class_docstr: The network architecture of the NextItNet model is formed of a stack of holed convolutional layers, which can
  efficiently increase the receptive fields without relying on the pooling operation.
  Also residual block structure is used to ease the optimization for much deeper networks.
  
  Note:
      As paper said, for comparison purpose, we only predict the next one item in our evaluation,
      and then stop the generating process. Although the number of parameters in residual block (a) is less
      than it in residual block (b), the performance of b is better than a.
      So in our model, we use residual block (b).
      In addition, when dilations is not equal to 1, the training may be slow. To  speed up the efficiency, please set the parameters "reproducibility" False.
class_docstr_tok: ["The","network","architecture","of","the","NextItNet","model","is","formed","of","a","stack","of","holed","convolutional","layers",",","which","can","efficiently","increase","the","receptive","fields","without","relying","on","the","pooling","operation",".","Also","residual","block","structure","is","used","to","ease","the","optimization","for","much","deeper","networks",".","Note",":","As","paper","said",",","for","comparison","purpose",",","we","only","predict","the","next","one","item","in","our","evaluation",",","and","then","stop","the","generating","process",".","Although","the","number","of","parameters","in","residual","block","(","a",")","is","less","than","it","in","residual","block","(","b",")",",","the","performance","of","b","is","better","than","a",".","So","in","our","model",",","we","use","residual","block","(","b",")",".","In","addition",",","when","dilations","is","not","equal","to","1",",","the","training","may","be","slow",".","To","speed","up","the","efficiency",",","please","set","the","parameters","``","reproducibility","''","False","."]
code: def calculate_loss(self, interaction):
      item_seq = interaction[self.ITEM_SEQ]
      # item_seq_len = interaction[self.ITEM_SEQ_LEN]
      seq_output = self.forward(item_seq)
      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)
      reg_loss = self.reg_loss(
          [self.item_embedding.weight, self.final_layer.weight]
      )
      loss = loss + self.reg_weight * reg_loss + self.reg_loss_rb()
      return loss
  
imports: import numpy
  import torch
  from torch import nn
  from torch.nn import functional
  from torch.nn.init import uniform_, xavier_normal_, constant_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import RegLoss, BPRLoss
label_desc: Use image node_id 5 for calling the NextItNet obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss