code: def get_data(num_quadrant_inputs, batch_size):
      transforms = Compose(
          [
              ToTensor(),
              MaskImages(num_quadrant_inputs=num_quadrant_inputs),
          ]
      )
      datasets, dataloaders, dataset_sizes = {}, {}, {}
      for mode in ["train", "val"]:
          datasets[mode] = CVAEMNIST(
              "../data",
              download=True,
              transform=transforms,
              train=mode == "train",
          )
          dataloaders[mode] = DataLoader(
              datasets[mode],
              batch_size=batch_size,
              shuffle=mode == "train",
              num_workers=0,
          )
          dataset_sizes[mode] = len(datasets[mode])
  
      return datasets, dataloaders, dataset_sizes
  
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 for calling a global function with example usage: get_data(num_quadrant_inputs, batch_size) and returns: datasets,  dataloaders,  dataset_sizes