code: def test_tfs_model_image_accelerator_not_supported(sagemaker_session):
      model = TensorFlowModel(
          "s3://some/data.tar.gz",
          role=ROLE,
          framework_version="1.13.0",
          sagemaker_session=sagemaker_session,
      )
  
      # assert error is not raised
  
      model.deploy(
          instance_type="ml.c4.xlarge",
          initial_instance_count=1,
          accelerator_type="ml.eia1.medium",
      )
  
      model = TensorFlowModel(
          "s3://some/data.tar.gz",
          role=ROLE,
          framework_version="2.1",
          sagemaker_session=sagemaker_session,
      )
  
      # assert error is not raised
  
      model.deploy(
          instance_type="ml.c4.xlarge", initial_instance_count=1
      )
  
      with pytest.raises(AttributeError) as e:
          model.deploy(
              instance_type="ml.c4.xlarge",
              accelerator_type="ml.eia1.medium",
              initial_instance_count=1,
          )
  
      assert (
          str(e.value)
          == "The TensorFlow version 2.1 doesn't support EIA."
      )
  
imports: from __future__ import absolute_import
  import io
  import json
  import logging
  import mock
  import pytest
  from mock import Mock, patch, ANY
  from sagemaker.serializers import CSVSerializer, IdentitySerializer
  from sagemaker.tensorflow import TensorFlow, TensorFlowModel, TensorFlowPredictor
label_desc: Use image node_id 5 for calling a global function with example usage: test_tfs_model_image_accelerator_not_supported(sagemaker_session) without return types