code: def guide(self, x, y=None):
      pyro.module("scanvi", self)
      with pyro.plate("batch", len(x)), poutine.scale(
          scale=self.scale_factor
      ):
          z2_loc, z2_scale, l_loc, l_scale = self.z2l_encoder(x)
          pyro.sample("l", dist.LogNormal(l_loc, l_scale).to_event(1))
          z2 = pyro.sample(
              "z2", dist.Normal(z2_loc, z2_scale).to_event(1)
          )
  
          y_logits = self.classifier(z2)
          y_dist = dist.OneHotCategorical(logits=y_logits)
          if y is None:
              # x is unlabeled so sample y using q(y|z2)
              y = pyro.sample("y", y_dist)
          else:
              # x is labeled so add a classification loss term
              # (this way q(y|z2) learns from both labeled and unlabeled data)
              classification_loss = y_dist.log_prob(y)
              # Note that the negative sign appears because we're adding this term in the guide
              # and the guide log_prob appears in the ELBO as -log q
              pyro.factor(
                  "classification_loss",
                  -self.alpha * classification_loss,
                  has_rsample=False,
              )
  
          z1_loc, z1_scale = self.z1_encoder(z2, y)
          pyro.sample("z1", dist.Normal(z1_loc, z1_scale).to_event(1))
  
imports: import argparse
  import matplotlib.pyplot
  import numpy
  import torch
  import torch.nn
  from matplotlib.patches import Patch
  from torch.distributions import constraints
  from torch.nn.functional import softmax, softplus
  from torch.optim import Adam
  import pyro
  import pyro.distributions
  import pyro.poutine
  from pyro.contrib.examples.scanvi_data import get_data
  from pyro.distributions.util import broadcast_shape
  from pyro.infer import SVI, TraceEnum_ELBO, config_enumerate
  from pyro.optim import MultiStepLR
label_desc: Use image node_id 3 for calling the SCANVI obj's underlying member method code with example usage: obj.guide(x, y) without return types