code: def benchmark(args):
      if args.amp:
          _logger.warning(
              "Overriding precision to 'amp' since --amp flag set."
          )
          args.precision = (
              "amp"
              if args.amp_dtype == "float16"
              else "_".join(["amp", args.amp_dtype])
          )
      _logger.info(
          f"Benchmarking in {args.precision} precision. "
          f'{"NHWC" if args.channels_last else "NCHW"} layout. '
          f'torchscript {"enabled" if args.torchscript else "disabled"}'
      )
  
      bench_kwargs = vars(args).copy()
      bench_kwargs.pop("amp")
      model = bench_kwargs.pop("model")
      batch_size = bench_kwargs.pop("batch_size")
  
      bench_fns = (InferenceBenchmarkRunner,)
      prefixes = ("infer",)
      if args.bench == "both":
          bench_fns = (InferenceBenchmarkRunner, TrainBenchmarkRunner)
          prefixes = ("infer", "train")
      elif args.bench == "train":
          bench_fns = (TrainBenchmarkRunner,)
          prefixes = ("train",)
      elif args.bench.startswith("profile"):
          # specific profiler used if included in bench mode string, otherwise default to deepspeed, fallback to fvcore
          if "deepspeed" in args.bench:
              assert (
                  has_deepspeed_profiling
              ), "deepspeed must be installed to use deepspeed flop counter"
              bench_kwargs["profiler"] = "deepspeed"
          elif "fvcore" in args.bench:
              assert (
                  has_fvcore_profiling
              ), "fvcore must be installed to use fvcore flop counter"
              bench_kwargs["profiler"] = "fvcore"
          bench_fns = (ProfileRunner,)
          batch_size = 1
  
      model_results = OrderedDict(model=model)
      for prefix, bench_fn in zip(prefixes, bench_fns):
          run_results = _try_run(
              model,
              bench_fn,
              bench_kwargs=bench_kwargs,
              initial_batch_size=batch_size,
              no_batch_size_retry=args.no_retry,
          )
          if prefix and "error" not in run_results:
              run_results = {
                  "_".join([prefix, k]): v
                  for k, v in run_results.items()
              }
          model_results.update(run_results)
          if "error" in run_results:
              break
      if "error" not in model_results:
          param_count = model_results.pop(
              "infer_param_count",
              model_results.pop("train_param_count", 0),
          )
          model_results.setdefault("param_count", param_count)
          model_results.pop("train_param_count", 0)
      return model_results
  
imports: import argparse
  import csv
  import json
  import logging
  import time
  from collections import OrderedDict
  from contextlib import suppress
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.parallel
  from timm.data import resolve_data_config
  from timm.layers import set_fast_norm
  from timm.models import create_model, is_model, list_models
  from timm.optim import create_optimizer_v2
  from timm.utils import setup_default_logging, set_jit_fuser, decay_batch_step, check_batch_size_retry, ParseKwargs, reparameterize_model
label_desc: Use image node_id 8 for calling a global function with example usage: benchmark(args) and returns: model_results