code: def predict(self, data):
      assert self.model is not None
  
      is_regression = (
          data.learning_task == LearningTask.REGRESSION
          or "SVC" in self.params["operator"]
      )
  
      with Timer() as t:
          predict_data = self.get_data(data.X_test)
          if self.params["transform"]:
              self.predictions = self.model.transform(predict_data)
          elif is_regression:
              self.predictions = self.model.predict(predict_data)
          else:
              self.predictions = self.model.predict_proba(predict_data)
  
      return t.interval
  
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 HBBackend obj's underlying member method code with example usage: obj.predict(data) and returns: t