class_docstr: CTC-based grapheme-to-phoneme model.
class_docstr_tok: ["CTC-based","grapheme-to-phoneme","model","."]
code: def forward(self, input_ids, attention_mask, input_len):
      if self.mode == "byt5":
          encoded_input = self.encoder(
              input_ids=input_ids, attention_mask=attention_mask
          )[0]
          encoded_len = input_len
          # encoded_input = [B, seq_len, hid_dim]
          # swap seq_len and hid_dim dimensions to get [B, hid_dim, seq_len]
          encoded_input = encoded_input.transpose(1, 2)
      elif self.mode == "conformer_bpe":
          input_embedding = self.embedding(input_ids)
          input_embedding = input_embedding.transpose(1, 2)
          encoded_input, encoded_len = self.encoder(
              audio_signal=input_embedding, length=input_len
          )
      else:
          raise ValueError(
              f"{self.mode} is not supported. Choose from {self.supported_modes}"
          )
  
      log_probs = self.decoder(encoder_output=encoded_input)
      greedy_predictions = log_probs.argmax(dim=-1, keepdim=False)
      return log_probs, greedy_predictions, encoded_len
  
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 4 for calling the CTCG2PModel obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, input_len) and returns: log_probs,  greedy_predictions,  encoded_len