code: def test_save_from_mlpackage(self):
      class Model(torch.nn.Module):
          def forward(self, x):
              return x
  
      example_input = torch.rand(1, 3, 50, 50)
      traced_model = torch.jit.trace(Model().eval(), example_input)
  
      model = coremltools.convert(
          traced_model,
          inputs=[coremltools.TensorType(shape=example_input.shape)],
          convert_to="mlprogram",
      )
  
      author = "Bobby Joe!"
      model.author = author
  
      save_dir = tempfile.TemporaryDirectory(suffix=".mlpackage").name
  
      model.save(save_dir)
      loaded_model = MLModel(save_dir)
  
      assert loaded_model.author == author
  
      _remove_path(save_dir)
  
imports: import os
  import shutil
  import tempfile
  import numpy
  import pytest
  import coremltools
  from coremltools import ComputeUnit, utils
  from coremltools._deps import _HAS_TORCH
  from coremltools.converters.mil import Builder
  from coremltools.libmodelpackage import ModelPackage
  from coremltools.models import MLModel
  from coremltools.models.utils import _MLPACKAGE_AUTHOR_NAME, _WEIGHTS_DIR_NAME
  from coremltools.proto import Model_pb2
label_desc: Use image node_id 4 for calling the TestMLModel obj's underlying member method code with example usage: obj.test_save_from_mlpackage() and returns: x