def create_model_from_job(self, training_job_name, name=None, role=None, primary_container_image=None,
                              model_data_url=None, env=None, vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT):
        """Create an Amazon SageMaker ``Model`` from a SageMaker Training Job.

        Args:
            training_job_name (str): The Amazon SageMaker Training Job name.
            name (str): The name of the SageMaker ``Model`` to create (default: None).
                If not specified, the training job name is used.
            role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``, specified either by an IAM role name or
                role ARN. If None, the ``RoleArn`` from the SageMaker Training Job will be used.
            primary_container_image (str): The Docker image reference (default: None). If None, it defaults to
                the Training Image in ``training_job_name``.
            model_data_url (str): S3 location of the model data (default: None). If None, defaults to
                the ``ModelS3Artifacts`` of ``training_job_name``.
            env (dict[string,string]): Model environment variables (default: {}).
            vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on the model.
                Default: use VpcConfig from training job.
                * 'Subnets' (list[str]): List of subnet ids.
                * 'SecurityGroupIds' (list[str]): List of security group ids.

        Returns:
            str: The name of the created ``Model``.
        """
        training_job = self.sagemaker_client.describe_training_job(TrainingJobName=training_job_name)
        name = name or training_job_name
        role = role or training_job['RoleArn']
        env = env or {}
        primary_container = container_def(
            primary_container_image or training_job['AlgorithmSpecification']['TrainingImage'],
            model_data_url=model_data_url or training_job['ModelArtifacts']['S3ModelArtifacts'],
            env=env)
        vpc_config = _vpc_config_from_training_job(training_job, vpc_config_override)
        return self.create_model(name, role, primary_container, vpc_config=vpc_config)