class_docstr: TorchRankerAgent implementation of the crossencoder.
  
  It is a standalone Agent. It might be called by the Both Encoder.
class_docstr_tok: ["TorchRankerAgent","implementation","of","the","crossencoder",".","It","is","a","standalone","Agent",".","It","might","be","called","by","the","Both","Encoder","."]
code: def build_model(self):
      return BertWrapper(
          BertModel.from_pretrained(self.pretrained_path),
          1,
          add_transformer_layer=self.opt["add_transformer_layer"],
          layer_pulled=self.opt["pull_from_layer"],
          aggregation=self.opt["bert_aggregation"],
      )
  
file_path: ParlAI/parlai/agents/bert_ranker/cross_encoder_ranker.py
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  from parlai.utils.torch import concat_without_padding
  from parlai.core.torch_ranker_agent import TorchRankerAgent
  from parlai.zoo.bert.build import download
  from .bert_dictionary import BertDictionaryAgent
  from .helpers import BertWrapper, BertModel, add_common_args, surround, MODEL_PATH
  import os
label_desc: Use image node_id 3 for calling the CrossEncoderRankerAgent obj's underlying member method code with example usage: obj.build_model() and returns: BertWrapper