docstr: Augments source and target ids in the batches with padding symbol
  to make the lengths of all sentences in the batches equal.
docstr_tok: ["Augments","source","and","target","ids","in","the","batches","with","padding","symbol","to","make","the","lengths","of","all","sentences","in","the","batches","equal","."]
code: def pad_batches(self, ids):
      """
      Augments source and target ids in the batches with padding symbol
      to make the lengths of all sentences in the batches equal.
      """
  
      batches = []
      for batch_elem_len, batch_sent_ids in zip(
          self.batch_elem_lengths, self.batch_sent_ids
      ):
          batch = self.tokenizer.pad_id * np.ones(
              (len(batch_sent_ids), batch_elem_len), dtype=np.int64
          )
          for i, sentence_idx in enumerate(batch_sent_ids):
              batch[i][: len(ids[sentence_idx])] = ids[sentence_idx]
          batches.append(batch)
      return batches
  
imports: import io
  import json
  import logging
  import pickle
  from collections import OrderedDict
  from typing import Any
  import braceexpand
  import numpy
  import webdataset
  from torch.utils.data import IterableDataset
  from nemo.collections.nlp.data.data_utils.data_preprocessing import dataset_to_ids
  from nemo.core import Dataset
label_desc: Use image node_id 4 for calling the SentenceDataset obj's underlying member method code with example usage: obj.pad_batches(ids) and returns: batches