code: def microbenchmark(args, model, example_inputs):
      compiled_fn = compile_fx(
          torch.fx.symbolic_trace(model), example_inputs
      )
      cudagraphs_eager = cudagraphs_inner(
          model, example_inputs, copy_outputs=False
      )
      cudagraphs_jit = cudagraphs_inner(
          torch.jit.trace(model, example_inputs),
          example_inputs,
          copy_outputs=False,
      )
      return compute_speedups(
          args,
          [cudagraphs_eager, cudagraphs_jit, compiled_fn],
          example_inputs,
      )
  
imports: import argparse
  import inspect
  import sys
  import numpy
  import tabulate
  import torch
  import torch._inductor
  from torch._dynamo.backends.cudagraphs import cudagraphs_inner
  from torch._dynamo.testing import same
  from torch._inductor.compile_fx import compile_fx
  from torch._inductor.utils import timed
label_desc: Use image node_id 2 for calling a global function with example usage: microbenchmark(args, model, example_inputs) and returns: compute_speedups