class_docstr: E2E module for chainer backend.
  
  Args:
      idim (int): Dimension of the inputs.
      odim (int): Dimension of the outputs.
      args (parser.args): Training config.
      flag_return (bool): If True, train() would return
          additional metrics in addition to the training
          loss.
docstr: Get total subsampling factor.
docstr_tok: ["Get","total","subsampling","factor","."]
code: def get_total_subsampling_factor(self):
      """Get total subsampling factor."""
      return self.enc.conv_subsampling_factor * int(
          np.prod(self.subsample)
      )
  
imports: import logging
  import math
  import chainer
  import numpy
  from chainer import reporter
  from espnet.nets.chainer_backend.asr_interface import ChainerASRInterface
  from espnet.nets.chainer_backend.ctc import ctc_for
  from espnet.nets.chainer_backend.rnn.attentions import att_for
  from espnet.nets.chainer_backend.rnn.decoders import decoder_for
  from espnet.nets.chainer_backend.rnn.encoders import encoder_for
  from espnet.nets.e2e_asr_common import label_smoothing_dist
  from espnet.nets.pytorch_backend.e2e_asr import E2E
  from espnet.nets.pytorch_backend.nets_utils import get_subsample
label_desc: Use image node_id 2 for calling the E2E obj's underlying member method code with example usage: obj.get_total_subsampling_factor() and returns: unknown