code: def convert(self, model, data, test_data, args, model_name):
      self.configure(data, model, args)
      self.batch_benchmark = args.batch_benchmark
      extra_config = {constants.N_THREADS: self.params["nthread"]}
  
      with Timer() as t:
          if self.batch_benchmark:
              self.model = convert(
                  model,
                  self.backend,
                  test_data,
                  device=self.params["device"],
                  extra_config=extra_config,
              )
          else:
              remainder_size = (
                  test_data.shape[0] % self.params["batch_size"]
              )
              self.model = convert_batch(
                  model,
                  self.backend,
                  test_data,
                  remainder_size,
                  device=self.params["device"],
                  extra_config=extra_config,
              )
  
      if data.learning_task == LearningTask.REGRESSION:
          self.predict_fn = self.model.predict
      else:
          self.predict_fn = self.model.predict_proba
  
      return t.interval
  
imports: from abc import ABC, abstractmethod
  import numpy
  from benchmarks.timer import Timer
  from benchmarks.datasets import LearningTask
  from hummingbird.ml import constants
  from hummingbird.ml import convert, convert_batch
label_desc: Use image node_id 2 for calling the HBBackend obj's underlying member method code with example usage: obj.convert(model, data, test_data, args, model_name) and returns: t