of `last_window`. In backtesting, this error may be "
              f"caused by using an `initial_train_size` too small."
          )
      )
      with pytest.raises(ValueError, match=err_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 7 for calling a global function with example usage: test_predict_ValueError_when_all_equivalent_values_are_missing() without return types