code: def weighted_cross_entropy(
      logits: torch.Tensor, labels: torch.Tensor, weights: torch.Tensor
  ):
      # Flatten the logits, labels, and weights tensors
      logits = logits.view(
          -1, logits.size(-1)
      )  # logits becomes of shape [batch_size*sequence_length, vocab_size]
      labels = labels.view(
          -1
      )  # labels becomes of shape [batch_size*sequence_length]
      weights = weights.view(
          -1
      )  # weights becomes of shape [batch_size*sequence_length]
  
      # Compute the unweighted cross entropy loss
      losses = torch.nn.functional.cross_entropy(
          logits, labels, reduction="none"
      )
  
      # Apply the weights to the losses and compute their sum
      return (weights * losses).sum()
  
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 1 for calling a global function with example usage: weighted_cross_entropy(logits, labels, weights) and returns: unknown