class_docstr: Guassian Diffusion for large-scale recommendation.
class_docstr_tok: ["Guassian","Diffusion","for","large-scale","recommendation","."]
code: def Encode(self, batch):
      batch = self.dropout(batch)
      if self.n_cate == 1:
          hidden = self.encoder(batch)
          mu = hidden[:, : self.in_dims[-1]]
          logvar = hidden[:, self.in_dims[-1] :]
  
          if self.training and self.reparam:
              latent = self.reparamterization(mu, logvar)
          else:
              latent = mu
  
          kl_divergence = -0.5 * torch.mean(
              torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=1)
          )
  
          return batch, latent, kl_divergence
  
      else:
          batch_cate = []
          for i in range(self.n_cate):
              batch_cate.append(batch[:, self.category_idx[i]])
          # [batch_size, n_items] -> [[batch_size, n1_items], [batch_size, n2_items], [batch_size, n3_items]]
          latent_mu = []
          latent_logvar = []
          for i in range(self.n_cate):
              hidden = self.encoder[i](batch_cate[i])
              latent_mu.append(hidden[:, : self.decode_dim[i][0]])
              latent_logvar.append(hidden[:, self.decode_dim[i][0] :])
          # latent: [[batch_size, latent_size1], [batch_size, latent_size2], [batch_size, latent_size3]]
  
          mu = torch.cat(tuple(latent_mu), dim=-1)
          logvar = torch.cat(tuple(latent_logvar), dim=-1)
          if self.training and self.reparam:
              latent = self.reparamterization(mu, logvar)
          else:
              latent = mu
  
          kl_divergence = -0.5 * torch.mean(
              torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=1)
          )
  
          return (
              torch.cat(tuple(batch_cate), dim=-1),
              latent,
              kl_divergence,
          )
  
imports: import os
  import numpy
  import torch
  import torch.nn.functional
  import torch.nn
  from recbole.model.init import xavier_normal_initialization
  from recbole.model.layers import MLPLayers
  from recbole.model.general_recommender.diffrec import DiffRec, DNN, ModelMeanType, mean_flat
label_desc: Use image node_id 2 for calling the AutoEncoder obj's underlying member method code with example usage: obj.Encode(batch) and returns: batch,  latent,  kl_divergence, torch,  latent,  kl_divergence