class_docstr: Base class for encoder decoder RNNT-based models.
class_docstr_tok: ["Base","class","for","encoder","decoder","RNNT-based","models","."]
code: def predict_step(self, batch, batch_idx, dataloader_idx=0):
      signal, signal_len, transcript, transcript_len, sample_id = batch
  
      # forward() only performs encoder forward
      if isinstance(batch, DALIOutputs) and batch.has_processed_signal:
          encoded, encoded_len = self.forward(
              processed_signal=signal,
              processed_signal_length=signal_len,
          )
      else:
          encoded, encoded_len = self.forward(
              input_signal=signal, input_signal_length=signal_len
          )
      del signal
  
      (
          best_hyp_text,
          all_hyp_text,
      ) = self.decoding.rnnt_decoder_predictions_tensor(
          encoder_output=encoded,
          encoded_lengths=encoded_len,
          return_hypotheses=False,
      )
  
      sample_id = sample_id.cpu().detach().numpy()
      return list(zip(sample_id, best_hyp_text))
  
imports: import copy
  import json
  import os
  import tempfile
  from math import ceil
  from typing import Dict, List, Optional, Tuple, 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.data.audio_to_text_dali import AudioToCharDALIDataset, DALIOutputs
  from nemo.collections.asr.data.audio_to_text_lhotse import LhotseSpeechToTextBpeDataset
  from nemo.collections.asr.losses.rnnt import RNNTLoss, resolve_rnnt_default_loss_name
  from nemo.collections.asr.metrics.wer import WER
  from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel
  from nemo.collections.asr.modules.rnnt import RNNTDecoderJoint
  from nemo.collections.asr.parts.mixins import ASRModuleMixin
  from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTDecoding, RNNTDecodingConfig
  from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType
  from nemo.collections.common.data.lhotse import get_lhotse_dataloader_from_config
  from nemo.collections.common.parts.preprocessing.parsers import make_parser
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.classes.mixins import AccessMixin
  from nemo.core.neural_types import AcousticEncodedRepresentation, AudioSignal, LengthsType, NeuralType, SpectrogramType
  from nemo.utils import logging
label_desc: Use image node_id 15 for calling the EncDecRNNTModel obj's underlying member method code with example usage: obj.predict_step(batch, batch_idx, dataloader_idx) and returns: list