code: def __init__(
      self, hidden_dim, latent_dim, input_dim, mixture_weights
  ):
      super(CompositePrior, self).__init__()
  
      self.mixture_weights = mixture_weights
  
      self.mu_prior = nn.Parameter(
          torch.Tensor(1, latent_dim), requires_grad=False
      )
      self.mu_prior.data.fill_(0)
  
      self.logvar_prior = nn.Parameter(
          torch.Tensor(1, latent_dim), requires_grad=False
      )
      self.logvar_prior.data.fill_(0)
  
      self.logvar_uniform_prior = nn.Parameter(
          torch.Tensor(1, latent_dim), requires_grad=False
      )
      self.logvar_uniform_prior.data.fill_(10)
  
      self.encoder_old = Encoder(hidden_dim, latent_dim, input_dim)
      self.encoder_old.requires_grad_(False)
  
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 CompositePrior object from inherited base classes: nn with example: obj = CompositePrior(hidden_dim, latent_dim, input_dim, mixture_weights)