docstr: Loads a datamodule from a path.
  
  Args:
      path (Union[str, Path]): Path to the datamodule
  
  Returns:
      TabularDatamodule (TabularDatamodule): The datamodule loaded from the path
docstr_tok: ["Loads","a","datamodule","from","a","path",".","Args",":","path","(","Union","[","str",",","Path","]",")",":","Path","to","the","datamodule","Returns",":","TabularDatamodule","(","TabularDatamodule",")",":","The","datamodule","loaded","from","the","path"]
code: def load_datamodule(cls, path: Union[str, Path]):
      """Loads a datamodule from a path.
  
      Args:
          path (Union[str, Path]): Path to the datamodule
  
      Returns:
          TabularDatamodule (TabularDatamodule): The datamodule loaded from the path
      """
      if isinstance(path, str):
          path = Path(path)
      if not path.exists():
          raise FileNotFoundError(f"{path} does not exist.")
      datamodule = joblib.load(path)
      return datamodule
  
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 40 for calling the TabularDatamodule obj's underlying member method code with example usage: obj.load_datamodule(cls, path) and returns: datamodule