code: def test_roberta_tensorizer_input_exceeds_max_seq_len(self):
      roberta = self._mock_roberta_tensorizer(max_seq_len=28)
  
      rand_tokens = self.get_rand_tokens([25, 15, 5, 30])
      expected_batch_size = 4
      expected_token_size = 28
  
      (
          tokens,
          pad_mask,
          start_indices,
          end_indices,
          positions,
      ) = roberta.tensorize(tokens=squeeze_2d(rand_tokens))
  
      sig_idxs = [1 + len(t) + 1 for t in rand_tokens]
      expected_token_padding = [
          expected_token_size - num for num in sig_idxs
      ]
  
      padding_key = {
          tokens: 200,
          pad_mask: 0,
          start_indices: 0,
          end_indices: 0,
          positions: 0,
      }
  
      # verify padding
      for output_tensor, pad_val in padding_key.items():
          self.validate_padding(
              output_tensor,
              pad_val,
              significant_idxs=sig_idxs,
              expected_batch_size=expected_batch_size,
              expected_token_padding=expected_token_padding,
          )
  
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 15 for calling the TensorizerTest obj's underlying member method code with example usage: obj.test_roberta_tensorizer_input_exceeds_max_seq_len() without return types