code: def guide(self, x):
      # group all the latent weights into one large latent variable
      global_group = self.group(match="w_.*")
      global_mean = pyro.param(
          "w_mean",
          lambda: rand_tensor(global_group.event_shape, 0.5, 0.1),
      )
      global_scale = softplus(
          pyro.param(
              "w_scale",
              lambda: rand_tensor(global_group.event_shape, 0.0, 0.1),
          )
      )
      # use a mean field Normal distribution on all the ws
      global_group.sample(
          "ws", Normal(global_mean, global_scale).to_event(1)
      )
  
      # group all the latent zs into one large latent variable
      local_group = self.group(match="z_.*")
      x_shape = x.shape[:1] + local_group.event_shape
  
      with self.plate("data", x.size(0)):
          local_mean = pyro.param(
              "z_mean", lambda: rand_tensor(x_shape, 0.5, 0.1)
          )
          local_scale = softplus(
              pyro.param(
                  "z_scale", lambda: rand_tensor(x_shape, 0.0, 0.1)
              )
          )
          # use a mean field Normal distribution on all the zs
          local_group.sample(
              "zs", Normal(local_mean, local_scale).to_event(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 for calling the MyEasyGuide obj's underlying member method code with example usage: obj.guide(x) without return types