code: def should_pad_bench_key(
      mat1: Tensor, mat2: Tensor, op, input: Optional[Tensor] = None
  ) -> str:
      def tensor_key(t):
          return (t.shape, t.stride(), t.dtype)
  
      tf32_key = (
          None
          if mat1.dtype != torch.float32
          else torch.backends.cuda.matmul.allow_tf32
      )
      key = (
          tensor_key(mat1),
          tensor_key(mat2),
          op,
          input if input is None else tensor_key(input),
          tf32_key,
      )
  
      return str(key)
  
imports: import functools
  from typing import List, Optional
  import torch
  from torch import Tensor
  from torch._inductor import utils
  from torch.utils._mode_utils import no_dispatch
  from torch.utils._triton import has_triton
  from ..pattern_matcher import fwd_only, joint_fwd_bwd, Match, register_replacement
label_desc: Use image node_id 17 for calling a global function with example usage: should_pad_bench_key(mat1, mat2, op, input) and returns: str, t,  t,  t