code: def compute_elbo(self, x, c, pred_model):
      c = torch.tensor(c).float()
      c = c.view(c.shape[0], 1)
      em, ev = self.encoder(torch.cat((x, c), 1))
      kl_divergence = 0.5 * torch.mean(
          em**2 + ev - torch.log(ev) - 1, axis=1
      )
  
      z = self.sample_latent_code(em, ev)
      dm = self.decoder(torch.cat((z, c), 1))
      log_px_z = torch.tensor(0.0)
  
      x_pred = dm
      return (
          torch.mean(log_px_z),
          torch.mean(kl_divergence),
          x,
          x_pred,
          torch.argmax(pred_model(x_pred), dim=1),
      )
  
imports: import torch
  import torch.utils.data
  from torch import nn
label_desc: Use image node_id 7 for calling the CF_VAE obj's underlying member method code with example usage: obj.compute_elbo(x, c, pred_model) and returns: torch,  torch,  x,  x_pred,  torch