code: def meta_conv_backward(
      grad_output: torch.Tensor,
      input: torch.Tensor,
      weight: torch.Tensor,
      bias_sizes,
      stride,
      padding,
      dilation,
      transposed,
      output_padding,
      groups,
      output_mask,
  ):
      return (
          torch.empty_like(input),
          torch.empty_like(weight),
          torch.empty((bias_sizes), device="meta"),
      )
  
imports: from typing import List, Optional, Union
  import torch
  from torch.utils._pytree import tree_map
label_desc: Use image node_id 4 for calling a global function with example usage: meta_conv_backward(grad_output, input, weight, bias_sizes, stride, padding, dilation, transposed, output_padding, groups, output_mask) and returns: torch,  torch,  torch