docs: def _create_sagemaker_model(self, instance_type, accelerator_type=None, tags=None):
          """Create a SageMaker Model Entity
  
          Args:
              instance_type (str): The EC2 instance type that this Model will be used for, this is only
                  used to determine if the image needs GPU support or not.
              accelerator_type (str): Type of Elastic Inference accelerator to attach to an endpoint for model loading
                  and inference, for example, 'ml.eia1.medium'. If not specified, no Elastic Inference accelerator
                  will be attached to the endpoint.
              tags(List[dict[str, str]]): Optional. The list of tags to add to the model. Example:
                      >>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
                      For more information about tags, see https://boto3.amazonaws.com/v1/documentation\
                      /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
  
          """
          container_def = self.prepare_container_def(instance_type, accelerator_type=accelerator_type)
          self.name = self.name or utils.name_from_image(container_def['Image'])
          enable_network_isolation = self.enable_network_isolation()
          self.sagemaker_session.create_model(self.name, self.role,
                                              container_def, vpc_config=self.vpc_config,
                                              enable_network_isolation=enable_network_isolation,
                                              tags=tags)