code: def guide(self, x):
      x_size = x.size(0)
  
      # define a helper function to sample z's for a single layer
      def sample_zs(name, width):
          alpha_z_q = pyro.param(
              "alpha_z_q_%s" % name,
              lambda: rand_tensor(
                  (x_size, width), self.alpha_init, self.sigma_init
              ),
          )
          mean_z_q = pyro.param(
              "mean_z_q_%s" % name,
              lambda: rand_tensor(
                  (x_size, width), self.mean_init, self.sigma_init
              ),
          )
          alpha_z_q, mean_z_q = softplus(alpha_z_q), softplus(mean_z_q)
          pyro.sample(
              "z_%s" % name,
              Gamma(alpha_z_q, alpha_z_q / mean_z_q).to_event(1),
          )
  
      # define a helper function to sample w's for a single layer
      def sample_ws(name, width):
          alpha_w_q = pyro.param(
              "alpha_w_q_%s" % name,
              lambda: rand_tensor(
                  (width), self.alpha_init, self.sigma_init
              ),
          )
          mean_w_q = pyro.param(
              "mean_w_q_%s" % name,
              lambda: rand_tensor(
                  (width), self.mean_init, self.sigma_init
              ),
          )
          alpha_w_q, mean_w_q = softplus(alpha_w_q), softplus(mean_w_q)
          pyro.sample(
              "w_%s" % name, Gamma(alpha_w_q, alpha_w_q / mean_w_q)
          )
  
      # sample the global weights
      with pyro.plate("w_top_plate", self.top_width * self.mid_width):
          sample_ws("top", self.top_width * self.mid_width)
      with pyro.plate(
          "w_mid_plate", self.mid_width * self.bottom_width
      ):
          sample_ws("mid", self.mid_width * self.bottom_width)
      with pyro.plate(
          "w_bottom_plate", self.bottom_width * self.image_size
      ):
          sample_ws("bottom", self.bottom_width * self.image_size)
  
      # sample the local latent random variables
      with pyro.plate("data", x_size):
          sample_zs("top", self.top_width)
          sample_zs("mid", self.mid_width)
          sample_zs("bottom", self.bottom_width)
  
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 3 for calling the SparseGammaDEF obj's underlying member method code with example usage: obj.guide(x) without return types