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,
              "extra_config": args.extra,
              "transform": True
              if args.operator
              not in [
                  "LogisticRegression",
                  "SGDClassifier",
                  "LogisticRegressionCV",
                  "SGDClassifier",
                  "LinearSVC",
                  "NuSVC",
                  "SVC",
                  "DecisionTreeClassifier",
                  "MLPClassifier",
                  "BernoulliNB",
              ]
              else False,
              "n_classes": 231
              if isinstance(model, PolynomialFeatures)
              else 20
              if data.learning_task == LearningTask.REGRESSION
              else len(set(data.y_test)),
              "operator": args.operator,
          }
      )
  
imports: from abc import ABC, abstractmethod
  import numpy
  from sklearn.preprocessing.data import PolynomialFeatures
  from sklearn.svm.classes import LinearSVC, NuSVC, SVC
  import sys
  from benchmarks.timer import Timer
  from benchmarks.datasets import LearningTask
  from hummingbird.ml import constants
  from hummingbird.ml import 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