code: def __init__(self, hidden_dim, latent_dim, input_dim, eps=1e-1):
      super(Encoder, self).__init__()
  
      self.fc1 = nn.Linear(input_dim, hidden_dim)
      self.ln1 = nn.LayerNorm(hidden_dim, eps=eps)
      self.fc2 = nn.Linear(hidden_dim, hidden_dim)
      self.ln2 = nn.LayerNorm(hidden_dim, eps=eps)
      self.fc3 = nn.Linear(hidden_dim, hidden_dim)
      self.ln3 = nn.LayerNorm(hidden_dim, eps=eps)
      self.fc4 = nn.Linear(hidden_dim, hidden_dim)
      self.ln4 = nn.LayerNorm(hidden_dim, eps=eps)
      self.fc5 = nn.Linear(hidden_dim, hidden_dim)
      self.ln5 = nn.LayerNorm(hidden_dim, eps=eps)
      self.fc_mu = nn.Linear(hidden_dim, latent_dim)
      self.fc_logvar = nn.Linear(hidden_dim, latent_dim)
  
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 1 to create a new Encoder object from inherited base classes: nn with example: obj = Encoder(hidden_dim, latent_dim, input_dim, eps)