def METHOD_NAME(
    dfs: Dict[str, pd.DataFrame]
) -> Dict[str, Dict[str, Any]]:
    """
    Transform the dict of <positive label, feature balance measures DataFrame>
    pairs into a dictionary acceptable by the RAI dashboard.
    :param dfs: A dict of <positive label, measure DataFrame> pairs.
    :type dfs: Dict[str, pd.DataFrame]
    :return: A dictionary of feature balance measures.
    :rtype: Dict[str, Dict[str, Any]]
    """
    all_measures: Dict[str, Dict[str, Any]] = {}
    try:
        for pos_label, df in dfs.items():
            measures: Dict[str, Dict[str, Any]] = {}
            rows: List[Any] = df.reset_index(drop=True).to_dict(
                orient="records"
            )
            # Transform { "FeatureName": "Col1", "MeasureA": 0.5, ... } to
            # { "Col1": { "MeasureA": 0.5, ... } }
            # which allows us to treat FeatureNames as the keys of the dict
            for row in rows:
                feature_name: str = row.pop(Constants.FEATURE_NAME.value)
                if feature_name not in measures:
                    measures[feature_name] = []
                measures[feature_name].append(row)
            all_measures[pos_label] = measures
    except Exception as e:
        # Warning instead of error so that other measures can be computed
        warnings.warn(f"Failed to transform feature measures due to {e!r}.")
    return all_measures