code: def _collate_fn(self, batch):
      graphemes_batch = [entry["graphemes"] for entry in batch]
  
      # Encode inputs (graphemes)
      # for ByT5 encoder
      if isinstance(self.tokenizer_graphemes, PreTrainedTokenizerBase):
          input_encoding = self.tokenizer_graphemes(
              graphemes_batch,
              padding="longest",
              max_length=self.max_source_len,
              truncation=True,
              return_tensors="pt",
          )
          input_ids, attention_mask = (
              input_encoding.input_ids,
              input_encoding.attention_mask,
          )
          input_len = torch.sum(attention_mask, 1) - 1
      else:
          # for Conformer encoder
          input_ids = [
              self.tokenizer_graphemes.text_to_ids(sentence)
              for sentence in graphemes_batch
          ]
          input_len = [len(entry) for entry in input_ids]
          max_len = max(input_len)
          input_ids = [
              entry + [0] * (max_len - entry_len)
              for entry, entry_len in zip(input_ids, input_len)
          ]
          attention_mask = None  # not used with Conformer encoder
          input_ids = torch.tensor(input_ids)
          input_len = torch.tensor(input_len)
  
      # inference
      if not self.with_labels:
          output = (input_ids, attention_mask, input_len)
      # Encode targets (phonemes)
      else:
          targets = [torch.tensor(entry["target"]) for entry in batch]
          target_lengths = [
              torch.tensor(entry["target_len"]) for entry in batch
          ]
          max_target_len = max(target_lengths)
  
          padded_targets = []
          for target, target_len in zip(targets, target_lengths):
              pad = (0, max_target_len - target_len)
              target_pad = torch.nn.functional.pad(
                  target, pad, value=len(self.labels)
              )
              padded_targets.append(target_pad)
  
          padded_targets = torch.stack(padded_targets)
          target_lengths = torch.stack(target_lengths)
          output = (
              input_ids,
              attention_mask,
              input_len,
              padded_targets,
              target_lengths,
          )
      return output
  
file_path: NeMo/nemo/collections/tts/g2p/data/ctc.py
imports: import json
  import os
  from typing import List
  import torch
  from transformers import PreTrainedTokenizerBase
  from nemo.core.classes import Dataset
  from nemo.utils import logging
label_desc: Use image node_id 5 for calling the CTCG2PBPEDataset obj's underlying member method code with example usage: obj._collate_fn(batch) and returns: output