code: def __init__(
      self,
      num_genes,
      num_labels,
      l_loc,
      l_scale,
      latent_dim=10,
      alpha=0.01,
      scale_factor=1.0,
  ):
      assert isinstance(num_genes, int)
      self.num_genes = num_genes
  
      assert isinstance(num_labels, int) and num_labels > 1
      self.num_labels = num_labels
  
      # This is the dimension of both z1 and z2
      assert isinstance(latent_dim, int) and latent_dim > 0
      self.latent_dim = latent_dim
  
      # The next two hyperparameters determine the prior over the log_count latent variable `l`
      assert isinstance(l_loc, float)
      self.l_loc = l_loc
      assert isinstance(l_scale, float) and l_scale > 0
      self.l_scale = l_scale
  
      # This hyperparameter controls the strength of the auxiliary classification loss
      assert isinstance(alpha, float) and alpha > 0
      self.alpha = alpha
  
      assert isinstance(scale_factor, float) and scale_factor > 0
      self.scale_factor = scale_factor
  
      super().__init__()
  
      # Setup the various neural networks used in the model and guide
      self.z2_decoder = Z2Decoder(
          z1_dim=self.latent_dim,
          y_dim=self.num_labels,
          z2_dim=self.latent_dim,
          hidden_dims=[50],
      )
      self.x_decoder = XDecoder(
          num_genes=num_genes, hidden_dims=[100], z2_dim=self.latent_dim
      )
      self.z2l_encoder = Z2LEncoder(
          num_genes=num_genes, z2_dim=self.latent_dim, hidden_dims=[100]
      )
      self.classifier = Classifier(
          z2_dim=self.latent_dim,
          hidden_dims=[50],
          num_labels=num_labels,
      )
      self.z1_encoder = Z1Encoder(
          num_labels=num_labels,
          z1_dim=self.latent_dim,
          z2_dim=self.latent_dim,
          hidden_dims=[50],
      )
  
      self.epsilon = 5.0e-3
  
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 1 to create a new SCANVI object from inherited base classes: nn with example: obj = SCANVI(num_genes, num_labels, l_loc, l_scale, latent_dim, alpha, scale_factor)