code: def train(
      device,
      dataloaders,
      dataset_sizes,
      learning_rate,
      num_epochs,
      early_stop_patience,
      model_path,
      pre_trained_baseline_net,
  ):
      # clear param store
      pyro.clear_param_store()
  
      cvae_net = CVAE(200, 500, 500, pre_trained_baseline_net)
      cvae_net.to(device)
      optimizer = pyro.optim.Adam({"lr": learning_rate})
      svi = SVI(
          cvae_net.model, cvae_net.guide, optimizer, loss=Trace_ELBO()
      )
  
      best_loss = np.inf
      early_stop_count = 0
      Path(model_path).parent.mkdir(parents=True, exist_ok=True)
  
      for epoch in range(num_epochs):
          # Each epoch has a training and validation phase
          for phase in ["train", "val"]:
              running_loss = 0.0
              num_preds = 0
  
              # Iterate over data.
              bar = tqdm(
                  dataloaders[phase],
                  desc="CVAE Epoch {} {}".format(epoch, phase).ljust(
                      20
                  ),
              )
              for i, batch in enumerate(bar):
                  inputs = batch["input"].to(device)
                  outputs = batch["output"].to(device)
  
                  if phase == "train":
                      loss = svi.step(inputs, outputs)
                  else:
                      loss = svi.evaluate_loss(inputs, outputs)
  
                  # statistics
                  running_loss += loss / inputs.size(0)
                  num_preds += 1
                  if i % 10 == 0:
                      bar.set_postfix(
                          loss="{:.2f}".format(
                              running_loss / num_preds
                          ),
                          early_stop_count=early_stop_count,
                      )
  
              epoch_loss = running_loss / dataset_sizes[phase]
              # deep copy the model
              if phase == "val":
                  if epoch_loss < best_loss:
                      best_loss = epoch_loss
                      torch.save(cvae_net.state_dict(), model_path)
                      early_stop_count = 0
                  else:
                      early_stop_count += 1
  
          if early_stop_count >= early_stop_patience:
              break
  
      # Save model weights
      cvae_net.load_state_dict(torch.load(model_path))
      cvae_net.eval()
      return cvae_net
  
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 for calling a global function with example usage: train(device, dataloaders, dataset_sizes, learning_rate, num_epochs, early_stop_patience, model_path, pre_trained_baseline_net) and returns: cvae_net