code: def run(self, *input_tensors: tf.Tensor) -> Tuple[tf.Tensor, ...]:
      if self.device.type is DeviceType.GPU and not self._is_gpu_ready:
          self.set_model_on_gpu()
      with tf.device(self.device.to_tf_format()):
          res = self.model(input_tensors)
      if not isinstance(res, tuple):
          return (res,)
      return res
  
imports: import pickle
  from pathlib import Path
  from typing import Tuple, Union, Dict, Type
  from nebullvm.config import TENSORFLOW_BACKEND_FILENAMES
  from nebullvm.core.models import DeviceType, Device, ModelParams
  from nebullvm.operations.inference_learners.base import TensorflowBaseInferenceLearner, LearnerMetadata
  from nebullvm.optional_modules.tensorflow import tensorflow
label_desc: Use image node_id 3 for calling the TensorflowBackendInferenceLearner obj's underlying member method code with example usage: obj.run() and returns: res, res