class_docstr: Memory Network agent.
  
  Tips:
  - time features are necessary when memory order matters
  - multiple hops allow multiple steps of reasoning, but also seem to make it
      easier to learn to read the memories if you have at least two hops
  - 'adam' seems to work very poorly compared to 'sgd' for hogwild training
class_docstr_tok: ["Memory","Network","agent",".","Tips",":","-","time","features","are","necessary","when","memory","order","matters","-","multiple","hops","allow","multiple","steps","of","reasoning",",","but","also","seem","to","make","it","easier","to","learn","to","read","the","memories","if","you","have","at","least","two","hops","-","'adam","'","seems","to","work","very","poorly","compared","to","'sgd","'","for","hogwild","training"]
docstr: Override options in vectorize from parent.
docstr_tok: ["Override","options","in","vectorize","from","parent","."]
code: def vectorize(self, *args, **kwargs):
      """
      Override options in vectorize from parent.
      """
      kwargs["add_start"] = False
      kwargs["add_end"] = False
      return super().vectorize(*args, **kwargs)
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  from functools import lru_cache
  import torch
  from parlai.core.torch_ranker_agent import TorchRankerAgent
  from .modules import MemNN, opt_to_kwargs
label_desc: Use image node_id 9 for calling the MemnnAgent obj's underlying member method code with example usage: obj.vectorize() and returns: super