code: def run_inference_for_epoch(
      data_loaders, losses, periodic_interval_batches
  ):
      """
      runs the inference algorithm for an epoch
      returns the values of all losses separately on supervised and unsupervised parts
      """
      num_losses = len(losses)
  
      # compute number of batches for an epoch
      sup_batches = len(data_loaders["sup"])
      unsup_batches = len(data_loaders["unsup"])
      batches_per_epoch = sup_batches + unsup_batches
  
      # initialize variables to store loss values
      epoch_losses_sup = [0.0] * num_losses
      epoch_losses_unsup = [0.0] * num_losses
  
      # setup the iterators for training data loaders
      sup_iter = iter(data_loaders["sup"])
      unsup_iter = iter(data_loaders["unsup"])
  
      # count the number of supervised batches seen in this epoch
      ctr_sup = 0
      for i in range(batches_per_epoch):
          # whether this batch is supervised or not
          is_supervised = (
              i % periodic_interval_batches == 1
          ) and ctr_sup < sup_batches
  
          # extract the corresponding batch
          if is_supervised:
              (xs, ys) = next(sup_iter)
              ctr_sup += 1
          else:
              (xs, ys) = next(unsup_iter)
  
          # run the inference for each loss with supervised or un-supervised
          # data as arguments
          for loss_id in range(num_losses):
              if is_supervised:
                  new_loss = losses[loss_id].step(xs, ys)
                  epoch_losses_sup[loss_id] += new_loss
              else:
                  new_loss = losses[loss_id].step(xs)
                  epoch_losses_unsup[loss_id] += new_loss
  
      # return the values of all losses
      return epoch_losses_sup, epoch_losses_unsup
  
imports: import argparse
  import torch
  import torch.nn
  from utils.custom_mlp import MLP, Exp
  from utils.mnist_cached import MNISTCached, mkdir_p, setup_data_loaders
  from utils.vae_plots import mnist_test_tsne_ssvae, plot_conditional_samples_ssvae
  from visdom import Visdom
  import pyro
  import pyro.distributions
  from pyro.contrib.examples.util import print_and_log
  from pyro.infer import SVI, JitTrace_ELBO, JitTraceEnum_ELBO, Trace_ELBO, TraceEnum_ELBO, config_enumerate
  from pyro.optim import Adam
label_desc: Use image node_id 1 for calling a global function with example usage: run_inference_for_epoch(data_loaders, losses, periodic_interval_batches) and returns: epoch_losses_sup,  epoch_losses_unsup