code: def execute(
      self,
      model: Any,
      input_data: Union[Iterable, Sequence, DataManager],
      metric_drop_ths: float = None,
      metric: Union[str, Callable] = None,
      optimization_time: str = "constrained",
      dynamic_info: Dict = None,
      config_file: str = None,
      ignore_compilers: List[str] = None,
      ignore_compressors: List[str] = None,
      store_latencies: bool = False,
      **kwargs,
  ):
      self.logger.info(
          "Running Speedster on {}{}".format(
              self.device.type.name,
              f":{self.device.idx}"
              if self.device.type is not DeviceType.CPU
              else "",
          )
      )
  
      result = self.optimize_inference_op.to(self.device).execute(
          model=model,
          input_data=input_data,
          metric_drop_ths=metric_drop_ths,
          metric=metric,
          optimization_time=optimization_time,
          dynamic_info=dynamic_info,
          config_file=config_file,
          ignore_compilers=ignore_compilers,
          ignore_compressors=ignore_compressors,
          store_latencies=store_latencies,
          **kwargs,
      )
  
      if result.optimized_model is None:
          return None
  
      opt_metric_drop = (
          f"{result.metric_drop:.4f}"
          if result.metric_drop > MIN_NUMBER
          else "0"
      )
  
      self._send_feedback(result, store_latencies=store_latencies)
  
      table = [
          [
              "backend",
              result.original_model.framework.name,
              result.optimized_model.inference_learner.name,
              "",
          ],
          [
              "latency",
              f"{result.original_model.latency_seconds:.4f} sec/batch",
              f"{result.optimized_model.latency_seconds:.4f} sec/batch",
              f"{result.original_model.latency_seconds / result.optimized_model.latency_seconds:.2f}x",  # noqa: E501
          ],
          [
              "throughput",
              f"{result.original_model.throughput:.2f} " f"data/sec",
              f"{result.optimized_model.throughput:.2f} " f"data/sec",
              f"{result.optimized_model.throughput / result.original_model.throughput:.2f}x",  # noqa: E501
          ],
          [
              "model size",
              f"{result.original_model.size_mb:.2f} MB",
              f"{result.optimized_model.size_mb:.2f} MB",
              f"{min(int((result.optimized_model.size_mb-result.original_model.size_mb) / result.original_model.size_mb * 100), 0)}%"  # noqa: E501
              if result.original_model.size_mb > 0
              else "NA",
          ],
          ["metric drop", "", opt_metric_drop, ""],
          [
              "techniques",
              "",
              f"{_convert_technique(result.optimized_model.technique)}",
              "",
          ],
      ]
      headers = [
          "Metric",
          "Original Model",
          "Optimized Model",
          "Improvement",
      ]
  
      # change format to the logger, avoiding printing verbose info
      # to the console (as date, time, etc.)
      self.logger.remove()
      handler_id = self.logger.add(
          sys.stdout, format="<level>{message}</level>"
      )
      hw_info = get_hw_setup(self.device)
      hw_name = (
          hw_info.cpu
          if self.device.type is DeviceType.CPU
          else hw_info.accelerator
      )
      self.logger.info(
          (
              f"\n[Speedster results on {hw_name}]\n"
              f"{tabulate(table, headers, tablefmt='heavy_outline')}"
          )
      )
  
      if (
          result.original_model.latency_seconds
          / result.optimized_model.latency_seconds
          < 2
      ):
          self.logger.warning(
              f"\nMax speed-up with your input parameters is "
              f"{result.original_model.latency_seconds / result.optimized_model.latency_seconds:.2f}x. "  # noqa: E501
              f"If you want to get a faster optimized model, "
              f"see the following link for some suggestions: "
              f"https://docs.nebuly.com/Speedster/advanced_"
              f"options/#acceleration-suggestions\n"
          )
  
      self.logger.remove(handler_id)
      setup_logger()
  
      return result.optimized_model.inference_learner
  
imports: import json
  import pickle
  import sys
  from typing import Any, Union, Iterable, Sequence, Dict, Callable, List
  from loguru import logger
  from nebullvm import setup_logger
  from nebullvm.config import MIN_NUMBER
  from nebullvm.core.models import OptimizeInferenceResult, DeviceType
  from nebullvm.operations.base import Operation
  from nebullvm.operations.optimizations.optimize_inference import OptimizeInferenceOp
  from nebullvm.tools.data import DataManager
  from nebullvm.tools.feedback_collector import FeedbackCollector
  from tabulate import tabulate
  from nebullvm.tools.hardware_utils import get_hw_setup
  from nebullvm.tools.utils import get_model_size_mb, get_model_name, generate_model_id
label_desc: Use image node_id 3 for calling the SpeedsterRootOp obj's underlying member method code with example usage: obj.execute(model, input_data, metric_drop_ths, metric, optimization_time, dynamic_info, config_file, ignore_compilers, ignore_compressors, store_latencies) and returns: result, None