code: def predict(self, data):
      import onnxruntime as ort
  
      assert self.model is not None
  
      remainder_sess = None
      sess_options = ort.SessionOptions()
      sess_options.intra_op_num_threads = self.params["nthread"]
      sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
      sess = ort.InferenceSession(
          self.model.SerializeToString(), sess_options=sess_options
      )
      if self.remainder_model is not None:
          remainder_sess = ort.InferenceSession(
              self.remainder_model.SerializeToString(),
              sess_options=sess_options,
          )
  
      batch_size = (
          1
          if self.params["operator"] == "xgb"
          else self.params["batch_size"]
      )
      input_name = sess.get_inputs()[0].name
      is_regression = (
          data.learning_task == LearningTask.REGRESSION
          or "SVC" in self.params["operator"]
      )
      if is_regression:
          output_name_index = 0
      else:
          output_name_index = 1
      output_name = sess.get_outputs()[output_name_index].name
  
      with Timer() as t:
          predict_data = ScoreBackend.get_data(data.X_test)
          total_size = len(predict_data)
          iterations = total_size // batch_size
          iterations += 1 if total_size % batch_size > 0 else 0
          iterations = max(1, iterations)
  
          for i in range(0, iterations):
              start = i * batch_size
              end = min(start + batch_size, total_size)
  
              if self.params["operator"] == "xgb":
                  self.predictions[start:end, :] = sess.run(
                      [output_name],
                      {input_name: predict_data[start:end, :]},
                  )
              else:
                  if (
                      i == iterations - 1
                      and self.remainder_model is not None
                  ):
                      pred = remainder_sess.run(
                          [output_name],
                          {input_name: predict_data[start:end, :]},
                      )
                  else:
                      pred = sess.run(
                          [output_name],
                          {input_name: predict_data[start:end, :]},
                      )
  
                  if is_regression:
                      self.predictions = pred[0]
                  else:
                      self.predictions = list(
                          map(lambda x: list(x.values()), pred[0])
                      )
  
      del sess
      if remainder_sess is not None:
          del remainder_sess
  
      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 4 for calling the ONNXMLBackend obj's underlying member method code with example usage: obj.predict(data) and returns: t