code: def test_batch_size(self):
      # 1 per batch
      aug = nas.LambadaAug(
          model_dir=self.model_dir, threshold=None, batch_size=1
      )
      aug_data = aug.augment(self.data)
      self.assertEqual(len(aug_data), len(self.data))
  
      # batch size = input size
      aug = nas.LambadaAug(
          model_dir=self.model_dir,
          threshold=None,
          batch_size=len(self.data),
      )
      aug_data = aug.augment(self.data)
      self.assertEqual(len(aug_data), len(self.data))
  
      # batch size < input size
      aug = nas.LambadaAug(
          model_dir=self.model_dir,
          threshold=None,
          batch_size=len(self.data) + 1,
      )
      aug_data = aug.augment(self.data)
      self.assertEqual(len(aug_data), len(self.data))
  
      # input size > batch size
      aug = nas.LambadaAug(
          model_dir=self.model_dir, threshold=None, batch_size=2
      )
      aug_data = aug.augment(self.data * 2)
      self.assertEqual(len(aug_data), len(self.data) * 2)
  
file_path: nlpaug/test/augmenter/sentence/test_lambada.py
imports: import unittest
  import os
  import torch
  import pandas
  from dotenv import load_dotenv
  import nlpaug.augmenter.sentence
label_desc: Use image node_id 2 for calling the TestLambadaAug obj's underlying member method code with example usage: obj.test_batch_size() without return types