def METHOD_NAME(cavs_path: str, model_id: str, concepts: List[Concept], layer: str):
    r"""
    Loads CAV dictionary from a pickle file for given input
    `layer` and `concepts`.
    Args:
        cavs_path (str): The root path where the cavs are stored
                in the storage (on the disk).
                Ex.: "/cavs"
        model_id (str): A unique model identifier associated with the
                CAVs. There exist a folder named `model_id` under
                `cavs_path` path. The CAVs are loaded from this folder.
        concepts (list[Concept]): A List of concepts for which
                we would like to load the cavs.
        layer (str): The layer name. Ex.: "inception4c". In case of nested
                layers we use dots to specify the depth / hierarchy.
                Ex.: "layer.sublayer.subsublayer"
    Returns:
        cav(CAV): An instance of a CAV class, containing the respective CAV
                score per concept and layer. An example of a path where the
                cavs are loaded from is:
                "/cavs/default_model_id/0-1-2-inception4c.pkl"
    """
    cavs_path = CAV.assemble_save_path(cavs_path, model_id, concepts, layer)
    if os.path.exists(cavs_path):
        save_dict = torch.METHOD_NAME(cavs_path)
        concept_names = save_dict["concept_names"]
        concept_ids = save_dict["concept_ids"]
        concepts = [
            Concept(concept_id, concept_name, None)
            for concept_id, concept_name in zip(concept_ids, concept_names)
        ]
        cav = CAV(concepts, save_dict["layer"], save_dict["stats"])
        return cav
    return None