text: 
  import torch
  
  
  def concat_fun(inputs, axis=-1):
      if len(inputs) == 1:
          return inputs[0]
      else:
          return torch.cat(inputs, dim=axis)
  
  def combined_dnn_input(sparse_embedding_list, dense_value_list=[]):
      if len(sparse_embedding_list) > 0 and len(dense_value_list) > 0:
          sparse_dnn_input = torch.flatten(
              torch.cat(sparse_embedding_list, dim=-1), start_dim=1)
          dense_dnn_input = torch.flatten(
              torch.cat(dense_value_list, dim=-1), start_dim=1)
          return concat_fun([sparse_dnn_input, dense_dnn_input])
      elif len(sparse_embedding_list) > 0:
          return torch.flatten(torch.cat(sparse_embedding_list, dim=-1), start_dim=1)
      elif len(dense_value_list) > 0:
          return torch.flatten(torch.cat(dense_value_list, dim=-1), start_dim=1)
      else:
          raise NotImplementedError