code: def meta_hardtanh_backward(
      grad_out: torch.Tensor,
      input: torch.Tensor,
      min_val: int,
      max_val: int,
  ):
      grad_in = torch.empty_like(input)
      return grad_in
  
imports: from typing import List, Optional, Union
  import torch
  from torch.utils._pytree import tree_map
label_desc: Use image node_id 13 for calling a global function with example usage: meta_hardtanh_backward(grad_out, input, min_val, max_val) and returns: grad_in