class_docstr: Same as QlibDataLoader. The fields can be define by config
class_docstr_tok: ["Same","as","QlibDataLoader",".","The","fields","can","be","define","by","config"]
code: def load_group_df(
      self,
      instruments,
      exprs: list,
      names: list,
      start_time: Union[str, pd.Timestamp] = None,
      end_time: Union[str, pd.Timestamp] = None,
      gp_name: str = None,
  ) -> pd.DataFrame:
      if instruments is None:
          warnings.warn(
              "`instruments` is not set, will load all stocks"
          )
          instruments = "all"
      if isinstance(instruments, str):
          instruments = D.instruments(
              instruments, filter_pipe=self.filter_pipe
          )
      elif self.filter_pipe is not None:
          warnings.warn(
              "`filter_pipe` is not None, but it will not be used with `instruments` as list"
          )
  
      freq = (
          self.freq[gp_name]
          if isinstance(self.freq, dict)
          else self.freq
      )
      inst_processors = (
          self.inst_processors
          if isinstance(self.inst_processors, list)
          else self.inst_processors.get(gp_name, [])
      )
      df = D.features(
          instruments,
          exprs,
          start_time,
          end_time,
          freq=freq,
          inst_processors=inst_processors,
      )
      df.columns = names
      if self.swap_level:
          df = (
              df.swaplevel().sort_index()
          )  # NOTE: if swaplevel, return <datetime, instrument>
      return df
  
imports: import abc
  import pickle
  from pathlib import Path
  import warnings
  import pandas
  from typing import Tuple, Union, List
  from qlib.data import D
  from qlib.utils import load_dataset, init_instance_by_config, time_to_slc_point
  from qlib.log import get_module_logger
  from qlib.utils.serial import Serializable
label_desc: Use image node_id 2 for calling the QlibDataLoader obj's underlying member method code with example usage: obj.load_group_df(instruments, exprs, names, start_time, end_time, gp_name) and returns: df