code: def test_can_build_keras_to_coreml_to_relay():
      """Test multiple conversion paths and importing from a saved file."""
      model = keras.models.Sequential()
      model.add(
          keras.layers.Conv2D(
              filters=6,
              kernel_size=(1, 1),
              activation="relu",
              padding="same",
              input_shape=(3, 3, 1),
              data_format="channels_first",
          )
      )
  
      with tempfile.TemporaryDirectory() as tmpdir:
          kmodel_fn = path.join(tmpdir, "c1mdl.h5")
          model.save(kmodel_fn)
  
          mdl = cm.convert(
              kmodel_fn,
              convert_to="neuralnetwork",
              minimum_deployment_target=cm.target.macOS11,
          )
          model_file = path.join(tmpdir, "c1.mlmodel")
          mdl.save(model_file)
  
          mdl = cm.models.MLModel(model_file)
          desc = mdl.get_spec().description
          iname = desc.input[0].name
          ishape = desc.input[0].type.multiArrayType.shape
          shape_dict = {}
          for i in mdl.get_spec().description.input:
              iname = i.name
              ishape = i.type.multiArrayType.shape
              shape_dict[iname] = ishape
          mod, params = relay.frontend.from_coreml(mdl, shape_dict)
  
          with tvm.transform.PassContext(opt_level=3):
              relay.build(mod, "llvm", params=params)
  
imports: from os import path
  from enum import Enum
  import tempfile
  import numpy
  import model_zoo
  import coremltools
  from coremltools.models.neural_network import NeuralNetworkBuilder
  from coremltools.models import datatypes
  from tensorflow import keras
  import tvm
  import tvm.topi.testing
  import tvm.testing
  from tvm.contrib import graph_executor
  from tvm.topi.testing import conv2d_nchw_python
  from tvm import relay
label_desc: Use image node_id 42 for calling a global function with example usage: test_can_build_keras_to_coreml_to_relay() without return types