class_docstr: DSSM respectively expresses user and item as low dimensional vectors with mlp layers,
  and uses cosine distance to calculate the distance between the two semantic vectors.
code: def forward(self, interaction):
      # user_sparse_embedding shape: [batch_size, user_token_seq_field_num + user_token_field_num , embed_dim] or None
      # user_dense_embedding shape: [batch_size, user_float_field_num] or [batch_size, user_float_field_num, embed_dim] or None
      # item_sparse_embedding shape: [batch_size, item_token_seq_field_num + item_token_field_num , embed_dim] or None
      # item_dense_embedding shape: [batch_size, item_float_field_num] or [batch_size, item_float_field_num, embed_dim] or None
      embed_result = self.double_tower_embed_input_fields(interaction)
      user_sparse_embedding, user_dense_embedding = embed_result[:2]
      item_sparse_embedding, item_dense_embedding = embed_result[2:]
  
      user = []
      if user_sparse_embedding is not None:
          user.append(user_sparse_embedding)
      if (
          user_dense_embedding is not None
          and len(user_dense_embedding.shape) == 3
      ):
          user.append(user_dense_embedding)
  
      embed_user = torch.cat(user, dim=1)
  
      item = []
      if item_sparse_embedding is not None:
          item.append(item_sparse_embedding)
      if (
          item_dense_embedding is not None
          and len(item_dense_embedding.shape) == 3
      ):
          item.append(item_dense_embedding)
  
      embed_item = torch.cat(item, dim=1)
  
      batch_size = embed_item.shape[0]
      user_dnn_out = self.user_mlp_layers(
          embed_user.view(batch_size, -1)
      )
      item_dnn_out = self.item_mlp_layers(
          embed_item.view(batch_size, -1)
      )
      score = torch.cosine_similarity(user_dnn_out, item_dnn_out, dim=1)
      return score.squeeze(-1)
  
file_path: RecBole/recbole/model/context_aware_recommender/dssm.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.layers import MLPLayers
label_desc: Use image node_id 3 for calling the DSSM obj's underlying member method code with example usage: obj.forward(interaction) and returns: score