class_docstr: Second stage pretraining of BERT based language model
  for entity linking task. An implementation of Liu et. al's
  NAACL 2021 paper Self-Alignment Pretraining for Biomedical Entity Representations.
class_docstr_tok: ["Second","stage","pretraining","of","BERT","based","language","model","for","entity","linking","task",".","An","implementation","of","Liu","et",".","al's","NAACL","2021","paper","Self-Alignment","Pretraining","for","Biomedical","Entity","Representations","."]
code: def output_types(self) -> Optional[Dict[str, NeuralType]]:
      return {"logits": NeuralType(("B", "D"), LogitsType())}
  
imports: from typing import Dict, Optional
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from transformers import AutoTokenizer
  from nemo.collections.common.losses import MultiSimilarityLoss
  from nemo.collections.nlp.data import EntityLinkingDataset
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.core.classes.common import typecheck
  from nemo.core.classes.exportable import Exportable
  from nemo.core.neural_types import LogitsType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling the EntityLinkingModel obj's underlying member method code with example usage: obj.output_types() and returns: dict