code: def get_accuracy(data_loader, classifier_fn, batch_size):
      """
      compute the accuracy over the supervised training set or the testing set
      """
      predictions, actuals = [], []
  
      # use the appropriate data loader
      for xs, ys in data_loader:
          # use classification function to compute all predictions for each batch
          predictions.append(classifier_fn(xs))
          actuals.append(ys)
  
      # compute the number of accurate predictions
      accurate_preds = 0
      for pred, act in zip(predictions, actuals):
          for i in range(pred.size(0)):
              v = torch.sum(pred[i] == act[i])
              accurate_preds += v.item() == 10
  
      # calculate the accuracy between 0 and 1
      accuracy = (accurate_preds * 1.0) / (
          len(predictions) * batch_size
      )
      return accuracy
  
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 2 for calling a global function with example usage: get_accuracy(data_loader, classifier_fn, batch_size) and returns: accuracy