class_docstr: Generic base for evaluations of models.
docstr: Returns a dataframe which contains the performance metrics as columns
  and the configurations as multi-index.
  
  Args:
      std: Whether to include the standard deviation of performance metrics in the dataframe.
docstr_tok: ["Returns","a","dataframe","which","contains","the","performance","metrics","as","columns","and","the","configurations","as","multi-index",".","Args",":","std",":","Whether","to","include","the","standard","deviation","of","performance","metrics","in","the","dataframe","."]
code: def dataframe(self, std: bool = True) -> pd.DataFrame:
      """
      Returns a dataframe which contains the performance metrics as columns
      and the configurations as multi-index.
  
      Args:
          std: Whether to include the standard deviation of performance metrics in the dataframe.
      """
      # Should implement this for ensembles as well
      index_df = Config.to_dataframe(
          cast(List[Config[ModelConfig]], self.configurations)
      )
      # Reorder columns
      column_order = ["dataset"] + [
          c for c in index_df.columns.tolist() if c != "dataset"
      ]
      index = pd.MultiIndex.from_frame(index_df[column_order])
      df = Performance.to_dataframe(self.performances, std=std)
      df.index = index
      return df.sort_index()
  
imports: from dataclasses import dataclass
  from typing import cast, Generic, List, TypeVar
  import pandas
  from tsbench.config import Config, EnsembleConfig, ModelConfig
  from tsbench.evaluations.metrics import Performance
label_desc: Use image node_id 1 for calling the Evaluations obj's underlying member method code with example usage: obj.dataframe(std) and returns: df