code: def _run_encoder(self, batch):
      src = batch["src"]
      src_len = batch["srclen"]
      batch_size = len(batch["srclen"])
  
      enc_out, enc_final_hs, src_len = self.model.encoder(src, src_len)
  
      if src_len is None:
          assert not isinstance(
              enc_out, tuple
          ), "Ensemble decoding only supported for text data"
          src_len = (
              torch.Tensor(batch_size)
              .type_as(enc_out)
              .long()
              .fill_(enc_out.size(1))
          )
      return src, enc_final_hs, enc_out, src_len
  
imports: import torch
  from torch.nn.functional import log_softmax
  from torch.nn.utils.rnn import pad_sequence
  import codecs
  from time import time
  from math import exp
  from itertools import count, zip_longest
  from copy import deepcopy
  import onmt.model_builder
  import onmt.decoders.ensemble
  from onmt.constants import DefaultTokens
  from onmt.translate.beam_search import BeamSearch, BeamSearchLM
  from onmt.translate.greedy_search import GreedySearch, GreedySearchLM
  from onmt.utils.misc import tile, set_random_seed, report_matrix
  from onmt.utils.alignment import extract_alignment, build_align_pharaoh
  from onmt.modules.copy_generator import collapse_copy_scores
  from onmt.constants import ModelTask
  from onmt.transforms import TransformPipe
label_desc: Use image node_id 4 for calling the Translator obj's underlying member method code with example usage: obj._run_encoder(batch) and returns: src,  enc_final_hs,  enc_out,  src_len