class_docstr: Hybrid CTC/Masked LM Encoder-Decoder model (Mask-CTC)
class_docstr_tok: ["Hybrid","CTC\/Masked","LM","Encoder-Decoder","model","(","Mask-CTC",")"]
code: def batchify_nll(
      self,
      encoder_out: torch.Tensor,
      encoder_out_lens: torch.Tensor,
      ys_pad: torch.Tensor,
      ys_pad_lens: torch.Tensor,
      batch_size: int = 100,
  ):
      raise NotImplementedError
  
imports: import logging
  from contextlib import contextmanager
  from itertools import groupby
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.asr.ctc import CTC
  from espnet2.asr.decoder.mlm_decoder import MLMDecoder
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet2.asr.espnet_model import ESPnetASRModel
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet2.asr.postencoder.abs_postencoder import AbsPostEncoder
  from espnet2.asr.preencoder.abs_preencoder import AbsPreEncoder
  from espnet2.asr.specaug.abs_specaug import AbsSpecAug
  from espnet2.layers.abs_normalize import AbsNormalize
  from espnet2.text.token_id_converter import TokenIDConverter
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet.nets.beam_search import Hypothesis
  from espnet.nets.e2e_asr_common import ErrorCalculator
  from espnet.nets.pytorch_backend.maskctc.add_mask_token import mask_uniform
  from espnet.nets.pytorch_backend.nets_utils import th_accuracy
  from espnet.nets.pytorch_backend.transformer.label_smoothing_loss import LabelSmoothingLoss
label_desc: Use image node_id 5 for calling the MaskCTCModel obj's underlying member method code with example usage: obj.batchify_nll(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, batch_size) without return types