def METHOD_NAME(
        lookoutvision_client, project_name, training_results, tag_key=None,
        tag_key_value=None):
    """
    Creates a version of a Lookout for Vision model.
    :param lookoutvision_client: A Boto3 Lookout for Vision client.
    :param project_name: The name of the project in which you want to create a
                         model.
    :param training_results: The Amazon S3 location where training results are stored.
    :param tag_key: The key for a tag to add to the model.
    :param tag_key_value - A value associated with the tag_key.
    return: The model status and version.
    """
    try:
        logger.info("Training model...")
        output_bucket, output_folder = training_results.replace(
            "s3://", "").split("/", 1)
        output_config = {
            "S3Location": {"Bucket": output_bucket, "Prefix": output_folder}}
        tags = []
        if tag_key is not None:
            tags = [{"Key": tag_key, "Value": tag_key_value}]
        response = lookoutvision_client.METHOD_NAME(
            ProjectName=project_name, OutputConfig=output_config, Tags=tags)
        logger.info("ARN: %s", response["ModelMetadata"]["ModelArn"])
        logger.info("Version: %s", response["ModelMetadata"]["ModelVersion"])
        logger.info("Started training...")
        print("Training started. Training might take several hours to complete.")
        # Wait until training completes.
        finished = False
        status = "UNKNOWN"
        while finished is False:
            model_description = lookoutvision_client.describe_model(
                ProjectName=project_name,
                ModelVersion=response["ModelMetadata"]["ModelVersion"])
            status = model_description["ModelDescription"]["Status"]
            if status == "TRAINING":
                logger.info("Model training in progress...")
                time.sleep(600)
                continue
            if status == "TRAINED":
                logger.info("Model was successfully trained.")
            else:
                logger.info(
                    "Model training failed: %s ",
                    model_description["ModelDescription"]["StatusMessage"])
            finished = True
    except ClientError:
        logger.exception("Couldn't train model.")
        raise
    else:
        return status, response["ModelMetadata"]["ModelVersion"]