code: def meta_cudnn_bn_backward(
      dY: torch.Tensor,
      input: torch.Tensor,
      weight: torch.Tensor,
      running_mean,
      running_var,
      save_mean,
      save_invstd,
      eps,
      reserve,
  ):
      dX = torch.empty_like(input)
      dgamma = torch.empty_like(weight)
      dbeta = torch.empty_like(weight)
      return dX, dgamma, dbeta
  
imports: from typing import List, Optional, Union
  import torch
  from torch.utils._pytree import tree_map
label_desc: Use image node_id 17 for calling a global function with example usage: meta_cudnn_bn_backward(dY, input, weight, running_mean, running_var, save_mean, save_invstd, eps, reserve) and returns: dX,  dgamma,  dbeta