How to use the sagemaker.fw_utils.create_image_uri function in sagemaker

actual_train_args = sagemaker_session.method_calls[0][2]
    assert actual_train_args == expected_train_args

    model = tf.create_model()

    environment = {
        "Environment": {
            "SAGEMAKER_SUBMIT_DIRECTORY": "s3://mybucket/sagemaker-tensorflow-2017-11-06-14:14:15.673/source/sourcedir.tar.gz",  # noqa: E501
            "SAGEMAKER_PROGRAM": "dummy_script.py",
            "SAGEMAKER_REQUIREMENTS": "dummy_requirements.txt",
            "SAGEMAKER_ENABLE_CLOUDWATCH_METRICS": "false",
            "SAGEMAKER_REGION": "us-west-2",
            "SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
        },
        "Image": create_image_uri("us-west-2", "tensorflow", INSTANCE_TYPE, tf_version, "py2"),
        "ModelDataUrl": "s3://m/m.tar.gz",
    }
    assert environment == model.prepare_container_def(INSTANCE_TYPE)

    assert "cpu" in model.prepare_container_def(INSTANCE_TYPE)["Image"]
    predictor = tf.deploy(1, INSTANCE_TYPE)
    assert isinstance(predictor, TensorFlowPredictor)

def test_create_image_uri_bad_python():
    with pytest.raises(ValueError):
        fw_utils.create_image_uri(MOCK_REGION, MOCK_FRAMEWORK, "ml.c4.large", "1.0rc", "py0")

"us-iso-east-1", "tensorflow-scriptmode", "ml.m4.xlarge", "1.13.1", "py3"
    )
    assert (
        image_uri
        == "886529160074.dkr.ecr.us-iso-east-1.c2s.ic.gov/tensorflow-training:1.13.1-cpu-py3"
    )

    image_uri = fw_utils.create_image_uri(
        "us-iso-east-1", "tensorflow-scriptmode", "ml.p3.2xlarge", "1.14", "py2"
    )
    assert (
        image_uri
        == "886529160074.dkr.ecr.us-iso-east-1.c2s.ic.gov/tensorflow-training:1.14-gpu-py2"
    )

    image_uri = fw_utils.create_image_uri(
        "us-iso-east-1", "tensorflow-serving", "ml.m4.xlarge", "1.13.0"
    )
    assert (
        image_uri == "886529160074.dkr.ecr.us-iso-east-1.c2s.ic.gov/tensorflow-inference:1.13.0-cpu"
    )

    image_uri = fw_utils.create_image_uri("us-iso-east-1", "mxnet", "ml.p3.2xlarge", "1.4.1", "py3")
    assert image_uri == "886529160074.dkr.ecr.us-iso-east-1.c2s.ic.gov/mxnet-training:1.4.1-gpu-py3"

    image_uri = fw_utils.create_image_uri(
        "us-iso-east-1", "mxnet-serving", "ml.c4.2xlarge", "1.4.1", "py3"
    )
    assert (
        image_uri == "886529160074.dkr.ecr.us-iso-east-1.c2s.ic.gov/mxnet-inference:1.4.1-cpu-py3"
    )

def test_create_image_uri_bah():
    image_uri = fw_utils.create_image_uri(
        MOCK_BAH_REGION, MOCK_FRAMEWORK, "ml.p3.2xlarge", "1.0rc", "py3"
    )
    assert {
        image_uri == "217643126080.dkr.ecr.me-south-1.amazonaws.com/sagemaker-mlfw:1.0rc-gpu-py3"
    }

def test_optimized_family():
    image_uri = fw_utils.create_image_uri(
        MOCK_REGION,
        MOCK_FRAMEWORK,
        "ml.p3.2xlarge",
        "1.0.0",
        "py3",
        optimized_families=["c5", "p3"],
    )
    assert (
        image_uri == "520713654638.dkr.ecr.mars-south-3.amazonaws.com/sagemaker-mlfw:1.0.0-p3-py3"
    )

