code: def prepare_data_loaders(self):
      if self.rgb:
          logging.debug("Load RGB train and valid")
          (
              self._rgb_source_by_split["train"],
              self._rgb_source_by_split["valid"],
          ) = self._source_access_dict["rgb"].get_train_valid(
              self._valid_split_ratio
          )
          if self.class_ids is not None:
              self._rgb_source_by_split["train"] = get_class_subset(
                  self._rgb_source_by_split["train"], self.class_ids
              )
              self._rgb_source_by_split["valid"] = get_class_subset(
                  self._rgb_source_by_split["valid"], self.class_ids
              )
  
          (
              self._rgb_target_by_split["train"],
              self._rgb_target_by_split["valid"],
          ) = self._target_access_dict["rgb"].get_train_valid(
              self._valid_split_ratio
          )
          if self.class_ids is not None:
              self._rgb_target_by_split["train"] = get_class_subset(
                  self._rgb_target_by_split["train"], self.class_ids
              )
              self._rgb_target_by_split["valid"] = get_class_subset(
                  self._rgb_target_by_split["valid"], self.class_ids
              )
  
          logging.debug("Load RGB Test")
          self._rgb_source_by_split["test"] = self._source_access_dict[
              "rgb"
          ].get_test()
          self._rgb_target_by_split["test"] = self._target_access_dict[
              "rgb"
          ].get_test()
          if self.class_ids is not None:
              self._rgb_source_by_split["test"] = get_class_subset(
                  self._rgb_source_by_split["test"], self.class_ids
              )
              self._rgb_target_by_split["test"] = get_class_subset(
                  self._rgb_target_by_split["test"], self.class_ids
              )
  
      if self.flow:
          logging.debug("Load flow train and valid")
          (
              self._flow_source_by_split["train"],
              self._flow_source_by_split["valid"],
          ) = self._source_access_dict["flow"].get_train_valid(
              self._valid_split_ratio
          )
          if self.class_ids is not None:
              self._flow_source_by_split["train"] = get_class_subset(
                  self._flow_source_by_split["train"], self.class_ids
              )
              self._flow_source_by_split["valid"] = get_class_subset(
                  self._flow_source_by_split["valid"], self.class_ids
              )
  
          (
              self._flow_target_by_split["train"],
              self._flow_target_by_split["valid"],
          ) = self._target_access_dict["flow"].get_train_valid(
              self._valid_split_ratio
          )
          if self.class_ids is not None:
              self._flow_target_by_split["train"] = get_class_subset(
                  self._flow_target_by_split["train"], self.class_ids
              )
              self._flow_target_by_split["valid"] = get_class_subset(
                  self._flow_target_by_split["valid"], self.class_ids
              )
  
          logging.debug("Load flow Test")
          self._flow_source_by_split["test"] = self._source_access_dict[
              "flow"
          ].get_test()
          self._flow_target_by_split["test"] = self._target_access_dict[
              "flow"
          ].get_test()
          if self.class_ids is not None:
              self._flow_source_by_split["test"] = get_class_subset(
                  self._flow_source_by_split["test"], self.class_ids
              )
              self._flow_target_by_split["test"] = get_class_subset(
                  self._flow_target_by_split["test"], self.class_ids
              )
  
file_path: pykale/kale/loaddata/video_multi_domain.py
imports: import logging
  import numpy
  from sklearn.utils import check_random_state
  from kale.loaddata.dataset_access import get_class_subset
  from kale.loaddata.multi_domain import DatasetSizeType, MultiDomainDatasets, WeightingType
  from kale.loaddata.sampler import FixedSeedSamplingConfig, MultiDataLoader
  from kale.loaddata.video_access import get_image_modality
label_desc: Use image node_id 2 for calling the VideoMultiDomainDatasets obj's underlying member method code with example usage: obj.prepare_data_loaders() without return types