code: def __init__(
      self,
      model_name,
      detail=False,
      device="cuda",
      torchscript=False,
      torchcompile=None,
      aot_autograd=False,
      reparam=False,
      precision="float32",
      fuser="",
      num_warm_iter=10,
      num_bench_iter=50,
      use_train_size=False,
      **kwargs,
  ):
      self.model_name = model_name
      self.detail = detail
      self.device = device
      (
          self.amp_dtype,
          self.model_dtype,
          self.data_dtype,
      ) = resolve_precision(precision)
      self.channels_last = kwargs.pop("channels_last", False)
      if self.amp_dtype is not None:
          self.amp_autocast = partial(
              torch.cuda.amp.autocast, dtype=self.amp_dtype
          )
      else:
          self.amp_autocast = suppress
  
      if fuser:
          set_jit_fuser(fuser)
      self.model = create_model(
          model_name,
          num_classes=kwargs.pop("num_classes", None),
          in_chans=3,
          global_pool=kwargs.pop("gp", "fast"),
          scriptable=torchscript,
          drop_rate=kwargs.pop("drop", 0.0),
          drop_path_rate=kwargs.pop("drop_path", None),
          drop_block_rate=kwargs.pop("drop_block", None),
          **kwargs.pop("model_kwargs", {}),
      )
      if reparam:
          self.model = reparameterize_model(self.model)
      self.model.to(
          device=self.device,
          dtype=self.model_dtype,
          memory_format=torch.channels_last
          if self.channels_last
          else None,
      )
      self.num_classes = self.model.num_classes
      self.param_count = count_params(self.model)
      _logger.info(
          "Model %s created, param count: %d"
          % (model_name, self.param_count)
      )
  
      data_config = resolve_data_config(
          kwargs, model=self.model, use_test_size=not use_train_size
      )
      self.input_size = data_config["input_size"]
      self.batch_size = kwargs.pop("batch_size", 256)
  
      self.compiled = False
      if torchscript:
          self.model = torch.jit.script(self.model)
          self.compiled = True
      elif torchcompile:
          assert (
              has_compile
          ), "A version of torch w/ torch.compile() is required, possibly a nightly."
          torch._dynamo.reset()
          self.model = torch.compile(self.model, backend=torchcompile)
          self.compiled = True
      elif aot_autograd:
          assert has_functorch, "functorch is needed for --aot-autograd"
          self.model = memory_efficient_fusion(self.model)
          self.compiled = True
  
      self.example_inputs = None
      self.num_warm_iter = num_warm_iter
      self.num_bench_iter = num_bench_iter
      self.log_freq = num_bench_iter // 5
      if "cuda" in self.device:
          self.time_fn = partial(cuda_timestamp, device=self.device)
      else:
          self.time_fn = timestamp
  
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 1 to create a new BenchmarkRunner object  with example: obj = BenchmarkRunner(model_name, detail, device, torchscript, torchcompile, aot_autograd, reparam, precision, fuser, num_warm_iter, num_bench_iter, use_train_size)