code: def classify_latents(epochs, option):
      model.eval()
      vae = unpack_model(option)
      if "_" not in args.model:
          epochs *= 10  # account for the fact the mnist-svhn has more examples (roughly x10)
      classifier = Latent_Classifier(args.latent_dim, 10).to(device)
      criterion = nn.CrossEntropyLoss()
      optimizer = optim.Adam(classifier.parameters(), lr=0.001)
  
      for epoch in range(
          epochs
      ):  # loop over the dataset multiple times
          running_loss = 0.0
          total_iters = len(train_loader)
          print("\n====> Epoch: {:03d} ".format(epoch))
          for i, data in enumerate(train_loader):
              # get the inputs
              x, targets = unpack_data_mlp(data, option)
              x, targets = x.to(device), targets.to(device)
              with torch.no_grad():
                  qz_x_params = vae.enc(x)
                  zs = vae.qz_x(*qz_x_params).rsample()
              optimizer.zero_grad()
              outputs = classifier(zs)
              loss = criterion(outputs, targets)
              loss.backward()
              optimizer.step()
              # print statistics
              running_loss += loss.item()
              if (i + 1) % 1000 == 0:
                  print(
                      "iteration {:04d}/{:d}: loss: {:6.3f}".format(
                          i + 1, total_iters, running_loss / 1000
                      )
                  )
                  running_loss = 0.0
      print("Finished Training, calculating test loss...")
  
      classifier.eval()
      total = 0
      correct = 0
      with torch.no_grad():
          for i, data in enumerate(test_loader):
              x, targets = unpack_data_mlp(data, option)
              x, targets = x.to(device), targets.to(device)
              qz_x_params = vae.enc(x)
              zs = vae.qz_x(*qz_x_params).rsample()
              outputs = classifier(zs)
              _, predicted = torch.max(outputs.data, 1)
              total += targets.size(0)
              correct += (predicted == targets).sum().item()
      print(
          "The classifier correctly classified {} out of {} examples. Accuracy: "
          "{:.2f}%".format(correct, total, correct / total * 100)
      )
  
file_path: mmvae/src/report/analyse_ms.py
imports: import argparse
  import os
  import sys
  import torch
  import torch.nn
  import torch.optim
  import inspect
  import models
  from helper import Latent_Classifier, SVHN_Classifier, MNIST_Classifier
  from utils import Logger, Timer
label_desc: Use image node_id 1 for calling a global function with example usage: classify_latents(epochs, option) without return types