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 _benchmark_presets(self):
      list_suffix = self.config["LIST_SUFFIX"]
      for field in self.inter_feat:
          if field + list_suffix in self.inter_feat:
              list_field = field + list_suffix
              setattr(self, f"{field}_list_field", list_field)
      self.set_field_property(
          self.item_list_length_field,
          FeatureType.TOKEN,
          FeatureSource.INTERACTION,
          1,
      )
      self.inter_feat[self.item_list_length_field] = self.inter_feat[
          self.item_id_list_field
      ].agg(len)
  
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 5 for calling the SequentialDataset obj's underlying member method code with example usage: obj._benchmark_presets() without return types