code: def masked_whiten(values, mask, shift_mean=True):
      """Whiten values with masked values."""
      mean, var = masked_mean(values, mask), masked_var(values, mask)
      whitened = (values - mean) * torch.rsqrt(var + 1e-8)
      if not shift_mean:
          whitened += mean
      return whitened
  
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 6 for calling a global function with example usage: masked_whiten(values, mask, shift_mean) and returns: whitened