class_docstr: E2E module.
  
  :param int idim: dimension of inputs
  :param int odim: dimension of outputs
  :param Namespace args: argument Namespace containing options
class_docstr_tok: ["E2E","module",".",":","param","int","idim",":","dimension","of","inputs",":","param","int","odim",":","dimension","of","outputs",":","param","Namespace","args",":","argument","Namespace","containing","options"]
docstr: E2E batch beam search.
  
  :param list xs:
      list of input source text feature arrays [(T_1, D), (T_2, D), ...]
  :param Namespace trans_args: argument Namespace containing options
  :param list char_list: list of characters
  :param torch.nn.Module rnnlm: language model module
  :return: N-best decoding results
  :rtype: list
docstr_tok: ["E2E","batch","beam","search",".",":","param","list","xs",":","list","of","input","source","text","feature","arrays","[","(","T_1",",","D",")",",","(","T_2",",","D",")",",","...","]",":","param","Namespace","trans_args",":","argument","Namespace","containing","options",":","param","list","char_list",":","list","of","characters",":","param","torch.nn.Module","rnnlm",":","language","model","module",":","return",":","N-best","decoding","results",":","rtype",":","list"]
code: def translate_batch(self, xs, trans_args, char_list, rnnlm=None):
      """E2E batch beam search.
  
      :param list xs:
          list of input source text feature arrays [(T_1, D), (T_2, D), ...]
      :param Namespace trans_args: argument Namespace containing options
      :param list char_list: list of characters
      :param torch.nn.Module rnnlm: language model module
      :return: N-best decoding results
      :rtype: list
      """
      prev = self.training
      self.eval()
  
      # 1. Encoder
      if self.multilingual:
          ilens = np.fromiter(
              (len(xx[1:]) for xx in xs), dtype=np.int64
          )
          hs = [to_device(self, torch.from_numpy(xx[1:])) for xx in xs]
      else:
          ilens = np.fromiter((len(xx) for xx in xs), dtype=np.int64)
          hs = [to_device(self, torch.from_numpy(xx)) for xx in xs]
      xpad = pad_list(hs, self.pad)
      hs_pad, hlens, _ = self.enc(self.dropout(self.embed(xpad)), ilens)
  
      # 2. Decoder
      hlens = torch.tensor(
          list(map(int, hlens))
      )  # make sure hlens is tensor
      y = self.dec.recognize_beam_batch(
          hs_pad, hlens, None, trans_args, char_list, rnnlm
      )
  
      if prev:
          self.train()
      return y
  
imports: import argparse
  import logging
  import math
  import os
  import chainer
  import nltk
  import numpy
  import torch
  from chainer import reporter
  from espnet.nets.e2e_asr_common import label_smoothing_dist
  from espnet.nets.mt_interface import MTInterface
  from espnet.nets.pytorch_backend.initialization import uniform_init_parameters
  from espnet.nets.pytorch_backend.nets_utils import get_subsample, pad_list, to_device
  from espnet.nets.pytorch_backend.rnn.argument import add_arguments_rnn_attention_common, add_arguments_rnn_decoder_common, add_arguments_rnn_encoder_common
  from espnet.nets.pytorch_backend.rnn.attentions import att_for
  from espnet.nets.pytorch_backend.rnn.decoders import decoder_for
  from espnet.nets.pytorch_backend.rnn.encoders import encoder_for
  from espnet.utils.fill_missing_args import fill_missing_args
label_desc: Use image node_id 10 for calling the E2E obj's underlying member method code with example usage: obj.translate_batch(xs, trans_args, char_list, rnnlm) and returns: y