class_docstr: Generic class for a next item recommender model. All next item recommendation models should inherit from this class.
  
  Parameters
  ----------------
  name: str, required
      Name of the recommender model.
  
  trainable: boolean, optional, default: True
      When False, the model is not trainable.
  
  verbose: boolean, optional, default: False
      When True, running logs are displayed.
  
  Attributes
  ----------
  num_users: int
      Number of users in training data.
  
  num_items: int
      Number of items in training data.
  
  total_users: int
      Number of users in training, validation, and test data.
      In other words, this includes unknown/unseen users.
  
  total_items: int
      Number of items in training, validation, and test data.
      In other words, this includes unknown/unseen items.
  
  uid_map: int
      Global mapping of user ID-index.
  
  iid_map: int
      Global mapping of item ID-index.
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docstr: Predict the scores for all items based on input history items
  
  Parameters
  ----------
  history_items: list of lists
      The list of history items in sequential manner for next-item prediction.
  
  Returns
  -------
  res : a Numpy array
      Relative scores of all known items
docstr_tok: ["Predict","the","scores","for","all","items","based","on","input","history","items","Parameters","--","--","--","--","--","history_items",":","list","of","lists","The","list","of","history","items","in","sequential","manner","for","next-item","prediction",".","Returns","--","--","--","-","res",":","a","Numpy","array","Relative","scores","of","all","known","items"]
code: def score(self, user_idx, history_items, **kwargs):
      """Predict the scores for all items based on input history items
  
      Parameters
      ----------
      history_items: list of lists
          The list of history items in sequential manner for next-item prediction.
  
      Returns
      -------
      res : a Numpy array
          Relative scores of all known items
  
      """
      raise NotImplementedError(
          "The algorithm is not able to make score prediction!"
      )
  
imports: import copy
  import inspect
  import os
  import pickle
  import warnings
  from datetime import datetime
  from glob import glob
  import numpy
  from ..exception import ScoreException
  from ..utils.common import clip
label_desc: Use image node_id 2 for calling the NextItemRecommender obj's underlying member method code with example usage: obj.score(user_idx, history_items) without return types