code: def abs_diff_masked_tensors(tensor_1, tensor_2, mask_1, mask_2):
      diffs = []
      for l1, l2, m1, m2 in zip(tensor_1, tensor_2, mask_1, mask_2):
          diff = apply_mask(l1, m1) - apply_mask(l2, m2)
          diffs.append(diff.sum())
      return abs(sum(diffs))
  
imports: import copy
  import fnmatch
  import gc
  import re
  import tempfile
  import unittest
  import pytest
  import torch
  from huggingface_hub import HfApi, HfFolder, delete_repo
  from parameterized import parameterized
  from pytest import mark
  from requests.exceptions import HTTPError
  from transformers import AutoTokenizer
  from trl import AutoModelForCausalLMWithValueHead, AutoModelForSeq2SeqLMWithValueHead, PPOConfig, PPOTrainer, set_seed
  from trl.core import respond_to_batch
  from .testing_constants import CI_HUB_ENDPOINT, CI_HUB_USER, CI_HUB_USER_TOKEN
  from .testing_utils import require_peft, require_torch_multi_gpu
label_desc: Use image node_id 2 for calling a global function with example usage: abs_diff_masked_tensors(tensor_1, tensor_2, mask_1, mask_2) and returns: abs