code: def main(args) -> dict:
      pipeline = load_pipeline(args)
  
      # warmup.
      run_inference(pipeline, args)
      run_inference(pipeline, args)
  
      trace_path = (
          args.ckpt.replace("/", "_")
          + f"bf16@{not args.no_bf16}-sdpa@{not args.no_sdpa}-bs@{args.batch_size}-fuse@{args.enable_fused_projections}-upcast_vae@{args.upcast_vae}-steps@{args.num_inference_steps}-unet@{args.compile_unet}-vae@{args.compile_vae}-mode@{args.compile_mode}-change_comp_config@{args.change_comp_config}-do_quant@{args.do_quant}.json"
      )
      runner = functools.partial(profiler_runner, trace_path)
      with torch.autograd.profiler.record_function("sdxl-brrr"):
          runner(run_inference, pipeline, args)
      return trace_path
  
file_path: diffusion-fast/run_profile.py
imports: import torch
  from torch._inductor import config
  import functools
  import sys
  from utils.benchmarking_utils import create_parser
  from utils.pipeline_utils import load_pipeline
label_desc: Use image node_id 3 for calling a global function with example usage: main(args) and returns: trace_path