code: def setup_dataloader_from_config(self, cfg: DictConfig):
      dataset = Text2SparqlDataset(
          filepath=cfg.filepath,
          encoder_tokenizer=self.encoder_tokenizer,
          decoder_tokenizer=self.decoder_tokenizer,
          encoder_add_special_tokens=self.encoder_add_special_tokens,
          decoder_add_special_tokens=self.decoder_add_special_tokens,
          max_seq_length=self._cfg.max_seq_length,
          num_samples=cfg.get("num_samples", -1),
          convert_labels=self._cfg.convert_labels,
      )
  
      return torch.utils.data.DataLoader(
          dataset=dataset,
          batch_size=self._cfg.batch_size,
          shuffle=cfg.shuffle,
          num_workers=cfg.get("num_workers", 2),
          pin_memory=cfg.get("pin_memory", False),
          drop_last=cfg.get("drop_last", False),
          collate_fn=dataset.collate_fn,
      )
  
imports: from typing import Dict, List, Optional, Tuple
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from transformers import AutoModel, BartForConditionalGeneration, EncoderDecoderModel
  from nemo.collections.common.metrics import Perplexity
  from nemo.collections.nlp.data.text2sparql import Text2SparqlDataset
  from nemo.collections.nlp.modules.common.tokenizer_utils import get_tokenizer
  from nemo.core.classes.common import typecheck
  from nemo.core.classes.modelPT import ModelPT
  from nemo.core.neural_types import ChannelType, MaskType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 14 for calling the Text2SparqlModel obj's underlying member method code with example usage: obj.setup_dataloader_from_config(cfg) and returns: torch