code: def configure(self, data, model, args):
      self.params.update(
          {
              "batch_size": len(data.X_test)
              if args.batch_size == -1
              or len(data.X_test) < args.batch_size
              else args.batch_size,
              "input_size": data.X_test.shape[1]
              if isinstance(data.X_test, np.ndarray)
              else len(data.X_test.columns),
              "device": "cpu" if args.gpu is False else "cuda",
              "nthread": args.cpus,
              "n_classes": 1
              if data.learning_task == LearningTask.REGRESSION
              else model.n_classes_,
          }
      )
  
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 3 for calling the ScoreBackend obj's underlying member method code with example usage: obj.configure(data, model, args) without return types