code: def _collate_fn(self, batch_data):
      # generate batch
      batch_size = len(batch_data)
      tensorized = OrderedDict()
      for d in batch_data:
          for k, v in d.items():
              tensorized.setdefault(k, []).append(v)
      assert len(tensorized) == 5
  
      tensorized["token_ids"] = torch.LongTensor(
          tensorized["token_ids"]
      )
      tensorized["token_mask"] = torch.LongTensor(
          tensorized["token_mask"]
      )
      tensorized["token_types"] = torch.LongTensor(
          tensorized["token_types"]
      )
      tensorized["seq_len"] = torch.LongTensor(tensorized["seq_len"])
      return tensorized
  
imports: from abc import ABC
  import csv
  from collections import OrderedDict
  import numpy
  import torch
  from torch.utils.data import DataLoader
  from torch.utils.data import Dataset, BatchSampler
  from megatron import print_rank_0, get_args, get_tokenizer
  from megatron.data.biencoder_dataset_utils import make_attention_mask
label_desc: Use image node_id 2 for calling the CustomDataLoader obj's underlying member method code with example usage: obj._collate_fn(batch_data) and returns: tensorized