class_docstr: E2E module.
  
  Args:
      idim (int): Input dimmensions.
      odim (int): Output dimmensions.
      args (Namespace): Training config.
      ignore_id (int, optional): Id for ignoring a character.
      flag_return (bool, optional): If true, return a list with (loss,
      loss_ctc, loss_att, acc) in forward. Otherwise, return loss.
class_docstr_tok: ["E2E","module",".","Args",":","idim","(","int",")",":","Input","dimmensions",".","odim","(","int",")",":","Output","dimmensions",".","args","(","Namespace",")",":","Training","config",".","ignore_id","(","int",",","optional",")",":","Id","for","ignoring","a","character",".","flag_return","(","bool",",","optional",")",":","If","true",",","return","a","list","with","(","loss",",","loss_ctc",",","loss_att",",","acc",")","in","forward",".","Otherwise",",","return","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.encoder.conv_subsampling_factor * int(
          np.prod(self.subsample)
      )
  
imports: import logging
  import math
  from argparse import Namespace
  from distutils.util import strtobool
  import chainer
  import chainer.functions
  import numpy
  from chainer import reporter
  from espnet.nets.chainer_backend.asr_interface import ChainerASRInterface
  from espnet.nets.chainer_backend.transformer import ctc
  from espnet.nets.chainer_backend.transformer.attention import MultiHeadAttention
  from espnet.nets.chainer_backend.transformer.decoder import Decoder
  from espnet.nets.chainer_backend.transformer.encoder import Encoder
  from espnet.nets.chainer_backend.transformer.label_smoothing_loss import LabelSmoothingLoss
  from espnet.nets.chainer_backend.transformer.training import CustomConverter, CustomParallelUpdater, CustomUpdater
  from espnet.nets.ctc_prefix_score import CTCPrefixScore
  from espnet.nets.e2e_asr_common import ErrorCalculator, end_detect
  from espnet.nets.pytorch_backend.nets_utils import get_subsample
  from espnet.nets.pytorch_backend.transformer.plot import PlotAttentionReport
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