code: def _forward_pass_and_assert_neftune_hook(
      module_without_hook, module_with_hook, mode
  ):
      assert module_with_hook.get_input_embeddings()._forward_hooks
      assert (
          not module_without_hook.get_input_embeddings()._forward_hooks
      )
  
      if mode == "train":
          module_without_hook.train()
          module_with_hook.train()
      elif mode == "eval":
          module_without_hook.eval()
          module_with_hook.eval()
  
      input_tensor = torch.tensor([[1, 2, 3]])
      output_tensor_with_noise = (
          module_with_hook.get_input_embeddings()(input_tensor)
      )
      output_tensor_without_noise = (
          module_without_hook.get_input_embeddings()(input_tensor)
      )
  
      if mode == "train":
          assert not torch.equal(
              output_tensor_with_noise, output_tensor_without_noise
          )
      elif mode == "eval":
          assert torch.equal(
              output_tensor_with_noise, output_tensor_without_noise
          )
  
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 18 for calling a global function with example usage: _forward_pass_and_assert_neftune_hook(module_without_hook, module_with_hook, mode) without return types