code: def __init__(
      self,
      instruments="csi500",
      start_time=None,
      end_time=None,
      freq="day",
      infer_processors=_DEFAULT_INFER_PROCESSORS,
      learn_processors=_DEFAULT_LEARN_PROCESSORS,
      fit_start_time=None,
      fit_end_time=None,
      filter_pipe=None,
      inst_processors=None,
      **kwargs,
  ):
      infer_processors = check_transform_proc(
          infer_processors, fit_start_time, fit_end_time
      )
      learn_processors = check_transform_proc(
          learn_processors, fit_start_time, fit_end_time
      )
  
      data_loader = {
          "class": "QlibDataLoader",
          "kwargs": {
              "config": {
                  "feature": self.get_feature_config(),
                  "label": kwargs.pop("label", self.get_label_config()),
              },
              "filter_pipe": filter_pipe,
              "freq": freq,
              "inst_processors": inst_processors,
          },
      }
  
      super().__init__(
          instruments=instruments,
          start_time=start_time,
          end_time=end_time,
          data_loader=data_loader,
          learn_processors=learn_processors,
          infer_processors=infer_processors,
          **kwargs,
      )
  
imports: from ...data.dataset.handler import DataHandlerLP
  from ...data.dataset.processor import Processor
  from ...utils import get_callable_kwargs
  from ...data.dataset import processor
  from inspect import getfullargspec
label_desc: Use image node_id 1 to create a new Alpha360 object from inherited base classes: DataHandlerLP with example: obj = Alpha360(instruments, start_time, end_time, freq, infer_processors, learn_processors, fit_start_time, fit_end_time, filter_pipe, inst_processors)