docstr: Function that loads the validation set.
  
  Args:
      batch_size (Optional[int], optional): Batch size. Defaults to `self.batch_size`.
  
  Returns:
      DataLoader: Validation dataloader
docstr_tok: ["Function","that","loads","the","validation","set",".","Args",":","batch_size","(","Optional","[","int","]",",","optional",")",":","Batch","size",".","Defaults","to","`","self.batch_size","`",".","Returns",":","DataLoader",":","Validation","dataloader"]
code: def val_dataloader(
      self, batch_size: Optional[int] = None
  ) -> DataLoader:
      """Function that loads the validation set.
  
      Args:
          batch_size (Optional[int], optional): Batch size. Defaults to `self.batch_size`.
  
      Returns:
          DataLoader: Validation dataloader
      """
      return DataLoader(
          self.validation_dataset,
          batch_size or self.batch_size,
          shuffle=False,
          num_workers=self.config.num_workers,
          pin_memory=self.config.pin_memory,
      )
  
imports: import re
  import warnings
  from enum import Enum
  from pathlib import Path
  from typing import Dict, Iterable, List, Optional, Tuple, Union
  import joblib
  import numpy
  import pandas
  import pytorch_lightning
  import torch
  from omegaconf import DictConfig
  from pandas import DataFrame, DatetimeTZDtype, to_datetime
  from pandas.tseries import offsets
  from pandas.tseries.frequencies import to_offset
  from sklearn.base import TransformerMixin, copy
  from sklearn.preprocessing import FunctionTransformer, LabelEncoder, PowerTransformer, QuantileTransformer, StandardScaler
  from torch.utils.data import DataLoader, Dataset
  from pytorch_tabular.config import InferredConfig
  from pytorch_tabular.utils import get_logger
  from .categorical_encoders import OrdinalEncoder
label_desc: Use image node_id 36 for calling the TabularDatamodule obj's underlying member method code with example usage: obj.val_dataloader(batch_size) and returns: DataLoader