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.
docstr: Return size of dataset.
docstr_tok: ["Return","size","of","dataset","."]
code: def __len__(self):
      """Return size of dataset."""
      return len(self.data)
  
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 3 for calling the MNISTM obj's underlying member method code with example usage: obj.__len__() and returns: len