code: def benchmark_transformer(
      model_info, dtype
  ) -> Iterator[benchmark.Timer]:
      device = "cuda"
  
      model_name, model_factory, input_shape = model_info
  
      inp = torch.randn(input_shape, dtype=dtype, device=device)
  
      for mod_name, mod_apply in MODIFIERS:
          model: nn.Module = model_factory()
          model = mod_apply(model).to(device).to(dtype)
  
          # Make sure we don't have errors
          out = model(inp)
          grad = out.clone()
          out.backward(grad)
  
          yield benchmark.Timer(
              stmt="model(inp).backward(grad)",
              globals={
                  "model": model,
                  "inp": inp,
                  "grad": grad,
              },
              label="fw+bw",
              description=mod_name,
              sub_label=model_name,
          )
  
imports: import itertools
  from functools import partial, reduce
  from typing import Iterator
  import timm
  import torch
  import torch.nn
  from timm.models.layers import Mlp
  from timm.models.vision_transformer import Attention
  from timm.models.vision_transformer import Block
  from torch.utils import benchmark
  from utils import benchmark_main_helper
  import xformers.ops
label_desc: Use image node_id 6 for calling a global function with example usage: benchmark_transformer(model_info, dtype) without return types