class_docstr: CTC-attention hybrid Encoder-Decoder model
class_docstr_tok: ["CTC-attention","hybrid","Encoder-Decoder","model"]
code: def _calc_batch_ctc_loss(
      self,
      speech: torch.Tensor,
      speech_lengths: torch.Tensor,
      text: torch.Tensor,
      text_lengths: torch.Tensor,
  ):
      if self.ctc is None:
          return
      assert text_lengths.dim() == 1, text_lengths.shape
      # Check that batch_size is unified
      assert (
          speech.shape[0]
          == speech_lengths.shape[0]
          == text.shape[0]
          == text_lengths.shape[0]
      ), (
          speech.shape,
          speech_lengths.shape,
          text.shape,
          text_lengths.shape,
      )
  
      # for data-parallel
      text = text[:, : text_lengths.max()]
  
      # 1. Encoder
      encoder_out, encoder_out_lens = self.encode(
          speech, speech_lengths
      )
      if isinstance(encoder_out, tuple):
          encoder_out = encoder_out[0]
  
      # Calc CTC loss
      do_reduce = self.ctc.reduce
      self.ctc.reduce = False
      loss_ctc = self.ctc(
          encoder_out, encoder_out_lens, text, text_lengths
      )
      self.ctc.reduce = do_reduce
      return loss_ctc
  
imports: import logging
  from contextlib import contextmanager
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.asr.ctc import CTC
  from espnet2.asr.decoder.abs_decoder import AbsDecoder
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  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.asr.transducer.error_calculator import ErrorCalculatorTransducer
  from espnet2.asr_transducer.utils import get_transducer_task_io
  from espnet2.layers.abs_normalize import AbsNormalize
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.train.abs_espnet_model import AbsESPnetModel
  from espnet.nets.e2e_asr_common import ErrorCalculator
  from espnet.nets.pytorch_backend.nets_utils import th_accuracy
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
  from espnet.nets.pytorch_backend.transformer.label_smoothing_loss import LabelSmoothingLoss
label_desc: Use image node_id 11 for calling the ESPnetASRModel obj's underlying member method code with example usage: obj._calc_batch_ctc_loss(speech, speech_lengths, text, text_lengths) and returns: loss_ctc