code: def __init__(self):
      # define the sizes of the layers in the deep exponential family
      self.top_width = 100
      self.mid_width = 40
      self.bottom_width = 15
      self.image_size = 64 * 64
      # define hyperparameters that control the prior
      self.alpha_z = torch.tensor(0.1)
      self.beta_z = torch.tensor(0.1)
      self.alpha_w = torch.tensor(0.1)
      self.beta_w = torch.tensor(0.3)
      # define parameters used to initialize variational parameters
      self.alpha_init = 0.5
      self.mean_init = 0.0
      self.sigma_init = 0.1
  
imports: import argparse
  import errno
  import os
  import numpy
  import torch
  import wget
  from torch.nn.functional import softplus
  import pyro
  import pyro.optim
  from pyro.contrib.easyguide import EasyGuide
  from pyro.contrib.examples.util import get_data_directory
  from pyro.distributions import Gamma, Normal, Poisson
  from pyro.infer import SVI, TraceMeanField_ELBO
  from pyro.infer.autoguide import AutoDiagonalNormal, init_to_feasible
label_desc: Use image node_id 1 to create a new SparseGammaDEF object  with example: obj = SparseGammaDEF()