code: def expected_pandas_dataframe(request):
      """
      This is a pytest fixture. It's a function that can be passed to a
      test so that we have a single block of code that can generate testing
      examples.
  
      We're using `params` in the call to declare that we want multiple versions
      to be generated. This is similar to the parametrize decorator, but it's difference
      because we can re-use `pd.Series` in multiple tests.
      """
      levels = request.param[0]
      levels_names = [levels] if isinstance(levels, str) else levels
  
      column_names = []
      for level in levels_names:
          _ = [
              f"{level}",
              f"{level}_lower_bound",
              f"{level}_upper_bound",
          ]
          column_names.append(_)
  
      expected_df = pd.DataFrame(
          data=request.param[1],
          columns=np.concatenate(column_names),
          index=pd.RangeIndex(start=10, stop=11, step=1),
      )
  
      return levels, expected_df
  
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregMultiSeries import ForecasterAutoregMultiSeries
  from sklearn.compose import ColumnTransformer
  from sklearn.preprocessing import StandardScaler
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import OneHotEncoder
  from sklearn.linear_model import LinearRegression
  from .fixtures_ForecasterAutoregMultiSeries import series
  from .fixtures_ForecasterAutoregMultiSeries import exog
  from .fixtures_ForecasterAutoregMultiSeries import exog_predict
label_desc: Use image node_id 1 for calling a global function with example usage: expected_pandas_dataframe(request) and returns: levels,  expected_df