code: def test_get_realigned_target_and_prediction_tensors_for_inference(
      tokenizer,
  ):
      of_name = "out_1"
      vocab_size = 8
  
      # Scenario 1: Prediction and target tensors have the same length, so nothing should change
      targets = {of_name: torch.tensor([[78, 79, 504, 76, 397, 84, 0]])}
      predictions = {
          of_name: {
              PREDICTIONS: torch.tensor(
                  [[78, 79, 504, 76, 397, 84, 0]], dtype=torch.int64
              ),
              PROBABILITIES: torch.randn(1, 7, vocab_size).to(
                  torch.float32
              ),
              LOGITS: torch.randn(1, 7, vocab_size).to(torch.float32),
          }
      }
      (
          updated_targets,
          updated_predictions,
      ) = get_realigned_target_and_prediction_tensors_for_inference(
          targets, predictions, of_name, tokenizer
      )
  
      assert targets == updated_targets
      assert predictions == updated_predictions
      assert (
          predictions[of_name][PREDICTIONS].shape[1]
          == targets[of_name].shape[1]
      )
      assert (
          predictions[of_name][PROBABILITIES].shape[1]
          == targets[of_name].shape[1]
      )
      assert (
          predictions[of_name][LOGITS].shape[1]
          == targets[of_name].shape[1]
      )
  
      # Scenario 2: Prediction length is longer than the target tensor, so we need to realign the target tensor
      targets = {of_name: torch.tensor([[78, 79, 504, 76, 397, 84, 0]])}
      predictions = {
          of_name: {
              PREDICTIONS: torch.tensor(
                  [[98, 47, 78, 79, 504, 76, 397, 84, 0]],
                  dtype=torch.int64,
              ),
              PROBABILITIES: torch.randn(1, 9, vocab_size).to(
                  torch.float32
              ),
              LOGITS: torch.randn(1, 9, vocab_size).to(torch.float32),
          }
      }
      (
          updated_targets,
          updated_predictions,
      ) = get_realigned_target_and_prediction_tensors_for_inference(
          targets, predictions, of_name, tokenizer
      )
  
      for key in updated_predictions.keys():
          assert torch.equal(
              updated_predictions[key][PREDICTIONS],
              predictions[key][PREDICTIONS],
          )
          assert torch.equal(
              updated_predictions[key][PROBABILITIES],
              predictions[key][PROBABILITIES],
          )
          assert torch.equal(
              updated_predictions[key][LOGITS], predictions[key][LOGITS]
          )
  
      assert torch.equal(
          updated_targets[of_name],
          torch.tensor([[78, 79, 504, 76, 397, 84, 0, 1, 1]]),
      )
  
      # Scenario 3: Target length is longer than the prediction tensor, so we need to realign them
      targets = {
          of_name: torch.tensor([[98, 47, 78, 79, 504, 76, 397, 84, 0]])
      }
      predictions = {
          of_name: {
              PREDICTIONS: torch.tensor(
                  [[78, 79, 504, 76, 397, 84, 0]], dtype=torch.int64
              ),
              PROBABILITIES: torch.randn(1, 7, vocab_size).to(
                  torch.float32
              ),
              LOGITS: torch.randn(1, 7, vocab_size).to(torch.float32),
          }
      }
      (
          updated_targets,
          updated_predictions,
      ) = get_realigned_target_and_prediction_tensors_for_inference(
          targets, predictions, of_name, tokenizer
      )
  
      assert torch.equal(updated_targets[of_name], targets[of_name])
  
      assert torch.equal(
          updated_predictions[of_name][PREDICTIONS],
          torch.tensor([[78, 79, 504, 76, 397, 84, 0, 1, 1]]),
      )
      assert (
          updated_predictions[of_name][PROBABILITIES].shape[1]
          == targets[of_name].shape[1]
      )
      assert (
          updated_predictions[of_name][LOGITS].shape[1]
          == targets[of_name].shape[1]
      )
  
      assert torch.equal(
          updated_predictions[of_name][PROBABILITIES][0][-1],
          torch.zeros(vocab_size),
      )
      assert torch.equal(
          updated_predictions[of_name][PROBABILITIES][0][-2],
          torch.zeros(vocab_size),
      )
      assert not torch.equal(
          updated_predictions[of_name][PROBABILITIES][0][-3],
          torch.zeros(vocab_size),
      )
  
      assert torch.equal(
          updated_predictions[of_name][LOGITS][0][-1],
          torch.zeros(vocab_size),
      )
      assert torch.equal(
          updated_predictions[of_name][LOGITS][0][-2],
          torch.zeros(vocab_size),
      )
      assert not torch.equal(
          updated_predictions[of_name][LOGITS][0][-3],
          torch.zeros(vocab_size),
      )
  
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 16 for calling a global function with example usage: test_get_realigned_target_and_prediction_tensors_for_inference(tokenizer) without return types