docstr: Generic data loader function
  
  :param df: Input tensor
  
  :return: Returns the constructed dataloader
docstr_tok: ["Generic","data","loader","function",":","param","df",":","Input","tensor",":","return",":","Returns","the","constructed","dataloader"]
code: def create_data_loader(self, df):
      """
      Generic data loader function
  
      :param df: Input tensor
  
      :return: Returns the constructed dataloader
      """
      return DataLoader(
          df,
          batch_size=self.args["batch_size"],
          num_workers=self.args["num_workers"],
      )
  
imports: import logging
  import os
  from argparse import ArgumentParser
  import mlflow
  import mlflow.pytorch
  import pytorch_lightning
  import torch
  from pytorch_lightning.callbacks import LearningRateMonitor
  from pytorch_lightning.callbacks import ModelCheckpoint
  from pytorch_lightning.callbacks.early_stopping import EarlyStopping
  from torch.nn import functional
  from torch.utils.data import DataLoader
  from torch.utils.data import random_split
  from torchvision import datasets
  from torchvision import transforms
label_desc: Use image node_id 3 for calling the MNISTDataModule obj's underlying member method code with example usage: obj.create_data_loader(df) and returns: DataLoader