code: def test_batch_size(self):
      # 1 per batch
      aug = naw.ContextualWordEmbsAug(
          model_path="distilbert-base-uncased",
          model_type="bert",
          batch_size=1,
      )
      aug_data = aug.augment(self.texts)
      self.assertEqual(len(aug_data), len(self.texts))
  
      # batch size = input size
      aug = naw.ContextualWordEmbsAug(
          model_path="distilbert-base-uncased",
          model_type="bert",
          batch_size=len(self.texts),
      )
      aug_data = aug.augment(self.texts)
      self.assertEqual(len(aug_data), len(self.texts))
  
      # batch size > input size
      aug = naw.ContextualWordEmbsAug(
          model_path="distilbert-base-uncased",
          model_type="bert",
          batch_size=len(self.texts) + 1,
      )
      aug_data = aug.augment(self.texts)
      self.assertEqual(len(aug_data), len(self.texts))
  
      # input size > batch size
      aug = naw.ContextualWordEmbsAug(
          model_path="distilbert-base-uncased",
          model_type="bert",
          batch_size=2,
      )
      aug_data = aug.augment(self.texts * 2)
      self.assertEqual(len(aug_data), len(self.texts) * 2)
  
imports: import unittest
  import os
  from dotenv import load_dotenv
  import torch
  import nlpaug.augmenter.word
  import nlpaug.model.lang_models
label_desc: Use image node_id 9 for calling the TestContextualWordEmbsAug obj's underlying member method code with example usage: obj.test_batch_size() without return types