class_docstr: Defines and formats data for the Alpha158 dataset.
  
  Attributes:
    column_definition: Defines input and data type of column used in the
      experiment.
    identifiers: Entity identifiers used in experiments.
docstr: Reverts any normalisation to give predictions in original scale.
  
  Args:
    predictions: Dataframe of model predictions.
  
  Returns:
    Data frame of unnormalised predictions.
code: def format_predictions(self, predictions):
      """Reverts any normalisation to give predictions in original scale.
  
      Args:
        predictions: Dataframe of model predictions.
  
      Returns:
        Data frame of unnormalised predictions.
      """
      output = predictions.copy()
  
      column_names = predictions.columns
  
      for col in column_names:
          if col not in {"forecast_time", "identifier"}:
              # Using [col] is for aligning with the format when fitting
              output[col] = self._target_scaler.inverse_transform(
                  predictions[[col]]
              )
  
      return output
  
file_path: qlib/examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py
imports: import data_formatters.base
  import libs.utils
  import sklearn.preprocessing
label_desc: Use image node_id 5 for calling the Alpha158Formatter obj's underlying member method code with example usage: obj.format_predictions(predictions) and returns: output