code: def test_create_attention_mask_last_token_padding(tokenizer):
      input_ids = torch.tensor([3, 4, tokenizer.pad_token_id])
      attention_mask = create_attention_mask(input_ids, tokenizer)
      assert attention_mask[-1] == 1
  
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 8 for calling a global function with example usage: test_create_attention_mask_last_token_padding(tokenizer) without return types