code: def _forward(
      input: Tensor,
      affine: bool,
      mean: Tensor,
      invstd: Tensor,
      weight: Tensor,
      bias: Tensor,
  ) -> Tensor:
      if affine:
          return (input - mean) * (
              invstd * weight.reshape_as(mean)
          ) + bias.reshape_as(mean)
      else:
          return (input - mean) * invstd
  
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 1 for calling a global function with example usage: _forward(input, affine, mean, invstd, weight, bias) and returns: unknown, unknown