code: def test_pad_target_tensor_for_fine_tuning():
      of_name = "out_1"
      prediction = {
          of_name: {
              PREDICTIONS: torch.tensor(
                  [
                      [
                          764,
                          764,
                          764,
                          764,
                          764,
                          764,
                          764,
                          578,
                          619,
                          841,
                          182,
                          905,
                          483,
                          764,
                      ]
                  ]
              )
          }
      }
  
      # Scenario 1: Entire target tensor was passed into model inputs
      model_input = torch.tensor(
          [[0, 0, 24, 52, 654, 529, 221, 78, 79, 504, 76, 397, 84, 0]]
      )
      target = {of_name: torch.tensor([[78, 79, 504, 76, 397, 84, 0]])}
      expected_target = {
          of_name: torch.tensor(
              [
                  [
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      78,
                      79,
                      504,
                      76,
                      397,
                      84,
                      0,
                  ]
              ]
          )
      }
      updated_targets = pad_target_tensor_for_fine_tuning(
          target, prediction, model_input, of_name
      )
      assert torch.equal(
          expected_target[of_name], updated_targets[of_name]
      )
  
      # Scenario 2: Entire target tensor was not passed into model inputs
      model_input = torch.tensor(
          [[13, 24, 395, 13, 46, 57, 52, 41, 45, 37, 51, 14, 380, 435]]
      )
      target = {of_name: torch.tensor([[78, 79, 504, 76, 397, 84, 0]])}
      expected_target = {
          of_name: torch.tensor(
              [
                  [
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                  ]
              ]
          )
      }
      updated_targets = pad_target_tensor_for_fine_tuning(
          target, prediction, model_input, of_name
      )
      assert torch.equal(
          expected_target[of_name], updated_targets[of_name]
      )
  
      # Scenario 3: Partial target tensor was passed into model inputs
      model_input = torch.tensor(
          [[0, 0, 24, 52, 654, 529, 221, 78, 79, 504, 76, 78, 79, 504]]
      )
      target = {of_name: torch.tensor([[78, 79, 504, 76, 397, 84, 0]])}
      expected_target = {
          of_name: torch.tensor(
              [
                  [
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      -100,
                      78,
                      79,
                      504,
                  ]
              ]
          )
      }
      updated_targets = pad_target_tensor_for_fine_tuning(
          target, prediction, model_input, of_name
      )
      assert torch.equal(
          expected_target[of_name], updated_targets[of_name]
      )
  
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 15 for calling a global function with example usage: test_pad_target_tensor_for_fine_tuning() without return types