code: def test_mil_as_package(self):
      num_tokens = 3
      embedding_size = 5
  
      class TestModule(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.embedding = torch.nn.Embedding(
                  num_tokens, embedding_size
              )
  
          def forward(self, x):
              return self.embedding(x)
  
      model = TestModule()
      model.eval()
  
      example_input = torch.randint(
          high=num_tokens, size=(2,), dtype=torch.int64
      )
      traced_model = torch.jit.trace(model, example_input)
  
      temp_package_dir = tempfile.TemporaryDirectory(
          suffix=".mlpackage"
      )
      for converted_package_path in [None, temp_package_dir.name]:
          mlmodel = coremltools.convert(
              traced_model,
              package_dir=converted_package_path,
              source="pytorch",
              convert_to="mlprogram",
              compute_precision=coremltools.precision.FLOAT32,
              inputs=[
                  coremltools.TensorType(
                      name="input",
                      shape=example_input.shape,
                      dtype=example_input.numpy().dtype,
                  )
              ],
          )
  
          assert isinstance(mlmodel, MLModel)
  
          package_path = tempfile.mkdtemp(suffix=".mlpackage")
          mlmodel.save(package_path)
  
          assert ModelPackage.isValid(package_path)
          assert os.path.exists(
              ModelPackage(package_path).getRootModel().path()
          )
  
          # Read back the saved bundle and compile
          mlmodel2 = MLModel(
              package_path, compute_units=ComputeUnit.CPU_ONLY
          )
  
          if utils._macos_version() >= (12, 0):
              result = mlmodel2.predict(
                  {
                      "input": example_input.cpu()
                      .detach()
                      .numpy()
                      .astype(np.float32)
                  }
              )
  
              # Verify outputs
              expected = model(example_input)
              name = list(result.keys())[0]
              np.testing.assert_allclose(
                  result[name], expected.cpu().detach().numpy()
              )
  
          # Cleanup package
          shutil.rmtree(package_path)
  
          tmp_package_path = mlmodel.package_path
          assert os.path.exists(tmp_package_path)
          del mlmodel
          if converted_package_path is not None:
              # Verify we leave the provided package dir alone
              assert os.path.exists(tmp_package_path)
  
      temp_package_dir.cleanup()
  
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 11 for calling the TestMLModel obj's underlying member method code with example usage: obj.test_mil_as_package() and returns: self