code: def load_data():
      inpath = get_data_directory(__file__)
      X_np, Y = multi_mnist.load(inpath)
      X_np = X_np.astype(np.float32)
      X_np /= 255.0
      X = torch.from_numpy(X_np)
      # Using FloatTensor to allow comparison with values sampled from
      # Bernoulli.
      counts = torch.FloatTensor([len(objs) for objs in Y])
      return X, counts
  
imports: import argparse
  import math
  import os
  import time
  from functools import partial
  import numpy
  import torch
  import visdom
  from air import AIR, latents_to_tensor
  from viz import draw_many, tensor_to_objs
  import pyro
  import pyro.contrib.examples.multi_mnist
  import pyro.optim
  import pyro.poutine
  from pyro.contrib.examples.util import get_data_directory
  from pyro.infer import SVI, JitTraceGraph_ELBO, TraceGraph_ELBO
label_desc: Use image node_id 5 for calling a global function with example usage: load_data() and returns: X,  counts