code: def execute(
      self,
      model: Any,
      input_data: DataManager,
      dl_framework: DeepLearningFramework,
  ) -> BenchmarkOriginalModelResult:
      self.logger.info("Benchmark performance of original model")
  
      self.outputs = [
          tuple(
              COMPUTE_OUTPUT_FRAMEWORK[dl_framework](
                  model, tuple(input_tensors[0]), self.device
              )
          )
          for input_tensors in input_data
      ]
  
      inputs = input_data.get_list(QUANTIZATION_DATA_NUM)
      self.measure_result, _ = COMPUTE_LATENCY_FRAMEWORK[dl_framework](
          inputs, model, self.device
      )
      self.logger.info(
          f"Original model latency: {self.measure_result} sec/iter"
      )
  
      return BenchmarkOriginalModelResult(
          latency_seconds=self.measure_result,
          model_outputs=self.outputs,
      )
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/measures/measures.py
imports: from typing import List, Tuple, Any, Callable, Dict
  import numpy
  from nebullvm.config import QUANTIZATION_DATA_NUM
  from nebullvm.core.models import BenchmarkOriginalModelResult, DeepLearningFramework
  from nebullvm.operations.inference_learners.base import BaseInferenceLearner
  from nebullvm.operations.measures.base import Measure
  from nebullvm.operations.measures.utils import compute_torch_latency, compute_tf_latency, compute_onnx_latency, compute_relative_difference
  from nebullvm.tools.data import DataManager
  from nebullvm.tools.onnx import run_onnx_model
  from nebullvm.tools.pytorch import run_torch_model
  from nebullvm.tools.tf import run_tf_model
label_desc: Use image node_id 2 for calling the LatencyOriginalModelMeasure obj's underlying member method code with example usage: obj.execute(model, input_data, dl_framework) and returns: BenchmarkOriginalModelResult