code: def meta_cudnn_bn(
      input: torch.Tensor,
      weight,
      bias,
      running_mean,
      running_var,
      training,
      momentum,
      eps,
  ):
      n_input = input.size(1)
  
      output = torch.empty_like(input)
      running_mean = torch.empty((n_input), device="meta")
      running_var = torch.empty((n_input), device="meta")
      reserve = torch.empty((0), dtype=torch.uint8, device="meta")
      return output, running_mean, running_var, reserve
  
imports: from typing import List, Optional, Union
  import torch
  from torch.utils._pytree import tree_map
label_desc: Use image node_id 16 for calling a global function with example usage: meta_cudnn_bn(input, weight, bias, running_mean, running_var, training, momentum, eps) and returns: output,  running_mean,  running_var,  reserve