class_docstr: This is Field-Aware Factorization Machine Module for FFM.
code: def _emb_token_ffm_input(self, token_ffm_input):
      # get token field-aware embeddings
      token_input_x_emb = []
      if len(self.token_feature_names) > 0:
          token_input_x = token_ffm_input + token_ffm_input.new_tensor(
              self.token_offsets
          ).unsqueeze(0)
          token_input_x_emb = [
              self.token_embeddings[i](token_input_x)
              for i in range(self.num_fields)
          ]  # [num_fields: [batch_size, num_token_features, emb_dim]]
  
      return token_input_x_emb
  
imports: import numpy
  import torch
  import torch.nn
  from torch.nn.init import xavier_normal_, constant_
  from recbole.model.abstract_recommender import ContextRecommender
label_desc: Use image node_id 4 for calling the FieldAwareFactorizationMachine obj's underlying member method code with example usage: obj._emb_token_ffm_input(token_ffm_input) and returns: token_input_x_emb