code: def calculate_loss(self, interaction):
      same_item_index = self.same_item_index
      sem_pos_lengths = []
      sem_pos_seqs = []
      dataset = self.dataset
      target_items = interaction[self.ITEM_ID]
      for i, item_id in enumerate(target_items):
          item_id = item_id.item()
          targets_index = same_item_index[item_id]
          lens = len(targets_index)
          if lens == 0:
              print("error")
          while True:
              sample_index = random.choice(targets_index)
              cur_item_list = interaction[self.ITEM_SEQ][i].to("cpu")
              sample_item_list = dataset.inter_feat[self.ITEM_SEQ][
                  sample_index
              ]
              are_equal = torch.equal(cur_item_list, sample_item_list)
              sample_item_length = dataset.inter_feat[
                  self.ITEM_SEQ_LEN
              ][sample_index]
              if not are_equal or lens == 1:
                  sem_pos_lengths.append(sample_item_length)
                  sem_pos_seqs.append(sample_item_list)
                  break
  
      sem_pos_lengths = torch.stack(sem_pos_lengths).to(self.device)
      sem_pos_seqs = torch.stack(sem_pos_seqs).to(self.device)
  
      interaction.update(
          Interaction(
              {
                  "sem_aug": sem_pos_seqs,
                  "sem_aug_lengths": sem_pos_lengths,
              }
          )
      )
  
      item_seq = interaction[self.ITEM_SEQ]
      item_seq_len = interaction[self.ITEM_SEQ_LEN]
      seq_output = self.forward(item_seq, item_seq_len)
      pos_items = interaction[self.POS_ITEM_ID]
      if self.loss_type == "BPR":
          neg_items = interaction[self.NEG_ITEM_ID]
          pos_items_emb = self.item_embedding(pos_items)
          neg_items_emb = self.item_embedding(neg_items)
          pos_score = torch.sum(
              seq_output * pos_items_emb, dim=-1
          )  # [B]
          neg_score = torch.sum(
              seq_output * neg_items_emb, dim=-1
          )  # [B]
          loss = self.loss_fct(pos_score, neg_score)
      else:  # self.loss_type = 'CE'
          test_item_emb = self.item_embedding.weight
          logits = torch.matmul(
              seq_output, test_item_emb.transpose(0, 1)
          )
          loss = self.loss_fct(logits, pos_items)
  
      # Unsupervised NCE
      if self.ssl in ["us", "un"]:
          aug_seq_output = self.forward(item_seq, item_seq_len)
          nce_logits, nce_labels = self.info_nce(
              seq_output,
              aug_seq_output,
              temp=self.tau,
              batch_size=item_seq_len.shape[0],
              sim=self.sim,
          )
  
          loss += self.lmd * self.aug_nce_fct(nce_logits, nce_labels)
  
      # Supervised NCE
      if self.ssl in ["us", "su"]:
          sem_aug, sem_aug_lengths = (
              interaction["sem_aug"],
              interaction["sem_aug_lengths"],
          )
          sem_aug_seq_output = self.forward(sem_aug, sem_aug_lengths)
  
          sem_nce_logits, sem_nce_labels = self.info_nce(
              seq_output,
              sem_aug_seq_output,
              temp=self.tau,
              batch_size=item_seq_len.shape[0],
              sim=self.sim,
          )
  
          loss += self.lmd_sem * self.aug_nce_fct(
              sem_nce_logits, sem_nce_labels
          )
  
      if self.ssl == "us_x":
          aug_seq_output = self.forward(item_seq, item_seq_len)
          sem_aug, sem_aug_lengths = (
              interaction["sem_aug"],
              interaction["sem_aug_lengths"],
          )
  
          sem_aug_seq_output = self.forward(sem_aug, sem_aug_lengths)
          sem_nce_logits, sem_nce_labels = self.info_nce(
              aug_seq_output,
              sem_aug_seq_output,
              temp=self.tau,
              batch_size=item_seq_len.shape[0],
              sim=self.sim,
          )
  
          loss += self.lmd_sem * self.aug_nce_fct(
              sem_nce_logits, sem_nce_labels
          )
  
          # frequency domain loss
          if self.fredom:
              seq_output_f = torch.fft.rfft(
                  seq_output, dim=1, norm="ortho"
              )
              aug_seq_output_f = torch.fft.rfft(
                  aug_seq_output, dim=1, norm="ortho"
              )
              sem_aug_seq_output_f = torch.fft.rfft(
                  sem_aug_seq_output, dim=1, norm="ortho"
              )
              if self.fredom_type in ["us", "un"]:
                  loss += (
                      0.1
                      * abs(seq_output_f - aug_seq_output_f)
                      .flatten()
                      .mean()
                  )
              if self.fredom_type in ["us", "su"]:
                  loss += (
                      0.1
                      * abs(seq_output_f - sem_aug_seq_output_f)
                      .flatten()
                      .mean()
                  )
              if self.fredom_type == "us_x":
                  loss += (
                      0.1
                      * abs(aug_seq_output_f - sem_aug_seq_output_f)
                      .flatten()
                      .mean()
                  )
  
      return loss
  
imports: import random
  import numpy
  import torch
  from torch import nn
  import torch.nn.functional
  import math
  import torch.nn.functional
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
  from recbole.data.interaction import Interaction
label_desc: Use image node_id 10 for calling the FEARec obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss