code: def test_batch_encoding_with_labels(self):
      batch = BatchEncoding(
          {"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}
      )
      tensor_batch = batch.convert_to_tensors(tensor_type="np")
      self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
      self.assertEqual(tensor_batch["labels"].shape, (2,))
      # test converting the converted
      with CaptureStderr() as cs:
          tensor_batch = batch.convert_to_tensors(tensor_type="np")
      self.assertFalse(
          len(cs.err), msg=f"should have no warning, but got {cs.err}"
      )
  
      batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
      tensor_batch = batch.convert_to_tensors(
          tensor_type="np", prepend_batch_axis=True
      )
      self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
      self.assertEqual(tensor_batch["labels"].shape, (1,))
  
imports: import os
  import pickle
  import tempfile
  import unittest
  from typing import Callable, Optional
  import numpy
  from transformers import PreTrainedTokenizerFast
  from transformers import BatchEncoding, BertTokenizer, BertTokenizerFast, PreTrainedTokenizer, TensorType, TokenSpan, is_tokenizers_available
  from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
  from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow
label_desc: Use image node_id 10 for calling the TokenizerUtilsTest obj's underlying member method code with example usage: obj.test_batch_encoding_with_labels() without return types