code: def validate_padding(
      self,
      output_tensor: torch.Tensor,
      pad_val: int,
      significant_idxs: List[int],
      expected_batch_size: int,
      expected_token_padding: List[int],
  ):
      output_list = output_tensor.tolist()
      self.assertEqual(len(output_list), expected_batch_size)
      for i in range(expected_batch_size):
          # indices that store significant values
          actual_idxs = significant_idxs[i]
          # create a list of size(expected_padding) filled with the value of pad_idx
          expected_padding_list = [pad_val] * expected_token_padding[i]
          # slice the given output_list from the last substantive index
          actual_padding_list = output_list[i][actual_idxs:]
          self.assertEqual(expected_padding_list, actual_padding_list)
  
imports: import random
  import unittest
  from typing import List, Tuple
  import torch
  from pytext.torchscript.tensorizer import ScriptBERTTensorizer, ScriptRoBERTaTensorizer, ScriptRoBERTaTensorizerWithIndices, ScriptXLMTensorizer
  from pytext.torchscript.tensorizer.tensorizer import VocabLookup
  from pytext.torchscript.tokenizer import ScriptDoNothingTokenizer
  from pytext.torchscript.tokenizer.tokenizer import ScriptTokenizerBase
  from pytext.torchscript.utils import squeeze_1d, squeeze_2d
  from pytext.torchscript.vocab import ScriptVocabulary
label_desc: Use image node_id 7 for calling the TensorizerTest obj's underlying member method code with example usage: obj.validate_padding(output_tensor, pad_val, significant_idxs, expected_batch_size, expected_token_padding) without return types