code: def __init__(
      self, z_dim, hidden_1, hidden_2, pre_trained_baseline_net
  ):
      super().__init__()
      # The CVAE is composed of multiple MLPs, such as recognition network
      # qφ(z|x, y), (conditional) prior network pθ(z|x), and generation
      # network pθ(y|x, z). Also, CVAE is built on top of the NN: not only
      # the direct input x, but also the initial guess y_hat made by the NN
      # are fed into the prior network.
      self.baseline_net = pre_trained_baseline_net
      self.prior_net = Encoder(z_dim, hidden_1, hidden_2)
      self.generation_net = Decoder(z_dim, hidden_1, hidden_2)
      self.recognition_net = Encoder(z_dim, hidden_1, hidden_2)
  
imports: from pathlib import Path
  import numpy
  import torch
  import torch.nn
  from tqdm import tqdm
  import pyro
  import pyro.distributions
  from pyro.infer import SVI, Trace_ELBO
label_desc: Use image node_id 1 to create a new CVAE object from inherited base classes: nn with example: obj = CVAE(z_dim, hidden_1, hidden_2, pre_trained_baseline_net)