code: def masked_var(values, mask, unbiased=True):
      """Compute variance of tensor with masked values."""
      mean = masked_mean(values, mask)
      centered_values = values - mean
      variance = masked_mean(centered_values**2, mask)
      if unbiased:
          mask_sum = mask.sum()
          if mask_sum == 0:
              raise ValueError(
                  "The sum of the mask is zero, which can happen when `mini_batch_size=1`"
                  "; try increase the `mini_batch_size` or `gradient_accumulation_steps`"
              )
          # note that if mask_sum == 1, then there is a division by zero issue
          # to avoid it you just need to use a larger minibatch_size
          bessel_correction = mask_sum / (mask_sum - 1)
          variance = variance * bessel_correction
      return variance
  
imports: import math
  import time
  import warnings
  from collections.abc import Mapping
  from typing import Any, Dict, List, Optional, Union
  import numpy
  import torch
  import torch.nn.functional
  from datasets import Dataset
  from huggingface_hub import PyTorchModelHubMixin
  from torch.cuda.amp import autocast
  from torch.nn.utils.rnn import pad_sequence
  from torch.utils.data import DataLoader
  from transformers import DataCollatorForLanguageModeling, PreTrainedTokenizer, PreTrainedTokenizerFast
  from llm_studio.src.utils.modeling_utils import unwrap_model
label_desc: Use image node_id 5 for calling a global function with example usage: masked_var(values, mask, unbiased) and returns: variance