code: def predict(self, predict_data):
      assert self.model is not None
  
      batch_size = (
          1
          if self.params["operator"] == "xgb"
          else self.params["batch_size"]
      )
      if self.is_regression:
          output_name_index = 0
      else:
          output_name_index = 1
  
      input_name = self.sess.get_inputs()[0].name
      output_name = self.sess.get_outputs()[output_name_index].name
  
      with Timer() as t:
          total_size = len(predict_data)
          iterations = total_size // batch_size
          iterations += 1 if total_size % batch_size > 0 else 0
          iterations = max(1, iterations)
          self.predictions = np.empty(
              [total_size, self.params["n_classes"]], dtype="f4"
          )
          for i in range(0, iterations):
              start = i * batch_size
              end = min(start + batch_size, total_size)
  
              if self.params["operator"] == "xgb":
                  pred = self.sess.run(
                      [output_name],
                      {input_name: predict_data[start:end, :]},
                  )
                  if type(pred) is list:
                      pred = pred[0]
                  self.predictions[start:end, :] = pred
              elif self.params["operator"] == "lgbm" or "rf":
                  if (
                      total_size > batch_size
                      and i == iterations - 1
                      and self.remainder_model is not None
                  ):
                      pred = self.remainder_sess.run(
                          [output_name],
                          {input_name: predict_data[start:end, :]},
                      )
                  else:
                      pred = self.sess.run(
                          [output_name],
                          {input_name: predict_data[start:end, :]},
                      )
  
                  if self.is_regression:
                      self.predictions[start:end, :] = pred[0]
                  else:
                      self.predictions[start:end, :] = list(
                          map(lambda x: list(x.values()), pred[0])
                      )
  
      if self.is_regression:
          self.predictions = self.predictions.flatten()
  
      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 4 for calling the ONNXMLBackend obj's underlying member method code with example usage: obj.predict(predict_data) and returns: t