class_docstr: This is Field-Aware Factorization Machine Module for FFM.
code: def _emb_float_ffm_input(self, float_ffm_input):
      # get float field-aware embeddings
      float_input_x_emb = []
      if len(self.float_feature_names) > 0:
          base, index = torch.split(float_ffm_input, [1, 1], dim=-1)
          index = index.squeeze(-1).long()
          index = index + index.new_tensor(
              self.float_offsets
          ).unsqueeze(0)
          float_input_x_emb = [
              self.float_embeddings[i](index) * base
              for i in range(self.num_fields)
          ]  # [num_fields: [batch_size, num_float_features, emb_dim]]
  
      return float_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 5 for calling the FieldAwareFactorizationMachine obj's underlying member method code with example usage: obj._emb_float_ffm_input(float_ffm_input) and returns: float_input_x_emb