code: def meta_cudnn_rnn_backward(
      input: torch.Tensor,
      weight: torch.Tensor,
      weight_stride0: int,
      hx: torch.Tensor,
      cx: Optional[torch.Tensor] = None,
      *args,
      **kwargs,
  ):
      print(input, weight, hx, cx)
      grad_input = torch.empty_like(input)
      grad_weight = torch.empty_like(weight)
      grad_hx = torch.empty_like(hx)
      grad_cx = (
          torch.empty_like(cx)
          if cx is not None
          else torch.empty((), device="meta")
      )
      return grad_input, grad_weight, grad_hx, grad_cx
  
imports: from typing import List, Optional, Union
  import torch
  from torch.utils._pytree import tree_map
label_desc: Use image node_id 7 for calling a global function with example usage: meta_cudnn_rnn_backward(input, weight, weight_stride0, hx, cx) and returns: grad_input,  grad_weight,  grad_hx,  grad_cx