code: def var_decomposition(
      input: Tensor,
      dim: Optional[List[int]] = None,
      correction: Optional[Number] = None,
      keepdim: bool = False,
  ) -> Tensor:
      if dim is None:
          dim_i: List[int] = []
          dim = dim_i
  
      if isinstance(dim, (tuple, list)) and len(dim) == 0:
          n = input.numel()
      else:
          n = 1
          for dim_i in dim:  # type: ignore[assignment]
              n *= input.shape[dim_i]  # type: ignore[call-overload]
  
      mean = aten.mean(input, dim, True)
      sub = input - mean
      sq = sub * sub
      sum = aten.sum(sq, dim, keepdim)
  
      if correction is None:
          denom = float(n - 1)
      else:
          if isinstance(correction, int):
              denom = float(n - correction)
          elif isinstance(correction, float):
              denom = float(n) - correction
          else:
              raise RuntimeError("correction must be int or float")
  
      return sum / max(0, denom)
  
file_path: pytorch/torch/jit/_decompositions.py
imports: import torch
  from torch import Tensor
  import inspect
  import warnings
  from typing import Dict, List, Optional, Set
  from torch.types import Number
label_desc: Use image node_id 4 for calling a global function with example usage: var_decomposition(input, dim, correction, keepdim) and returns: unknown