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_dataloader(
      self, cfg: Dict, is_index_data: bool = False
  ) -> "torch.utils.data.DataLoader":
      dataset = EntityLinkingDataset(
          tokenizer=self.tokenizer,
          data_file=cfg.data_file,
          max_seq_length=cfg.max_seq_length,
          is_index_data=is_index_data,
      )
  
      return torch.utils.data.DataLoader(
          dataset=dataset,
          batch_size=cfg.batch_size,
          collate_fn=dataset.collate_fn,
          shuffle=cfg.get("shuffle", True),
          num_workers=cfg.get("num_workers", 2),
          pin_memory=cfg.get("pin_memory", False),
          drop_last=cfg.get("drop_last", False),
      )
  
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 10 for calling the EntityLinkingModel obj's underlying member method code with example usage: obj.setup_dataloader(cfg, is_index_data) and returns: torch