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 CTC probability calculation.
  
  :param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax)
  :param torch.Tensor ilens: batch of lengths of input sequences (B)
  :param torch.Tensor ys_pad: batch of padded token id sequence tensor (B, Lmax)
  :return: CTC probability (B, Tmax, vocab)
  :rtype: float ndarray
code: def calculate_all_ctc_probs(self, xs_pad, ilens, ys_pad):
      """E2E CTC probability calculation.
  
      :param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax)
      :param torch.Tensor ilens: batch of lengths of input sequences (B)
      :param torch.Tensor ys_pad: batch of padded token id sequence tensor (B, Lmax)
      :return: CTC probability (B, Tmax, vocab)
      :rtype: float ndarray
      """
      probs = None
      if self.mtlalpha == 0:
          return probs
  
      self.eval()
      with torch.no_grad():
          # 0. Frontend
          if self.frontend is not None:
              hs_pad, hlens, mask = self.frontend(
                  to_torch_tensor(xs_pad), ilens
              )
              hs_pad, hlens = self.feature_transform(hs_pad, hlens)
          else:
              hs_pad, hlens = xs_pad, ilens
  
          # 1. Encoder
          hpad, hlens, _ = self.enc(hs_pad, hlens)
  
          # 2. CTC probs
          probs = self.ctc.softmax(hpad).cpu().numpy()
      self.train()
      return probs
  
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.frontends.feature_transform import feature_transform_for
  from espnet.nets.pytorch_backend.frontends.frontend import frontend_for
  from espnet.nets.pytorch_backend.initialization import lecun_normal_init_parameters, set_forget_bias_to_one
  from espnet.nets.pytorch_backend.nets_utils import get_subsample, pad_list, to_device, to_torch_tensor
  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.nets.scorers.ctc import CTCPrefixScorer
  from espnet.utils.fill_missing_args import fill_missing_args
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, ilens, ys_pad) and returns: probs, probs