class_docstr: DiffRec is a generative recommender model which infers users' interaction probabilities in a denoising manner.
  Note that DiffRec simultaneously ranks all items for each user.
  We implement the the DiffRec model with only user dataloader.
class_docstr_tok: ["DiffRec","is","a","generative","recommender","model","which","infers","users","'","interaction","probabilities","in","a","denoising","manner",".","Note","that","DiffRec","simultaneously","ranks","all","items","for","each","user",".","We","implement","the","the","DiffRec","model","with","only","user","dataloader","."]
code: def calculate_loss(self, interaction):
      user = interaction[self.USER_ID]
      x_start = self.get_rating_matrix(user)
  
      batch_size, device = x_start.size(0), x_start.device
      ts, pt = self.sample_timesteps(batch_size, device, "importance")
      noise = torch.randn_like(x_start)
      if self.noise_scale != 0.0:
          x_t = self.q_sample(x_start, ts, noise)
      else:
          x_t = x_start
  
      model_output = self.mlp(x_t, ts)
      target = {
          ModelMeanType.START_X: x_start,
          ModelMeanType.EPSILON: noise,
      }[self.mean_type]
  
      assert model_output.shape == target.shape == x_start.shape
  
      mse = mean_flat((target - model_output) ** 2)
  
      reloss = self.reweight_loss(
          x_start, x_t, mse, ts, target, model_output, device
      )
      self.update_Lt_history(ts, reloss)
  
      # importance sampling
      reloss /= pt
      mean_loss = reloss.mean()
      return mean_loss
  
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 8 for calling the DiffRec obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: mean_loss