code: def collate_test_dataset(
      batch: List[
          Tuple[
              np.ndarray,
              np.ndarray,
              np.ndarray,
              np.ndarray,
              np.ndarray,
              np.ndarray,
              np.ndarray,
              np.ndarray,
          ]
      ],
      pad_token_id: int,
  ) -> Tuple[
      torch.LongTensor,
      torch.LongTensor,
      torch.LongTensor,
      torch.LongTensor,
      torch.LongTensor,
      torch.LongTensor,
      torch.LongTensor,
      torch.LongTensor,
  ]:
      """collate batch of test items
      Args:
          batch: A list of tuples of (input_ids, input_mask, segment_ids, input_ids_for_subwords, input_mask_for_subwords, segment_ids_for_subwords, character_pos_to_subword_pos, fragment_indices).
          pad_token_id: integer id of padding token (to use in padded_input_ids, padded_input_ids_for_subwords)
      """
      max_length = 0
      max_length_for_subwords = 0
      max_length_for_fragment_indices = 1  # to avoid empty tensor
      for (
          input_ids,
          input_mask,
          segment_ids,
          input_ids_for_subwords,
          input_mask_for_subwords,
          segment_ids_for_subwords,
          character_pos_to_subword_pos,
          fragment_indices,
      ) in batch:
          if len(input_ids) > max_length:
              max_length = len(input_ids)
          if len(input_ids_for_subwords) > max_length_for_subwords:
              max_length_for_subwords = len(input_ids_for_subwords)
          if len(fragment_indices) > max_length_for_fragment_indices:
              max_length_for_fragment_indices = len(fragment_indices)
  
      padded_input_ids = []
      padded_input_mask = []
      padded_segment_ids = []
      padded_input_ids_for_subwords = []
      padded_input_mask_for_subwords = []
      padded_segment_ids_for_subwords = []
      padded_character_pos_to_subword_pos = []
      padded_fragment_indices = []
      for (
          input_ids,
          input_mask,
          segment_ids,
          input_ids_for_subwords,
          input_mask_for_subwords,
          segment_ids_for_subwords,
          character_pos_to_subword_pos,
          fragment_indices,
      ) in batch:
          if len(input_ids) < max_length:
              pad_length = max_length - len(input_ids)
              padded_input_ids.append(
                  np.pad(
                      input_ids,
                      pad_width=[0, pad_length],
                      constant_values=pad_token_id,
                  )
              )
              padded_input_mask.append(
                  np.pad(
                      input_mask,
                      pad_width=[0, pad_length],
                      constant_values=0,
                  )
              )
              padded_segment_ids.append(
                  np.pad(
                      segment_ids,
                      pad_width=[0, pad_length],
                      constant_values=0,
                  )
              )
              padded_character_pos_to_subword_pos.append(
                  np.pad(
                      character_pos_to_subword_pos,
                      pad_width=[0, pad_length],
                      constant_values=0,
                  )
              )
          else:
              padded_input_ids.append(input_ids)
              padded_input_mask.append(input_mask)
              padded_segment_ids.append(segment_ids)
              padded_character_pos_to_subword_pos.append(
                  character_pos_to_subword_pos
              )
  
          if len(input_ids_for_subwords) < max_length_for_subwords:
              pad_length = max_length_for_subwords - len(
                  input_ids_for_subwords
              )
              padded_input_ids_for_subwords.append(
                  np.pad(
                      input_ids_for_subwords,
                      pad_width=[0, pad_length],
                      constant_values=pad_token_id,
                  )
              )
              padded_input_mask_for_subwords.append(
                  np.pad(
                      input_mask_for_subwords,
                      pad_width=[0, pad_length],
                      constant_values=0,
                  )
              )
              padded_segment_ids_for_subwords.append(
                  np.pad(
                      segment_ids_for_subwords,
                      pad_width=[0, pad_length],
                      constant_values=0,
                  )
              )
          else:
              padded_input_ids_for_subwords.append(
                  input_ids_for_subwords
              )
              padded_input_mask_for_subwords.append(
                  input_mask_for_subwords
              )
              padded_segment_ids_for_subwords.append(
                  segment_ids_for_subwords
              )
  
          if len(fragment_indices) < max_length_for_fragment_indices:
              # we use [0, 1, 0] as padding value for fragment_indices, it corresponds to [CLS] token, which is ignored and won't affect anything
              p = np.zeros(
                  (max_length_for_fragment_indices, 3), dtype=int
              )
              p[:, 1] = 1
              p[:, 2] = 0
              padded_fragment_indices.append(p)
              if len(fragment_indices) > 0:
                  padded_fragment_indices[-1][
                      : fragment_indices.shape[0],
                      : fragment_indices.shape[1],
                  ] = fragment_indices  # copy actual fragment_indices to the beginning
          else:
              padded_fragment_indices.append(fragment_indices)
  
      return (
          torch.LongTensor(np.array(padded_input_ids)),
          torch.LongTensor(np.array(padded_input_mask)),
          torch.LongTensor(np.array(padded_segment_ids)),
          torch.LongTensor(np.array(padded_input_ids_for_subwords)),
          torch.LongTensor(np.array(padded_input_mask_for_subwords)),
          torch.LongTensor(np.array(padded_segment_ids_for_subwords)),
          torch.LongTensor(
              np.array(padded_character_pos_to_subword_pos)
          ),
          torch.LongTensor(np.array(padded_fragment_indices)),
      )
  
imports: import pickle
  from io import BytesIO
  from typing import Dict, List, Optional, Tuple
  import braceexpand
  import numpy
  import torch
  import webdataset
  from nemo.collections.nlp.data.spellchecking_asr_customization.bert_example import BertExampleBuilder
  from nemo.core.classes.dataset import Dataset, IterableDataset
  from nemo.core.neural_types import ChannelType, IntType, LabelsType, MaskType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: collate_test_dataset(batch, pad_token_id) and returns: torch,  torch,  torch,  torch,  torch,  torch,  torch,  torch