code: def run(
      self, *input_tensors: torch.Tensor
  ) -> Tuple[torch.Tensor, ...]:
      if self.device.type is DeviceType.TPU and not self._is_gpu_ready:
          self.set_model_on_gpu()
      if self.device.type is DeviceType.TPU:
          input_tensors = (
              t.to(self.device.to_torch_format()) for t in input_tensors
          )
      with torch.no_grad():
          res = self.model(*input_tensors)
          if not isinstance(res, tuple):
              return (res,)
          return tuple(out for out in res)
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/inference_learners/torch_xla.py
imports: import os
  import pickle
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from typing import Tuple, Union
  from nebullvm.core.models import Device, DeviceType, ModelParams
  from nebullvm.operations.inference_learners.base import PytorchBaseInferenceLearner, LearnerMetadata
  from nebullvm.optional_modules.torch import torch
  from nebullvm.tools.transformations import MultiStageTransformation
label_desc: Use image node_id 2 for calling the TorchXLAInferenceLearner obj's underlying member method code with example usage: obj.run() and returns: tuple, res