class_docstr: CTC-based grapheme-to-phoneme model.
class_docstr_tok: ["CTC-based","grapheme-to-phoneme","model","."]
docstr: Runs model inference.
  
  Args:
      Config: configuration file to set up DataLoader
  Returns:
      all_preds: model predictions
docstr_tok: ["Runs","model","inference",".","Args",":","Config",":","configuration","file","to","set","up","DataLoader","Returns",":","all_preds",":","model","predictions"]
code: def _infer(
      self,
      config: DictConfig,
  ) -> List[int]:
      """
      Runs model inference.
  
      Args:
          Config: configuration file to set up DataLoader
      Returns:
          all_preds: model predictions
      """
      # store predictions for all queries in a single list
      all_preds = []
      mode = self.training
      try:
          device = "cuda" if torch.cuda.is_available() else "cpu"
          # Switch model to evaluation mode
          self.eval()
          self.to(device)
  
          infer_datalayer = self._setup_infer_dataloader(config)
  
          for batch in infer_datalayer:
              input_ids, attention_mask, input_len = batch
              log_probs, greedy_predictions, encoded_len = self.forward(
                  input_ids=input_ids.to(device),
                  attention_mask=attention_mask
                  if attention_mask is None
                  else attention_mask.to(device),
                  input_len=input_len.to(device),
              )
  
              (
                  preds_str,
                  _,
              ) = self.decoding.ctc_decoder_predictions_tensor(
                  log_probs,
                  decoder_lengths=encoded_len,
                  return_hypotheses=False,
              )
              all_preds.extend(preds_str)
  
              del greedy_predictions
              del log_probs
              del batch
              del input_len
      finally:
          # set mode back to its original value
          self.train(mode=mode)
      return all_preds
  
imports: import os
  import string
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Union
  import torch
  from hydra.utils import instantiate
  from omegaconf import DictConfig, ListConfig, OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from transformers import AutoConfig, AutoModel, AutoTokenizer
  from nemo.collections.tts.g2p.data.ctc import CTCG2PBPEDataset
  from nemo.collections.tts.models.base import G2PModel
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 12 for calling the CTCG2PModel obj's underlying member method code with example usage: obj._infer(config) and returns: all_preds