class_docstr: :class:`SequentialDataset` is based on :class:`~recbole.data.dataset.dataset.Dataset`,
  and provides augmentation interface to adapt to Sequential Recommendation,
  which can accelerate the data loader.
  
  Attributes:
      max_item_list_len (int): Max length of historical item list.
      item_list_length_field (str): Field name for item lists' length.
code: def _aug_presets(self):
      list_suffix = self.config["LIST_SUFFIX"]
      for field in self.inter_feat:
          if field != self.uid_field:
              list_field = field + list_suffix
              setattr(self, f"{field}_list_field", list_field)
              ftype = self.field2type[field]
  
              if ftype in [FeatureType.TOKEN, FeatureType.TOKEN_SEQ]:
                  list_ftype = FeatureType.TOKEN_SEQ
              else:
                  list_ftype = FeatureType.FLOAT_SEQ
  
              if ftype in [
                  FeatureType.TOKEN_SEQ,
                  FeatureType.FLOAT_SEQ,
              ]:
                  list_len = (
                      self.max_item_list_len,
                      self.field2seqlen[field],
                  )
              else:
                  list_len = self.max_item_list_len
  
              self.set_field_property(
                  list_field,
                  list_ftype,
                  FeatureSource.INTERACTION,
                  list_len,
              )
  
      self.set_field_property(
          self.item_list_length_field,
          FeatureType.TOKEN,
          FeatureSource.INTERACTION,
          1,
      )
  
file_path: RecBole/recbole/data/dataset/sequential_dataset.py
imports: import numpy
  import torch
  from recbole.data.dataset import Dataset
  from recbole.data.interaction import Interaction
  from recbole.utils.enum_type import FeatureType, FeatureSource
label_desc: Use image node_id 3 for calling the SequentialDataset obj's underlying member method code with example usage: obj._aug_presets() without return types