code: def setup_class(self):
      spec = Model_pb2.Model()
      spec.specificationVersion = coremltools.SPECIFICATION_VERSION
  
      features = ["feature_1", "feature_2"]
      output = "output"
      for f in features:
          input_ = spec.description.input.add()
          input_.name = f
          input_.type.doubleType.MergeFromString(b"")
  
      output_ = spec.description.output.add()
      output_.name = output
      output_.type.doubleType.MergeFromString(b"")
  
      lr = spec.glmRegressor
      lr.offset.append(0.1)
      weights = lr.weights.add()
      coefs = [1.0, 2.0]
      for i in coefs:
          weights.value.append(i)
  
      spec.description.predictedFeatureName = "output"
      self.spec = spec
  
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 1 for calling the TestMLModel obj's underlying member method code with example usage: obj.setup_class() without return types