code: def decode(self, z, h):
      zh = torch.cat([z, h], 1)
      h = self.decoder(zh)
      if self.likelihood == "mult":
          return torch.softmax(h, dim=1)
      else:
          return torch.sigmoid(h)
  
file_path: cornac/cornac/models/cvaecf/cvaecf.py
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from tqdm.auto import trange
  from ...utils import estimate_batches
label_desc: Use image node_id 6 for calling the CVAE obj's underlying member method code with example usage: obj.decode(z, h) and returns: torch, torch