code: def meta_conv_1(
      input_tensor: torch.Tensor,
      weight: torch.Tensor,
      bias: torch.Tensor,
      stride: List[int],
      padding: List[int],
      dilation: List[int],
      is_transposed: bool,
      output_padding: List[int],
      groups: int,
      *extra_args,
  ):
      out = meta_conv(
          input_tensor,
          weight,
          bias,
          stride,
          padding,
          dilation,
          is_transposed,
          output_padding,
          groups,
      )
      return out
  
imports: from typing import List, Optional, Union
  import torch
  from torch.utils._pytree import tree_map
label_desc: Use image node_id 3 for calling a global function with example usage: meta_conv_1(input_tensor, weight, bias, stride, padding, dilation, is_transposed, output_padding, groups) and returns: out