code: def prepare_container_def(self, instance_type, accelerator_type=None):
          
          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)
docs: 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.