def test_create_image_uri_merged_gov_regions():
    image_uri = fw_utils.create_image_uri(
        "us-iso-east-1", "tensorflow-scriptmode", "ml.m4.xlarge", "1.13.1", "py3"
    )
    assert (
        image_uri
        == "886529160074.dkr.ecr.us-iso-east-1.c2s.ic.gov/tensorflow-training:1.13.1-cpu-py3"
    )

    image_uri = fw_utils.create_image_uri(
        "us-iso-east-1", "tensorflow-scriptmode", "ml.p3.2xlarge", "1.14", "py2"
    )
    assert (
        image_uri
        == "886529160074.dkr.ecr.us-iso-east-1.c2s.ic.gov/tensorflow-training:1.14-gpu-py2"
    )

    image_uri = fw_utils.create_image_uri(

def test_create_image_uri_merged_gov_regions():
    image_uri = fw_utils.create_image_uri(
        "us-iso-east-1", "tensorflow-scriptmode", "ml.m4.xlarge", "1.13.1", "py3"
    )
    assert (
        image_uri
        == "886529160074.dkr.ecr.us-iso-east-1.c2s.ic.gov/tensorflow-training:1.13.1-cpu-py3"
    )

    image_uri = fw_utils.create_image_uri(
        "us-iso-east-1", "tensorflow-scriptmode", "ml.p3.2xlarge", "1.14", "py2"
    )
    assert (
        image_uri
        == "886529160074.dkr.ecr.us-iso-east-1.c2s.ic.gov/tensorflow-training:1.14-gpu-py2"
    )

    image_uri = fw_utils.create_image_uri(
        "us-iso-east-1", "tensorflow-serving", "ml.m4.xlarge", "1.13.0"
    )
    assert (
        image_uri == "886529160074.dkr.ecr.us-iso-east-1.c2s.ic.gov/tensorflow-inference:1.13.0-cpu"
    )

    image_uri = fw_utils.create_image_uri("us-iso-east-1", "mxnet", "ml.p3.2xlarge", "1.4.1", "py3")
    assert image_uri == "886529160074.dkr.ecr.us-iso-east-1.c2s.ic.gov/mxnet-training:1.4.1-gpu-py3"

operations for Airflow to perform. (Upload `source_dir` )

    Args:
        model (sagemaker.model.FrameworkModel): The framework model
        instance_type (str): The EC2 instance type to deploy this Model to. For
            example, 'ml.p2.xlarge'.
        s3_operations (dict): The dict to specify S3 operations (upload
            `source_dir` ).

    Returns:
        dict: The container information of this framework model.
    """
    deploy_image = model.image
    if not deploy_image:
        region_name = model.sagemaker_session.boto_session.region_name
        deploy_image = fw_utils.create_image_uri(
            region_name,
            model.__framework_name__,
            instance_type,
            model.framework_version,
            model.py_version,
        )

    base_name = utils.base_name_from_image(deploy_image)
    model.name = model.name or utils.name_from_base(base_name)

    bucket = model.bucket or model.sagemaker_session._default_bucket
    script = os.path.basename(model.entry_point)
    key = "{}/source/sourcedir.tar.gz".format(model.name)

    if model.source_dir and model.source_dir.lower().startswith("s3://"):
        code_dir = model.source_dir

def _neo_image(self, region, target_instance_type, framework, framework_version):
        """
        Args:
            region:
            target_instance_type:
            framework:
            framework_version:
        """
        return fw_utils.create_image_uri(
            region,
            "neo-" + framework.lower(),
            target_instance_type.replace("_", "."),
            framework_version,
            py_version="py3",
            account=self._neo_image_account(region),
        )

def train_image(self):
        """Return the Docker image to use for training.

        The :meth:`~sagemaker.estimator.EstimatorBase.fit` method, which does
        the model training, calls this method to find the image to use for model
        training.

        Returns:
            str: The URI of the Docker image.
        """
        if self.image_name:
            return self.image_name
        return create_image_uri(
            self.sagemaker_session.boto_region_name,
            self.__framework_name__,
            self.train_instance_type,
            self.framework_version,  # pylint: disable=no-member
            py_version=self.py_version,  # pylint: disable=no-member
        )