class_docstr: Guassian Diffusion for large-scale recommendation.
class_docstr_tok: ["Guassian","Diffusion","for","large-scale","recommendation","."]
code: def Decode(self, batch):
      if (
          len(self.out_dims) == 0 or self.n_cate == 1
      ):  # one-layer decoder
          return self.decoder(batch)
      else:
          batch_cate = []
          start = 0
          for i in range(self.n_cate):
              end = start + self.decode_dim[i][0]
              batch_cate.append(batch[:, start:end])
              start = end
          pred_cate = []
          for i in range(self.n_cate):
              pred_cate.append(self.decoder[i](batch_cate[i]))
          pred = torch.cat(tuple(pred_cate), dim=-1)
  
          return pred
  
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 4 for calling the AutoEncoder obj's underlying member method code with example usage: obj.Decode(batch) and returns: self, pred