code: def get_size(self):
      try:
          if hasattr(self.model, "core_model"):
              return len(pickle.dumps(self.model.core_model, -1))
          else:
              # Normal torch model
              return len(pickle.dumps(self.model, -1))
      except RuntimeError:
          with TemporaryDirectory() as tmp_dir:
              self.save(tmp_dir)
              return sum(
                  os.path.getsize(Path(tmp_dir) / f)
                  for f in os.listdir(Path(tmp_dir))
                  if os.path.isfile(Path(tmp_dir) / f)
              )
  
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 3 for calling the TorchXLAInferenceLearner obj's underlying member method code with example usage: obj.get_size() and returns: len, len, sum