code: def predict(self, predict_data):
      assert self.predict_fn is not None
  
      # For the batch by batch prediction case, we do not want to include the cost of
      # doing final outputs concatenation into time measurement
      with Timer() as t:
          if self.batch_benchmark:
              self.predictions = self.predict_fn(predict_data)
          else:
              self.predictions = self.predict_fn(
                  predict_data, concatenate_outputs=False
              )
  
      if not self.batch_benchmark:
          self.predictions = np.concatenate(self.predictions)
  
      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 3 for calling the HBBackend obj's underlying member method code with example usage: obj.predict(predict_data) and returns: t