class_docstr: This is a abstract sequential recommender. All the sequential model should implement This class.
class_docstr_tok: ["This","is","a","abstract","sequential","recommender",".","All","the","sequential","model","should","implement","This","class","."]
code: def __init__(self, config, dataset):
      super(SequentialRecommender, self).__init__()
  
      # load dataset info
      self.USER_ID = config["USER_ID_FIELD"]
      self.ITEM_ID = config["ITEM_ID_FIELD"]
      self.ITEM_SEQ = self.ITEM_ID + config["LIST_SUFFIX"]
      self.ITEM_SEQ_LEN = config["ITEM_LIST_LENGTH_FIELD"]
      self.POS_ITEM_ID = self.ITEM_ID
      self.NEG_ITEM_ID = config["NEG_PREFIX"] + self.ITEM_ID
      self.max_seq_length = config["MAX_ITEM_LIST_LENGTH"]
      self.n_items = dataset.num(self.ITEM_ID)
  
      # load parameters info
      self.device = config["device"]
  
imports: from logging import getLogger
  import numpy
  import torch
  import torch.nn
  from recbole.model.layers import FMEmbedding, FMFirstOrderLinear, FLEmbedding
  from recbole.utils import ModelType, InputType, FeatureSource, FeatureType, set_color
label_desc: Use image node_id 1 to create a new SequentialRecommender object from inherited base classes: AbstractRecommender with example: obj = SequentialRecommender(config, dataset)