code: def trainer_weighted_loss(model_output, labels, shift_labels=True):
      logits = (
          model_output["logits"]
          if isinstance(model_output, dict)
          else model_output[0]
      )
      if shift_labels:
          logits = logits[..., :-1, :].contiguous()
          labels = labels[..., 1:].contiguous()
  
      weights = create_weighted_mask(labels)
      return weighted_cross_entropy(logits, labels, weights)
  
imports: import math
  import os
  from contextlib import contextmanager
  from functools import partial
  from typing import List
  import numpy
  import torch
  import torch.cuda
  from accelerate.logging import get_logger
  from datasets import set_caching_enabled
  from torch.utils.data import DataLoader, RandomSampler
  from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFDPOTrainerBuilder
  from axolotl.utils.distributed import is_main_process, reduce_and_broadcast, zero_first
  from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
label_desc: Use image node_id 3 for calling a global function with example usage: trainer_weighted_loss(model_output, labels, shift_labels) and returns: weighted_cross_entropy