code: def test_make_batch_with_pre_tokenized_input(self) -> None:
      # case: pass in both raw text and pre-tokenized and the return is
      #       levearging pre-tokenized based on the mock tokenizer
      mega_batch = [
          (None, None, [["1", "1", "1"]], None, None, 1),
          (None, None, [["1", "1"]], None, None, 2),
      ]
      batches = self.module.make_batch(mega_batch, {})
      self.assertEqual(
          batches,
          [
              [
                  (None, None, [["1", "1"]], None, None, 2),
                  (None, None, [["1", "1", "1"]], None, None, 1),
              ],
          ],
      )
  
      # case: if only pass in raw text the return is only considering the
      #       raw text based on the mock tokenizer
      mega_batch = [
          (["1 1 1"], None, None, None, None, 1),
          (["1 1"], None, None, None, None, 2),
      ]
      batches = self.module.make_batch(mega_batch, {})
      self.assertEqual(
          batches,
          [
              [
                  (["1 1"], None, None, None, None, 2),
                  (["1 1 1"], None, None, None, None, 1),
              ],
          ],
      )
  
imports: import random
  import string
  import unittest
  from typing import List, Optional, Tuple
  import torch
  from pytext.data.tensorizers import TensorizerScriptImpl
  from pytext.torchscript.module import PyTextEmbeddingModule, ScriptPyTextEmbeddingModule
  from pytext.torchscript.tensorizer.tensorizer import ScriptTensorizer, VocabLookup
  from pytext.torchscript.utils import pad_2d_mask, ScriptBatchInput
  from pytext.torchscript.vocab import ScriptVocabulary
  from pytorch.text.fb.utils.transform_utils import pad_2d
label_desc: Use image node_id 14 for calling the ScriptPyTextEmbeddingModuleTest obj's underlying member method code with example usage: obj.test_make_batch_with_pre_tokenized_input() without return types