code: def _inverse_transform_target(
      ts_analysis: Dict[str, Dict],
      predictions: pd.DataFrame,
      freq,
      group: tuple,
  ):
      predictions = deepcopy(
          predictions
      )  # needed because methods like get_level_values actually have undisclosed side effects  # noqa
      transformer = ts_analysis["stl_transforms"][group]["transformer"]
      if (
          isinstance(predictions.index, pd.MultiIndex)
          and len(predictions.index.levels) > 1
      ):
          temp_s = predictions.droplevel(0)
          temp_s.index = pd.date_range(
              start=temp_s.index[0], freq=freq, periods=len(temp_s)
          )
          return transformer.inverse_transform(temp_s.to_period())
      elif isinstance(predictions.index, pd.MultiIndex):
          temp_s = pd.Series(data=predictions)
          temp_s.index = predictions.index.levels[
              0
          ]  # warning: do not use get_level_values as it removes inferred freq
          return transformer.inverse_transform(temp_s.to_period())
      else:
          return transformer.inverse_transform(predictions.to_period())
  
imports: from typing import Dict
  from copy import deepcopy
  import pandas
label_desc: Use image node_id 2 for calling a global function with example usage: _inverse_transform_target(ts_analysis, predictions, freq, group) and returns: transformer, transformer, transformer