code: def forward(self, x, dropout_prob):
      x = F.normalize(x)
      x = F.dropout(x, dropout_prob, training=self.training)
  
      h1 = self.ln1(swish(self.fc1(x)))
      h2 = self.ln2(swish(self.fc2(h1) + h1))
      h3 = self.ln3(swish(self.fc3(h2) + h1 + h2))
      h4 = self.ln4(swish(self.fc4(h3) + h1 + h2 + h3))
      h5 = self.ln5(swish(self.fc5(h4) + h1 + h2 + h3 + h4))
      return self.fc_mu(h5), self.fc_logvar(h5)
  
imports: import numpy
  from copy import deepcopy
  import torch
  import torch.nn
  import torch.nn.functional
  from recbole.model.abstract_recommender import GeneralRecommender, AutoEncoderMixin
  from recbole.model.init import xavier_normal_initialization
  from recbole.utils import InputType
label_desc: Use image node_id 2 for calling the Encoder obj's underlying member method code with example usage: obj.forward(x, dropout_prob) and returns: self,  self