def METHOD_NAME(
    self,
    dataset: DatasetConfig,
    candidates: Optional[List[T]] = None,
    max_count: int = 10,
) -> List[Recommendation[T]]:
    """
    This method takes a dataset and a set of constraints and outputs a set
    of recommendations. The recommendations provide both the configurations
    of the recommended model as well as the expected performance.
    Args:
        dataset: The configuration of the dataset for which to recommend a model.
        candidates: A list of model configurations that are allowed to be recommended. If
            `None`, any model configuration is permitted.
        max_count: The maximum number of models to recommend.
    Returns:
        The recommendations which (approximately) satisfy the provided constraints.
    """
    model_configs = self.generator.generate(candidates)
    configs = [Config(m, dataset) for m in model_configs]
    performances = self._get_performances(configs)
    # We construct a data frame, extracting the performance metrics to minimize.
    # Then, we invert the performance metrics for the metrics to maximize.
    df = Performance.to_dataframe(performances)[self.objectives]
    # Then, we perform a nondominated sort
    argsort = argsort_nondominated(
        df.to_numpy(),  # type: ignore
        dim=df.columns.tolist().index(self.focus)
        if self.focus is not None
        else None,
        max_items=max_count,
    )
    # And get the recommendations
    result = []
    for choice in cast(List[int], argsort):
        config = configs[choice]
        recommendation = Recommendation(config.model, performances[choice])
        result.append(recommendation)
    return result