class_docstr: This torchvision image transformation prepares the MNIST digits to be
  used in the tutorial. Depending on the number of quadrants to be used as
  inputs (1, 2, or 3), the transformation masks the remaining (3, 2, 1)
  quadrant(s) setting their pixels with -1. Additionally, the transformation
  adds the target output in the sample dict as the complementary of the input
code: def __init__(self, num_quadrant_inputs, mask_with=-1):
      if num_quadrant_inputs <= 0 or num_quadrant_inputs >= 4:
          raise ValueError(
              "Number of quadrants as inputs must be 1, 2 or 3"
          )
      self.num = num_quadrant_inputs
      self.mask_with = mask_with
  
file_path: pyro/examples/cvae/mnist.py
imports: import numpy
  import torch
  from torch.utils.data import DataLoader, Dataset
  from torchvision.transforms import Compose, functional
  from pyro.contrib.examples.util import MNIST
label_desc: Use image node_id 1 to create a new MaskImages object  with example: obj = MaskImages(num_quadrant_inputs, mask_with)