class_docstr: E2E module.
  
  :param List idims: List of dimensions of inputs
  :param int odim: dimension of outputs
  :param Namespace args: argument Namespace containing options
docstr: E2E CTC probability calculation.
  
  :param List xs_pad_list: list of batch (torch.Tensor) of padded input sequences
                          [(B, Tmax_1, idim), (B, Tmax_2, idim),..]
  :param List ilens_list:
      list of batch (torch.Tensor) of lengths of input sequences [(B), (B), ..]
  :param torch.Tensor ys_pad:
      batch of padded character id sequence tensor (B, Lmax)
  :return: CTC probability (B, Tmax, vocab)
  :rtype: float ndarray or list
code: def calculate_all_ctc_probs(self, xs_pad_list, ilens_list, ys_pad):
      """E2E CTC probability calculation.
  
      :param List xs_pad_list: list of batch (torch.Tensor) of padded input sequences
                              [(B, Tmax_1, idim), (B, Tmax_2, idim),..]
      :param List ilens_list:
          list of batch (torch.Tensor) of lengths of input sequences [(B), (B), ..]
      :param torch.Tensor ys_pad:
          batch of padded character id sequence tensor (B, Lmax)
      :return: CTC probability (B, Tmax, vocab)
      :rtype: float ndarray or list
      """
      probs_list = [None]
      if self.mtlalpha == 0:
          return probs_list
  
      self.eval()
      probs_list = []
      with torch.no_grad():
          # 1. Encoder
          for idx in range(self.num_encs):
              hs_pad, hlens, _ = self.enc[idx](
                  xs_pad_list[idx], ilens_list[idx]
              )
  
              # 2. CTC loss
              ctc_idx = 0 if self.share_ctc else idx
              probs = self.ctc[ctc_idx].softmax(hs_pad).cpu().numpy()
              probs_list.append(probs)
      self.train()
      return probs_list
  
imports: import argparse
  import logging
  import math
  import os
  from itertools import groupby
  import chainer
  import numpy
  import torch
  from chainer import reporter
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.e2e_asr_common import label_smoothing_dist
  from espnet.nets.pytorch_backend.ctc import ctc_for
  from espnet.nets.pytorch_backend.nets_utils import get_subsample, pad_list, to_device, to_torch_tensor
  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, encoder_for
  from espnet.nets.scorers.ctc import CTCPrefixScorer
  from espnet.utils.cli_utils import strtobool
label_desc: Use image node_id 15 for calling the E2E obj's underlying member method code with example usage: obj.calculate_all_ctc_probs(xs_pad_list, ilens_list, ys_pad) and returns: probs_list, probs_list