code: def masked_var(
      values: torch.Tensor, mask: torch.Tensor, unbiased: bool = True
  ) -> torch.Tensor:
      """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 gc
  import random
  import warnings
  from contextlib import contextmanager
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.rnn import pad_sequence
  from transformers import top_k_top_p_filtering
  from .import_utils import is_npu_available, is_xpu_available
label_desc: Use image node_id 9 for calling a global function with example usage: masked_var(values, mask, unbiased) and returns: variance