code: def masked_mean(values, mask, axis=None):
      """Compute mean of tensor with a masked values."""
      if axis is not None:
          return (values * mask).sum(axis=axis) / mask.sum(axis=axis)
      else:
          return (values * mask).sum() / mask.sum()
  
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 4 for calling a global function with example usage: masked_mean(values, mask, axis) and returns: unknown, unknown