code: def _encode(self, encoder_input):
      encoder_outs = []
      incremental_states = {}
      for model in self.models:
          if not self.retain_dropout:
              model.eval()
          if isinstance(model.decoder, FairseqIncrementalDecoder):
              incremental_states[model] = {}
          else:
              incremental_states[model] = None
          encoder_out = model.encoder(*encoder_input)
          encoder_outs.append(encoder_out)
      return encoder_outs, incremental_states
  
imports: import math
  from typing import List, Tuple
  import torch
  from fairseq import search, utils
  from fairseq.models import FairseqIncrementalDecoder
  from pytorch_translate import utils
  from torch import Tensor
label_desc: Use image node_id 13 for calling the SequenceGenerator obj's underlying member method code with example usage: obj._encode(encoder_input) and returns: encoder_outs,  incremental_states