code: def format_predictions(
      raw_predictions: np.array,
      dataset_df: pd.DataFrame,
      target_column: str,
      id_column: str = None,
  ):
      """Organize components into a pandas.DataFrame that is properly formatted and ready to save
  
      Parameters
      ----------
      raw_predictions: np.array
          The actual predictions that were made and that should inhabit the column named
          `target_column` in the result
      dataset_df: pd.DataFrame
          The original data provided that yielded `raw_predictions`. If `id_column` is not None, it
          must be in `dataset_df`. In practice, expect this value to be one of the following:
          :attr:`experiments.BaseExperiment.train_dataset`,
          :attr:`experiments.BaseExperiment.holdout_dataset`, or
          :attr:`experiments.BaseExperiment.test_dataset`
      target_column: str
          The name for the result column containing `raw_predictions`
      id_column: str, or None, default=None
          If not None, must be the name of a column in `dataset_df`, the contents of which will be
          included as a column in the result and are assumed to be sample identifiers of some kind
  
      Returns
      -------
      predictions: pd.DataFrame
          Dataframe containing the formatted predictions"""
      predictions = pd.DataFrame()
      if id_column is not None:
          predictions[id_column] = dataset_df[id_column]
      predictions[target_column] = raw_predictions
      predictions.reset_index(inplace=True, drop=True)
      return predictions
  
label_desc: Use image node_id 1 for calling a global function with example usage: format_predictions(raw_predictions, dataset_df, target_column, id_column) and returns: predictions