class_docstr: EulerNet is a context-based recommendation model.
  It can adaptively learn the arbitrary-order feature interactions in a complex vector space
  by conducting space mapping according to Euler's formula. Meanwhile, it can jointly capture
  the explicit and implicit feature interactions in a unified model architecture.
class_docstr_tok: ["EulerNet","is","a","context-based","recommendation","model",".","It","can","adaptively","learn","the","arbitrary-order","feature","interactions","in","a","complex","vector","space","by","conducting","space","mapping","according","to","Euler","'s","formula",".","Meanwhile",",","it","can","jointly","capture","the","explicit","and","implicit","feature","interactions","in","a","unified","model","architecture","."]
code: def calculate_loss(self, interaction):
      label = interaction[self.LABEL]
      output = self.forward(interaction)
      return self.loss(output, label) + self.RegularLoss(
          self.reg_weight
      )
  
file_path: RecBole/recbole/model/context_aware_recommender/eulernet.py
imports: import torch
  import torch.nn
  from torch.nn.init import xavier_normal_, constant_
  from recbole.model.abstract_recommender import ContextRecommender
  from recbole.model.loss import RegLoss
label_desc: Use image node_id 4 for calling the EulerNet obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: unknown