class_docstr: Transformer Ranker Agent.
  
  Implementation of a TorchRankerAgent, where the model is a Transformer
docstr: Override to include vectorization of memories.
docstr_tok: ["Override","to","include","vectorization","of","memories","."]
code: def vectorize(self, *args, **kwargs):
      """
      Override to include vectorization of memories.
      """
      kwargs["add_start"] = False
      kwargs["add_end"] = False
      obs = super().vectorize(*args, **kwargs)
      if self.opt["use_memories"]:
          obs = self._vectorize_memories(obs)
      return obs
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  from parlai.core.agents import Agent
  from parlai.utils.torch import padded_3d
  from parlai.core.torch_classifier_agent import TorchClassifierAgent
  from parlai.core.torch_ranker_agent import TorchRankerAgent
  from parlai.core.torch_generator_agent import TorchGeneratorAgent
  from parlai.utils.misc import recursive_getattr, warn_once
  from parlai.utils.logging import logging
  from parlai.utils.fsdp import should_use_fsdp
  from .modules import TransformerMemNetModel, TransformerGeneratorModel, TransformerLinearWrapper
  import torch
label_desc: Use image node_id 6 for calling the TransformerRankerAgent obj's underlying member method code with example usage: obj.vectorize() and returns: obs