class_docstr: A deep neural network for the reverse diffusion preocess.
class_docstr_tok: ["A","deep","neural","network","for","the","reverse","diffusion","preocess","."]
code: def forward(self, x, timesteps):
      time_emb = timestep_embedding(timesteps, self.time_emb_dim).to(
          x.device
      )
      emb = self.emb_layer(time_emb)
      if self.norm:
          x = F.normalize(x)
      x = self.drop(x)
      h = torch.cat([x, emb], dim=-1)
      h = self.mlp_layers(h)
      return h
  
imports: import enum
  import math
  import copy
  import numpy
  import torch
  import torch.nn.functional
  import torch.nn
  from recbole.model.init import xavier_normal_initialization
  from recbole.utils.enum_type import InputType
  from recbole.model.abstract_recommender import AutoEncoderMixin, GeneralRecommender
  from recbole.model.layers import MLPLayers
  import typing
label_desc: Use image node_id 2 for calling the DNN obj's underlying member method code with example usage: obj.forward(x, timesteps) and returns: h