code: def _launch_time(self, trial):
      batch_size = trial.suggest_categorical(
          "batch_size", self.batch_sizes
      )
      input_length = trial.suggest_categorical(
          "input_length", self.input_lengths
      )
  
      model_input_names = set(self.preprocessor.model_input_names)
  
      # onnxruntime benchmark
      print("Running ONNX Runtime time benchmark.")
      ort_benchmark = TimeBenchmark(
          self.ort_model,
          input_length=input_length,
          batch_size=batch_size,
          model_input_names=model_input_names,
          warmup_runs=self.time_benchmark_args["warmup_runs"],
          duration=self.time_benchmark_args["duration"],
      )
      optimized_time_metrics = ort_benchmark.execute()
  
      # pytorch benchmark
      print("Running Pytorch time benchmark.")
      torch_benchmark = TimeBenchmark(
          self.torch_model,
          input_length=input_length,
          batch_size=batch_size,
          model_input_names=model_input_names,
          warmup_runs=self.time_benchmark_args["warmup_runs"],
          duration=self.time_benchmark_args["duration"],
      )
      baseline_time_metrics = torch_benchmark.execute()
  
      time_evaluation = {
          "batch_size": batch_size,
          "input_length": input_length,
          "baseline": baseline_time_metrics,
          "optimized": optimized_time_metrics,
      }
  
      self.return_body["evaluation"]["time"].append(time_evaluation)
  
      return 0, 0
  
imports: import copy
  import os
  from pathlib import Path
  from transformers import pipeline
  from transformers.onnx import FeaturesManager
  from transformers.onnx.utils import get_preprocessor
  from onnxruntime.quantization import QuantFormat, QuantizationMode, QuantType
  from ...pipelines import ORT_SUPPORTED_TASKS
  from ...pipelines import pipeline
  from ...runs_base import Run, TimeBenchmark, get_autoclass_name, task_processing_map
  from ..None import ORTQuantizer
  from ..configuration import QuantizationConfig
  from ..modeling_ort import ORTModel
  from ..preprocessors import QuantizationPreprocessor
  from .calibrator import OnnxRuntimeCalibrator
  from .utils import task_ortmodel_map
label_desc: Use image node_id 2 for calling the OnnxRuntimeRun obj's underlying member method code with example usage: obj._launch_time(trial) and returns: int,  int