code: def _track_running_stats(
      running_mean: Tensor,
      running_var: Tensor,
      momentum: float,
      mean: Tensor,
      var: Tensor,
      total_count: Tensor,
  ) -> None:
      unbiased_var = var * (total_count / (total_count - 1))
      running_mean += momentum * (mean.reshape(-1) - running_mean)
      running_var += momentum * (unbiased_var.reshape(-1) - running_var)
  
file_path: fairscale/fairscale/experimental/nn/sync_batchnorm.py
imports: from typing import Any, Dict, List, Optional, Tuple
  import torch
  from torch import Tensor
  import torch.distributed
  from torch.distributed import ProcessGroup
  from fairscale.internal import torch_version
  from fairscale.nn.checkpoint import is_checkpointing, is_recomputing
label_desc: Use image node_id 2 for calling a global function with example usage: _track_running_stats(running_mean, running_var, momentum, mean, var, total_count) without return types