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 setup_validation_data(
      self, val_data_config: Optional[DictConfig]
  ):
      if not val_data_config or not val_data_config.data_file:
          logging.info(
              f"Dataloader config or file_path or processed data path for the val dataset is missing, \
                          so no data loader for validation is created!"
          )
  
          self._validation_dl = None
          return
  
      self._validation_dl = self.setup_dataloader(cfg=val_data_config)
  
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 9 for calling the EntityLinkingModel obj's underlying member method code with example usage: obj.setup_validation_data(val_data_config) without return types