class_docstr: Base class for encoder decoder CTC-based models.
class_docstr_tok: ["Base","class","for","encoder","decoder","CTC-based","models","."]
code: def predict_step(self, batch, batch_idx, dataloader_idx=0):
      (
          video_signal,
          video_signal_len,
          transcript,
          transcript_len,
          sample_id,
      ) = batch
      log_probs, encoded_len, predictions = self.forward(
          input_video_signal=video_signal,
          input_video_signal_length=video_signal_len,
      )
  
      (
          transcribed_texts,
          _,
      ) = self.wer.decoding.ctc_decoder_predictions_tensor(
          decoder_outputs=log_probs,
          decoder_lengths=encoded_len,
          return_hypotheses=False,
      )
  
      sample_id = sample_id.cpu().detach().numpy()
      return list(zip(sample_id, transcribed_texts))
  
imports: import copy
  import json
  import os
  import tempfile
  from math import ceil
  from typing import Dict, List, Optional, Union
  import torch
  from omegaconf import DictConfig, OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from tqdm.auto import tqdm
  from nemo.collections.asr.data import audio_to_text_dataset
  from nemo.collections.asr.losses.ctc import CTCLoss
  from nemo.collections.asr.metrics.wer import WER
  from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel
  from nemo.collections.asr.parts.mixins import ASRModuleMixin, InterCTCMixin
  from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecoding, CTCDecodingConfig
  from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType
  from nemo.collections.multimodal.speech_cv.data import video_to_text_dataset
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.classes.mixins import AccessMixin
  from nemo.core.neural_types import LabelsType, LengthsType, LogprobsType, NeuralType, VideoSignal
  from nemo.utils import logging
label_desc: Use image node_id 13 for calling the VisualEncDecCTCModel obj's underlying member method code with example usage: obj.predict_step(batch, batch_idx, dataloader_idx) and returns: list