class_docstr: Information retrieval model which jointly encodes both query and passage
  and passes them to BERT encoder followed by a fully-connected layer for
  similarity score prediction.
class_docstr_tok: ["Information","retrieval","model","which","jointly","encodes","both","query","and","passage","and","passes","them","to","BERT","encoder","followed","by","a","fully-connected","layer","for","similarity","score","prediction","."]
code: def output_types(self) -> Optional[Dict[str, NeuralType]]:
      return self.sim_score_regressor.output_types
  
file_path: NeMo/nemo/collections/nlp/models/information_retrieval/bert_joint_ir_model.py
imports: from typing import Dict, Optional
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from nemo.collections.common.losses import SmoothedCrossEntropyLoss
  from nemo.collections.nlp.models.information_retrieval.base_ir_model import BaseIRModel
  from nemo.collections.nlp.modules.common import SequenceRegression
  from nemo.collections.nlp.modules.common.tokenizer_utils import get_tokenizer
  from nemo.core.classes.common import typecheck
  from nemo.core.neural_types import NeuralType
label_desc: Use image node_id 2 for calling the BertJointIRModel obj's underlying member method code with example usage: obj.output_types() and returns: self