code: def run(self):
      def _step(detail=False):
          self.optimizer.zero_grad()  # can this be ignored?
          t_start = self.time_fn()
          t_fwd_end = t_start
          t_bwd_end = t_start
          with self.amp_autocast():
              output = self.model(self.example_inputs)
              if isinstance(output, tuple):
                  output = output[0]
              if detail:
                  t_fwd_end = self.time_fn(True)
              target = self._gen_target(output.shape[0])
              self.loss(output, target).backward()
              if detail:
                  t_bwd_end = self.time_fn(True)
          self.optimizer.step()
          t_end = self.time_fn(True)
          if detail:
              delta_fwd = t_fwd_end - t_start
              delta_bwd = t_bwd_end - t_fwd_end
              delta_opt = t_end - t_bwd_end
              return delta_fwd, delta_bwd, delta_opt
          else:
              delta_step = t_end - t_start
              return delta_step
  
      _logger.info(
          f"Running train benchmark on {self.model_name} for {self.num_bench_iter} steps w/ "
          f"input size {self.input_size} and batch size {self.batch_size}."
      )
  
      self._init_input()
  
      for _ in range(self.num_warm_iter):
          _step()
  
      t_run_start = self.time_fn()
      if self.detail:
          total_fwd = 0.0
          total_bwd = 0.0
          total_opt = 0.0
          num_samples = 0
          for i in range(self.num_bench_iter):
              delta_fwd, delta_bwd, delta_opt = _step(True)
              num_samples += self.batch_size
              total_fwd += delta_fwd
              total_bwd += delta_bwd
              total_opt += delta_opt
              num_steps = i + 1
              if num_steps % self.log_freq == 0:
                  total_step = total_fwd + total_bwd + total_opt
                  _logger.info(
                      f"Train [{num_steps}/{self.num_bench_iter}]."
                      f" {num_samples / total_step:0.2f} samples/sec."
                      f" {1000 * total_fwd / num_steps:0.3f} ms/step fwd,"
                      f" {1000 * total_bwd / num_steps:0.3f} ms/step bwd,"
                      f" {1000 * total_opt / num_steps:0.3f} ms/step opt."
                  )
          total_step = total_fwd + total_bwd + total_opt
          t_run_elapsed = self.time_fn() - t_run_start
          results = dict(
              samples_per_sec=round(num_samples / t_run_elapsed, 2),
              step_time=round(
                  1000 * total_step / self.num_bench_iter, 3
              ),
              fwd_time=round(1000 * total_fwd / self.num_bench_iter, 3),
              bwd_time=round(1000 * total_bwd / self.num_bench_iter, 3),
              opt_time=round(1000 * total_opt / self.num_bench_iter, 3),
              batch_size=self.batch_size,
              img_size=self.input_size[-1],
              param_count=round(self.param_count / 1e6, 2),
          )
      else:
          total_step = 0.0
          num_samples = 0
          for i in range(self.num_bench_iter):
              delta_step = _step(False)
              num_samples += self.batch_size
              total_step += delta_step
              num_steps = i + 1
              if num_steps % self.log_freq == 0:
                  _logger.info(
                      f"Train [{num_steps}/{self.num_bench_iter}]."
                      f" {num_samples / total_step:0.2f} samples/sec."
                      f" {1000 * total_step / num_steps:0.3f} ms/step."
                  )
          t_run_elapsed = self.time_fn() - t_run_start
          results = dict(
              samples_per_sec=round(num_samples / t_run_elapsed, 2),
              step_time=round(
                  1000 * total_step / self.num_bench_iter, 3
              ),
              batch_size=self.batch_size,
              img_size=self.input_size[-1],
              param_count=round(self.param_count / 1e6, 2),
          )
  
      _logger.info(
          f"Train benchmark of {self.model_name} done. "
          f"{results['samples_per_sec']:.2f} samples/sec, {results['step_time']:.2f} ms/sample"
      )
  
      return 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 3 for calling the TrainBenchmarkRunner obj's underlying member method code with example usage: obj.run() and returns: results, delta_fwd,  delta_bwd,  delta_opt, delta_step