Return a container definition with framework configuration set in model environment variables.

        Args:
            instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.
            accelerator_type (str): The Elastic Inference accelerator type to deploy to the instance for loading and
                making inferences to the model. For example, 'ml.eia1.medium'. Note: accelerator types are not
                supported by SKLearnModel.

        Returns:
            dict[str, str]: A container definition object usable with the CreateModel API.

def prepare_container_def(self, instance_type, accelerator_type=None):
        """Return a container definition with framework configuration set in model environment variables.

        Args:
            instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.
            accelerator_type (str): The Elastic Inference accelerator type to deploy to the instance for loading and
                making inferences to the model. For example, 'ml.eia1.medium'. Note: accelerator types are not
                supported by SKLearnModel.

        Returns:
            dict[str, str]: A container definition object usable with the CreateModel API.
        """
        if accelerator_type:
            raise ValueError("Accelerator types are not supported for Scikit-Learn.")

        deploy_image = self.image
        if not deploy_image:
            image_tag = "{}-{}-{}".format(self.framework_version, "cpu", self.py_version)
            deploy_image = default_framework_uri(
                self.__framework_name__,
                self.sagemaker_session.boto_region_name,
                image_tag)

        deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
        self._upload_code(deploy_key_prefix)
        deploy_env = dict(self.env)
        deploy_env.update(self._framework_env_vars())

        if self.model_server_workers:
            deploy_env[MODEL_SERVER_WORKERS_PARAM_NAME.upper()] = str(self.model_server_workers)
        return sagemaker.container_def(deploy_image, self.model_data, deploy_env)