class_docstr: MNIST-M Dataset.
  Auto-downloads the dataset and provide the torch Dataset API.
  
  Args:
      root (str): path to directory where the MNISTM folder will be created (or exists.)
      train (bool, optional): defaults to True.
          If True, loads the training data. Otherwise, loads the test data.
      transform (callable, optional): defaults to None.
          A function/transform that takes in
          an PIL image and returns a transformed version.
          E.g., ``transforms.RandomCrop``
          This preprocessing function applied to all images (whether source or target)
  
      target_transform (callable, optional): default to None, similar to transform.
          This preprocessing function applied to all target images, after `transform`
  
      download (bool optional): defaults to False.
          Whether to allow downloading the data if not found on disk.
code: def _check_exists(self):
      return os.path.exists(
          os.path.join(
              self.root, self.processed_folder, self.training_file
          )
      ) and os.path.exists(
          os.path.join(self.root, self.processed_folder, self.test_file)
      )
  
file_path: pykale/kale/loaddata/mnistm.py
imports: from __future__ import print_function
  import errno
  import logging
  import os
  import torch
  import torch.utils.data
  from PIL import Image
label_desc: Use image node_id 4 for calling the MNISTM obj's underlying member method code with example usage: obj._check_exists() and returns: unknown