class_docstr: Factorization Machine considers the second-order interaction with features to predict the final score.
class_docstr_tok: ["Factorization","Machine","considers","the","second-order","interaction","with","features","to","predict","the","final","score","."]
code: def forward(self, interaction):
      fm_all_embeddings = self.concat_embed_input_fields(
          interaction
      )  # [batch_size, num_field, embed_dim]
      y = self.first_order_linear(interaction) + self.fm(
          fm_all_embeddings
      )
      return y.squeeze(-1)
  
file_path: RecBole/recbole/model/context_aware_recommender/fm.py
imports: import torch.nn
  from torch.nn.init import xavier_normal_
  from recbole.model.abstract_recommender import ContextRecommender
  from recbole.model.layers import BaseFactorizationMachine
label_desc: Use image node_id 3 for calling the FM obj's underlying member method code with example usage: obj.forward(interaction) and returns: y