code: def model(self, x, y=None):
      # Register various nn.Modules with Pyro
      pyro.module("scanvi", self)
  
      # This gene-level parameter modulates the variance of the observation distribution
      theta = pyro.param(
          "inverse_dispersion",
          10.0 * x.new_ones(self.num_genes),
          constraint=constraints.positive,
      )
  
      # We scale all sample statements by scale_factor so that the ELBO is normalized
      # wrt the number of datapoints and genes
      with pyro.plate("batch", len(x)), poutine.scale(
          scale=self.scale_factor
      ):
          z1 = pyro.sample(
              "z1",
              dist.Normal(0, x.new_ones(self.latent_dim)).to_event(1),
          )
          # Note that if y is None (i.e. y is unobserved) then y will be sampled;
          # otherwise y will be treated as observed.
          y = pyro.sample(
              "y",
              dist.OneHotCategorical(
                  logits=x.new_zeros(self.num_labels)
              ),
              obs=y,
          )
  
          z2_loc, z2_scale = self.z2_decoder(z1, y)
          z2 = pyro.sample(
              "z2", dist.Normal(z2_loc, z2_scale).to_event(1)
          )
  
          l_scale = self.l_scale * x.new_ones(1)
          l = pyro.sample(
              "l", dist.LogNormal(self.l_loc, l_scale).to_event(1)
          )
  
          # Note that by construction mu is normalized (i.e. mu.sum(-1) == 1) and the
          # total scale of counts for each cell is determined by `l`
          gate_logits, mu = self.x_decoder(z2)
          # TODO revisit this parameterization if torch.distributions.NegativeBinomial changes
          # from failure to success parametrization;
          # see https://github.com/pytorch/pytorch/issues/42449
          nb_logits = (l * mu + self.epsilon).log() - (
              theta + self.epsilon
          ).log()
          x_dist = dist.ZeroInflatedNegativeBinomial(
              gate_logits=gate_logits,
              total_count=theta,
              logits=nb_logits,
          )
          # Observe the datapoint x using the observation distribution x_dist
          pyro.sample("x", x_dist.to_event(1), obs=x)
  
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 2 for calling the SCANVI obj's underlying member method code with example usage: obj.model(x, y) without return types