code: def execute(self):
      inputs = {}
  
      checked_inputs = {
          "input_ids",
          "attention_mask",
          "token_type_ids",
          "pixel_values",
      }
      if "input_ids" in self.model_input_names:
          inputs["input_ids"] = torch.randint(
              high=1000, size=(self.batch_size, self.input_length)
          )
      if "attention_mask" in self.model_input_names:
          inputs["attention_mask"] = torch.ones(
              self.batch_size, self.input_length, dtype=torch.int64
          )
      if "token_type_ids" in self.model_input_names:
          inputs["token_type_ids"] = torch.ones(
              self.batch_size, self.input_length, dtype=torch.int64
          )
      if "pixel_values" in self.model_input_names:
          # TODO support grayscale?
          inputs["pixel_values"] = torch.rand(
              self.batch_size,
              3,
              self.model.config.image_size,
              self.model.config.image_size,
              dtype=torch.float32,
          )
  
      if np.any(
          [k not in checked_inputs for k in self.model_input_names]
      ):
          raise NotImplementedError(
              f"At least an input in {self.model_input_names} has no dummy generation for time benchmark."
          )
  
      # Warmup
      for _ in trange(self.warmup_runs, desc="Warming up"):
          self.model.forward(**inputs)
  
      if self.benchmark_duration != 0:
          benchmark_duration_ns = (
              self.benchmark_duration * SEC_TO_NS_SCALE
          )
          print(
              f"Running time tracking in {self.benchmark_duration:.1f}s."
          )
          while sum(self.latencies) < benchmark_duration_ns:
              # TODO not trak GPU/CPU <--> numpy/torch, need to change the implementation of forward
              with self.track():
                  self.model.forward(**inputs)
  
          self.finalize(benchmark_duration_ns)
  
          return self.to_dict()
      else:
          benchmarks_stats = {
              "nb_forwards": 0,
              "throughput": -1,
              "latency_mean": -1,
          }
          return benchmarks_stats
  
imports: import os
  import subprocess
  from contextlib import contextmanager
  from time import perf_counter_ns
  from typing import Set
  import numpy
  import optuna
  import torch
  import transformers
  from datasets import Dataset
  from tqdm import trange
  from .None import version
  from .utils.preprocessing import ImageClassificationProcessing, QuestionAnsweringProcessing, TextClassificationProcessing, TokenClassificationProcessing
  from .utils.runs import RunConfig, cpu_info_command
label_desc: Use image node_id 6 for calling the TimeBenchmark obj's underlying member method code with example usage: obj.execute() and returns: self, benchmarks_stats