code: def launch_eval(self):
      kwargs = self.task_processor.get_pipeline_kwargs()
  
      # transformers pipelines are smart enought to detect whether the tokenizer or feature_extractor is needed
      ort_pipeline = _optimum_pipeline(
          task=self.task,
          model=self.ort_model,
          tokenizer=self.preprocessor,
          feature_extractor=self.preprocessor,
          accelerator="ort",
          **kwargs,
      )
  
      transformers_pipeline = _transformers_pipeline(
          task=self.task,
          model=self.torch_model,
          tokenizer=self.preprocessor,
          feature_extractor=self.preprocessor,
          **kwargs,
      )
  
      eval_dataset = self.get_eval_dataset()
  
      print("Running evaluation...")
      baseline_metrics_dict = self.task_processor.run_evaluation(
          eval_dataset, transformers_pipeline, self.metric_names
      )
      optimized_metrics_dict = self.task_processor.run_evaluation(
          eval_dataset, ort_pipeline, self.metric_names
      )
  
      baseline_metrics_dict.pop("total_time_in_seconds", None)
      baseline_metrics_dict.pop("samples_per_second", None)
      baseline_metrics_dict.pop("latency_in_seconds", None)
  
      optimized_metrics_dict.pop("total_time_in_seconds", None)
      optimized_metrics_dict.pop("samples_per_second", None)
      optimized_metrics_dict.pop("latency_in_seconds", None)
  
      self.return_body["evaluation"]["others"]["baseline"].update(
          baseline_metrics_dict
      )
      self.return_body["evaluation"]["others"]["optimized"].update(
          optimized_metrics_dict
      )
  
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 3 for calling the OnnxRuntimeRun obj's underlying member method code with example usage: obj.launch_eval() without return types