code: def test_has_padding_token_with_padding_tokens(tokenizer):
      input_sentence = "This is an example sentence."
      input_ids = tokenizer([input_sentence])
      input_ids["input_ids"] = torch.tensor(input_ids["input_ids"])
      padded_input_ids = torch.nn.functional.pad(
          input_ids["input_ids"],
          (10 - len(input_ids["input_ids"]), 1),
          value=1,
      )
  
      assert has_padding_token(padded_input_ids, tokenizer)
  
imports: import pytest
  import torch
  from transformers import AutoConfig, AutoModelForCausalLM
  from ludwig.constants import LOGITS, PREDICTIONS, PROBABILITIES
  from ludwig.modules.training_hooks import NEFTuneHook
  from ludwig.utils.llm_utils import add_left_padding, create_attention_mask, FALLBACK_CONTEXT_LEN, find_last_matching_index, generate_merged_ids, get_context_len, get_realigned_target_and_prediction_tensors_for_inference, has_padding_token, pad_target_tensor_for_fine_tuning, remove_left_padding
  from ludwig.utils.tokenizers import HFTokenizer
label_desc: Use image node_id 4 for calling a global function with example usage: test_has_padding_token_with_padding_tokens(tokenizer) without return types