class_docstr: 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
      """
      ret = None
      if self.mtlalpha == 0:
          return ret
  
      self.eval()
      with torch.no_grad():
          self.forward(xs_pad, ilens, ys_pad)
      for name, m in self.named_modules():
          if isinstance(m, CTC) and m.probs is not None:
              ret = m.probs.cpu().numpy()
      self.train()
      return ret
  
imports: import logging
  import math
  from argparse import Namespace
  import numpy
  import torch
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.ctc_prefix_score import CTCPrefixScore
  from espnet.nets.e2e_asr_common import ErrorCalculator, end_detect
  from espnet.nets.pytorch_backend.ctc import CTC
  from espnet.nets.pytorch_backend.e2e_asr import CTC_LOSS_THRESHOLD, Reporter
  from espnet.nets.pytorch_backend.nets_utils import get_subsample, make_non_pad_mask, th_accuracy
  from espnet.nets.pytorch_backend.rnn.decoders import CTC_SCORING_RATIO
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
  from espnet.nets.pytorch_backend.transformer.argument import add_arguments_transformer_common
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention, RelPositionMultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.decoder import Decoder
  from espnet.nets.pytorch_backend.transformer.dynamic_conv import DynamicConvolution
  from espnet.nets.pytorch_backend.transformer.dynamic_conv2d import DynamicConvolution2D
  from espnet.nets.pytorch_backend.transformer.encoder import Encoder
  from espnet.nets.pytorch_backend.transformer.initializer import initialize
  from espnet.nets.pytorch_backend.transformer.label_smoothing_loss import LabelSmoothingLoss
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask, target_mask
  from espnet.nets.pytorch_backend.transformer.plot import PlotAttentionReport
  from espnet.nets.scorers.ctc import CTCPrefixScorer
  from espnet.utils.fill_missing_args import fill_missing_args
label_desc: Use image node_id 11 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: ret, ret